Tom Sercu
@TomSercu
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Building @evoscaleai - Frontier AI for biology. Ex-Meta FAIR, Ex-IBM Research. Alum @NYU, @ugent.
New York
Joined February 2012
About 2.5 years ago we started EvolutionaryScale as an AI research company for biology. We got one of the first 1,000+ H100 GPU clusters in the world, built a world class team, and trained frontier AI models - shipped ESM3 and ESM Cambrian for proteins, and way beyond that we’ve
Today CZI is announcing an unprecedented new scientific initiative to build the future of AI-powered biology. I am joining CZI to lead this initiative as Head of Science, and the EvolutionaryScale team is joining forces with Biohub. This is the first large scale scientific
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My main goal here is speed, reducing friction. Shorten the context input-> useful result output cycle
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Bonus points if it automatically adjusts style to the app I'm in (email, slack, document context). Bonus points if I can provide examples of my msg style. Bonus points if I can route to different llms, but I feel like any modern frontier llm + minimal context eng could work.
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AI App request: On laptop, I hit a shortcut. App opens the mic, I talk, screen contents as background context. Send it to LLM to process and insert a processed version of my thoughts where my cursor is. Optionally a temp overlay to iterate together. Who is building this?
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TIL that intelligence likely evolved twice; once in birds and then again in mammals. Key evidence seems to be that distinct cell types and brain regions are used to perform similar computations in both birds and mammals. From Quanta magazine.
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The story behind the proto-transformers, 2yrs before the "attention is all you need" paper came out. It's got many of the key ideas of modern LLMs
Ten years ago in 2015 we published a paper called End-to-End Memory Networks ( https://t.co/dPmf5Fk3FW). Looking back, this paper had many of the ingredients of current LLMs. Our model was the first language model that completely replaced RNN with attention. It had dot-product
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Protein function often depends on protein dynamics. To design proteins that function like natural ones, how do we predict their dynamics? @HWaymentSteele and I are thrilled to share the first big, experimental datasets on protein dynamics and our new model: Dyna-1! 🧵
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🔥 ESM3 on the cover of science 🙏
Researchers have developed a deep learning protein language model, ESM3, that enables programmable protein design. Learn more in this week's issue of Science: https://t.co/PndjVQWjT5
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New Paper Alert! 🚀 We introduce Path Planning (P2), a sampling approach to optimizing token unmasking order in Masked Diffusion Models (MDMs). SOTA results across language, math, code, and biological sequence (Protein and RNA)—all without training. https://t.co/PIsgCS1Hqg 🧵👇
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📢Excited to introduce NanoCas -our new mini CRISPR system that can reach tissues previously out of reach! By shrinking CRISPR to 1/3 its normal size, we can now edit genes in muscle, heart & brain that were difficult to access before. Summary & link to paper:
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This is the first generative protein language model trained on three protein features: sequence, structure, and function. Using GFP as a test case, the model generated a bright functional protein with low sequence homology to known proteins – estimated to be equivalent to a
An #AI model created to design proteins simulates 500 million years of protein evolution in developing a previously unknown bright fluorescent protein. Learn more in a new Science study: https://t.co/klcE9wFRco
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So excited about the progress by EvolutionaryScale with ESM3 now in @ScienceMagazine, ESM Cambrian, and more to be announced. Thrilled to continue our collaboration! https://t.co/RDZ1TXXgdS
We're thrilled to present ESM3 in @ScienceMagazine. ESM3 is a generative language model that reasons over the three fundamental properties of proteins: sequence, structure, and function. Today we're making ESM3 available free to researchers worldwide via the public beta of an API
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Thrilled that the ESM3 paper is now published in @ScienceMagazine! 🌐🧬 We are also announcing public beta availability of the models via the Forge API, including demos to generate proteins and predictions with ESM3 without need to write any code!
We're thrilled to present ESM3 in @ScienceMagazine. ESM3 is a generative language model that reasons over the three fundamental properties of proteins: sequence, structure, and function. Today we're making ESM3 available free to researchers worldwide via the public beta of an API
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Whoa! When a large language of life model generates a protein equivalent to ~500 million years of evolution. @ScienceMagazine
https://t.co/p6GWxC15Zm
@THayes427 @proteinrosh @EvoscaleAI @arcinstitute @UCBerkeley
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Really exciting new series of highly efficient protein language models from @EvoscaleAI
Introducing ESM Cambrian. Unsupervised learning can invert biology at scale to reveal the hidden structure of the natural world. We’ve scaled up compute and data to train a new generation of protein language models. ESM C defines a new state of the art for protein
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Today we’re releasing ESM C 300M, and 600M with open weights. ESM C 6B is available immediately on EvolutionaryScale Forge for academic use, and AWS Sagemaker for commercial use. ESM C will be on NVIDIA BioNemo soon. We’re excited to see what you build with ESM!
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Announcing ESM Cambrian. ESM C defines a new state of the art for protein sequence modeling. ESM C is a drop in replacement for ESM2 with much better performance across the board. ESM C models are available for academic and commercial use to enable scientists and builders.
Introducing ESM Cambrian. Unsupervised learning can invert biology at scale to reveal the hidden structure of the natural world. We’ve scaled up compute and data to train a new generation of protein language models. ESM C defines a new state of the art for protein
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Sry, we forgot our password. PIKA 1.5 IS HERE. With more realistic movement, big screen shots, and mind-blowing Pikaffects that break the laws of physics, there’s more to love about Pika than ever before. Try it.
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An incredible day for our field. Congratulations David Baker, Demis Hassabis, and John Jumper!!
BREAKING NEWS The Royal Swedish Academy of Sciences has decided to award the 2024 #NobelPrize in Chemistry with one half to David Baker “for computational protein design” and the other half jointly to Demis Hassabis and John M. Jumper “for protein structure prediction.”
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