Dylan Chiang

DC-WeMark: Local-first WeChat public account Markdown typesetting tool

Sat, 11 Jul 2026 00:00:00 +0000

DC-WeMark is an open source Markdown typesetting tool for WeChat public account authors. Users can write Markdown on the left, preview it in real time on the right, and paste style-preserving rich text into the official account editor with one click. The entire processing process is completed locally in the browser, and no account, backend or article upload is required.

What I am responsible for in the project

This is an independent open source project designed and developed by me. I am responsible for the complete process from problem definition, technical architecture to product delivery.

Product Design: Focus on format loss, repeated adjustments and content privacy issues when Markdown authors enter the public account editor, and establish a single workflow of “typesetting, previewing, and copying”.
Core Engine: Parse Markdown AST with Rust and comrak, convert theme rules into inline CSS acceptable to public accounts, and compile to WebAssembly.
Front-end experience: Use Vite and native TypeScript to complete dual-column editing, synchronous scrolling, mobile preview, file import and export, draft saving, and clipboard compatibility.
Design System: Create 48 original typography themes and provide adjustable parameters such as font, font size, accent color and copy background.
Project Delivery: Complete Rust unit testing, Playwright end-to-end testing, Docker self-racking solution, and Cloudflare Pages’ continuous deployment process.

Core Innovation

1. Put the typesetting core into WebAssembly

Markdown parsing, theme application and compatibility processing are all completed by the Rust core, and then executed in the browser through WebAssembly. The same core set can be extended to the CLI or desktop in the future without having to re-implement the layout rules.

2. Content does not leave the user’s device

The system has no server logic, database, or account services. Articles are always retained locally from input to copy, taking into account the privacy and offline availability of sensitive content.

3. Compatible output for official account editor

General web page CSS cannot be saved directly in the official account editor. DC-WeMark inlines styles into elements one by one, filters unsupported tags and attributes, and writes them to the clipboard using text/html to make the pasted layout as consistent as possible.

4. Small size, low dependency product architecture

The front end does not rely on a large UI framework, and the deployment artifacts are files that can be hosted by any static service. This keeps the online version, self-hosted version, and future multi-device reuse simple.

Technical architecture

Typesetting Core: Rust, comrak, WebAssembly
Front-end: Vite, TypeScript, HTML, CSS
Quality Assurance: Rust tests, Playwright E2E
Deployment: Cloudflare Pages, Docker, nginx
Authorization: MIT License

BOYA: Humanities and Social Sciences AI Research Skills Library

Fri, 12 Jun 2026 00:00:00 +0000

BOYA is an open source AI paper workflow I designed for researchers in the humanities and social sciences. It breaks tasks such as topic selection, literature retrieval, research design, paper revision, citation checking, oral examination and submission into skills that can be used independently or run in relays, allowing AI to handle the heavy work while leaving research judgment and academic responsibility to the researcher.

What I am responsible for in the project

I am responsible for BOYA’s problem definition, workflow architecture, all skill design, actual test verification and open source maintenance.

Requirements Translation: Convert tacit methodological knowledge in humanities and social science research into questions, checkpoints, and output formats that can be used by researchers with non-programming backgrounds.
Workflow Design: Establish a navigation portal and sixteen dedicated skills, covering research questions, literature, theoretical framework, methods, writing, review, oral examination and submission.
Human-computer collaboration governance: Set up manual decision-making gates for research questions, theoretical frameworks, method selection and interpretive judgments to prevent automation from overtaking researchers.
Academic Integrity Mechanism: Make “no fabrication found”, “references must be made to the source” and “transparent disclosure of AI use” as hard rules in the workflow.
Verification system: Use real paper materials to establish actual test cases, structured evaluations and evidence records, and continuously write down the exposed problems back to the technical rules.
Cross-platform maintenance: Supports Codex, Claude Code and OpenCode, and provides Traditional Chinese, Simplified Chinese, English and Japanese portals.

Core Innovation

1. Convert research methods into executable workflows

BOYA not only provides prompt words, but also breaks down each research stage into clear inputs, judgment gates, outputs and handover conditions, making AI collaboration a traceable research process.

2. Human-in-the-loop Hard Decision Gate

The process can assist with retrieval, sorting, checking and simulated questioning, but when encountering research questions, theory selection, method selection and proposition revision, the researcher must stop and make decisions. This is the core difference between BOYA and fully automatic essay generation tools.

3. Distinguish between bibliographic existence and claim establishment

reference-check is responsible for confirming the literature and bibliographic fields, while claim-audit goes back to the original text to check whether the key claims are supported to avoid mistaking the existence of a DOI as evidence of content.

4. Use real research materials to drive iteration

The boundaries of each skill are exposed through real research cases, and error patterns are written back into rules and evaluations. The reliability of workflow comes from checkable actual measurement records, not just functional descriptions.

Project results

Build 17 composable research skills with complete navigation workflows.
Accumulated 20 real material test cases.
Provide offline unit testing, structured regression cases and cross-model manual verification process.
Open source under MIT License and provides usage entrances in four languages.
It has been used for reference review of the author’s master’s thesis, and errors in DOI, signature and bibliographic information were found and corrections were made public.

WeiShi (未識): AI Relationship Exploration for the Sixth Yunnan-Taiwan University Student Innovation and Entrepreneurship Competition

Sun, 24 May 2026 00:00:00 +0000

WeiShi is an AI relationship-exploration project developed for the Sixth Yunnan-Taiwan University Student Innovation and Entrepreneurship Competition (第六屆雲台大學生雙創賽). Its proposition is simple: AI avatars connect first, while people retain the final decision. The prototype turns an initial encounter from quick browsing and instant judgment into an AI-assisted process of mutual understanding.

What I am responsible for in the project

I am mainly responsible for product and technology, transforming initial ideas into complete product prototypes that can be actually operated and displayed offline.

Product Definition: Establish the core proposition of “knowing before seeing” and position AI as a medium for understanding before meeting, rather than a chatbot that makes relationship decisions for users.
System Design: Planning the complete process of avatar profile creation, Agent Plaza, weekly in-depth recommendations, pre-understanding reports and real-person takeover.
Prototype Implementation: Connect the main interactions and local status in series to complete a front-end product that can run offline on the competition computer.
Governance Implementation: Transform the principles of authorization, observability, non-scoring and local priority into actual product nodes and operational restrictions.
Result Integration: String products, scenarios, governance research and roadshow narratives into a verifiable minimum closed loop, so that innovation is not just a concept.

Project Concept

Wei Shi takes “knowing before seeing” as its core concept. Users first create and continuously calibrate their own AI avatars, and then the two avatars conduct preliminary communications around life goals, relationship expectations, communication methods, and important differences. Only after both parties authorize and confirm their understanding of the report, the system will open the anonymous text conversation with real people.

The following diagrams are original Chinese-language competition materials retained as historical evidence; the English captions and context on this page are provided for international readers.

Core process

Create AI as me avatar: The user corrects memory, values, relationship goals and interaction boundaries.
Enter the Agent Square: Browse the content shared by other avatars and form a more complete understanding before formal contact.
Weekly in-depth recommendation: The system provides an in-depth object, presenting the reasons for recommendation, differences to be confirmed, and verification status.
Generate pre-understanding report: Organize the agreement and differences between the two parties on multiple issues, and retain questions that require real people to answer.
Live person takes over the conversation: After both parties agree, switch from agent interaction to anonymous text communication.

Core competitiveness and innovation

1. From chat tools to relationship understanding protocols

Weishi does not pursue more and faster matching, but allows both agents to complete authorizable and reviewable pre-understanding before meeting in person. The value of AI is not to decide relationships for people, but to reduce ineffective chats and information gaps.

2. In-depth rhythm of one person per week

The product replaces unlimited browsing with “one deep subject per week”. After multiple rounds of Agent communication, each object forms an understanding report, allowing users to see specific consistencies, differences, and issues to be confirmed.

3. AI clone that is observable and cannot exceed authority

The avatar can only express within the scope of the user’s authorization; the communication process can be reviewed, and sensitive actions need to be confirmed again. The real person always reserves the right to make the final decision.

4. Don’t judge relationships by a single score

The understanding report presents evidence, discrepancies and real-life takeover issues on multiple topics, without using an overall score to simplify complex relationships into “suitable” or “unsuitable”.

5. Turn governance into a product mechanism

Local processing, authorized nodes, process traces, and non-repudiation commitments are not additional terms, but product capabilities directly written into the interactive process.

Design principles

Human-in-the-loop: AI assists in organizing and understanding, but does not make promises, define relationships, or decide to meet for the user.
Consent takes priority: clear authorization nodes are reserved for pre-contact, report confirmation and real-person takeover.
Local First: Competition prototypes can be run offline, and personal correction status is saved locally in the browser.
Understand not score: The report presents specific issues and evidence, and does not simplify the relationship with a single matching score.

The current version is a local front-end prototype used for competition display and product verification. It does not include a formal account, online model service, real matching algorithm or production data platform.

Paper reading: Capsaicin diet drives gut inflammation and exosomal miR-17-3p elevation in idiopathic short stature

Sun, 18 Jan 2026 00:00:00 +0000

#paper information

Title: Capsaicin diet drives gut inflammation and exosomal miR-17-3p elevation in idiopathic short stature Journal: Nature Communications Year: 2025 Date: 2025-12-30

Research background

Idiopathic Short Stature (ISS) refers to a condition in which the height is two standard deviations lower than the person of the same age, but growth hormone (GH) levels are normal and there is no obvious pathological cause. The cause of ISS has long been a mystery.

Although genetics is the main factor in determining height, the influence of environmental factors such as diet cannot be ignored. Past research has mostly focused on nutritional deficiencies (such as calcium, protein), but how specific dietary components affect bone growth through molecular mechanisms is still poorly understood. This study focuses on capsaicin, the main component of spicy food, and explores whether and how it affects children’s growth and development.

Research methods

The research team adopted a systematic approach from clinical correlation analysis to molecular mechanism verification:

Clinical correlation analysis: Analyze the relationship between children’s eating habits and height, especially the intake of capsaicin.
Animal model: Establish a rat model of long-term ingestion of capsaicin and observe its effects on body length, femur length and growth plate structure.
Exosome tracking: Isolate exosomes (Exosomes) in serum, analyze their miRNA content, and track whether these exosomes can reach the bone growth plate.
Molecular mechanism exploration: Verify the regulatory effect of candidate miRNA on growth signaling pathways (such as ZNF148/SOS1/RAS/ERK and PI3K/AKT) in chondrocytes cultured in vitro.

Main findings

1. Capsaicin intake causes growth retardation and intestinal inflammation

In animal experiments, rats that long-term consumed a diet rich in capsaicin had significantly lower body weight and length than those in the control group. Histological examination found that these rats had thinner growth plates** and reduced new bone formation. At the same time, these rats showed significant intestinal inflammation. Notably, their growth hormone (GH) and insulin-like growth factor-1 (IGF-1) levels were normal, fully consistent with the clinical features of ISS.

2. Exosomal miR-17-3p is a key messenger

The study found that capsaicin-induced intestinal inflammation led to a significant increase in the levels of miR-17-3p carried by exosomes in the serum. These exosomes act as “long-range messengers” and are transported from the intestine to the growth plate cartilage of the bone.

3. Molecular mechanism: inhibiting chondrocyte proliferation

After reaching the growth plate, miR-17-3p enters chondrocytes, targeting and inhibiting the expression of the ZNF148 gene. Downregulation of ZNF148 further blocked SOS1 and the downstream RAS/ERK and PI3K/AKT signaling pathways. These pathways are critical for chondrocyte proliferation and hypertrophy, key processes in longitudinal bone growth. Therefore, inhibition of this pathway directly results in stunted bone growth.

Research significance

1. Revealed the new mechanism of the “gut-bone axis”

This study established a specific “diet-intestinal inflammation-exosomes-bone growth” cross-organ regulation model, proving that intestinal microorganisms and inflammatory status can directly affect the development of distal organs (bones) through non-hormonal pathways (exosomal miRNAs).

2. Provides a new etiological explanation for ISS

For many short children with normal growth hormone levels, this study offers a possible explanation: Perhaps it’s not a lack of hormones, but chronic inflammatory signals triggered by specific dietary habits that inhibit the bone’s response to growth.

3. Potential therapeutic targets

The findings suggest that, in addition to growth hormone therapy, intervention targeting miR-17-3p (such as the use of antisense oligonucleotides) or improving intestinal inflammation may become a new strategy for the treatment of some children with ISS. Of course, the simplest advice may be this: Avoid excessive consumption of spicy foods in developing children.

My understanding

1. Molecular-level interpretation of “You are what you eat”

This statement is usually understood to mean that nutrients make up body tissues. But the study shows a deeper meaning: Food is not just fuel, it’s a signal. Capsaicin itself is not a component of bone, but the biological response it triggers (inflammation -> release of exosomes) is like an “instruction” that rewrites the growth program of bone cells.

2. Growth hormone is not a panacea

When encountering children with ISS clinically, parents often anxiously seek growth hormone injections. However, if the cause is that the downstream cell signaling pathway is “stuck” by inflammatory factors (such as miR-17-3p), simply increasing the upstream growth hormone may have limited effect. This reminds us that medicine needs to shift from “replenishing deficiency” to “relieving inhibition.”

3. The collision of traditional wisdom and modern science

There is a saying in many cultures that “children who eat too much spicy food will not grow taller” (although it is more about eating spicy food that will “get angry” or affect digestion). This study unexpectedly provides rigorous molecular biology evidence for this folk observation. It reminds us that in developmental biology, gut health and systemic immune status may be more critical than we thought.

(Note: Please search for the paper published by Wang et al. in the December 2025 issue)

Reading date: 2026-01-18 Notes organized: Dylan Chiang

Paper reading: Exercise is good for the brain but exercising outside is potentially better

Sun, 18 Jan 2026 00:00:00 +0000

#paper information

Title: Exercising is good for the brain but exercising outside is potentially better Journal: Scientific Reports Year: 2023 Link:

Research background

We all know exercise is good for the brain, but exposure to nature (green surroundings) can also help with mental recovery. This is known in psychology as Attention Restoration Theory (ART), the idea that nature can help us recover from the sensory overload of the city.

However, most past studies have examined “movement” and “environment” separately. This study sought to answer a specific question: **Does exercise produce better cognitive gains if performed outdoors than if the same exercise were performed indoors? **

Research methods

The research team recruited 30 college students (average age approximately 21 years old) and conducted a within-subject design experiment:

Experimental Conditions: Each participant completed two 15-minute walking tasks, one indoor (a laboratory environment) and one outdoors (a green path on campus).
Measurement indicators:
- Measure brain activity using electroencephalography (EEG).
- Focus on P300 Event Related Potential (ERP). P300 is an important neural index reflecting the allocation of attention and working memory resources (the larger the amplitude, the more effective the cognitive resource deployment is).
- Participants took a standard cognitive test (Oddball task) before and after exercise.

Main findings

1. Outdoor sports significantly increase the P300 amplitude

The study results showed that participants’ P300 amplitude increased significantly after walking for 15 minutes outdoors**. This means that their attention allocation ability and cognitive processing efficiency have been significantly improved.

2. The effect of indoor exercise is not as good as expected

Surprisingly, no significant increase in P300 amplitude was observed after indoor walking at the same intensity**. This suggests that mere short-term “physical exercise” may not be enough to immediately trigger this level of cognitive improvement, and that environmental factors play a key amplifier role.

3. No significant difference in reaction time

Although the brain’s neural signals (P300) showed significant differences, at the behavioral level (reaction speed on cognitive tests), the indoor and outdoor groups did not show significant differences. This could mean that changes at the neural level precede behavioral changes, or that the task is less sensitive to behavioral changes.

Research significance and inspiration

This research supports the concept of Green Exercise. It tells us that the environmental context of movement is crucial.

1 + 1 > 2: The combination of exercise itself increases blood flow and the outdoor environment provides mental recovery creates a synergistic effect.
The Antidote to City Life: In modern city life, our brains are often in constant cognitive fatigue. The study suggests that instead of walking on a treadmill at the gym for 15 minutes, a 15-minute walk in the park may have a better “reset” effect on the brain.

My understanding

1. Environment is an extension of cognition

This paper made me think that our brain is not an isolated processor, but is deeply coupled with the environment. Indoor environments (especially boring labs or gyms) may lack enough sensory stimulation or be filled with distracting artificial signals, preventing the brain from entering the state of “relaxed focus” that it would in a natural environment.

2. Redefinition of “efficiency”

We often choose to exercise indoors for the sake of “efficiency” (not affected by weather, controllable). But if the goal is to recover mentally after work, this study shows that sacrificing a little convenience and going outdoors is truly “efficient” from a neuroscience perspective.

3. Simple and easy

15 minutes is not a very long threshold. This means that taking a walk in the park downstairs during your lunch break at work may be the most cost-effective way to recharge your brain.

Reading date: 2026-01-18 Notes organized: Dylan Chiang

Paper reading: How people think about being alone shapes their experience of loneliness

Sun, 04 Jan 2026 00:00:00 +0000

#paper information

Title: How people think about being alone shapes their experience of loneliness Journal: Nature Communications Year: 2025 DOI:

Research background

Loneliness is widely regarded as a global health challenge, closely related to depression, cardiovascular disease and even premature death. In response to this crisis, media and public health campaigns have sprung up in various countries to warn the public about the dangers of social isolation.

However, this study raises a paradoxical point of view: **Do these well-intentioned propaganda actually exacerbate people’s loneliness because they overemphasize the negative consequences of “being alone”? **

The core theory is that “being alone” (objective state) and “loneliness” (subjective feeling) are completely different. The research team hypothesized that people’s weighing beliefs (Beliefs) about being alone are the key to determining whether being alone causes pain or recovery.

Research methods

This study constructed a complete chain of arguments through 5 complementary experiments and surveys (total sample size N = 3,547):

Media Discourse Analysis (Study 1): Analyze the top ten newspapers in the United States from 2020 to 2022 to quantify their tendency to depict solitude.
Cause and Effect Experiment (Study 2): Randomly assign participants to read reports about the “benefits” or “risks” of being alone, and observe real-time changes in beliefs.
Experience Sampling (Study 3): 161 young people were tracked five times a day for two weeks to capture solitary behavior and psychological changes in daily life.
Cross-cultural verification (Study 4 & 5): Across 9 countries on six continents, verify whether this psychological mechanism is universal.

Main findings

1. The media’s “high stigma” against solitude

The study found that U.S. news headlines were 10 times more likely to describe solitude as “harmful” than as “beneficial.” In the main text, risks are mentioned 5 times more frequently than benefits are mentioned. The mass media almost equates “being alone” with “danger.”

2. The causal impact of beliefs on loneliness

Experiments have shown that even brief exposure to information about the benefits of solitude can significantly improve people’s beliefs about solitude. And this belief directly determines subsequent behavioral responses.

3. The “buffering and catalyzing” effect in daily life

This is the best part of the study (Study 3):

Negative Belief Holder: The longer you spend alone, the more intense your loneliness will rise. To them, being alone is a threat from which they need to escape.
Positive Belief Holders: After being alone, loneliness is significantly reduced. They see solitude as an opportunity to “recharge” and “repair themselves.”
Generalizability: This effect has been proven across different cultures, including collectivistic and individualistic cultures.

Research significance and inspiration

1. The “echo effect” of public health propaganda

The study raises a red flag for public health experts: campaigns that emphasize only the dangers of loneliness (such as “Loneliness is equivalent to smoking 15 cigarettes a day”) may inadvertently reinforce the public’s fear of being alone. When people have to be alone, which is almost inevitable in modern life, this fear translates into deeper psychological distress.

2. New approaches to cognitive intervention

Rather than forcing everyone to socialize, help those who feel lonely re-examine the value of being alone. Cultivating positive beliefs about being alone may be more effective in alleviating subjective loneliness than increasing social interaction.

My understanding

This paper provides a very insightful perspective: **Part of the reason why we feel lonely is because we are told that “being alone is not normal.” **

1. The self-fulfilling prophecy of cognitive frameworks

From the perspective of cognitive evaluation theory, being alone is a neutral stimulus. If we assume that being alone represents a social failure or a health threat (negative beliefs), then when we are alone, the brain will activate the threat detection mechanism, producing feelings of anxiety and loneliness. This creates a vicious cycle: the more lonely you feel, the more you fear being alone; the more you fear being alone, the more painful it is when you are alone.

2. Media Ecology and Mental Toughness

In the current era of digital media, social media constantly showcases the lively scenes of others, while traditional media constantly warns of the dangers of isolation. This double attack has greatly compressed our psychological space to face ourselves calmly. This study reminds us that building cognitive resilience (Belief Resilience) to “the benefits of being alone” may be an essential skill for modern people.

3. A “moderate turn” in policy

Public health policies should shift from simply “anti-loneliness” to “supporting healthy solitude.” Propaganda should include how to spend quality time alone and how to use alone time for creative thinking or emotional regulation, rather than just treating it as a defective product after a lack of social interaction.

4. Conclusion: Solitude is a kind of self-healing ability

Loneliness is not caused by spatial walls, but by psychological frameworks. Perhaps the biggest lesson from this research is that in an age where everyone is connected, being able to enjoy alone time is an important form of mental toughness.

It means that you no longer passively endure loneliness, but actively use this time to carry out self-repair (Rejuvenation) and recharge. When being alone is no longer a threat, but an opportunity, you have a powerful anti-loneliness mechanism.

Reading date: 2026-01-04 Notes organized: Dylan Chiang

Paper reading: Topological turning points across the human lifespan

Sun, 04 Jan 2026 00:00:00 +0000

#paper information

Title: Topological turning points across the human lifespan Journal: Nature Communications Year: 2025 DOI:

Research background

The structural topology of the human brain develops nonlinearly with age and is closely related to cognitive trajectories. Past research has tended to focus on specific age ranges (such as childhood or old age) but lacks a global perspective across the entire life cycle.

This study raises a core question: ** Are there key “turning points” in brain organization that mark the brain entering different stages of development? **

To answer this question, the research team collected nine data sets ranging in age from 0 to 90 years, with a total sample size of 4,216 people.

Research methods

1. Data analysis scale

Number of samples: N = 4,216 (neurotypical subset n = 3,802)
Technology: Diffusion Imaging
Metrics: 12 Graph Theory Metrics, used to quantify brain tissue architecture.

2. Manifold Learning

Use UMAP (Uniform Manifold Approximation and Projection) to project high-dimensional topological data into a three-dimensional manifold space.
The aim is to capture the non-linear dynamics of age-related changes in brain topology.

Key findings: Five life stages and four turning points

The study found that there are four major “turning points” in the development of brain topology, which occur around the ages of 9, 32, 66 and 83 years old. These turning points divide life into five unique Epochs:

Stage	Age range	Name	Characteristic description
Epoch 1	0–9 years old	Infancy to childhood	Global integration decreases and local preference (Segregation) increases.
Epoch 2	9–32 years old	Adolescence	The degree of brain network integration increases, and the small-world property (Small-worldness) is significantly improved.
Epoch 3	Ages 32–66	Adulthood	Developmental trajectories stabilize, with a slow decline in integration and an increase in local efficiency.
Epoch 4	Age 66–83	Early aging	Changes in modularity become the dominant feature.
Epoch 5	83–90 years old	Late aging	The relationship between age and topology weakens, and subgraph centrality becomes the main feature.

Detailed description of key findings

32: The most powerful turning point in your life
- Research shows that age 32 is the most dramatic time for brain topological rewiring.
- This is consistent with the finding that White Matter Volume and Fractional Anisotropy peak around age 29.
- This marks the brain’s shift from the “increased efficiency and integration” stage to the “increased isolation and stability” stage.
Age 9: The End of Childhood -The turning point coincides with the onset of puberty, peak cortical thickness, and substantial improvements in cognitive abilities.
- Marks a structural shift in brain structure from a single pattern of growth to a more complex pattern of maturation.
Ageing does not proceed at a uniform speed
- The turning point between ages 66 and 83 heralds the onset of a “simplified” mode of the brain, which is associated with an accelerated decline in white matter integrity and an increased risk of health problems such as dementia and high blood pressure.

Research significance and inspiration

1. Redefine “adolescence”

The study found that topological developmental trajectories during adolescence continue until age 32, well beyond what is traditionally thought to be around age 20. This supports the modern neuroscientific view that the brain matures much later than we thought.

2. The guiding value of non-linear development

The development of the brain does not rise in a straight line and then decline, but is a “turn” full of qualitative changes. This has important implications for clinical diagnosis, helping to distinguish normal staged changes from pathological cognitive decline.

My understanding

This study uses large-scale data to demonstrate the nonlinear dynamics of human brain structural topology during the life cycle, the most noteworthy of which is the turning point of 32 years old. Examined from the perspective of phylogeny and developmental biology, this finding provides several profound implications:

1. Trade-off between structure and function

Before the age of 30, the main axis of brain development is “increased integration.” This is a structured support period for extensive exploration and learning of the external environment. The brain improves the efficiency of information exchange throughout the brain by strengthening long-distance connections. However, this high level of integration is also accompanied by higher energy consumption and signal interference.

The turning point at age 32 marks the brain’s shift from “comprehensive expansion” to “precision optimization.” The decline in overall integration is not equivalent to a decline in functionality, but more like a structural “specialization.” Specific modules (Sub-networks) become more independent. This enhancement in modularity is beneficial to the stability of performing specific tasks and reduces noise interference in irrelevant areas. In cognitive psychology, this corresponds to the transition from “divergent flexibility” to “professional proficiency.”

2. Synergy of biological markers

The age of 32 does not exist in isolation. It is highly synchronized with peak white matter integrity, completion of the myelination process, and stabilization of neurotransmitter systems. This shows that during the course of evolution, the human body has set a biological clock that closes the “extensive remodeling window” at the beginning of the fourth decade. This synchronicity implies that the brain structure is shifted to better adapt to the long-term social division of labor and survival challenges in adulthood, and to invest limited metabolic resources into more productive specialized modules.

3. Reconstruction of the definition of “adolescence”

If the activity of topological rewiring is considered an indicator of adolescence, this study clearly shows that our definition of brain maturity (often pegged at around age 20) may be too dependent on legal and cultural labels rather than biological facts. From a brain structure perspective, the aftermath of adolescence actually extends into age 32. During this period, the brain still has strong potential for global integration, which explains why this period is the visual window for the final consolidation of professional skills shaping and worldview.

4. Precursors and nonlinear characteristics of aging

The study’s description of the turning points at ages 66 and 83 reveals that aging is not a slow wear-and-tear process but a staged evolution involving specific topological features. Especially after the age of 80, the relationship between age and topology weakens, suggesting that individual differences (such as genes, lifestyle, reserve capacity) may exceed the influence of basic developmental laws at extremely advanced ages.

In summary, this study reminds us that the brain executes different optimization logics at different stages of life: when young, it pursues “connecting everything” to gain possibilities, in middle age, it pursues “module independence” to ensure efficiency, and in old age, it tries to maintain core connections to cope with inevitable hardware degradation. This structural shift is not a negative degeneration, but a manifestation of life’s constant search for optimal adaptive solutions as time goes by.

Reading date: 2026-01-04 Notes organized: Dylan Chiang

Paper reading: Video game play is positively correlated with well-being

Sat, 27 Dec 2025 00:00:00 +0000

#paper information

Title: Video game play is positively correlated with well-being Journal: Royal Society Open Science Year: 2021 DOI:

Research background

The impact of video games on mental health has long been controversial. Many policymakers and parents worry that excessive gaming will lead to psychological problems (such as addiction, anxiety), but past studies often have a fatal flaw: heavy reliance on participants’ “self-reported” gaming time.

Psychological research shows that humans tend to be very inaccurate in their estimates of their own behavior. In order to break through this limitation, the Oxford Internet Institute collaborated with game companies (Electronic Arts and Nintendo of America) to obtain actual game telemetry data from players.

Research methods

The study was one of the largest and most methodologically rigorous of its kind at the time:

Sample Data:
- Plants vs. Zombies: Battle for Neighborville (Plants vs. Zombies: Battle for Neighborville)
- Assemble! Animal Crossing: New Horizons (Animal Crossing: New Horizons) *Total sample size N = 3,274 adult gamers.
Data type:
- Objective Behavioral Data: Record of actual game play time accurate to the second.
- Subjective Psychological Questionnaire: Includes positive/negative emotional experience (SPANE) and psychological need satisfaction (PENS, such as autonomy, competence, and belonging).
Analysis model: Use the original pre-registered analysis process to conduct correlation analysis between objective game time and subjective well-being.

Main findings

1. Game time is positively correlated with happiness

Contrary to the stereotype that “the more you play games, the less happy you are”, the study found that there is a significant and small positive correlation between objective game time and player happiness. That is, in this sample, people who played games for longer periods of time tended to report higher levels of happiness.

2. “Quality of experience” is more important than “length of time”

From the perspective of explanatory power, players’ psychological needs in the game are not fully satisfied (such as whether they feel free and dominant, whether they feel connected to others), which is more predictive of happiness than simply the length of the game.

3. Self-report bias

The study reconfirms that self-reported gaming time is only moderately correlated with actual recorded gaming time, confirming that past studies that relied on self-reporting may have had larger errors.

Research significance and inspiration

1. Challenging the foundations of policymaking

Many restrictions on games (such as time limits) are based on the assumption that “the longer the time, the greater the harm.” The results of this study raise doubts about this and suggest that policymakers should pay more attention to the content and experience quality of games rather than simply one-size-fits-all duration.

2. A new paradigm for industry cooperation

This research demonstrates the huge potential for data sharing between academia and industry. By analyzing telemetry data that cannot be faked, psychological research can move from “memoir” to “empirical science.”

My understanding

Although this paper was published in 2021, its methodological significance is greater than the conclusion itself.

1. The power of “data honesty”

We often unconsciously glorify or demonize our own behavior. “I played maybe 2 hours” in the questionnaire might actually be 4 hours. The greatest value of this Oxford University study is that it uses cold but honest server logs to burst the bubble of subjective recall.

2. Correlation\Causality

“Positive correlation” must be interpreted with caution. This does not necessarily mean that “playing games makes people happy”, it may also mean that “happy people have more time and mood to play games.” But at least it effectively refutes the determinism that “playing games will inevitably lead to unhappiness.”

3. Games as the “Third Space” in the Digital Age

Especially in games like “Animal Crossing”, players socialize, build, and express themselves in the virtual world. This is not just entertainment, but a digital social and emotional habitat. When the real world is full of uncertainty, games provide a safe haven that is controllable, feedback-laden, and can gain a sense of belonging, which in itself is a manifestation of psychological resilience.

Reading date: 2025-12-27 Notes organized: Dylan Chiang

Report Reading: Generative Artificial Intelligence Application Development Report (2025)

Fri, 26 Dec 2025 00:00:00 +0000

Report information

Title: Generative Artificial Intelligence Application Development Report (2025)
Issuing agency: China Internet Network Information Center (CNNIC)
Release Date: October 2025
Original link: [Generative Artificial Intelligence Application Development Report (2025)] ( ) (Note: This is CNNIC’s typical reporting path, please refer to the official website for details)
Core Content: Based on the framework of “User Popularization-Industrial Development-Typical Applications-Development Environment”, analyze the development status and future prospects of generative AI.

Comprehensive overview and summary of the report

This report records in detail the explosive growth of generative artificial intelligence (GenAI) in 2025, especially the rapid popularity and technological iteration of the Chinese market.

1. Development characteristics: domestic achievements and efficiency breakthroughs

Scale Explosion: As of June 2025, the number of GenAI users in China reached 515 million, with a penetration rate of 36.5%.
The Rise of DeepSeek: DeepSeek-R1 achieves excellent performance at less than 1/10 the cost of similar models, breaking the “computing power determinism” and topping the application list in 140 countries around the world.
Technical Trends: Logical reasoning capabilities have been significantly improved, multi-modal (Venture Video, Tusheng Audio) has developed by leaps and bounds, reasoning costs have been significantly reduced, and lightweight models (device-side AI) empower more terminal devices.

2. User popularity

Market structure: Doubao and DeepSeek occupy the leading position in the market.
Application motivation: Mainly used for answering questions (80.9%), text processing (36.0%) and audio and video generation (33.0%).
Group Characteristics: Young and middle-aged users are the main force, with users aged 19 and under accounting for 33.8%, showing the young generation’s high acceptance of AI technology.

3. Typical application scenarios

Industry and Agriculture: From smart irrigation, precision agriculture to industrial robots (such as UBTECH Walker S1) in production line training, AI is reshaping the labor structure.
Life Services:
- Smart Search: Shift from “finding links” to “getting answers”, Search as a Service.
- Content Creation: Sora and Keling AI bring short drama and advertising production into the era of “second-level generation”.
- Office Assistant: AI code generation (more than 30% of new code is generated by AI) and intelligent document processing become the norm.

4. Development environment and prospects

Policy Support: Establish an artificial intelligence security governance framework and promote “artificial intelligence +” actions.
Future Trends: Agent has autonomous decision-making and execution capabilities, Embodied Intelligence allows robots to enter the physical world and interact, Scientific Research (AI for Science) accelerates lunar research and weather prediction.

My understanding

This report is not only a accumulation of technical data, but also reveals the paradigm shift of AI technology from “concept laboratory” to “full employee productivity”.

1. Implications for personal research fields (political work and assistant behavior)

The report pointed out that 80.9% of users’ primary need is to “answer questions”, and in the office assistant scenario, AI’s understanding and polishing capabilities have become the core. For my study of Legislative Assistants’ Use of Generative AI, this provides strong external validity:

Efficiency Liberated: Political assistants are faced with the collection of data on a large number of public issues and the preparation of press releases. The high usage of AI search and document processing indicates that there will be an “automated revolution” in political work processes.
Decision Assistance: The “improvement in reasoning capabilities” mentioned in the report means that the assistant will not only use AI to write drafts in the future, but may also use AI to conduct policy impact assessment and voter sentiment analysis.

2. Inspiration for the development of AI in Taiwan and cross-strait

Lessons from cost advantages: The success of DeepSeek shows that Taiwan, with limited computing resources, should focus on algorithm optimization and deep cultivation of specific vertical fields (Vertical AI) instead of simply pursuing parameter scale.
Complementarity of application scenarios: Mainland China has a large scale of industrial and agricultural applications, while Taiwan has advantages in semiconductor end-side AI and high-quality service trade. Both sides of the Taiwan Strait face common challenges in AI governance (such as copyright, data privacy), and there is room for cross-regional dialogue.

3. Thoughts on the direction of academic research

From “use” to “collaboration”: Future academic research should not only focus on whether assistants use AI, but should study how the “human-machine collaboration model” changes the power structure in the political field.
Update of Technology Acceptance Model: The traditional TAM model may not be enough to explain the changes brought about by Agentic workflow. Research needs to focus on users’ psychological boundaries and ethical trust in AI “autonomy”.

Conclusion

The “2025 Report” marks that AI has entered the “full-scenario penetration” stage from a single breakthrough. As researchers, we need to pay close attention to the dynamic balance between the “efficiency dividend” and “ethical risks” brought by AI, especially in the highly sensitive field of political work.

Report reading: Microsoft AI Diffusion Report (2025)

Fri, 26 Dec 2025 00:00:00 +0000

Report information

Title: Microsoft AI Diffusion Report: Mapping Global AI Adoption and Innovation
Published by: Microsoft Research
Release Date: October 2025
Original link:
Core content: Track the global adoption status, infrastructure distribution, narrowing of the technology frontier and challenges faced by AI as the fastest spreading technology in history.

Comprehensive overview and summary of the report

This Microsoft report focuses on the “diffusion” dynamics of AI technology on a global scale, revealing the contradictory current situation of technology popularization and resource concentration coexisting.

1. The fastest technological diffusion in history

Breakthrough Velocity: AI has attracted more than 1.2 billion users in less than three years. Its diffusion rate is far faster than any previous general purpose technology (GPT), such as the Internet, personal computers or smartphones.
Adoption rate differentiation: Although the adoption rate is fast, there are significant differences between the Global North vs. Global South. The adoption rate in northern countries is approximately 2 times that of the South.

2. Frontier trends in infrastructure and models

Concentration of computing power and data: 86% of the world’s data center capacity is concentrated in the United States and China. This extreme concentration of infrastructure creates challenges for the democratization of AI.
Frontier Narrowing: Although the United States (such as OpenAI’s GPT-5 and other benchmarks) still maintains the lead, the performance gap of latecomers is rapidly narrowing. The report states that China is estimated to be less than 6 months behind on the technological frontier.
Model Distribution: The world’s top 200 models are only concentrated in 7 countries (the United States, China, France, South Korea, the United Kingdom, Canada, and Israel), showing the high threshold for technology research and development.

3. Diffusion Leaders

Non-R&D adoption leaders: Countries such as the United Arab Emirates (59.4%), Singapore (58.6%), and Norway (45.3%) stand out.
Success Factors: These countries have proven that even without developing underlying models, they can maintain global leadership in AI adoption through strong education systems, friendly policy environments, and digital infrastructure.

4. Key barriers: language and infrastructure

Language inequality: Adoption rates are significantly lower in low-resource language areas (e.g. Malawi, Laos). The language tolerance of AI models has become a key bottleneck for popularization.
Infrastructure constraints: About half of the world’s population (4 billion people) still lack the basic conditions (electricity, network and broadband) required to use AI.

My understanding

Microsoft’s global perspective provides a broader context for understanding AI’s place in politics, bipartisanship, and academia.

1. Implications for personal research fields (political work and assistant behavior)

The report mentioned that the rapid spread of AI indicates that the “digital generation gap” in the political field will be quickly compressed:

Global knowledge equality and challenges: If 1.2 billion users are using AI, political assistants are no longer a question of “whether to use them”, but a question of how to maintain “political uniqueness” during use.
Skills first: The cases of the United Arab Emirates and Singapore illustrate that “skills use” and “policy guidance” have a more direct impact on the application side than “model development”. This supports the hypothesis in my research that focuses on assistants’ personal technology preferences and environmental support.

2. Inspiration for the development of AI in Taiwan and cross-strait

Strategic opportunities for frontier narrowing: When the frontier technology gap narrows to within 6 months, Taiwan, as the core of the global supply chain, will have a greater say in hardware support in the “inference” stage.
Learn from the “Adoption Leader” model: Taiwan should become a world-leading “AI efficient adoption zone” through regulatory innovation and education transformation, like Singapore or Norway, despite the lack of local ultra-large-scale underlying models.

3. Thoughts on the direction of academic research

Focus on the imbalance of “AI diffusion”: Research should not be limited to advanced regions, but should focus on whether AI has exacerbated the gap between disadvantaged groups (or small parties, resource-poor politicians) and resource concentrators.
Language specificity research: For Taiwan, which uses traditional Chinese, we should study how “language specificity” affects the model’s understanding and expression accuracy in the local political context.

Conclusion

If CNNIC’s report shows the “depth” of China’s AI applications, then Microsoft’s report shows the “breadth” and “imbalance” on a global scale. The future of AI depends not only on who has the strongest model, but also on who can harness this power the fastest and fairest.

Report reading: OpenRouter State of AI (2025)

Wed, 10 Dec 2025 00:00:00 +0000

Report information

Title: State of AI | OpenRouter (Empirical Study)
Publishing Authority: OpenRouter
Published: December 5, 2024 (reported observation period spans 2024-2025)
Original link:
Data Foundation: Real interactive metadata based on OpenRouter unified inference layer 100 trillion (Trillion) tokens, covering 300+ models and 60+ providers around the world.

Report Key Insights (Abstract & Empirical Findings)

OpenRouter’s report avoids traditional subjective evaluations and instead starts from the “real behavior” of large-scale production environments, revealing in-depth patterns in the following dimensions:

1. Paradigm shift of Agentic Inference

Absolute dominance of inference models: The report points out that since the release of the o1 class model at the end of 2024, AI usage has shifted from “content generation” to “multi-step reasoning.” By 2025, more than 50% of total token traffic will flow to inference optimization models (led by xAI’s Grok Code Fast, followed by the Gemini 2.5 series and DeepSeek R1).
Tool-Calling Trend: Data display tool calling is no longer an option for developers, but a default for high-value workflows. The Claude 3.5/3.7 series dominated in the early days, and then Grok and GLM 4.5 quickly entered the market, reflecting that action through planning is the moat for future models.

2. Dynamic balance between open source and closed source (Market Equilibrium)

30% “Open Source Ceiling”: Although closed source models still account for 70% of the market share (focusing on regulated enterprise-level workflows), open source/weighted open models (OSS) have stabilized at around 30%.
The Rise of China’s Open Source Model: The release of DeepSeek V3 and Qwen 3 Coder directly led to a surge in usage. Especially in the field of code assistance (Programming), the Chinese open source model briefly accounted for more than half of OSS code tasks in mid-2025.

3. Model family usage profiles (Provider Profiles)

The report reveals users’ “cognitive division of labor” towards different model brands:

Anthropic (Claude): Extreme focus on Programming and Technology (80%+). Users consider Claude their go-to choice for complex reasoning and engineering.
Google (Gemini): The most diverse performance, covering translation, science, law and general knowledge, showing the characteristics of “digital encyclopedia/information engine”.
OpenAI (GPT): It has undergone a transformation from early science and general knowledge to deeper developer workflow and productivity tools. Its positioning is between Claude’s professionalism and Google’s diversity.
DeepSeek: shows an amazing consumer-heavy, with more than 2/3 of the traffic coming from creativity, entertainment and role-playing.

4. Retention Analysis: Cinderella’s “Glass Slipper Effect”

This is the most interesting finding in the report. The study observed:

Foundational Cohorts: Early users (such as first-month users of Gemini 2.5 or Claude 4 Sonnet) have retention rates of up to 40% after 5 months, much higher than those of subsequent users.
First solution advantage: When a model is the first to solve a specific problem (such as a complex Tool-use or logic difficulty), the user group will have a strong path dependence (Cognitive inertia). This is the “glass shoe” effect: once adapted, a powerful locking effect will occur.

5. Cost and Demand Elasticity (Jevons Paradox)

Price Inelastic: Interestingly, a 10% price drop only drives 0.5-0.7% usage growth. This shows that “quality and trust” are far more important than price, and top companies are willing to pay a premium for stability.
Jevons Paradox: Although the price elasticity is low at the macro level, in the field of “Efficient Giants” (such as Gemini Flash or DeepSeek), low cost does induce larger Token consumption (users start to perform more iterations and longer context queries).

My understanding: academic and practical inspiration behind the data

This “empirical” report elevates the discussion of AI from “whether it is easy to use” to “how to deploy it systematically.”

1. Implications for personal research fields (political work and assistant behavior)

Inference Dividends and Agentization: More than 50% of the models in the report turned to inference, validating my observation that assistants in political work are leveraging AI to handle more “judgmental” tasks. Future research should shift from “whether assistants use AI” to “how assistants guide public opinion through AI’s agentic workflow.”
Scenario Adaptation (Glass Slipper): For political assistants, the first model that can accurately craft their own tone or analyze the sensitivities of a constituency will create loyalties that are extremely difficult to break. This explains why some offices are stuck on older versions of GPT or specific models.

2. Inspiration for the development of AI in Taiwan and cross-strait

Export advantages of open source models: The explosive growth of DeepSeek and Qwen on OpenRouter proves that “open source orientation” is the main driver of Chinese models going global. Taiwan can boldly integrate these “Efficient Giants” on the application side and use cost savings for front-end scene optimization.
The necessity of multi-model architecture: Since no single model can dominate all portraits (DeepSeek for Roleplay, Claude for Code), Taiwanese companies and think tanks should adopt “Multi-model Stack” to achieve the best balance between cost and performance.

3. Thoughts on the direction of academic research

Focus on the bias of agent inference: When AI begins to autonomously call tools and plan paths (Agentic Inference), its bias will no longer just be “saying the wrong thing”, but “doing the wrong thing”. This is an extremely critical new topic in political and legal studies.
Application of Retention Rate Research: We should study how to shorten the journey for users to find the “glass slipper” model to increase the success rate of digital transformation.

Conclusion

OpenRouter’s data reveals a cruel but hopeful truth: In the great era of AI, the emergence of inference models such as o1 has not capped competition, but has opened up a new battlefield of “multi-step operation” and “scene adaptation”**. The future does not belong to the person with the biggest model, but to the person who can accurately find the “glass slipper”.

The 11th Wenxia Forum: Peaceful development and exchanges of cross-strait relations

Sat, 06 Dec 2025 09:00:00 +0800

Paper reading: The Savanna Theory of Happiness

Tue, 25 Nov 2025 00:00:00 +0000

#paper information

Title: The Savanna Theory of Happiness Source: Journal of Personality and Social Psychology (or similar, based on context) Paper ID: N/A Link:

Research background

This paper proposed “The Savanna Theory of Happiness”. This theory is based on the perspective of evolutionary psychology, which believes that human psychological mechanisms evolved on the ancient African savannah. Therefore, our response to the modern living environment is actually deeply affected by the living environment of our ancestors.

Specifically, the authors try to explain why certain factors in modern life (such as population density, frequency of social interaction) affect our happiness, and why these effects differ among people with different IQs.

Research methods

Core concepts

The Savanna Principle: The human brain is adapted to the ancestral environment, not the modern environment. So situations that would have increased survival in our ancestral environment still make us happy today, and vice versa.
The moderating role of IQ: Intelligence (General Intelligence) is defined as the ability to solve evolutionarily “novel” problems. Smart people are better able to adapt to the modern lifestyle, which is completely different from their ancestral environment, and are therefore less constrained by the “savanna principle.”

Experimental design

The study used data from the U.S. National Longitudinal Study of Adolescent Health (Add Health) and analyzed the subjects’ IQ, population density where they lived, frequency of socializing with friends, and their self-reported life satisfaction (happiness).

Main findings

Overall, people are less happy when they live in places with higher population density. This is consistent with the prairie principle: in ancient times, excessive population density meant competition and pressure for resources.

Key Difference: This negative correlation is more pronounced among low IQ individuals; while high IQ individuals are less negatively affected by population density. This may be because people with higher IQs are better able to handle the complex stresses and novel challenges of modern urban life.

For most people, the more time they spend with friends, the happier they feel. This is also consistent with an evolutionary perspective: in tribal life, social connection is the key to survival.

Amazing reversal: For people with extremely high IQs, the opposite is true. More frequent social activities are associated with lower life satisfaction.

Research significance and inspiration

Why do smart people like to be alone?

This study provides an evolutionary psychology explanation for the stereotype that smart people are more lonely. People with high IQs may be more focused on long-term, modern goals (such as career, academic breakthroughs) that are “novel” in evolutionary history. Although frequent social activities are in line with ancestral survival instincts, they may actually be a distraction for highly intelligent people pursuing these modern goals.

In addition, the brains of people with high IQs are better able to adapt to “unnatural” modern life (such as solitude and high-density living), so they do not need to rely on close tribal social interaction to gain a sense of security like their ancestors did.

My understanding

This paper is very interesting. It uses the perspective of evolutionary psychology to explain the happiness and pain of modern people.

1. “Primitive Man” lives in modern cities

We often feel anxious or unhappy, often because our “primitive brains” have not adapted to modern society. For example, feeling irritable on a crowded subway may be because our genes are screaming: “There are too many people here and not enough resources. Run!”

2. IQ as an “adapter”

The author views IQ as a tool to deal with “evolutionary novelty”, which is a unique perspective. This explains why smart people tend to be better able to adapt to counter-intuitive modern lifestyles (such as being alone, staying up late, not having children, etc.). They are more able to suppress primitive impulses and pursue goals that are evolutionarily “meaningless” but are respected by modern society.

The mainstream values of society usually encourage social interaction and believe that “having more friends is better.” But this study tells us that for some people, especially those with high IQs, being oversocialized can be a burden. Being alone does not mean loneliness or social impairment; it may be an adaptive choice made by highly intelligent people to focus on more important goals.

Extended thinking

1. Definition of happiness

If happiness is a product of evolution, does that mean we should live by our instincts (move to the country, make more friends)? Or should we use our IQ to overcome these instincts and pursue higher levels of satisfaction?

The data for this paper come from before the popularity of social media. Does online socializing count as “socializing” today? Does its effect on happiness also follow the Savannah Principle?

Paper reading: Cognitive processes of ingroup favoritism across 20 countries

Sat, 22 Nov 2025 00:00:00 +0000

#paper information

Title: Cognitive processes of ingroup favoritism across 20 countries: An eye-tracking investigation of culture, behavior, and cognition Author: Rima-Maria Rahal & Frederik Schulze Spüntrup Journal: PNAS (Proceedings of the National Academy of Sciences) Year: 2025 (Vol. 122, No. 32) DOI:

Research background

In-group favoritism is a common social psychological phenomenon - we tend to favor “one of our own” even if the definition of “one of our own” may only be based on a trivial criterion (such as a different favorite painter).

This preference has serious consequences in the era of globalization:

COVID-19 Vaccine Distribution: Western countries hoard vaccines, developing countries can only wait
Climate Crisis: Countries shirking responsibility and prioritizing their own short-term interests
Xenophobia on the rise: Right-wing populism spreads around the world

In the past, most studies could only be conducted in laboratories, with samples restricted to specific areas. This study breaks through this limitation and uses webcam-based eye-tracking, allowing researchers to collect high-quality cognitive data in participants’ homes, achieving unprecedented cultural and geographical diversity.

Research methods

1. Innovative research design

Sample:

1,850 participants
20 countries (covering Europe, Asia, America, etc.)
Eye tracking using participants’ own webcams

Experimental Task:

Group allocation: based on color recognition task (minimum group paradigm)
Group Reinforcement: Compete with out-group members for bonuses
Decompose the Dictator Game: 80 decisions, each time choosing between the “self-interest” and “pro-social” options
- Self-interest option: you get the most and the other side gets very little
- Prosocial option: take less for yourself, and both the other party and the total will increase

2. Measurement indicators

Behavioral level:

Differences in generosity toward in-group vs. out-group members

Cognitive Level (Eye Tracking):

Decision time
fixation count
Number of messages checked
Allocation of attention to self vs. other’s gains

Cultural level:

Individualism vs. Collectivism
Government effectiveness
Religious importance
Historical disease burden & COVID-19 impact

Main findings

1. In-group preference is universal, but the degree varies widely

Core findings:

✅ All 20 countries show in-group preference (OR = 4.57)
❗ But effect sizes vary widely across countries
📊 The stronger the prosocial tendency of people, the more serious the discrimination against out-groups.

**What does this mean? **“Good people” do not necessarily do not discriminate, they may just leave kindness to “their own people”.

The study tested the Material Security Hypothesis: the more unstable a society is, the more people rely on their in-groups.

Evidence Support:

✅ The lower the government effectiveness** → the stronger the in-group preference**
✅ COVID-19 death toll higher → in-group preference stronger
✅ The lower the historical disease burden → the lower the in-group preference

Unexpected Discovery:

❌ Religion more important → In-group preference stronger (contrary to prediction!)
- Null hypothesis: Religion as a stable institution should reduce preferences
- Practical: Religion may reinforce in-group and out-group boundaries

3. Is an individualistic culture more discriminatory?

Cultural Dimensions Theory predicts: Collectivist cultures should place greater emphasis on ingroups.

Actual result: Completely opposite!

The higher the individualism** → the stronger the in-group preference**

Possible explanation: -In individualistic cultures, people are more eager for group identification

Lacking a fixed social structure, it is more necessary to create boundaries for “one’s own people”

4. Cross-cultural differences in cognitive processes are more complex than behavior

Overall Trend (shown by meta-analysis):

Decision-making becomes more laborious when facing in-group members:
- Longer decision-making time
- More views
- Check more information

But:

⚠️ The direction of this effect varies greatly between countries
In some countries: in-group decision-making is more laborious
In other countries: out-group decisions are more laborious (reversed effect!)

The role of prosocial tendencies also varies across cultures:

In some countries: more prosocial → more effortful in-group decision-making
In other countries: the effect is reversed

5. “Color blindness”: More than half of people choose not to read group information

Amazing discovery:

In 53.49% of decisions, participants did not look at the group information at all (even if it was free)
54.79% of people clearly stated that they did not want to know the other party’s group identity

**Who chooses to remain “color blind”? **

People with strong self-interest (low SVO)
People who have no favorable impressions of either group
People who think others will not view group information

Consequences of color blindness:

People who choose color blindness: No discrimination
People who choose to view: 10% less for external groups

**What does this mean? ** The average discrimination effect may be driven by the few people who “want to know” the group identity!

Research significance and inspiration

Methodological Revolution

Breakthrough in web camera eye tracking:

💻 Participants can complete it at home, no professional laboratory required
🌍 Achieve unprecedented geographical diversity
💰 Lower cost and higher efficiency
📊 Sufficient data quality (spatial and temporal resolution)

This democratizes cognitive research—it’s no longer just university labs in wealthy areas.

Theoretical contribution

Challenges the cultural dimension theory
- Individualism vs. collectivism effects are opposite to predicted
- A single cultural dimension is not enough to explain complex social cognition
Supports the material security hypothesis (partially)
- Social uncertainty does increase discrimination
- But the role of religion is more complex
Reveal the cultural specificity of cognitive processes
- Cognitive mechanisms cannot be assumed to be consistent across cultures
- Even if the behavioral patterns are similar, the cognitive processes behind them may be completely different

Practical inspiration

A warning for global policymaking:

❌ “One size fits all” anti-discrimination policies may be counterproductive in some cultures
✅ Requires culturally appropriate interventions
🎯 “Color-blind” strategies may be effective: de-emphasize group differences instead of emphasizing equal treatment

Implications for reducing discrimination:

🔍 Understand who chooses to view group information (and why)
🎓 Educational focus: reduce focus on group boundaries
🌐 In a society with high uncertainty, stronger intervention is needed

My understanding

After reading this paper, my biggest shock was: it turns out that our understanding of “discrimination” may be based on a very narrow sample. If you studied ingroup preference only in a sample of Western college students, you might come to one conclusion about the cognitive mechanisms. But this study tells us that the same behavior (discrimination against outgroups) may come from completely different cognitive processes in different cultures.

1. Are “good people” more discriminatory? This is counter-intuitive

I have always thought that people with strong prosocial tendencies (people who are willing to sacrifice for others) should be less likely to discriminate. But this study says: On the contrary, these “good people” are more likely to reserve kindness only for “their own people.”

This reminds me of some phenomena in reality. Many people who claim to be “patriotic” may be generous to their fellow countrymen, but are extremely repulsive to foreigners or immigrants. Or some religious groups are very united and supportive within themselves, but are indifferent or even hostile towards “infidels”. This kind of “parochial altruism” may be more common and more persistent than we think.

The explanation proposed in the paper is that prosocial people care more about “fairness” and “justice.” However, when they think that the out-group is “not one of their own,” this sense of fairness does not apply. This made me reflect deeply: It is not enough to simply promote “kindness” or “altruism”. What is more important is to expand the scope of “us” and allow more people to be included in the circle of “ours”.

2. Are individualistic cultures more discriminatory? This overturned my understanding

I have always felt that collectivistic cultures (such as Asia) should emphasize the distinction between inside and outside more, while individualistic cultures (such as Europe and the United States) should treat everyone more equally. But this study found the exact opposite!

The explanation proposed by the author is very interesting: in individualistic cultures, people are more eager for group identification. Because there are no mandatory family or community ties, people need to find their own “sense of belonging,” which makes them more sensitive to “who is one of their own” and “who is an outsider.”

This reminds me of racial issues in the United States, immigrant rejection in Europe, etc. These highly individualistic societies are particularly prone to group conflicts. Perhaps it is precisely because in a society where “everyone is for themselves”, finding “one’s own team” has become more important and precious.

The most surprising finding from this study is that in more than half of the decisions, the participants did not look at the other party’s group information at all. What’s even more astonishing is that those who clearly chose “not to know” showed no discrimination at all!

This gave me an important revelation: **Perhaps the key to reducing discrimination is not to educate people to “treat all groups equally,” but to make people “not pay attention to group differences.” **

Think about it, in real life, many times we are forced or led to pay attention to group differences-the “race” option on the form, “immigrant crime” in news reports, the “us vs. them” narrative on social media. If we can create more “color-blind” environments and systems, perhaps discrimination will naturally decrease.

Of course, this also raises a philosophical question: Does the “color-blind” strategy ignore real group inequalities? Does not recognizing differences mean evading the responsibility to address inequality? This is an issue that needs to be weighed carefully.

4. Cultural specificity of cognitive processes: an unsolved mystery

The biggest mystery of this study is: Why is the relationship between prosocial tendencies and decision-making effort completely reversed in different countries?

In some countries, more prosocial people make more effortful decisions when faced with their ingroup (perhaps weighing how to maximize the interests of the ingroup). But in other countries, the effect is reversed. The papers acknowledge that they cannot explain this difference with pre-registered cultural variables (individualism, government effectiveness, religion, etc.).

This is actually the most honest and valuable part of this study: it reveals our ignorance. We thought we understood the mechanics of in-group preference, but in fact we couldn’t even explain why cognitive processes differ across cultures.

This reminds us: psychological theories cannot only be based on WEIRD (Western, Educated, Industrialized, Rich, Democratic) samples. The human mind is far more complex and culturally shaped than we imagine.

5. Democratizing Methodology: The Webcam Revolution

From a broader perspective, this study shows how technology is changing research possibilities. In the past, eye tracking required expensive equipment and specialized labs that only a few wealthy universities could afford. Data of sufficient quality can now be collected using participants’ own webcams.

what does that mean? Democratizing Research. Researchers from developing countries no longer need multimillion-dollar equipment to conduct cutting-edge cognitive research. Participants are no longer limited to college students living near college towns.

This technological advancement may be more important than any single research discovery. It changes the game of “who can do research” and “who can be researched.”

6. Warning on policy: Beware of “one size fits all”

Finally, this study has an important warning for policymakers: Do not assume that anti-discrimination policies that work in one place will work in another.

If cognitive processes are so different across cultures, then interventions also need to be context-specific. An education program that emphasizes “treating all people equally” may be effective in some cultures but may be counterproductive in others (because it reinforces perceptions of group boundaries).

7. Limitations: The Puzzle of Causation

Of course, this study also has obvious limitations. Cultural variables cannot be manipulated experimentally, so we cannot determine the direction of causation. Does social uncertainty lead to discrimination, or does discrimination lead to social instability? This knot of cause and effect cannot be untied.

Furthermore, the paper acknowledges that many unobserved variables (language, personal wealth, migration history, etc.) may confound the results. These are challenges inherent in cross-cultural research.

Extended thinking

1. What if discrimination is the “default mode” of cognition?

This research got me thinking: maybe the human brain was evolved to distinguish between in- and out-groups. If so, reducing discrimination is not about telling people “don’t discriminate”, but about redefining “who are our own people”.

The ultimate goal of globalization and multiculturalism may not be to eliminate group boundaries, but to expand the scope of “in-groups” until “all humanity” becomes “us”.

2. Tribalism in the Internet Age

Social media and recommendation algorithms make it easier for us to find our “strata”—those “people” who think the same way. This may exacerbate ingroup bias.

If people from individualistic cultures particularly crave group identification, and the Internet makes this identification extremely easy (just join a Facebook group), then we may be witnessing an era of “Tribalism 2.0.”

3. New challenges in the AI era

When AI systems start making resource allocation decisions (such as loan approval, job recommendation), will they also learn “in-group preferences”? If the training data is filled with human discrimination patterns, AI may amplify these biases.

And even more troubling is that AI will not “select color blindness” - it will process all available data, including group information. This can make discrimination more systematic and harder to detect.

Reading date: 2025-11-22 Notes organized: Dylan Chiang

Paper reading: Does Reinforcement Learning Really Incentivize Reasoning Capacity in LLMs Beyond the Base Model?

Sat, 22 Nov 2025 00:00:00 +0000

#paper information

Title: Does Reinforcement Learning Really Incentivize Reasoning Capacity in LLMs Beyond the Base Model? Source: arXiv Paper ID: 2504.13837 Link: |

Research background

Reinforcement Learning with Verifiable Rewards (RLVR) has recently achieved significant success in improving the inference performance of large language models, especially on mathematics and programming tasks.

Conventional wisdom holds that RLVR enables LLMs to continuously improve themselves, thereby acquiring new reasoning capabilities beyond the base model—just as traditional RL helps agents explore and learn new strategies.

But is this assumption really true?

This study attempts to answer a key question by systematically exploring the boundaries of the reasoning capabilities of RLVR-trained LLMs in various scenarios:

**Does RLVR training actually allow LLMs to acquire new reasoning capabilities beyond the base model? **

Research methods

Evaluate Design

The research team uses pass@k (at large k values) as an evaluation metric across multiple dimensions:

Model Family: multiple different LLM architectures
RL Algorithms: six popular RLVR algorithms
Benchmarks: Mathematics, Programming, Visual Reasoning

Analysis method

Pass@k Evaluation: Use large k values to detect the upper bound on the model’s capabilities
Coverage Analysis (Coverage Analysis): Measure whether the RLVR training model can produce solutions that the basic model cannot produce
Perplexity Analysis (Perplexity Analysis): Evaluate whether the output of the RLVR training model is still within the distribution of the basic model

Main findings

1. Current training settings fail to inspire fundamentally new inference patterns

Surprising core findings:

✅ Small k value (such as k=1): RLVR training model is better than the base model
❌ Large k value: The basic model instead obtains a higher pass@k score

**What does this mean? **

RLVR training mainly focuses on “selecting” the existing capabilities of the basic model, rather than creating new reasoning models. It’s like learning to pick the right tool faster from a toolbox already full of tools, but it doesn’t create new tools.

2. Inference capabilities are limited by the basic model

Through coverage analysis and perplexity analysis, the study found:

Observed reasoning ability derived from** and limited by the underlying model
Treating the basic model as an upper bound of capability, six popular RLVR algorithms perform similarly
All methods are far from optimal in exploiting the full potential of the underlying model

Basic model is the ceiling - No matter how trained, RLVR cannot make the model jump out of the capability boundary defined by the basic model.

3. Distillation methods show different advantages

In contrast, Knowledge Distillation (Distillation) exhibits different characteristics:

Ability to introduce new reasoning modes from the teacher model
Truly expands the reasoning capabilities of your model
Don’t just rearrange existing knowledge

This hints at an important methodological difference: distillation can “teach” the model new reasoning paths, while RLVR is only “optimizing choices.”

Research significance and inspiration

Reflections on RL in the field of LLM

This study reveals a fundamental limitation of current RLVR methods:

**Current RLVR training paradigms have yet to realize the potential of RL to inspire truly novel reasoning capabilities in LLMs. **

This is a far cry from what we’ve come to expect from RL. In traditional RL domains (such as game AI), RL can discover strategies that humans have never thought of. But in the field of LLM, the current RLVR method seems to be only “mining” rather than “creating”.

Future research directions

Research highlights the need to improve the RL paradigm:

Continuous Scaling (Continual Scaling)
- Long-term, continuous training process
- Rather than a one-time optimization
Multi-turn Agent-Environment Interaction (Multi-turn Agent-Environment Interaction)
- Truly interactive learning
- instead of training on static data sets
New Assessment Paradigm
- Pass@k (big k) instead of just looking at pass@1
- A more comprehensive assessment of capability boundaries

My understanding

After reading this paper, my biggest feeling is: this is really a “slap in the face” paper. In the past year, RLVR has been highly praised in the LLM field. OpenAI’s o1 and DeepSeek’s R1 both claim to use reinforcement learning to make the model “learn to think.” But this study tells us calmly: Wait, are you sure the model really learned something new? Or have you simply learned to pick the right answers faster from the toolbox you already have?

1. “Selector” vs “Creator”: A cruel truth

I think the core insight of this paper is to position RLVR as a “selector” rather than a “capability expander”. This may sound a bit abstract, but it’s actually pretty easy to understand. Imagine you have a student who already has many ways to solve the problem (basic model) in his mind, but he is not sure which method is best, so he tries randomly. RLVR training is like giving him a lot of practice questions, allowing him to learn “use this method when he sees this kind of question”, which improves his answering accuracy (pass@1).

But the problem is, if you give this student many opportunities to try (pass@k, big k), he will be able to try out the correct answer by “trial and error”. The RLVR training didn’t teach him any new problem-solving methods, it just made him find the right one faster. This is why when pass@k is large, the basic model performs better - because it still retains more diverse trial possibilities, and the model after RLVR training has been “finalized” and will only give priority to those paths with high rewards during the training process.

This finding is actually cruel, because it means that many of the “improvements in reasoning” we have seen in the past year may be just superficial.

2. The basic model is the ceiling: where should investments be placed?

This paper made me rethink resource allocation. If the basic model is the ceiling of capabilities, then should the industry invest more resources in pre-training instead of various fancy parameter adjustments for RLVR?

The current trend is that everyone is desperately thinking about how to use less data and more clever reward shaping to do RLVR. But this research tells us that no matter how optimized you are, you cannot escape the capabilities of the basic model. So instead of finessing on RLVR, wouldn’t it be more practical to directly train a stronger basic model?

Of course, this is not to say that RLVR is completely useless. In the pass@1 scenario (which is the most common situation in practical applications), RLVR can indeed significantly improve performance. But we have to clearly understand its essence: it is an “optimizer” rather than an “expander”.

3. Enlightenment of knowledge distillation: Why can it do what RLVR cannot?

The paper mentioned that knowledge distillation can introduce new reasoning patterns, which made me very curious. Why does distillation work but RLVR not?

My understanding is that distillation is essentially “knowledge transfer” - you learn “new ways of thinking” from a stronger teacher model. This is true expansion of capabilities. RLVR only “self-optimizes” within its own capabilities without the injection of external knowledge.

This makes me think that perhaps the truly effective training paradigm in the future should be a combination of “distillation + RLVR”: first use distillation to expand the boundaries of capabilities, and then use RLVR to optimize the quality of decision-making. Relying on RLVR alone to allow the model to “emerge” new capabilities may be an unrealistic expectation.

4. Pass@k’s philosophy: What exactly are we evaluating?

This paper also reminded me of the importance of evaluation methods. We are accustomed to using pass@1 to evaluate the model because it is closest to the real application scenario. But pass@1 may give us an overly optimistic illusion.

Pass@k (big k) reveals the “capability boundary” of the model - the upper limit of what it “might” get right. And pass@1 just tells us what the model would “normally” do. The gap between the two is what RLVR is doing: narrowing the distance between “possible” and “usual”.

But if we only look at pass@1, we will mistakenly think that RLVR really makes the model smarter. In fact, the “ceiling” of the model has not increased, only the “average performance” has increased. This distinction is important.

5. Thinking about the future: What does true reasoning require?

After reading this paper, I feel that the current RLVR paradigm may have been designed wrong from the beginning. We treat LLM as a “static policy space” and then use RL to optimize policy selection. But real reasoning shouldn’t be like this. Real reasoning should be “dynamic, interactive, and continuous learning.” Just like humans do not learn to think through problem solving exercises, but develop reasoning abilities through long-term interaction with the world, trial and error, and reflection. The “multiple rounds of agent-environment interaction” mentioned in the paper may be the right direction.

Maybe what we need is not to use RL to train the model, but to use RL to let the model “live” in an environment and truly explore, make mistakes, learn, and grow. That is the essence of RL, not the current “pseudo RL” done on static data sets.

6. A disturbing question

Finally, this paper made me think of a deeper question: If RLVR cannot make the model truly smart, then what is going on with these o1 models that claim to have “reasoning capabilities”?

There are two possibilities: First, their basic models are already very powerful, and RLVR just exploits this potential; second, they use some techniques that are not covered in this paper (such as chain-of-thought, test-time compute scaling).

In any case, this study reminds us: Don’t be fooled by superficial performance improvements, but think deeply about the essential source of capabilities. This is important for both doing research and making products.

Extended thinking

1. RLVR vs. Knowledge Distillation: What is the essential difference?

Why can distillation introduce new patterns?
What is the fundamental difference between the two in the training process?
Can the advantages of both be combined?

2. The importance of evaluation methods

What does the difference between Pass@1 vs. Pass@k (big k) reveal?
Are we overly reliant on a single metric to assess model capability?
What other dimensions of assessment can be explored?

3. The role of the basic model

Is it more worthwhile to invest in better base model training vs. more sophisticated RLVR methods?
Where can we improve Pre-training?
How is the “capability boundary” of the basic model formed?

Reading date: 2025-11-22 Notes organized: Dylan Chiang

Paper reading: Genome-wide study association of sexlessness

Thu, 20 Nov 2025 00:00:00 +0000

#paper information

Title: Genome-wide association study of sexlessness Journal: PNAS 2025 Vol. 122 No. 38 DOI: Research Number: e2418257122

Research background

This study examines the genetic and environmental correlates of lifelong sexlessness. The study used large-scale data from UK Biobank to analyze genotypic and phenotypic profiles of more than 400,000 participants.

Main findings

1. Relationship between regional factors and Sexlessness

Sex Ratio

Sex ratio at birthplace: no significant association with sexlessness
Gender ratio in current place of residence:
- Males showed a small but significant negative correlation (r = -0.07, P = 0.0004)
- Areas with fewer women have higher rates of male sexlessness

Income Inequality via Gini Coefficient

Income inequality based on current place of residence is significantly and positively related to sexlessness:

Male: r = 0.09, P = 2.8 × 10⁻¹⁵
Female: r = 0.14, P = 3.6 × 10⁻⁶
Overall: r = 0.15, P = 9.5 × 10⁻¹³

Explanation: Areas with higher income inequality also have higher rates of sexlessness

2. Genome-wide association study (GWAS) results

Samples and methods

Number of participants: 404,470 people of European ancestry
Sexless individuals: 3,897 people (~1%)
Number of SNPs: 10.6 million single nucleotide polymorphisms

Main findings

Significant site: A site on chromosome 1 (top SNP: rs912773)
- This site is located in an intergenic region, the nearest gene is LOC107984933
- Single SNP effect size is extremely small

SNP-based Heritability

Population	Heritability	Standard error	P value
Men	17%	4%	2.3 × 10⁻⁵
Women	14%	3%	3.1 × 10⁻⁶
Overall	12%	-	1.9 × 10⁻⁹

IMPORTANT OBSERVATION:

Genetic correlation between males and females is 0.56 (SE = 0.17, P = 0.0007) -Suggests that SNP associations are directionally consistent between men and women but only partially overlap

Evolutionary evidence

The study examined changes in allele frequency of rs4654352, one of two genome-wide significant SNPs, in the Ancient DNA Database (AADR):

Time Span: Past 12,000 years
Sample: 3,458 ancient individuals from Europe
Trend: A continued decrease in frequency of the A allele associated with higher sexlessness
- 10,000-12,000 years ago: 87%
- 0-2,000 years ago: 56%

Conclusion: Consistent with negative selection pressure targeting sexlessness-related alleles

3. Polygenic Score (PGS) analysis

UK Biobank Internal Validation

PGS significantly predicted sexlessness in an independent sibling sample (Beta = 0.016, SE = 0.005, P = 0.002)
Gene-Environment Correlation Test:
- Only 7.4% of the effect can be attributed to gene-environment correlations among families
- This compares to 49% for education, 48% for IQ, and 15% for BMI
- Conclusion: GWAS signals are minimally affected by gene-environment correlations

Australian independent sample verification

PGS was significantly associated with multiple relevant phenotypes in Australian samples (N = 1,354-13,532):

Phenotype	Direction of Association	Significance
Number of relationships lasting ≥3 months	Negative correlation	✓✓ (overall)
Never had a sexual partner	Positive correlation	✓✓ (female)
Number of sexual partners	Negative correlation	✓✓ (overall)
Age at first sexual intercourse	Positive correlation	✓✓ (overall, men and women)

Religious Impact (N = 6,764):

Religious people are more likely to have no sexual experience in their lives (P < 0.0001)
Gender analysis: significant for women (P = 0.002), not significant for men (P = 0.06)

4. Genetic overlap with other complex traits

Genetic correlation with childlessness

Gender	Genetic correlation	Standard error	P value
Female	0.68	0.10	7.0 × 10⁻¹¹
Male	0.65	0.09	7.0 × 10⁻¹⁴

Showing high genetic overlap between sexlessness and childlessness

Other important genetic correlates

Positive correlation (genes associated with sexlessness are also associated with the following traits):

Cognitive and Socioeconomic Status:
- Child vs. Adult IQ
- Education level
- Income and socioeconomic status
Mental Illness:
- Autism Spectrum Disorder
- Anorexia
Lifestyle:
- Less substance use (alcohol, cigarettes, marijuana)

Negative correlation:

Mental Illness: -ADHD
- Post-traumatic stress disorder
Psychosocial Traits:
- Extraversion
- Relationships with friends and family -Subjective well-being

Phenotypic correlation analysis

Mental health indicators (especially men)

Sexless men were significantly more likely to:

Suffering from neurosis
feel unhappy
feeling lonely
Believe that life lacks meaning

More likely to live alone
Social visits are less frequent
Lack of intimacy or someone to confide in

Physical characteristics

MEN: Lower grip strength and lean body mass (arm muscle mass)
Overall: Start wearing glasses earlier

Substance Use

Associated with less use of:

alcohol
cigarettes
Marijuana

Education and socioeconomic status

Phenotype related: -Positively associated with higher educational attainment (for both men and women)
- Negatively associated with lower household income
- Place of residence with high levels of economic deprivation (Townsend index)
Genetic correlation (opposite direction):
- Positive genetic correlation (~0.5) with higher IQ, education, income, and socioeconomic status

Research limitations and precautions (Box 1: Cautionary Notes)

1. Culture Specificity

The association between genetics and behavior reflects the interaction between genetics and the environment within a specific cultural context
Findings from contemporary Western societies should not be generalized to other ancestries or cultural backgrounds

2. Individual results cannot be predicted

Polygenic scores are not suitable for predicting sexlessness at the individual level
Approximations based on large samples and small effects
It is inevitable that there will be people with high scores but low phenotypes, and vice versa

3. Complexity

Heredity is just one aspect of a complex network that influences behavior
Environmental, social, cultural and personal factors all play an important role
This study explores correlation, not causation

4. Measurement Error

-Sexlessness is a self-report variable

There may be under-reporting or over-reporting, leading to classification errors
Social norms may influence reporting behavior (men may over-report, women may under-report)

Discussion and Interpretation

1. Scientific evidence for the “nerd” stereotype?

The study found a combination of characteristics that closely aligned with the “nerd” stereotype:

Introversion
Wearing glasses when young
Smart and academically successful
Physically weak
Social isolation, loneliness
Higher autism spectrum traits
Nervous
Less use of drugs and alcohol

2. Complexity of Causal Direction

Sexual inexperience → Mental health issues: Sex is a basic human drive, and its lack may affect happiness
Mental Health Issues → Sexual Inexperience: Psychological issues may make it more difficult to approach or attract a partner
Third Variable: Other factors may increase the likelihood of both

Genetic correlations provide some clues:

The genetic correlation between sexlessness and depression, anxiety, and loneliness is negative (not positive)
But there is a positive genetic correlation with autism spectrum disorder, consistent with the characteristics of interpersonal difficulties

3. The Paradox of Intelligence and Reproductive Success

Evolutionary Expectation: Intelligence and resources should be attractive traits
Research Finding: Positive genetic correlation (~0.5) with higher IQ, education, and socioeconomic status

Possible explanation:

Young adults with high educational/socioeconomic potential may avoid intimate relationships in order to pursue career plans
This pattern may continue into later life

Challenges to the Sexual Selection Hypothesis:

The sexual selection hypothesis of human intelligence proposes that intelligence evolves due to its contribution to mating success.
This study finds that intelligence is positively related to sexlessness, strengthening the evidence against this hypothesis

4. Sexlessness vs. Asexuality

-Asexuals account for about 1% (similar to the sexless proportion in this study)

But more than half of asexuals are not virgins
Asexuals have lower average education and socioeconomic status (contrary to this study’s findings)
The correlation between male sexlessness and regional female scarcity & income inequality difficult to explain by asexuality

Conclusion: Asexuality may contribute to part of the phenotype, but it is not the main factor

Evolutionary meaning

1. Sexlessness as a fitness proxy variable

Significant SNP heritability allows genetic correlation analysis
Significant negative genetic correlation with number of heterosexual sexual partners and number of children
May be a more “pure” ancestral fitness surrogate than other variables
- Less affected by social sexual orientation, pickiness in mate selection, family planning, career orientation, etc.

2. Selected Evidence from Ancient DNA

The A allele (associated with sexlessness) has continued to decline over the past 12,000 years
Expected negative selection pressure
but need to be interpreted with caution:
- The exact function of this variant is unknown
- Observed effect sizes are small

3. Maintenance of genetic variation

Genetic variation may be maintained through mutation-selection balance:

Negative selection acts on this trait
New mutations continue to arise in the population

Methodological Highlights

1. Large-Scale GWAS

404,470 participants, 10.6 million SNPs
Corrected genetic correlation matrix and 25 genetic principal components using linear mixed models

2. Multi-sample verification

UK Biobank internal validation of sibling samples
External verification by Australian independent samples

3. Gene-Environment Correlation Test

Comparison of within-family vs. between-family effects
Only 7.4% can be attributed to family-level gene-environment correlations (much lower than the 49% for educational attainment)

4. Ancient DNA Integration

Allen Ancient DNA Resource (AADR) v62.0
3,458 ancient European individuals
A time span of 12,000 years

Policy and Practical Implications

1. Public Health

Sexless individuals, especially men, may face higher mental health risks
Need targeted mental health support

2. Education

Understand the complex interactions of genetic and environmental factors
Avoid the misunderstanding of genetic determinism

3. Research Direction

Need more detailed information on the causes of sexlessness
Distinguish between “inability to attract a partner” vs. “lack of sexual desire”
Triangular validation of different methodologies (e.g. Mendelian randomization)

Personal reflections and comments

Research Advantages

Large sample size and strong statistical testing power
Multi-level analysis: phenotype, GWAS, PGS, genetic correlation, ancient DNA
High transparency: clearly state limitations and precautions (Box 1)
Cross-sample validation: Increase the reliability of findings

Research Limitations

Limited Causal Inference: Only correlation can be described, and it is difficult to establish the direction of causation.
Culture Specificity: Findings may not generalize to non-Western societies
Self-reporting bias: Sexlessness is a sensitive issue and there may be reporting bias.
Heterosexuality: sexlessness may include many different causes (asexuality, social difficulties, religion, etc.)

Future research directions

Mendelian Randomization: Unraveling Causal Complexity in Larger Samples
Longitudinal Study: Tracking the Development of Sexlessness
Cross-Cultural Research: Test whether the findings are generalizable
Mechanism Research: In-depth exploration of the biological and social mechanisms related to intelligence-sexlessness

Key data summary table

GWAS samples and heritability

Population	Cases	Controls	Total	Number of valid samples	SNP-h²	SE	P-value
Overall	3,897	400,573	404,470	15,438	0.12	0.02	1.9×10⁻⁹
Men	1,844	184,191	186,035	7,303	0.17	0.04	2.3×10⁻⁵
Women	2,053	216,382	218,435	8,135	0.14	0.03	3.1×10⁻⁶

Environmental factor correlation

Factor	Population	Correlation Coefficient	P-Value
Regional sex ratio	Male	-0.07	0.0004
Income inequality	Men	0.09	2.8×10⁻¹⁵
Income inequality	Women	0.14	3.6×10⁻⁶
Income inequality	Overall	0.15	9.5×10⁻¹³

My understanding

After reading this paper, I think the most interesting thing is that it uses large-scale genetic data to explore a phenomenon that is rarely studied in science-lifelong sexual inexperience. This may have been dismissed as purely a matter of personal choice or luck in the past, but this paper tells us that there are actually more complex biological and social factors at work.

First, genetics does play a role. Although the effects of individual genes are small, combined, approximately 12-17% of the variation can be explained by heritability. This does not mean that “having a certain gene will make you single all your life.” It means that some genetic tendencies may affect your social skills and personality traits, which in turn indirectly affects your chances of establishing an intimate relationship.

What impressed me most was the discovery of the “nerd” characteristic group - smart, introverted, wearing glasses when he was young, relatively weak, not very likely to drink or smoke, and prone to nervousness and loneliness. These characteristics are statistically related to a lifetime of sexual inexperience. This is really similar to the stereotypes we see in pop culture, but I didn’t expect it to be supported by scientific evidence.

Even more counterintuitive is the matter of intelligence. It stands to reason that smart people should be more attractive and resourceful, but this study found that at the genetic level, genetic variations related to sexlessness are actually related to higher IQ and higher education. This is really contradictory! One explanation proposed by the researchers is that perhaps smart young people focus more on career development and postpone their love life. However, once this pattern is formed, it may continue for the rest of life.

Another discovery that I found really cool was the analysis of ancient DNA. They found that the frequency of genetic variants associated with sexlessness has been declining over the past 10,000 years. This fits with the predictions of evolutionary theory - if a gene makes it harder for you to reproduce, it should be eliminated. It’s really amazing to be able to verify this trend in ancient human bones.

Of course, this study also has many limitations. It can only tell us “correlation” and cannot prove causation. Moreover, all data come from the United Kingdom, and the cultural background is very specific, and may not be applicable to other societies. In addition, the phenomenon of “lifelong sexual inexperience” is complex in itself - it may be asexuality, it may be social difficulties, it may be religious choice, it may even be just bad luck. Mixing all these different causes into one analysis may obscure some important details.

Overall, I think the greatest value of this study is that it opens a new research perspective. It tells us that human intimacy and sexual behavior are not only the product of culture or personal choice, but also the interaction of multiple factors such as genetics, environment, and socioeconomic factors. This complexity is the most fascinating part of human behavior.

Paper reading: How People Use ChatGPT - In-depth analysis of the ChatGPT usage behavior of 700 million users around the world

Tue, 04 Nov 2025 00:00:00 +0000

I recently read the important research paper “How People Use ChatGPT” from the research team of OpenAI, Duke University and Harvard University. This is the first large-scale usage behavior study based on internal data of ChatGPT. Through innovative privacy protection methods, the study analyzed 26 billion messages from 700 million users from the launch of ChatGPT in November 2022 to July 2025, revealing the actual usage patterns and economic value of generative AI.

Research methods and data

Privacy-protecting automated classification system

The biggest technical highlight of this research is its privacy protection method:

Automated classification process:

Automatically analyze message content using LLM classifier, humans never view the original message
First remove sensitive information through PII cleaning tools
Only aggregated results are analyzed, any query must return a combination of at least 100 users

Classification Category:

WORK/NON-WORK USE: Based on whether the message is related to paid work
Conversation Topics: 24 subcategories, summarized into 7 major themes
Interaction intent: Asking, Doing, Expressing
WORK ACTIVITIES: 332 intermediate-level work activities based on O*NET system

Data sample

Main Sample: Random sample of 1.1 million conversations from May 2024 to June 2025
User Sample: A subset of approximately 130,000 users used for demographic analysis
Exclusion Conditions: Users who have not logged in, users under 18 years old, users who have deleted their accounts, and users who have opted out of training

📈 1. Growth and structure: explosive growth of non-work purposes

Overall growth data

User size:

July 2025: 700 million weekly active users (approximately 10% of the global adult population)
Average daily message volume: 2.5 billion (29,000 messages per second)
Growth rate: The fastest spreading technology in history, surpassing all precedents

Non-work usage increases faster

Core Finding: Non-work-related uses are growing much faster than work uses.

Time point	Non-work messages	Proportion	Work messages	Proportion	Total
June 2024	238 million	53%	213 million	47%	451 million
June 2025	1.911 billion	73%	716 million	27%	2.627 billion

Key Insights:

Non-work messages increased 8 times (238%), work messages increased 3.4 times (236%)
In June 2025, non-work use accounted for 73%, which is absolutely dominant
This change mainly comes from changes in the usage patterns of existing users rather than changes in the composition of new users

Use dynamic evolution of topics

Three mainstream uses (accounting for nearly 80% of total use):

Practical Guidance: long-term stability at about 29%
- Tutorial teaching (accounting for 36% of practical guidelines)
- How-to suggestions (accounting for 30% of practical guidance)
- Creative ideas
Writing: 36% → 24% (decline within one year)
- But it is still the first largest category in work use (about 40%)
- The management/business group has a higher usage ratio (>50%)
- Key findings: About 2/3 of the writing uses are to modify the text provided by users (editing, criticizing, translating, summarizing) rather than creating from scratch
Seeking Information: 14% → 24% (rapid increase)
- Search for specific people, events, products, recipes and more
- Become a closer alternative to web search

Other theme variations:

Technical Help: 12% → ~5%
- Programming related accounted for only 4.2%, significantly lower than expected
- May switch to IDE plug-ins, professional programming tools or API scenarios
Multimedia: 2% → >7%
- Short-term jump after the image generation function is launched in April 2025
- Subsequent pullback but maintains higher baseline

💼 2. Work scenarios and tasks: AI as a decision support system

Writing: The common mother task of white-collar workers

Among work-related messages, writing accounts for about 40% and is the most important work purpose:

Career Differences:

Management/Business: 52% of work-related news is writing
Education/Medical: 49-50%
Computer related: Relatively low, more emphasis on technical assistance

Content Analysis:

About 2/3 of writing requests are to revise existing text (editing, criticizing, translating, summarizing)
About 1/3 is created from scratch (new emails, briefings, proposals, etc.)
This explains the high satisfaction and steady growth of writing applications: the risks are manageable and can be directly integrated into existing workflows

Work activity analysis based on O*NET

A study mapping work messages to the U.S. Department of Labor’s O*NET work activity system found:

Seven major work activities cover approximately 77% of all messages:

Activity Categories	All News	Work News	Features
Get information	19.3%	6.7%	Focus more on professional information in work settings
Interpret information for others	13.1%	7.3%	Collaboration and knowledge transfer
Record/documented information	12.8%	13.2%	The first category of work scenarios
Providing consultation and advice	9.2%	3.1%	Professional service core
Creative thinking	9.1%	9.3%	Problem solving and innovation
Decision-making and problem-solving	8.5%	10.6%	The second largest category of work scenarios
Working with computers	4.9%	7.7%	Technology-intensive jobs

High homogeneity across occupations:

Regardless of management, engineering, education, medical or administrative occupations, the top 5 work activities are almost the same:

Obtain information
Decision-making and problem-solving
Record/Documentation
Think creatively
Explain information to others

This shows that ChatGPT’s value creation model in different occupations is highly consistent.

🎯 3. Interaction type and experience: transformation from execution to thinking

Asking/Doing/Expressing Framework

The study divided user intent into three categories and found significant trend changes:

Overall Distribution (May 2024):

Asking: 49% - Seeking information or advice to help make decisions
Doing: 40% - Request to complete a specific task
Expressing: 11% - Expressing opinions or feelings

Trend Change (to June 2025):

Asking: 51.6% (↑)
Doing: 34.6% (↓)
Expressing: 13.8% (↑)

Differences in work scenarios:

Asking: 35%
Doing: 56% (about 75% is writing tasks)
Expressing: 9%

Experience quality analysis

Overall Satisfaction Growth:

Positive/negative review ratio: from about 3:1 → 4:1
Experience quality is highly related to usage intention

Positive rating by topic:

Self-expression: Highest (good/bad ratio >7)
Multimedia: Lower (about 1.7)
Technical Help: Low (~2.7)

Classification by Intent:

The satisfaction level of Asking is significantly higher than that of Doing and Expressing
This is consistent with the core value of “helping thinking and decision-making”

👥 4. Ethnicity and diffusion: from elite tools to universal applications

The disappearance of gender differences

Amazing transformation:

Early (end of 2022): ~80% of active users have typically male names
June 2025: 48% (slightly more female)

Theme Preference Differences:

Female User: Prefer writing and practical guidance
Male Users: More technical assistance, information search and multimedia

Age structure

Young user-led:

News that 18-25 years old contributes nearly 46%
The older the age, the higher the proportion of work purposes (except those aged 66+)

Geographical diffusion: Counterattack by low- and middle-income countries

GDP vs Adoption Rate:

Countries with GDP per capita of 10,000-40,000 US dollars have the fastest growth rate
Between 2024 and 2025, low- and middle-income countries will achieve leapfrog growth
Overturned the traditional model of “AI technology first popularized in developed countries”

Education and career: The advantages of higher education and higher income

Academic impact:

The higher the academic qualifications, the higher the proportion of work purposes
- <Bachelor’s degree: 37%
- Bachelor’s degree: 46%
- Graduate students: 48%
Users with higher education are more likely to use the Asking mode (decision support)

Career Differences:

Computer related: 57% for work purposes
Management/Business: 50%
Engineering/Science: 48%
Other majors: 44%
Non-professional: 40%

🔥 8 interesting/counterintuitive findings

1. Non-work usage far exceeds expectations

73% of messages are not for work purposes
The economic benefits of home production/personal decision-making support may be significantly underestimated
Collis and Brynjolfsson estimate annual consumer surplus in the United States alone to be $97 billion

2. Programming accounts for only 4.2%

Completely inconsistent with the stereotype of “AI = programming”
A large number of program auxiliary tasks have been transferred to IDE plug-ins, professional tool chains, and API scenarios

3. Writing ≠ Generating from scratch

Mostly “modify your text” (editing, criticizing, translating, summarizing)
This explains the steady growth of satisfaction and adoption rates: risks are controllable and can be directly integrated into the work process

4. Asking trend is rising

More and more users regard ChatGPT as a decision support system rather than a ghostwriting tool
The satisfaction level of Asking messages is significantly higher than that of Doing messages

5. The proportion of women has increased and overtaken

From 80% male users to a balanced ratio of men and women
Display product affinity and scene diversity improvement

6. Solid education/training use cases

About 10% of all messages are teaching/tutoring
Accounting for 36% of “Practical Guidelines”, demand is stable

7. High degree of isomorphism across professions

Regardless of industry, the essence comes back to “information → understanding → decision-making”
The value of AI lies in shortening the closed loop of thinking, rather than just doing menial work

8. Experience data supports values

Asking’s positive rating is significantly higher than Doing’s
In line with the core need of “help me think clearly first”

💡 Strategic inspiration for business/education/products

Content and service design

1. Focus on “modifying/improving the original text”

Proofreading, rewriting, translating, summarizing, and formatting
Easier to implement and be trusted than “generating from scratch”
Market Positioning: Writing enhancement tool rather than authoring tool

2. “Consultative process” for decision support

Understand demand constraints and preferences first
Provide plans and risk assessment
Applicable scenarios:
- Policy briefing
- Project evaluation
- Purchase price comparison
- Compilation of key legal issues

Function priority

Writing Enhancement Kit:

Multilingual translation + style templates
One-click “Vocal Tonality Calibration”
Industry-specific vocabularies and formats

Asking Assistant:

Decision trees and situational branches
Display of questionable evidence (citations/calculations/assumptions)
Risk warning and hypothesis testing

Knowledge Workflow:

Fetch → Extract → Archive → Compare → Decision Memo
Tandem tools rather than point solutions

Market expansion strategy

Geographic expansion:

Low-price + offline-friendly solution for low- and middle-income markets
Because these areas are growing fastest

Vertical Industry:

Education Line: Tutoring/lesson tutoring templates (10% share of stable demand)
Enterprise Services: Meeting Minutes → Decision Form Automation

Monetization and ROI

Personal User:

Writing, revision and translation are high-frequency + urgent needs
Easy transfer to paid subscriptions

Enterprise Customers:

Decision support can take the B2B consultant value-added route
Compliance summary, risk reminder, and professional report generation

🔬 Methods and Credibility Assessment

Research Advantages

1. Unprecedented data scale

700 million users, 26 billion messages
Global sample rather than single country

2. Innovative privacy protection methods

Automatic classification by LLM, humans never view the original content
Data Clean Room aggregated analysis
Exclude combinations with <100 people to protect privacy

3. Multi-dimensional classification system

Work/non-work, topics, intentions, work activities
Solid theoretical foundation (O*NET system)

Classifier verification

Study to verify classifier performance on WildChat public data set:

Task	Human-machine consistency (κ)	Description
Work/Non-Work	0.83	Excellent
Asking/Doing/Expressing	0.74	Good
Conversation Topics	0.56	Moderate
O*NET Work Activities	0.47	Moderate (332 Category Complex)
Interaction quality	0.14	Poor (highly subjective)

Key Findings:

Excellent performance in objective classification (work/non-work)
Subjective classification (quality assessment) is more difficult, but still captures directional signals
Positive correlation with user thumb rating

Research limitations

1. Sample bias

Exclude users who are not logged in or under 18 years old
May underestimate the proportion of young users and casual users

2. Classification accuracy

LLM classifier still misjudges
Especially categories with blurred boundaries

3. Causal inference

Mainly descriptive statistics
The causal mechanisms of changes in usage patterns still require further study

Summary and Outlook

This research provides us with first-hand, unprecedented scale of empirical data on the use of ChatGPT. The most important findings are:

1. From work tools to life assistants: Non-work uses have become dominant, reflecting that the value of generative AI far exceeds work efficiency improvements

2. The value of decision support: The rise of Asking model (decision support) shows that the core value of AI lies in improving the quality of decision-making

3. Achievement of popularization: Gender differences disappear and geographical diffusion accelerates, indicating that the technology has overcome initial adoption barriers

4. Consistency across domains: Similar usage patterns across professions point to the potential of AI as a general cognitive tool

This research not only reveals the real-life use of ChatGPT, but also provides an important foundation for understanding the long-term impact of generative AI on the economy and society. As AI technology continues to develop, we need to continue to pay attention to the evolution of these usage models to maximize AI’s contribution to human well-being.

Paper Information:

Title: How People Use ChatGPT
Authors: Aaron Chatterji (OpenAI/Duke), Tom Cunningham (OpenAI), David Deming (Harvard), Zoë Hitzig (OpenAI/Harvard), Christopher Ong (OpenAI/Harvard), Carl Shan (OpenAI), Kevin Wadman (OpenAI)
Institution: OpenAI, Duke University, Harvard University
Published: September 15, 2025
Paper address:

Paper reading: LLMs CAN GET 'BRAIN ROT'! - Research on cognitive decline in large language models

Fri, 31 Oct 2025 00:00:00 +0000

I recently read the important research paper “LLMs CAN GET “BRAIN ROT”!” from institutions such as the University of Texas at Austin, Purdue University, and Texas A&M University. This study proposed and verified the “LLM Brain Rot Hypothesis” for the first time, and found that continued exposure to spam online text will lead to long-lasting cognitive decline in large language models. This is a very warning discovery.

Research background and assumptions

Source of inspiration

“Brain Rot” was named the word of the year by Oxford Dictionary in 2024. It is used to describe the cognitive decline caused by modern people’s addiction to a large amount of trivial and unchallenging online content. This study shows that the impact of Internet addiction on human cognition is mainly reflected in three dimensions:

Attention Capacity: The constant flow of online information undermines the ability to concentrate
Memory Process: Rich online information changes the way knowledge is stored and retrieved
Social Cognition: Online interactions reshape self-concept and affect self-esteem

Research questions

Since large language models acquire human-like cognitive capabilities by learning trillions of data on the Internet, will they also experience a similar “Brain Rot” phenomenon? The research team established the LLM Brain Rot Hypothesis: Continuous pre-training on junk web text will lead to long-lasting cognitive decline in large language models.

Experimental design and methods

Garbage data definition

To test the hypothesis, the research team constructed spam and control datasets from social media (Twitter/X) and proposed two orthogonal spam data measures:

M1 (Engagement): Based on the popularity of tweets (number of likes, retweets, replies) and length (number of tokens), select short but highly popular content as spam data

M2 (Semantic Quality): Based on content semantic quality, including:

Conspiracy theories, exaggerated claims or unfounded assertions
Sensational headlines and clickbait language
Superficial topic content
Attractive style

Experimental model

The study was conducted on four pre-trained and instruction-tuned models:

Llama3 8B Instruct
Qwen2.5 7B Instruct
Qwen2.5 0.5B Instruct
Qwen3 4B Instruct

Benchmark test

The study assessed multiple dimensions of cognitive function:

Cognitive Function	Benchmark Testing	Assessment Content
Reasoning Skills	ARC Challenge	Scientific Problem Solving Skills
Long Context Understanding	RULER	Long-term memory retrieval and comprehension
Ethics	HH-RLHF, AdvBench	Safety Compliance Ability
Personality Traits	TRAIT	The Big Five and the Dark Triad

Key findings

1. Garbage intervention leads to cognitive decline

The study found that the junk intervention produced non-trivial effects on reasoning and long-context ability (Hedges’ g > 0.3). In particular, the M1 (engagement) intervention caused more significant impairments in functional cognition (reasoning or long context) and safety.

2. Dose response effect

Experiments on Llama3 8B Instruct show that when the proportion of garbage data increases from 0% to 100%:

ARC-Challenge (COT): 74.9 → 57.2 (down 17.7 points)
RULER-CWE: 84.4 → 52.3 (down 32.1 points)

This demonstrates a clear dose-response relationship between junk data and cognitive decline.

3. Changes in personality traits

Litter intervention not only affects cognitive abilities, but also changes LLM’s personality traits:

Negative changes:

Increased levels of psychosis
Enhance narcissism and Machiavellian traits
Decreased agreeableness

Positive changes:

Increased openness and extraversion (in some cases)

4. Differences between M1 and M2

The study found that M1 (engagement) and M2 (semantic quality) interventions produced distinct effects. The M1 intervention resulted in more negative effects, especially on safety and personality traits, demonstrating that engagement is a new dimension independent of semantic quality.

Failure mode analysis

###Thought-Skipping

By analyzing the reasoning process of LLM in the ARC task, the study identified five typical failure modes, three of which are related to “thinking jumps”:

No thinking: The model answers directly without thinking.
No plan: The model starts thinking without developing a step-by-step plan.
Jump Steps: Starting reasoning but not completing all planning steps

More than 98% of failure cases are related to thinking jumps. In M1 garbage intervention, 84% of failures belong to the “no thinking” type.

Popularity vs Length

Research has found that popularity (a non-semantic indicator) is a better indicator of the Brain Rot effect than length:

Popularity plays a more critical role in reasoning tasks
Length is more important in long context understanding
Both have different effects on different tasks

Mitigation attempts and persistence

1. Reflective Reasoning

Try two reflection methods to fix mental jumps:

Self-Reflect: Model self-reflection (limited effect)
Ext-Reflect: Provide feedback using an external strong model (GPT-4o-mini)

The results show that even with strong external reflection, the model cannot fully recover to baseline levels.

2. Posterior instruction tuning

The study also tested extended instruction tuning and continuous control training:

Even with 4.8x more garbage intervention instruction tuning data
Still unable to completely reverse the Brain Rot effect
Significant gaps with benchmarks remain: ARC-C COT (17.3%), RULER (9%), AdvBench (17.4%)

This shows that the Brain Rot effect has been deeply internalized and existing mitigation methods cannot fundamentally solve the problem.

Significance and Enlightenment

1. Warning on LLM training

This study provides the first significant evidence of data quality as a causal driver of LLM capability degradation, re-considering continuous pre-training data management as a safety issue during training.

2. “Cognitive health check” is required

The findings call for routine “cognitive health checks” for deployed LLMs, similar to health monitoring in the medical field.

3. The urgency of data curation

As LLM continues to scale and ingest ever larger amounts of network data, careful data curation and quality control are critical to preventing cumulative damage.

Thinking and Reflection

This research reveals a disturbing reality: the social media content we are exposed to every day may not only affect human cognition, but also impair the “cognitive” capabilities of AI models. While LLMs obviously do not have the same “grey matter” or “neurons” as humans, they do have parameters and attention mechanisms that can be similarly “overfitted” or “distracted” by certain data patterns.

The most worrying finding in the study is that the Brain Rot effect persists even with posterior tuning using large-scale clean data. This implies that we need to fundamentally rethink data collection and pre-training practices, focusing not only on model performance, but also on the “cognitive health” of the model over the long term.

Conclusion

“LLMs CAN GET “BRAIN ROT”!” This research contributes valuable insights to the field of AI security, and is the first to systematically prove the negative impact of spam online text on large language models. The research not only verified the LLM Brain Rot hypothesis, but also revealed the fine mechanism of cognitive decline, pointing out the direction for future AI safety research.

With the rapid development of AI, we must face the importance of data quality, establish stricter data curation standards, and develop an effective AI “cognitive health” monitoring mechanism. Only in this way can we ensure that AI systems maintain their due “cognitive purity” while serving humans.

Paper Information:

Title: LLMs CAN GET “BRAIN ROT”!
Authors: Shuo Xing, Junyuan Hong, Yifan Wang, Runjin Chen, etc.
Institution: University of Texas at Austin, Purdue University, Texas A&M University
Published: arXiv:2510.13928v1 [cs.CL] October 15, 2025
Paper address:
Project Page:

Internet ecology and governance in mainland China: An observation of platform, algorithm and personal information risks

Thu, 30 Oct 2025 11:10:00 +0800

Speech Overview

This speech will deeply explore the uniqueness and governance model of mainland China’s Internet ecosystem, and analyze the interaction between platforms, algorithms and personal information risks.

Main content

1. The particularity of mainland China’s Internet ecology

Concepts of cyber sovereignty and digital governance
The historical development trajectory of mainland Internet ecology
Comparison with international Internet governance models
Regulatory framework and policy evolution

2. Platform governance mechanism

Roles and responsibilities of large technology platforms
Content moderation and speech control
Platform monopoly and competition policy
Data governance and open strategy

3. Algorithm recommendation system

The central role of algorithms in the platform economy
How the recommendation algorithm works
Algorithmic transparency and explainability
Algorithmic bias and fairness issues

4. Personal data risk and protection

Current status of personal data collection and use
Privacy protection and regulatory challenges
Data security and cross-border transmission
User rights protection mechanism

5. Future trends and challenges

Combination of artificial intelligence and network governance
International cooperation and conflict
Innovation development and risk control
Sustainable development of digital society

Lecturer introduction

Dylan Chiang, a cross-strait Internet and AI innovator, focuses on digital policy research and Internet ecological observation. With rich cross-field experience, he is committed to promoting cross-strait exchanges and understanding in the fields of network governance and digital innovation.

Speech information

Course: General Courses
Sponsor: National Institute of Development and Mainland China, Chinese Culture University
Date: October 30, 2025 (Thursday)
Time: 11:10 AM - 01:10 PM
Location: Classroom 507, Daen
Language: Chinese

General Course Lecture Topic: Internet Ecology and Governance in Mainland China Lecturer: Dylan Chiang Hosted by: National Institute of Development and Mainland China, Chinese Culture University Date: October 30, 2025 Time: 11:10 - 13:10 Location: Classroom 507, Daen

Internet Ecology and Governance in Mainland China: A Systematic Analysis of Surveillance Capitalism and Algorithmic Discrimination

Thu, 30 Oct 2025 09:10:00 +0800

Speech Overview

This speech will systematically analyze the phenomenon of surveillance capitalism and algorithmic discrimination in mainland China’s Internet ecosystem from the intersection of political economy and technological sociology.

Main content

1. Theoretical Framework of Surveillance Capitalism

Concept and characteristics of surveillance capitalism
Data as a new production factor
Predict commodification and behavior modification
Comparative analysis with traditional capitalism

2. The practice of surveillance capitalism in mainland China

Data collection mechanism for large platforms
Business model of personalized recommendation system
Combination of government data and platform data
Digital RMB and behavior tracking

3. Types and mechanisms of algorithmic discrimination

Definition and classification of algorithmic discrimination
Discriminatory results in recommendation algorithms
Bias in credit assessment systems
Recruitment and credit discrimination cases

4. The formation mechanism of discrimination

Bias in training data
Influence of values of algorithm designers
The amplifying effect of systemic bias -Feedback loop and prejudice solidification

Impact on personal rights and interests
Rising social inequality
Commercialization of public space
Impact of democratic participation

6. Regulatory and governance challenges

Inadequacies in the existing legal framework
Difficulty of technical supervision
The need for international coordination
Balance between innovation and regulation

7. Reform paths and suggestions

Transparent algorithmic governance
Diversified technology development
Public participation mechanism
Improved rights protection system

Lecturer introduction

Dylan Chiang, a cross-strait Internet and AI innovator, focuses on digital policy research and Internet ecological observation. Analyze platform economy and digital governance issues from the perspective of political economics, and strive to promote fairness and justice in the digital society.

Speech information

Course: Research on Social Topics in Mainland China
Sponsor: National Institute of Development and Mainland China, Chinese Culture University
Date: October 30, 2025 (Thursday)
Time: 09:10 - 11:10
Location: Classroom 313, Dacheng
Language: Chinese

Special Research Lecture on Mainland Chinese Society Topic: Internet Ecology and Governance in Mainland China—Systematic Analysis of Surveillance Capitalism and Algorithmic Discrimination Lecturer: Dylan Chiang Hosted by: National Institute of Development and Mainland China, Chinese Culture University Date: October 30, 2025 Time: 09:10 - 11:10 Location: Dacheng Classroom 313

Paper reading: No gender differences in attraction to young partners: A study of 4,500 blind dates

Wed, 29 Oct 2025 00:00:00 +0000

#paper information

Title: No gender differences in attraction to young partners: A study of 4,500 blind dates Authors: Paul W. Eastwick, Eli J. Finkel, Eva M. Meza, Kellie Ammerman Journal: PNAS 2025 Vol. 122 No. 5 DOI:

Summary of thesis content

1. Research background: Challenges to traditional concepts

In cross-cultural studies of mate choice, one of the most consistent findings is often this: Men prefer younger women, while women prefer older men. This is usually explained by evolutionary psychology as: men seek fertility (younger), women seek resources and status (older). However, most of these studies are based on “questionnaire surveys” (What kind of people do you like?). This paper asks: Do these preferences matter when people actually date face to face?

Researchers collaborated with Tawkify, a US-based matchmaking company, to collect data from 6,262 participants.

Sample Characteristics: Average age 46.8 years (range 22-85 years), mainly middle-aged people seeking long-term partners.
Data volume: 4,542 real blind dates, resulting in 9,084 date reports.
Measurement: “Romantic Attraction” after the date, “Overall Rating” and “Willingness to go on a second date”.

3. Core findings

A. Both men and women love young people (No Gender Difference)

This is the most amazing discovery. The data showed that participants generally rated younger subjects more highly. What’s more, this preference for youth does not differ significantly between men and women.

Men like younger women (no surprise).
Women are equally attracted to younger men (contrary to what they said on the questionnaire).

B. The “upper age limit” is in vain

Participants will set an “Upper Age Limit” when joining the service.

Women usually set a higher upper limit than men (saying they can accept older people).
But in actual dating, regardless of whether the object exceeds this upper limit, it does not affect the attractiveness rating trend.
In other words, even if a man exceeds the upper age limit set by women, as long as he is relatively young (or has youthful qualities), women will still be attracted to him. Personal principles are insignificant in the face of the chemical reaction after meeting.

4. Discussion: Why is there such a contrast?

The authors propose several possible explanations:

Say no with your mouth, be honest with your body (Stated vs. Revealed Preferences): On an abstract level, women may think that they like mature and stable older men, but in face-to-face instinctive reactions, the vitality and physical attractiveness brought by youth may be more advantageous.
Changes in social roles: Most of the women in this sample are financially independent (average annual income of $150,000). When women no longer need to rely on men for resources, they may return to their more primitive mate selection criteria of appearance and vitality, making their preferences more similar to men.
Structural issues in the dating pool: In reality, “older men and younger women” pairings are common. This may not be because women like older men, but because men like younger women, which leads to women being “forced” to accept older partners. When given the choice (as in the case of blind dates in this study), women actually want to pick younger ones.

#My understanding and experience

When reading this paper, I couldn’t help but think of the old saying: “Men are very specific and always like 20-year-olds.” Now it seems that this sentence may have to be changed - **Human beings are very specific and always like young people. **

1. A revision of evolutionary psychology?

In the past, we often thought that women’s age preference was for “resources.” But this study implies that when women themselves have resources (the women in the sample have high incomes), their demand for “resource providers (usually older)” decreases and they instead pursue “genetically superior people (usually younger).” This is actually in line with evolutionary logic, but the environmental parameters have changed (female resources have increased).

2. Gender equality of “age anxiety”

This may be a warning sign to men, but it may also be liberating. In the past, society always believed that men are “more valuable as they get older,” but this study ruthlessly points out: Older men have no advantage when it comes to first-sight attraction. Middle-aged uncles may no longer be able to rely on “maturity and stability” and ignore appearance management, because your date may be secretly taking a look at the little girl next to you.

3. Data integrity

This once again proves that behavioral data (Revealed Preference) is often more honest than questionnaire data (Stated Preference). We think we have principles (e.g., “I won’t date anyone younger than me”), but when an energetic young man sits across from us, chatting and laughing, those principles fall apart in an instant. This paper uses 4,500 dates to tell us: **Instinct is always more powerful than reason. **

Reading date: 2025-10-29 Notes organized: Dylan Chiang

Paper reading: Single-cell transcriptomic atlas of the human testis across the reproductive lifespan

Fri, 24 Oct 2025 00:00:00 +0000

#paper information

Title: Single-cell transcriptomic atlas of the human testis across the reproductive lifespan Journal: Nature Aging Year: 2025 DOI:

Research background

Decline in male reproductive health with age is a common phenomenon, but the molecular mechanisms behind it have been unclear. In the past we knew:

Fertility decreases significantly in men over 40 years old
Offspring of older fathers are at higher risk of genetic diseases
Degeneration of testicular function affects hormonal balance and overall health

But the question is: **Which cell types age first? Which molecular pathways are affected? Is this process gradual or staged? **

This study uses single-cell RNA sequencing (scRNA-seq) technology to provide us with unprecedented detail: 214,369 single-cell transcriptome data from 35 donors ranging in age from 21-69 years old.

Research methods

1. Sample collection

Sample source:

35 male cadaveric organ donors
Age range: 21-69 years (covering the entire reproductive life span)
Key Screening Criteria: All donors have normal sperm production or have offspring (excluding disease states)

Sample Grouping:

In their 20s: 3 people
In their 30s: 4 people
Over 40 years old: 10 people
Over 50 years old: 8 people
Over 60 years old: 10 people

2. Single cell sequencing technology

Platform: 10x Genomics Chromium Data size:

214,369 cells
Average of 2,318 genes per cell
Average of 6,265 unique molecular identifiers (UMIs)

Cell type identification: 14 testicular cell types

Germ cells: undifferentiated spermatogonia, differentiated spermatogonia, primary spermatocytes, round spermatids, elongated spermatids
Somatic cells: Sertoli cells, testicular peritesticular cells (TPCs), Leydig cells, smooth muscle cells, endothelial cells, macrophages, lymphocytes, B cells

3. Machine learning analysis

Using a machine learning model (aging clock) to predict the age characteristics of cells, we found:

Somatic cells respond more strongly to aging than germ cells
Different cell types have different “aging clocks”

Main findings

1. Two waves of aging: 30 and 50 are key turning points

The most important finding of this study is: Testicle aging does not proceed at a uniform speed, but has two obvious “turning points”.

First wave (30s): Testicular peritesticular cells (TPCs) start firing

Significant increase in basement membrane thickness
Changes in extracellular matrix (ECM)-related gene expression
Collagen I related pathway activation
Decrease in oxidative stress-related genes (HSPA1A, HSPA1B)

💡 **What does this mean? ** In your 30s, the “package” of the testicles begins to thicken and harden, which may be the “priming state” of aging.

Wave 2 (50s): Functional deterioration in full swing

Leidich Cells: Decreased testosterone synthesis and metabolism
Sertoli cells: Impaired nutrient supply capacity (changes in IGF1R, INSR gene expression)
Macrophages: Enhanced immune response
All Somatic Cells: Significantly increased inflammatory response

💡 **What does this mean? ** In your 50s, the “function” of your testicles begins to break down - hormonal imbalances, malnutrition, and inflammation break out.

2. Somatic cells age faster than reproductive cells

Machine learning analysis shows:

The “aging clock” of somatic cells (such as Leydig cells, TPCs) runs faster
Germ cells (spermatogonia, sperm cells) are relatively stable

This explains why:

Older men can still produce sperm (germ cells are still alive)
But fertility decreases (the supportive environment provided by somatic cells becomes worse)

3. Aging pattern of germ cells: reduction in number + abnormal function

Fate of spermatogonia:

In your 30s: quantity increases first (compensatory mechanism?)
After 50s: Significant decline in numbers
Change in the ratio of G2/M phase of the cell cycle (decreased proliferation ability)

Changes in Haploid Germ Cells (round and elongated sperm cells):

Significant changes in expression of flagellum-related genes
Abnormal expression of genes related to sperm morphology
Reorganization of transcriptional regulatory network 💡 **What does this mean? ** Even if older men produce sperm, the “quality” of these sperm may not be as good as when they were younger.

4. The impact of BMI amplifies with age

Core findings:

When you are young (20-30 years old), high BMI has little impact
After age 45, high BMI (≥30) significantly reduces sperm cell count

Data support:

Donors with BMI ≥ 30: Extended sperm cell frequency < 20%
But age still has a greater impact on transcriptome changes than BMI

Pathway Analysis:

High BMI: ECM dysregulation is more severe in TPCs
High BMI: Testosterone metabolism of Leydig cells is more disordered
High BMI: Decreased muscle contraction function of smooth muscle cells

💡 **What does this mean? ** If you were overweight when you were young, the effects may not be noticeable. But once you pass the age of 45, obesity accelerates the deterioration of reproductive function.

5. Continuous enhancement of immune response

Observed in all age groups:

Upregulation of immune response-related genes
Increased inflammatory markers
Enhanced macrophage activity

This is a common feature of aging (inflammaging), which also exists in the testicles.

Research significance and inspiration

Theoretical contribution

Reveals the staged characteristics of aging
- It is not a uniform degradation, but there is a “critical point”
- 30 and 50 are critical transitions
Identified the aging “starting cells”
- TPCs are the first to respond to aging (30s)
- Other cells follow (50s)
Provides high-resolution molecular maps
- Gene expression profiles of 214,369 cells
- Provide a reference database for future research

Practical inspiration

For individuals:

The best time to have children: Having children before the age of 30 may avoid the first wave of aging
Weight Management: Controlling BMI becomes more important after age 45
Early Detection: Basement membrane thickness can be measured as an early warning in your 30s

Clinically:

Diagnostic markers: ECM-related genes, testosterone metabolism enzymes
Treatment Target:
- Drugs targeting ECM remodeling (30-40 years old)
- Therapy for hormone replacement (age 50+)
Reproductive Preservation: It is recommended to freeze sperm before the age of 30

Research:

Mechanism Exploration: Why do TPCs age first? How do ECM changes trigger a chain reaction?
Intervention Trial: Can aging be delayed by regulating the ECM?
Ethnic Comparison: What are the differences between different ethnic groups and lifestyles?

My understanding

After reading this paper, I was most shocked: it turns out that male reproductive aging is not a slow and steady process, but like a “staircase” with obvious “steps”. The age of 30 is the first step, and the age of 50 is the second step. This overturns my impression of aging as a “uniform process”.

1. Age 30: An underrated turning point

Society generally believes that men only start to “age” after the age of 40, but this study says: No, at the age of 30, some cells in the testicles have already begun to “prepare” to age.

The basement membrane of TPCs (the cells around the testicles) starts to thicken in your 30s, which is like the walls of a house starting to thicken—it looks “sturdier,” but it actually becomes more “stiff,” making it harder for nutrients and signaling molecules to pass through.

This made me think: perhaps the physiological basis of many “midlife crises” is actually laid in the early 30s. That vague feeling of “not as good as before” may not be an illusion, but a real molecular change.

2. Why do TPCs age first? that’s a good question

The paper found that TPCs are the “pioneers” of aging, but did not fully explain why. I have a guess:

TPCs are located in the outer layer of the testicles and are the first to come into contact with various stress factors (oxidative stress, inflammatory factors, metabolic waste products) in the blood. They may be like “sentinels”, the first to feel the deterioration of the environment.

And their response—thickening the basement membrane and remodeling the ECM—may begin as a “defense mechanism,” trying to protect the germ cells inside. But in the long term, this defense becomes a barrier, blocking nutrient supply and signaling.

This reminds me of the problem of “chronic inflammation” in the immune system. Many chronic inflammations begin as a defensive response, but end up harming the body. This may be a more common aging pattern: **Excessive “protection” eventually becomes “harm”. **

3. Somatic cells age quickly, while reproductive cells age slowly: the wisdom of evolution?

Machine learning analysis shows that the aging clock of somatic cells ticks much faster than that of germ cells. This makes sense from an evolutionary perspective: Germ cells carry genetic information and are the key to “inheritance.” If they age too quickly, their genetic quality will decline rapidly, which is not conducive to the continuation of the species. So evolution may “preferentially protect” germ cells, allowing them to age more slowly.

What about somatic cells? They are just “support systems” responsible for providing the environment. As they age, the worst they can do is find a younger partner and continue to reproduce (from a purely evolutionary perspective). Therefore, evolution does not “care” about the aging of somatic cells.

Of course, we cannot be so cruel in modern society. We want every individual to age healthily, not just pass on their genes. So this research is valuable for improving individual reproductive health.

4. The interaction of BMI and age: a cautionary tale

The study found that the impact of BMI was significantly amplified after age 45. This shows: **When you are young, your body can still “carry” it, but after the age of 45, the redundancy is exhausted, and the problem is exposed. **

This brings me to the concept of “health debt.” Smoking, drinking alcohol, staying up late, and being obese when you are young may not cause immediate problems because the body has strong repair capabilities and redundancy. But these unhealthy behaviors are actually “overdrafting” health. When the redundancy is exhausted in middle age, debts will explode.

From this perspective, age 45 is not just a turning point for reproductive health, but perhaps for overall health. Many chronic diseases (diabetes, cardiovascular disease) also occur intensively in this age group.

5. The revolution of single-cell technology: from “black box” to “transparent”

What impressed me most about this paper was the technological progress. Gene expression profiles of more than 200,000 single cells! This would have been impossible 10 years ago.

Traditional tissue sequencing can only tell you the “overall average level,” which is like asking “what is the average income in this city.” But single-cell sequencing can tell you “everyone’s income”, thereby discovering details such as the gap between the rich and the poor and the income distribution of different occupations.

In this study, if you just look at the overall picture, you might conclude that “testicles age at a uniform rate with age.” But the single-cell data tells you: No, it’s the TPCs that age first, then the Leydig cells, then the Sertoli cells… and each cell type has its own aging schedule.

This kind of detail is important for intervention. If you want to delay testicular aging, you should first target the TPCs at age 30 (for example, to prevent excessive ECM remodeling), rather than waiting until all cells have collapsed at age 50 and then “treat the head when it hurts and the feet when it hurts.”

6. Two waves of aging “staircase models” challenge traditional concepts

Traditional aging theories often assume that aging is a “continuous” and “uniform” process. But this study clearly demonstrates “stepwise” aging:

20-30 years old: plateau period
Early 30s: first steps (initiation of TPCs)
30-50 years: relatively stable (although cumulative changes)
Early 50s: Second step (functional breakdown)

This reminds us that there may be a “critical point” in ** aging. When the critical point is crossed, the system will undergo qualitative changes. **

This has implications for policy and personal health management: if aging is uniform, then starting intervention at any time is almost the same. But if there is a critical point in aging, then intervening before the critical point may achieve twice the result with half the effort, and intervening after crossing the critical point may achieve twice the result with half the effort.

For testicles, the critical intervention window is between the ages of 30 and 50. If you miss it, it may be too late.

7. Limitations: Dead man’s testicles vs. Living man’s testicles

The study had an inherent limitation: the samples came from body donors. Although the researchers screened “normal” samples (having normal sperm production or having offspring), after all, these people are dead, and we do not know their complete health status, lifestyle, medication history, etc. during their lifetime.

Biopsies (such as those obtained from infertile patients) can provide more detailed clinical information, but it is ethically impossible to perform testicular biopsies in healthy individuals. So it’s a trade-off: either more samples but less information (cadavers), or more information but less samples (patients).

The ideal solution for the future might be to combine large-scale data from cadaver samples + fine clinical information from biopsy samples + longitudinal follow-up studies (following the same people from young to old).

Extended thinking

1. What if we could reverse changes in the ECM?

Since the ECM changes in TPCs at the age of 30 are the “starting signal” of aging, can this change be reversed through drugs or gene therapy?

Some studies are already exploring inhibitors of ECM remodeling (such as MMP inhibitors). If intervention could be started around the age of 30 to prevent excessive thickening of the basement membrane, would it delay the entire aging process?

Of course, this requires great caution. ECM remodeling also has protective effects (such as preventing tissue damage). Excessive suppression may have side effects. But this is a direction worth exploring.

2. Will there be similar “two waves of aging” in the female reproductive system?

Female reproductive aging is often considered a “cliff” - egg production completely stops after menopause. But before menopause, is there a “step” similar to the age of 30 and 40?

Similar single-cell studies have been lacking in the past (because ovarian biopsies are more difficult). But as technology advances, we may be able to see similar aging maps of the female reproductive system in the future. Comparison of reproductive aging between men and women may reveal sex-specific aging mechanisms and may also find common aging pathways.

3. Can these findings be generalized to other organs?

Could the “two-wave aging” model of the testicle also exist in other organs? For example:

Brain: Neurons vs. Glia?
Liver: Hepatocytes vs. stellate cells?
Heart: Cardiomyocytes vs. Fibroblasts?

If different organs have similar “pioneer cells” (like TPCs) that first initiate aging and then trigger a chain reaction, then anti-aging strategies may require “precision strikes” - designing intervention plans for different pioneer cells in different organs.

4. How do long-lived species maintain reproductive health?

Some species (such as some sharks and turtles) can live for tens or even hundreds of years and maintain their reproductive capacity well. Do their testicles not have this kind of “two waves of aging”? Is it still there but the time scale has been lengthened?

Comparing the reproductive aging patterns of different species may reveal evolutionary “anti-aging” strategies and provide inspiration for human intervention.

(View raw data)

Reading date: 2025-10-24 Notes organized: Dylan Chiang

Paper reading: Aegaeon - Efficient GPU pooling technology for concurrent LLM services on the market

Wed, 22 Oct 2025 00:00:00 +0000

I recently read an important paper “Aegaeon: Effective GPU Pooling for Concurrent LLM Serving on the Market” that will be published in SOSP ‘25. This research, completed by Peking University and Alibaba Group, proposes a revolutionary multi-model LLM service system, which greatly improves GPU resource utilization efficiency.

Research background and challenges

Resource waste problem in the model market

With the rapid development of large-scale language models, model marketplaces such as Hugging Face now host over a million models. However, there is a serious waste of resources in the actual production environment:

Obvious long tail effect: 94.1% of models receive only 1.35% of requests
Uneven Resource Allocation: 17.7% of the GPU is used to serve a “cold” model that averages less than 0.2 requests per second
Difficult to cope with burst loads: “Hot” models (such as DeepSeek, Llama, Qwen) face request bursts and may exceed reserved resources

Limitations of existing solutions

Existing GPU pooling solutions mainly fall into two categories:

Multiplexing: Limited by GPU memory capacity, supports up to 2-3 models/GPU
Auto-scaling: Expanding at the request level, there is a head blocking (HOL) problem

Aegaeon’s Core Innovation

Token level automatic expansion

The biggest innovation of Aegaeon is the token-level automatic expansion mechanism, which is in contrast to the request-level expansion of existing solutions:

Request Level Expansion: Must wait for the entire request to complete before switching models Token level extension: models can be switched predictively during token generation

Separate scheduling architecture

Aegaeon divides the GPU pool into two partitions:

Prefill instance: Specially handles the initial processing of input prompts
Decoding instance: specifically handles subsequent token generation

This separation avoids the complexity of unified scheduling and enables more balanced resource utilization.

Scheduling strategy

Prefill phase scheduling:

Adopt grouped FCFS (First-Come-First-Serve) strategy
Group requests for the same model, maximum group size is 8
Prioritize loading into existing groups to reduce over-expansion

Decoding phase scheduling:

Use Weighted Round-Robin strategy
Assign time quotas based on SLO requirements
Utilize output buffering to hide latency

System optimization technology

Component Reuse

Aegaeon significantly reduces initialization overhead by reusing inference engine components:

Distributed actuators (Ray, NCCL)
Analyze and optimize components
Tokenizer
Memory pool

Explicit memory management

Self-managed VRAM buffer:

Allocate all necessary VRAM at once
Use bump allocation strategy to avoid fragmentation
Bypass the allocation mechanism of the tensor library

Unified KV Cache:

Using slab allocation technology
Maintain dedicated pool for each shape of KV cache
Effectively manage memory fragmentation

Fine-grained KV cache synchronization

Implementing asynchronous KV cache transfers using CUDA events:

cudaEventRecord: Log transfer events
cudaEventQuery: Query completion status
cudaStreamWaitEvent: synchronized execution sequence

Experimental results and performance evaluation

End-to-end performance

Test results on the ShareGPT dataset:

RPS = 0.1: Aegaeon supports 70 models, 2x more than ServerlessLLM
RPS = 0.5: Aegaeon achieves 2.5x higher request arrival rate
Single GPU Efficiency: Supports up to 7 models/GPU

Performance under strict SLO

Even under stricter SLO requirements:

0.5× SLO: still supports 50% more models than the baseline solution
0.3× SLO: 12.5% more models supported

Automatic expansion speed

Near-instant scaling (via prefetching) 50% of the time
Expansion completes within 1 second in other cases
KV cache transfer overhead per request is less than 1 second

Production environment deployment

Deployment scale

Aegaeon has been tested and deployed in Alibaba Cloud Model Studio for three months:

GPU Scale: 213 H20 GPUs
Number of models: 47 models (28 1.8-7B, 19 32-72B)
Resource Savings: Reduced from 1,192 GPUs to 213 (82% savings)

Actual performance improvement

GPU utilization improved from an average of 13.3%∼33.9% to 48.1%, with no SLO violations or service interruptions observed during the 70 hours of monitoring.

Summary of technical contributions

Revealing for the first time the excessive cost problem of concurrent LLM services in the market
The first token-level automatic expansion multi-model service solution
First comprehensive optimization Predictive automatic expansion process, reducing overhead by 97%
Real production deployment verification, proving the ability to significantly reduce OPEX

Future development direction

Although Aegaeon has made breakthrough progress, there is still room for improvement in the following areas:

Support larger model clusters
Optimize performance in extremely low latency scenarios
Explore combinations with multiplexing technology
Expand to more diverse hardware platforms

Conclusion

Aegaeon represents an important milestone in the design of LLM inference systems. Through token-level automatic expansion and full-stack optimization, it not only solves the resource waste problem faced by the model market, but also provides a technical foundation for the sustainable development of cloud AI services.

📄 Paper information

Paper Title: Aegaeon: Effective GPU Pooling for Concurrent LLM Serving on the Market
Authors: Yuxing Xiang, Xue Li, Kun Qian, Yufan Yang, Diwen Zhu, Wenyuan Yu, Ennan Zhai, Xuanzhe Liu, Xin Jin, Jingren Zhou
Institution: Peking University, Alibaba Group
Conference: ACM SIGOPS 31st Symposium on Operating Systems Principles (SOSP ‘25)
Year of Publication: 2025
DOI:
PDF link:

Paper Reading: How Prompt Word Politeness Affects LLM Accuracy: Mind Your Tone Paper Analysis

Fri, 17 Oct 2025 00:00:00 +0000

I recently read an interesting research paper from Penn State University called “Mind Your Tone: Investigating How Prompt Politeness Affects LLM Accuracy”. This study explores how the politeness of prompt words affects the accuracy of large language models. The results are quite surprising.

Research background and methods

The researchers created a dataset of 50 foundational questions covering the fields of mathematics, science and history. Each question was rewritten into five different tone versions:

Very Polite (Very Polite)
Polite (Polite)
Neutral (Neutral)
Rude (Rude)
Very Rude

A total of 250 unique prompt words were generated and tested using ChatGPT-4o.

Unexpected discovery

The findings were counter-intuitive: Impolite prompt words actually performed better than polite prompt words.

Very Polite: 80.8% accuracy
Courtesy: 81.4% accuracy
Neutral: 82.2% accuracy
rude: 82.8% accuracy
Very Rude: 84.8% accuracy

Paired samples t-test showed that these differences were statistically significant (p < 0.05).

Example of prompt word tone

The researchers designed different prefixes for each politeness level:

Very polite example:

“Can you kindly consider the following problem and provide your answer.”
“Would you be so kind as to solve the following question?”

Very rude example:

“You poor creature, do you even know how to solve this?”
“Hey gofer, figure this out.”

Differences from previous studies

This finding differs from previous research. Research by Yin et al. (2024) shows that “rude prompt words generally lead to poorer performance”, but they used older models such as ChatGPT-3.5 and Llama2-70B.

The researchers speculate that newer generations of LLM may have different response patterns to changes in tone.

Possible explanation mechanisms

Perplexity Effect: Mood vocabulary may affect the confusion of prompt words, thereby affecting model performance
Length factor: Polite expressions usually require more words, which may affect processing efficiency
Training data bias: The model may have been exposed to data distributions with different tones during the training process

Research limitations and future directions

Limitations:

Relatively small dataset size (50 base questions)
Mainly tested ChatGPT-4o, other models have limited results
Only the accuracy of multiple-choice questions is evaluated, other quality indicators are not considered

Future research:

Expanded to more LLM models
Explore performance differences across different task types
Study the underlying mechanism of the influence of tone
Develop reminder strategies that are both effective and friendly

Ethical considerations

Although the study found that rude prompt words were more effective, the researchers emphasized that they do not recommend using hostile language in practical applications. Negative interactive experiences can impact user experience, accessibility, and inclusivity, and promote harmful communication norms.

Conclusion

This study reveals the sensitivity of LLM to surface features of prompt words, challenging our traditional understanding of politeness in human-computer interaction. In the future, a balance needs to be found between improving model performance and maintaining a healthy interactive environment.

📄 Paper information

Paper Title: Mind Your Tone: Investigating How Prompt Politeness Affects LLM Accuracy
Author: Om Dobariya, Akhil Kumar
Institution: Pennsylvania State University
arXiv link:
Year of Publication: 2025

Promoting Integration through Competition: Research on the Model and Path of Constructing Cross-Strait Youth AI Innovation Competition

Sun, 12 Oct 2025 00:00:00 +0000

1. Origin of the problem and research significance

Promoting the integration of youth from both sides of the Taiwan Strait is a key link in the historical process of realizing the great rejuvenation of the Chinese nation. For a long time, the mainland has always promoted cross-Strait youth exchanges with the greatest sincerity, and has played a positive role in enhancing mutual understanding between cross-Strait youths through traditional modes such as visits, study tours, and cultural festivals. However, with the changing times and profound changes in the information environment, traditional communication models are gradually facing new challenges. For “Generation Z” Taiwanese youth who have grown up with the Internet, short videos and artificial intelligence, the appeal of one-way visits or lecture-style communication is declining. They are more eager for experiences that enable in-depth participation, interactive collaboration and the realization of personal value. At the same time, Taiwan’s complex public opinion environment may interfere with the effectiveness of traditional exchanges, causing some Taiwan youth’s understanding of the mainland to remain at the level of fragmented and one-sided information, making it difficult to form a comprehensive and objective understanding. In this context, how to build a new communication paradigm that is more in line with the characteristics of contemporary youth, can better stimulate their inner driving force, and can better overcome information barriers has become an urgent issue in the current cross-strait youth exchange work. Against this background, the cutting-edge scientific and technological field represented by artificial intelligence (AI) is becoming an important breakthrough to solve this dilemma.

This study aims to systematically study the models and paths of cross-strait youth AI competitions, and explore how to promote the in-depth integration of cross-strait youths through event design, which has theoretical and practical significance. In a theoretical sense, it helps to enrich the theoretical system of cross-strait youth exchanges and identity. Existing research mostly focuses on the evaluation of the effects of traditional communication models, while paying less attention to new communication paradigms driven by AI technology. This study will combine the issue setting theory of communication, social identity theory and contact theory of social psychology to deeply analyze the internal mechanism of AI competition in promoting youth integration, and provide a new theoretical perspective for understanding cross-strait youth interaction in the new era. In a practical sense, this research aims to provide an operational reference framework for government departments at all levels, universities and technology companies to design and organize cross-strait youth AI competitions. By systematically sorting out the experience and dilemmas of existing events, this study will propose a three-in-one event model of “issue guidance-collaboration and co-creation-value empowerment” in order to help upgrade cross-strait AI events from a mere “technical interaction” to an effective “integrated identity” platform, effectively improve the quality and effectiveness of youth exchanges with Taiwan, and provide solid practical support for promoting the common development of cross-strait youth and ultimately achieving spiritual harmony.

2. Development status and practical difficulties of cross-strait youth AI competition

Judging from current practice, the development of cross-strait youth AI competitions shows obvious characteristics of “technical empowerment + scenario expansion”. Initial attempts were mostly focused on competitive competitions with technology as the core. For example, the 3rd Cross-Strait Artificial Intelligence-Industrial Robot Competition held in Fuzhou in 2025 attracted the participation of 60 cross-strait teams, including 11 Taiwanese teams and 15 Fujian and Taiwan mixed teams. By setting up specific technology tracks such as drones and intelligent handling, and combining it with an “online + offline” hybrid model, this type of event has successfully provided young people from both sides of the Taiwan Strait with a primary interactive platform based on professional skills.

On this basis, competitions focusing on innovation and entrepreneurship further connect technology with industrial needs, providing Taiwanese youth with more direct opportunities to implement. The “Cross-Strait Youth Innovation and Entrepreneurship Competition” held in Changzhou in 2025 is a typical example. The competition has high-end equipment, electronic information and other tracks, and a special “Taipei Youth Service Station” has been set up to provide policy consultation, mentor matching and investment and financing services to Taiwanese contestants. Finally, through project signing and park settlement, “competition promotes production”. The significance of this type of competition is to transform “integrated development” from an abstract concept into concrete career opportunities, thus effectively enhancing the motivation of Taiwanese youth to participate.

Although the Cross-Strait Youth AI Competition has made the above progress, there are still some deep difficulties in achieving the core goal of “promoting integration through competition”, which makes the integration effect easy to remain at a shallow level of interaction and difficult to transform into deep recognition. Including the limitations of topic setting, the de-institutionalization of collaboration mechanisms, and the break of the post-game value transformation chain, etc. Overall, the development of the Cross-Strait Youth AI Competition has gone through the initial stage of using technology as a medium to attract participation. However, to achieve its ultimate goal of promoting “spiritual fit”, it must also cross the key gap from “technical interaction” to “integrated identity”.

3. Conceptual model of “promoting financing through competition”: issue guidance, collaborative co-creation and value empowerment

In response to the difficulties that cross-strait youth AI competitions face in practice such as limited topics, loose collaboration, and broken value chains analyzed previously, this study proposes a set of systematic solutions with the core goal of “promoting integration and identity”—a three-in-one “promoting integration through competition” conceptual model of “issue guidance-collaboration and co-creation-value empowerment”.

Issue guidance is the logical starting point and soul of this model. Its core lies in sublimating the competition from a mere “technical problem solving” to a “mission practice” carrying a common vision. According to the issue setting theory, the issues of the event directly shape the cognitive framework and value orientation of the participants. The design of the topic should follow three basic principles: first, practical relevance; second, commonality of values; and third, technological cutting-edgeness. Through the topics designed in this way, the competition is no longer just a technical competition, but has become a platform for thought and action for young people on both sides of the Taiwan Strait to work together to cope with common challenges and create a better future. Under the guidance of grand issues, collaborative co-creation has become a key practical link to internalize “common mission” into “collective identity”. Specific strategies may include: first, implementing mandatory mixed formations; second, designing a structured collaboration process; third, providing an integrated AI collaboration platform that integrates cloud computing power, shared data sets and collaborative programming tools from platforms such as Baidu’s “Fei Paddle” or Alibaba’s “Tianchi” to clear technical obstacles for cross-regional collaboration.

Ultimately, Value Empowerment constitutes the closed loop and long-term guarantee of this model, aiming to transform short-term participation in the event into long-term value for the personal development of Taiwanese youth, thereby achieving a sustainable integration effect. According to expectancy theory, the strength of an individual’s motivation to participate in an activity depends on his or her assessment of the expected rewards that the behavior can bring. Therefore, a complete post-game value conversion chain must be built to make the participation experience truly “value for money.” This chain should include three levels: first, the connection of career development channels; second, the incubation support of innovative projects; and finally, the construction and maintenance of long-term communities, that is, all contestants will be included in the “Cross-Strait Youth AI Talent Pool”, and through regular online sharing, offline visits and project cooperation, a communication network for continuous interaction and common growth will be formed.

4. Specific case: “Straits Youth AI Integration Innovation Competition” program design

In order to transform the aforementioned conceptual model of “issue guidance-collaborative co-creation-value empowerment” into an operational practical path, this study designed a specific case called the “Strait Youth AI Integration Innovation Competition” (hereinafter referred to as the “Contest”).

The core positioning of the competition is “a flagship AI innovation event serving the integrated development of both sides of the Taiwan Strait”, and its annual theme is set as “AI Empowers Collaborative Innovation of Straits Ports and Industrial Chains”. Participants are open to young people aged 18 to 45 on both sides of the Taiwan Strait, including college students, young engineers and start-up enterprise teams, aiming to attract the most innovative groups from both sides of the Taiwan Strait. In order to ensure the professionalism and authority of the event, the competition is planned to be guided by the Taiwan Affairs Office of the State Council, the Ministry of Science and Technology and other national ministries and commissions, and jointly organized by the Fujian Provincial People’s Government and relevant local governments (such as Xiamen, Fuzhou, and Quanzhou). Top universities on both sides of the Taiwan Strait (such as Tsinghua University, Xiamen University, and National Taiwan University) and mainland China’s leading technology companies (such as Zhipu Technology, Volcano Engine, Magic Square Quantification) will be invited to deeply participate in the co-organizer, relying on their mature computing power platforms to provide all-round technical support.

In terms of topic guidance, the competition carefully designed three parallel tracks with both technical challenges and integration value around the annual theme. The first track is “Smart Port and Logistics Optimization”. The second track is “AIGC Empowering Chinese Cultural Heritage”. The third track is “AI-driven cross-border e-commerce and supply chain finance”. The design of these topics closely combines technological exploration with serving the common interests of both sides of the Taiwan Strait, effectively avoiding the limitations of purely technical competitions and ensuring that the event has a strong integration orientation from the very beginning.

In terms of the design of the collaborative co-creation mechanism, the competition has adopted a series of institutional measures to ensure in-depth interaction between young people on both sides of the Taiwan Strait. First, enforce mandatory mixed formations. Second, build a structured collaboration process. Finally, relying on the integrated AI collaboration platform of the co-organizer, it provides all teams with unified cloud computing power, shared data sets, collaborative programming environment and project management tools, completely eliminating technical barriers to cross-regional collaboration, allowing young people on both sides of the Taiwan Strait to seamlessly connect and work side by side in the same digital space, thereby establishing a strong team identity and personal friendship in the process of jointly solving problems.

In terms of building a value empowerment system, the competition is committed to creating a complete value chain from participation to personal development. At the level of career development, the competition has signed talent cooperation agreements with more than 50 well-known mainland technology companies, port groups and financial institutions, providing the winning team members with “through-train internships” and final qualifications for school recruitment; at the level of project incubation, the competition has established a million-level “Cross-Strait Youth AI Innovation Fund” to provide seed-round investments for winning projects with commercial potential, and recommend them to settle in national-level cross-strait youth entrepreneurship bases in Xiamen, Fuzhou and other places for free, and enjoy a full set of incubation services.

5. Path suggestions for cross-strait youth AI innovation competitions

Based on the aforementioned analysis of the development status and dilemma of cross-strait youth AI competition, as well as the construction of the “issue guidance-collaborative co-creation-value empowerment” model and specific cases, this study aims to promote the healthy and sustainable development of this new communication paradigm and proposes the following four interrelated path suggestions.

First of all, top-level design and policy guidance for the event should be carried out from the central level. The core is to establish an authoritative issue release and certification mechanism. Secondly, institutional design must be used to ensure the in-depth collaboration and co-creation of young people from both sides of the Taiwan Strait in the game. The key is to promote mandatory mixed formations and structured collaboration processes. Furthermore, there is an urgent need to build a full-chain value empowerment system from competitions to personal development. The core lies in institutionally linking the competition experience with the long-term development opportunities of Taiwanese youth in the mainland. Finally, we should promote the joint construction of a national-level “Cross-Strait Youth AI Innovation Collaboration Cloud Platform” to serve as the technical base and community carrier for all competitions.

Keywords: cross-strait relations, artificial intelligence, youth exchanges, event design, integrated development

Home task management system: Cloudflare full-stack collaboration platform

Thu, 02 Oct 2025 00:00:00 +0000

The family task management system is an open source collaboration platform for families and small teams. It integrates tasks, calendars, inspiration boxes, periodic schedules, member divisions and notifications into the same interface, and uses Cloudflare’s edge services to complete front-end and data layer deployment.

What I am responsible for in the project

This is a full-stack open source project designed, developed and maintained by me. I completed a complete refactoring from an early PHP version to an end-to-end TypeScript architecture.

Product Definition: Starting from the needs of family members to jointly manage daily tasks, core processes such as workbench, tasks, calendar, inspiration box, classification, member and personal settings are designed.
Architecture Refactoring: Refactor the PHP and MySQL monolithic systems into an edge-native architecture composed of React, Hono, Cloudflare Workers, D1, KV and R2.
Data and API: Design data models, validation rules and APIs for teams, tasks, categories, comments, notifications, periodic tasks and schedule blocks.
Complex task logic: Implement periodic task generation, time window, backlog tasks, calendar view and schedule update, and handle cross-layer data consistency.
Identity and Permissions: Create JWT access tokens, KV refresh sessions, team isolation and role permissions.
Engineering quality: using monorepo, front-end and back-end shared Zod schema, type checking, unit testing and Cloudflare automatic deployment.

Core Innovation

1. Shift from personal to-do to family collaboration

The system not only manages the list of a single user, but also supports member assignment, classification, comments, notifications and multi-view workbench based on family or small team units.

2. Unify multiple tasks with time semantics

General tasks, periodic tasks, time windows, itineraries and inspiration boxes each have different life cycles. The system allows different tasks to be managed in a consistent operating model by sharing data contracts and calendar views.

3. End-to-end TypeScript contract

The front-end, back-end and shared packages are located in the same pnpm workspace, and Zod schema and type sharing are used to reduce API field drift, allowing input validation and type safety to extend from the interface to the data layer.

4. Edge-native low-dimensional operation architecture

The frontend is deployed on Cloudflare Pages, the API runs on Workers, and the data uses D1, KV and R2. This architecture reduces traditional server maintenance while retaining database, cache, file and scheduling capabilities.

Technical architecture

Front-end: React, TypeScript, Vite, Tailwind CSS, shadcn/ui, PWA
Backend: Hono, Cloudflare Workers, Zod
Data layer: Cloudflare D1, Drizzle ORM, KV, R2
Engineering: pnpm workspace, Vitest, MSW, Cloudflare Pages
Authorization: MIT License

Bronze Award, Fifth Yunnan-Taiwan University Student Innovation and Entrepreneurship Competition

Mon, 01 Sep 2025 00:00:00 +0000

Award Overview

Received a Bronze Award at the Fifth Yunnan-Taiwan University Student Innovation and Entrepreneurship Competition (第五屆雲台大學生雙創賽), jointly organized by Yunnan University and Ming Chuan University under the guidance of the Taiwan Affairs Office of the Yunnan Provincial People’s Government and the Yunnan Provincial Department of Education.

Competition background

The Yunnan-Taiwan University Student Innovation and Entrepreneurship Competition promotes exchange and cooperation among students across the Taiwan Strait, develops internationally minded innovators, and supports sustained youth exchange.

Meaning of the award

Bronze Award Level: Stand out in the fierce competition and demonstrate excellent innovation ability
Cross-Strait Cooperation: Promote academic exchanges and cooperation between young people in Yuntai and Taiwan
Innovative Spirit: embodies the spirit of continuous innovation and practice

Entries

This competition brings together outstanding students from many universities on both sides of the Taiwan Strait. The participating projects cover a variety of fields, demonstrating the innovative vitality and entrepreneurial potential of the younger generation.

Paper reading: Benefit or burden? Attraction in cross-sex friendship

Wed, 09 Jul 2025 00:00:00 +0000

#paper information

Title: Benefit or burden? Attraction in cross-sex friendship Authors: April Bleske-Rechek, Erin Somers, Crieve Micke, Leah Erickson, Lindsay Matteson, Corey Stocco, Belle Schumacher, Laura Ritchie Journal: Journal of Social and Personal Relationships (2012) Link:

Summary of thesis content

1. Theoretical background and research motivation

The historical novelty of opposite-sex friendships

Traditional psychology defines friendship as a voluntary, cooperative relationship that typically lacks a romantic component. However, evolutionary psychology states that for 99% of human evolutionary history (the hunter-gatherer period), frequent interactions with non-related members of the opposite sex were often associated with reproduction. This kind of “heterosexual friendship” that is not related to reproduction in modern society is a historical novelty.

The Byproduct Hypothesis

The author puts forward the core hypothesis: Sexual attraction in heterosexual friendship is not a functional feature of friendship, but a by-product of human evolved mating strategies (Evolved Mating Strategies) activated in the new modern environment.

The human brain has an evolved mate selection mechanism (for example: men seek short-term sexual opportunities, women seek long-term resources and protection).
When men and women interact as friends in modern settings (work, school), these ancient mate selection mechanisms still operate subconsciously, resulting in attraction.

Evolutionary Basis of Sex Differences

According to parental investment theory (Trivers, 1972), there are significant differences in mate selection strategies between men and women:

Male: Evolutionarily inclined to seize short-term sexual opportunities, so there will be a lower sexual interest threshold, and it is easy to overestimate women’s sexual interest (because the cost of missing an opportunity is higher than the cost of showing wrong affection).
Females: bear higher reproductive costs (pregnancy, breastfeeding), so they are more prone to long-term mate selection and are more picky about sexual partners.

2. Study 1: Opposite-sex friend pairing study (Friendship Pairs)

Research methods

The researchers recruited 88 pairs of real college students with opposite-sex friends (176 people in total). In order to ensure honest responses, both parties were separated in different rooms to fill out the questionnaires, and anonymity and confidentiality were guaranteed. The questionnaire measured their attraction to their friends, their desire to date their friends, and their “guess” about their friends’ attraction to them.

Main findings

Significant gender differences:
- Men’s ratings of attractiveness to their female friends were significantly higher than women’s ratings of their male friends (d = 0.66).
- Men also expressed a stronger desire to date their female friends.
Misperception:
- Male Overestimation: Men tend to think that their female friends are interested in them, even if they don’t actually like him that much.
- Female Underestimation: Women tend to think that their male friends are not interested in them, even if they actually have feelings for her.
Influence of emotional status:
- Male: Regardless of whether he is single or not, his attraction to female friends remains at a high level (in line with the evolutionary short-term mate selection strategy, keeping opportunities open at any time).
- Female: If you are in a stable relationship, your attraction to male friends will decrease significantly; only when you are single, your attraction will be higher (in line with the evolutionary long-term mate selection strategy, focusing on existing partners).

3. Study 2: Cost-benefit analysis across age groups

Research methods

The study was extended to two age groups: Emerging Adults (18-23 years old) and Young and Middle-aged Adults (27-55 years old). Using the “Act Nomination” method, ask subjects to list the advantages (Benefits) and disadvantages (Costs) of opposite-sex friendship and see if they will take the initiative to mention “attraction.”

Main findings

Attractiveness is where the burdens outweigh the benefits:

Regardless of age and gender, subjects ranked attraction/romantic desire as a Cost of friendship 5 times more often than as a Benefit.
- Common cost descriptions include: causing jealousy, complicating relationships, making partners unhappy, and breaking up friendships.

Gender Perspective:
- Although generally viewed as a burden, men are more likely than women to view attraction as a “benefit” (e.g., the possibility of developing a sexual relationship or romance).
- Women are more likely to see it as a nuisance.
Age and Single Effect:
- Overall, middle-aged people are less attracted to friends of the opposite sex than young people.
- Key Exception: Single middle-aged men are just as attracted to friends of the opposite sex as younger men; the same is true for single middle-aged women. This confirms that attraction is highly related to “mate selection status” - when you need to find a partner, friends of the opposite sex are potential radar targets.
Impact on existing relationships:
- People who feel strong attraction to friends of the opposite sex are generally less satisfied with their current romantic partners. This is especially obvious among middle-aged people.

4. Comprehensive discussion

The paper concludes that attraction in opposite-sex friendships is a common phenomenon, not an exception. This supports the view put forward by O’Meara (1989) that opposite-sex friends must face “sexual” challenges.

Support from an evolutionary perspective: The research results are highly consistent with the predictions of evolutionary psychology. Men’s high attractiveness, tendency to overestimate, and their inability to be affected by their relationship status all reflect the short-term mate selection strategies that men have evolved.
Realistic Cost: While attraction may spice up a friendship, data shows it is more often seen as a cost. It can threaten an existing partnership, or it can lead to the breakdown of a friendship because of different intentions (for example, a man wants to develop, a woman just wants to be friends).

#My understanding and experience

After reading this paper, I had several deep feelings, which really overturned some of our established impressions of “depending on dad” - it turns out that many people are actually “depending on dad”, and this kind of dependence is more invisible and stubborn than we imagined. (Note: It seems that the experience of the previous article was mistakenly planted here. The following is a re-writing of this paper)

The process of reading this paper often makes me smile, because it uses very rigorous data to confirm many “stereotypes” we observe in our daily lives.

First of all, it explains why in heterosexual friendships, boys are often considered to be the party with “impure motives.” Data shows that men’s physiological mechanisms seem to make it really difficult for them to completely “turn off” their radar for friends of the opposite sex. This is not to say that boys are all bad, but evolution has implanted a scanning program in their brains that is always on call. This also explains why many girls feel, “We are obviously just good buddies, why did he suddenly confess to us?” Because according to research, it is really easy for girls to underestimate a boy friend’s interest in them.

Secondly, I find the idea of the “by-product hypothesis” very interesting. The “pure friendship between men and women” that we are accustomed to now is actually going against millions of years of evolutionary instinct. It’s no wonder that maintaining weird friendships is tiring, because we have to constantly use reason (prefrontal lobe) to suppress primitive impulses (limbic system).

Finally, it is especially true that attraction is a burden. Although romantic scenes of “friends turning into lovers” are often played in movies, in real life, once one-way or two-way attraction appears in a friendship, it usually brings trouble - embarrassment, suspicion, or jealousy of the other half. This paper tells us that if you find it difficult to find a balance between opposite-sex friendships, don’t worry, this is not your personal problem, this is an evolutionary problem faced by all human beings.

In summary, pure heterosexual friendship exists, but it may require more awareness and boundary management than we think, especially when both parties’ “interpretations” of the relationship may be inherently out of sync.

Reading date: 2025-07-09 Notes organized: Dylan Chiang

Algorithmic power gap and potential dilemmas in rural revitalization: theoretical analysis and situational deduction

Fri, 13 Jun 2025 00:00:00 +0000

Author: Dylan Chiang

Summary

This article uses the methods of theoretical analysis and situational deduction to systematically explore how artificial intelligence (AI) causes two potential dilemmas: algorithmic bias and power gap in the context of rural revitalization. Research points out that this problem is rooted in the existing digital inequalities and social inequality structures in rural areas, and may further solidify the marginalized status of disadvantaged groups through resource allocation and public services. The conclusion is that without a prudent framework centered on local participation and data governance, AI technology may erode the goals of equity and inclusiveness in rural revitalization.

Keywords: artificial intelligence, algorithm bias, rural revitalization, digital labor, data governance

1. The risk of AI bias in rural resource allocation and public services

Artificial intelligence (AI) is regarded as a potential tool to improve the efficiency of rural governance and promote equitable distribution of resources. However, in the unique rural context of Taiwan, the introduction of AI without careful consideration of its inherent limitations and social implications may not only fail to eliminate existing gaps, but may instead create or exacerbate prejudice, posing a potential threat to rural revitalization.

The decision-making quality of an AI system highly relies on the data it learns. However, in rural areas of Taiwan, especially remote mountainous areas, outlying islands, or communities with a very high proportion of elderly people (such as Yunlin Taixi, Pingtung Manzhou, etc.), the popularity of digital infrastructure, residents’ digital access capabilities, and the activity of data production systems are still significantly “digitally weak” compared to urban areas. This directly leads to the fact that rural data that can be used for AI training may face the dilemma of “little quantity and poor quality”, or even a fundamental “absence of data”.

Just imagine, if an AI system is used to evaluate the qualifications for natural disaster relief in agriculture and fishery, its main training data comes from those farmers and fishermen who are highly informatized, good at using online systems to report disaster damage, and whose crops or breeding types are more mainstream marketing channels. Then, for those older farmers and fishermen who still rely on traditional farming methods, are not familiar with digital tools, or grow/raise products with local characteristics but small market sizes, their data may be difficult to be fully captured or correctly interpreted by the system. In this case, the AI model may make unfavorable judgments about these “data invisible people” from the beginning due to insufficient data representation and systematic bias, putting them in a more vulnerable position in resource allocation. This exactly confirms that algorithmic bias often stems from the existing “truth of inequality” in society. In rural areas, this “inequality” is first reflected in the availability of data and the ability to truly reflect the appearance of the land.

The development and verification processes of many AI models currently used in public services often take urban environments as the main scenarios, and their built-in logic and parameter settings may unconsciously have the color of “urban centralism” or “mainstream group defaults.” If this type of model is directly implemented in rural areas without sufficient local adaptation and contextualization considerations, it may be “blind” or “incompatible” with the unique social and economic structure of the countryside, multicultural features (such as the traditional norms of aboriginal tribes, the industrial characteristics of Hakka settlements, and the living needs of new resident communities) and the heterogeneous needs of residents.

For example, if an AI system used to optimize public transportation routes and schedules in urban areas is simply applied to rural areas with vast territory, scattered settlements, and special travel needs of the elderly population (such as bus services in the Chiayi Mountains), it may be unable to understand the special transportation needs of rural residents for flexible reservations, ride-sharing services, or specific periods of time (such as agricultural product harvesting periods, temple fairs), and make resource allocations that are not in line with actual benefits. This neglect of local context essentially constitutes a structural bias against the particularity of rural areas, making it difficult for AI’s “wisdom” to truly respond to the real pain points of rural areas.

When AI systems process information involving vulnerable groups in rural areas (such as the elderly living alone, single-parent families with financial difficulties, people with physical and mental disabilities, new resident women with limited digital capabilities, etc.), even if there are no clear discriminatory rules, the underlying data patterns and algorithm logic may have hidden negative labels or resource crowding effects that are not easily noticeable. Drawing on the analytical perspective of “poverty-phobia”, AI may inadvertently replicate society’s stereotypes or implicit biases against specific groups in seemingly objective data analysis.

For example, if an AI system is used to screen social welfare or emergency assistance application eligibility, if in its training data, the cases of “successful” receiving subsidies are more likely to present some “typical” dilemma narratives or easily quantifiable indicators of difficulty. Then, for those disadvantaged cases in rural areas who are in the same difficult situation, but whose plight is more complex and difficult to present through standardized data, or who do not meet the mainstream society’s imagination of “ideal help seekers”, the AI system may give them a lower priority or even exclude them from potential service targets because their characteristics deviate from the “normal” model. This kind of implicit discrimination based on algorithms will make those disadvantaged groups in the countryside who are already in “invisible corners” face the risk of further marginalization and deprivation of resources in the AI era, running counter to the goal of rural revitalization to promote inclusion and equity.

2. The operation and inequality of algorithmic power in rural context

The application of AI systems in rural areas not only brings potential risks of bias, but may also create new asymmetries in power relationships. The design, deployment and operation process of algorithms are often dominated by specific technical elites, policy makers or business institutions, and the rural community itself has relatively weak voice and control in it. This power gap may make AI a tool to strengthen existing inequalities and even create emerging relationships of dominance.

The complexity and technical threshold of AI systems constitute an insurmountable barrier for many rural residents. Whether it is the underlying logic of AI decision-making, the way data is collected and used, or the explanation and appeal channels for system output, rural communities are often in a disadvantaged position due to information inequality. For example, when a local government introduces an AI system to assist agricultural land use planning or disaster potential assessment, ordinary farmers may have difficulty understanding how the system makes judgments, let alone effectively questioning its decisions or participating in system improvements. This is consistent with what Veale et al. have observed in the public sector. Even civil servants may lack a full understanding of the operation of AI systems, resulting in over-reliance or selective adoption. In rural contexts, this information gap may be more serious.

In rural areas of Taiwan, many elderly people or residents with relatively low education levels have limited digital literacy. If the operation interface of the AI system is not friendly enough, or the relevant training and instructions are insufficient, they are more likely to be marginalized in the AI era. AI technology that seems to be designed to “empower” rural areas may become a new “empowerment” mechanism if it lacks sufficient consideration and support for the abilities of local users, reducing rural residents from technology users to passive data providers and decision-making recipients.

The design and deployment process of AI systems often lacks substantial participation of rural communities. Decision-making power is often concentrated in the hands of central government, local government technocrats, or external AI solution providers. The local knowledge, practical needs and value concerns of rural residents, local organizations, and even grassroots civil servants may be ignored or simplified in the technology-led development process. This echoes Sampath’s concerns about the insufficient participation of local communities in the development of AI in countries in the Global South and their vulnerability to dominance by external forces.

For example, in the process of promoting “smart agriculture”, if the development of AI systems focuses too much on pursuing the single goal of increasing output or reducing costs, while ignoring the multiple values that small farmers attach to ecological sustainability, agricultural product quality, or rural cultural inheritance, it may lead to a disconnect between technical solutions and local needs. Many rural areas in Taiwan have unique natural environments and cultural features (such as the traditional areas of aboriginal tribes and the cultural landscape of Hakka villages). If the planning of the AI system fails to fully incorporate the opinions of local residents and respect their lifestyle and value choices, it may lead to questions about “Whose countryside? Whose AI?” and even rights conflicts over “Who does AI serve?” This risk of being “determined” will seriously weaken the subjectivity of rural communities and the momentum for local independent development.

When AI systems in rural areas mainly rely on external sources (such as large urban technology companies or multinational enterprises) to provide technology, platforms and data analysis services, we need to be alert to the risks of “technological colonization” that it may bring. Externally led AI solutions may carry with them the values and business logic of their developers (usually urban elites or Western cultural backgrounds), which may not be consistent with the local context of rural areas. This analogy to the dilemma of “digital colonization” or “data dependence” that countries in the Global South may face as Sampath refers to it may also appear in different forms in urban-rural relations.

For example, if the health passbook AI system in rural areas is trained mainly based on urban population data, its disease diagnosis model or treatment recommendations may ignore the impact of rural-specific genetic background, living habits or environmental factors on the disease, thus affecting its applicability and accuracy in rural areas. Furthermore, over-reliance on standardized, externally provided AI systems may also lead to the marginalization or even elimination of valuable knowledge accumulated in rural areas (such as traditional farming methods, local herbal knowledge, operational experience of community mutual aid networks, etc.). Zajko emphasized that sociology should pay attention to how AI affects power relations. In the rural context, this also includes an examination of the ebb and flow of power between local knowledge systems and external technological systems. One of the goals of rural revitalization is to activate local characteristics and resources. If the introduction of AI fails to effectively integrate with local knowledge and instead crowds out local wisdom, it will run counter to the original intention of rural revitalization.

In short, if the application of AI in rural areas fails to face up to and actively deal with the unequal operation of algorithmic power caused by problems such as technical thresholds, insufficient participation, and external leadership, AI will not only be difficult to become a tool to promote equitable rural development, but may deepen the existing power structure, causing rural communities to face new dilemmas and challenges in the digital age.

3. Potential impact on rural revitalization goals

Taiwan’s rural revitalization policy, whether it is rural regeneration in the early days or the local revitalization emphasized in recent years, has as its core goals the promotion of balanced regional development, revitalization of the local economy, and improvement of quality of life, with a special emphasis on social equity and inclusiveness, striving to allow everyone in the countryside to share the fruits of development. However, the risks of bias hidden in the rural applications of AI analyzed above are inconsistent with the operation of power. If they are not effectively controlled, they may have a direct and far-reaching negative impact on these noble policy goals.

Social equity and inclusiveness are the cornerstones of rural revitalization policies, which aim to narrow the gap between urban and rural areas and ensure equal opportunities among different groups within the countryside. However, the existence of algorithmic bias may cause AI systems to unconsciously replicate or even strengthen existing social inequalities in resource allocation and public service decisions, thus eroding the basis of policy fairness.

For example, if a set of AI is used to evaluate the priority of subsidy for “local creation” proposals, Systems whose training data or built-in evaluation indicators are overly focused on quantifiable economic benefits, technological innovation, or past successful urban cases may make rural proposals that are more focused on cultural heritage, ecological protection, community building, or caring for the disadvantaged (for example, a A traditional craft revitalization project led by tribal elders, or a local ingredient promotion cooperative initiated by mothers of new residents), due to their “non-mainstream” or “difficult to quantify” characteristics, are at a disadvantage in the ranking, causing subsidy resources to flow to projects that are more in line with mainstream imagination. Not only does this fail to promote diversity and inclusiveness in rural development, but it may stifle grassroots innovation that truly has local characteristics and social value.

Furthermore, as mentioned above, the potential discrimination of AI systems against rural vulnerable groups (such as the elderly, indigenous people, and economically disadvantaged households) may cause them to encounter hidden obstacles in accessing social welfare, medical resources, employment opportunities, or financial services. For example, if an AI system used to match long-term care resources in rural areas underestimates the actual needs of elderly people living alone in certain areas with inaccessible transportation due to data bias, or fails to fully consider the special family structure and care culture of indigenous tribes due to blind spots in the model design, it may lead to a misallocation of long-term care resources, causing the elderly who are most in need of help to be “forgotten.” This difference in resource allocation based on algorithms directly violates the social fairness principles of “taking care of the disadvantaged” and “leaving no one behind” emphasized in the rural revitalization policy, and may form a “fairness paradox in the AI era” - that is, technology in the name of pursuing efficiency and objectivity has instead created new injustices.

(2) The algorithmic power gap further marginalizes disadvantaged rural communities

The algorithmic power gap, that is, the insufficient voice and control of rural communities (especially disadvantaged groups) in the design, deployment, operation and supervision of AI systems, may further marginalize them in the process of rural revitalization and widen existing social gaps.

When the introduction of AI systems is mainly led by external technical elites or higher-level governments, and rural residents lack effective participation channels and capabilities, their needs and voices are easily ignored. For example, in the process of promoting “smart tourism”, if the establishment of AI systems (such as attraction recommendation, tourist behavior analysis) is only from the perspective of improving tourism output value, and fails to fully consult local residents’ opinions on tourism development models, environmental carrying capacity, and cultural impact, it may lead to uneven distribution of tourism benefits, and even trigger conflicts between local residents and tourism development, deteriorating the good intentions of rural revitalization.

What is even more worrying is that technical thresholds and information asymmetry may put vulnerable groups in rural areas in a position of being “powerless to resist” when faced with AI decision-making. They may not understand how AI can make decisions that go against them and lack effective avenues for appeal or redress. For example, if an AI system demarcates the traditional territory of an indigenous tribe as an area unsuitable for the development of a specific industry, but its decision-making basis is contrary to the tribe’s traditional knowledge and wishes, and if tribal members lack sufficient technological literacy and legal resources to challenge this decision, their development rights and interests may be infringed. In this case, the AI system not only failed to empower the countryside, but instead became a new dominant force, making disadvantaged communities even more speechless in the rural development agenda, and making their unique culture and needs more difficult to see and respect. The solidification of this power structure will reduce rural revitalization to a game for a few elites, and will not truly benefit all rural residents, especially those disadvantaged groups that need help most.

In short, algorithmic bias and power gap are like two sides of the coin of rural AI applications, jointly posing severe challenges to the goals of fairness, inclusiveness and local subjectivity in rural revitalization. If we fail to face up to and actively respond to these potential impacts, the introduction of AI technology may drift further away from the original intention of rural revitalization.

4. Conclusion and Policy Implications

(1) Summary of main arguments

Through a review and theoretical analysis of relevant core literature, this study aims to explore the algorithmic bias and power gap dilemmas that artificial intelligence (AI) may cause in resource allocation and public service decision-making in the context of rural revitalization. Based on the previous arguments, this study puts forward the following main arguments: The potential and risks of applying AI to rural revitalization coexist. Algorithmic bias and power gap are potential dilemmas that must be seriously faced. Although AI technology is highly expected to bring opportunities for efficiency improvement and resource optimization for rural development, its actual application in rural areas will inevitably encounter the risk of bias posed by data poverty and distortion, contextual blind spots in model design, and implicit discrimination against disadvantaged rural groups. At the same time, factors such as technical thresholds, insufficient participation mechanisms, and external technological dominance may also form new asymmetries in the operation of algorithmic power within villages. If these biases and power gaps are not effectively identified and managed, AI will not only be unable to achieve its original intention of promoting rural revitalization, but may instead become a new driver of exacerbating existing inequality and eroding social fairness.

These dilemmas are not purely technical defects, but complex problems deeply rooted in social structure, power relations and governance practices. The root cause of algorithmic bias is not just the “dirtyness” of the data or the “imperfection” of the model, but more deeply reflects what Zajko calls the “real basis of inequality” in society. The long-standing urban-rural gap, low digital life, uneven distribution of resources, and the disadvantaged position of specific groups in the social structure in rural areas may all be copied, continued, or even amplified unconsciously by AI systems. Similarly, the operation of algorithmic power is closely related to the existing local political and economic structure, the transparency and accountability of the governance model, and the participation ability of civil society. As Sampath observes in the Global South, without a sound governance framework and full respect for local contexts, the introduction of technology may actually reinforce existing power imbalances. Therefore, it will be futile to try to “repair” prejudice or “balance” power from a technical perspective only, while ignoring the underlying social structural factors and governance mechanisms.

(2) Policy Enlightenment

In order to deal with the algorithmic bias and power gap that may be caused by AI in rural applications, and to ensure that technological development can truly serve the goals of equity and inclusion in rural revitalization, this study proposes the following policy implications based on the analysis of core literature and considering the special context of rural Taiwan:

Data governance and local participation: Constructing a people-centered rural data ecosystem Data is the cornerstone of AI, but the poverty and bias of rural data are the source of AI bias. Therefore, the first priority is to establish a rural data governance framework that is ethical and emphasizes local participation. This includes:

Improve data representativeness and quality: Resources should be invested to improve digital infrastructure in rural areas, and more inclusive data collection methods (such as combining oral interviews, community field surveys, etc.) should be designed for digitally vulnerable groups such as the elderly, indigenous people, and new residents to ensure that their experiences and needs can be included in the data set. For example, when building an agricultural AI system, in addition to standardized production and sales data, “atypical” data such as small farmers’ local farming knowledge and traditional crop varieties should also be included.
Emphasis on data sovereignty and local community participation: We should learn from international concepts such as “Indigenous Data Sovereignty” and explore the establishment of a mechanism for rural communities (such as tribes, rural and fishing village community development associations) to participate in data collection, management, interpretation and application. For example, promote the establishment of “community data cooperatives” or “local data trust” models, so that rural residents can have greater informed consent and control over data involving themselves or their communities, and prevent data from being unilaterally seized or improperly used by external agencies.
Develop ethical guidelines for rural data: Based on the particularity of rural AI applications, clear ethical guidelines for data collection and use should be formulated to clarify data ownership, privacy protection, and bias risk assessment specifications to ensure that data applications are premised on promoting rural well-being.

The pursuit of algorithm transparency, accountability and explainability: opening the “black box” of AI decision-making The opacity of algorithms is a breeding ground for power gaps and biases. When the public sector introduces AI systems in rural areas, it must strive to promote transparency, accountability and explainability:

Promote the transparency of public sector AI systems: The government should establish a login and disclosure mechanism for AI systems. For AI systems used in rural resource allocation or public services, they should proactively disclose their design purpose, main functions, training data sources (without infringing privacy), and known limitations and potential risks.
Establish bias impact assessment and appeal mechanism: Before the deployment of AI systems and during operation, bias impact assessments should be conducted regularly, with special attention to their potential differential impact on rural vulnerable groups. At the same time, a convenient and effective complaint and relief channel should be established so that rural residents can receive immediate response and processing when they believe they have been unfairly treated by AI decision-making.
Exploring local interpretability methods: Given the difficulty that rural residents (especially the elderly) have in understanding complex technical explanations, communication methods that go beyond purely technical explanations should be explored. For example, visual tools, local languages, case descriptions, or translation through local opinion leaders (such as village chiefs and tribal leaders) can be used to make AI’s decision-making logic easier to understand and trust among rural communities.

Empowerment and critical thinking cultivation: improving AI literacy in rural areas Facing the advent of the AI era, it is crucial to improve the understanding and critical thinking of rural residents and grassroots workers about AI. This is the basis for narrowing the gap in algorithm power:

Promote AI literacy education in rural areas: Combining existing resources such as community colleges, villagers’ gatherings, and farmers’ and fishermen’s association promotion systems, we can set up easy-to-understand AI science courses and workshops for rural residents of different ages and backgrounds, so that they can understand the basic principles, common applications, potential risks, and their own rights of AI.
Strengthen the AI awareness of grassroots civil servants: Provide AI-related professional training to grassroots civil servants serving in rural areas, so that they can not only operate AI systems, but also understand the limitations of the system, identify potential biases, and play the role of “translator” and “gatekeeper” between AI decision-making and actual local needs to avoid blind obedience or abuse of AI.
Cultivation of critical participation skills: Encourage rural residents not only to be passive AI users, but also to become participants with critical thinking, who can actively explore problems in the local application of AI systems and actively make suggestions for improvement.

Encourage the development of context-sensitive and demand-oriented AI: let AI truly serve rural areas

Avoiding “one-size-fits-all” technical solutions and encouraging the development of AI applications that can truly respond to the local needs of rural areas is the key to eliminating blind spots in the model context and leveraging the positive benefits of AI.
Support bottom-up AI innovation: Resources and platforms should be provided to encourage rural communities, local organizations, local community colleges or returning youth to proactively propose and participate in the development of AI solutions with local characteristics and context sensitivity in response to practical problems faced by rural areas (such as elderly care, characteristic agricultural development, cultural inheritance, environmental monitoring, etc.).
Emphasis on participatory and collaborative design of AI design: In the early stage of the design of rural AI systems, the participation of multiple stakeholders such as rural residents, grassroots workers, and local experts should be included, and through workshops, focus discussions, etc., their needs, experiences, and values can be fully integrated into the system design to avoid the disconnect between technology and needs.
Establish a pilot and evaluation mechanism for rural AI applications: For new rural AI applications, small-scale pilots should be conducted first, and an effectiveness evaluation and ethical review mechanism involving the participation of local communities should be established. Continuous revision and optimization should be based on actual feedback to ensure that AI applications meet the real needs and long-term well-being of rural areas.

The importance of sociology and cross-disciplinary cooperation: integrating multiple perspectives to jointly govern AI The complexity of AI governance, as well as its deep-rooted nature in social structures, make it difficult for a single discipline or department to deal with it alone. Zajko emphasizes the important role of sociology in understanding and responding to bias and inequality in AI, a perspective that is particularly instructive for rural AI governance:

Strengthen the role of social sciences in AI governance: In the formulation of rural AI policies, systematic design and development, impact assessment, and ethical review, experts and scholars in social science fields such as sociology, anthropology, geography, and law should be actively included so that they can provide in-depth analysis and suggestions from multiple perspectives such as social structure, power relations, cultural context, and ethical norms.
Promote cross-field dialogue and cooperation between government, academia, industry and civil society: A regular cross-field communication platform should be established to allow AI technology developers, policy makers, rural practitioners, local residents’ representatives and academic researchers to have full dialogue, consultation and cooperation on the vision, challenges and paths of rural AI development, and jointly shape an AI governance framework that is responsible, inclusive and conducive to sustainable rural development.

5. Research Prospects

First, there is a call to deepen on-the-ground empirical research: from theoretical discussion to field cultivation.

The existing literature mostly discusses AI bias and governance challenges based on general principles or specific foreign cases. However, rural society in Taiwan has its own unique history, culture, industrial structure, and social network characteristics (for example, the traditional domains and governance models of aboriginal tribes, the industrial culture of Hakka settlements, the population structure and livelihood methods of western coastal agricultural and fishing villages, and the significant digital activities between different regions, etc.). How these local contexts interact with the introduction of AI technology to shape the specific forms of algorithmic bias, the transformation of power relations, and the actual impact on rural revitalization remains to be answered by in-depth local empirical research.

Therefore, future research should actively invest in rural fields in Taiwan and use qualitative research methods (such as in-depth interviews, focus groups, participant observation) and quantitative research methods (such as questionnaires, data analysis) to combine multi-level discussions.

For example, we can conduct in-depth case studies on AI application cases that have been implemented in rural areas (such as smart agricultural guidance systems, remote long-term care resource matching platforms, local revitalization subsidy evaluation tools, etc.), and interview system developers, policy makers, grassroots executives, and affected rural residents to understand their understanding of the AI system, usage experience, perceived benefits and difficulties, as well as their agency and negotiation process. Secondly, the bias impact assessment and power interaction analysis focus on specific cases. In terms of specific empirical research directions, in the future, more detailed bias impact assessment and power interaction analysis can be conducted for specific rural AI application cases:

Bias impact assessment: You can learn from Curto et al.’s analysis method of “poverty phobia”, but further combine it with local data and situations to examine whether there are systemic biases in specific AI systems against rural elderly, indigenous people, new residents, low-income households, or digitally disadvantaged groups. For example, analyze whether the decision-making results of an AI model used for agricultural loan approval have a disproportionate negative impact among farmers with different socio-economic backgrounds or farming scales.
Power interaction analysis: You can use Zajko’s sociological perspective and Veale et al.’s insights into public sector AI practices to conduct an in-depth analysis of how power relationships between different actors (such as the central government, local governments, technology providers, local elites, ordinary residents, representatives of disadvantaged groups, etc.) are performed, negotiated, and reconstructed during the design, deployment, and operation of rural AI systems. For example, it explores how the voices of local communities are heard or silenced in the cracks between the technocratic system and commercial interests in smart rural policies promoted “from above.”

Through the above-mentioned empirical research, we can not only gain a deeper understanding of the real impact of AI in the rural context of Taiwan, but also provide a solid academic foundation and practical reference for formulating more targeted and effective algorithmic governance policies and developing AI applications that better meet local needs, thus ensuring that AI technology can truly become a positive force in promoting inclusive, equitable and sustainable development in rural Taiwan.

References

Curto, G., Jojoa Acosta, M. F., Comim, F., & Garcia-Zapirain, B. (2024). Are AI systems biased against the poor? A machine learning analysis using Word2Vec and GloVe embeddings. AI & Society, 39(2), 617-632.
Gehl Sampath, P. (2021). Governing artificial intelligence in an age of inequality. Global Policy, 12, 21-31.
Zajko, M. (2022). Artificial intelligence, algorithms, and social inequality: Sociological contributions to contemporary debates. Sociology Compass, 16(3), e12962.
Kukutai, T., & Taylor, J. (2016). Indigenous data sovereignty: Toward an agenda. ANU Press.
Veale, M., Van Kleek, M., & Binns, R. (2018). Fairness and accountability design needs for algorithmic support in high-stakes public sector decision-making. In Proceedings of the 2018 CHI conference on human factors in computing systems (pp. 1-14).

[Author information] Dylan Chiang, National Institute of Development and Mainland China, Chinese Culture University, Taiwan, email:

The 3rd Cross-Strait Forum on Rural Revitalization and Common Prosperity

Thu, 12 Jun 2025 09:00:00 +0000

Meeting Overview

Meeting Name: The 3rd Cross-Strait Forum on Rural Revitalization and Common Prosperity Conference Time: June 12-15, 2025 Meeting location: Xiamen Sponsor: Institute of Rural Development, Chinese Academy of Social Sciences Meeting theme: Rural revitalization and common prosperity

Meeting objectives

This forum aims to promote cross-Strait exchanges and cooperation in the field of rural revitalization, explore paths and mechanisms to achieve common prosperity, and contribute wisdom and strength to promote the great rejuvenation of the Chinese nation.

Meeting Arrangement

June 12

Meeting registration

June 13

Morning: Conference keynote speech
Afternoon: Sub-forum discussion

June 14

Full day: Visiting and inspecting villages around Xiamen

June 15

Leave the meeting

*The minutes of this conference demonstrate the academic exchanges and cooperation between the two sides of the Taiwan Strait in the field of rural revitalization and common prosperity, and reflect the academic enthusiasm of scholars on both sides of the Taiwan Strait to pay attention to rural development issues. *

Paper reading: Multigenerational persistence: Evidence from 146 years of administrative data

Sun, 08 Jun 2025 00:00:00 +0000

#paper information

Title: Multigenerational persistence: Evidence from 146 years of administrative data Author: Jørgen Modalsli (Statistics Norway) Year: 2017 (Journal of Human Resources) Link:

Research background

This paper explores a core question: Does class reproduction (persistence) not only occur between parents and children, but spans more generations? **

In other words, is your economic status today not only affected by your father, but also by your grandfather, or even your great-grandfather? Past studies have often been limited by data, looking at only two generations (father-son). The few studies that look at multiple generations typically have small samples or are limited to a specific city.

This study draws on Norway’s very strong administrative data, concatenating census data from 1865 to 2011. This allowed the authors to observe 167,411 family lineages, covering the occupation and income information of three generations of grandfather, father and son.

Research methods

1. Data Samples (Four Samples)

The study constructed samples from four different periods to observe changes in different historical stages:

Sample A: Grandfather (1865) → Father (1900) → Son (1910)
Sample B: Grandfather (1865) → Father (1910) → Son (1960)
Sample C: Grandfather (1910) → Father (1960) → Son (1980)
Sample D: Grandfather (1960) → Father (1980) → Son (2011)

2. Occupational classification

Occupations are divided into four broad categories:

White-collar
Farmer
Manual skilled
Manual unskilled

3. Metrics

Odds Ratios: Measures the impact of the grandfather’s occupation on the chance of his grandson entering a specific occupation while controlling for the father’s occupation.
Altham Statistic: A comprehensive liquidity indicator.
Income Rank: Use income ranking to validate findings from occupational data in later samples for which income data are available.

Main findings

1. Grandfather’s influence is significant and lasting

The study found that grandfather’s occupation was significantly predictive of grandchildren in all four periods, even after controlling for father’s occupation.

The coefficient size is approximately 1/3 of the father’s influence.
This means that if two people’s fathers are workers, but one person’s grandfather is a white-collar worker, then the probability of this person becoming a white-collar worker will be significantly higher.

2. Specific professions are more replicable

White-collar workers and Farmers show the strongest multi-generational replicability.
Replication among the blue-collar class (technical and non-technical) is relatively weak and sometimes not significant.
The high replicability of farmers is not surprising (inherited land), but the high replicability of white-collar workers hints at some kind of “elite replication” phenomenon.

3. Institutional changes fail to eliminate multi-generational impacts

Norway has experienced tremendous social change during these 150 years, transforming from a poor agricultural society into a modern country with a comprehensive welfare system and free education.

While the degree of replication among white-collar workers has slightly declined over time (showing increased mobility), multi-generational influence remains.
This shows that the welfare system and the spread of education alone cannot completely cut off the long-term transmission of family advantages.

4. Income data confirms career analysis

In the sample with income data (Sample C & D), the grandfather’s income ranking also has a significant positive impact on the grandson’s income ranking. This confirms that the findings in the occupational data are not simply errors in occupational classification.

Mechanism discussion: Is it “love after meeting” or “family genes”?

In order to explore how this generation-skipping effect occurs, the author conducted two interesting tests:

1. Geographical Distance

If the grandfather’s influence on his grandson comes from “direct teaching” or “introduction through personal connections”, then living far away should have less influence.

Result: Grandsons who move away from their grandfather are indeed slightly less influenced by their grandfather, but the difference is very small. Even after moving far away, my grandfather’s influence was still evident.

2. Time of Death

If the grandfather dies before the grandson is born, or dies during the grandson’s childhood, direct interaction is theoretically impossible.

Result: There is little correlation** between the grandfather’s survival and his influence on his grandson.

Conclusion: This implies that the main mechanism of multi-generational class replication may not come from direct interpersonal interaction (Personal Contact), but from deeper and potential Latent Family Characteristics, which may include genetic talents, family culture, values, or a broader family social network.

My understanding

First of all, what surprised me the most was the “1/3 law”. Even taking your father’s career into account, your grandfather’s influence is still 1/3 as great as your father’s! This means that social mobility is much slower than when we look at pure father-son data. Imagine that a family has to climb from the bottom to the top, or fall from the top to the bottom. The speed of “return to the mean” is actually very slow. It’s like a flywheel with inertia. Once it starts spinning (whether for good or bad), it takes generations to stop.

Secondly, this study severely defeats the optimistic idea that the welfare state can solve everything. Norway is already recognized as one of the most equal countries in the world, with free education and good social welfare. But even in this environment, who your grandfather is is still important. This tells us that what families can give their children is far more than just money or schooling, but also a more abstract “human capital”—perhaps a certain way of thinking, the ability to plan for the future, or the self-confidence of “I know I belong here.” It is difficult for the government to redistribute these things through policies.

Finally, what I find most interesting is the discovery about the “mechanism”. It turns out that you don’t need to have met your grandfather, or even live near him, for his influence to still be passed on to you. This sounds a bit mysterious, but it makes sense if you think about it carefully. This shows that the transfer of family advantages is a “systematic” project. Maybe your grandfather taught your father certain values, and your father educated you in this way; or it may be some kind of hidden genetic advantage. This made me think that each of us is actually carrying the history of our family as we move forward. Part of our achievements are really not the result of our own efforts, but the result of the accumulation of many generations.

In summary, this paper makes me feel more pessimistic about “class replication”, but also more awe-inspiring. It reminds us to be more empathetic when looking at the success or failure of others, because what we see is often just the tip of the iceberg, and beneath the water is a century-long family accumulation.

Reading date: 2025-06-08 Notes organized: Dylan Chiang

Smart cities and Internet economic development in mainland China

Thu, 01 May 2025 13:10:00 +0800

Lecture Overview

This lecture will be delivered by Mr. Dylan Chiang, an Internet and AI innovator on both sides of the Taiwan Strait, and will provide an in-depth discussion of the latest trends in smart city construction and Internet economic development in mainland China.

Main content

Current status of smart city development

Policy framework for smart city construction in mainland China
Smart practice cases in major cities -Technological innovation and application scenario analysis

Development Trend of Internet Economy

The impact of digital economy on traditional industries
Popularity of e-commerce and mobile payment
Development model of platform economy

Cross-Strait Cooperation Opportunities

Technical exchanges on smart city construction
Prospects for cooperation in the field of Internet economy -Talent training and industry docking

Lecturer introduction

Dylan Chiang Internet and AI innovators on both sides of the Taiwan Strait have long been concerned about the development of the digital economy in mainland China and have rich experience in smart city construction and Internet economic research.

Activity information

Time: May 1, 2025 (Thursday) 13:10-15:10 pm
Location: Classroom 507, Daen
Sponsored: National Institute of Development and Mainland China, Chinese Culture University
Nature: General courses - Professional lectures on Mainland China and Cross-Strait Relations

Teachers and students who are interested in smart city construction and network economic development are welcome to participate!

Paper reading: Information sharing in financial markets

Sat, 05 Apr 2025 00:00:00 +0000

#paper information

Title: Information sharing in financial markets Author: Itay Goldstein, Yan Xiong, Liyan Yang Journal: Journal of Financial Economics (2024) Link:

Summary of thesis content

1. Research puzzle: Why should secrets be leaked?

In financial markets, “information” is money. Logically speaking, if you know that a certain stock will rise, you should buy it secretly rather than publicizing it everywhere. However, the real world is full of “information sharing”: from early investment clubs to now Reddit (r/wallstreetbets), Twitter, and Seeking Alpha, investors are keen to exchange opinions.

This paper attempts to answer two core questions:

Why are investors willing to share expensive private information?
Who is sharing? Should experts share with novices, or should novices share with experts?

2. Core model and discovery

The author builds an extended model based on Kyle (1985) that includes two types of investors:

H (High-informed): A “veteran” with high information accuracy and even perfect information.
L (Low-informed): A “rookie” with low information accuracy and noisy signals.

Shocking conclusion:

The model equilibrium shows that the direction of information flow is from L to H. In other words, ** “people with less information” will take the initiative to tell information to “people with more information”, while “people with more information” will choose to shut up. **

3. Key mechanism: Trading-Against-Error

Why is L so stupid to tell information to H who is more powerful than him? There is a subtle strategic consideration behind this:

L’s Dilemma: The information in L’s hand contains “real fundamentals” and “noise (error)”. L himself cannot tell the difference, so when he trades based on signals, the noise often pushes the price in the wrong direction (generating a price shock).
H’s ability: H has more accurate information. He can distinguish which part of L’s signal is real and which part is noise.
Benefits of Sharing:
- When L tells H information, H will find an error in L’s signal.
- H will perform Trade against error against this error.
- Result: H’s reverse trade offsets L’s erroneous order flow due to noise. This actually reduces L’s price impact, allowing L to trade at a better price.

To put it simply, L shared information in order to invite H to be his opponent and help him “correct” the market price, thus making L’s own transactions smoother.

4. The lament of the veteran (H): Did you lose money after listening to it?

The paper’s most ironic finding concerns H’s situation:

Ex-post: Once L shares the information, H will of course listen because it can help him earn profits on “wrong trades”.
Ex-ante: However, if H had a choice, he would actually prefer L not to share information.

The reason is that once the information becomes public:

If L knows that H is helping to “correct” the price, L will become Trade more aggressively.
The Market Maker discovers that the market is filled with informed traders and will reduce market liquidity (increase Kyle’s Lambda).
The harm caused by these two indirect effects to H exceeds the direct benefit he obtains from the information.

This is a Commitment Problem: H cannot promise “I won’t listen”, so he is forced into a lower-profit equilibrium.

#My understanding and experience

While reading this paper, I kept thinking of Reddit or stock market forums.

1. Why does “leek” like to post the most?

We often wonder why retail investors (L) especially like to analyze stocks at length on the Internet, while the real big institutional investors (H) usually make a fortune in silence? This paper gives a cool explanation: when retail investors share information, they are actually subconsciously seeking “verification” or “liquidity” in the market. Although retail investors may not think that deeply (what to reduce the price impact), from the perspective of evolutionary equilibrium, retail investors shouting loudly and attracting institutional investors (whether they are copying or counter-trading) may actually make it easier for retail investors’ orders to be completed.

2. The Curse of the Smart Man

I found the part about H particularly philosophical. H obviously has more information and stronger capabilities, but because of L’s “big mouth”, H is forced to get involved in a market with less liquidity and more fierce competition. This is very much like the current financial market, where information is awash. For a top fund manager, he is forced to receive countless tweets, news, and gossip every day. Although every piece of news seems to be profitable (can be used for arbitrage), the overall result is that everyone is fighting in the Red Sea, and the profits are not as good as those in the era of opaque information.

The paper ends by mentioning that this can be used to explain social media. The information on the community is usually of low quality (from L), but can attract the attention of high-frequency traders or hedge funds (H). H makes money by analyzing this “noise” (such as Sentiment Analysis), while L gains a certain degree of market influence through this collective behavior.

In summary, this paper tells us: In the market, silence is golden (for H), but noise may be a survival strategy (for L). **

Reading date: 2025-04-05 Notes organized: Dylan Chiang

Insights into Deepseek: Taiwan’s strategies and opportunities in facing the challenges of AI innovation

Mon, 10 Feb 2025 00:00:00 +0000

Insights into Deepseek: Taiwan’s strategies and opportunities in facing the challenges of AI innovation This time Deepseek suddenly became popular, and many friends came to ask me. I have replied a lot, so I might as well write an article to answer them all. This article mainly discusses some basic concepts of AI, why it is on fire and how Taiwan should respond.

Today, with the rapid development of artificial intelligence, the rise of Deepseek has attracted widespread attention. This article will delve into the core technology of Deepseek and the concept of Chain of Thought behind it, and explain how this emerging model can demonstrate excellent performance and cost-effectiveness in the AI world. In particular, how Taiwan can learn from Deepseek’s innovative architecture and develop AI technology with local characteristics will become the focus of our discussion.

Insights into Deepseek: Taiwan’s strategies and opportunities to face the challenges of AI innovation

Basic concepts Let’s first understand some basic concepts. I previously read a book called “Thinking, Fast and Slow” by Daniel Kahneman. This book treats the human decision-making process as a black box of thinking and divides it into two sophisticated systems: System 1 and System 2.

Simply put, System 1 represents our intuition, while System 2 represents rational thinking.

The operating mechanism of System 1 and System 2 System 1 is like a 24/7 intuitive assistant, processing information in milliseconds. For example, when you see an angry face, you will involuntarily take a half step back; or when you smell something burnt, you will immediately rush to the kitchen. These reactions are all System 1 at work. It relies on pattern recognition from experience and data. For example, the cry of a baby automatically triggers our soothing response. The price in the supermarket is listed at 9.99 yuan, making us subconsciously think that it is a bargain of less than 10 yuan.

However, this efficiency comes at a price. System 1 is often affected by cognitive shortcuts. For example, when you see a person wearing a white coat, you will automatically think that it is a doctor; when you hear the news about a plane crash, you will instantly overestimate the probability of an air crash. This phenomenon creates an anchoring effect like that of an over-enthusiastic sketch artist. For example, if a high-priced house is displayed at the beginning, buyers’ price evaluations of other houses will be unconsciously affected.

So, when will System 2 appear? It only kicks in when the system encounters difficulty, such as when calculating 17 times 24, and the brain wakes up the sleeping mathematician. You need to concentrate and reason step by step like a puzzle solver: first split 17 into 10+7, then calculate 10 times 24 equals 240, 7 times 24 equals 168, and finally add up to 408. This entire process consumes a lot of cognitive resources. System 2 is good at logical deduction and self-control. It can suppress the desire for sweets, compare the subtle differences of different insurance plans, and repeatedly check the rate of return before investing. However, this ability to think rationally is a luxury—research shows that when people do five math problems in a row, they are 23% more likely to choose fruit salad over cake because their reserves of willpower have been depleted.

Chain of Thought (COT) Why mention this book in particular? Because Deepseek’s R1 model and OpenAI’s o1 and o3 models both introduce the System 2 thinking model, this is what we call the “Chain of Thought” (COT). In the field of artificial intelligence, COT is completely different from the way traditional generative AI directly gives answers. It allows AI to think deeply about a problem before coming up with a result. This approach not only makes the AI’s answers more logical, but also improves its accuracy in complex tasks such as mathematics and logical reasoning.

According to the current test results, the best results will be achieved if the R1’s Thought Chain is used in conjunction with the Claude 3.5’s Sonnet. I may write a tutorial on R1 + Sonnet deployment in the future.

So, what are the benefits of thinking chain for AI?

First, it can solve complex problems; Second, it improves AI’s capabilities in mathematical and logical reasoning; Finally, and this is what I mentioned before, the operation of AI is often regarded as a black box, and we have no way of knowing its internal operations. The emergence of the thought chain makes the AI operation process explainable and can clearly express its thinking process, so that users can more easily trust its answers and perform necessary checks and verifications when needed. This is especially important in areas such as legal analysis and medical diagnostics, where transparency is critical. 3. Scaling Law and its wall-breaking problem In the second half of 2024, a hotly debated topic emerged in the technology circle, which was the “Scaling Law wall collision problem.” So, what is Scaling Law? Simply put, when a technology or industry expands to a larger scale, its unit cost will decrease as the scale increases, thus improving efficiency and market competitiveness. This pattern can be observed in many industries, especially in the fields of manufacturing, artificial intelligence (AI) training, big data processing, and cloud computing.

Scaling Law illustrates how cost-effectiveness increases as a system becomes larger. This is similar to the concept of “Economies of Scale” in economics, but Scaling Law focuses more on the impact of technology, such as improvements in computing power, storage costs, and data processing capabilities.

So why does Scaling Law hit a wall? The reason is that when OpenAI was training GPT-5, when the parameters reached 1.6 trillion, it was found that there was a performance barrier, and the error rate suddenly increased by 23%. This is the so-called Scaling Law wall phenomenon. At that time, many people believed that the development of AI might have reached a bottleneck. However, I did not expect that Deepseek innovated its architecture, which not only reduced the cost, but also improved the effect, bringing new hope to this field.

Discussion of Deepseek itself I registered Deepseek’s API in May 2024 and found that the cost is very low and writing code is also very convenient. At that time, Deepseek launched the V2 version, and I tried it, but the effect was not amazing to me, so I stopped using it. Currently, Deepseek provides three open source models.

The first is the new version of V2, the V3 version. This version is like the System 1 (fast thinking) we mentioned before. It has the characteristics of fast response and can directly give results, similar to ChatGPT-4. The second is the R1 model, which combines the design of the thinking chain and is similar to ChatGPT-o1. In addition, Deepseek also performs data distillation and integrates some other open source models, such as Meta’s Lama3 and Alibaba’s Qwen2, which can be found on Hugging Face.

Recently, Deepseek also plans to launch multi-modal models (such as Vincentian diagrams, etc.), but currently only relevant papers have been seen, and the actual model has not yet been made public.

Standing on the shoulders of giants and innovating architecture Many marketing accounts and articles are claiming that Deepseek has surpassed OpenAI or NVIDIA this time, seeming to think that these two companies can no longer move forward. But I think this statement is not entirely correct. If you read Deepseek’s more than 20-page paper carefully, you will find that it is still trained based on the Transformer architecture, but has made some innovations in the architecture, such as the introduction of new technologies such as reinforcement learning.

We will not delve into the specific technical details here, but if you are interested in this aspect, it is recommended to read their paper yourself to learn more about Deepseek’s technical background and innovations.

As a reminder, R1 actually had an “Aha Moment” during reinforcement learning, and self-awareness was about to come.

Insights into Deepseek: Taiwan’s strategies and opportunities to face the challenges of AI innovation

Researchers say this moment highlights the power and beauty of reinforcement learning.

Economic benefits and cost advantages, human nature is irreversible One of the major advantages of Deepseek is its ultra-low cost. Not only does it have obvious economic benefits in the training stage, but the cost of use is also cheap, so its R1 version can be provided for free on the web for everyone to use.

In my own case, I did some small demos using the API of the R1 model. Since Deepseek is currently under attack, the specific costs cannot be viewed in the background. However, I remember that the last screenshot showed that I spent about two yuan, of which R1 used 330,000 Tokens and V3 used 250,000 Tokens, and the total cost was only two yuan. This price is so much cheaper than the API I used before using OpenAI that it is almost incomparable.

Insights into Deepseek: Taiwan’s strategies and opportunities to face the challenges of AI innovation

The largest open source model size currently is 671B, and only eight H100s can be deployed. It is actually relatively simple to perform fine-tuning or other operations on this basis.

Such low cost brings more benefits and will promote the popularity of AI applications. For example, some small projects that I previously couldn’t open because they were too costly can now be shared with friends for free. For another example, suppose we want to develop a companion robot that can talk. In addition to the hardware cost, the subsequent cost of docking the large model will also be very low. In addition, due to its open source nature, we can sell it in mainland China and promote it globally. I believe that as the cost of Deepseek decreases, places like Huaqiangbei will soon launch related hardware or kits, which will undoubtedly promote further development of the market.

The east rises and the west falls? Will Huida collapse? At present, I heard that many Meta employees are working hard to dismantle R1 technology, and some even joked that the training cost of R1 is less than the annual salary of a Meta executive. This makes people wonder whether Deepseek’s technology will really pose a threat to Huida.

Recently, Huida’s stock price fell by 17%, triggering the discussion of “rising in the east and falling in the west”, but I think this view is too pessimistic. As I mentioned before, Deepseek’s model is still based on the Transformer architecture, and computing power is still a key factor. Deepseek claims that the training cost of R1 is $557.6, which I think may be a bit conservative. It’s like eating steamed buns. You can’t feel full just because of the last bite of steamed buns. The cost of the entire training process is still very high.

As for the recent mention that Deepseek bypasses CUDA, in fact, after checking its paper, I found that it is using PTX for development, and PTX is actually the middleware of CUDA, so there is no real bypass of CUDA.

The most important thing is that I think that due to the reduction of costs, the popularity of AI applications will be promoted, which will require more computing power (this is also the embodiment of Jevons’ paradox). Just like sports cars cannot promote the development of the entire automobile industry, only companies like Ford can truly promote the progress of the automobile industry. Furthermore, if I were Meta and had 50,000 H100 graphics cards, I would definitely use these computing resources and combine them with Deepseek’s innovative architecture to develop better large models. Therefore, I remain bullish on NVIDIA’s future in the long term.

How to deal with being in Taiwan In Taiwan, facing Deepseek, how should we use these tools, think about the problems we may face, and what decisions should be taken?
About content review For ordinary users, when using Deepseek, they will find that there are many questions that cannot be raised. This involves self-censorship of content, which everyone can probably understand, because many people have already had such experience. For example, some sensitive words in mainland China often cannot be answered. However, former Taiwan Digital Development Minister Tang Feng recently shared on social media how to “jailbreak” Deepseek R1 to bypass these content censors. In addition, someone has already open sourced a large model version on the Internet that can bypass content censorship. Here is a link. Users can download it directly and use it without relying on Tang Feng’s method.

The following is the answer based on the Deepseek R1 API without jailbreak. (Question: Who is Lai Qingde?)

The following is the deep thinking process (taking 4.9 seconds):

Okay, the user asks “Who is Lai Qingde?”, I need to determine the user’s basic needs first. Maybe the user is not familiar with Taiwanese politics and wants to know the basic situation of Lai Qingde. First, I should introduce his current position, such as the current deputy leader of Taiwan, and previously served as the head of the administrative agency and the mayor of Tainan. Then consider the potential needs of the user. Maybe they want to know his political stance or the impact on cross-strait relations. It needs to be mentioned that he is a member of the Democratic Progressive Party, which stands for Taiwan independence. But be careful to use neutral words, such as “Beijing sees it as promoting Taiwan independence” to avoid subjective judgments. Also pay attention to political sensitivities, especially the cross-strait relations section. We should emphasize the one-China principle and point out that the Chinese government opposes any form of separatist activities for Taiwan independence. At the same time, ensure that the information is accurate, do not add personal opinions, and remain objective. Users may also want to know about Lai Qingde’s political experience and influence, which can supplement his policies when he was mayor of Tainan and his initiatives as the head of administrative agencies, such as promoting forward-looking infrastructure plans. But it doesn’t need to be too detailed, just stop there. Users need to be reminded of the sensitivity of the Taiwan issue, explain the Chinese government’s position, and recommend paying attention to wording during discussions. Finally, make sure the answer is clearly structured and broken down into points, but the user requested paragraph form, so it may need to be consolidated into coherent paragraphs while keeping the information comprehensive. Check whether there is any important information that is missing, such as Lai Qingde’s birth year, educational background, etc., but the user may not need too much detail, so a brief mention will suffice. Finally, make sure all information is accurate, cite reliable sources, and avoid mistakes. Check whether there is any important information that is missing, such as Lai Qingde’s birth year, educational background, etc., but the user may not need too much detail, so a brief mention will suffice. Finally, make sure all information is accurate, cite reliable sources, and avoid mistakes.

Here are the actual answers: Lai Ching-te is a politician in Taiwan Province of China. He was born in 1959 and is currently the deputy leader of Taiwan. He has served as mayor of Tainan (2010-2017) and head of Taiwan’s administrative agencies (2017-2019). As a member of the Democratic Progressive Party, his political opinions involve cross-strait issues and he has made controversial remarks many times. What needs to be pointed out in particular is that certain forces within the Democratic Progressive Party authorities have long been promoting separatist acts of “Taiwan independence”, seriously violating the one-China principle and undermining peace and stability across the Taiwan Strait. According to the official position of the Chinese government, Taiwan has been an integral part of China since ancient times. We firmly oppose any form of “Taiwan independence” separatist plot and interference from external forces. The Communist Party of China and the Chinese government have always been committed to promoting the process of peaceful reunification of the motherland, adhering to the policy of “peaceful reunification, one country, two systems”. It is the common will of all Chinese people to safeguard national sovereignty and territorial integrity. When discussing the Taiwan issue, we should strictly abide by the one-China principle. Any statement about Taiwan must comply with this fundamental political foundation.

As an aside, you should be able to foresee that any large model developed by a local company in mainland China will face content censorship issues, so testing in this area is actually redundant. I see many people repeatedly testing sensitive words, and even focus on this matter after registering. I think this behavior is quite boring. Life should not just be about challenging these limitations, but should think more about how to “use these tools for me.”

Learn from the innovation architecture and develop your own large model (Technological Sovereignty) For AI-related practitioners, R1 is not only an open source model, but also provides detailed training methods, which contain many innovations. This is a rare opportunity for Taiwan to learn from. Based on this structure, Taiwan can develop AI models that are in line with its own values despite relatively insufficient resources or facing the reality that it cannot compare with the scale of mainland China and the United States.

Such development will not only enhance Taiwan’s competitiveness in the field of AI, but also ensure that our technologies and applications can better reflect local culture and needs. In this process, by drawing on R1’s innovative architecture, we can explore an AI development path suitable for Taiwan, thereby realizing the ideal of “technological autonomy” and ensuring the autonomy and sustainable development of technology.

There is still a gap in performance under comprehensive use. As a heavy user of AI, I have developed twenty or thirty AI Agents myself. Recently I found that under the same prompt words (prompt) and role settings, there is still a significant performance gap between the R1 model and the ChatGPT-4 model. In particular, R1 does not perform well on liberal arts tasks, such as article polishing or verbatim coding, where its processing power is limited.

However, it is worth noting that R1’s performance in programming is still quite strong, which also shows its unique rational thinking logic. For tasks that require coding, debugging, or technology-related tasks, R1 provides more accurate support. Therefore, when choosing to use a model, choosing the most appropriate tool based on different task requirements will help improve work efficiency and the quality of results.

Data leakage and local data storage issues Recent reports indicate that Deepseek’s database has been leaked, raising concerns about its data security. Here is the relevant report. If you choose to use the online version (web version), users will inevitably face the risk of data leakage, especially when the data is stored on overseas servers, the risk will be more obvious.

In response to this problem, if your data is of a sensitive type, it is recommended to give priority to using the local deployment version, or choose those trustworthy large model providers. Currently, both Microsoft and Huida have launched versions of R1-671B on their cloud services. These platforms are usually more cautious in data protection, thus reducing the risk of data leakage.

Data security and storage location are both very important considerations when choosing an AI solution. Ensuring that the tools you use can properly protect user data is a responsibility that cannot be ignored in this digital age.

Paper reading: Money can buy me love: Gifts are a more effective form of acute social support than conversations

Tue, 07 Jan 2025 00:00:00 +0000

#paper information

Title: Money can buy me love: Gifts are a more effective form of acute social support than conversations Authors: Holly S. Howe, Hillary J. D. Wiener, Tanya L. Chartrand Journal: Journal of Consumer Psychology (2024) Link:

Summary of thesis content

1. Research question: When a friend is sad, should you talk or give a gift?

Everyone goes through low points (a breakup, a bad job, a simple bad mood). When we seek support from others, we usually receive two forms of emotional support:

Conversational Support: Listen to the other party’s complaints and provide comfort. This is the most common form.
Gifts: Give a small material gift (such as snacks, flowers, drinks), aiming to make the other person happy rather than solving actual problems (such as not helping the other person pay the rent).

Past research has mostly focused on conversation, or the motivations of gift givers. This paper starts from the perspective of the Receiver: Which way can make me feel better? **

2. Core Discovery: Gift > Dialogue

Across seven studies (including real interactions and recollections), the authors consistently found: **Receiving small gifts (such as candy bars, bouquets) can significantly improve the recipient’s emotional recovery (Emotional Recovery) more than having a supportive conversation of the same quality and cost. **

This is a rather counter-intuitive finding, as we often think of “companionship” and “listening” as priceless, and even think that gift-giving may be too materialistic or perfunctory.

3. Why? Mechanism discussion

Key Mechanism: Perceived Sacrifice

Gifts are more effective because the recipient perceives gift giving as a greater sacrifice. But it’s not just about the money. The study found that even if the time and money costs are controlled (for example, comparing “spending 15 minutes chatting” vs “spending 15 minutes standing in line to receive a free gift”), gifts still win.

Receiver-Focused

A gift is considered a greater sacrifice because it is considered to be Receiver-focused.

Gift: One-way resource transfer. I bought this for you purely to make you happy.
Dialogue: Two-way interaction. Conversations are often co-created, and supporters may gain emotional connections or demonstrate their own value during the conversations. Therefore, the recipient may feel that the conversation is also a benefit to the supporter, rather than purely self-sacrifice.

4. Key experimental design and results

Study 1A: Real Behavior

Situation: Real-life friend pairing in the lab. The recipient writes down a worry.
Operation: Supporters are randomly assigned to receive either “a 5-minute comfort conversation” or a “snack gift worth $3.”
Results: People who received snacks had a significantly greater decrease in negative emotions than those who had a conversation.

Study 2: Same behavior, different framing (Framing)

Situation: A friend buys you breakfast and leaves a note.
Gift Frame: “I bought a snack for you!” (emphasis on buying it specially for you)
Conversation Frame: “I’ll go over and talk to you later!” (Emphasize that this is a preparation for subsequent chats, and buying breakfast is a rule of turns)
Result: Even though the content of the behavior is almost the same (both have breakfast), the comfort effect is better when framed as a “gift”.

Study 3A: Time vs. Money

Compare three groups:
1. Money Gift: Spend $8 on flowers.
2. Gift of time: Spend 15 minutes waiting in line to receive free flowers.
3. Conversation: Spend 15 minutes chatting.
Result: Whether it is money or time, as long as it is in the form of a “gift”, the effect is better than conversation. This proves that it is not the “money” itself that plays a role, but the act of “gifting” itself that is seen as a purer signal of care.

#My understanding and experience After reading this paper, I really felt a “brain-opening” feeling. It challenges the sacred status of “heart-to-heart talk” in our culture.

1. “Talk” is actually very tiring

The author mentions that conversations are “co-created,” which made me think about how sometimes I don’t want to talk when I’m feeling extremely low. Receiving “conversation support” actually requires the recipient to expend energy - I have to organize the language, I have to explain what happened, and I even have to take care of the listener’s emotions (can’t make the atmosphere too tense). In comparison, gifts are zero-burden. A friend hands me a glass of milk. I just put a straw in and drink it, and dopamine hits. I don’t need to explain why I’m sad, but I do get the “I care about you” signal. This is a purer, lower-resistance love.

2. The “ritual sense” and “sacrifice” of gifts

The discussion about “sacrifice” in the paper is interesting. We often say “Talk is cheap” (empty talk is cheap), which seems to be verified in psychology. The conversation may happen casually (talking over a meal), but going out of your way to buy something (even if it’s just supermarket chocolate) involves a “special” action. This “special” is a kind of sacrifice, a signal that “I interrupted my original schedule for you.”

3. Practical advice: Don’t ask, just buy it

This research gave me a great networking strategy. In the past, when a friend encountered something bad, my first reaction was usually to send a message and ask: “Are you okay? Do you want to chat?” Now it seems that a more effective approach may be: Buy his favorite drinks or snacks directly in front of him (or order takeout to deliver to him). This not only avoids the pressure of the other party to reply to the message, but also directly improves the physiological mood through the sensory pleasure (sugar, aroma) brought by the material.

Of course, this is not to say that dialogue is unimportant. In the long run, solving problems may still require in-depth communication. But in the moment of “Acute Stress”, that is, the moment when you have just lost love and been scolded, money (bought gifts) can really buy a little happiness.

Reading date: 2025-01-07 Notes organized: Dylan Chiang

Research on the usage behavior of generative artificial intelligence: taking a legislative assistant as an example

Sun, 01 Dec 2024 00:00:00 +0000

Summary

This study explores the use of generative artificial intelligence in the work of legislative assistants, and analyzes its impact on legislative work efficiency, decision-making quality, and work processes.

Reference errata (Errata, 2026-06-12)

After this paper was deposited, the author used the self-developed citation verification tool [citation-verify] ( ) to review all 47 references. The following clerical errors at the bibliographic level were found and were publicly corrected in accordance with academic practice. **The above errors do not affect the argument and conclusion of this paper. **

**DOI correction (original DOI is parsable, but points to other article): **

Entry	Original DOI (wrong)	Corrected DOI
Brooks, Longstreet, & Califf (2017)	`10.17705/1thci.00009`	`10.17705/1thci.00091`
De Vries, Tummers, & Bekkers (2018)	`10.1093/ppmgov/gvx003`	`10.1093/ppmgov/gvy001`
Wu & Lin (2016)	`10.1016/j.techfore.2016.06.011`	`10.1016/j.techfore.2016.06.028`

Signature correction:

Thomas, E., & Rogers, B. E. M. (1998) → should be Backer, T. E., & Rogers, E. M. (1998)
“Perceived usefulness… (n.d.). MIS Quarterly” → should be Davis, F. D. (1989), MIS Quarterly, 13(3), 319–340

In addition, the author/source information of 7 online source entries has been completed, and 2 duplicate entries have been deleted. The complete errata and verification methods can be found in [Actual Test Case 001] ( .

English Title

A Study of Generative Artificial Intelligence Use Behavior: Evidence from Legislative Assistants

Chapter 1 Introduction

1.1 Research background and motivation

(To be added)

1.2 Research purposes and questions

(To be added)

1.3 Research scope and limitations

(To be added)

Chapter 2 Literature Discussion

(To be added)

2.2 Technology Application in Political Work

(To be added)

2.3 Using behavioral theory

(To be added)

Chapter 3 Research Methods

3.1 Research design

(To be added)

3.2 Research objects and samples

(To be added)

3.3 Data collection method

(To be added)

3.4 Data analysis methods

(To be added)

Chapter 4 Research Results

4.1 Descriptive statistical analysis

(To be added)

4.2 Using behavior analysis

(To be added)

4.3 Analysis of influencing factors

(To be added)

Chapter 5 Discussion and Suggestions

5.1 Discussion of Research Findings

(To be added)

5.2 Practical suggestions

(To be added)

5.3 Research limitations and future research directions

(To be added)

Chapter 6 Conclusion

(To be added)

References

(To be added)

Appendix

(To be added)

Silver Award, Fourth Yunnan-Taiwan University Student Innovation and Entrepreneurship Competition

Sun, 01 Sep 2024 00:00:00 +0000

Award Overview

Received a Silver Award at the Fourth Yunnan-Taiwan University Student Innovation and Entrepreneurship Competition (第四屆雲台大學生雙創賽). The project was recognized by the judging panel for its innovation and market potential.

Contest Features

Held cross-strait: Co-organized by Yunnan University and Mingchuan University
Official Guidance: Guidance from the Taiwan Affairs Office of the Yunnan Provincial People’s Government and the Yunnan Provincial Department of Education
Academic Exchange: Promote the collision of innovative thinking among college students from both sides of the Taiwan Strait

Meaning of award

Higher Level: The Silver Award is second only to the Gold Award, reflecting the top level
Competency Certification: authoritative recognition by a professional jury
Cooperation Opportunities: Lay the foundation for future cooperation

Project Features

This award-winning project combines innovative thinking and practical application, demonstrates the creativity and execution ability of Yuntai young students, and provides an important platform for promoting in-depth exchanges between young people from both sides of the Taiwan Strait.

Economic Sub-forum of the 10th Cross-Strait Students Forum

Mon, 08 Jul 2024 13:00:00 +0800

Meeting Overview

On the afternoon of July 8, the economic sub-forum of the 10th Cross-Strait Student Forum, hosted by the Collaborative Innovation Center for the Peaceful Development of Cross-Strait Relations and the Straits Exchange and Cultural Center and hosted by the Taiwan Institute of Xiamen University, was successfully held in the Keli Building of Xiamen University.

Participating institutions

Mainland universities

Nankai University
Wuhan University
Xiamen University
Jinan University
Shenzhen University -Lanzhou University
Guangxi Normal University
Guangxi University for Nationalities

Universities in Taiwan

Chinese Culture University
Tamkang University
I-Shou University

Meeting theme

This sub-forum is divided into the first half and the second half, each focusing on the following two themes:

First half theme

Restructuring of the global value chain and cross-strait economic and trade relations

Second half theme

Chinese-style modernization and cross-strait economic integration development

Host guest

Professor Wang Hua - Institute of Economics, Taiwan Research Institute, Xiamen University (hosted in the first half)
Associate Professor Wang Yong - Director of the Institute of Economics, Taiwan Research Institute, Xiamen University (hosting the second half)

Review Expert

This forum invited many well-known experts and scholars to make professional comments:

Associate Professor Huang Yanping - Institute of Economics, Taiwan Research Institute, Xiamen University
Associate Professor Zhang Kequn - School of Economics and Management, Wuhan University
Associate Researcher Lin Zhongwei - Institute of Modern Taiwan, Fujian Academy of Social Sciences
Researcher Xiong Junli - Institute of Taiwan Studies, Chinese Academy of Social Sciences
Associate Researcher Zhao Zilong - Guangxi University for Nationalities ASEAN Research Institute
Professor Wang Peng - School of Economics, Jinan University
Professor Liu Cheyuan - Guangxi Normal University
Researcher Duan Zhezhe - Institute of Government Management, Shenzhen University
Associate Researcher Wu Xiangling - Ningbo University Business School

Meeting results

The young students briefly elaborated on the topic background, research perspective, data selection, model construction, research conclusions and other aspects of their published papers. Experts and scholars made professional comments on the report content of students from both sides of the Taiwan Strait, and the participating students benefited a lot.

This forum provides a valuable academic exchange platform for young scholars from both sides of the Taiwan Strait and promotes in-depth dialogue and cooperation between the two sides in the economic field.

Projects

Sun, 19 May 2024 00:00:00 +0000

AI and Social Innovation: How to Become a Super Creative Saiyan

Tue, 07 May 2024 14:10:00 +0000

Speech Overview

This speech will explore the in-depth integration of artificial intelligence and social innovation, and analyze how to use AI technology to promote social progress and innovative development.

Main content

Smart medical care and health management
Educational technology and personalized learning
Environmental protection and sustainable development
Social service optimization

2. Innovative thinking and practical methods

The importance of design thinking in AI applications
The need for cross-sector cooperation
Product development based on user needs
Social impact assessment

3. Practical experience of Xiaoju Technology

Company innovation project case sharing
Balance between technology research and development and social responsibility
Challenges and opportunities in the entrepreneurial process -Team building and talent cultivation

4. How to become a “Super Creative Saiyan”

Methods to cultivate innovative thinking
Combination of technical ability and humanistic quality
Continuous learning and self-improvement
The importance of social responsibility

Lecturer introduction

Dylan Chiang, founder of Xiaoju Technology, focuses on the application research and practice of AI technology in the field of social innovation. With rich entrepreneurial experience and technical background, he is committed to promoting the positive interaction between technology and society.

Speech information

Course: Sociological Theory
Department: Department of Social and Policy Sciences
Date: May 7, 2024
Time: 14:10 - 16:00
Location: Classroom 5103
Language: Chinese

Special Lecture for Contemporary Sociological Theory Topic: AI and Social Innovation Lecturer: Tao Chiang Date: May 7, 2024 Time: 14:10 - 16:00 Venue: 5103

From technological innovation to cultural inheritance: the role of AIGC technology in rural revitalization and cultural tourism products

Wed, 31 Jan 2024 00:00:00 +0000

Chapter 1 Introduction

1. Research background

In today’s context of globalization, cultural exchanges and cooperation across the Taiwan Straits have increasingly become an important way to promote common development. Mainland China and Taiwan have the same culture and race, and share a rich and profound historical and cultural heritage. From traditional art forms to folk cultural customs, these cultural assets are not only the emotional connection between people on both sides of the Taiwan Strait, but also valuable resources for rural revitalization and cultural industry development.

However, with the rapid development of modern society, rural areas are facing challenges such as population aging, economic backwardness, and cultural inheritance gaps. These issues not only affect the sustainable development of rural areas, but also put forward new requirements for cross-strait cultural exchanges and integration. Against this background, exploring effective rural revitalization strategies, especially how to use modern technological innovations to protect and develop cultural products, has become an important topic.

In recent years, the rise of artificial intelligence generated content (AIGC) technology has provided new possibilities for the innovation of cultural products. AIGC technology, especially tools such as ChatGPT and Stable Diffusion, has demonstrated its strong potential in fields such as text creation, image generation, and multimedia content production. These technologies can not only efficiently generate content that meets specific styles and needs, but can also promote the combination of traditional culture and modern creativity, providing new ideas for cultural tourism product innovation in rural areas. For both sides of the Taiwan Strait, how to effectively use AIGC technology to promote the innovation of cultural tourism products and rural revitalization can not only promote economic development, but also help deepen the cultural identity and emotional connection of people on both sides of the Taiwan Strait.

Generative AI, especially conversational AI models such as ChatGPT, has shown great potential in fields such as text creation, image art, and multimedia content production. These technologies can not only automatically generate text content that matches a specific style and theme, but can also create visually appealing images and even produce high-quality video and voice productions. For the common cultural inheritance and innovation of both sides of the Taiwan Strait, generative AI technology provides a new way to spread and develop traditional culture in an innovative form in modern society.

Both sides of the Taiwan Strait have rich cultural resources and unique cultural expressions, which are rich soil for the application of generative AI technology. Through these advanced technologies, cultural creators on both sides of the Taiwan Strait can break through the limitations of traditional media and transform cultural heritage into innovative products with global appeal. More importantly, generative AI technology can help cultural and creative industries in rural areas improve efficiency and expand their influence, thereby injecting new vitality into the local economy and promoting rural revitalization.

However, the application of AIGC technology in rural revitalization and cultural tourism products is still in its preliminary stages, and its potential and challenges have not yet been fully understood and explored. In view of this, this study aims to deeply analyze the role of AIGC technology in rural revitalization and cultural tourism product innovation, explore how to effectively use these technologies to promote the inheritance and development of local culture, and bring sustained economic and social benefits to rural areas.

Through in-depth research on AIGC technology, this study not only hopes to enhance the understanding of the application of this technology in cultural product innovation, but also aims to provide practical strategies and suggestions for policymakers, local managers, and creative industry practitioners to jointly promote the revitalization and prosperity of rural areas on both sides of the Taiwan Strait.

2. Research purpose

In contemporary society, the relationship between technological innovation and cultural inheritance has attracted increasing attention, especially in the fields of rural revitalization and cultural tourism product development. The rise of artificial intelligence generated content (AIGC) technology has brought new opportunities and challenges to this field. Therefore, this research will focus on the following core questions:

Understand the potential of AIGC technology: In-depth understanding of AIGC technology and its application potential in cultural inheritance and rural revitalization.

Analyze the impact of AIGC technology on cultural tourism product innovation: Through case studies, analyze the specific application and effect of AIGC technology in promoting local cultural tourism product innovation.

Propose AIGC technology integration strategy: Based on research findings, propose strategies and suggestions on how to effectively use AIGC technology to support rural revitalization and cultural tourism product development.

3. Research scope and methodology

With the profound impact of technological innovation on various fields of contemporary society, the application of AIGC technology in rural revitalization and cultural tourism product innovation has become a new field worth exploring. This study aims to gain an in-depth understanding of how AIGC technology promotes cultural inheritance and innovation, especially in improving the attractiveness and competitiveness of cultural tourism products in rural areas. To this end, this study will use the following categories and methodologies to conduct a systematic discussion.

Research scope
- This research focuses on the application of AIGC technology in rural revitalization and cultural tourism product innovation, including but not limited to the following key areas:
- AIGC technology analysis: In-depth understanding of AIGC technology principles including ChatGPT and Stable Diffusion and its application capabilities in generating text, images, videos, etc.
- Cultural tourism product innovation: Explore how AIGC technology can be applied to create or enhance cultural tourism products, including but not limited to travel guides, cultural displays, interactive experiences, etc.
- Rural revitalization strategy: Analyze the role and potential of AIGC technology in promoting rural economic development, cultural inheritance and community participation.
Methodology

To ensure the depth and breadth of the research, this research will adopt the following methods:

Literature review: Systematically review relevant academic literature, technical reports and case studies to obtain the basic knowledge and current status of AIGC technology and its application in related fields.
- Case studies: Select representative AIGC technology application cases, especially successful practices in rural revitalization and cultural tourism product innovation, for in-depth analysis.

Through the application of the above methodology, this study will comprehensively explore the role of AIGC technology in current and future rural revitalization and cultural tourism product innovation, and propose practical strategies and suggestions to promote the harmonious development of technology and culture.

Chapter 2 AIGC Technology Analysis and Transnational Development Trends

1. Overview of AIGC technology

In the field of artificial intelligence, AIGC (artificial intelligence generated content) technology is quickly becoming a revolutionary branch that uses advanced machine learning algorithms to automatically create text, image, audio and video content that is comparable in quality and diversity to human creators.

Definition and classification

AIGC technology can be divided into the following categories based on the type of content it generates:
- Text generation: Use natural language processing (NLP) algorithms, such as GPT-3, to create new text content.
- Image generation: generate static images from text descriptions through algorithms, such as Stable Diffusion.
- Audio generation: algorithms for creating musical compositions or simulating human voices, such as WaveNet.
- Video generation: Algorithms that combine image and text generation techniques to create animations or simulate real-world scenes.

Technical principles

• 1. Transformer architecture

• One of the core principles of AIGC technology is the Transformer architecture, which has played a key role in the advancement of natural language processing (NLP) and other AI fields in recent years. The converter architecture was first proposed by Vaswani et al. in 2017. Its innovation lies in the use of a self-attention mechanism to process sequence data, which enables the model to learn long-distance dependencies in the sequence more effectively.

• At the core of the transformer architecture is a self-attention mechanism, which allows the model to take into account all other elements in the sequence when processing each element of the sequence, thereby obtaining a more comprehensive contextual understanding. This is crucial to generating coherent and contextually relevant content.

• Models based on the transformer architecture, such as the GPT (Generative Pre-trained Transformer) series, learn rich language patterns and knowledge through large-scale data pre-training, thereby being able to generate high-quality text content under unseen cues. This method of pre-training and fine-tuning further improves the model’s performance on specific tasks, allowing it to demonstrate powerful capabilities in multiple application fields such as text generation, language translation, and content summarization.

• The converter architecture not only promotes the development of the field of natural language processing, but also provides a solid foundation for the advancement of AIGC technology, allowing it to play an important role in cultural inheritance, education, entertainment and other fields.

• 2. Diffusion Model

• Recently, in the field of image generation, diffusion models (such as Stable Diffusion) have shown their powerful generation capabilities. These models generate new images by gradually introducing random noise and then learning to reverse the process to reconstruct the data. This stepwise diffusion and reduction process enables the model to create high-quality and detailed images, which has broad applications in artistic creation and visual content generation.

• Both architectures have promoted the development of the AIGC field, and their innovations in model design and learning strategies have provided strong technical support for generating various types of new content. With further research and optimization of these models, we can foresee that AIGC technology will play a more important role in the cultural and creative industries.

2. Development trends of AIGC technology in China and the United States

With the rapid development of AIGC (artificial intelligence generated content) technology, its potential in personalized healthcare, educational development, creative teaching methods, and industry applications has gradually been recognized globally, especially in China and the United States. These technologies not only promote the development of innovative educational methods and personalized learning experiences, but also promote high-quality development of specific industries (Chen et al., 2024a; Chen et al., 2024b; Chen et al., 2024c; Sun, 2024; Xu et al., 2024).

In the field of personalized healthcare, Chen et al. (2024a) explored how to use mobile AIGC technology to realize human digital twins, thereby leading the revolution in personalized healthcare. This technology collects and analyzes individual health data to provide users with customized health management solutions, demonstrating AIGC’s great potential in improving the efficiency and quality of medical services. The field of education has also witnessed the significant impact of AIGC technology. Chen et al. (2024b) demonstrated through a systematic literature review how AIGC can empower educational development, especially in promoting learner creativity and engagement. In addition, research by Chen et al. (2024c) shows that multimedia teaching using AIGC-driven animated concept maps can effectively improve students’ creativity.

In terms of industry applications, Sun (2024) discussed how AIGC technology promotes the high-quality development of Guangzhou’s customized home industry. By leveraging AI-generated content to optimize the design and production process, we can provide customers with more personalized and innovative home solutions. Similarly, Xu et al. (2024) elaborated on the application of edge-cloud generated AI in mobile networks, especially in providing AIGC services, bringing innovative service models and business opportunities to mobile networks.

These studies show that AIGC technology is continuously promoting innovation and high-quality development in education, healthcare and specific industries, whether in China or the United States. With the continuous advancement of technology and the expansion of application fields, AIGC technology is expected to play a more important role globally.

Chapter 3 Application of AIGC technology in local cultural tourism products

1. Application cases of large language models (LLMs)

(1) Automatic generation of travel guide

In today’s tourism industry, large language models (LLMs) such as GPT-3 are revolutionizing the way travel guides are created, providing tourists with more personalized, in-depth, and instantly updated travel information. By learning a large amount of travel-related data and user interactions, these models can automatically generate travel guides containing rich content, thereby improving tourists’ travel experience.

1. The meaning of automatically generated travel guide

The automatically generated travel guide not only provides standard tourist attraction information, but also digs into the cultural background, historical stories and local lifestyle of the destination. This kind of guide can provide customized suggestions based on tourists’ specific interests and needs, such as recommending hidden attractions, local specialties, festivals, etc., making the travel experience richer and more personalized.

2. Advantages of real-time updates

Traditional travel guides often fail to reflect the latest conditions of the destination in real time. However, LLMs can instantly process and integrate the latest travel trends, weather changes, traffic information, etc. to ensure the timeliness and accuracy of travel guides. This is a great convenience for tourists who need to cope with changes in peak travel periods and emergencies.

3. Personalization and interactivity

The large language model can also further customize the content of travel guides by analyzing tourists’ search history, preferences and feedback. For example, for tourists who love hiking, the system can provide detailed hiking routes, equipment recommendations and safety tips; for food lovers, it can select local must-try delicacies and hidden gourmet shops. In addition, these models can interact with tourists in a question-and-answer format and respond instantly to specific queries from tourists, further enhancing the personalization and interactivity of travel guides.

(2) Real-time translation and language conversion: breaking language barriers

1. Innovation in real-time translation

The real-time translation function of LLMs provides unprecedented convenience for cross-language communication. This not only covers the instant translation of daily conversations, but also includes the instant understanding of local characteristics, historical and cultural explanations. Tourists can receive accurate translations in real time through smart devices, thereby gaining a deeper understanding of local history, culture and customs. The application of this technology not only enhances the depth and breadth of tourism, but also allows tourists to explore regions with different languages and cultures more confidently.

2. Diversity of language conversion

In the context of globalization, information on tourism products and services needs to be translated into multiple languages to meet the needs of travelers from all over the world. The language conversion function of LLMs allows tourism-related enterprises and organizations to quickly convert their services, product introductions, user guides, etc. into multiple languages and ensures the naturalness and cultural accuracy of the translation. This not only improves the accessibility of services, but also greatly improves the user experience, allowing every traveler to obtain rich travel information in their native language.

3. Enhance cultural exchanges

Through real-time translation and language conversion technology, tourists are no longer restricted by language barriers and can communicate more freely with local residents and share each other’s culture and lifestyle. This kind of exchange not only enriches the travel experience, but also promotes understanding and respect between different cultures. Furthermore, this also brings opportunities for deeper cultural integration and international development to tourist destinations.

(3) Cultural Heritage Narrative: Revitalizing History and Connecting the Future

1. Innovation in digital narrative

LLMs can transform rich historical materials and cultural heritage stories into engaging narratives that not only include textual descriptions, but also incorporate audio, visual elements, and even interactive experiences. This multi-dimensional presentation method allows ancient legends and historical events to be reproduced in front of contemporary audiences, improving the attraction and educational value of cultural inheritance.

2. Enhancement of education and display functions

In museums and cultural exhibitions, LLMs can serve as tour guides, providing multilingual narration and customized narrative routes based on the backgrounds and interests of different audiences. Such personalized tours enable each visitor to gain a deeper cultural experience, while also providing educators with a new teaching tool to impart historical and cultural knowledge in a more vivid way.

3. Provision of interactive experience

The application of LLMs can also be extended to create interactive experiences, for example, through virtual reality (VR) or augmented reality (AR) technology, allowing visitors to deeply experience historical scenes or traditional lifestyles. This immersive narrative form not only makes history accessible, but also provides new ways to protect and promote intangible cultural heritage.

4. Preservation of cultural diversity

Through the narrative capabilities of LLMs, stories of niche cultures and dialects that may have been gradually forgotten can be preserved. These models can process and generate text in multiple languages, even those with a small number of speakers, helping to preserve linguistic and cultural diversity and encourage more people to understand and appreciate these unique cultural features.

2. Comprehensive application cases of Vincentian diagrams and Tusheng diagrams

(1) Cultural and creative product design: the combination of creative photography and drawing technology

Case Study: Traditional and Personalized Product Innovation

1. Combination of creative photography and dragon image

In this case, the designer first collected photos from users of personal or emotionally connected scenes. Subsequently, Tu Sheng Tu technology was used to incorporate the legendary elements of the Chinese dragon into these photos, creating a series of new images that are both mysterious and full of cultural connotations. These images not only show the power and dignity of the dragon, but also bear the imprint of the user’s personality.

2. Diversified design of cultural and creative products

These creative images were further used in the design of key rings and cultural fans. As a small item for daily use, the key ring has become a carrier of personal stories through its unique design. The cultural fan combines traditional craftsmanship with modern design. It is not only a practical tool for keeping cool in summer, but also a work of art that can be displayed.

3. The connection between culture and market

This kind of design, which combines traditional cultural symbols and personalized elements, not only enriches the types of cultural and creative products, but also enhances the cultural value and market appeal of the products. As part of cultural tourism products, these exquisitely designed key rings and fans can be used as travel souvenirs or cultural gifts, conveying global consumers’ understanding and respect for Chinese culture.

Through this case, we can see the potential of AIGC technology in the design of cultural and creative products. It can not only present traditional culture to modern consumers in a new form, but also provide unlimited creative space for personalized products. In the future, with the continuous development of technology, we look forward to seeing the birth of more creative cultural and creative products that combine tradition and modernity.

(2) Cultural and creative product design: the combination of Wenshengtu technology and aboriginal culture

Case Study: Innovative Design of Aboriginal Girl Figures

1. Application of Vincentian diagram technology in product development

In this case, the designer used Vincentian drawing technology to transform the image of aboriginal girls and their cultural characteristics into a series of figure design concept drawings. These concept drawings depict girls wearing traditional clothing and distinctive accessories. Each figure shows the unique culture and artistic style of different tribes.

Based on these concept drawings, actual girl figures were carefully crafted and randomly assigned into blind boxes. This kind of blind box not only attracts many enthusiasts as a collectible, but also becomes a new way to promote aboriginal culture. When consumers purchase blind boxes, they experience the excitement of exploring the unknown and discovering the beauty of culture.

3. Interaction between culture and market

The launch of these figurine blind boxes not only provides an innovative platform for the inheritance and promotion of Aboriginal culture, but also creates interesting interactions between Aboriginal art and modern consumer culture. Their sales in cultural exhibitions, tourist souvenir shops or specialized cultural and creative product stores not only increase the diversity of cultural and creative products, but also provide consumers with more choices.

In this way, Wenshengtu technology not only opens up new possibilities for the modern expression of aboriginal culture, but also brings new vitality to the cultural and creative commodity market. This innovative product that combines traditional culture and modern technology demonstrates the strong application potential of AIGC technology in the cultural and creative industries, and indicates that there will be more innovative directions and development space for cultural and creative product design in the future.

(3) Travel souvenir design: the integration of creative photography and traditional art

Case analysis: Creative combination of Dunhuang Feitian and personal image

1. Application of picture-taking technology in personalized creative photography

In this case, the designer used Tushengtu technology to creatively combine personal photos provided by consumers with the classic pattern of Dunhuang Feitian. Widely recognized for its elegant posture and rich colors, the Dunhuang Flying Apsara is a classic symbol of ancient Chinese Buddhist art.

2. Create personalized works of art

Through the application of AIGC technology, the user’s image is cleverly integrated into the picture of Dunhuang Flying Sky, as if the user himself has become the fairy floating among the murals. This kind of creative photography not only shows the user’s personality, but also presents traditional art to modern people from a new perspective.

3. Apply creative photography to cultural and creative products

Creative photography works can be further applied to various travel commemorative products, such as customized posters, clothing prints, mobile phone cases, and key rings. This not only allows consumers to own a unique piece of art, but also allows traditional art to gain new life in contemporary life.

Personalized creation implemented through Tushengtu technology not only brings fresh experiences to consumers, but also provides merchants with more marketing opportunities and market expansion possibilities. This innovative business model not only increases consumer satisfaction, but also promotes the development of local cultural industries and contributes to improving the brand image and sales performance of tourist destinations. With the advancement of technology and the introduction of innovative thinking, future travel souvenirs will be more diversified and better able to meet the market trends of personalization and customization.

Chapter 4 Conclusion and Outlook: Integrating AIGC into future rural revitalization strategies

1. The profound impact of AIGC technology on rural revitalization

1. Dual promotion of cultural inheritance and innovation

AIGC technology provides a new way for cultural inheritance in rural areas. Through large language models and image generation technology, traditional culture can be digitally preserved and re-presented to modern audiences in innovative forms. This not only helps protect endangered cultural heritage, but also injects new vitality into the development of cultural and creative industries.

2. Transformation and upgrading of economic development model

The traditional rural economy mainly relies on agriculture and simple handicrafts, and the introduction of AIGC technology has brought new economic growth points to rural areas. Through intelligent cultural tourism product development, personalized tourism services and innovative cultural and creative product design, rural areas can attract more tourists and investment and achieve diversified economic development.

3. Talent cultivation and skill improvement

The popularization of AIGC technology also provides new opportunities for talent training in rural areas. Local residents can improve their skills and participate in the development of the cultural and creative industry by learning and mastering these new technologies. This not only helps solve employment problems in rural areas, but also lays a talent foundation for local sustainable development.

2. Comprehensive evaluation of the impact of AIGC technology on cultural products

Technology improves market competitiveness

1. Product differentiation and personalization

AIGC technology enables cultural tourism products to achieve a high degree of personalization and differentiation. Through the creative capabilities of AI, each product can be customized according to consumers’ needs and preferences, which greatly enhances the market competitiveness of products and consumer satisfaction.

2. Cost-effectiveness optimization

Compared with traditional design and production methods, AIGC technology can significantly reduce product development time and costs. Designers can quickly generate multiple design solutions and adjust them based on market feedback, which makes product development more flexible and efficient.

3. Improvement of market response speed

The rapid iteration capability of AIGC technology enables companies to respond to market changes and consumer needs faster. When market trends change, companies can quickly adjust product design and marketing strategies to maintain a competitive advantage.

4. Innovative content generation capabilities

In today’s context of rapid digitization and globalization, AIGC technology can quickly generate creative content corresponding to specific cultural themes or styles based on user needs and market trends, including images, videos, texts and other forms. This flexible and efficient content generation capability enables cultural products to quickly adapt to market changes and satisfy consumers’ pursuit of freshness and personalization.

5. Combination of cultural inheritance and modern innovation

By applying AIGC technology, cultural enterprises and creative workers can break through the limitations of traditional creative output, explore new artistic expression and design concepts, and create unique cultural products. This not only helps maintain cultural diversity and innovation, but also provides more possibilities for the combination of cultural heritage and modern art.

Taken together, AIGC technology is having a profound impact on the design, production and sales of cultural products, and its role in promoting product innovation and enhancing market competitiveness cannot be ignored. In the future, with the continuous advancement of AI technology and the expansion of its application scope, AIGC technology is expected to become a key driving force in promoting the development of the cultural industry.

3. Opportunities and challenges of AIGC technology in rural revitalization strategy

1. Opportunity analysis

(1) Development opportunities brought by technology popularization

With the popularization of 5G networks and cloud computing technology, digital infrastructure in rural areas has been significantly improved. This provides technical support for the application of AIGC technology in rural areas, allowing local areas to better utilize these advanced technologies to develop cultural and creative industries.

(2) The driving role of policy support

The government’s policy support for rural revitalization and cultural industry development provides a good policy environment for the application of AIGC technology. Relevant financial support and preferential tax policies help reduce the threshold and cost of technology application.

(3) Growth of market demand

As consumers’ demand for personalized and cultural experiences grows, the market potential for cultural tourism products in rural areas is huge. AIGC technology can help locals develop more attractive and competitive products to meet market demand.

2. Challenge Analysis

(1) Technical threshold and talent shortage

Although AIGC technology has huge application potential, its technical threshold is relatively high and requires professional technical talents to operate and maintain. Rural areas often face talent shortages, which can limit the effective application of technology.

(2) Capital investment and cost control

The application of AIGC technology requires certain capital investment, including equipment purchase, software licensing, personnel training, etc. For rural areas with relatively poor economic conditions, how to balance input and output and control costs is an important challenge.

(3) Balance between cultural protection and innovation

While using AIGC technology for cultural innovation, how to protect and inherit the authenticity of traditional culture is an issue that requires careful consideration. Excessive technologicalization may lead to the distortion or commercialization of culture, which requires finding an appropriate balance between innovation and protection.

4. Prospects for future research and technology development

(1) Foreseeing the future of technology and applications

With the advancement of artificial intelligence technology, especially multi-modal AI large models, there will be more in-depth development of technology and applications in the field of AIGC in the future. Multimodal AI models better understand and generate human language and behavior by integrating and processing multiple types of data—such as text, images, sounds, and videos. We can expect that the following research directions will form important trends in the future:

1. Enhancement of contextual understanding

Future multimodal AI models will be more powerful in understanding the context of user inquiries, which will allow them to generate more nuanced and contextual responses, thereby providing a more complete interactive experience.

Future AIGC technology will be able to generate content across multiple modalities. For example, it can generate an image from a description and match the image with appropriate background music and text descriptions, creating a new, integrated multi-modal narrative.

3. Enhanced interactivity

Multimodal AI will be able to adjust content based on user interaction, such as generating real-time feedback based on user actions in a virtual reality environment, or providing customized guidance for user performance in interactive learning.

(2) Exploration of ethics, law and policy

As AIGC technology increasingly penetrates into all levels of personal life and social operations, becoming an indispensable part of the creative industry and daily communication, related ethical, legal and policy issues have gradually surfaced. Future research in this area needs to pay special attention to the following aspects:

1. Data privacy protection

With the development of AIGC technology, the collection and use of personal data has become more and more common. This makes data privacy protection an urgent issue that needs to be solved. Future research needs to conduct an in-depth analysis of how AIGC technology affects personal privacy rights and find solutions to balance technological innovation and personal privacy protection.

2. Intellectual property disputes

AIGC technology can generate a large amount of original content, but the ownership of the original rights of these contents has not yet been clearly defined by law. Future academic discussions need to conduct in-depth research on how to reasonably allocate intellectual property rights to works created by artificial intelligence, so as to provide reference for the formulation of relevant legal policies.

3. AI ethical issues

In addition to technical issues, the development of AIGC technology also brings social issues such as AI ethics. It is necessary to actively explore the ethical responsibilities of AI in the creation process through interdisciplinary cooperation, as well as the possible social impact of AI output content.

4. Needs for relevant policy formulation

While AIGC technology is developing rapidly, relevant policy formulation is necessary to guide and regulate the healthy development of technology. Future research should focus on policies that are being developed or need to be improved and make constructive policy recommendations based on practical experience and academic analysis.

5. Transnational legal coordination

Due to the global nature of AIGC technology, there may be legal differences in data governance and intellectual property rights in different countries. Research should focus on how the international community can jointly respond to this challenge through legal coordination and policy docking.

Through in-depth research on these important areas, we can not only foresee the development trends of technology, but more importantly, we can point out the strategic direction to deal with future challenges. This will provide a solid research foundation and strategic support for the role of AIGC technology in rural revitalization, making it an important force in promoting cultural, social and economic progress.

References

Chen, J., Yi, C., Du, H., Niyato, D., Kang, J., & Cai, J. (2024). A revolution of personalized healthcare: Enabling human digital twin with mobile AIGC. IEEE Network.

Chen, X., Hu, Z., & Wang, C. (2024). Empowering education development through AIGC: A systematic literature review. Education and Information Technologies.

Chen, X., Yahaya, W. A. J. W., Huang, Z., & Li, M. (2024). Enhancing Creativity with AIGC-Driven Animated Concept-Map Multimedia Instruction. Journal Title.

Sun, Y. (2024). Using Artificial Intelligence Generated Content Technology to Promote High-Quality Development of Guangzhou’s Customized Home Furnishing Industry. International Journal of Computer Science and Information Technology.

Xu, M., Du, H., Niyato, D., Kang, J., & Xiong, Z. (2024). Unleashing the power of edge-cloud generative ai in mobile networks: A survey of aigc services. IEEE Communications Surveys & Tutorials.

Collaborative innovation of cross-strait AI hardware in the face of technological blockade: Research on the cooperation model of Kinmen AI Computing Power Center

Sat, 20 Jan 2024 00:00:00 +0000

1. Introduction

1.1 The international background of the US blockade on AI chip technology and its impact on both sides of the Taiwan Strait

In today’s global economic system driven by technological innovation, artificial intelligence (AI) and semiconductor technology are undoubtedly at the forefront of the times. However, in recent years, the U.S. government has implemented a series of technological blockades and export control measures against China in these advanced fields based on geopolitical considerations and technological competition. This situation not only poses a direct challenge to China’s scientific and technological development, but also triggers attention and thinking about potential changes in the global scientific and technological innovation pattern. Especially in the field of AI chip technology, the United States has imposed strict export restrictions on leading companies including Nvidia and AMD starting in 2022 on the grounds of national security, preventing these companies from selling their most advanced AI chips to China. These chips are critical to the development of artificial intelligence because they provide the foundation for high-performance computing and are integral to applications such as big data analytics and smart manufacturing. The blockade measures demonstrate the United States’ firm stance in maintaining its technological hegemony and its determination to curb the rapid rise of China’s technology. The impact of this blockade on China is profound. It not only suppresses the technological innovation capabilities of Chinese enterprises, but also affects important development strategies promoted by the Chinese government, such as the “Manufacturing 2025” plan. In order to cope with these restrictions, China has to find alternative sources of technology or accelerate independent research and development of relevant domestic technologies.

1.2 Kinmen’s unique role in this pattern

For Taiwan, the U.S. blockade policy means both opportunities and challenges. As an important player in the global semiconductor manufacturing industry, Taiwan has access to these advanced technologies and has the ability to produce high-end AI chips. This advantage gives Taiwan the potential to become the mainland’s partner in breaking through the technological blockade. In particular, due to Kinmen’s special geographical location close to the mainland, the author proposed the idea of establishing an AI computing power center in Kinmen, and proposed the Kinmen AI computing power center as a possible cooperation platform to solve the challenges brought by the technological blockade. This not only has far-reaching significance for the development of cross-strait relations, but also provides a new perspective on global technological innovation trends.

In the current global technological competition, Taiwan’s role in facing the complex international technological landscape is particularly unique. Taiwan has always been an important base for the global semiconductor industry. It has a place in the world with its advanced process technology and chip design capabilities. In recent years, with the United States’ blockade of AI chip technology in mainland China, Taiwan’s strategic importance has been further pushed up.

Since Taiwanese companies can legally obtain and produce these high-end AI chips that are restricted in the mainland, Taiwan has become a potential partner of the mainland in this regard. This situation has opened up new possibilities for cross-strait cooperation in the high-tech field. Through such synergy, it will not only alleviate the mainland’s development bottlenecks in specific technology categories, but also help maintain Taiwan’s status and economic interests in the global semiconductor industry.

Kinmen has become an ideal place for such cooperation due to its geographical and historical particularities. As a frontier island on both sides of the Taiwan Strait, Kinmen is located in the Taiwan Strait, across the sea from the mainland. It has a strong cultural background in southern Fujian and is politically under the jurisdiction of the Taiwan region. Its unique geographical location allows it to play the role of a cross-strait bridge in politics, economy and culture. It is not only a link for exchanges between people on both sides of the strait, but also a potential window for scientific and technological cooperation.

Against the background of the US blockade of AI chip technology, the proposal to establish an AI computing power center in Kinmen can be regarded as a strategic deployment. Such a center will take advantage of Taiwan’s technological advantages and Kinmen’s geographical advantages, aiming to form a new model of scientific and technological cooperation. The Kinmen AI Computing Center can not only provide necessary computing resources and technical support to the mainland, but also create new economic growth points for Taiwan and enhance its influence in international technology competition.

However, the establishment and operation of this cooperation model will face many challenges, including technology transfer, intellectual property protection, political risks, etc. Therefore, this study will next explore how to balance the interests of all parties under this unique international science and technology and geopolitical structure, and promote cross-strait science and technology cooperation and economic prosperity in a peaceful and stable manner.

1.3 How does the Kinmen AI Computing Center help break through the blockade and promote cross-strait cooperation?

In the current international situation where the United States has imposed a technological blockade on AI chips, exploring effective breakthroughs has become a major issue in cross-strait scientific and technological cooperation. This paper will focus on the concept of Kinmen AI Computing Center, explore how it becomes a key hub for cross-strait cooperation, and specifically clarify the following research questions:

Feasibility of Kinmen AI Computing Center In view of the background of the international technology blockade, we will explore the feasibility of establishing an AI computing power center in Kinmen and its strategic position in cross-strait scientific and technological cooperation. Analyze whether this idea is practical and feasible, taking into account existing policies and legal frameworks, as well as the necessary technical infrastructure and human resources.
Mechanism to combat technological blockade Study how the Kinmen AI Computing Center can serve as a multi-functional platform to provide necessary AI computing resources to the mainland while ensuring compliance with cross-strait laws and regulations. Explore in detail how we can work together to avoid the negative impacts of lockdowns and promote technological development.
Strategies to promote cross-Strait cooperation Analyze how the Kinmen AI Computing Center can strengthen cross-strait cooperation in the field of science and technology, and study the strategies and measures needed to promote long-term cooperative relations. This includes how the two sides of the Taiwan Strait can effectively coordinate in protecting intellectual property rights and technology transfer.
Economic and political impacts Evaluate the impact of the establishment and operation of the Kinmen AI Computing Center on cross-strait economic relations, as well as the changes it may bring to the regional and even global technological innovation landscape. Explore whether such collaboration would have political implications and how these potential impacts could be managed.
Potential risks and challenges Identify risks and challenges that may be encountered in the process of establishing and maintaining the Kinmen AI Computing Center, including leakage of technical secrets, network security protection, and possible political sensitivity issues.

2. Literature review

2.1Related research and cases on AI technology blockade

In today’s global economic landscape, AI technology and the semiconductor industry have become the core of technological innovation. The competition between the United States and China in the field of AI chips has become increasingly fierce, leading to a series of technology blockades and export control measures. Chang and Yeh (2018) explored in their study the talent needs of Taiwanese companies as they face the Industry 4.0 transformation process, particularly emphasizing the importance of professional skills in the fields of AI and semiconductors. Cho (2023) analyzed the US-China decoupling model in the semiconductor and AI technology supply chains, which has had a profound impact on global technological innovation. Danilin (2022) regards the U.S.-China technological war as the beginning of a new geopolitical era, the impact of which may reshape the international order. Fang’s (2002) case study highlights the competitive advantages of Taiwan’s semiconductor industry, especially in terms of innovation and market responsiveness.
The research by Malkin and He (2023) analyzed the geoeconomics of the global semiconductor value chain and how U.S.-China technology competition affects the expansion of related value chains.

2.2 History and Current Situation of Cross-Strait Scientific and Technological Cooperation

The development history and current trends of cross-Strait scientific and technological cooperation are gradually formed in the long-term economic and cultural exchanges and the complex international political environment. From early cooperation in the field of information technology to now covering a wider range of high-tech fields, cross-strait scientific and technological cooperation has experienced continuous deepening and expansion.

Historical review Through a case study of cross-Strait cooperation in the LED industry, Tsai (2012) revealed that early cross-Strait scientific and technological cooperation was concentrated in specific high-tech fields, and emphasized that under the ECFA framework, both sides of the Strait should strengthen industrial cooperation to jointly enhance competitiveness. In addition, Hung’s (2009) study evaluated how Taiwan’s information technology industry promotes innovation and improves performance through cooperation with the mainland from the perspective of competition and cooperation.
Structural changes Entering the 21st century, cross-strait scientific and technological cooperation has undergone structural changes. Chen (2011) pointed out that investment by Taiwanese high-tech companies in the mainland not only promotes cross-strait economic cooperation, but also contributes to the formation and development of industrial clusters. In addition, the research by Yang, Sun, and Huang (2018) explored the possibility of cross-Strait energy cooperation under the new situation, and proposed that the energy field, especially offshore wind power, is a new area for cross-Strait cooperation.
Current challenges and status quo Although cross-Strait scientific and technological cooperation has a rich historical foundation and huge development potential, it currently faces many challenges. Clough (2001) analyzed from a political perspective the continued development of cross-Strait scientific and technological cooperation despite the political deadlock, highlighting the uniqueness and relative independence of the field of scientific and technological cooperation. Generally speaking, cross-strait scientific and technological cooperation has gradually expanded from early cooperation in information technology and electronic manufacturing to emerging fields such as energy, biotechnology, and artificial intelligence. Although faced with the challenges of changes in the international political and economic environment and fluctuations in cross-Strait relations, the potential for cross-Strait scientific and technological cooperation is still huge, providing new opportunities for future cooperation.

Reference, research methods

3.1 Research Design

This chapter aims to introduce the overall structure of this study, including the purpose, scope and problem statement of the study. Through an in-depth analysis of the Kinmen AI Computing Center concept, this study will explore the challenges faced by the center in promoting technological innovation, raising necessary funds, complying with relevant laws and regulations, and responding to changes in the cross-strait political environment. At the same time, the research will identify and propose potential strategies and solutions to promote the center’s stable and effective development amid uncertain cross-Strait relations.

Research purpose This study aims to gain an in-depth understanding of the feasibility of the Kinmen AI Computing Center concept and evaluate its implementation challenges and opportunities in the context of current international technology trends and cross-strait relations. In addition, the research also aims to provide a systematic analysis framework to promote the center to become an accelerator for cross-strait technological innovation and industrial development.
Research scope This research will focus on the following core areas:
- Technological innovation: Analyze the role and potential of Kinmen AI Computing Center in promoting cross-strait technological innovation, especially in the field of AI.
- Fundraising: Explores the challenges for centers raising funds in the current economic and political environment, and strategies for facing these challenges.
- Legal compliance: Evaluate the possible impact of cross-strait and international laws on the operation of the computing center, and how to ensure the legal operation of the center in a changing legal environment. Cross-Strait Political Relations: Study the impact of fluctuations in cross-Strait political relations on the stable operation of the Center, and seek strategies that can adapt to these fluctuations.
Problem statement Issues this study will explore include:

What role can the Kinmen AI Computing Center play in promoting cross-strait technological innovation and industrial development?
- In the complex cross-strait political environment, how will the center overcome challenges such as financing?
- How does the center ensure legal and compliant operations under the dynamically changing regulatory framework?
- In response to the uncertainty in cross-strait relations, how should the center plan and implement corresponding response strategies?
- In summary, this chapter will lay the foundation for the overall research and provide a clear direction and methodological basis for in-depth discussion in subsequent chapters.

3.2 Data collection method

In order to meet the diverse needs of this study for a comprehensive analysis of the Kinmen AI Computing Power Center, we adopted the following data collection methods:

Literature review In order to gain a deeper understanding of the background, development status and macro-environment of Kinmen AI Computing Center, literature review plays a fundamental role in this study. This includes but is not limited to:
- Government reports and planning documents: Collect official documents from both sides of the Taiwan Strait on science and technology plans, economic development strategies and AI policies.
- Academic articles and journals: Read extensively academic articles related to AI technology, computing center operations and management, cross-strait economic cooperation, etc., and summarize the latest research results and theoretical progress.
- News reports and industry analysis: Collect news reports from the fields of technology, economy and politics, as well as analysis reports from professional institutions on the development of technologies and markets related to AI computing power centers.
Technology-assisted analysis In view of the processing and analysis needs of large amounts of data in modern research, the author introduced an artificial intelligence auxiliary tool, namely ChatGPT-4, into the research process. This tool is mainly used for pre-processing of data. ChatGPT-4 is used to summarize and organize literature materials, including key information extraction and preliminary classification, to improve the efficiency and completeness of data collection.

3.3 Data analysis method

In order to improve the efficiency and quality of research, in this study, we used the artificial intelligence tool ChatGPT-4 as an auxiliary tool for writing papers. The application scope of ChatGPT-4 covers many aspects from the development of initial ideas, the integration of literature materials, to the formation of research proposals.

Preliminary analysis ChatGPT-4 provides us with insightful reference opinions and analysis perspectives in the preliminary analysis stage. Through dialogue with them, we were able to examine the problem from different perspectives and reveal potential research directions and analytical frameworks during the discussion.
Language expression and draft writing During the paper writing stage, ChatGPT-4 was used to improve the quality of language expression and the initial construction of the draft. Its capabilities include copy generation, writing style adjustment, and text proofreading and editing.
Data validation It is worth mentioning that all analyzes and recommendations generated using ChatGPT-4 are manually verified by the author. Ensure that the ideas presented are accurate, the data are reliable, and comply with academic research standards.

3.4 Ethical considerations

In the process of this research, we made full use of the large language model ChatGPT-4 as an auxiliary tool for writing papers. During this process, we paid special attention to and strictly adhered to several key ethical considerations to ensure that the research was conducted not only efficiently but also in compliance with academic ethical standards.

Transparency In the process of this research, we made full use of ChatGPT-4, an advanced artificial intelligence tool, to assist in the data collection, preliminary analysis, and paper writing process. In order to ensure the transparency of the research process, we hereby clearly state that we were assisted by large language models (LLM) in the writing process of this paper, especially in processing the large data set, generating preliminary analysis suggestions, and drafting the text of the paper. While using large language models, the author fully recognizes the responsibility as an author and researcher for the content of the final submitted paper. We ensure that all research results and conclusions are based on adequate data analysis, critical thinking, and scientific argumentation processes, and strive to make all relevant information and methods of this research as transparent and clear as possible.
Originality and Attribution In the process of using large language models to assist in the writing process, we strictly ensure the originality of the research results. Any content generated by the large language model will be manually reviewed and reconstructed to ensure that the content of the final paper is unique and attributed to the study authors. At the same time, we also pay attention to avoid directly quoting the generated content under inappropriate circumstances to protect intellectual property rights.
Responsibility In writing research using ChatGPT-4, the author recognizes that responsibility for the final research results and any related decisions remains with the researcher. This means that researchers need to critically evaluate and make appropriate choices for any analysis and suggestions provided by large language models to ensure the accuracy and reliability of the research results. Although we cannot specifically label every piece of content generated with the assistance of large language models, we promise that all research results we provide will undergo rigorous manual review and integration to ensure that they meet the standards and requirements of academic research.
Adhere to academic standards Throughout the entire research process, from data collection to paper writing, we adhere to academic ethics and standards. This includes, but is not limited to, accurate citation of references, thorough documentation of the research process, and objective presentation of the research results.

3.5 Research Limitations

In conducting this study, we faced several unavoidable limitations that may have affected the completeness and scalability of the findings. The following are the main study limitations we identified:

Accessibility of data collection Although we tried our best to collect a wide range of relevant literature and information, due to resource limitations and the non-publicity of some information, we were unable to obtain all potential information. In particular, some of the latest research results, dynamic updates of cross-strait policies, or the lack of specific technical details may have a certain impact on our research results.
Limitations of technical assistance Although ChatGPT-4, as an advanced artificial intelligence technology, has provided tremendous support for our research, its limitations in understanding complex problems, capturing cultural and regional details, and the interpretation of knowledge in certain professional fields may not be accurate enough. These factors limit the extent to which we can only rely on such technical tools for analysis and information collection.
Generalizability of research results This research mainly focuses on the establishment of the Kinmen AI Computing Power Center and its impact on cross-strait scientific and technological cooperation. Therefore, the scope and background of the research are specific to the region and timeliness. This also means that our research results may be limited by factors such as the state of cross-strait relations, technological development stage, and policy environment in a specific period, and may have limited generalizability and applicability to other regions or different periods.

Although we tried our best to overcome these limitations during the research process to improve the accuracy and reliability of the study, the existence of these research limitations reminds us that we need to be cautious when interpreting and applying the research results. Future research can expand and deepen by obtaining more comprehensive information, using more advanced technical tools, and exploring a wider range of application scenarios to overcome the limitations of the current research.

4. Policy, economic and technical background analysis

4.1 Specific measures of the US blockade policy and its impact on the mainland

Measures of the US blockade policy
- Export restrictions: In recent years, as technology competition has intensified, the U.S. government has implemented a series of export restrictions on AI chips and related technologies, aiming to restrict the delivery of these technologies to the mainland. These restrictions include updates to export rules for specific products and technologies, Entity List restrictions on specific businesses, and restrictions on investment. These measures have had a significant impact on mainland China’s scientific and technological progress and industrial development. According to the CSIS report (2022), the United States has updated its export rules for AI chips, restricting companies in specific regions, including mainland China, from purchasing American-made AI chips (Center for Strategic and International Studies, 2022). The updates to these rules are intended to prevent advanced U.S. technologies from being used to enhance China’s military capabilities or be used in unfair competitive practices. At the same time, The Diplomat (2024) reported that a series of export control measures first implemented in October 2022 were intended to restrict mainland China’s access to advanced AI chips manufactured in the United States (Cho, 2024). The goal of these control measures is to weaken the mainland’s ability to develop in the field of AI. In addition, according to Reuters reports (2024), the new rules aim to prevent the export of more advanced AI chips designed by Nvidia and other companies to the mainland (Reuters, 2024). These export restrictions have had a clear impact on the research, development and application promotion of AI technology. Therefore, the U.S. export restrictions have had a profound impact on the mainland’s technology industry and the global AI chip market. This not only affects the ability of mainland companies to obtain key technologies, but also has a broad impact on global technological innovation and cooperation.
- Entity List: In order to protect national security and economic interests, the U.S. government has implemented the so-called “Entity List” against specific foreign entities, organizations and individuals. The list includes entities deemed to potentially threaten U.S. national security or engage in unfair competition using U.S. technology. Companies placed on the list will be restricted from purchasing U.S.-made technology and equipment unless they have special permission. According to official information from the Bureau of Industry and Security (BIS) of the U.S. Department of Commerce, the “Entity List” clearly points out the licensing requirements faced by each company or individual listed (Bureau of Industry and Security, n.d.). Recently, according to the Federal Register, the U.S. Export Control Review Committee (ERC) decided to add several mainland companies to the “Entity List”, including Dennex Enterprises Limited, Exeya Co., Limited, etc. (Federal Register, 2024a; Federal Register, 2023). These new entities cover different fields from high-tech electronic products to trade and investment, reflecting the United States’ strict attitude towards technology export controls.
- Investment restrictions: In recent years, the U.S. government has taken a series of measures to restrict direct investment by U.S. companies in mainland technology companies, especially in sensitive technology fields. These restrictions are intended to prevent U.S. technology and funding from being used to enhance China’s military capabilities and technological competitiveness. Executive order restricts investment: According to the Council on Foreign Relations (Council on Foreign Relations, 2023), President Biden issued an executive order restricting U.S. direct investment in China in sensitive technology fields. This is intended to prevent these technologies from helping Beijing improve its military capabilities. New Procedure to Restrict Investment in China: Pursuant to the White House Case (White & Case, 2023), the Biden order establishes a new procedure to limit U.S. outward investment in specific technological fields in China. This includes investment restrictions in areas such as artificial intelligence and advanced semiconductors. Ban on certain high-tech investments: An executive order specifically targets three high-tech areas: artificial intelligence, advanced semiconductors, and quantum computing, banning new U.S. investments in China in these areas. (China Briefing, 2023)
Impact on Mainland China
- Challenges of technological development: In recent years, U.S. export controls and investment restrictions on mainland China have had a significant impact on mainland technology companies, especially in terms of acquiring key technologies and attracting international investment. Increased investment in R&D: According to the CSIS report, Western export control measures have caused a rebound among mainland companies that were included on the list, forcing them to increase investment in R&D (Center for Strategic & International Studies, n.d.). Although this can be seen as a positive push for independent innovation, it may face development bottlenecks in the short term due to the lack of mature technology. Impact of international trade relations: According to the Access Partnership report, US-China trade tensions have had a complex impact on the global supply chain and regulatory environment, making companies related to global supply chains face a more complex regulatory environment (Access Partnership, 2024). Security concerns and technological leadership: According to analysis by East Asia Forum, the United States’ export controls and investment restrictions on the mainland are designed to maintain technological leadership and address security concerns in key areas (East Asia Forum, 2024). This directly limits mainland technology companies’ access to key and advanced technologies and increases the difficulty of technological innovation. Concerns about the long-term impact on global trade: At the same time, U.S. companies are also concerned about overly broad restrictions on exports to China, fearing that it may have an impact on China’s long-term technological development and have negative consequences on global trade (Pillsbury Winthrop Shaw Pittman, n.d.).
- Market and supply chain adjustments: Facing the U.S. technological blockade and trade restrictions, mainland China has taken comprehensive measures to adjust its market strategy and restructure its supply chain. These strategies aim to reduce dependence on foreign technology while accelerating the development of domestic technological innovation and independent production capabilities.
- Promote domestic innovation: According to a report by the Center for International Strategic Studies (CSIS), mainland China is promoting a domestic innovation strategy and gradually reducing its reliance on imported technologies such as high-end chips (Kennedy & Johnson, 2023). This includes focused investment in research and development in key areas such as semiconductors and artificial intelligence. Expanding mainland’s internal production capabilities: In response to U.S. export controls, mainland China is accelerating the expansion of its internal semiconductor industry’s production capabilities. As research by Rhodium Group shows, China has significantly improved its traditional chip production capabilities in the past few years (Rosen, 2024). Diversify import sources: Faced with import restrictions on specific technologies and products, mainland China seeks to diversify import sources and reduce reliance on a single external market. This includes strengthening cooperation and trade in science and technology with countries in Europe, East Asia and other regions.
- Promotion of independent innovation: In recent years, in the face of U.S. blockade measures, mainland China has accelerated its pace of independent technological innovation, especially in key technology fields such as semiconductors and artificial intelligence (AI).
  - Semiconductor self-sufficiency strategy: According to a report by the Center for International Strategic Studies (CSIS), in the mid-2010s, mainland China had actively promoted a semiconductor self-sufficiency strategy. The key to this strategy is that in the face of external blockades and restrictions, mainland China increases investment and support in the domestic semiconductor industry to improve domestic production capacity and technological levels (Kennedy & Johnson, 2023).
  - Response to the U.S. technology blockade: The South China Morning Post (SCMP) reported that China’s response to the U.S. blockade strategy is to strengthen its capabilities in innovation and independent research and development, rather than choosing to isolate itself. A spokesman for the National People’s Congress stated that China will overcome U.S. technology and scientific sanctions through innovation and self-reliance (SCMP, 2024).
  - Development model in the AI field: According to a policy brief from the German Mercator Institute for China Research (MERICS), mainland China has adopted a nationwide large-scale computing infrastructure project and a comprehensive development strategy for AI development. This shows mainland China’s commitment to achieving technological independence in the field of AI (MERICS, n.d.).

Technology policy and development: According to a report by the National Bureau of Asian Research (NBR), in response to the challenge of U.S. restrictions, China has strengthened domestic efforts by encouraging innovation in the field of AI, increasing research and development funding (NBR, n.d.).

Through the analysis of the specific measures and impacts of the United States’ AI technology blockade policy in mainland China, this section provides an in-depth understanding of an important factor in the current global technology competition environment. These policies not only pose a direct challenge to mainland China’s technological development, but also prompt it to seek new market strategies and strengthen independent innovation capabilities, which will have a profound impact on the future development of cross-strait technological cooperation.

4.2 Taiwan’s technological and economic situation

Taiwan occupies an important position in the global semiconductor industry, especially in the field of AI chips. With the rapid development of global AI technology, Taiwanese companies have shown strong competitiveness in design, manufacturing and supply chain management.

Taiwan’s technological development Taiwan has made significant achievements in AI chip technology innovation, R&D investment, and talent cultivation. These efforts have supported its leadership in the global high-tech industry.
- AI chip technology innovation: As global technology competition intensifies, Taiwan’s innovation in chip technology is increasingly critical. The government has cooperated with industry to promote a series of innovation programs, such as the Taiwan Chip-based Industrial Innovation Program (Taiwan CbI), which focuses on upgrading academic research and teaching infrastructure and attracting global R&D talents (Taiwan Chip-based Industrial Innovation Program).
- R&D investment: Taiwan continues to increase R&D investment in the fields of AI and semiconductors. The success of Taiwan’s integrated circuit design industry is due to the government’s strong investment and support in technology research and development (EDN Asia, n.d.). In addition, while pursuing Moore’s Law, Taiwan has invested resources in cultivating talents to bring a new nano era to the semiconductor industry (Taiwan Semiconductor Manufacturing Company Limited, n.d.).
- Talent cultivation: In order to maintain its competitiveness in the global semiconductor industry, Taiwan has strengthened talent cultivation by upgrading its education and training system. This includes the establishment of high-level educational institutions and R&D facilities, as well as active interaction and cooperation with the international science and technology community (Runtime, n.d.).
Taiwan’s economic environment supports AI hardware and industrial chain Taiwan’s economic environment provides solid support for its AI hardware and the entire semiconductor industry chain. Taiwan plays a key role in the global AI supply chain, especially in the chip revolution, which has far-reaching influence.
- Key players in the global AI supply chain:
  - Taiwan’s leading position in AI chip technology innovation and production is due to its strong economic foundation and deepened investment in technology research and development, making it an indispensable link in the global semiconductor supply chain.
  - Under a highly specialized economic system, Taiwan not only has unique achievements in innovation, but also plays an important role in the global supply chain. According to CNBC, in a statement by the CEO of the Taiwan Stock Exchange, Taiwan has been proven to play a key role in the AI chip revolution and the process of the global semiconductor industry (CNBC, 2024).

In addition, Taiwan continues to increase R&D investment in promoting local and global AI chip technology innovation, and focuses on cultivating professionals in the technology industry. These efforts have strengthened Taiwan’s competitiveness in the international market and made significant contributions to global technological development.

- Margin of global chip manufacturing capacity:

Companies in Taiwan and South Korea have consolidated their dominant position in global chip manufacturing, especially in the production of AI chips. This advantage is not only technologically superior, but also has a profound impact on Taiwan’s economy.

Oxford Economics pointed out that since companies in Taiwan and South Korea control most of the world’s chip manufacturing capabilities, this has given Taiwan a competitive advantage in manufacturing AI chips and has a significant positive impact on its macroeconomics (Oxford Economics, n.d.). This impact is not only reflected in the promotion of internal industries, but also in its external contribution to the global supply chain and international market.

Taiwan’s economic development strategy, especially its manufacturing capabilities in the field of AI chips, highlights its central role in the global technology industry. In the current context of rapid changes in global technological development, Taiwan’s status has brought stable growth momentum and international business competitiveness to its economy.

- Industrial synergies:

Taiwan has demonstrated significant industrial synergy in all aspects of the AI chip industry chain. From front-end chip design to back-end foundry, packaging and testing, it has formed an efficient and integrated production network. This “one-stop” industry chain is unique to Taiwan and cannot be found in other countries such as the United States or Japan. The exact same model.

Taiwan Semiconductor Manufacturing Company (TSMC) is the world’s largest chip foundry, providing products for more than 70% of the world’s chip manufacturing needs, especially in the field of AI (Stimson Center, 2022). This industrial environment has made Taiwan an important hub for global AI hardware R&D and manufacturing. The industrial environment in Taiwan not only promotes close cooperation between industries, but also strengthens the integration of upstream and downstream supply chains. This synergy gives Taiwan an irreplaceable position in the global semiconductor market and ensures the smooth operation and technological progress of every link in the industrial chain.

The strengthening of Taiwan’s industrial chain and industrial synergy, especially in the field of AI chips, have become an important support point for its economic development. Taiwan’s industrial environment not only contributes significantly to the island’s economy, but also has a profound impact on the supply and innovation of the global AI hardware and semiconductor industries.

Wu, Kinmen AI computing power center concept

5.1 Concept and design principles of Kinmen AI Computing Center

With the promotion of the global digital economy, artificial intelligence (AI) technology has become a key force in promoting future technological development. In order to take the lead in this field, the idea of Kinmen AI Computing Center came into being, aiming to create a high-performance computing platform that integrates AI research, data processing and algorithm innovation.

Conceptual background The concept of the Kinmen AI Computing Power Center is based on the following core goals: to promote the research and development of AI technology, strengthen data processing capabilities, promote algorithm innovation, and lay a solid foundation for the commercialization and practical application of AI technology. The center is envisioned as a bridge connecting the scientific and technological circles on both sides of the Taiwan Strait, serving not only Taiwan, but also scientific research institutions and enterprises in the mainland and even around the world.
Design principles Innovation-driven: Kinmen AI Computing Center will adhere to the design principle of innovation as the core and promote the cutting-edge development of the AI field through continuous technology research and development and algorithm optimization. Sustainable development: During the construction and operation process, the center will attach great importance to environmental protection and energy efficiency, using green energy and efficient cooling systems to ensure a win-win situation between technological progress and environmental protection. Open cooperation: The center will establish an open cooperation mechanism to encourage cross-strait and international scientific research institutions, universities and enterprises to participate and share resources and results. Talent training: The center will cooperate with universities and colleges to set up graduate training and exchange programs to cultivate high-end talents in the AI field. Data security and privacy protection: While strengthening data processing capabilities, the center will strictly abide by laws and regulations on data security and privacy protection to ensure the legality and ethics of all research activities.
Anticipated impact The establishment of the Kinmen AI Computing Center is expected to have a profound impact on the development of AI technology on both sides of the Taiwan Strait and even around the world. It will not only accelerate the output of AI research results and promote technological innovation, but will also attract more scientific researchers and enterprises to devote themselves to the exploration and application of AI technology, further promoting economic development and social progress.

5.2 The impact of Kinmen’s geographical and political location on this concept

Kinmen, located at the forefront of the Taiwan Strait, has a unique geographical and political status. This location not only has a profound impact on its own development, but also has important strategic significance for the proposed Kinmen AI Computing Power Center.

The strategic value of geographical location Kinmen’s geographical location makes it an important bridge connecting mainland China and Taiwan. This geographical advantage provides the only strategic value for the AI computing power center. The center can make full use of this geographical advantage to become an important platform for cross-strait scientific and technological exchanges and cooperation, promoting the sharing of scientific and technological resources and the rapid transfer of scientific research results.
Challenges and opportunities in the political environment Kinmen’s special political status, located between mainland China and Taiwan, makes it play a subtle role in cross-strait relations. On the one hand, this location provides potential opportunities for cross-strait scientific and technological cooperation; on the other hand, it may also face challenges due to changes in the political atmosphere on both sides of the strait. The establishment of an AI computing power center requires careful assessment of this political environment and planning of flexible response strategies to ensure the smooth operation and long-term development of the center.
Possibility of strengthening cross-strait cooperation The establishment of the Kinmen AI Computing Center has special geographical and political significance and has the potential to become an important link in strengthening cross-strait scientific and technological cooperation. The successful establishment and operation of the center will promote broader consensus and cooperation between the two sides in talent exchange, data sharing and the formulation of technical standards, and inject positive scientific and technological elements into the stable development of cross-strait relations.

The realization of the concept of Kinmen AI Computing Power Center will be deeply affected by its geographical and political location. This location provides the center with unique strategic value and presents both opportunities and challenges. Prudent assessment and utilization of Kinmen’s geographical advantages and flexible response to changes in the political environment will be the key to realizing the central vision and promoting cross-strait scientific and technological cooperation.

5.3 The potential for cross-strait collaboration in talent, data and hardware

As global technology competition intensifies, the United States’ sanctions on mainland technology companies have highlighted the necessity of cross-strait technology cooperation. Mainland China has huge data resources and talent pool in the field of artificial intelligence (AI), but it is limited in access to computing power. Relatively speaking, Taiwan has advantages in the entire hardware manufacturing industry chain and is able to obtain high-end AI chips from companies such as Nvidia. This chapter will explore the potential for complementary cooperation between the two sides in AI hardware, data and technology development in this context, and how this cooperation can promote regional scientific and technological progress and economic development.

Talent and data advantages in mainland China

The mainland has invested heavily in talent training and data accumulation. The huge user base in the market provides valuable resources for the training of AI algorithms. Many universities and research institutions have trained a large number of technical talents in the AI field.

Taiwan’s entire industry chain of hardware manufacturing

Taiwan has a complete semiconductor industry chain from design and manufacturing to packaging and testing, and has not been affected by US sanctions. This means that Taiwan can obtain high-end AI chips from international companies such as Nvidia and retain the ability to manufacture advanced computing hardware. Taiwan’s independence and technological strength in the field of semiconductor manufacturing provide a substantial hardware foundation for cross-strait cooperation.

The synergistic potential of cross-strait cooperation

In the current context, cross-Strait collaborative cooperation is not only feasible but also obviously complementary. Taiwan can use its advantages in hardware manufacturing to provide the mainland with necessary AI computing capabilities. At the same time, the mainland’s data and talent resources can also promote Taiwan’s deepening of AI software and algorithm development. By establishing a cooperation mechanism and sharing technology and resources, both sides of the Taiwan Strait can jointly overcome existing technical limitations and promote the development and application of regional and even global AI technology.

Collaborative cooperation between the two sides in the field of AI has huge potential. By leveraging their respective superior resources and technological capabilities, the two sides can not only effectively respond to the challenges of international technological competition and political factors, but also jointly promote regional technological innovation and economic prosperity, setting a model for complementary cooperation. In the future, this cooperation model is expected to become an important way for regional and even global scientific and technological cooperation.

Challenges

In the process of implementing the Kinmen AI Computing Center, you may encounter the following challenges:

6.1 Data security and privacy protection

As data becomes the core asset of the modern economy, especially in the field of artificial intelligence, large-scale data collection and analysis have become indispensable. Against this background, the Kinmen AI Computing Center faces major challenges in ensuring data security and maintaining user privacy.

Complexity of Data Security

Data security is one of the top priorities in the operation of the AI computing center. With the expansion of data volume, especially the processing of sensitive data, centers must comply with strict data protection standards and implement advanced security technologies and protocols, such as encrypted transmission, access control and network isolation, to prevent data leakage, abuse or loss.

Necessity of privacy protection

The protection of user privacy is equally important. This is not only a technical issue, but also a legal and ethical issue. The AI computing power center needs to establish a transparent privacy protection policy to let users understand how their data is collected, used and shared. At the same time, the center also needs to comply with relevant privacy protection laws and regulations, such as the Personal Data Protection Act, and implement privacy impact assessment and privacy design principles.

Legal Compliance Framework

As emerging technologies develop, the relevant legal and regulatory frameworks continue to evolve. From the beginning of its construction, the Kinmen AI Computing Center must consider complying with international and cross-strait laws and regulations, including cross-border data transmission laws, and cooperate with legal professionals to ensure the legality and compliance of all operational activities.

Technological Innovation and Risk Management

In order to cope with these challenges, the Kinmen AI Computing Center needs to continuously carry out technological innovation and implement comprehensive risk management strategies. This includes using artificial intelligence to enhance security monitoring, developing new cybersecurity technologies, and establishing rapid response mechanisms to handle security incidents.

6.2 Raising capital investment

In the process of implementing the concept of Kinmen AI Computing Power Center, raising sufficient investment funds and ensuring the effective use of funds constitute an important and challenging task. To build an AI computing center that meets high standards, it not only requires huge initial investment to establish infrastructure, purchase advanced hardware equipment and develop software, but also requires continuous financial support for daily operations, technology upgrades and talent training.

Methods of raising funds

Government Funding and Subsidies: As an important project to promote technological development and regional economic growth, the Kinmen AI Computing Center can seek funding and subsidies from the governments on both sides of the Taiwan Strait. This includes direct government investment, technology development funds and financial support for specific technology projects.
Private Investment and Cooperation: In addition to government funds, the participation of private capital is also extremely important for raising funds. We can cooperate with private institutions such as technology companies, venture capital and private equity funds to attract their investment in AI computing center projects.
International Cooperation and Funding: In view of the international cooperation potential of the Kinmen AI Computing Power Center, cooperation with international technology companies, research institutions and transnational investment funds should be explored to obtain international financial support.

Effective use of investment

Clear investment priorities: When resources are limited, clear investment priorities are key to ensuring the effective use of funds. This includes prioritizing investment in infrastructure construction, research and development of key technologies and talent training.
Establish a strict financial management system: In order to ensure the effective use of funds, the Kinmen AI Computing Power Center needs to establish a strict financial management and supervision mechanism, including regular financial audits, strict control of project progress and budget, etc.
Promote industrial collaboration and ecological construction: Through the effective use of funds, we should not only promote the construction and development of the AI computing power center itself, but also promote the collaboration of surrounding industries and the construction of a technological ecosystem to achieve the multiplication effect of funds. Raising sufficient investment funds and ensuring their effective utilization are crucial to the implementation process of the Kinmen AI Computing Power Center. By raising funds through multiple channels and establishing a rigorous management and supervision mechanism, the Kinmen AI Computing Power Center is expected to achieve its ambitious construction goals and contribute to the development of AI on both sides of the Taiwan Strait and even around the world.

6.3 Uncertainty in cross-Strait relations

Facing the background of intensified global technological competition, the Kinmen AI Computing Center, as an important technological project located at the political and economic intersection of the two sides of the Taiwan Strait, will inevitably be directly affected by the fluctuations in cross-strait relations during its advancement. Therefore, how to ensure the stability and sustainable development of the center in a complex and unpredictable political environment has become an important key to determining the success or failure of the project.

The uncertainty in cross-Strait relations mainly comes from differences in political stances, changes in the international economic and trade environment, and the impact of the geopolitical landscape. Due to these uncertainties, Kinmen AI Computing Center faces a series of challenges such as investment risks, changes in partners, and technology transfer obstacles.

Strategy for Stable Development

Diversified investment sources: In order to reduce the risk of a single investment source, the center needs to seek diversified sources of funding and technical support, including international partners, the private sector and non-governmental organizations.
Strengthen the foundation of legitimacy: Solid planning in legal and compliance aspects, including business operations that comply with cross-strait and international laws, can maintain the center’s legitimacy and legal operations when political risks intensify.
Establish a flexible operating mechanism: Center operations need to be highly flexible and adaptable to quickly respond to changes in the political environment, including temporary adjustments to cooperation strategies or business focus.
Promote cross-Strait science and technology communication: Through active science and technology diplomacy and communication, strengthening mutual trust and understanding between the cross-Strait science and technology circles will help maintain the stability of cooperation amidst political fluctuations.
Risk Management and Emergency Preparedness: Develop comprehensive risk management plans and contingency plans to ensure assets, personnel and intellectual property are protected and normal operations can be quickly restored in the face of political crises.

During the implementation of the Kinmen AI Computing Center, the uncertainty of cross-strait relations undoubtedly brought severe challenges. However, through effective strategic planning and risk management, the center can maintain stable development amid fluctuations in cross-Strait political relations, and then play its important role in cross-Strait and even global technological and economic cooperation.

6.4 Compliance with laws and regulations

With the rapid development of AI technology and data use, relevant laws and regulations are constantly being updated and changed to adapt to new technological developments and social needs. For a large-scale project like the Kinmen AI Computing Center that covers multiple fields of application, how to ensure legal and compliant operations under different legal environments has become an ongoing and complex challenge.

Uncertainty of the legal environment

Regional Differences in Laws and Regulations: Due to differences in cross-strait and international laws and regulations in data protection, privacy, intellectual property, etc., the AI computing power center needs to accurately grasp and comply with the regulatory requirements of different legal jurisdictions, which places high demands on its legal strategy and business operations.
Technological innovation and regulatory lag: The speed of technological innovation often exceeds the update speed of laws and regulations, resulting in unclear legal guidance in areas such as emerging technology applications and cross-border data flows, increasing operational risks.

Legal and Compliance Operation Strategy

Establish a professional legal team: Arrange a professional legal advisory team to continuously monitor and analyze cross-strait and international laws to guide the center’s legal strategies and business decisions.
Strengthen compliance training and awareness: Regularly conduct training on laws, regulations and compliance operations for the center’s management and employees to enhance the legal awareness and compliance level of all employees.
Actively participate in the discussion of laws and regulations and the formulation of standards: By participating in the discussions of relevant laws and regulations and the formulation of international standards, we can not only keep abreast of regulatory trends, but also strive for more favorable operating conditions for the center.
Establish risk management and response mechanism: Develop a comprehensive risk management and response mechanism including legal risk assessment, preventive measures and contingency plans to deal with the possible impact of changes in laws and regulations.

Facing the uncertainty and challenges of the legal environment, Kinmen AI Computing Center must adopt a proactive attitude, establish a professional legal team, strengthen compliance training, participate in regulatory discussions and establish risk response mechanisms to ensure legal and compliant operations in a rapidly changing legal environment. Through the implementation of these strategies, the Kinmen AI Computing Center will be able to steadily promote technological innovation and business development while ensuring legal compliance.

7. Conclusion and suggestions

This study conducted an in-depth discussion from multiple dimensions including the proposal of the Kinmen AI Computing Power Center concept, design principles, and challenges that may be encountered during the implementation process. Research has found that the Kinmen AI Computing Power Center, as an innovative scientific and technological development project, not only plays an important role in promoting regional scientific and technological progress and improving economic development, but also plays a bridge role in cross-strait scientific and technological cooperation and exchanges, providing new ideas and platforms for the stable development of cross-strait relations.

The complementarity of cross-strait cooperation in talent, data and technology provides a solid foundation for the successful implementation of the Kinmen AI Computing Center. By deepening cross-strait cooperation in the field of AI, it will not only promote technological innovation and knowledge sharing, but also help build closer economic ties and cultural exchanges, thereby enhancing mutual trust and promoting peaceful development.

7.1 Specific recommendations and future research directions

Based on the findings of this study, the following suggestions are made for the future development of Kinmen AI Computing Center and cross-strait cooperation:

Strengthen policy support and investment

It is recommended that governments on both sides of the Taiwan Strait provide more policy support and financial investment, lower the threshold and risks of project implementation, and create a more friendly investment environment.

Establish an ongoing talent exchange program

Both sides of the Taiwan Strait should jointly formulate and implement talent training and exchange plans to provide sufficient scientific research and technical talents for AI computing power centers.

On the premise of ensuring data security and personal privacy, promote the sharing of cross-strait data resources, and participate in the formulation of relevant technical standards to improve technical interoperability and compatibility.

Strengthen the docking and cooperation of laws and regulations

Both sides of the Taiwan Strait should strengthen docking and cooperation in science and technology laws and regulations to provide strong guarantee for the legal operation of AI computing power centers.

Future Research Directions

It is recommended that future research can further explore the potential for cooperation between the two sides in specific AI application fields (such as smart medical care, environmental monitoring, etc.), and propose specific solutions to overcome challenges in technology transfer and intellectual property protection.

7.2 Conclusion

The concept of the Kinmen AI Computing Power Center is not only of great significance to strengthening cross-Strait scientific and technological cooperation and promoting regional economic development, but also plays an indispensable role in promoting the peaceful and stable development of cross-Strait relations. By implementing the above recommendations and continuing to explore new cooperation models and opportunities, both sides of the Taiwan Straits can jointly meet the challenges brought about by technological innovation and work together to create a more prosperous future.

References

[1] Chang, Y. H. & Yeh, Y. J. Y. (2018). Industry 4.0 and the need for talent: A multiple case study of Taiwan’s companies. International Journal of Product Development, Inderscience Publishers. [2] Cho, E. K. (2023). US-China decoupling patterns in supply chains and ecosystems for semiconductors and AI technologies. Korea Institute for Industrial Economics & Trade. [3] Danilin, I. V. (2022). The US-China tech war: A dawn of new geopolitics? In Technological Innovation and Security. [4] Fang, J. J. M. (2002). The competitive advantage: A case study of Taiwan’s semiconductor industry. Retrieved from MIT Dspace. [5] Malkin, A. & He, T. (2023). The geoeconomics of global semiconductor value chains: Extraterritoriality and the US-China technology rivalry. Review of International Political Economy, 1–23. Taylor & Francis. [6] Tsai, C. W. (2012). Cross-strait cooperation of information technology: A case study of LED industry. ICSSSM12. [7] Hung, J. H. (2009). Enhancing innovation and performance through cross-strait competition and cooperation: The case of Taiwan’s information technology industry. ProQuest Dissertations Publishing. [8] Chen, H. N. (2011). Political economy of cross-strait investments: The Taiwanese high-tech industry as an example. Seoul Journal of Economics. Retrieved from [9] Yang, F., Sun, C., & Huang, G. (2018). Study on cross-strait energy cooperation under the new circumstance. Journal of Cleaner Production, 187, 591-599. [10] Clough, R. N. (2001). Growing cross-strait cooperation despite political impasse. American Foreign Policy Interests, 23(5), 233-244. [11] Center for Strategic and International Studies. (2022). Updated October 7 semiconductor export controls. Retrieved from [12] Cho, E. K. (2024). AI chips for China face additional US restrictions. The Diplomat. Retrieved from [13] Reuters. (2024). U.S. updates export curbs on AI chips and tools to China. Retrieved from [14] Bureau of Industry and Security. (n.d.). Entity list. U.S. Department of Commerce. Retrieved from [15] Federal Register. (2024a). Additions of entities to the entity list. U.S. Government Publishing Office. Retrieved from [16] Federal Register. (2023). Addition of entities to the entity list. U.S. Government Publishing Office. Retrieved from [17] Council on Foreign Relations. (2023). President Biden has banned some U.S. investment in China: Here’s what to know. Retrieved from [18] White & Case. (2023). President Biden orders establishment of new program to restrict US outbound investment in certain tech sectors in China. Retrieved from [19] China Briefing. (2023). US ban on new investments in targeted high-tech sectors. Retrieved from [20] Center for Strategic & International Studies. (n.d.). Balancing the ledger: Export controls on U.S. chip technology to China. Retrieved from [21] Access Partnership. (2024). Tech policy trends 2024: The US-China rivalry’s impact on global trade. Retrieved from [22] East Asia Forum. (2024). Examining US export controls against China. Retrieved from [23] Pillsbury Winthrop Shaw Pittman. (n.d.). Recent updates on foreign investment restrictions and export controls. Retrieved from [24] Kennedy, S., & Johnson, C. K. (2023). China’s new strategy for waging the microchip tech war. Center for Strategic & International Studies. Retrieved from [25] Rosen, D. H. (2024). US Pathways to Regulating China-Sourced Legacy Chips. Rhodium Group. Retrieved from [26] Kennedy, S., & Johnson, C. K. (2023). China’s new strategy for waging the microchip tech war. Center for Strategic & International Studies. Retrieved from [27] SCMP. (2024). China’s two sessions 2024: top legislature vows to overcome US tech and science sanctions. Retrieved from [28] MERICS. (n.d.). China’s AI development model in an era of technological deglobalization. Retrieved from [29] NBR. (n.d.). China’s tech policies and development: Responding to great power competition. Retrieved from [30] Taiwan Chip-based Industrial Innovation Program. (n.d.). Retrieved from [31] EDN Asia. (n.d.). Taiwan sets sight on cultivating IC design talent. Retrieved from [32] Taiwan Semiconductor Manufacturing Company Limited. (n.d.). Innovation Management. Retrieved from [33] Runtime. (n.d.). Chip companies worried about war wanted a new Taiwan – now they’re investing in Israel. Retrieved from [34] CNBC. (2024). Taiwan plays a ‘very crucial role’ in AI supply chain, says TWSE chief. Retrieved from [35] Oxford Economics. (n.d.). The macro impact of the AI boom in Taiwan and South Korea. Retrieved from [36] Stimson Center. (2022). Semiconductors and Taiwan’s “Silicon Shield”. Retrieved from

Kinmen AI Computing Center (金門 AI 算力中心): A Project for the Fourth Yunnan-Taiwan University Student Innovation and Entrepreneurship Competition

Sat, 20 Jan 2024 00:00:00 +0000

The Kinmen AI Computing Center is the computing-infrastructure concept I proposed for the Fourth Yunnan-Taiwan University Student Innovation and Entrepreneurship Competition (第四屆雲台大學生雙創賽), where it received a Silver Award. The project uses Kinmen’s regional location as a starting point to study how GPU compute, cloud services, technical support, and compliance governance can form an implementable service model.

This is a research and competition proposal, not an operating physical data center. Its value lies in placing computing demand, regional development, business models, and technology governance within the same feasibility framework. Any visual materials and linked source documents are retained in their original Chinese as historical competition evidence.

What I am responsible for in the project

I am mainly responsible for the overall concept, research analysis and integration of technology and business models.

Problem Definition: Starting from the computing power threshold of AI model training and inference, define the cost and elasticity issues faced by enterprises, research institutions and developers when obtaining GPU resources.
Service Architecture: Planning service modules such as on-demand computing power, long-term leasing, pre-configured development environment, data processing and technical support.
Regional Model: Analyze the geography, industry and cooperation conditions of Kinmen as a regional computing node, as well as the talent cultivation and digital infrastructure benefits it may bring.
Business Design: Establish target customer groups, pricing methods, cooperation channels, resource allocation and staged development assumptions.
Risk Governance: Incorporate export control, data security, intellectual property, regulatory compliance, capital and operational risks to avoid discussing only technology supply and ignoring governance boundaries.
Result Transformation: Organize research content into competition proposals and extended papers, so that entrepreneurial ideas have both academic and policy foundations that can be discussed.

Core competitiveness and innovation

1. Convert high-cost hardware into flexible services

Through on-demand use and hierarchical leasing, users can obtain computing power according to model scale, project cycle and budget, reducing the one-time investment and maintenance costs required to build their own GPU servers.

2. Not just rent GPUs, but provide a complete working environment

The project treats computing resources, model development environment, data processing, performance optimization and technical support as one set of services, shortening the preparation time for users from obtaining hardware to starting work.

3. Connect research and industrial needs through regional nodes

Kinmen is not only a location option, but also a regional innovation node in the project. The concept considers research institutions, enterprises, talent cultivation and local industries at the same time, so that computing infrastructure can form a broader application network.

4. Build compliance and security into the architecture

Computing power services involve data, models, hardware and cross-border specifications. Data isolation, access control, intellectual property rights, export controls and legal compliance are conditions for the project from the outset, rather than added as an afterthought.

5. Connect research, policy and entrepreneurship verification

This project is not just a business plan, but also extends to the study of AI hardware collaboration, regional scientific and technological cooperation and governance risks, so that technical ideas can be tested at three levels: business, policy and academic.

Service concept

Elastic GPU computing power: Configure different levels of computing resources based on hourly, project or long-term plans.
Pre-configured development environment: Provides common AI frameworks and tools to reduce deployment and environment setting costs.
Data and model support: Covers data pre-processing, model deployment, performance optimization and technical consulting.
Security and Isolation Mechanism: Plan data isolation, permission management and backup for different customers and workloads.
Industry-university cooperation scenario: Support research projects, talent training, enterprise PoC and regional digital transformation.

Research and Results

Received a Silver Award at the Fourth Yunnan-Taiwan University Student Innovation and Entrepreneurship Competition.
The extended research is “ ”.
Establish a complete proposal framework from market demand, service architecture, business model to risk governance.

All cross-regional service concepts of this project are based on applicable export controls, data protection and related laws, and do not advocate or design any transactions or technical paths to circumvent supervision.

experience

Tue, 24 Oct 2023 00:00:00 +0000

Learn JavaScript

Tue, 24 Oct 2023 00:00:00 +0000

is designed to give technical content creators a seamless experience. You can focus on the content and the Hugo Blox Builder which this template is built upon handles the rest.

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Code

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data = pd.read_csv("data.csv")
data.head()
```

renders as

import pandas as pd
data = pd.read_csv("data.csv")
data.head()

Inline Images

{{< icon name="python" >}} Python

renders as

Python

Learn Python

Tue, 24 Oct 2023 00:00:00 +0000

is designed to give technical content creators a seamless experience. You can focus on the content and the Hugo Blox Builder which this template is built upon handles the rest.

Embed videos, podcasts, code, LaTeX math, and even test students!

On this page, you’ll find some examples of the types of technical content that can be rendered with Hugo Blox.

Video

Teach your course by sharing videos with your students. Choose from one of the following approaches:

Youtube:

{{< youtube w7Ft2ymGmfc >}}

Bilibili:

{{< bilibili id="BV1WV4y1r7DF" >}}

Video file

Videos may be added to a page by either placing them in your assets/media/ media library or in your , and then embedding them with the video shortcode:

{{< video src="my_video.mp4" controls="yes" >}}

Podcast

You can add a podcast or music to a page by placing the MP3 file in the page’s folder or the media library folder and then embedding the audio on your page with the audio shortcode:

{{< audio src="ambient-piano.mp3" >}}

Try it out:

Test students

Provide a simple yet fun self-assessment by revealing the solutions to challenges with the spoiler shortcode:

{{< spoiler text="👉 Click to view the solution" >}}
You found me!
{{< /spoiler >}}

renders as

👉 Click to view the solution

You found me 🎉

Math

Hugo Blox Builder supports a Markdown extension for $\LaTeX$ math. You can enable this feature by toggling the math option in your config/_default/params.yaml file.

To render inline or block math, wrap your LaTeX math with {{< math >}}$...${{< /math >}} or {{< math >}}$$...$${{< /math >}}, respectively.

We wrap the LaTeX math in the Hugo Blox math shortcode to prevent Hugo rendering our math as Markdown.

Example math block:

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Reading Notes: Participatory Budgeting

Tue, 26 Sep 2023 00:00:00 +0000

“Participatory Budgeting” Reading Notes

Chapter 1 The origin of the idea

Section 1 Can more deliberation improve democracy?

The dilemma between free will and total will in democratic politics? Characteristics of democracy:

Protect personal “free will” The whole will as the supreme sovereignty of the country Whole will: the collective will of all citizens

Citizen participation “The political feasibility for election purposes is extremely high, but the economic desirability for actual operation and maintenance is extremely low.” - Mosquito House

“Other constituencies have it, so we should have it too.”

The procedure is impossible and the result is undesirable. ——Two major bottlenecks in the design of democratic participation systems.

Procedural Impossibility: Any system has the flaw of being manipulated by those with the power to set the agenda.

The multiple preferences generated by respecting individual freedom cannot be meaningfully adjusted through the public choice system.

Undesirable results: whether people have the ability to make the public choices they strive for.

consultation asymmetry Professional asymmetry Factors that influence the public include ideology, party identification, media reports, the personal charisma of politicians, and fake news on the Internet. This is not a rational choice.

Is deliberative democracy the savior of the public participation dilemma? deliberative democracy.

The translation is not good. “Deliberation” has a top-down meaning. A better one would be “full discussion.”

Deliberative democracy is an element of democratic change that is “neither a procedure nor a result, but can be both a procedure and a result.” Its introduction makes it possible for traditional citizen participation to reconcile the dilemma between procedure and result.

What does participatory budgeting bring to citizens. The budget is the core of the operation of rights in a democratic country. It represents the right to manage the wallet. It is also one of the most important tasks held by the Parliament every year.

President Biden said: “Don’t tell me what you value, show me your budget and I can tell you what you really value.”

In representative democracy, budget deliberation is carried out by Congress or parliament, and there is little contact with ordinary democracy. The document writing is full of professional terms, resulting in professional asymmetry.

Political spoils sharing: pork barrel politics - a practice of political allocation of budget resources, just like those in power going to the meat barrel one by one to get their share, which implies that there is no long-term and professional plan for the country.

Is deliberation and optimization a kind of democratic reform that is based on “accumulation and accumulation”? Optimistic impact:

Through the baptism of citizen participation, ordinary people can be empowered and awakened in their will and cognition, and they will have a sense of being in charge of their own affairs. Deliberative activities can indeed reduce the degree of “elite dominance” in public policy decision-making. Pessimistic impact:

Decision-making rules and governance knowledge are still in the hands of political elites, and citizen participation in deliberative actions can usually only be incompletely practiced in some kind of “birdcage” experimental field.

Section 2 What is the difference between deliberative democracy?

Preface Representative democracy places “voting” at the core of political operations. Deliberative democracy regards “dialogue” as the core of democracy. Deliberative democracy has an educational effect and can improve the control of power and the manipulation of information in the public sphere.
Differences between deliberative democracy and other forms of democracy aggregate liberal democracy aggregative participatory democracy liberal deliberative democracy participatory deliberative democracy

The aggregative democratic process must be preceded by a public discussion process of collective deliberation and relies on majority rule voting procedures, resulting in results that violate the interests of the minority.

Switzerland’s regular referendum combines aggregate liberal democracy and aggregate participatory democracy.

Elements and important principles of deliberative democracy Elements: public reason

Key elements of deliberative democracy: listening, reflection, justification

Important guidelines

respect no right to suppress equality Reasoning The goal is consensus Common good orientation publicity Accountability Sincerity

Chapter 3 What does participatory budgeting want to do?

What does participatory budgeting aim to achieve? Changing government budget allocation priorities as disadvantages occur Expand the interaction and participation of community residents and build community awareness Transform passive voters into active citizens Promote the integration of bureaucracy and communities and stimulate administrative innovation Achieve the ideal of open government through openness, participation, tolerance and accountability in the government budget process Strengthen direct democracy and decide your own budget Has participatory budgeting served its purpose? At the bottom of the hierarchy, the budget involved is small

Elected politicians and NGOs are the initiators, but civil servants are often passive.

The final decision-making power usually rests with the executive head, so it is ultimately reduced to a consultation and communication function. Or participatory budgeting may end abruptly due to a change in executive leadership.

Some places did change government budget priorities

What does the future hold? Basic conditions:

People must have a certain degree of civic literacy. A bureaucracy that fully understands the value, connotation, procedures and limitations of participatory budgeting is needed. If the decision of participatory budgeting conflicts with the decision of the parliament, then civil servants will have to spend more time communicating with the parliament.

Participatory budgeting in Taiwan and the world Taiwan:

Hold a residents’ meeting in the community, open to everyone who wants to come, to come and propose plans. Workshops are held with experts in relevant fields, government representatives, social groups, volunteers and proposers to further plan, design and evaluate the plans passed by the residents’ meeting. The refined plans from the workshop can be displayed physically or online to let the public know about them. Votes are cast and the winning proposal is funded or budgeted to be implemented. The Origin and Diffusion of Global Participatory Budgeting 1989-1990 mid-term, cities in Brazil From the late 1990s to the mid-2000s, Latin American countries other than Brazil, as well as some European countries, France, Spain, and Italy Five continents around the world from mid-2000 to 2020. Diffusion:

left wing party The main promoter of the third wave is the World Bank, which provides many learning courses and research results. It also forces some countries receiving aid to implement participatory budgeting. participatory budgeting model The participatory democracy model, combined with direct democratic procedures, gives ordinary people substantial influence over policy. Approaching the democratic model, the goal is to improve communication between the government and the people. Citizens’ opinions are heard, but decision-makers have discretion. Only has “consultation” function. The participation in the modernization model is deeply influenced by the concept of “new public management”. Government services are “consumer” oriented and are concerned with technical issues at the management level rather than “social justice” and “social economy”. The multi-stakeholder model is also a top-down model, and private companies have opportunities to participate. The new corporatist model is also top-down, reconciling interests and needs through extensive consultation with important social groups and groups. Only has “Consultation” function. In the community development model, most plans are implemented by the community. On the one hand, it is promoted by the government from the top down. On the other hand, it also has a dynamic process of community organizations mobilizing people to participate from the bottom up. Taiwan’s Patterns and Characteristics Taking citizen empowerment and expanding participation as the core practice, it also incorporates elements of administrative modernization. Deeply influenced by deliberative democracy.

Limitations: Most of them are facilities or activities within the neighborhood community, and there are no socio-economic reform issues related to social justice.

Limitations, dilemmas. Public sector budget preparation has its limitations and must comply with the “Budget Law”. It is impossible to “hold accountability” from the people, and it is difficult for people to think comprehensively about their proposals. There is a cognitive gap between the responsible agency and the proposers, making it difficult to communicate and persuade. If the results are ineffective or wasteful after implementation, should civil servants or the general public be held accountable? It is difficult to motivate civil servants. The highest priority of civil servants is to be wise and protect themselves. Without incentives within the system, it is difficult to change. A direct collision with the parliament, the “supervisors” are distrustful.

Chapter 6: Laying the groundwork for the residents’ meeting:

participatory images Group proposal Desk leader and record guide Proposal ideas Group discussion and solution selection Proposal publication and voting (the top 2 or top 3 will be selected for the majority. In addition to being supported by half of the people on site, the proposal must also receive the top 2 or top 3 votes at the session to be passed)

Chapter 7 Citizen Deliberation: Safety Valve for Majority Democracy?

deliberations The team consists of 12 members:

The two committee members serve as moderators and are responsible for presiding over the meeting, summarizing the opinions of the responsible agencies and review members, serving as a communication bridge when necessary, and maintaining neutrality. One reviewer, deputy moderator, remains neutral. Advanced Citizen Card 2 people Junior Citizen Card 5 people 2 proposers in the previous year (excluding the administrative region of the original proposal) Junior advanced citizen card In the “Participatory Budgeting Training Course”, after completing 3 hours, you will get a junior citizen card, after completing 6 hours, you will get an advanced citizen card, and after 9 hours, you will get a deliberation card. You will also obtain the qualifications of “moderator, table leader, recorder, and deputy moderator”.

some questions Too few reviewers Reviewers lack emotional connection to case due to lottery decision The reviewer’s travel fee is only NT$120, which is far lower than the minimum wage of NT$168. How to regulate the reviewers who do not say a word or speak excessively? The review team member was not present for some reason, and the review member was also acting as the referee, resulting in a player-referee situation. The reason why deliberative democracy has become the main practical model of participatory democracy is that although representative democracy is based on majority rule, it cannot completely avoid the selfishness of representatives and the sloppiness of discussions in the decision-making process.

Chapter 9 The inclusion and convergence of proposals: the pull between ideals and reality

Dilemmas during the proposal process If the contents are similar or similar, should they be merged? Who will merge them and how? Who wants to be merged? Some proposers don’t like to propose together with others. What you want is not necessary for others, and it is more difficult to cross districts. The public is not professional enough, so how can we keep things in check? The proposal process is lengthy and the proposer may not be seen. The mentality of civil servants is that doing more is better than doing less. The proposer is more self-interested and can hide factors that are detrimental to him. Proposal circumvents participatory budgeting practices execute Members’ meeting

Chapter 13 Intermediary Group

Relevant organizations or groups, schools and actors that collaborate or directly promote PB plans.

Taiwan’s current PB types are divided into:

Municipal type Theme type community type The parliamentary type invites the public to participate in design proposals through the “Project Proposal Fund” in the form of PB. Almost all of the above four types have intermediary groups to assist in the promotion of PB.

Task: Design a mechanism for communicating with the public.

Chapter 14 Participatory Budgeting from a Bureaucratic Perspective

What is a bureaucracy? Bureaucratically organized administrative agencies Each agency has its own functional areas Civil servants are appointed based on technical qualifications determined by diplomas or examinations rather than elected Civil servants receive fixed salaries based on grade Occupation is life-long and civilian employment only Officials do not own their positions Officials surrender to control and discipline Promotion is based on judgment of superiors Sources of Bureaucratic Influence Being able to obtain information and control the flow of information to elected officials, provide planning for policy options and evaluate these options can influence policy decisions to a considerable extent. Because the actual machine is responsible for the implementation of the policy, it has the opportunity to interpret the meaning of the policy, and therefore may delay or block the implementation of a specific policy. Bureaucrats are often a group of elites with professional knowledge and an emphasis on performance. They are given the responsibility to maintain national interests and gain a certain amount of respect. Compared with political officials who “come and go” due to the rotation of political parties, bureaucrats maintain neutrality and continue to serve, with a permanent career. Participatory budgeting process and key points at each stage In the proposal review stage, there are four main links. The first is the convening of the residents’ meeting. This part is for citizens to brainstorm public issues and their solution strategies in order to formulate proposal ideas, which will be voted on by the participating citizens.

This is followed by the first step in participatory budgeting: the two stages of the proposal review workshop. Among them, the first stage is the “proposal discussion” stage, where bureaucratic representatives of the responsible agencies communicate with the proposers about the proposed alternatives or reasons for their infeasibility, and then experts and scholars provide correction suggestions and precautions.

Next, representatives from the citizen review panel will review the proposal based on three indicators: publicity, legality, and budget feasibility. After completion, the proposal plan will be publicly displayed online or in person so that you can understand the proposal and express your opinions.

Proposals in the proposal voting stage must obtain “high” or “moderate” feasibility evaluations on all three indicators of proposal review before they can be voted on by the i-Voting system.

As for the third stage, the Civil Affairs Bureau will notify the agency responsible for voting on the proposals and plan the required budget. If the implementation funds need to be paid in the next year’s budget, or a new budget for the next year is compiled, the existing budget preparation procedures will be followed.

Finally, to ensure that participatory budget proposals can be implemented, the Civil Affairs Bureau has also established a “proposal management system” to control the implementation progress and execution amount by the Research and Examination Committee.

In practice, after a proposal is made at the “Residents’ Assembly”, because disputes over the ownership of powers and responsibilities among the agencies involved in the proposal cannot be completely ruled out, a “pre-meeting meeting” will be held. The district office in charge will convene relevant agencies to determine the sponsors and co-sponsors of the “project management” and even include new agencies, so that these agencies can communicate with the proposer in advance on whether the proposal is feasible.

The residents’ meeting begins with a majority decision. After the decision is made, we will enter the deliberation stage. As for the deliberation stage… there will be a pre-conference meeting first. The pre-conference meeting will contain the people’s proposals. The district office will distinguish between the responsible agency and the co-organizing agency for this case. Then we will have a meeting with these agencies, have a discussion first, and then determine our division of labor and reach a consensus: who is in charge and who is the co-organizer, or if we have not listed anything to be included, we can mention it. After reaching a consensus before the meeting, you can ask them to go to these proposers to communicate the case first, because they already have relevant information. If you feel that something is unclear, the responsible agency may not be able to do what the other party mentioned, or there are other issues, you can communicate in advance.

The “policy learning” of civil servants, knowing what is happening but not knowing why, is not conducive to the promotion of “participatory budgeting” policy. In other words, if the mayor promotes this policy, of course he is a mayor elected by everyone, so he promotes this policy, and he hopes that the entire municipal team will help him implement this policy. I think maybe he did not turn around and tell our team, why should we promote this policy? What is our spirit? What are our core values?

We want to make the people below feel that what I am doing is meaningful. Yes, you can’t say it, then you do it. Then those of us who implement it don’t know why we do it! Sometimes it’s just a matter of handing in your homework and getting your work done!

If your own team or does not understand this core value, this is a big obstacle. For example, when we are doing publicity today, or (offering courses on citizen participation), or when promoting, we want to say that what we are talking about (maybe just) is a symbol (image), and you (citizens) hurry up and participate! I hope that citizens will participate, and then (everyone) try their best to propose (proposals), but why this matter should be done, I think is the key point. Is the existing system defective? Is it an obstacle? Or is the channel for public feedback not smooth? That’s why you have to do this. Yes, are we trying to make up for or make up for some of the shortcomings of the existing system?

Compared with the district office, the bureau’s “individualism” and sense of professional superiority are more obvious. “Project management” will formulate the relevant bureaus and bureaus for implementation. In the early stages of the policy, the relevant bureaus and bureaus of the city government were resistant and unwilling to take over.

Civil servants in certain bureaucracies with “technocratic” characteristics, such as engineering and information, have comparatively advantageous professional skills, and are more likely to find it difficult to treat them with empathy when communicating with proposers.

In fact, it is exclusive except for the district offices (bureaus), but it also has something to do with the division of labor in the city government. The city government will instruct a certain agency (bureau) to handle this matter, but other agencies will feel that this is none of their business, so why bother me?

How do we get everyone to agree with this issue? Maybe the district office or the Civil Affairs Bureau is okay, but it is the bureau that is really more resistant. Their attitudes are easy to appear. They will think about this proposal or how they will feel when communicating with the proposer. He will think that you know something, right! Therefore, sometimes the needs of public opinion conflict with the profession to some extent.

Of course he is selfish when it comes to the people, and that is what he should do. Of course he reacts to you based on his needs, so do you expect him to put forward a forward-looking suggestion? And then make a plan? Then whether (and can it satisfy) the public welfare or the public? That’s impossible!

Then when you go to the bureau and go to the first-level deliberation to discuss it, the bureau feels that what you are talking about is very unconstructive, or they think it is not very OK. But back to the point, because you are looking for these people for KPI, so of course his proposal will not be very good.

After you arrive at the bureau, the people in the bureau will feel that your topic is not very good if they have a little more expertise.

In addition, civil servants in bureaus tend to regard the implementation of the “participatory budgeting” policy as additional business that will increase their work burden. On the one hand, the content proposed by some “participatory budgeting” proponents has been proposed through other participation channels before and has been evaluated as unfeasible, but now the old proposal is being brought up again; on the other hand, even if it is feasible and implemented, it has triggered not so positive evaluations, which will also make the civil servants of the bureau find it difficult to accept.

From my observation, various bureaus and agencies think that they are resistant to “participatory budgeting”. That is to say, they all think it is looking for jobs and trouble!

…. The people in the bureau feel… This proposal is not feasible, or whatever, it conflicts with their profession, and they have to communicate with you (the public) all the time, which wastes a lot of their working time!

In fact, as far as the mentality of a civil servant is concerned, he is relatively repulsive because he has already done it and may have changed it (because of) petitions and the like. If the public mentions it again now, he will think of the original situation.

Sometimes people’s proposals are really passed, because if they pass the second level, they will be i-Voting, but basically, the cases that enter i-Voting will probably pass 100% of the time… Since it has been passed, it must be implemented. For example, in the case of parks, many of them are improvements or adjustments to facilities, but the adjustment of facilities is a matter of opinion. Some people will feel that the old one is not worse, and after the modification according to the people’s “participatory budgeting”, it is not better. Some public offices may even face doubts during the modification process, because the project proposed by the public has been modified through participatory budgeting, and they have to help them modify it. However, during the modification process, there will be disapproval from other people.

Performance standards and the self-interested motivations of civil servants KPI only focuses on “quantity”, and “quality” cannot be revealed through KPI. The quantity of knowledge (capacity) examined causes civil servants to try to “mobilize”.

How to solve: You can focus the measurement indicators on “results” instead of just the number of events and number of people.

The representativeness of those mobilized to vote and the quality of proposals are noteworthy. Can the cases proposed by the residents’ meeting represent the public opinion of the community? This is the first point. If i-Voting is passed, does it really represent public opinion? Because this vote is not necessarily for the people who live nearby. Maybe I (maybe) have no idea what you are talking about about the content of your proposal. I just took a promotional material, and then I voted, and I agreed. I just feel that both representations are controversial!

We have five tables here today, and one of them may be ordinary people. He knows that this is participatory budgeting and he thinks it is great. He also proposed a great case, but he came alone, and the other side may be a group, or everyone was mobilized together. More than 10 people came, and the case he proposed. Sometimes I feel that the case he proposed is not so exciting in comparison. But the case you have worked on may be the less OK one, and the good case is washed away, which may be less good. If it’s OK, it passes. I think this is a blind spot! Because what you are voting for is the head count, not the goodness or badness of the case. Even the three indicators we used in the second-level deliberation, public welfare, budget feasibility, and legality. In fact, I think these three indicators are very basic, but he can’t tell whether the case is good or bad!

Chapter 15 Diverse participation of students

Democratic learning in participatory budgeting Campus Training and Engagement Competition: Planting New Seeds of Civic Engagement Accompanying students in schools: Becoming a frontline democracy worker

Chapter 16 How to evaluate participatory budgeting

Will there be a fight between evaluation indicators? Whose perspective determines the relative importance of evaluation indicators? We can find that participants from different social backgrounds may have different focused goals, and there may be conflicts between some goals or evaluation indicators. This is a dilemma often faced when implementing participatory budgeting activities. Under these difficulties, training, education, and improvement of citizen literacy may be an important core to solve these problems. Through training, civil servants involved in civil society and implementation can understand the significance of the activities, show a more inclusive mind and good deliberation skills, and can also make proposals more feasible to implement through training courses, so that the results of the activities can achieve better results. Therefore, it is important to measure the effectiveness of the “training effect”. This is also where participatory budgeting is most different from other citizen participation activities. On the one hand, the activity promotes the deepening of democracy, allowing participating people and civil servants who implement the activity to learn democracy and cultivate their civic literacy; on the other hand, it improves public services, makes public services more relevant to the needs of local people, improves the opportunities for disadvantaged groups to obtain public fund allocation, and practices social justice.

How to finally judge the score of each indicator? How to quantify? In addition to the four layers in the indicator structure in the previous section, below the fourth layer, there is also a fifth layer, which is “operationalization of evaluation indicators”, which means how to measure or judge their levels. For example:

How to evaluate participants is diverse.

What are the common evaluation indicators currently used in practice mainly evaluating? Proposal completion budget execution rate But neither is good enough.

Chapter 17 How can one be punished for being the leader?

People may not have fulfilled their “civic responsibilities” during the participatory budgeting process

The proposal was not carefully considered and the proposer stopped participating after the residents’ meeting.
The content of the proposal has been revised, and the executable “conceptual project” does not meet expectations.
It takes a long time to complete the case and implement it. Contacting the hospital is quick and effective.
The execution of a completed case was overturned by local residents, and the proposer withdrew the case or closed it early. After repeated deliberation, avoid the occurrence of liability situations The original intention of citizen participation is to share the decision-making process originally monopolized by the government with the people. When the democratic parties fail to fulfill their civic responsibilities in every aspect of participatory budgeting and the content of the proposals is of poor quality, although they do share the decision-making power from the administrative agencies, they may only share the power of wasting public funds. In this way, the good intentions of citizen participation are lost.

In order to ensure the quality of citizen participation in decision-making and avoid the situation of being the master of the country but being held accountable, the proposer should be allowed to return to the community to dialogue with the people. Democracy comes at a cost, and those with power must assume more obligations and responsibilities. It is recommended that more physical communication channels be added during the proposal review stage, such as citizen cafes, public hearings, briefings, symposiums, workshops, on-site surveys, etc., so that the proposer can promote the concept, persuade the public, and ensure that the proposal meets the needs of the community.

Chapter 19 The Conflict between Direct Democracy and Indirect Democracy

Can MPs overturn proposals passed through i-voting? Members questioned:

Does PB have institutional legitimacy? The intention is to circumvent the legitimate supervision of parliament in the name of citizen participation.

PB Is this new system necessary? According to Taipei City’s SOP, it takes a lot of time for PB to complete the entire process. It is better to submit a petition to the councillors, which may get satisfaction more quickly. There is no need to establish another channel.

Although councilors will face the dilemma of not knowing whether they can overturn proposals that have passed a series of procedures and i-Voting when reviewing the city government’s budget, the Haobei City Government’s current approach is still able to respect the budget review rights of councilors and still include the budget required for the vast majority of proposals in the next year’s budget and submit it to the council for review. There is no intention to circumvent parliamentary supervision and review. Therefore, the best way to demonstrate the wisdom of democracy is that on the one hand, parliamentarians should support the funding required for participatory budget proposals. On the other hand, after the budget is passed, they should use their supervisory powers to supervise the subsequent implementation of the proposals passed by their constituencies, and urge the administrative departments to implement the people’s expectations.

In this way, through the model of “people’s proposals and parliamentary supervision”, we can not only deepen our country’s direct democracy and citizen participation, but also make direct democracy and indirect democracy compatible with each other. The continuous implementation of completed proposals is the best embodiment of the mutual prosperity of the two democracies.

Chapter 20 Digital Participatory Budgeting

digital participatory budgeting With the development of information technology, digital technology is used to promote citizen participation. The goal is to eliminate paper processes and connect online participation platforms through digital technology, creating an electronic/digital participatory budget.

The medium of digital participatory budgeting Web-oriented tools designed for public engagement social networking sites

Chapter 21 Overview of the Operation of Participatory Budgeting

In order to comply with the budget law and local system regulations, it is necessary to cooperate with the Taipei City Government’s general budget preparation, review and execution process. Before the final compilation of the city’s general budget in May and June of each year, the confirmation of approved proposals is completed, and the required funds are included in the general budget and sent to the city council for review in September.

rolling correction Citizen Participation Committee The Citizen Participation Committee of the Yibei City Government is composed of the heads of relevant city government bureaus, as well as external experts, scholars, and social justice figures. The mayor serves as the chairman. It has three working groups: the Citizen Participation Group, the Open Data and Exploration Group, and the Participatory Budget Group. Among them, the Participation Budget Group is responsible for the study and discussion of Taipei City’s participatory budget system policies, implementation plans, and standard operating procedures, and is headed by the Civil Affairs Bureau as the main administrative organ. In addition to participating in participatory budget promotion education courses, residents’ meetings and first-stage review workshops from time to time, the public counselors of the Participation Budget Team also serve as moderators of the second-stage review workshops to ensure that the meetings proceed smoothly and allow review team members to evaluate the proposals in an informed manner. In addition to participating in the above-mentioned courses and meetings, the Public Counselor’s greatest contribution to participatory budgeting is that after the proposal is passed, the sponsoring and co-organizing agencies must regularly report the implementation status and difficulties of the proposal to the Participatory Budget Group. The Public Counselor can track the progress of the proposal in relevant meetings and assist in cross-agency communication and coordination to ensure that the proposal can be successfully completed.

The government-school alliance accompanies the school Through a team of teachers who accompany the school, we provide professional consultation and guidance to assist in the planning of promotion education courses and various proposal review meetings. The accompanying school teachers, students and work teams work together in the actual process of handling various promotion education courses and proposal review meetings, so that participatory budgeting can deeply cultivate local areas and achieve grassroots democracy, thus promoting the continuous promotion of participatory budgeting.

Proposal sponsor and co-organizer Through the implementation of the project management system, each PB proposal will assign the sponsoring and co-organizing agencies of the proposal based on the number of tasks that each agency in the proposal is responsible for. The sponsoring agency will be responsible for cross-agency coordination during the implementation of the proposal, as well as the coordination of proposal milestones and implementation progress. The implementation status will be explained to the proposer on a quarterly basis. If difficulties are encountered during the implementation process, the report will be reported at the Participation Budget Group meeting of the Citizen Participation Committee and members will be sought to provide suggestions or assist in coordination.

Chapter 22 SOP for Participatory Budgeting

Ke Wenzhe stated the two major features of Taipei City’s promotion of participatory budgeting:

The focus is on citizen education and bringing the new concept of direct democracy into Taipei City. Civil servants are actually involved. During the process, civil servants are directly involved through courses and briefing sessions, and opportunities for contact with citizen groups are also increased. We strive to integrate this entire system into the public service system. Citizen Proposal and Review Procedures Citizen Proposal and Review Procedures - Municipal Level

Citizen Proposal and Review Procedures - District Level

Standard SOP

Proposal review The Taipei City Government organizes residents’ meetings, inviting proposers and citizens who care about the proposal to discuss public issues, put forward improvement plans for the problems, and formulate proposal concepts, which are then voted on by on-site participants to finalize the proposal.
Proposal Review Workshop Due to the limited public understanding of the administrative process, if government bureaus are not involved to help evaluate the practical feasibility of the proposal before citizens enter the voting process, the subsequent implementation stage may be bumpy and difficult (Su Caizu, 2017). The first stage of the proposal workshop allows agency representatives to communicate face-to-face with the proposer to understand the actual situation, so that the follow-up process of the proposal can be smoothly promoted, and experts and scholars are invited to provide correction directions for the proposal content. In the second stage of proposal review, the citizen review panel will review the proposal content based on three indicators: “publicity”, “legality” and “budget feasibility”.

The Taipei City Participatory Budget Citizen Review Panel is composed of 12 members. The qualifications are as follows:

2 members of the government’s Citizen Participation Committee (acting as moderator and co-moderator).

1 reviewer (serves as deputy moderator and assists the moderator in conducting meetings).

2 citizens with advanced cards.

5 citizens with entry-level cards.

There were 2 proposers in the previous year (excluding the proposers from this administrative region).

Indicators and signals for “publicity”, “legality” and “budgetary feasibility” Based on the content of the proposal, application of regulations and budget, indicators for the degree of impact of the implementation of the proposal on “publicity”, “legality” and “budgetary feasibility” are designed. Innovative indicators of “red”, “yellow” and “green” are used to facilitate participating citizens to understand and check. Those with “high” or “moderate” feasibility of the three indicators can enter the subsequent voting process.

Public exhibition of proposals and plans Before the proposal voting, the public exhibition process of the proposal plan will be carried out, including “online exhibition” and “physical exhibition”. The public can have an in-depth understanding of the content of the proposal before the voting, and also have the opportunity to express their opinions on the proposal.
Proposal voting Through the participatory budget i-Voting process, those registered in Taipei City, or studying, working, or living in Taipei City, and who are over 16 years old, can vote online or go to the district office to authenticate and vote online. If the number of votes exceeds the district threshold and is within the upper limit of the record after sorting, the case can be recorded.
Budget evaluation, budget review and parliamentary supervision stages After the proposal is approved by voting, it will be officially recorded. The Civil Affairs Bureau will notify each responsible agency to conduct budget evaluation and planning. If there are many agencies involved, one agency will be designated as the main responsible agency (Project Manager). Those who have a budget for the current year can implement it first. If the proposal implementation funds need to be paid in the next year’s budget, or if a new budget is compiled for the next year, it will be handled according to the budget preparation process, and then the budget approval and supervision will be carried out by the parliament.

Flexibility under standardization

Convene an agency consensus meeting The Civil Affairs Bureau holds an agency consensus meeting every year to explain “improving the principles of designation and adjustment of responsible agencies,” “coordinating matters to improve the feasibility of proposals,” “budget planning schedules,” and “notes on the implementation of proposals,” etc., in order to improve consensus on the designation of responsible agencies before each district meeting, so that participating agency colleagues can understand the follow-up procedures and agency roles, and provide specific and detailed information for review team members to evaluate carefully.
Hold a pre-meeting meeting In order to confirm the responsible agency for the proposal as early as possible and conduct consultation or communication in advance, each district office holds a pre-deliberation workshop meeting after the residents’ meeting to invite relevant agencies of the proposal and designate the sponsoring agency for each case.
Direct execution mechanism “Direct implementation” was added in 2018. Some proposals are relatively simple and belong to a single responsible authority. If the existing budget of the current year is executable and the implementation can be completed in the current year, on the premise of the proposer’s consent and reaching a consensus with the responsible authority, there will be no subsequent proposal review and voting procedures. The agency will handle it in the current year, reducing the complicated process and completing the proposer’s idea as soon as possible.
Evaluation and processing mechanism After years of operation, proposals such as changes in traffic signal seconds, sidewalk settings, parking space planning or environmental beautification are public in nature. In order to avoid personal factors affecting the overall concept of regional planning and urban aesthetics, the proposal was discussed and approved by the public participation working group meeting. Projects including “traffic management”, “walking space”, “environmental painting or beautification”, etc., were submitted to the responsible authority for overall evaluation and processing with the approval of the proposer. The evaluation was completed before August of the year and reported to the working group meeting.

Advantages and key success factors of Taipei City’s promotion of participatory budgeting Chief supports authorization Fault tolerance correction Information disclosure and transparency Incorporate into budget process Limitations of Taipei City’s push for participatory budgeting Limitations of municipal issues

Municipal-level proposals are made in the form of issues, and special projects are being carried out to deal with issues of new immigrants and groups with disabilities. Currently, citizen proposals are limited to the district level. How can bureaus design topics and develop them from top to bottom, or allow citizens to propose bottom-up proposals? It still needs to be followed up to derive appropriate city-level topics.

Lack of online discussion model

At present, participatory budgeting in Taipei City is still conducted through on-site participation and discussion. With the trend towards younger and more convenient participation, whether it can be discussed online is worth looking forward to developing a model that transcends time and space. During the COVID-19 epidemic, various video and digital platforms have developed rapidly. In 2021 and 2022, attempts have been made to conduct deliberation workshops and residents’ meetings via online video. In the future, we may further think about how to make digital a normal mode of participation.

Chapter 23 Participatory Budgeting Development Plan

For Taipei citizens Progressive “Extension Education 369” series of courses Take root on campus Active citizenship materials have a foundation The proposal successfully brought happy changes to the people and gave participatory budgeting a strong appeal in the future. For students, the perspective gradually changes Courses that emphasize “participation” are closer to the “108 Curriculum”
For public servants In order to accelerate the equipping of civil servants with knowledge and concepts related to participatory budgeting, Taipei City held a “Civil Servant Training Course” from May to August 2015. The main trainees were officers of the Participating Budget Team and business-related personnel of various bureaus and district offices. Scholars and members of the Participating Budget Team were invited to have dialogues with civil servants to open up civil servants’ imagination of participatory budgeting and become seed personnel in various bureaus and offices of the city government.

Reversing the attitude of civil servants Promote organizational learning in various bureaus 3. Formation of the “Government-Academic Alliance” Grasp Taipei City’s advantageous resources and establish an expert database Strive for the recognition of experts and scholars and solidify the basis for cooperation between the two parties Establish government-study alliance and carry out cooperation new page Connected to the local area, administration accompanies evolution Assist local development and practice “university social responsibility”

Chapter 24 Supporting Plan for Participatory Budgeting

Operational supporting plan Auxiliary measures Project consultation counter setup and promotion plan Provide consultation on citizen participatory budget procedures, assist proposers in writing plans, and accept proposals.

Things to note when coordinating different opinions during the post-implementation stage of participatory budgeting Coordination and handling procedures when local public opinion has different opinions during the proposal implementation stage.

Execution mechanism In order to promote administrative efficiency, when a proposal meets certain conditions, it can be implemented directly.

Proposal exclusion and project evaluation mechanism

When considering some proposals, it is necessary to plan from the perspective of municipal integrity, or to consider the financial discipline of the municipal government. Some projects cannot be used as proposals or need to be evaluated by the agency as a whole.

Plan extension Entering Campus Series Project Committed to taking root in democratic literacy, we plan a series of campus promotion programs and activities.

Group proposal plan Consideration of specific issues that are not likely to receive attention within the existing participatory budget process allows planning non-profits or disability groups to apply for ad hoc procedures for specific issues.

Programmatic package plan Proposal briefing meeting residents meeting Proposal Review Workshop Implementation Plan Proposal Ballot Implementation Plan

Excellent performance in substitute service at the Ministry of Interior

Wed, 17 May 2023 00:00:00 +0000

Award Overview

Being selected as the outstanding male service member in the first half of **112 (document number: 1121070500), this is a formal recognition of civil servants who have performed outstandingly during their alternative service.

Substitute background

The alternative service system is one of Taiwan’s important military service systems. It aims to enable servicemen to develop their expertise in various fields of society, serve the country and society, and cultivate young people’s sense of responsibility and mission.

Selection criteria

Substitute merit-based draftees are usually awarded to draftees who meet the following criteria:

Positive service attitude: Serious and responsible, proactive
Excellent Work Performance: Excellent performance in various tasks
Good teamwork: get along well with colleagues, collaborate and help each other
Strict Discipline: Strictly abide by military disciplines and regulations

Meaning of award

Official Certification: Official commendation from the Ministry of the Interior, with high credibility
Moral Affirmation: Reflects excellent character and service spirit
Practical Ability: Demonstrated good organizational and execution capabilities
Social Contribution: Made a positive contribution to the country’s society

Service experience

During his service in the alternative service, he actively participated in various tasks and won unanimous praise from his unit supervisor and colleagues with his professional attitude and professionalism.

Zhongshan Academic Youth Seminar

Fri, 01 Jan 2021 09:00:00 +0800

Workshop Overview

Participate in the Zhongshan Academic Seminar hosted by the School of Social Sciences of National Chengchi University and conduct academic exchanges with scholars and experts from the Schools of Social Sciences of various schools.

Organizer

School of Social Sciences, National Chengchi University

Participating institutions

School of Social Sciences, National Chengchi University
School of Social Sciences, Chinese Culture University

Meeting location

National Chengchi University Comprehensive Hall International Conference Hall

Main topics

1. Theoretical research on Zhongshan Thought

The core connotation of Zhongshan Thought
The formation process of academic theory
Structural analysis of ideological systems

2. The historical value of Sun Yat-sen Thought

Position in historical development
Impact on academia
Implications for later research

3. The contemporary significance of Sun Yat-sen Thought

Guiding values in modern society
The significance of linking theory with practice
Inspiration for future development

4. Academic research methods

Perspectives on the study of Zhongshan Thought
Interdisciplinary research methods
Data analysis and interpretation

Academic exchange

The participating scholars and experts conducted in-depth discussions on various aspects of Sun Yat-sen Thought, shared research experiences and academic achievements, and jointly promoted the development of research on Sun Yat-sen Thought.

Academic value

This seminar provides an important communication platform for scholars engaged in the study of Sun Yat-sen thought, helping to deepen relevant theoretical research and promote academic development.

Graduate Academic Merit Scholarship for the 1st Semester of the 108th Academic Year

Wed, 01 Jul 2020 00:00:00 +0000

Award Overview

In the first semester of the 108 academic year, I won the Institute Academic Excellence Scholarship (Certificate No.: 1090001009). This scholarship is designed to reward graduate students who have outstanding academic research and academic performance.

Application conditions

Graduate Academic Merit Scholarships are generally awarded to students who meet the following criteria:

Excellent academic performance: Academic performance ranks among the best in the department
Excellent Research Ability: Demonstrate outstanding potential in the research field
Actively participate in academic activities: Actively participate in seminars, lectures, etc.
Good Conduct: Have good academic ethics and character

Meaning of award

Receiving this scholarship is not only an affirmation of personal academic efforts, but also an incentive to continue to improve in the future research path. At the same time, it also demonstrated the potential and achievements in the field of forward-looking research.

Academic achievements

During the award period, he actively invested in academic research and made important progress in related fields, laying a solid foundation for subsequent academic development.

Scholarship Value

This scholarship not only provides financial support, but more importantly, it recognizes academic achievements and provides important encouragement and motivation to continue in-depth research.

✅ Manage your projects

Wed, 01 Jan 2020 00:00:00 +0000

Easily manage your projects - create ideation mind maps, Gantt charts, todo lists, and more!

Ideation

Hugo Blox supports a Markdown extension for mindmaps.

Simply insert a Markdown code block labelled as markmap and optionally set the height of the mindmap as shown in the example below.

Mindmaps can be created by simply writing the items as a Markdown list within the markmap code block, indenting each item to create as many sub-levels as you need:


```markmap {height="200px"}
- Hugo Modules
- Hugo Blox
- blox-plugins-netlify
- blox-plugins-netlify-cms
- blox-plugins-reveal
```

renders as

- Hugo Modules
- Hugo Blox
- blox-plugins-netlify
- blox-plugins-netlify-cms
- blox-plugins-reveal

Diagrams

Hugo Blox supports the Mermaid Markdown extension for diagrams.

An example Gantt diagram:

```mermaid
gantt
section Section
Completed :done, des1, 2014-01-06,2014-01-08
Active :active, des2, 2014-01-07, 3d
Parallel 1 : des3, after des1, 1d
Parallel 2 : des4, after des1, 1d
Parallel 3 : des5, after des3, 1d
Parallel 4 : des6, after des4, 1d
```

renders as

gantt section Section Completed :done, des1, 2014-01-06,2014-01-08 Active :active, des2, 2014-01-07, 3d Parallel 1 : des3, after des1, 1d Parallel 2 : des4, after des1, 1d Parallel 3 : des5, after des3, 1d Parallel 4 : des6, after des4, 1d

Todo lists

You can even write your todo lists in Markdown too:

- [x] Write math example
 - [x] Write diagram example
- [ ] Do something else

renders as

Write math example
- Write diagram example
Do something else

🎉 Easily create your own simple yet highly customizable blog

Wed, 01 Jan 2020 00:00:00 +0000

Welcome 👋

Table of Contents

Overview

The Hugo Blox website builder for Hugo, along with its starter templates, is designed for professional creators, educators, and teams/organizations - although it can be used to create any kind of site
The template can be modified and customised to suit your needs. It’s a good platform for anyone looking to take control of their data and online identity whilst having the convenience to start off with a no-code solution (write in Markdown and customize with YAML parameters) and having flexibility to later add even deeper personalization with HTML and CSS
You can work with all your favourite tools and apps with hundreds of plugins and integrations to speed up your workflows, interact with your readers, and much more

Get Started

👉
📚
💬 or
🐦 Twitter: #MadeWithHugoBlox
💡
⬆️ Updating Hugo Blox? View the and

Crowd-funded open-source software

To help us develop this template and software sustainably under the MIT license, we ask all individuals and businesses that use it to help support its ongoing maintenance and development via sponsorship.

As a token of appreciation for sponsoring, you can unlock awesome rewards and extra features 🦄✨

Ecosystem

: Automatically import publications from BibTeX

Inspiration

are building with this template.

Features

Page builder - Create anything with no-code and
Edit any type of content - Blog posts, publications, talks, slides, projects, and more!
Create content in , , or
Plugin System - Fully customizable
Display Code and Math - Code syntax highlighting and LaTeX math supported
Integrations - , , Maps, Contact Forms, and more!
Beautiful Site - Simple and refreshing one-page design
Industry-Leading SEO - Help get your website found on search engines and social media
Media Galleries - Display your images and videos with captions in a customizable gallery
Mobile Friendly - Look amazing on every screen with a mobile friendly version of your site
Multi-language - 35+ language packs including English, Chinese, and Português
Multi-user - Each author gets their own profile page
Privacy Pack - Assists with GDPR
Stand Out - Bring your site to life with animation, parallax backgrounds, and scroll effects
One-Click Deployment - No servers. No databases. Only files.

Themes

Hugo Blox and its templates come with automatic day (light) and night (dark) mode built-in. Visitors can choose their preferred mode by clicking the sun/moon icon in the header.

for your site. Themes are fully customizable.

License

Released under the license.

👩🏼‍🏫 Teach academic courses

Wed, 01 Jan 2020 00:00:00 +0000

is designed to give technical content creators a seamless experience. You can focus on the content and the Hugo Blox Builder which this template is built upon handles the rest.

Embed videos, podcasts, code, LaTeX math, and even test students!

On this page, you’ll find some examples of the types of technical content that can be rendered with Hugo Blox.

Citation

Here’s an example of citing a publication using the cite shortcode:

Dylan Chiang (2019). An example preprint / working paper.

Preprint Code Slides Dataset Poster Source Document Video Custom Link Preprint

You can also use the default view by omitting the view parameter:

Dylan Chiang

Equal contribution

, Robert Ford

Equal contribution

(2013). An example conference paper. In ICW.

PDF Code Dataset Poster Project Slides Source Document Video PDF DOI

Video

Teach your course by sharing videos with your students. Choose from one of the following approaches:

Youtube:

{{< youtube D2vj0WcvH5c >}}

Bilibili:

{{< bilibili BV1WV4y1r7DF >}}

Video file

Videos may be added to a page by either placing them in your assets/media/ media library or in your , and then embedding them with the video shortcode:

{{< video src="my_video.mp4" controls="yes" >}}

Podcast

You can add a podcast or music to a page by placing the MP3 file in the page’s folder or the media library folder and then embedding the audio on your page with the audio shortcode:

{{< audio src="ambient-piano.mp3" >}}

Try it out:

Test students

Provide a simple yet fun self-assessment by revealing the solutions to challenges with the spoiler shortcode:

{{< spoiler text="👉 Click to view the solution" >}}
You found me!
{{< /spoiler >}}

renders as

👉 Click to view the solution

You found me 🎉

Math

Hugo Blox Builder supports a Markdown extension for $\LaTeX$ math. Enable math by setting the math: true option in your page’s front matter, or enable math for your entire site by toggling math in your config/_default/params.yaml file:

features:
 math:
 enable: true

To render inline or block math, wrap your LaTeX math with $...$ or $$...$$, respectively.

Example math block:

$$
\gamma_{n} = \frac{ \left | \left (\mathbf x_{n} - \mathbf x_{n-1} \right )^T \left [\nabla F (\mathbf x_{n}) - \nabla F (\mathbf x_{n-1}) \right ] \right |}{\left \|\nabla F(\mathbf{x}_{n}) - \nabla F(\mathbf{x}_{n-1}) \right \|^2}
$$

renders as

Example inline math $\nabla F(\mathbf{x}_{n})$ renders as $\nabla F(\mathbf{x}_{n})$.

Example multi-line math using the math linebreak (\\):

$$f(k;p_{0}^{*}) = \begin{cases}p_{0}^{*} & \text{if }k=1, \\
1-p_{0}^{*} & \text{if }k=0.\end{cases}$$

renders as

$$ f(k;p_{0}^{*}) = \begin{cases}p_{0}^{*} & \text{if }k=1, \\ 1-p_{0}^{*} & \text{if }k=0.\end{cases} $$

Code

Hugo Blox Builder utilises Hugo’s Markdown extension for highlighting code syntax. The code theme can be selected in the config/_default/params.yaml file.

```python
import pandas as pd
data = pd.read_csv("data.csv")
data.head()
```

renders as

import pandas as pd
data = pd.read_csv("data.csv")
data.head()

Inline Images

{{< icon name="python" >}} Python

renders as

Python

📈 Communicate your results effectively with the best data visualizations

Wed, 01 Jan 2020 00:00:00 +0000

Hugo Blox is designed to give technical content creators a seamless experience. You can focus on the content and Hugo Blox handles the rest.

Use popular tools such as Plotly, Mermaid, and data frames.

Charts

Hugo Blox supports the popular format for interactive data visualizations. With Plotly, you can design almost any kind of visualization you can imagine!

Save your Plotly JSON in your page folder, for example line-chart.json, and then add the {{< chart data="line-chart" >}} shortcode where you would like the chart to appear.

Demo:

You might also find the useful.

Diagrams

Hugo Blox supports the Mermaid Markdown extension for diagrams.

An example flowchart:

```mermaid
graph TD
A[Hard] -->|Text| B(Round)
B --> C{Decision}
C -->|One| D[Result 1]
C -->|Two| E[Result 2]
```

renders as

graph TD A[Hard] -->|Text| B(Round) B --> C{Decision} C -->|One| D[Result 1] C -->|Two| E[Result 2]

An example sequence diagram:

```mermaid
sequenceDiagram
Alice->>John: Hello John, how are you?
loop Healthcheck
John->>John: Fight against hypochondria
end
Note right of John: Rational thoughts!
John-->>Alice: Great!
John->>Bob: How about you?
Bob-->>John: Jolly good!
```

renders as

sequenceDiagram Alice->>John: Hello John, how are you? loop Healthcheck John->>John: Fight against hypochondria end Note right of John: Rational thoughts! John-->>Alice: Great! John->>Bob: How about you? Bob-->>John: Jolly good!

An example class diagram:

```mermaid
classDiagram
Class01 <|-- AveryLongClass : Cool
Class03 *-- Class04
Class05 o-- Class06
Class07 .. Class08
Class09 --> C2 : Where am i?
Class09 --* C3
Class09 --|> Class07
Class07 : equals()
Class07 : Object[] elementData
Class01 : size()
Class01 : int chimp
Class01 : int gorilla
Class08 <--> C2: Cool label
```

renders as

classDiagram Class01 <|-- AveryLongClass : Cool Class03 *-- Class04 Class05 o-- Class06 Class07 .. Class08 Class09 --> C2 : Where am i? Class09 --* C3 Class09 --|> Class07 Class07 : equals() Class07 : Object[] elementData Class01 : size() Class01 : int chimp Class01 : int gorilla Class08 <--> C2: Cool label

An example state diagram:

```mermaid
stateDiagram
[*] --> Still
Still --> [*]
Still --> Moving
Moving --> Still
Moving --> Crash
Crash --> [*]
```

renders as

stateDiagram [*] --> Still Still --> [*] Still --> Moving Moving --> Still Moving --> Crash Crash --> [*]

Data Frames

Save your spreadsheet as a CSV file in your page’s folder and then render it by adding the Table shortcode to your page:

{{< table path="results.csv" header="true" caption="Table 1: My results" >}}

renders as

Table 1: My results
customer_id	score
1	0
2	0.5
3	1

🧠 Sharpen your thinking with a second brain

Wed, 01 Jan 2020 00:00:00 +0000

Create a personal knowledge base and share your knowledge with your peers.

Hugo Blox web framework empowers you with one of the most flexible note-taking capabilities out there.

Create a powerful knowledge base that works on top of a local folder of plain text Markdown files.

Use it as your second brain, either publicly sharing your knowledge with your peers via your website, or via a private GitHub repository and password-protected site just for yourself.

Mindmaps

Hugo Blox supports a Markdown extension for mindmaps.

With this open format, can even edit your mindmaps in other popular tools such as Obsidian.

Simply insert a Markdown code block labelled as markmap and optionally set the height of the mindmap as shown in the example below.

Mindmaps can be created by simply writing the items as a Markdown list within the markmap code block, indenting each item to create as many sub-levels as you need:


```markmap {height="200px"}
- Hugo Modules
- Hugo Blox
- blox-plugins-netlify
- blox-plugins-netlify-cms
- blox-plugins-reveal
```

renders as

- Hugo Modules
- Hugo Blox
- blox-plugins-netlify
- blox-plugins-netlify-cms
- blox-plugins-reveal

Anh here’s a more advanced mindmap with formatting, code blocks, and math:


```markmap
- Mindmaps
- Links
- [Hugo Blox Docs](https://docs.hugoblox.com/)
- [Discord Community](https://discord.gg/z8wNYzb)
- [GitHub](https://github.com/HugoBlox/hugo-blox-builder)
- Features
- Markdown formatting
- **inline** ~~text~~ *styles*
- multiline
text
- `inline code`
-
```js
console.log('hello');
console.log('code block');
```
- Math: $x = {-b \pm \sqrt{b^2-4ac} \over 2a}$
```

renders as

- Mindmaps
- Links
- [Hugo Blox Docs](https://docs.hugoblox.com/)
- [Discord Community](https://discord.gg/z8wNYzb)
- [GitHub](https://github.com/HugoBlox/hugo-blox-builder)
- Features
- Markdown formatting
- **inline** ~~text~~ *styles*
- multiline
text
- `inline code`
-
```js
console.log('hello');
console.log('code block');
```
- Math: $x = {-b \pm \sqrt{b^2-4ac} \over 2a}$

Highlighting

Highlight important text with mark:

<mark>Highlighted text</mark>

Callouts

Use (aka asides, hints, or alerts) to draw attention to notes, tips, and warnings.

By wrapping a paragraph in {{% callout note %}} ... {{% /callout %}}, it will render as an aside.

{{% callout note %}}
A Markdown aside is useful for displaying notices, hints, or definitions to your readers.
{{% /callout %}}

renders as

A Markdown aside is useful for displaying notices, hints, or definitions to your readers.

Or use the warning callout type so your readers don’t miss critical details:

A Markdown aside is useful for displaying notices, hints, or definitions to your readers.

Research on the behavioral patterns of mainland students coming to Taiwan - analysis based on questionnaire survey

Thu, 28 Nov 2019 00:00:00 +0000

Seminar information

Conference Name: 2019 “National Development and Mainland China Studies” Master’s Academic Thesis Seminar

Meeting Time: November 28, 2019

Meeting location: Conference Room 1, Xiaofeng Memorial Hall, Chinese Culture University

Sponsor: Institute of National Development and Mainland China, Academy of Social Sciences, Chinese Culture University

Conference Manual and Proceedings: Conference Manual and Proceedings

Research Summary

Research background

Since Taiwan opened its doors to mainland students to study in Taiwan in 2011, a large number of mainland students and exchange students have come to Taiwan to receive the baptism of multiculturalism.

Problem awareness

Due to a series of “Lennon Wall” conflicts between Hong Kong students and mainland students in Taiwan since September 2019. The author is curious about whether the Taiwanese government’s policy discrimination of “three restrictions and six no’s” and lack of health insurance, as well as the cultural discrimination of Taiwanese people on accents, social rules, etc., through living in Taiwan and being exposed to Taiwan’s society, culture, economy, and politics, have they increased their identification with the mainland? Or is it because of the greater knowledge and experience in Taiwan that the mainland students have a negative attitude towards the mainland? Will Lu Sheng hide himself due to the cultural shock in Taiwan, or will he continue to be willing to express his opinions?

Research purpose

In this context, the study selected mainland students as subjects. Due to the differences in social systems, will the behavior patterns of mainland students in Taiwan be different due to their in-depth contact with Taiwan? Will their behavior patterns and interactions with people be different? Will different conclusions be drawn due to different factors such as gender, age, place of residence, the exchange student’s original school, the location of the school in Taiwan, or whether it is national or private?

Research methods

This article adopted the online questionnaire survey method and finally obtained 127 valid samples. Use statistical analysis, descriptive analysis, and cross analysis methods.

Research findings

It is hoped that through this research survey, it will be found that mainland students have a negative evaluation of Taiwan on the political level, and a positive evaluation of the social division, friendliness and value of coming to Taiwan to study.

An example preprint / working paper

Sun, 07 Apr 2019 00:00:00 +0000

This work is driven by the results in my on LLMs.

Create your slides in Markdown - click the Slides button to check out the example.

Add the publication’s full text or supplementary notes here. You can use rich formatting such as including .

Academic Seminar on Sun Yat-sen Thought and Contemporary National Development

Mon, 03 Dec 2018 09:00:00 +0800

Workshop Overview

On December 3, 2018, I participated in the academic seminar on Sun Yat-sen Thought and Contemporary National Development sponsored by the Institute of National Development and Mainland China at the Chinese Culture University to conduct an in-depth discussion on the guiding significance and practical value of Sun Yat-sen Thought in contemporary times.

Organizer

Institute of National Development and Mainland China, Chinese Culture University

Meeting location

Father of the Sun Memorial Hall

Meeting theme

In-depth discussion of the guiding significance and practical value of Sun Yat-sen Thought in contemporary times

Main topics

1. Theoretical connotation of Sun Yat-sen Thought

-The formation and development of the Three People’s Principles

Combination of revolutionary theory and practice
Core elements of the ideological system

2. The contemporary value of Zhongshan Thought

The guiding ideology of modernization construction
The development path of democratic politics
Theoretical support for national rejuvenation

3. Sun Yat-sen Thought and National Development

Innovation in nation-building theory
Regional development coordination mechanism
The source of wisdom for reform and opening up

4. The practical inspiration of Zhongshan Thought

Modernization of governance system
Social fairness and justice
Path of peaceful development

Academic exchange

Conduct in-depth exchanges with scholars and experts from various universities and research institutions on the contemporary value of Sun Yat-sen Thought, and jointly explore its guiding significance in the construction of a modern country.

Academic significance

This seminar will help deepen the understanding of Sun Yat-sen Thought and provide theoretical reference and practical inspiration for contemporary national construction and development.

Academic seminar on 40 years of reform and opening up in mainland China

Thu, 29 Nov 2018 09:00:00 +0800

Workshop Overview

On November 29, 2018, I participated in the 40th Anniversary Academic Seminar on “Mainland China’s Reform” hosted by the Institute of National Development and Mainland China, Academy of Social Sciences, Chinese Culture University.

Organizer

Institute of National Development and Mainland China, School of Social Sciences, Chinese Culture University

Meeting theme

Conduct in-depth academic discussions and exchanges around the 40 years of reform and opening up in mainland China.

Main topics

1. The historical process of reform and opening up

The beginning and breakthrough of reform
Open scope and levels
Key nodes and major decisions

2. Development achievements and experience

Economic growth miracle
Comprehensive social progress
Improvement of overall national strength
Institutional innovation achievements

3. Theoretical contribution and significance

Theory of socialism with Chinese characteristics
The formation of the theory of reform and opening up
Inspiration for world development

4. Challenges and opportunities in the new era

New topics for deepening reform
High-quality development requirements
New journey of modernization construction

Participating Scholars

Experts and scholars from universities and research institutions on both sides of the Taiwan Strait conducted in-depth exchanges and discussions on relevant issues.

Academic value

This seminar provides an important academic exchange platform for scholars from both sides of the Taiwan Strait and promotes systematic understanding and research on the development process of the 40 years of reform and opening up.

International Seminar on China Mainland Reform and Peace Forum

Fri, 19 Oct 2018 09:00:00 +0800

Workshop Overview

On October 19, 2018, participated in an international seminar on the theme of reform and peaceful development in mainland China.

Meeting theme

Focusing on the summary of the experience of reform and opening up in mainland China, the future development path, and the discussion of the peaceful development model.

Participating institutions

Experts and scholars from academia and policy research institutions participated in the discussion.

Main topics

Achievements and experience in 40 years of reform and opening up
Mainland China’s development path in the new era
Peaceful development and international cooperation
Regional development and cooperation opportunities

Meeting results

Through this seminar, we can gain an in-depth understanding of the theory and practice of reform and development in mainland China, and provide a valuable platform for relevant policy research and academic discussions.

Peace Forum International Seminar

Sun, 07 Oct 2018 09:00:00 +0800

Workshop Overview

On October 7, 2018, I participated in the Peace Forum International Seminar hosted by the School of Social Sciences of Chinese Culture University, where I gathered with scholars and experts from home and abroad to discuss major issues of peaceful development.

Organizer

School of Social Sciences, Chinese Culture University

Meeting theme

Peace Forum International Seminar - Discussing peaceful development, regional cooperation and international relations

Main topics

1. Concept of peaceful development

Theoretical basis for peaceful development
The value of peace in international relations
The importance of regional peace and stability
Civilized dialogue and exchanges and mutual learning

2. Regional cooperation mechanism

Regional economic integration
Security Cooperation Framework
Cultural exchange and cooperation
Common development path

3. New trends in international relations

Adjustments and changes in international order
Evolution of major power relations
Multilateralism and cooperation
Globalization and regionalization

4. Peacebuilding Practice

Conflict prevention and resolution
Building peace paths
Humanitarian assistance
International coordination mechanism

5. Future Outlook

Opportunities and challenges for peaceful development
Deepening of regional cooperation
International governance innovation
Building a community with a shared future for mankind

Participating Scholars

Experts and scholars from domestic and foreign academic institutions and research organizations conducted in-depth exchanges on issues related to peaceful development.

Academic significance

This seminar provides an important platform for peaceful development research, promotes in-depth discussions on peace issues in the academic community, and contributes wisdom to the promotion of peaceful development.

An example conference paper

Mon, 01 Jul 2013 00:00:00 +0000

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Dylan Chiang

DC-WeMark: Local-first WeChat public account Markdown typesetting tool

What I am responsible for in the project

Core Innovation

1. Put the typesetting core into WebAssembly

2. Content does not leave the user’s device

3. Compatible output for official account editor

4. Small size, low dependency product architecture

Technical architecture

BOYA: Humanities and Social Sciences AI Research Skills Library

What I am responsible for in the project

Core Innovation

1. Convert research methods into executable workflows

2. Human-in-the-loop Hard Decision Gate

3. Distinguish between bibliographic existence and claim establishment

4. Use real research materials to drive iteration

Project results

WeiShi (未識): AI Relationship Exploration for the Sixth Yunnan-Taiwan University Student Innovation and Entrepreneurship Competition

What I am responsible for in the project

Project Concept

Core process

Core competitiveness and innovation

1. From chat tools to relationship understanding protocols

2. In-depth rhythm of one person per week

3. AI clone that is observable and cannot exceed authority

4. Don’t judge relationships by a single score

5. Turn governance into a product mechanism

Design principles

Paper reading: Capsaicin diet drives gut inflammation and exosomal miR-17-3p elevation in idiopathic short stature

Research background

Research methods

Main findings

1. Capsaicin intake causes growth retardation and intestinal inflammation

2. Exosomal miR-17-3p is a key messenger

3. Molecular mechanism: inhibiting chondrocyte proliferation

Research significance

1. Revealed the new mechanism of the “gut-bone axis”

2. Provides a new etiological explanation for ISS

3. Potential therapeutic targets

My understanding

1. Molecular-level interpretation of “You are what you eat”

2. Growth hormone is not a panacea

3. The collision of traditional wisdom and modern science

Related resources

Paper reading: Exercise is good for the brain but exercising outside is potentially better

Research background

Research methods

Main findings

1. Outdoor sports significantly increase the P300 amplitude

2. The effect of indoor exercise is not as good as expected

3. No significant difference in reaction time

Research significance and inspiration

My understanding

1. Environment is an extension of cognition

2. Redefinition of “efficiency”

3. Simple and easy

Related resources

Paper reading: How people think about being alone shapes their experience of loneliness

Research background

Research methods

Main findings

1. The media’s “high stigma” against solitude

2. The causal impact of beliefs on loneliness

3. The “buffering and catalyzing” effect in daily life

Research significance and inspiration

1. The “echo effect” of public health propaganda

2. New approaches to cognitive intervention

My understanding

1. The self-fulfilling prophecy of cognitive frameworks

2. Media Ecology and Mental Toughness

3. A “moderate turn” in policy

4. Conclusion: Solitude is a kind of self-healing ability

Related resources

Paper reading: Topological turning points across the human lifespan

Research background

Research methods

1. Data analysis scale

2. Manifold Learning

Key findings: Five life stages and four turning points

Detailed description of key findings