I know it's been lifetimes since they put this out, but what did you think of this press release that seems to have more details? www.anthropic.com/institute/re...
Who's Who of AI
Ben Recht
Why they matter
Researcher with public evidence across AI research, Compute & infrastructure.
- AI signals
- 4 past 30d
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- 3 distinct domains
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- Latest signal
- 3d ago
Blog: https://argmin.substack.com/
Webpage: https://people.eecs.berkeley.edu/~brecht/
The Irrational Decision: How We Gave Computers the Power to Choose for Us. (Princeton) https://press.princeton.edu/books/hardcover/9780691272443/the-irrational-decision
What they're sharing
When AI builds itself anthropic.com
The AI Researcher Who Just Quit Anthropic Says It’s ‘Crunch Time for Humanity’ wired.com
cdn.openai.com
nytimes.com
Dario Amodei — We Must Pace the Frontier darioamodei.com
Are Transformers necessary? A data-centric view on generalization simons.berkeley.edu
What is the objective of reasoning with reinforcement learning? arxiv.org
argmin.net
Reify This theideasletter.org
The Artificiality of Alignment joinreboot.org
Silicon Valley Is Turning Into Its Own Worst Fear buzzfeednews.com
OpenAI’s President Gave Millions to Trump. He Says It’s for Humanity wired.com
Articles & links
It seems brave young Coxon has no insight beyond that of your average Twitter addict. But kudos to him for quitting.
The full commentary from mathematicians asked to review the proof is thoughtful and worth a read: cdn.openai.com/pdf/74c24085...
AI Weekly's analysis
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- An internal OpenAI model produced a proof disproving Erdős's 1946 unit distance conjecture; the paper posted to OpenAI's CDN calls itself a human-verified version of the AI's work.
- The AI's original construction beat Erdős's ceiling by only ε ~ 10^-38; Princeton's Will Sawin later pushed the exponent to roughly n^1.014.
- Scott Aaronson says his former student Lijie Chen 'simply gave GPT the problem' and it returned a several-page argument that held up under human review.
Read full analysis →
I don't understand how people can report on companies complaining about distillation and IP theft without mentioning these companies' entire tech stack depends on distillation and IP theft. It's an important detail!
Oh boy, Dario has put up another insufferable post. darioamodei.com/post/we-must...
Check out this talk by Ludwig Schmidt from a few years ago. The transformer is important not because of some magical geometry fact, but because it's the most efficient at memorizing massive datasets given our current hardware constraints. simons.berkeley.edu/talks/ludwig...
AI Weekly's analysis
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- Ludwig Schmidt's August 2023 Berkeley talk surveys robustness findings from over 200 models to isolate why CLIP holds up under distribution shifts.
- Controlled experiments attribute CLIP's robustness to the pre-training dataset rather than language supervision alone.
- The talk points at LAION-5B and DataComp as data-side efforts, and introduces fine-tuning via weight interpolation.
Read full analysis →
This is probably too mathy, but the first few sections might help?
AI Weekly's analysis
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- A new arXiv note argues several RL algorithms for LLMs with binary rewards are stochastic gradient ascent on a monotone transform of one probability.
- Rejection sampling optimizes the logarithm of that probability; the GRPO algorithm optimizes the arcsine of its square root.
- The two-sentence abstract by Damek Davis and Benjamin Recht was submitted on October 15, 2025.
Read full analysis →
OpenAI’s faster Fourier transform is less a mathematical breakthrough and more a malicious exploit of mathematical traditions.
In _The Ideas Letter_, Leif Weatherby, Tyler Shoemaker, and I wrote about the creation of reality through pseudoscience, religion, and psychosis. Of course, the piece is mostly about AI.
This is very good and was rather prescient: joinreboot.org/p/alignment
Ted Chiang wrote the definitive essay on p(doom) in 2017. It's worth reading again in light of this week's events.
Also www.wired.com/story/openai...
Their own posts
Recent commentary
Mechanistic Interpretability is making the rounds again, so I'm re-upping my ask for a short explainer (under 1,000 words) of how topic modeling is supposed to solve AI safety.
Have to appreciate the audacity of Anthropic defending three successive model releases with diminishing quality by claiming it's both intentional and for our own good.
I'm disappointed that the heated fight between tech bros and medtwitter about midjourney ultrasound snake oil hasn't broken containment over here.
Isn't putting METR & Redwood Research in charge of investigating the OpenAI/Hugging Face incident like putting Irregular in charge of cybersecurity testing?
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