Our paper[1] is on the cover[2] of Nature! Thank you @Nature, for featuring our work on the cover and @MKrzywinski, for the cover art. [1] Paper: https://t.co/5BuxYHByXV [2] Cover: https://t.co/2KoVM6ayBc

Read more about AI co-scientist here, including novel and useful medical insights it has already discovered! Better understanding the universe around us has always been my passion and primary motivation for building AI - a dream that now feels very close.

Hypothesis generation and testing is a critical capability for AGI imo. Super excited about our AI co-scientist and other AI for Science work which are important steps towards that. We're on the cusp of an incredible new golden age of AI accelerated scientific discovery.

Great work with a team across @GoogleAI @GoogleDeepMind @googlecloud, @Mysiak @vivnat @alan_karthi Alexander Daryin Annalisa Pawlosky @taotu831 @ynhnx @_e_d_v_ @OpsBug @apalepu13 @KhaledSaab11 @RyutaroTanno @AvinatanH48021 @pushmeet @ymatias and more.

Excited to see the AI co-scientist unveiled! This system of multiple Gemini-powered agents collaborates to propose novel scientific hypotheses (some of these have already been validated), showcasing AI's potential in Science. Read more in the technical papers listed on our blog.

We are starting to see what "AI will accelerate science" actually looks like. This Google paper describes novel discoveries being made by AI working with human co-scientists (something I think we have all been waiting to see), along with an early version of an AI scientist.

Introducing our AI co-scientist, a multi-agent AI system built with Gemini 2.0. We think of it as a virtual collaborator for scientists, using advanced reasoning to synthesize a huge amount of literature, generate novel hypotheses, and suggest detailed research plans. We’re

Thank you so much, @vivnat!

I am convinced if we get this right and do it well, such AI systems like the AI co-scientist will have a profound impact on humanity. Together with a stellar team across @GoogleAI, @GoogleDeepMind and @googlecloud. Its been an immense pleasure to work with such incredible, kind

Google builds AI ‘co-scientist’ tool to speed up research

Today we introduce an AI co-scientist system, designed to go beyond deep research tools to aid scientists in generating novel hypotheses & research strategies. Learn more, including how to join the Trusted Tester Program, at https://t.co/1eqmTTZOLr

Directed evolution is key for unlocking new protein function But is difficult and time consuming So how can we accelerate protein design by 10-100x? With AI! Now introducing EVOLVEpro, an LLM-based model for evolving proteins rapidly and efficiently https://t.co/aYj25y6hpQ

Excited to share Tx-LLM, a Large Language Model for Therapeutics. Developing therapeutics is a long road, and we're excited about the possibility of AI to contribute. https://t.co/8p4ACCpDB5

@IAmBiotech #BIO2024 Using AI/ML for Identification and Optimization of Therapeutic Molecules Michal Preminger @mpreminger Justin Scheer @JNJInnovation Joe McDonald @Odyssey_Tx Mark DePristo @MarkDePristo @BigHatBio Eeshit Dhaval Vaishnav @_e_d_v_ @Sequome Jim Edwards https://t.co/BPtj78G1Dk

@GaryMarcus @anshulkundaje @BoWang87 @manoliskellis Building sequence-to-gene expression models that decipher the regulatory evolution of yeast, with deep learning: https://t.co/leseuW3PlT

Tune in🎙: https://t.co/M3Bk2RYTGx Spotify: https://t.co/0fiATaSv3N Apple: https://t.co/5W2ZJO9tXj Google: https://t.co/XB40a8Lrah

@Nature cover: https://t.co/M0LP6jEVpn Thank you everyone for your kind words and wishes. [n/n]

Today's @nature cover on "oracle," the deep neural network that predicts gene expression from a promoter DNA sequence, trained by tens of millions, paper by @_e_d_v_ and colleagues, link above Art by @MKrzywinski

Neural networks that accurately predict expression from promoter sequences allows promoter engineering and simulation studies of evolution. @_e_d_v_ @JenniferMolinet @MoranYassour @XAdiconis @jzlevin @FcocubillosR @CarldeBoerPhD https://t.co/q2oopYVtu6

Over the past year, life science is getting transformed by #AI: —Accurately predicting protein structure from amino acid sequence —Accurately predicting RNA structure —Step closer to predicting gene expression from DNA sequence (this week) https://t.co/XXZbhdDAtJ @_e_d_v_ @MIT