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Researcher with public evidence across AI research, NLP & language.
A general purpose (nothing special) internal OpenAI model solved one of the most famous previously unsolved problems in discrete geometry The solution involved far too many decisions for a human to feasibly explore openai.com/index/model-...
“It's literally the gulag,” one of the employees claims. “You have zero purpose in life all of a sudden, you barely interact with anyone, you just have these tasks every week." www.wired.com/story/mark-z...
wow, the Huggingface guys made an annotated version of Magnifica Humanitas huggingface.co/spaces/socie...
“I don't understand how any of these AI labs are playing this off like this is 'just something that happens.’ It's not. It's negligence.”
2) Characterizing Narrative Content in Web-scale LLM Pretraining Data, by @teagrjohnson.bsky.social, @elliottash.bsky.social, @andrewpiper.bsky.social, @mariaa.bsky.social arxiv.org/abs/2606.19468 Why: annotation and analysis of narrative features across the pretraining data (…
Those “who own our algorithms, who own our cable news networks . . . they and their platforms are engineering our emotions so they can profit off our pain. They are selling us conflict, and they’re calling it connection.” www.ft.com/content/63e8...
jacobin.com/2026/07/ai-n... “AI is a machine that takes good jobs and makes them precarious. It’s a machine for turning stable employment into gig work”
An incomplete list: 1) Explaining Attention with Program Synthesis, by Amiri Hayes, @belindazli.bsky.social and arxiv.org/abs/2606.19317 Why: LMs can generate code that can explain/replace attention heads! 🤯 (Program synthesis 🤝 Interpretability)
Does physical modeling matter or should we just do data-driven stuff? I feel like this paper characterizes things perfectly: arxiv.org/abs/2410.23179. Physics-based modeling is always helpful but the relative gain closes at scale
4) Why Larger Models Learn More: Effects of Capacity, Interference, and Rare-Task Retention arxiv.org/abs/2605.29548
"I do not understand why anyone who believes that AI will eliminate most jobs and much of humanity’s relevance would rush to build that future." Really???? The answer is money about.fb.com/news/2026/08...
Does anyone know why MASS (arxiv.org/pdf/1905.02450) didn't take off, while BART did? (OK maybe no one remembers BART today, but it was popular around 2019-2022)
Tip on reviewing ARR papers in 2026: - Check the references FIRST. I wasted 40 minutes reviewing a paper before realizing the references were LLM-generated.
So much academic writing is bland and uninspiring. And LLMs will make it worse. What your favorite examples of *good* writing?
Excited to teach my first ML class tomorrow!
If we over-rely on LLMs for science, I fear we will remain stuck in the convex hull of what already exists. Is anyone studying this?
I'm so tired of seeing LLM-generated reviews. They just wasting all our time. If this continues, "peer review" will mean nothing
It was a pleasure to moderate the panel at the ACL 2026 SRW: "The next big questions in NLP: What should students work on?" Huge thanks to our panelists, @boydgraber.bsky.social, @igurevych.bsky.social, Preslav Nakov and Yulia Tsvetkov for their thoughtful answers and helpful advice! Takeaways 👇
I wish I could just go to an island, no internet, no deadlines, no AI. Just a library, a coffeeshop, and people to collaborate with.
Claude keeps making the stupidest mistakes, mistakes no person would make. The problem with AI is it’s so hard to form a mental model to predict or understand its failures, the way we do with other people.
I asked GPT 5.6 Sol to solve the Collatz conjecture. Why is it searching the NOAA fisheries website??
It's crazy to think it took only 4 years to go from "LLMs are only good for generating misinformation at scale" to them proving new theorems, writing code, and writing (mostly) accurate literature surveys.
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