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10月13日 周二
Hacker News 熱門83

“AI”不是工具——Rich Hickey

https://gist.github.com/svieira/619d60dea942a8d4498cba3bb6b1... 评论网址：https://news.ycombinator.com/item?id=50060269 得分：105 评论数：46

Hacker News AI39

Ask HN: How to slow down AI development speed(also, estimate my ideas)

Hello everybody. So, the first: we should abandon likes and go to top-lists instead of them, and say "this is on first place of importance", "this is third place", and finally we will find the medium: "2nd place in average" . To avoid sybil-attacks and get needed money, we may register proofs that we are really person with this e-mail, date and place of birth, first and last name to charity companies - they may reveal data about you like "this is a unique person", "this is 18+ person" etc., whether we want to deanonymize registered person or not.(Proof of Personhood/PoP + Sovereign Identity?) Then, we should alarm/whistleblow the AI disaster as the most important things in the world* Then, we should watch somehow who uses/trains AI. Some suggestions: OS, providers, antivirus systems. If you're closing this data or openly train AI too fast - Then we will use cryptocurrency of smart-contracts to "sanction" personally - people in your country will never accept money that has ever been into your hands and will buy only goods with mark on it "Income from this product will never be into hands of those who are in not in our white list(detailed citizens) etc., and they will never be into hands of those people are in black list (Tainted Coins Audit?)* Then, we should create IdeaPledge. Manifest is too long and AI-generated(so that I can't post it here), but TL;DR we should stop be such commies on the Internet information, and still not be greedy: we should try to agree with any information producer how much does his information cost. We should add payments like "If you're rich - pay 10x as much of the base cost, if you're poor - for free, but you should prove you studied something". and similar production - we should make internet-economics work not for free and not for corporations, but for world improvement, and ideas how to slow down AI development this way will be much better. P.S. AI mentioned you may be too worried about privacy(personally I don't mind it roughly speaking about myself), so may you suggest any alternatives/trade-offs? --- * We may also add weights to voices * Also, this is a good place for suggesting your dreams about the perfect world. ** Also, we may just boycott a lot of US military this way and economize a lot of money for much more useful purposes Comments URL: https://news.ycombinator.com/item?id=50061674 Points: 3 # Comments: 0

10月12日 周一
The Decoder54

Anthropic's Andrej Karpathy says "the external world has seen the preview" of what AI can actually do

Andrej Karpathy, co-founder of OpenAI and now at Anthropic, sees a widening knowledge gap in AI. About 75 percent of the world's population has barely touched LLMs. Roughly 5,000 people, meanwhile, have access to internal frontier systems whose agent swarms do work that would take top experts years. The public, at best, sees a weird chatbot. The article Anthropic's Andrej Karpathy says "the external world has seen the preview" of what AI can actually do appeared first on The Decoder .

arXiv cs.LG54

Equality, Equity, and Causality in Fairness Research: A Commentary on Cheng (2026)

arXiv:2607.17679v2 Announce Type: replace-cross Abstract: This is an invited commentary on the Psychometrika focus article "Fairness Issues and Evaluation in Psychometrics and AI/ML: What Can We Learn from Each Field?" by Ying Cheng (2026, doi:10.1017/psy.2026.10110). Cheng offers a systematic comparison between long-standing test fairness and modern algorithmic fairness. Her mapping of the entire testing workflow onto the AI/ML fairness paradigm, rather than only the final selection stage, is a crucial contribution to interdisciplinary fairness research. This commentary extends her discussion by examining two conceptual issues: the distinction between equality and equity, and the role of causality in fairness research. The focus article and this commentary point to directions for future fairness research across the psychometrics and AI/ML communities.