Excited to share our perspective in Cell, where we discuss “AI scientists” as collaborative AI agents designed to empower biomedical research cell.com/cell/fulltext/… While the concept of an “AI scientist” is aspirational, advances in agent-based AI are paving the way

Congratulations, Tal Ifargan, Lukas Hafner, Roy Kishony, and team! This is incredibly exciting work! As AI continues to transform every aspect of research, this couldn't be more timely.The last step? Engineering some(thing)one to collect the data at the bench, maybe soon? :D

I think this is so amazing. The way Roy explained this aspect of his work to me is that he’s making the slow parts of science fast - I agree and think it’s a major contribution, in the future we may all have more time for the fun parts of science including mentoring!

AI to drive to research goals from data to publication? A glimpse of which fraction of biomedical research? NEJM AI ai.nejm.org/doi/10.1056/AI…

Agree with this and it’s not so different from the folks who complain AI will break something in science or the practice of it (usually something that’s already deeply broken or aggressively suboptimal)

🎉 Big news! Our Nucleotide Transformer foundation models for genomics were just published in Nature Methods! 🚀 So proud of this incredible team InstaDeep! ⭐Paper: go.nature.com/3OA7dWr 📕Research briefing: go.nature.com/3BbSPQY

How will AI reshape the way we publish and read papers? In 30 years, will there be such a thing as "a paper"? How will academia deal with it?

It's so great to be knitting wire cells again, this time for a 32-cell snowflake yeast colony for Will Ratcliff! 🦠 What a beautiful model organism that Will's lab uses to study the evolution of multicellularity. Stay tuned to see the sculpture grow! 😉 #sciart #snowflakeyeast

using the same color names, comparing different dimensionality reduction algorithms (pca, tsne and umap):

people in academia are way too negative about chatGPT, LLMs, etc.. why are folks so allergic to machine learning?

Timely discussion about whether/what guidance is needed in straight-to-publication data-analysed-by-AI NEJM AI ai.nejm.org/doi/full/10.10… Bonus: an Asimov story reference.

A study by Tal Ifargan and colleagues demonstrates a potential for AI-driven acceleration of scientific discovery in biomedical research and beyond, while enhancing, rather than jeopardizing, traceability, transparency, and verifiability. nejm.ai/4f8JAPA

Indeed, mistakes in research are inevitable, but the key is to leverage AI to address them more effectively and expose them transparently. We should harness AI to make science more reproducible and verifiable.

Original Article by Tal Ifargan et al.: Autonomous LLM-Driven Research — from Data to Human-Verifiable Research Papers nejm.ai/4f8JAPA #ArtificialIntelligence

Listen to this one to hear a debate about some of the best medical AI papers of the past few years, lots of laughter, and a rare long-form glimpse into Isaac Kohane’s special mentoring style.

We’re releasing Humanity’s Last Exam, a dataset with 3,000 questions developed with hundreds of subject matter experts to capture the human frontier of knowledge and reasoning. State-of-the-art AIs get <10% accuracy and are highly overconfident. pak.ai Scale.ai

📢 First paper from my Postdoc is out. The field of stem cell-based embryo models is flourishing. These models mimic critical stages of embryo development, providing powerful tools to study processes that are tricky to dissect in natural embryos. Many approaches are being used to

Legitmately thrilled to share our latest work, in which Fernando Rossine solved an experimental challenge in plasmid biology as old as the field: measuring how plasmids compete and evolve within individual cells!

How do we build AI systems that enable deeper, not just faster, science? I came across a very thought-provoking article by @nisheethvinoi on “What Counts as Discovery?”