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Today we're releasing the structures of over 600M metagenomic proteins—the least understood proteins on earth. A view into the dark matter of the protein universe. Explore the ESM Metagenomic Atlas here: esmatlas.com

We increase protein family annotation of the Unified Human Gastrointestinal Proteome (UHGP) using DPCfam clustering. Nice effort by our PhD Federico Barone @areasciencepark, Elena Tea alessio ansuini and great collaboration with Marco Punta COSR Lab Preprint: bit.ly/42bV3HY

Open expression of interest for two postdoc positions in #cancer #datascience at Area Science Park and slug trafficking on a joint project led by Giulio Caravagna and me. Join us to develop cool computational methods for single-cell and long-read data! More info👉 shorturl.at/lptNT

We will present our work “The geometry of hidden representations of large transformer models” at #NeurIPS2023 Great effort from Lucrezia Valeriani Diego #fracuturello alessio ansuini at Area Science Park, and great collaboration with Ale Laio @SISSAschool! 📝 arxiv.org/pdf/2302.00294…

Can changes in the geometry of hidden representations of transformers help identify the layers that encode semantic-rich features of data? Join our poster presentation 乳rIPS. Session 4, Poster #506. #deeplearning #AreaSciencePark arxiv.org/abs/2302.00294

We have trained ESM3 and we're excited to introduce EvolutionaryScale. ESM3 is a generative language model for programming biology. In experiments, we found ESM3 can simulate 500M years of evolution to generate new fluorescent proteins. Read more: evolutionaryscale.ai/blog/esm3-rele…

I just landed in Vancouver to present NeurIPS Conference the findings of our new work! Few-shot learning and fine-tuning change the hidden layers of LLMs in a dramatically different way, even when they perform equally well on multiple-choice question-answering tasks. 🧵1/6

🚨🚨 Excited to share our latest paper, now on arXiv.org! 🖼️ We studied how unified VLMs, trained to generate both text and images (e.g., @MetaAI's Chameleon), exchange information between modalities, comparing them to standard VLMs. Deep dive:👇