Lukas Hafner
@LostInTranscrip
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Biologist lost somewhere between the scales | Biology x ML @TechnionLive previously @BIUPasteur @lpiparis_ @Biologie_UNIGE Opinions are my own& mostly wrong
Haifa, Israel
Joined March 2019
Excited to see our data-to-paper framework out @NEJM_AI ! https://t.co/LEMuAFzP82
ai.nejm.org
Artificial intelligence (AI) promises to accelerate scientific discovery, but it remains unclear whether AI systems can perform fully autonomous research, and whether they can do so while adhering ...
Our data-to-paper AI is officially out! https://t.co/wpKcdAElKr Autonomously analyzes data and writes human-verifiable papers! Fun AI-human copiloting! Papers are "data-chained": click-traced to the raw code. Try it out: https://t.co/yhqDfTj4aB
@TalIfargan @LostInTranscrip
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1/ Do you have a favorite protein you wish you could dissect residue by residue? 🔬 Excited to share our platform for mutational scanning at endogenous loci in yeast (no ectopic expression needed!)
biorxiv.org
Proteins operate through a few critical residues, yet most proteins remain uncharacterized at the deep molecular resolution, particularly within essential genes, where functional dissection is...
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🌎👩🔬 For 15+ years biology has accumulated petabytes (million gigabytes) of🧬DNA sequencing data🧬 from the far reaches of our planet.🦠🍄🌵 Logan now democratizes efficient access to the world’s most comprehensive genetics dataset. Free and open. https://t.co/dDBtAjfdYL
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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?”
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Legitmately thrilled to share our latest work, in which @fernpizza solved an experimental challenge in plasmid biology as old as the field: measuring how plasmids compete and evolve within individual cells!
@baym Here we show that within-cell competition is key to plasmid evolution. Link to the paper! (2/n)
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📢 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
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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 @zakkohane’s special mentoring style.
Dr. @zakkohane discusses the evolving landscape of AI in medicine and shares insights on health care system challenges, the Human Values Project, and his perspectives on the most significant AI developments of 2024. Listen to the full episode: https://t.co/Kv56B1rYJq
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Original Article by @TalIfargan et al.: Autonomous LLM-Driven Research — from Data to Human-Verifiable Research Papers https://t.co/jtJEOwCVO7
#ArtificialIntelligence
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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.
"Merely" having AI oversee study design and manuscript construction (much less than proposed in this article) would reduce errors that have led to irreproducible results and unintentionally misleading figures/tables.
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A study by @TalIfargan 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. https://t.co/jtJEOwCVO7
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Timely discussion about whether/what guidance is needed in straight-to-publication data-analysed-by-AI @NEJM_AI
https://t.co/3HNLOqNgbO Bonus: an Asimov story reference.
ai.nejm.org
In this edition of NEJM AI, Ifargan and colleagues present data-to-paper, an autonomous platform designed to mimic human scientific practice by guiding a large language model through a stepwise res...
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people in academia are way too negative about chatGPT, LLMs, etc.. why are folks so allergic to machine learning?
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using the same color names, comparing different dimensionality reduction algorithms (pca, tsne and umap):
✨How AI sees colors: a color space made from their interconnected linguistic dimensions🌈 I extracted all the words for colors from wiki and mapped their embeddings from llms to a 3D color space, making it a semantic one purely derived from languages of the hive mind. 𓉤
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It's so great to be knitting wire cells again, this time for a 32-cell snowflake yeast colony for @wc_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
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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 a safe bet that there will be a lot more capability (Science Goal/Question->AI->Data->Publication) in the same vein in the near future. Will pose interesting questions for academia and the publication-industrial-conplex immediately.
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🎉 Big news! Our Nucleotide Transformer foundation models for genomics were just published in @naturemethods! 🚀 So proud of this incredible team @instadeepai! ⭐Paper: https://t.co/N66V8Ay2Er 📕Research briefing: https://t.co/jEny6IlIai
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AI to drive to research goals from data to publication? A glimpse of which fraction of biomedical research? @NEJM_AI
ai.nejm.org
Artificial intelligence (AI) promises to accelerate scientific discovery, but it remains unclear whether AI systems can perform fully autonomous research, and whether they can do so while adhering ...
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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)
The people freaking out about AI "destroying education" are telling on themselves. They're so indoctrinated by our Industrial Revolution-era factory model of education that they can't imagine alternatives. But every major technology - from books to electricity to the internet -
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Excited to share our perspective in @CellCellPress, where we discuss “AI scientists” as collaborative AI agents designed to empower biomedical research https://t.co/jfCUoG0d5u While the concept of an “AI scientist” is aspirational, advances in agent-based AI are paving the way
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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!
Our data-to-paper AI is officially out! https://t.co/wpKcdAElKr Autonomously analyzes data and writes human-verifiable papers! Fun AI-human copiloting! Papers are "data-chained": click-traced to the raw code. Try it out: https://t.co/yhqDfTj4aB
@TalIfargan @LostInTranscrip
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