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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...

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Jason Kelly

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22,629 просмотров • 6 месяцев назад •via X (Twitter)

ОбразованиеНаука и технологии
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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 🧵
1:00

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 🧵

Jason Kelly

12,691 просмотров • 6 месяцев назад

We raised a $40M Series A to build the automated lab for agentic biology. Last week, we shared the work we did with Paul Jankura: Claude designed proteins, sent them to our automated wet lab, and got real experimental data back. We believe this is where biology is going: AI agents designing experiments, running them in automated wet labs, learning from the results and iterating. But AI can only move as fast as the experiments behind it. To use the potential of AI to cure all diseases, we need to build high-throughput wet lab infrastructure: a “biological gigafactory”. That’s exactly what we’re doing at Adaptyv. Over the past year we’ve grown our lab throughput by over 5x and onboarded more than 100 customers, ranging from bio AI labs like Chai Discovery and Boltz to pharmas like Roche and Novo Nordisk to dozens and dozens of new startups that use AI to radically speed up drug discovery.
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We raised a $40M Series A to build the automated lab for agentic biology. Last week, we shared the work we did with Paul Jankura: Claude designed proteins, sent them to our automated wet lab, and got real experimental data back. We believe this is where biology is going: AI agents designing experiments, running them in automated wet labs, learning from the results and iterating. But AI can only move as fast as the experiments behind it. To use the potential of AI to cure all diseases, we need to build high-throughput wet lab infrastructure: a “biological gigafactory”. That’s exactly what we’re doing at Adaptyv. Over the past year we’ve grown our lab throughput by over 5x and onboarded more than 100 customers, ranging from bio AI labs like Chai Discovery and Boltz to pharmas like Roche and Novo Nordisk to dozens and dozens of new startups that use AI to radically speed up drug discovery.

Adaptyv Bio

181,592 просмотров • 27 дней назад

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!
0:59

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!

Cristian Ponce

1,140,918 просмотров • 2 месяцев назад

Introducing the USC Physical Superintelligence (PSI) Lab ( We are rebranding to better reflect our current focus. From here on out, we are tackling one thing: solving robotics and physical intelligence with every model, every bug, and every line of code. And yes, we are hiring at all levels, especially PhDs in this cycle and potential PostDocs who are excited about robotics. We hope you can join us in this journey! 1/9
4:14

Introducing the USC Physical Superintelligence (PSI) Lab ( We are rebranding to better reflect our current focus. From here on out, we are tackling one thing: solving robotics and physical intelligence with every model, every bug, and every line of code. And yes, we are hiring at all levels, especially PhDs in this cycle and potential PostDocs who are excited about robotics. We hope you can join us in this journey! 1/9

Yue Wang

25,715 просмотров • 9 месяцев назад

Today, we're delighted to announce Phylo's partnership with Ono Pharma to deploy Biomni Lab to its discovery scientists. At Phylo, we believe agentic AI will fundamentally change how new medicines are discovered. Ono is a global leader in innovative drug discovery with a rich history of scientific leadership. Together, we will embed AI agents throughout the research process, accelerating the path from complex questions to scientific discoveries. We're honored to partner with Ono on this transformation. Read more:
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Today, we're delighted to announce Phylo's partnership with Ono Pharma to deploy Biomni Lab to its discovery scientists. At Phylo, we believe agentic AI will fundamentally change how new medicines are discovered. Ono is a global leader in innovative drug discovery with a rich history of scientific leadership. Together, we will embed AI agents throughout the research process, accelerating the path from complex questions to scientific discoveries. We're honored to partner with Ono on this transformation. Read more:

Kexin Huang

20,361 просмотров • 1 месяц назад

AI Scientists are starting to actually do science. Not just answer questions. Not just run workflows. Introducing AutoScientists: a decentralized team of AI agents that can generate hypotheses, design experiments, write code, test ideas, analyze failures, and revise strategy as evidence accumulates. Because real research is not a to do list of tasks. It is a living search process. Leads emerge, failures matter, teams form around what works, and priorities shift when evidence changes. Much like how a lab of scientists would work on cutting edge research together. Across GPT training optimization, biomedical ML, and protein fitness prediction, this decentralized structure consistently does better research. Learn more 👇 Shanghua Gao Marinka Zitnik Kempner Institute at Harvard University DBMI at Harvard Med Harvard University
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AI Scientists are starting to actually do science. Not just answer questions. Not just run workflows. Introducing AutoScientists: a decentralized team of AI agents that can generate hypotheses, design experiments, write code, test ideas, analyze failures, and revise strategy as evidence accumulates. Because real research is not a to do list of tasks. It is a living search process. Leads emerge, failures matter, teams form around what works, and priorities shift when evidence changes. Much like how a lab of scientists would work on cutting edge research together. Across GPT training optimization, biomedical ML, and protein fitness prediction, this decentralized structure consistently does better research. Learn more 👇 Shanghua Gao Marinka Zitnik Kempner Institute at Harvard University DBMI at Harvard Med Harvard University

Ada Fang

74,186 просмотров • 3 месяцев назад

AI has solved software. Biology is the next frontier. We're hiring across every team at Adaptyv. We’ve built the best automated lab for protein designers to experimentally test their AI-designed proteins. Today, the most advanced protein design companies run their wet-lab work on Adaptyv, from the biggest biopharmas to frontier AI labs to dozens of virtual biotech startups. Demand has grown faster than we have, so we’re hiring across the board: • Bio: Research associates, scientists and lab technicians to develop and run new assays at scale. • Lab automation: Engineers and interns to onboard new lab instruments and scale our automation infrastructure. • Software: Product and backend engineers to scale LabOS, our internal lab orchestration platform, our API for agents and the data pipelines that turn messy physical-world data into clean results. • Partnerships, customer success and operations: Building partnerships with AI & pharma labs, making sure customers understand their data and can run more campaigns, making sure the company operations run smoothly
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AI has solved software. Biology is the next frontier. We're hiring across every team at Adaptyv. We’ve built the best automated lab for protein designers to experimentally test their AI-designed proteins. Today, the most advanced protein design companies run their wet-lab work on Adaptyv, from the biggest biopharmas to frontier AI labs to dozens of virtual biotech startups. Demand has grown faster than we have, so we’re hiring across the board: • Bio: Research associates, scientists and lab technicians to develop and run new assays at scale. • Lab automation: Engineers and interns to onboard new lab instruments and scale our automation infrastructure. • Software: Product and backend engineers to scale LabOS, our internal lab orchestration platform, our API for agents and the data pipelines that turn messy physical-world data into clean results. • Partnerships, customer success and operations: Building partnerships with AI & pharma labs, making sure customers understand their data and can run more campaigns, making sure the company operations run smoothly

Adaptyv Bio

15,109 просмотров • 3 месяцев назад

The scarce thing in a data center is not manpower, but instinct that only comes from years on the floor. Kingston Kuan and I spent the past month with data center operators and industrial robotics startups. Most robotics companies are focused on robots as a productivity amplifiers: 24/7 uptime, five days of work done in two. Few are focused on the potential of robots to change how people work altogether. We want to show what it looks like to rethink human-robot collaboration, using AI so a shrinking pool of experts can meet the increasing demands of future infrastructure.
1:00

The scarce thing in a data center is not manpower, but instinct that only comes from years on the floor. Kingston Kuan and I spent the past month with data center operators and industrial robotics startups. Most robotics companies are focused on robots as a productivity amplifiers: 24/7 uptime, five days of work done in two. Few are focused on the potential of robots to change how people work altogether. We want to show what it looks like to rethink human-robot collaboration, using AI so a shrinking pool of experts can meet the increasing demands of future infrastructure.

Brandon Ong

59,678 просмотров • 3 месяцев назад

One of the most exciting (and challenging) applications of physical AI is thinking about how it can accelerate scientific discovery. Imagine a robot that can pipette, handle stem cells, operate existing lab machinery, 24/7 - a physical AI scientist. This is what the new episode of Greylock Partners Change Agents is all about, featuring Michelle Lee, founder and CEO of Medra. We talk about building an AI-powered lab, how accelerating physical AI lab experimentation is the key to unlocking drug development, and how Medra is automating experimentation today inside pharma companies. 00:00 Teaser 00:56 Medra’s founding mission 04:17 Hypotheses are not enough 13:27 Medra’s differentiation 20:22 The experimentation bottleneck 27:06 Refining the agent harness 29:05 The role of the physical lab 33:23 Managing context across different customers 34:59 Working with models 40:26 Towards personalized medicine and drug discovery 43:15 AWS for life sciences Thank you for joining Michelle Lee
46:43

One of the most exciting (and challenging) applications of physical AI is thinking about how it can accelerate scientific discovery. Imagine a robot that can pipette, handle stem cells, operate existing lab machinery, 24/7 - a physical AI scientist. This is what the new episode of Greylock Partners Change Agents is all about, featuring Michelle Lee, founder and CEO of Medra. We talk about building an AI-powered lab, how accelerating physical AI lab experimentation is the key to unlocking drug development, and how Medra is automating experimentation today inside pharma companies. 00:00 Teaser 00:56 Medra’s founding mission 04:17 Hypotheses are not enough 13:27 Medra’s differentiation 20:22 The experimentation bottleneck 27:06 Refining the agent harness 29:05 The role of the physical lab 33:23 Managing context across different customers 34:59 Working with models 40:26 Towards personalized medicine and drug discovery 43:15 AWS for life sciences Thank you for joining Michelle Lee

Corinne Marie Riley

39,467 просмотров • 18 дней назад

I made an AI data science team with Python. I'm excited to share my new project, the AI Data Science Team, a Python package for creating AI co-pilots and agents to assist in common data science tasks. I walk you through the GitHub page, showcasing the progress and potential of this project. I'll explain how these agents can streamline tasks like code generation and modeling pipeline creation, making data science projects more efficient. Introduction to AI Data Science Team Project Integrating Human-in-the-Loop Example: Accessing Agents for Feature Engineering Setting up Feature Engineering Agent Code On GitHub: Python AI-Tips Newsletter (stay updated as I build this project in the open):
12:57

I made an AI data science team with Python. I'm excited to share my new project, the AI Data Science Team, a Python package for creating AI co-pilots and agents to assist in common data science tasks. I walk you through the GitHub page, showcasing the progress and potential of this project. I'll explain how these agents can streamline tasks like code generation and modeling pipeline creation, making data science projects more efficient. Introduction to AI Data Science Team Project Integrating Human-in-the-Loop Example: Accessing Agents for Feature Engineering Setting up Feature Engineering Agent Code On GitHub: Python AI-Tips Newsletter (stay updated as I build this project in the open):

Matt Dancho (Business Science)

11,799 просмотров • 1 год назад

1/ We are speed running industrial robotics. It took us just 19 days from the first day of data collection to filming a 2.5-hour continuous run of our model autonomously breaking down industrial containers — zero human intervention. The data efficiency of our DVA model is fundamentally changing how fast we bring robots out of the lab and into the factory. Autonomous operation with 3 hours of data collection at a customer factory.
4:32

1/ We are speed running industrial robotics. It took us just 19 days from the first day of data collection to filming a 2.5-hour continuous run of our model autonomously breaking down industrial containers — zero human intervention. The data efficiency of our DVA model is fundamentally changing how fast we bring robots out of the lab and into the factory. Autonomous operation with 3 hours of data collection at a customer factory.

Rhoda AI

26,105 просмотров • 6 месяцев назад

In Xi'an, China, AI and robotics are powering a 24/7 "lights-out" car factory at Geely's intelligent production base. Machines and data work together across every step of the process, delivering precision manufacturing at scale. With an annual capacity of up to 300,000 vehicles, this smart factory shows how automation is shaping the future of mobility. #SmartManufacturing #ChinaInnovation #FutureMobility
0:50

In Xi'an, China, AI and robotics are powering a 24/7 "lights-out" car factory at Geely's intelligent production base. Machines and data work together across every step of the process, delivering precision manufacturing at scale. With an annual capacity of up to 300,000 vehicles, this smart factory shows how automation is shaping the future of mobility. #SmartManufacturing #ChinaInnovation #FutureMobility

China Xinhua News

11,224 просмотров • 3 месяцев назад

🤖 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! 🔗👇
2:24

🤖 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! 🔗👇

Stephanie Zhan

25,946 просмотров • 2 лет назад

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.
2:48

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

385,932 просмотров • 1 год назад

Terence Tao, UCLA mathematics professor and Fields Medallist, on why AI may be succeeding at science while scientists get worse at it: Tao starts by naming the tension directly: "There is this paradox that on the one hand AIs are becoming more powerful and more capable and making fewer mistakes, and they are ostensibly achieving a lot of the goals that we think scientists are trying to do. They're running experiments. They're analyzing data. They're writing papers." Experiments run, data analyzed, papers written — the exact outputs any university or funding body would cite as proof that science is working. Tao's concern is what those outputs stop telling you: "It may be that it comes at the cost of the AI picks up some skill but no human scientist gets any better at doing the science." The skill still accumulates, just in the wrong place. The system gets more capable while the people operating it stand still, because the work that used to build a researcher is now the work being handed off. And the cost shows up in the one thing scientists are supposed to be able to do: "No human can communicate exactly what just happened and why. This scientific discovery is interesting, why this proof is new and what features it has and how it connects." That final clause is the sharp end of it. Tao is describing a result nobody can place — a proof that arrives with no one able to say what makes it new, how it's built, or how it connects to the rest of the field. Understanding is a separate achievement from getting the answer, and it can quietly disappear while the answers keep arriving on schedule. Which is why he thinks the target itself needs re-examining: "We may have to sort of redesign our conception of what science is and what we actually want out of science. What exactly is science for and what are we trying to do? And is there a danger that we are optimizing the wrong thing when we are pointing our AI tools at science?"
0:52

Terence Tao, UCLA mathematics professor and Fields Medallist, on why AI may be succeeding at science while scientists get worse at it: Tao starts by naming the tension directly: "There is this paradox that on the one hand AIs are becoming more powerful and more capable and making fewer mistakes, and they are ostensibly achieving a lot of the goals that we think scientists are trying to do. They're running experiments. They're analyzing data. They're writing papers." Experiments run, data analyzed, papers written — the exact outputs any university or funding body would cite as proof that science is working. Tao's concern is what those outputs stop telling you: "It may be that it comes at the cost of the AI picks up some skill but no human scientist gets any better at doing the science." The skill still accumulates, just in the wrong place. The system gets more capable while the people operating it stand still, because the work that used to build a researcher is now the work being handed off. And the cost shows up in the one thing scientists are supposed to be able to do: "No human can communicate exactly what just happened and why. This scientific discovery is interesting, why this proof is new and what features it has and how it connects." That final clause is the sharp end of it. Tao is describing a result nobody can place — a proof that arrives with no one able to say what makes it new, how it's built, or how it connects to the rest of the field. Understanding is a separate achievement from getting the answer, and it can quietly disappear while the answers keep arriving on schedule. Which is why he thinks the target itself needs re-examining: "We may have to sort of redesign our conception of what science is and what we actually want out of science. What exactly is science for and what are we trying to do? And is there a danger that we are optimizing the wrong thing when we are pointing our AI tools at science?"

Big Brain AI

42,366 просмотров • 10 дней назад

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.
4:31

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

17,092 просмотров • 3 месяцев назад

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.
1:45

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

297,748 просмотров • 6 месяцев назад

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...
18:18

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

196,710 просмотров • 9 месяцев назад

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.
59:28

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

134,134 просмотров • 2 месяцев назад

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
0:38

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

197,980 просмотров • 2 месяцев назад