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AI + lab robotics will change experimental science. 1. Data (both positive and negative results) shared daily among a team of AI agents pursuing different hypothesis 2. Lab robotics running 24/7 with lab experiments written in code that are easily reproducible Human scientists can shepherd the whole process with... show more

Jason Kelly

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22,629 görüntüleme • 4 ay önce •via X (Twitter)

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Awesome being on the Sequoia Capital Training Data podcast with Pat Grady and Sonya Huang 🐥. Topic links in the 🧵 The most human part of intelligence is curiosity, and science is formalized curiosity. If we want AGI — doing the work of science is the training set. Fun to talk history of Ginkgo Bioworks, our project with OpenAI where GPT-5 ran our Autonomous Lab, what new biotech apps are coming, and more. Also — you (or your moltbot) can order experiments from Ginkgo Bioworks Cloud Lab if you don't have your own lab or buy our robots to make your own autonomous lab. Technical details of our OpenAI partnership also in 🧵

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Automating the lab bench is the best thing we can do for AI in biology. Most experiments are still run by hand. Every biologist's handiwork is unique and every lab is a little different. So, biology faces widespread reproducibility issues. But, AI will demand more reproducible data than we can produce, and generate more ideas than we can test. Experiments must be communicated and executed in a standardized way to generate reproducible data. So, Tetsuwan is building a lab where users specify experiments in an exact syntax. These experiments are executed by an automated platform to generate transparent, reproducible output. The user never needs physical access to a lab. This is a biology lab you can use like a computer. Our platform, built and tested with pilot labs over the past two years, lets users configure automated workflows rapidly & precisely. Later this year, we will bring our first services online, focusing on functional screens for protein design. Alex and I met at Caltech, where she was one year my senior. We're building this company because we were little kids who wanted to be biologists that grew up into adults who resented the lab bench. We want biology to be about asking questions, not the painful and frustrating manual process of asking them. For those looking to receive updates on our pilot services, looking for a meaningful job, or just to learn more about our work, see the links in the comments!

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🤖 New Sequoia Capital Training Data pod drop with GitHub CEO Thomas Dohmke! A legendary company now at $2B ARR, with 2-year old Copilot now adopted by 77K+ orgs. CEO Thomas Dohmke chats with me and Sonya Huang 🐥 about: 1⃣Github Copilot and Workspace ushering in a new era of 1B developers 2⃣Agent applications across the developer workflow like code scanning Autofix 3⃣What he's excited about in AI in 1, 5, 10 years from today outside of developers, & more! 🔗👇

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Modern AI is confined to the digital world. At Skild AI, we are building towards AGI for the real world, unconstrained by robot type or task — a single, omni-bodied brain. Today, we are sharing our journey, starting with early milestones, with more to come in the weeks ahead. Our Mission: Artificial General Intelligence grounded in the physical world. We believe AGI that can truly understand and reason in the real world can only be built through grounding in the physical world. Our Vision: Any robot, Any task, One brain. We tackle robotics in its full generality – building a continually improving, omni-bodied brain that can control any hardware for any task. Who are we? A passionate group of scientists & engineers driven by our shared vision. We have been researching AI and robotics for more than a decade. Our team includes pioneers of self-supervised learning, curiosity-driven exploration, end-to-end sim2real for visual locomotion, dexterous manipulation, learning from human videos, robot parkour, and many more. Many of these works have won awards at top-tier AI and Robotics conferences. Our team has also built production-ready systems at Anduril, Tesla, Nvidia, Meta, Kitty Hawk, Google, Everyday Robotics, and Amazon. Join us in our mission to build the robot brains of tomorrow.

Modern AI is confined to the digital world. At Skild AI, we are building towards AGI for the real world, unconstrained by robot type or task — a single, omni-bodied brain. Today, we are sharing our journey, starting with early milestones, with more to come in the weeks ahead. Our Mission: Artificial General Intelligence grounded in the physical world. We believe AGI that can truly understand and reason in the real world can only be built through grounding in the physical world. Our Vision: Any robot, Any task, One brain. We tackle robotics in its full generality – building a continually improving, omni-bodied brain that can control any hardware for any task. Who are we? A passionate group of scientists & engineers driven by our shared vision. We have been researching AI and robotics for more than a decade. Our team includes pioneers of self-supervised learning, curiosity-driven exploration, end-to-end sim2real for visual locomotion, dexterous manipulation, learning from human videos, robot parkour, and many more. Many of these works have won awards at top-tier AI and Robotics conferences. Our team has also built production-ready systems at Anduril, Tesla, Nvidia, Meta, Kitty Hawk, Google, Everyday Robotics, and Amazon. Join us in our mission to build the robot brains of tomorrow.

Skild AI

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Physics AI 🌊 Jensen just unveiled the NVIDIA Isaac GR00T Reference Humanoid Robot at GTC Taipei--the world’s first open-source humanoid robot reference design Built on Jetson Thor edge computing and the Isaac GR00T open platform, it features a Unitree H2 Plus body with Sharpa five-finger dexterous hands and 75 degrees of freedom. Even before Physical AI truly explodes, NVIDIA has built a complete end-to-end full-stack infrastructure: >chips (Jetson Thor + Blackwell), >synthetic data (GR00T-Dreams), >world models (Cosmos), >foundation models (GR00T N-series VLA), >high-fidelity simulation platforms (Isaac Sim + Isaac Lab + Omniverse). Dozens of humanoid robot companies ,including Agility Robotics, Aglie robotics,Boston Dynamics, Figure, 1X, NEURA Robotics, XPENG Robotics, and more are already running large-scale daily simulation training on this platform, rapidly iterating perception, reasoning, and actions in virtual environments, speeding up the jump from lab prototypes to real factory deployment. By providing an open reference design and full-stack tools, NVIDIA has dramatically lowered the barrier, letting every robotics team join the Physical AI wave with low cost and high efficiency.

Physics AI 🌊 Jensen just unveiled the NVIDIA Isaac GR00T Reference Humanoid Robot at GTC Taipei--the world’s first open-source humanoid robot reference design Built on Jetson Thor edge computing and the Isaac GR00T open platform, it features a Unitree H2 Plus body with Sharpa five-finger dexterous hands and 75 degrees of freedom. Even before Physical AI truly explodes, NVIDIA has built a complete end-to-end full-stack infrastructure: >chips (Jetson Thor + Blackwell), >synthetic data (GR00T-Dreams), >world models (Cosmos), >foundation models (GR00T N-series VLA), >high-fidelity simulation platforms (Isaac Sim + Isaac Lab + Omniverse). Dozens of humanoid robot companies ,including Agility Robotics, Aglie robotics,Boston Dynamics, Figure, 1X, NEURA Robotics, XPENG Robotics, and more are already running large-scale daily simulation training on this platform, rapidly iterating perception, reasoning, and actions in virtual environments, speeding up the jump from lab prototypes to real factory deployment. By providing an open reference design and full-stack tools, NVIDIA has dramatically lowered the barrier, letting every robotics team join the Physical AI wave with low cost and high efficiency.

CyberRobo

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If AI scientists are writing millions of papers, many of which are slop, and some of which are incremental progress, how would we identify the one or two which come up with an extremely productive new idea? In 1948, Shannon was one of hundreds of engineers at Bell Labs working on how to cleanly send voice signals over noisy copper wires. His paper sat in the same technical journal as reports on reducing static and building better filters. How would you recognize that he has come up with this very general framework for thinking about information and communication channels, which over the coming decades would have enormous use from domains as far apart as cryptography to genetics to quantum mechanics? It seems like it can take fields multiple decades to recognize the significance of unifying new concepts. Because it is on that time scale that the fruits of such general concepts lead to new discoveries across many different fields. We’ve managed to solve this peer review problem for human scientists (at least somewhat). Now we’ll need to do it at a much greater scale for the mass of AI science that will be thrown at us.

If AI scientists are writing millions of papers, many of which are slop, and some of which are incremental progress, how would we identify the one or two which come up with an extremely productive new idea? In 1948, Shannon was one of hundreds of engineers at Bell Labs working on how to cleanly send voice signals over noisy copper wires. His paper sat in the same technical journal as reports on reducing static and building better filters. How would you recognize that he has come up with this very general framework for thinking about information and communication channels, which over the coming decades would have enormous use from domains as far apart as cryptography to genetics to quantum mechanics? It seems like it can take fields multiple decades to recognize the significance of unifying new concepts. Because it is on that time scale that the fruits of such general concepts lead to new discoveries across many different fields. We’ve managed to solve this peer review problem for human scientists (at least somewhat). Now we’ll need to do it at a much greater scale for the mass of AI science that will be thrown at us.

Dwarkesh Patel

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Google’s Quantum AI Found Something Disturbing in String Theory. The Results Are Unsettling...🧐🤔 Google’s Quantum AI may have just opened a door that scientists weren’t ready for. While running experiments connected to string theory, researchers claim the system uncovered results that were not only unexpected — but deeply unsettling. Could this be the first sign that our understanding of the universe is incomplete? In this documentary-style breakdown, we’ll look at what the AI discovered, why physicists are both excited and disturbed, and how these findings could change the future of science. This is not just theory anymore — it’s a glimpse into something far bigger than we imagined...

Google’s Quantum AI Found Something Disturbing in String Theory. The Results Are Unsettling...🧐🤔 Google’s Quantum AI may have just opened a door that scientists weren’t ready for. While running experiments connected to string theory, researchers claim the system uncovered results that were not only unexpected — but deeply unsettling. Could this be the first sign that our understanding of the universe is incomplete? In this documentary-style breakdown, we’ll look at what the AI discovered, why physicists are both excited and disturbed, and how these findings could change the future of science. This is not just theory anymore — it’s a glimpse into something far bigger than we imagined...

UFO mania

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Microsoft CEO, Satya Nadella: “At Microsoft, more than 60% of our code is already written by AI agents. We are planning to scale it to 2–20 million agents, all running in a loop.” In this 1-hour talk, Microsoft’s CEO discusses the future of agentic AI with the LinkedIn founder. Watch it today, then read how to build a self-improving agentic system in the article below.

Microsoft CEO, Satya Nadella: “At Microsoft, more than 60% of our code is already written by AI agents. We are planning to scale it to 2–20 million agents, all running in a loop.” In this 1-hour talk, Microsoft’s CEO discusses the future of agentic AI with the LinkedIn founder. Watch it today, then read how to build a self-improving agentic system in the article below.

Movez

133,748 görüntüleme • 18 gün önce

A flying robot that moves like a snake in the air. 🐍🤖 This multi-jointed aerial robot can bend its body mid-flight and grasp objects with precision. A new generation of drones is going beyond flying they’re learning how to interact with the world. 🎥 Media: JSK Lab at The University of Tokyo ⚠️ This content is shared for informational purposes only. CTO Robotics Media is a media platform and does not own or develop the technology shown. Credit belongs to the original creators. #Robotics #Engineering #Drone #AI

A flying robot that moves like a snake in the air. 🐍🤖 This multi-jointed aerial robot can bend its body mid-flight and grasp objects with precision. A new generation of drones is going beyond flying they’re learning how to interact with the world. 🎥 Media: JSK Lab at The University of Tokyo ⚠️ This content is shared for informational purposes only. CTO Robotics Media is a media platform and does not own or develop the technology shown. Credit belongs to the original creators. #Robotics #Engineering #Drone #AI

CTO ROBOTICS Media

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In the AI era, the traditional biopharma industry is the underdog. Big tech and AI labs are building wet labs. China has overtaken Europe in molecules produced. But the tools available to the industry discuss science, not do it. The hard problem in AI for science is at the interface between the physical and digital worlds. We built an AI Scientist at that seam. It wires together the digital and physical worlds of R&D. Predictive models, data infrastructure, wet lab execution feed into a single loop that reasons, acts, and improves with every experiment. Our ambition: get molecules to the clinic twice as fast. Last fall I wrote about why biotech needs to be rebuilt for the AI era. Today I'm sharing the next chapter: what the AI Scientist is, a blueprint for how it works, and why even Richard Feynman couldn't hack it in a wet lab.

In the AI era, the traditional biopharma industry is the underdog. Big tech and AI labs are building wet labs. China has overtaken Europe in molecules produced. But the tools available to the industry discuss science, not do it. The hard problem in AI for science is at the interface between the physical and digital worlds. We built an AI Scientist at that seam. It wires together the digital and physical worlds of R&D. Predictive models, data infrastructure, wet lab execution feed into a single loop that reasons, acts, and improves with every experiment. Our ambition: get molecules to the clinic twice as fast. Last fall I wrote about why biotech needs to be rebuilt for the AI era. Today I'm sharing the next chapter: what the AI Scientist is, a blueprint for how it works, and why even Richard Feynman couldn't hack it in a wet lab.

Sajith Wickramasekara

30,646 görüntüleme • 3 ay önce

“You just had a huge bioleak in Seattle” This story highlights a critical point in the Biolab debate. These experiments using vaccine components are going on all over the World without the scrutiny it deserves. Genomics expert Kevin McKernan tells Danny Jones the story about “a lab leak that didn’t hit the radar anywhere”. Kevin McKernan - “some lab was working on a vaccine.. something very similar to Pfizer’s vaccine plasmid... that somehow infected al the lab people.... and then it got out of the lab and infected their housemates...” “It should have been followed up by the CDC” But it wasn’t. GROK summarises the published paper, saying it was “likely from lab aerosol exposure during plasmid work (common in BSL-2 cloning). The plasmid DNA lingered and could transfer home. It was downplayed as a "false positive" issue but highlights real biosafety risks”. This makes it crystal clear... with DNI Tulsi Gabbard just releasing more documents about Biolab funding in Ukraine... it's not just GOF you have to worry about. And “there are hundreds of labs around the World doing those same type of experiments” And this one all went down at a Children’s Research Institute.

“You just had a huge bioleak in Seattle” This story highlights a critical point in the Biolab debate. These experiments using vaccine components are going on all over the World without the scrutiny it deserves. Genomics expert Kevin McKernan tells Danny Jones the story about “a lab leak that didn’t hit the radar anywhere”. Kevin McKernan - “some lab was working on a vaccine.. something very similar to Pfizer’s vaccine plasmid... that somehow infected al the lab people.... and then it got out of the lab and infected their housemates...” “It should have been followed up by the CDC” But it wasn’t. GROK summarises the published paper, saying it was “likely from lab aerosol exposure during plasmid work (common in BSL-2 cloning). The plasmid DNA lingered and could transfer home. It was downplayed as a "false positive" issue but highlights real biosafety risks”. This makes it crystal clear... with DNI Tulsi Gabbard just releasing more documents about Biolab funding in Ukraine... it's not just GOF you have to worry about. And “there are hundreds of labs around the World doing those same type of experiments” And this one all went down at a Children’s Research Institute.

Humanspective

18,145 görüntüleme • 1 ay önce

How can generative AI and Robotics help advance drug discovery？ 🚀 Excited to introduce LUMI-lab! A foundation model-driven Self-Driving Lab (SDL) for autonomous ionizable lipid discovery in mRNA delivery 🤖🔍 🔬 What is LUMI-lab? LUMI-lab integrates molecular foundation models with autonomous robotic experiments to efficiently explore new LNPs (lipid nanoparticles, mRNA delivery vehicles) with minimal wet-lab data. 🔥 Key Highlights: - 🧠 Foundation model trained on 28M molecules using a three-step strategy: - Unsupervised pretraining to capture broad molecular knowledge - Continual pretraining to specialize in lipid-like molecules - Active learning fine-tuning within a closed-loop experimental system - 🤖 1,700+ new LNPs synthesized & tested across 10 iterative cycles - 🧪 Brominated lipids autonomously identified as a novel structural feature that enhances mRNA transfection—an insight previously unrecognized in LNP design - 🏆 20.3% in vivo CRISPR gene editing efficiency in lung epithelial cells—the highest reported for inhaled LNPs 🚀 Why it matters? LNPs are the backbone of mRNA therapeutics, yet discovery has been slow due to data scarcity. LUMI-lab shows that AI-powered autonomous labs can accelerate mRNA delivery innovation🚀💡 🌐 Beyond mRNA drugs, LUMI-lab exemplifies a scalable framework for AI-driven molecular discovery, pushing boundaries in material science & drug delivery. 📜 Read the preprint: 🔗 💻 Code available on GitHub: 🔗 #AI #DrugDiscovery #mRNA #LNP #SyntheticBiology 🙏 A huge team effort behind this work, with special appreciation to Bowen LI for driving the project. Kudos to Haotian Cui, @YueXu1995, Kuan Pang, Gen Li, and Bettycat567!

How can generative AI and Robotics help advance drug discovery？ 🚀 Excited to introduce LUMI-lab! A foundation model-driven Self-Driving Lab (SDL) for autonomous ionizable lipid discovery in mRNA delivery 🤖🔍 🔬 What is LUMI-lab? LUMI-lab integrates molecular foundation models with autonomous robotic experiments to efficiently explore new LNPs (lipid nanoparticles, mRNA delivery vehicles) with minimal wet-lab data. 🔥 Key Highlights: - 🧠 Foundation model trained on 28M molecules using a three-step strategy: - Unsupervised pretraining to capture broad molecular knowledge - Continual pretraining to specialize in lipid-like molecules - Active learning fine-tuning within a closed-loop experimental system - 🤖 1,700+ new LNPs synthesized & tested across 10 iterative cycles - 🧪 Brominated lipids autonomously identified as a novel structural feature that enhances mRNA transfection—an insight previously unrecognized in LNP design - 🏆 20.3% in vivo CRISPR gene editing efficiency in lung epithelial cells—the highest reported for inhaled LNPs 🚀 Why it matters? LNPs are the backbone of mRNA therapeutics, yet discovery has been slow due to data scarcity. LUMI-lab shows that AI-powered autonomous labs can accelerate mRNA delivery innovation🚀💡 🌐 Beyond mRNA drugs, LUMI-lab exemplifies a scalable framework for AI-driven molecular discovery, pushing boundaries in material science & drug delivery. 📜 Read the preprint: 🔗 💻 Code available on GitHub: 🔗 #AI #DrugDiscovery #mRNA #LNP #SyntheticBiology 🙏 A huge team effort behind this work, with special appreciation to Bowen LI for driving the project. Kudos to Haotian Cui, @YueXu1995, Kuan Pang, Gen Li, and Bettycat567!

Bo Wang

27,626 görüntüleme • 1 yıl önce

The AI-first rocket scientist. Founder of Plasmos Inc Ali Baghchehsara takes us into his lab. These are engines that move satellites around in space. He came up with a design that joins both an electric as well as gas fired. So it can move both efficiently and quickly with less weight, size, and cost. I wonder what Steve Jurvetson thinks (he is the most into space person I have met, early investor in SpaceX and has a museum of space artifacts). I'm working with some friends to turn this kind of thing into an AI-first community and video show that I call "Unaligned." Who else should I interview that is using AI first in their work? It's OK if the others aren't rocket scientists.

The AI-first rocket scientist. Founder of Plasmos Inc Ali Baghchehsara takes us into his lab. These are engines that move satellites around in space. He came up with a design that joins both an electric as well as gas fired. So it can move both efficiently and quickly with less weight, size, and cost. I wonder what Steve Jurvetson thinks (he is the most into space person I have met, early investor in SpaceX and has a museum of space artifacts). I'm working with some friends to turn this kind of thing into an AI-first community and video show that I call "Unaligned." Who else should I interview that is using AI first in their work? It's OK if the others aren't rocket scientists.

Robert Scoble

516,855 görüntüleme • 3 yıl önce