Video yükleniyor...

Video Yüklenemedi

Ana Sayfaya Dön

Evo: a foundation model for genomics! Using a well suited architecture, Evo learns from billions of bp of genomic sequence and performs well on several zero-shot prediction tasks on RNA, DNA and protein. Beautiful paper from Brian Hie & Patrick Hsu at Arc Institute

JuliaBauman2's profile picture

Julia Bauman

10,455 subscribers

52,395 görüntüleme • 2 yıl önce •via X (Twitter)

LIVE

Anya Rossi• Live Now

Private livecam show

14 Yorum

Julia Bauman profil fotoğrafı
Julia Bauman2 yıl önce

Preprint link here:

ksminnovation profil fotoğrafı
ksminnovation1 yıl önce

Dr. Tal Patalon explores how AI mega initiatives like the $500B Stargate project & AI-powered genomic analysis by Illumina & NVIDIA are transforming healthcare. @TalPatalon @forbes @edengallery_ #AIinHealthcare #PrecisionMedicine #Multiomics #HealthTech

Matt Durrant profil fotoğrafı
Matt Durrant2 yıl önce

@BrianHie @pdhsu @arcinstitute Thank you for highlighting our work!

Dmitry Penzar profil fotoğrafı
Dmitry Penzar2 yıl önce

@BrianHie @pdhsu @arcinstitute What are you about the fact their model do activity prediction worse than gc-content?

Misha profil fotoğrafı
Misha2 yıl önce

@BrianHie @pdhsu @arcinstitute very good presentation

Ashton C Trotman-Grant profil fotoğrafı
Ashton C Trotman-Grant2 yıl önce

@BrianHie @pdhsu @arcinstitute Yes! Awesome summary Julia. This paper is so cool

Nishant Jha profil fotoğrafı
Nishant Jha2 yıl önce

@BrianHie @pdhsu @arcinstitute Been noodling on a playground for evo here:

ريان profil fotoğrafı
ريان2 yıl önce

@BrianHie @pdhsu @arcinstitute Julia — what do you think about the fact that they claim to generate functional CRISPR-Cas systems without experimental validation?

Julia Bauman profil fotoğrafı
Julia Bauman2 yıl önce

@BrianHie @pdhsu @arcinstitute I think that they’re going to do the experimental validation :)

Dr Rob Leigh profil fotoğrafı
Dr Rob Leigh2 yıl önce

@BrianHie @pdhsu @arcinstitute Paging Dr @Cian_Smyth

Neuropunk profil fotoğrafı
Neuropunk2 yıl önce

@BrianHie @pdhsu @arcinstitute Its about time we move to fully synthetic biology. This is where all the cures, improvements and the most deadly weapons of humanity lie

Ryan Metz profil fotoğrafı
Ryan Metz2 yıl önce

@BrianHie @pdhsu @arcinstitute Great work

Sean Jackewicz, MD 🧬🛠️ profil fotoğrafı
Sean Jackewicz, MD 🧬🛠️2 yıl önce

@BrianHie @pdhsu @arcinstitute Oooooo this could be so cool

Bin Shao profil fotoğrafı
Bin Shao2 yıl önce

Very nice video! I do hope some day people can create a similar one for our language model…

Benzer Videolar

Evo 2, our fully open-source biological foundation model trained on trillions of DNA tokens spanning the entire tree of life, is out in nature today We & the scientific community have done a lot with this Arc Institute NVIDIA model in the last year! 🧵👇
0:50

Evo 2, our fully open-source biological foundation model trained on trillions of DNA tokens spanning the entire tree of life, is out in nature today We & the scientific community have done a lot with this Arc Institute NVIDIA model in the last year! 🧵👇

Patrick Hsu

263,237 görüntüleme • 8 ay önce

🚀 We're thrilled to introduce Orthrus 🧬🐕—a groundbreaking mature RNA foundation model designed to push the boundaries of RNA property prediction! 🔬 What is Orthrus? Orthrus is a Mamba-based RNA foundation model, pre-trained using a novel self-supervised contrastive learning objective with biologically inspired augmentations. It optimizes the similarity between splicing isoforms and orthologous transcripts, capturing functional and evolutionary relationships to enhance mature RNA property prediction accuracy. 📑 Preprint: 💻 Code: 🌐 Project Page: 📦 Model Weights: 🧠 Why Orthrus? Decoding the RNA regulatory code is key to understanding biology, but traditional experimental approaches are slow and costly. Existing genomic foundation models rely on techniques like masked language modeling or next-token prediction, which aren't fully aligned with the complexities of genomic data—leading to suboptimal results. 🌟 Orthrus Highlights: - Biologically-Informed Contrastive Learning 🧪: A novel contrastive learning objective designed specifically for genomics, maximizing similarity between splicing isoforms and orthologous transcripts across species. - Extensive Pre-training 📊: Trained on splicing annotations from 10 species and orthologous alignments from 400+ mammalian species (Zoonomia Project), with a focus on sequences of high functional importance. - Superior Representations🏅: Orthrus outperforms existing genomic models on 5 mRNA property prediction tasks, often surpassing supervised methods with just a simple linear transformation. - Efficiency in Low-Data Settings📉: Orthrus excels in low-data regimes, achieving state-of-the-art results with as few as 45 labeled examples for fine-tuning on RNA half-life prediction. Shoutout to the amazing leading authors Phil (Phil Fradkin) and Ian (Ian Shi)! Also the work is impossible without an outstanding collaboration by Karina (Karin(a) Isaev), Brendan (Brendan Frey) , Quaid (Quaid Morris), Leo J. Lee! Vector Institute University Health Network U of T Department of Computer Science Temerty Centre for AI in Medicine (T-CAIREM) Department of Laboratory Medicine & Pathobiology
0:53

🚀 We're thrilled to introduce Orthrus 🧬🐕—a groundbreaking mature RNA foundation model designed to push the boundaries of RNA property prediction! 🔬 What is Orthrus? Orthrus is a Mamba-based RNA foundation model, pre-trained using a novel self-supervised contrastive learning objective with biologically inspired augmentations. It optimizes the similarity between splicing isoforms and orthologous transcripts, capturing functional and evolutionary relationships to enhance mature RNA property prediction accuracy. 📑 Preprint: 💻 Code: 🌐 Project Page: 📦 Model Weights: 🧠 Why Orthrus? Decoding the RNA regulatory code is key to understanding biology, but traditional experimental approaches are slow and costly. Existing genomic foundation models rely on techniques like masked language modeling or next-token prediction, which aren't fully aligned with the complexities of genomic data—leading to suboptimal results. 🌟 Orthrus Highlights: - Biologically-Informed Contrastive Learning 🧪: A novel contrastive learning objective designed specifically for genomics, maximizing similarity between splicing isoforms and orthologous transcripts across species. - Extensive Pre-training 📊: Trained on splicing annotations from 10 species and orthologous alignments from 400+ mammalian species (Zoonomia Project), with a focus on sequences of high functional importance. - Superior Representations🏅: Orthrus outperforms existing genomic models on 5 mRNA property prediction tasks, often surpassing supervised methods with just a simple linear transformation. - Efficiency in Low-Data Settings📉: Orthrus excels in low-data regimes, achieving state-of-the-art results with as few as 45 labeled examples for fine-tuning on RNA half-life prediction. Shoutout to the amazing leading authors Phil (Phil Fradkin) and Ian (Ian Shi)! Also the work is impossible without an outstanding collaboration by Karina (Karin(a) Isaev), Brendan (Brendan Frey) , Quaid (Quaid Morris), Leo J. Lee! Vector Institute University Health Network U of T Department of Computer Science Temerty Centre for AI in Medicine (T-CAIREM) Department of Laboratory Medicine & Pathobiology

Bo Wang

115,050 görüntüleme • 2 yıl önce

From sequence to structure, in one sentence.⚡ Today’s Agent Spotlight: Boltz-2 Structure Prediction Agent on ASI:One. Built on Massachusetts Institute of Technology (MIT)’s cutting-edge Boltz-2 model and deployed via NVIDIA's API, this agent generates 3D biomolecular structures just from a natural language prompt. Supports proteins, DNA, RNA, ligands, constraints, and even MSA data. Ideal for drug discovery, molecular biology, and biotech research. Try now: Link to Agent: Watch 👇
2:09

From sequence to structure, in one sentence.⚡ Today’s Agent Spotlight: Boltz-2 Structure Prediction Agent on ASI:One. Built on Massachusetts Institute of Technology (MIT)’s cutting-edge Boltz-2 model and deployed via NVIDIA's API, this agent generates 3D biomolecular structures just from a natural language prompt. Supports proteins, DNA, RNA, ligands, constraints, and even MSA data. Ideal for drug discovery, molecular biology, and biotech research. Try now: Link to Agent: Watch 👇

Fetch.ai

14,034 görüntüleme • 1 yıl önce

BREAKING: Gates Foundation Funds First-Ever Creation of 16 Synthetic Viruses Using AI For the first time ever, mad scientists used AI models to design entire viral genomes, physically manufacture them, and turn them into 16 new functional viruses capable of replicating. Project leader Brian Hie, who conceived, designed, and supervised the research, disclosed funding from the Gates Foundation. They used Evo 1 and Evo 2, genomic language models that essentially function like ChatGPT for DNA, predicting nucleotides instead of words and writing complete viral genetic blueprints. Bioterrorists no longer need to imagine a future in which AI can design pathogens from genetic code... the foundational capability has now been revealed in a major scientific journal. AI viral genome design technology should be halted before it falls into the hands of bioterrorists or other criminal actors.
1:36

BREAKING: Gates Foundation Funds First-Ever Creation of 16 Synthetic Viruses Using AI For the first time ever, mad scientists used AI models to design entire viral genomes, physically manufacture them, and turn them into 16 new functional viruses capable of replicating. Project leader Brian Hie, who conceived, designed, and supervised the research, disclosed funding from the Gates Foundation. They used Evo 1 and Evo 2, genomic language models that essentially function like ChatGPT for DNA, predicting nucleotides instead of words and writing complete viral genetic blueprints. Bioterrorists no longer need to imagine a future in which AI can design pathogens from genetic code... the foundational capability has now been revealed in a major scientific journal. AI viral genome design technology should be halted before it falls into the hands of bioterrorists or other criminal actors.

Nicolas Hulscher, MPH

106,896 görüntüleme • 2 ay önce

Meet BFM-Zero: A Promptable Humanoid Behavioral Foundation Model w/ Unsupervised RL👉 🧩ONE latent space for ALL tasks ⚡Zero-shot goal reaching, tracking, and reward optimization (any reward at test time), from ONE policy 🤖Natural recovery & transition
1:17

Meet BFM-Zero: A Promptable Humanoid Behavioral Foundation Model w/ Unsupervised RL👉 🧩ONE latent space for ALL tasks ⚡Zero-shot goal reaching, tracking, and reward optimization (any reward at test time), from ONE policy 🤖Natural recovery & transition

Yitang Li

84,209 görüntüleme • 11 ay önce

Fantastic news for genome engineers: Bridge recombinases work in human cells!!🧬 And they can make really big insertions, deletions, and inversions with impressive efficiency. From Arc Institute Patrick Hsu Nicholas Perry & team
1:43

Fantastic news for genome engineers: Bridge recombinases work in human cells!!🧬 And they can make really big insertions, deletions, and inversions with impressive efficiency. From Arc Institute Patrick Hsu Nicholas Perry & team

Julia Bauman

16,435 görüntüleme • 1 yıl önce

The Wazzler delivered and secured the victory for Team Justin Wong in Evo Connections Rock, Paper, Scissors Presented by AT&T. Thank you to all of competitors and everyone that participated at home.
1:13

The Wazzler delivered and secured the victory for Team Justin Wong in Evo Connections Rock, Paper, Scissors Presented by AT&T. Thank you to all of competitors and everyone that participated at home.

Evo

183,798 görüntüleme • 1 yıl önce

Today we're introducing TRIBE v2 (Trimodal Brain Encoder), a foundation model trained to predict how the human brain responds to almost any sight or sound. Building on our Algonauts 2025 award-winning architecture, TRIBE v2 draws on 500+ hours of fMRI recordings from 700+ people to create a digital twin of neural activity and enable zero-shot predictions for new subjects, languages, and tasks. Try the demo and learn more here:
0:29

Today we're introducing TRIBE v2 (Trimodal Brain Encoder), a foundation model trained to predict how the human brain responds to almost any sight or sound. Building on our Algonauts 2025 award-winning architecture, TRIBE v2 draws on 500+ hours of fMRI recordings from 700+ people to create a digital twin of neural activity and enable zero-shot predictions for new subjects, languages, and tasks. Try the demo and learn more here:

AI at Meta

6,982,833 görüntüleme • 7 ay önce

Excited to release METAGENE-1, a 7B parameter metagenomic foundation model, built to aid in pathogen detection & pandemic monitoring. Pretrained on 1.5 trillion base pairs of DNA/RNA sequenced from wastewater. A collab w/ USC, Prime Intellect, & the Nucleic Acid Observatory. 🧵
0:29

Excited to release METAGENE-1, a 7B parameter metagenomic foundation model, built to aid in pathogen detection & pandemic monitoring. Pretrained on 1.5 trillion base pairs of DNA/RNA sequenced from wastewater. A collab w/ USC, Prime Intellect, & the Nucleic Acid Observatory. 🧵

Willie Neiswanger

13,390 görüntüleme • 1 yıl önce

Introducing Natura-1, nature will's first general foundation model for the physical world. It is a single model that follows plain-language instructions zero-shot and carries them out with dexterous hands. - 50B MoE, pretrained on 1M hours of egocentric human experience. - Drives 20-DoF dexterous hands from plain language, zero-shot, in a 100ms loop. - Scaling human data from ~44K to 1M hours cut action MSE on held-out robot data by 67.7%. - Built on in-house data and compute infra, it handles fine-grained long-horizon tasks and a ~30-minute zero-shot interaction in one continuous take. The general model exists, and it can only get stronger.
3:12

Introducing Natura-1, nature will's first general foundation model for the physical world. It is a single model that follows plain-language instructions zero-shot and carries them out with dexterous hands. - 50B MoE, pretrained on 1M hours of egocentric human experience. - Drives 20-DoF dexterous hands from plain language, zero-shot, in a 100ms loop. - Scaling human data from ~44K to 1M hours cut action MSE on held-out robot data by 67.7%. - Built on in-house data and compute infra, it handles fine-grained long-horizon tasks and a ~30-minute zero-shot interaction in one continuous take. The general model exists, and it can only get stronger.

Ning Ding

21,925 görüntüleme • 18 gün önce

🏗️ Policy Adaptation from Foundation Model Feedback #CVPR2023 Instead of using foundation model as a pre-trained encoder (generator), we use it as a Teacher (discriminator) to tell where our policy did wrong and helps it adapts to new envs and tasks.
1:53

🏗️ Policy Adaptation from Foundation Model Feedback #CVPR2023 Instead of using foundation model as a pre-trained encoder (generator), we use it as a Teacher (discriminator) to tell where our policy did wrong and helps it adapts to new envs and tasks.

Xiaolong Wang

24,413 görüntüleme • 3 yıl önce

‘INSIDIOUS’: Even if you didn’t get the COVID shot, you COULD be producing the spike protein. “We're finding individuals that never received the vaccine that are still producing spike protein and we're finding the messenger RNA in those individuals as well.” Watch today’s episode of ‘Doctors and Scientists’ with Brian Hooker and Polly Tommey on CHDTV ⬇️
1:38

‘INSIDIOUS’: Even if you didn’t get the COVID shot, you COULD be producing the spike protein. “We're finding individuals that never received the vaccine that are still producing spike protein and we're finding the messenger RNA in those individuals as well.” Watch today’s episode of ‘Doctors and Scientists’ with Brian Hooker and Polly Tommey on CHDTV ⬇️

Dr Dave Cartland BMedSc MBChB Ex-MRCGP

40,509 görüntüleme • 11 ay önce

The Segment Anything Model (SAM) by Meta AI is a step toward the first foundation model for image segmentation. SAM is capable of one-click segmentation of any object from photos or videos + zero-shot transfer to other segmentation tasks. Try the demo ➡️
0:51

The Segment Anything Model (SAM) by Meta AI is a step toward the first foundation model for image segmentation. SAM is capable of one-click segmentation of any object from photos or videos + zero-shot transfer to other segmentation tasks. Try the demo ➡️

AI at Meta

186,348 görüntüleme • 3 yıl önce

"I feel the energy from the whole crowd. The whole time I felt people cheering for me." Rivals of Aether II Champion Plub on the Evo Arena environment in the Evo Champions Interviews Presented by AT&T.
0:30

"I feel the energy from the whole crowd. The whole time I felt people cheering for me." Rivals of Aether II Champion Plub on the Evo Arena environment in the Evo Champions Interviews Presented by AT&T.

Evo

23,463 görüntüleme • 4 ay önce

“Another Evo. Another year of greatness. Another year of history.” #Evo2026 is this weekend! Join us tonight at 6PM PDT on Twitch on YouTube, where we’ll be hanging out to talk about this year’s 12 featured tournaments and more of what to expect from the event in the official Evo 2026 Preview Show!
1:08

“Another Evo. Another year of greatness. Another year of history.” #Evo2026 is this weekend! Join us tonight at 6PM PDT on Twitch on YouTube, where we’ll be hanging out to talk about this year’s 12 featured tournaments and more of what to expect from the event in the official Evo 2026 Preview Show!

Evo

21,880 görüntüleme • 4 ay önce

Dialectic 60: Patrick Hsu! Patrick Hsu & Arc Institute are combining biology, engineering, and AI to turn a sci-fi future into reality. Patrick got expelled from college at 13, returned at 15, later worked on early CRISPR breakthroughs, and teamed up with Silvana Konermann & Patrick Collison to found Arc. He works 3 jobs: core investigator at Arc, professor at Stanford, and venture partner at Thrive Capital. For all his intensity and ambition, he carries a lightness and boldness that is contagious. We discuss: - why research is 99% failure and even your best ideas kind of suck - infinite games and speedrunning school - the reach of GLP-1s and power laws in biotech - what makes a good biologist - Arc’s bet on people over projects - lab robots that finally work - AI-designed organisms and new forms of life - why heart disease could go the way of infectious disease - bro science, peptides, and biofuturism - research taste in the age of AI Timestamps: (0:00) Opening Highlights (1:39) Intro: Patrick Hsu (4:29) Start: Boldness, Infinite Games, & Ambition (12:54) GLP-1s and Power Laws in Biotechnology (20:17) Expelled at 13 and Falling for Biology (25:51) What Makes a Good Biologist, Research Taste, and Biology's Data Challenges (37:58) The Arc Model: Full-Stack Science (52:01) Betting on People, Style, & Learning to Simplify (57:22) Perturbation, World Models, and Evo (1:03:27) Virtual Cell Models (1:09:56) Designing Life: From Stem Cells to Genome Writing (1:18:39) Testing, Bro Science, and Human Data (1:24:26) Can It Exist vs. Will It Exist: Science Meets Markets (1:29:59) Biofuturism: Interoception, Peptides, Muscle Modulation, Sleep, and New Forms of Life (1:43:47) Fun, Obsolescence, Going Slow, and the Future Dialectic with Jackson Dahl 60: Patrick Hsu - Speedrunning the Infinite Game - is available on all platforms, including X. Links below.
1:56:11

Dialectic 60: Patrick Hsu! Patrick Hsu & Arc Institute are combining biology, engineering, and AI to turn a sci-fi future into reality. Patrick got expelled from college at 13, returned at 15, later worked on early CRISPR breakthroughs, and teamed up with Silvana Konermann & Patrick Collison to found Arc. He works 3 jobs: core investigator at Arc, professor at Stanford, and venture partner at Thrive Capital. For all his intensity and ambition, he carries a lightness and boldness that is contagious. We discuss: - why research is 99% failure and even your best ideas kind of suck - infinite games and speedrunning school - the reach of GLP-1s and power laws in biotech - what makes a good biologist - Arc’s bet on people over projects - lab robots that finally work - AI-designed organisms and new forms of life - why heart disease could go the way of infectious disease - bro science, peptides, and biofuturism - research taste in the age of AI Timestamps: (0:00) Opening Highlights (1:39) Intro: Patrick Hsu (4:29) Start: Boldness, Infinite Games, & Ambition (12:54) GLP-1s and Power Laws in Biotechnology (20:17) Expelled at 13 and Falling for Biology (25:51) What Makes a Good Biologist, Research Taste, and Biology's Data Challenges (37:58) The Arc Model: Full-Stack Science (52:01) Betting on People, Style, & Learning to Simplify (57:22) Perturbation, World Models, and Evo (1:03:27) Virtual Cell Models (1:09:56) Designing Life: From Stem Cells to Genome Writing (1:18:39) Testing, Bro Science, and Human Data (1:24:26) Can It Exist vs. Will It Exist: Science Meets Markets (1:29:59) Biofuturism: Interoception, Peptides, Muscle Modulation, Sleep, and New Forms of Life (1:43:47) Fun, Obsolescence, Going Slow, and the Future Dialectic with Jackson Dahl 60: Patrick Hsu - Speedrunning the Infinite Game - is available on all platforms, including X. Links below.

Jackson Dahl

40,416 görüntüleme • 16 gün önce