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Fei-Fei Li ( Fei-Fei Li ) beautifully explains Robotics. She defines robotics not by form, like humanoids or cars, but by function: they are any "embodied machines" that must perceive, understand, and act within a physical, 3D space. This core requirement is "spatial intelligence," the unifying principle of all...

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Rohan Paul

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41,441 次观看 • 8 个月前 •via X (Twitter)

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📁 Fei-Fei Li founder of World Labs, says the next leap in AI is not language. Human intelligence does not just speak, it moves, perceives, and acts in the physical world. Spatial intelligence is the real core of intelligence. From text to space, from models to 3D and 4D worlds, from understanding words to interacting with reality. The next chapter is not read, it is inhabited.
1:41

📁 Fei-Fei Li founder of World Labs, says the next leap in AI is not language. Human intelligence does not just speak, it moves, perceives, and acts in the physical world. Spatial intelligence is the real core of intelligence. From text to space, from models to 3D and 4D worlds, from understanding words to interacting with reality. The next chapter is not read, it is inhabited.

Jon Hernandez

20,592 次观看 • 8 个月前

World Labs CEO Dr. Fei-Fei Li & SceniX Co-Founder Yunzhu Li on the data bottleneck in robotics and how world models help: "The lack of data in training, the lack of data in evaluation, this is very, very different from language models, where data is abundant on the internet." "And we know that in order for robotics to work, we have to somehow unlock the power of scaling law. But where does that come from?" "It's a profound problem that everybody's battling with in robotics." "We see a lot of unlock in being able to do this whole process through the modeling of the environments." "Being able to create these digital worlds... that's just going to unlock so much more potential for being able to replace all the costly and the unsafe data in the real environments with the data generated from the worlds for robots to be able to do scalable learning and evaluations." Fei-Fei Li Yunzhu Li martin_casado
1:49

World Labs CEO Dr. Fei-Fei Li & SceniX Co-Founder Yunzhu Li on the data bottleneck in robotics and how world models help: "The lack of data in training, the lack of data in evaluation, this is very, very different from language models, where data is abundant on the internet." "And we know that in order for robotics to work, we have to somehow unlock the power of scaling law. But where does that come from?" "It's a profound problem that everybody's battling with in robotics." "We see a lot of unlock in being able to do this whole process through the modeling of the environments." "Being able to create these digital worlds... that's just going to unlock so much more potential for being able to replace all the costly and the unsafe data in the real environments with the data generated from the worlds for robots to be able to do scalable learning and evaluations." Fei-Fei Li Yunzhu Li martin_casado

a16z

77,065 次观看 • 2 个月前

Fei-Fei Li on what simulation can do for robotics that real-world data can't: "You play out events that haven't happened, or cannot happen... And while you play it out, you learn how to act in it." "That's really important in robotics, because we cannot possibly have enough real-world data for that." "Here is a real life example: the industry of self-driving cars. Waymo has officially said they use billions of hours of simulation." "Cars are the simplest kind of robots. So clearly simulation plays a huge role in robotic learning." Fei-Fei Li Yunzhu Li World Labs martin_casado
1:30

Fei-Fei Li on what simulation can do for robotics that real-world data can't: "You play out events that haven't happened, or cannot happen... And while you play it out, you learn how to act in it." "That's really important in robotics, because we cannot possibly have enough real-world data for that." "Here is a real life example: the industry of self-driving cars. Waymo has officially said they use billions of hours of simulation." "Cars are the simplest kind of robots. So clearly simulation plays a huge role in robotic learning." Fei-Fei Li Yunzhu Li World Labs martin_casado

a16z

35,794 次观看 • 2 个月前

World Labs co-founders Justin Johnson and Dr. Fei-Fei Li say Atlas unifies the two things computer vision has always kept apart: Justin: "Historically, reconstruction has been its own subfield in computer vision with its own specialized tasks and models. Generation is what all the text-to-video models are really good at." "Those are great for creative applications if I want to imagine something that's never been there before." "But now with Atlas, for the first time, we're putting these two different parts of visual intelligence together in one model. It can do both 3D reconstruction and generation together in one architecture." "We had to make it multimodal from the start. This thing natively works on text, images, videos, and camera poses as a native input to the model... It uses 3D as a native modality that it works on." Fei-Fei: "Computer vision has been around for more than half a century... Our field traditionally has multiple tracks. You go to a computer vision conference: you have the pixel generation track, you have some recognition track, and you have a 3D reconstruction track." "This is an elegant model that unifies the problem of reconstruction and generation by anchoring on viewpoints and viewpoint estimation. That's just incredibly powerful." Justin Johnson Fei-Fei Li
1:52

World Labs co-founders Justin Johnson and Dr. Fei-Fei Li say Atlas unifies the two things computer vision has always kept apart: Justin: "Historically, reconstruction has been its own subfield in computer vision with its own specialized tasks and models. Generation is what all the text-to-video models are really good at." "Those are great for creative applications if I want to imagine something that's never been there before." "But now with Atlas, for the first time, we're putting these two different parts of visual intelligence together in one model. It can do both 3D reconstruction and generation together in one architecture." "We had to make it multimodal from the start. This thing natively works on text, images, videos, and camera poses as a native input to the model... It uses 3D as a native modality that it works on." Fei-Fei: "Computer vision has been around for more than half a century... Our field traditionally has multiple tracks. You go to a computer vision conference: you have the pixel generation track, you have some recognition track, and you have a 3D reconstruction track." "This is an elegant model that unifies the problem of reconstruction and generation by anchoring on viewpoints and viewpoint estimation. That's just incredibly powerful." Justin Johnson Fei-Fei Li

a16z

40,937 次观看 • 26 天前

World Labs' Dr. Fei-Fei Li on why the 3D world and spatial intelligence are fundamentally different from language models: "Language is fundamentally a purely generated signal. There's no language out there. You don't go out in the nature and there's words written in the sky for you." "Whatever data you feed in, you pretty much can just somehow regurgitate with enough generalizability the same data out, and that's language to language." "But the 3D world is not. There is a 3D world out there that follows laws of physics, that has its own structures due to materials and many other things. And to fundamentally back that information out and be able to represent it and be able to generate it is just fundamentally quite a different problem." "We will be borrowing similar ideas or useful ideas from language and LLMs, but this is fundamentally philosophically, to me, a different problem." Fei-Fei Li
1:03

World Labs' Dr. Fei-Fei Li on why the 3D world and spatial intelligence are fundamentally different from language models: "Language is fundamentally a purely generated signal. There's no language out there. You don't go out in the nature and there's words written in the sky for you." "Whatever data you feed in, you pretty much can just somehow regurgitate with enough generalizability the same data out, and that's language to language." "But the 3D world is not. There is a 3D world out there that follows laws of physics, that has its own structures due to materials and many other things. And to fundamentally back that information out and be able to represent it and be able to generate it is just fundamentally quite a different problem." "We will be borrowing similar ideas or useful ideas from language and LLMs, but this is fundamentally philosophically, to me, a different problem." Fei-Fei Li

a16z

51,559 次观看 • 4 天前

BREAKING: $AMD AMD Acquires World Labs in $8.2 Billion Deal for AI Expertise. Stanford professor and World Labs founder Fei-Fei Li sits down with Jany Talk to break down what everyone gets wrong about AI - and why she still believes in people, not machines. Fei-Fei Li co-created ImageNet before most of the world understood why data would matter more than algorithms. She's spent a decade building computer vision and robotics from the ground up. Now, as the founder of World Labs, she's betting on spatial intelligence as the missing piece of AGI - and she's here to tell you why almost everyone is thinking about AI wrong. Fei-Fei Li: "AI Is a Tool, Not the World" - On Curiosity, AGI and Being Human. Full interview here:
1:04:46

BREAKING: $AMD AMD Acquires World Labs in $8.2 Billion Deal for AI Expertise. Stanford professor and World Labs founder Fei-Fei Li sits down with Jany Talk to break down what everyone gets wrong about AI - and why she still believes in people, not machines. Fei-Fei Li co-created ImageNet before most of the world understood why data would matter more than algorithms. She's spent a decade building computer vision and robotics from the ground up. Now, as the founder of World Labs, she's betting on spatial intelligence as the missing piece of AGI - and she's here to tell you why almost everyone is thinking about AI wrong. Fei-Fei Li: "AI Is a Tool, Not the World" - On Curiosity, AGI and Being Human. Full interview here:

NextFin

32,356 次观看 • 4 天前

World Labs CEO Dr. Fei-Fei Li explains says "world model" has become an overloaded term & explains what each kind of world model does: "Right now there are three ways of calling world models when it comes to spatial intelligence." "One is what I call a renderer, when the model puts beautiful pixels on the screen." "Another kind of world model is what we call a planner. That is more for machines, more for robots." "The third kind, which I think is the linchpin of the three, is a simulator." "A simulator could become a renderer. The simulator could become a planner. But this layer is a huge critical path to unlock spatial intelligence. And that's what World Labs is working on." Fei-Fei Li at Bloomberg Tech live with Emily Chang
2:08

World Labs CEO Dr. Fei-Fei Li explains says "world model" has become an overloaded term & explains what each kind of world model does: "Right now there are three ways of calling world models when it comes to spatial intelligence." "One is what I call a renderer, when the model puts beautiful pixels on the screen." "Another kind of world model is what we call a planner. That is more for machines, more for robots." "The third kind, which I think is the linchpin of the three, is a simulator." "A simulator could become a renderer. The simulator could become a planner. But this layer is a huge critical path to unlock spatial intelligence. And that's what World Labs is working on." Fei-Fei Li at Bloomberg Tech live with Emily Chang

a16z

112,217 次观看 • 3 个月前

I don't know if you really understand what these people just did... They built an AI that looks at a few casual real-world videos or photos and instantly turns them into a full 3D playground. You can MOVE THE CAMERA HOWEVER YOU WANT, change the lighting, move objects around, and even let physics play out like in the real world. View events from impossible angles because Atlas knows what was there. This is massive! Let's take robotics. Until now training robots meant collecting tons of expensive real-world data. Now a handful of recordings can become thousands of different simulated situations. Robots can practice the same task in new rooms, with different objects and lighting without anyone having to film it all again. For the industry it means we can train robots way faster and cheaper. Home robots, warehouse robots, whatever... they can learn new environments at scale instead of staying stuck in the lab. This is one of the first real bridges between “cool AI video models” and actual physical machines that have to work in the real world. Blog: The team behind World Labs: Fei-Fei Li, Justin Johnson, Ben Mildenhall and Yunzhu Li. The video is from David Pantera. Follow for more insights into robotics and AI. —— Weekly robotics and AI insights. Subscribe free:
0:49

I don't know if you really understand what these people just did... They built an AI that looks at a few casual real-world videos or photos and instantly turns them into a full 3D playground. You can MOVE THE CAMERA HOWEVER YOU WANT, change the lighting, move objects around, and even let physics play out like in the real world. View events from impossible angles because Atlas knows what was there. This is massive! Let's take robotics. Until now training robots meant collecting tons of expensive real-world data. Now a handful of recordings can become thousands of different simulated situations. Robots can practice the same task in new rooms, with different objects and lighting without anyone having to film it all again. For the industry it means we can train robots way faster and cheaper. Home robots, warehouse robots, whatever... they can learn new environments at scale instead of staying stuck in the lab. This is one of the first real bridges between “cool AI video models” and actual physical machines that have to work in the real world. Blog: The team behind World Labs: Fei-Fei Li, Justin Johnson, Ben Mildenhall and Yunzhu Li. The video is from David Pantera. Follow for more insights into robotics and AI. —— Weekly robotics and AI insights. Subscribe free:

Ilir Aliu

273,195 次观看 • 1 个月前

World Labs CEO Dr. Fei-Fei Li says AI must change how we teach and evaluate students: "AI must change learning. AI must change K-16 learning." "The most precious resource of our entire world is human capital." "When we have gotten the technology that can answer standardized tests... when AI can do better than an average human, it's not about humans are bad. It's about we need to change the education system." "We need to change how we evaluate. We need to change the way we empower teachers to educate the next generation of students where they can use these tools, be empowered, and do things that we can never imagine." "All of the kids today should not be scared of AI. They should feel the human agency to lead AI, to use AI in the right way, and to use AI to make the impact that they want to make for the world." Fei-Fei Li at Bloomberg Tech live with Emily Chang
1:35

World Labs CEO Dr. Fei-Fei Li says AI must change how we teach and evaluate students: "AI must change learning. AI must change K-16 learning." "The most precious resource of our entire world is human capital." "When we have gotten the technology that can answer standardized tests... when AI can do better than an average human, it's not about humans are bad. It's about we need to change the education system." "We need to change how we evaluate. We need to change the way we empower teachers to educate the next generation of students where they can use these tools, be empowered, and do things that we can never imagine." "All of the kids today should not be scared of AI. They should feel the human agency to lead AI, to use AI in the right way, and to use AI to make the impact that they want to make for the world." Fei-Fei Li at Bloomberg Tech live with Emily Chang

a16z

82,011 次观看 • 3 个月前

THE GODMOTHER OF AI JUST REVEALED WHAT COMES AFTER CHATGPT. Fei-Fei Li is the legendary scientist who sparked the modern AI revolution. Now, she says chatbots have hit a wall because words alone cannot cook a meal, drive a car, or put out a fire. To solve this, she raised a billion dollars to build "World Models." Instead of just predicting text, this new AI actually understands physical physics, 3D space, and time. She says teaching AI how the physical world works is the only way we will ever get robots into our homes. Watch this 👇
1:27

THE GODMOTHER OF AI JUST REVEALED WHAT COMES AFTER CHATGPT. Fei-Fei Li is the legendary scientist who sparked the modern AI revolution. Now, she says chatbots have hit a wall because words alone cannot cook a meal, drive a car, or put out a fire. To solve this, she raised a billion dollars to build "World Models." Instead of just predicting text, this new AI actually understands physical physics, 3D space, and time. She says teaching AI how the physical world works is the only way we will ever get robots into our homes. Watch this 👇

Katyayani Shukla

35,042 次观看 • 1 个月前

Dr. Fei-Fei Li just called out the biggest blind spot in the entire AI industry. We have been building half of human intelligence. And calling it the finish line. Li: “If you look at human intelligence, it pretty much boils down to two buckets.” The first bucket is language. Symbolic reasoning. Communication. The ability to think in words and abstractions. That’s what every major AI lab has spent the last decade building. The second bucket is the one the industry has almost entirely ignored. Li: “We call that in AI spatial intelligence.” How humans and animals perceive, navigate, and interact with the three-dimensional physical world. How we reach for objects. How we move through space. How we build and manipulate physical reality. From painting masterpieces to constructing the pyramids, non-verbal spatial intelligence is what actually shapes the world. Language describes reality. Spatial intelligence acts on it. And the gap between those two things is the gap between a chatbot and a robot. Li: “When this technology is ready, the robotic revolution is gonna start. We’re already seeing that trend.” Every robot is a moving agent. Every moving agent requires spatial intelligence to function in the real world. The humanoid robots being deployed in factories right now are hitting the ceiling of what language models alone can power. Spatial intelligence is the unlock. But Li didn’t stop at robotics. Li: “From a geopolitics point of view, this is part of the technology that goes straight into weapons.” Autonomous drone swarms. Battlefield navigation. Physical target acquisition without human oversight. Every military application of AI that operates in the real world runs on spatial intelligence. The nation that masters the transition from static text to dynamic three-dimensional perception doesn’t just win the software race. It commands the physical battlefield. The AI arms race just broke out of the data center. It’s operating in three dimensions now.
3:08

Dr. Fei-Fei Li just called out the biggest blind spot in the entire AI industry. We have been building half of human intelligence. And calling it the finish line. Li: “If you look at human intelligence, it pretty much boils down to two buckets.” The first bucket is language. Symbolic reasoning. Communication. The ability to think in words and abstractions. That’s what every major AI lab has spent the last decade building. The second bucket is the one the industry has almost entirely ignored. Li: “We call that in AI spatial intelligence.” How humans and animals perceive, navigate, and interact with the three-dimensional physical world. How we reach for objects. How we move through space. How we build and manipulate physical reality. From painting masterpieces to constructing the pyramids, non-verbal spatial intelligence is what actually shapes the world. Language describes reality. Spatial intelligence acts on it. And the gap between those two things is the gap between a chatbot and a robot. Li: “When this technology is ready, the robotic revolution is gonna start. We’re already seeing that trend.” Every robot is a moving agent. Every moving agent requires spatial intelligence to function in the real world. The humanoid robots being deployed in factories right now are hitting the ceiling of what language models alone can power. Spatial intelligence is the unlock. But Li didn’t stop at robotics. Li: “From a geopolitics point of view, this is part of the technology that goes straight into weapons.” Autonomous drone swarms. Battlefield navigation. Physical target acquisition without human oversight. Every military application of AI that operates in the real world runs on spatial intelligence. The nation that masters the transition from static text to dynamic three-dimensional perception doesn’t just win the software race. It commands the physical battlefield. The AI arms race just broke out of the data center. It’s operating in three dimensions now.

Dustin

122,861 次观看 • 7 个月前

Today at Stanford, Fei-Fei Li (Fei-Fei Li),Cofounder/CEO World Labs, gave one of the clearest explanations I’ve heard of what a World Model really is. She broke it down into three layers: 1️⃣ Rendering — What does the world look like? This is where most of today’s video generation models operate: generating increasingly realistic and beautiful pixels. The question is: Can AI generate what the world looks like? The primary consumer is humans. 2️⃣ Simulation — How does the world actually work? Fei-Fei gave a simple example: “How will this bottle move? If I pour the water out, how will the water flow?” This goes far beyond generating something that looks realistic. The model needs to understand physics, spatial relationships, cause and effect, and how the world changes over time. The consumers are both humans and machines. 3️⃣ Planning — What should happen next? This is where things get really interesting. AI doesn't just render the world or simulate what might happen. It uses its understanding of the world to decide: What should I do next? At this layer, the primary consumer is the machine itself. And this connects directly to two enormous opportunities: Autonomous driving and robotics. The progression is powerful: Rendering → Simulation → Planning The real promise of World Models isn't simply generating better videos. It's building AI that can understand the world, predict what happens next, and ultimately take intelligent action in the physical world.
3:54

Today at Stanford, Fei-Fei Li (Fei-Fei Li),Cofounder/CEO World Labs, gave one of the clearest explanations I’ve heard of what a World Model really is. She broke it down into three layers: 1️⃣ Rendering — What does the world look like? This is where most of today’s video generation models operate: generating increasingly realistic and beautiful pixels. The question is: Can AI generate what the world looks like? The primary consumer is humans. 2️⃣ Simulation — How does the world actually work? Fei-Fei gave a simple example: “How will this bottle move? If I pour the water out, how will the water flow?” This goes far beyond generating something that looks realistic. The model needs to understand physics, spatial relationships, cause and effect, and how the world changes over time. The consumers are both humans and machines. 3️⃣ Planning — What should happen next? This is where things get really interesting. AI doesn't just render the world or simulate what might happen. It uses its understanding of the world to decide: What should I do next? At this layer, the primary consumer is the machine itself. And this connects directly to two enormous opportunities: Autonomous driving and robotics. The progression is powerful: Rendering → Simulation → Planning The real promise of World Models isn't simply generating better videos. It's building AI that can understand the world, predict what happens next, and ultimately take intelligent action in the physical world.

PaulFang

11,774 次观看 • 1 个月前

Old footage sitting in your camera roll could now be reconstructable as a 3D scene. World Labs co-founders Ben Mildenhall and Fei-Fei Li on how Atlas got there: Ben: "In a casual sense... I took three photos of this object, or six photos of this room. I look at the photos, I can understand in my mind how those piece together. I can fill in the gaps and get it." "But there's never really been any reconciliation between those data-driven priors and the brute force dense reconstruction, which is much more akin to scientific or medical imaging... When we say dense, we really mean dense." "This room, I want like 100, 200, 300 photos to capture it. And what we're trying to do is bring that down to like three. We're saying like 50, 100x reduction." "At that scale it completely flips that calculus on its head of what type of captures you reconstruct. You can go back to existing imagery you have. You can go to stuff you find on the internet and even build scenes out of that. You can go to casual videos and unearth a lot of footage that in the past we would never have treated as reconstructable, and bring it to life as 3D." "This is something we've been playing around with a lot with Atlas. Taking old clips. I've taken a bunch of my own old captures that never worked before and put them through the system and seen a reconstruction for the first time." Fei-Fei: "The Stanford demo is underappreciated. Anywhere between 3 to 25 images, you can reconstruct that entire Stanford quad... Everything you see is generated, but according to the laws of reconstruction. And this is really magical." Ben Mildenhall Fei-Fei Li
2:01

Old footage sitting in your camera roll could now be reconstructable as a 3D scene. World Labs co-founders Ben Mildenhall and Fei-Fei Li on how Atlas got there: Ben: "In a casual sense... I took three photos of this object, or six photos of this room. I look at the photos, I can understand in my mind how those piece together. I can fill in the gaps and get it." "But there's never really been any reconciliation between those data-driven priors and the brute force dense reconstruction, which is much more akin to scientific or medical imaging... When we say dense, we really mean dense." "This room, I want like 100, 200, 300 photos to capture it. And what we're trying to do is bring that down to like three. We're saying like 50, 100x reduction." "At that scale it completely flips that calculus on its head of what type of captures you reconstruct. You can go back to existing imagery you have. You can go to stuff you find on the internet and even build scenes out of that. You can go to casual videos and unearth a lot of footage that in the past we would never have treated as reconstructable, and bring it to life as 3D." "This is something we've been playing around with a lot with Atlas. Taking old clips. I've taken a bunch of my own old captures that never worked before and put them through the system and seen a reconstruction for the first time." Fei-Fei: "The Stanford demo is underappreciated. Anywhere between 3 to 25 images, you can reconstruct that entire Stanford quad... Everything you see is generated, but according to the laws of reconstruction. And this is really magical." Ben Mildenhall Fei-Fei Li

a16z

48,823 次观看 • 28 天前

World Labs co-founders Dr. Fei-Fei Li and Justin Johnson on compute as the constraint, and the Slack message that convinced them to go all-in on their Atlas model: Fei-Fei: "I think [we] have total conviction about the scaling law." "I do think the exact architecture choices and data mixtures is where the devil's in the details. I watched Justin and his team going from 'we really don't know how long this is gonna take,' to 'maybe sign of life,' to 'wow, this is gonna work.'" Justin: "We're basically at the beginning, and we're basically limited by compute at this point." "During development, we trained a sequence of models, the first couple rungs of the scaling ladder. Each time we made the model bigger, each time we trained it for longer, each time we put it on more chips, it got significantly better." Fei-Fei: "Here's a little bit of an insider story... There was one day in early summer... Ben and Justin feed [a smaller model] into the viewpoint generation... Remember that famous garden table from the NeRF paper?... Overnight we all saw the Slack from Ben that our camera flew under the table." "That morning, the three of us looked at each other in the eyes and said, 'That's it. We're gonna build this.' We made a decision within five seconds. No one has ever seen this result." Fei-Fei Li Justin Johnson Ben Mildenhall martin_casado
2:21

World Labs co-founders Dr. Fei-Fei Li and Justin Johnson on compute as the constraint, and the Slack message that convinced them to go all-in on their Atlas model: Fei-Fei: "I think [we] have total conviction about the scaling law." "I do think the exact architecture choices and data mixtures is where the devil's in the details. I watched Justin and his team going from 'we really don't know how long this is gonna take,' to 'maybe sign of life,' to 'wow, this is gonna work.'" Justin: "We're basically at the beginning, and we're basically limited by compute at this point." "During development, we trained a sequence of models, the first couple rungs of the scaling ladder. Each time we made the model bigger, each time we trained it for longer, each time we put it on more chips, it got significantly better." Fei-Fei: "Here's a little bit of an insider story... There was one day in early summer... Ben and Justin feed [a smaller model] into the viewpoint generation... Remember that famous garden table from the NeRF paper?... Overnight we all saw the Slack from Ben that our camera flew under the table." "That morning, the three of us looked at each other in the eyes and said, 'That's it. We're gonna build this.' We made a decision within five seconds. No one has ever seen this result." Fei-Fei Li Justin Johnson Ben Mildenhall martin_casado

a16z

41,593 次观看 • 3 天前

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

386,301 次观看 • 1 年前

Asked why humanoid robots still can't load a dishwasher after $6 billion in funding, Fei-Fei Li says the number is too small: Emily Chang: "Funding for humanoids hit $6 billion, but they still can't load my dishwasher as fast as I can. They still can't go get my Amazon packages. Will world models, world labs, close the gap between hype and reality?" "That's a loaded question, Emily." "First of all, robotics is gonna be one of the most important revolution in human industrialization." "$6 billion is too small, right?" "If you look at self-driving cars investment, if you look at language models investment, it took way more than six billion dollars." The $6 billion is Chang's total for humanoid funding, not Li's own measurement. Chang also says World Labs, which Li co-founded, has raised $1 billion of its own. - Fei-Fei Li (Fei-Fei Li), co-founder and CEO of World Labs and Stanford professor, with Emily Chang on The Circuit (Bloomberg TV), August 19, 2026.
0:34

Asked why humanoid robots still can't load a dishwasher after $6 billion in funding, Fei-Fei Li says the number is too small: Emily Chang: "Funding for humanoids hit $6 billion, but they still can't load my dishwasher as fast as I can. They still can't go get my Amazon packages. Will world models, world labs, close the gap between hype and reality?" "That's a loaded question, Emily." "First of all, robotics is gonna be one of the most important revolution in human industrialization." "$6 billion is too small, right?" "If you look at self-driving cars investment, if you look at language models investment, it took way more than six billion dollars." The $6 billion is Chang's total for humanoid funding, not Li's own measurement. Chang also says World Labs, which Li co-founded, has raised $1 billion of its own. - Fei-Fei Li (Fei-Fei Li), co-founder and CEO of World Labs and Stanford professor, with Emily Chang on The Circuit (Bloomberg TV), August 19, 2026.

Karl Mehta

42,855 次观看 • 10 天前

Dr. Fei-Fei Li (Fei-Fei Li) is known as the “godmother of AI.” For the past two decades, she’s been at the center of AI’s most significant breakthroughs, including: - Spearheading ImageNet, the dataset that sparked the AI explosion we’re living through right now. - Leading work at Stanford Artificial Intelligence Laboratory (SAIL) - Serving as Chief Scientist of AI/ML at Google Cloud - Co-founding Stanford’s Institute for Human-Centered AI - Serving on the United Nations AI Scientific Advisory Board - Being named as Time's 100 most influential people in AI In this conversation, Fei-Fei shares the rarely told history of how we got to today—and what comes next. We discuss: 🔸 The backstory on ImageNet 🔸 Why robotics faces unique challenges compared with language models and what’s needed to overcome them 🔸 Why Fei-Fei believes AI won’t replace humans but will require us to take responsibility for ourselves 🔸 Why world models and spatial intelligence represent the next frontier in AI, beyond large language models 🔸 The surprising applications of Marble, from movie production to psychological research 🔸 How to participate in AI regardless of your role 🔸 Much more Listen now 👇 • YouTube: • Spotify: • Apple: Thank you to our wonderful sponsors for supporting the podcast: 🏆 Figma Make — A prompt-to-code tool for making ideas real: 🏆 Justworks — The all-in-one HR solution for managing your small business with confidence: 🏆 Sinch — Build messaging, email, and calling into your product:
1:40

Dr. Fei-Fei Li (Fei-Fei Li) is known as the “godmother of AI.” For the past two decades, she’s been at the center of AI’s most significant breakthroughs, including: - Spearheading ImageNet, the dataset that sparked the AI explosion we’re living through right now. - Leading work at Stanford Artificial Intelligence Laboratory (SAIL) - Serving as Chief Scientist of AI/ML at Google Cloud - Co-founding Stanford’s Institute for Human-Centered AI - Serving on the United Nations AI Scientific Advisory Board - Being named as Time's 100 most influential people in AI In this conversation, Fei-Fei shares the rarely told history of how we got to today—and what comes next. We discuss: 🔸 The backstory on ImageNet 🔸 Why robotics faces unique challenges compared with language models and what’s needed to overcome them 🔸 Why Fei-Fei believes AI won’t replace humans but will require us to take responsibility for ourselves 🔸 Why world models and spatial intelligence represent the next frontier in AI, beyond large language models 🔸 The surprising applications of Marble, from movie production to psychological research 🔸 How to participate in AI regardless of your role 🔸 Much more Listen now 👇 • YouTube: • Spotify: • Apple: Thank you to our wonderful sponsors for supporting the podcast: 🏆 Figma Make — A prompt-to-code tool for making ideas real: 🏆 Justworks — The all-in-one HR solution for managing your small business with confidence: 🏆 Sinch — Build messaging, email, and calling into your product:

Lenny Rachitsky

250,455 次观看 • 10 个月前

World Labs CEO Fei-Fei Li on the need for world models: "There's no language out there. You don't go out in nature and there's words written in the sky for you." "There is a 3D world out there that follows laws of physics, that has its own structures due to materials. To fundamentally back that information out and be able to represent it and be able to generate it is just fundamentally quite a different problem." "We will be borrowing useful ideas from language and LLMs, but this is fundamentally, philosophically a different problem."
1:03

World Labs CEO Fei-Fei Li on the need for world models: "There's no language out there. You don't go out in nature and there's words written in the sky for you." "There is a 3D world out there that follows laws of physics, that has its own structures due to materials. To fundamentally back that information out and be able to represent it and be able to generate it is just fundamentally quite a different problem." "We will be borrowing useful ideas from language and LLMs, but this is fundamentally, philosophically a different problem."

a16z

203,474 次观看 • 1 个月前

World Labs co-founders Justin Johnson and Dr. Fei-Fei Li say LLMs use next-token prediction, but spatial intelligence has its own equivalent: Justin: "The soft definition of AI-completeness is there's this fundamental primitive that's an AI task. But if I could solve this AI task in its full, broadest generality, it would solve any intelligence problem." "The classic example in LLMs is that next-token prediction is AI-complete... I think from Ilya: there's a mystery novel, the thing has to read the whole novel, and the final sentence is, 'And the killer was.' Predict the next token. You could basically frame any kind of intelligence task in terms of that." "So clearly next-token prediction is something people believe is AI-complete." "New-view prediction, this primitive that we have in Atlas, especially generative new-view prediction, is also AI-complete." "I want to have a world where Martin is writing a proof of the Riemann hypothesis on the blackboard, and then the camera pans over to the next whiteboard." Fei-Fei: "Evolution had to solve new-viewpoint prediction by making animals move. Nature gave animals eyes, but nature didn't give trees eyes. Why? Because when you move, you see a new viewpoint... We do believe very strongly that next-viewpoint prediction is the equivalent of next-token prediction." Justin Johnson Fei-Fei Li martin_casado
1:43

World Labs co-founders Justin Johnson and Dr. Fei-Fei Li say LLMs use next-token prediction, but spatial intelligence has its own equivalent: Justin: "The soft definition of AI-completeness is there's this fundamental primitive that's an AI task. But if I could solve this AI task in its full, broadest generality, it would solve any intelligence problem." "The classic example in LLMs is that next-token prediction is AI-complete... I think from Ilya: there's a mystery novel, the thing has to read the whole novel, and the final sentence is, 'And the killer was.' Predict the next token. You could basically frame any kind of intelligence task in terms of that." "So clearly next-token prediction is something people believe is AI-complete." "New-view prediction, this primitive that we have in Atlas, especially generative new-view prediction, is also AI-complete." "I want to have a world where Martin is writing a proof of the Riemann hypothesis on the blackboard, and then the camera pans over to the next whiteboard." Fei-Fei: "Evolution had to solve new-viewpoint prediction by making animals move. Nature gave animals eyes, but nature didn't give trees eyes. Why? Because when you move, you see a new viewpoint... We do believe very strongly that next-viewpoint prediction is the equivalent of next-token prediction." Justin Johnson Fei-Fei Li martin_casado

a16z

88,392 次观看 • 27 天前