Lukas Hafner

Biologist lost somewhere between the scales | Biology x ML
@TechnionLive
previously @BIUPasteur @lpiparis_ @Biologie_UNIGE

Opinions are my own& mostly wrong

[Lukas Hafner @LostInTranscrip]

Excited to see our data-to-paper framework out @NEJM_AI !

https://t.co/LEMuAFzP82

[Roy Kishony @RoyKishony]

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

[Oded Rechavi @OdedRechavi]

Most experiments fail, and negative results rarely get published. This means LLMs are unaware of the outcomes of most experiments.

[Lukas Hafner @LostInTranscrip]

@caldarellip1 Interesting. But I think the capacity for direction and responsibility will be just completely dependent on the ability of being able to judge in the first place.

Also, to what degree can taste be learned without the need to execute (as it is so cheap)?

[Lukas Hafner @LostInTranscrip]

With AI getting better and better: will "taste" become the "key" human skill; e.g. the sense of knowing what is "good" and/or "true"?

[Umberto Aiello @UmbertoAiello]

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!) https://t.co/hCs3TGrfay

[Rayan Chikhi @RayanChikhi]

🌎👩‍🔬 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

[Yunha Hwang @Micro_Yunha]

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?”

[Michael Baym @baym]

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!

[Paolo Caldarelli @caldarellip1]

📢 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? 👇
https://t.co/MlJ8PUzlSl

[Dan Hendrycks @hendrycks]

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

[Arjun (Raj) Manrai @arjunmanrai]

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.

[NEJM AI @NEJM_AI]

Original Article by @TalIfargan et al.: Autonomous LLM-Driven Research — from Data to Human-Verifiable Research Papers https://t.co/jtJEOwCVO7

#ArtificialIntelligence

[Tal Ifargan @TalIfargan]

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.

[NEJM AI @NEJM_AI]

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

[Isaac Kohane @zakkohane]

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.

[Andrew Kern @pastramimachine]

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

[Lukas Hafner @LostInTranscrip]

@yoginho @pastramimachine I have the impression that already before AI, we had an issue with a "deluge of crap". To filter out the good, verifiable and meaningful content is one of the major challenges we face today in science - independent if it is coming from an AI or a human

[Lukas Hafner @LostInTranscrip]

@pastramimachine Also, there is the other misconception that AI necesseraly means that no human is involved anymore. But AI-human copiloting can be actually both be fun and powerful

[Lukas Hafner @LostInTranscrip]

@pastramimachine Maybe bc most scientists think that the use of AI in science must jeopardize key scientific values like verifiability? In data-to-paper, we tried to use AI to enhance them instead: https://t.co/LEMuAFzP82

[Lukas Hafner @LostInTranscrip]

@ipfconline1 @karinv @ConversationEDU @jblefevre60 @Khulood_Almani @ahier We also built a (bit different) AI scientist system:
https://t.co/LEMuAFzP82

The mentioned risks can (and should!) be mitigated, as we argue, by using AI in a transparent, verifiable and traceable manner

(+ many issues are not inherent to AI, but to the publication "industry")