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
We speak at ≈ 8k words per hour. 300b tokens ≈ 230b words ≈ 10'000 years speaking 8h per day.
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re "disclosing AI use in papers" discussion that re-emerges: please lets not do it as a reviewer or a reader of a paper, i do not care what part of the ideation, research, proof, implementation or writing was done by AI, and how the credit is split between the AI and human author. at the end of the day, humans sign on the resulting paper, and this is all i care about. that they stand behind the text they produced. i don't care if the idea is theirs or came from the AI or is a shared process. i do care that they found it compelling enough to publish. in any case i don't trust the authors to accurately describe how the idea evolved between them and the AI: even if they do try and be honest, i think in most cases its a complex process the author themselves don't fully understand. when i work with a student, i certainly do want them to explain how they used AI and which parts of the work, idea or argument are primarily AI driven. but this is in order to guide and improve the student's work and process, not because i care about the credit assignment. it's part of their training and my mentoring, and should not leak into the final work. the readers and reviewers should not be bothered with it. so please. let just have people submit work without the silly AI disclosures, and be very trigger-happy on rejecting papers that are clear AI slop, or that are hard to evaluate due to what may be improper AI use. (in the short term, we will likely see an increase in conference submissions that have the same idea, coming from different authors having a similar process with a shared AI. i think this will sort itself in the long run by reducing the credit assigned to such works in the community, but in the short term we may want the conference management software to actively look for such similar submission, and maybe review them in bulk)
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Lukas Hafner retweeted
This is so inginius (and I am worried likely true): "in the future our entire work will just be to prove we are human". What a cool and scary idea; perfect for a Black Mirror episode!
Replying to @netflix
@netflix 1/ Black Mirror episode pitch: In the future, AI does all the work. Humans just live off universal basic income. Except there's one taks left: proving you're not a robot. And it's slowly eating people's entire lives (if you have a phone, maybe you can relate!)
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Lukas Hafner retweeted
Most experiments fail, and negative results rarely get published. This means LLMs are unaware of the outcomes of most experiments.
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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!) doi.org/10.1101/2025.09.09.6…
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Lukas Hafner retweeted
🌎👩‍🔬 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. doi.org/10.1101/2024.07.30.6…
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Lukas Hafner retweeted
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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📢 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 investigate them—what was ours? 👇 nature.com/articles/s41467-0…
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Lukas Hafner retweeted
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. @ai_risk @scaleai
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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: nejm.ai/ep26
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Lukas Hafner retweeted
Original Article by @TalIfargan et al.: Autonomous LLM-Driven Research — from Data to Human-Verifiable Research Papers nejm.ai/4f8JAPA #ArtificialIntelligence
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Lukas Hafner retweeted
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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Lukas Hafner retweeted
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. nejm.ai/4f8JAPA
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Lukas Hafner retweeted
Andrew Kern@pastramimachine7 Dec 2024
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 #aiart #llm #touchdesigner #datavisualization #dataviz #audiovisual
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Lukas Hafner retweeted
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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🎉 Big news! Our Nucleotide Transformer foundation models for genomics were just published in @naturemethods! 🚀 So proud of this incredible team @instadeepai! ⭐Paper: go.nature.com/3OA7dWr 📕Research briefing: go.nature.com/3BbSPQY
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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 - has transformed how humans learn. If AI can do your homework, maybe homework isn't how we should measure learning anymore. The goal of education is to create lasting cognitive change - literally rewiring neural pathways as we master reading, writing, and analytical thinking. Our current system of standardized assignments and generic grading is just one deeply flawed path to that destination. When someone says "AI is great for learning but will destroy education," what they're really admitting is that our education system is broken and desperately needs disruption. The fact that so many educators can't envision alternatives proves how effectively this system has educated the creativity and vision right out of us. Time to break free.
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