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Memories are made by breaking DNA — and fixing it nature.com/articles/d4158…

In a guest post for Understanding AI (Timothy B. Lee), I write about how I got fooled by AI-for-science hype, and what it taught me. I argue that AI is unlikely to revolutionize science, and much more likely to be a normal tool of incremental, uneven scientific progress.

🔥 Time for my first bioML blog post! This one is for all the folks getting into ML-for-protein-design. ✨ "Just know stuff, proteinML edition" kidger.site/thoughts/just-… This is intended as a curriculum-with-context, as a starting point for the field. 1/2

Today, we’re announcing the first major discovery made by our AI Scientist with the lab in the loop: a promising new treatment for dry AMD, a major cause of blindness. Our agents generated the hypotheses, designed the experiments, analyzed the data, iterated, even made figures

Thanks for the invitation Cecile🥂 I'll be sharing our ICML paper MedXpertQA, which introduces our challenging medical benchmark with focused discussions on reasoning assessment. Join us if you're interested in relevant topics!

Drugs currently in clinical trials will likely not be impacted by AI owlposting.com/p/drugs-curren… first 'argument' post ive made in awhile. two months in the making and 4 rewrites and my own opinion being changed in the process of writing it all down thesis: due to [a lot of

It's actually very easy to find examples of AI-enabled medical advances. These people are just very incurious, and self-reporting that they don't think about anything other than scantily clad women on social media.

I am a little late to the party, having just read the FH Robin paper a couple of times myself. I will skip over anything related to whether ROCK inhibitors are actually a novel insight, I don't think that's really that interesting and I don't know enough about optho research to

hello beautiful people of twitter, I've decided to start sharing my notes from reading AI&Bio papers on substack <3 (essentially because reading and writing are great but sadly I am incapable of doing anything without a deadline🥲) open.substack.com/pub/lindsaytts…

New lab preprint! 🚀 Modeling complex data distributions is tough. We designed GDEs, a new framework that tackles this head-on! GDEs generalize across text, images & MANY bio apps (think virtual cells, spatial bio, viral genome tracking). Thread 👇

Interesting paper! I believe the reason why Figure 1 works essentially comes down to a situation we called lucky hit. That is, regardless of the ground truth, most sampled rollouts are wrong and thus receive zero reward — and these zero rewards are actually correct. For example,

🧠 Again! Introducing: MARTI — Multi-Agent Reinforced Training and Inference A unified framework for LLM-based Multi-Agent Systems with centralized interaction & distributed policy training. Supports structured workflows (debate, MoA, chain), custom rewards, and 3rd-party MAS

I had a great film professor who forbade us from speaking badly about our work just before showing it. if we did, we had to start over. I thought this was brilliant and have found it to be applicable to many other areas of life

This year, there have been various pieces of evidence that AI agents are starting to be able to conduct scientific research and produce papers end-to-end, at a level where some of these generated papers were already accepted by top-tier conferences/workshops. Intology’s

wild to me how much of a nothing burger AI has been so far. it’s been 2.5 years and the most tangible effects of AI are solving the monomer protein structure prediction problem, zero-shot binder design to arbitrary receptors, predicting cellular morphology changes under chemical

AI person getting bombarded by math + biology for a week (fully self-imposed)⬇️ open.substack.com/pub/lindsaytts…