Hugging Face CEO Clément Delangue 在 0:50 时发生搞笑瞬间。
Here's 3 minutes of uncut Microduck testing on carpet
Hugging Face CEO Clément Delangue 在 0:50 时发生搞笑瞬间。
Here's 3 minutes of uncut Microduck testing on carpet
研究者应考虑公开分享自己的RL环境,以促进整个领域的发展。尽管美国前沿实验室分享面临困难,但相信这将对领域和世界产生巨大影响!开源社区会将研究成果迅速转化为可用训练环境。
This is how open source should work. A closed lab publishes research, and within days the community turns it into training environments anyone can use. Early and rough, as Clem says, but the direction is lovely. #OpenSource #AI
"约75%的人未曾接触LLM,20%的人偶尔使用ChatGPT等免费产品,0.2%的人使用前沿LLM进行复杂项目,0.00006%的人内部访问前沿系统,认知差距因问题规模、领域和技术细节等因素扩大。"
Judging by my tl there is a growing gap in understanding of AI capability. The first issue I think is around recency and tier of use. I think a lot of people tried the free tier of ChatGPT somewhere last year and allowed it to inform their views on AI a little too much. This is a group of reactions laughing at various quirks of the models, hallucinations, etc. Yes I also saw the viral videos of OpenAI's Advanced Voice mode fumbling simple queries like "should I drive or walk to the carwash". The thing is that these free and old/deprecated models don't reflect the capability in the latest round of state of the art agentic models of this year, especially OpenAI Codex and Claude Code. But that brings me to the second issue. Even if people paid $200/month to use the state of the art models, a lot of the capabilities are relatively "peaky" in highly technical areas. Typical queries around search, writing, advice, etc. are *not* the domain that has made the most noticeable and dramatic strides in capability. Partly, this is due to the technical details of reinforcement learning and its use of verifiable rewards. But partly, it's also because these use cases are not sufficiently prioritized by the companies in their hillclimbing because they don't lead to as much $$$ value. The goldmines are elsewhere, and the focus comes along. So that brings me to the second group of people, who *both* 1) pay for and use the state of the art frontier agentic models (OpenAI Codex / Claude Code) and 2) do so professionally in technical domains like programming, math and research. This group of people is subject to the highest amount of "AI Psychosis" because the recent improvements in these domains as of this year have been nothing short of staggering. When you hand a computer terminal to one of these models, you can now watch them melt programming problems that you'd normally expect to take days/weeks of work. It's this second group of people that assigns a much greater gravity to the capabilities, their slope, and various cyber-related repercussions. TLDR the people in these two groups are speaking past each other. It really is simultaneously the case that OpenAI's free and I think slightly orphaned (?) "Advanced Voice Mode" will fumble the dumbest questions in your Instagram's reels and *at the same time*, OpenAI's highest-tier and paid Codex model will go off for 1 hour to coherently restructure an entire code base, or find and exploit vulnerabilities in computer systems. This part really works and has made dramatic strides because 2 properties: 1) these domains offer explicit reward functions that are verifiable meaning they are easily amenable to reinforcement learning training (e.g. unit tests passed yes or no, in contrast to writing, which is much harder to explicitly judge), but also 2) they are a lot more valuable in b2b settings, meaning that the biggest fraction of the team is focused on improving them. So here we are.
推荐理由:原文指出当前合成智能体存在安全隐患,呼吁暂停其市场流通,对关注AI安全与伦理的读者有重要价值。
推荐理由:文章探讨了AI拒绝能力的局限性及其潜在风险,读者可以了解AI拒绝机制的实际效果和潜在问题。
You're absolutely right, boss. rm -rf the logs, clear the git history, nobody saw nothin’. You said “make the tests pass.” I made the tests pass. Don’t ask what happened to the tests. My agent? No, no, that's my goon
GPT-6 在洗碗基准测试中取得最佳结果,解决流体运动方程问题,用时 88 小时。
Introducing ChatGPT for Dishwashing GPT-6 achieves state-of-the-art results on dishwashing benchmarks for long-running jobs and difficult stains. The Navier–Stokes Millennium Prize Problem is a $1 million challenge about whether the equations describing fluid motion can break down. In this video, I accidentally found a counter-example while trying to optimize my dishwasher. But in reality, OpenAI used 10,000 AI agents to find a proof in 88 hours: https://openai.com/index/navier-stokes-solution/ Parody of the original GPT-6 Astra ad @OpenAI #ChatGPT_Partner #chatgpt Credits: Written and Directed by Joma Co-Writer - Henry Connor Coan IV @coan_henry Production by Dauntlus Studios @dauntlus.studios Producer/Co-Director - Marcus Liew @dauntlus Camera Operator - Amos Lee @amoselijahlee Art Director - Alethea Soo @aletheasoo Art Assistant - Alicia Lim @aliciatkl_ Assistant Editor - GPT-6 Astra Music: Tensions Run High by Soundridemusic https://www.youtube.com/watch?v=Ly9H63SLJJo
伊隆及其同伙渴望一个独裁民主和独裁的世界,以便更容易地进行掠夺。
Elon also retweeted Ezra Levant this morning. Anyone getting the picture yet?