Jake Feala
@FealaJake
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Automating the loop of science. Co-founder @LilaSciences.
Joined July 2020
My biggest career aspiration is to lock an ML model in a room with lab automation and a problem (new drug target, novel molecule, engineered cell), turn off the lights, and come back in a month to gather the solution. 1/3.
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RT @OptimalBayes: @Sara_Imari Causation is just an arbitrary line we draw when a model provides highly accurate predictive & explanatory po….
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Mining targets in the early days of The Cancer Genome Atlas was a highlight of my career.
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RT @wasserstein_rao: Automating experiments with AI is going to be really hard because automation is way less about high-dimensional Bayesi….
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RT @US_FDA: In a historic first for the agency, @DrMakaryFDA today announced an aggressive timeline to scale use of artificial intelligence….
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Take online company reviews with a grain of salt (my review of glassdoor and reddit reviews). Link 👇.
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RT @OptimalBayes: @Sara_Imari This is just not true. Automating science is easy in theory, just hard in practice. In theory, all we have to….
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Deep Research has become my personalized content generator. Thought I'd start sharing some interesting ones.
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Gospel. Lila was built on the power of closed-loop workflows in science.
Progress in biology is too slow. Even simple experiments often take several days of work to complete. Software, by contrast, compiles in seconds. It's what makes programming "flow-inducing.". We need faster feedback loops in biology. Our latest article explains where to start🔻
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Wolfram suggests some systems are “computationally irreducible,” i.e. you can only get the complete solution through full simulation. I’d love to know if some subsystems (obviously not all) in biology are *fundamentally* irreducible and therefore we can only hope to either.
obviously we know a ton about biology, and obviously there is a ton we don't understand. But what is the ratio?.Are we almost there in our quest to understand cell behavior? Or have we landed on tiny islands of comprehensibility among a vast sea of irreducible complexity?.
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RT @koeng101: I saw this paper, and several people sent it to me. Quite similar to what I've been doing for a while, so I wanted to give my….
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RT @jrkelly: @FealaJake Vast sea still I think, certainly for higher organisms. Maybe there is hope in microbes.
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obviously we know a ton about biology, and obviously there is a ton we don't understand. But what is the ratio?.Are we almost there in our quest to understand cell behavior? Or have we landed on tiny islands of comprehensibility among a vast sea of irreducible complexity?.
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then again, CS majors get to learn about superposition, eigenvectors, and high-dimensional spaces in their classes. Biology learns "pleiotropy" and "polypharmacology" but those terms don't bring a deep intuition about how systems work.
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Ironically, biology needs to re-learn from CS/tech that complex systems need to be poked and prodded as a whole, intact gooey blob – not broken into a million parts to understand each one's special function.
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Science is a never-ending wheel of creating models and refining them via experimentation. AI will revolutionize science not as an all-knowing oracle, but by accelerating turns of this entire wheel.
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RT @kulesatony: 🚀 Fellowship applications are OPEN for Encode: AI for Science. What if you could use AI to.- Design shape-shifting robots….
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I'm representing Lila at GTC today from 2p-7p. Stop by our booth and chat about autonomous science!.
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