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🤖 Physical AI is evolving weekly, but many models are trained on robots with limited payload capacity--restricting their real-world utility. 💪 To truly deploy AI in physical spaces, we need stronger hardware to unlock these models' full potential. Vega is capable of lifting 15+ lbs! 🚀 What applications will... show more

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Vega's strength could revolutionize aerospace components and Industry 4.0 applications. Exciting times for Physical AI!

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World models that aren't grounded in the real world will never reach their potential for physical AI. 🌎 Just as RAG cut hallucination in LLMs, location-specific grounding lets robots choose the right action in the right place. Read more about Niantic Spatial’s thinking: #WorldModels #PhysicalAI #AI #EmbodiedAI #SpatialAI #Robotics

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Data for the Real World Diana & Austin Tindle AI has become superhuman at code, language, and images because it has enormous amounts of data to learn from. But our understanding of the physical world is still limited by sparse sensors and models built largely on intuition. Cheaper sensors and better foundation models could unlock entirely new datasets for energy, agriculture, logistics, construction, and the climate. Once you can accurately model the physical world, you can begin to control it.

Data for the Real World Diana & Austin Tindle AI has become superhuman at code, language, and images because it has enormous amounts of data to learn from. But our understanding of the physical world is still limited by sparse sensors and models built largely on intuition. Cheaper sensors and better foundation models could unlock entirely new datasets for energy, agriculture, logistics, construction, and the climate. Once you can accurately model the physical world, you can begin to control it.

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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 👇

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 👇

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Physical AI hits production speed on automotive assembly! 👀 Sanctuary AI just achieved 99,5%+ success on a wire plugging task at a Tier 1 automotive supplier, 2.54 seconds per task, validated against live production benchmarks. The system matched the customer's actual production line throughput. The task sounds simple. But if you look closer, it's not. Inserting flexible wires into moving targets on a conveyor requires contact-rich dexterity, force sensing, real-time adaptation. Materials shift. Targets move. This has stayed out of reach for traditional automation. Instead of waiting for humanoid hardware to mature, Sanctuary AI is deploying Physical AI on existing commercial robotic platforms (Universal Robots in this case). So their solution is fully hardware-agnostic. Deploy today on what you already have. Scale to next-gen humanoids later. Olivia Norton (co-founder, CTO): "Physical AI adoption is gated by AI that meets both performance and cycle time requirements. That's what customers are seeking, and that's what we are delivering." Looks like manufacturing, logistics, and other labor-constrained industries don't need to wait. Read more here: ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →

Physical AI hits production speed on automotive assembly! 👀 Sanctuary AI just achieved 99,5%+ success on a wire plugging task at a Tier 1 automotive supplier, 2.54 seconds per task, validated against live production benchmarks. The system matched the customer's actual production line throughput. The task sounds simple. But if you look closer, it's not. Inserting flexible wires into moving targets on a conveyor requires contact-rich dexterity, force sensing, real-time adaptation. Materials shift. Targets move. This has stayed out of reach for traditional automation. Instead of waiting for humanoid hardware to mature, Sanctuary AI is deploying Physical AI on existing commercial robotic platforms (Universal Robots in this case). So their solution is fully hardware-agnostic. Deploy today on what you already have. Scale to next-gen humanoids later. Olivia Norton (co-founder, CTO): "Physical AI adoption is gated by AI that meets both performance and cycle time requirements. That's what customers are seeking, and that's what we are delivering." Looks like manufacturing, logistics, and other labor-constrained industries don't need to wait. Read more here: ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →

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Breaking news: Cosmos 3 is here. They are attempting to do something completely new 🤯 Why is Physical AI much harder than building a chatbot? Understanding the world is not enough, robots need to predict it and act inside it. That's the idea behind NVIDIA Cosmos 3: → Reasoning model understands what's happening from video, images, text, and actions. → World model generates future states of the environment. → Action model generates the actions needed to achieve a goal. Previous systems often stitched these capabilities together using separate models. Cosmos 3 combines them into a single architecture with two components: ▪️ Reasoner Tower Analyzes observations and builds an understanding of objects, motion, interactions, and physical context. ▪️ Generator Tower Uses that understanding to generate future videos and action sequences that obey physical constraints. So Cosmos 3 moves from: Perception → Model A Prediction → Model B Actions → Model C to: Perception + Prediction + Actions → One unified system. The goal is to make robots, autonomous vehicles, and smart environments better at answering three questions: 1. What is happening? 2. What will happen next? 3. What should I do? That's a big shift from today's AI, which mostly focuses on generating text. And check the benchmarks! Physical AI needs to generate decisions that survive contact with the real world. 🚗🤖

Breaking news: Cosmos 3 is here. They are attempting to do something completely new 🤯 Why is Physical AI much harder than building a chatbot? Understanding the world is not enough, robots need to predict it and act inside it. That's the idea behind NVIDIA Cosmos 3: → Reasoning model understands what's happening from video, images, text, and actions. → World model generates future states of the environment. → Action model generates the actions needed to achieve a goal. Previous systems often stitched these capabilities together using separate models. Cosmos 3 combines them into a single architecture with two components: ▪️ Reasoner Tower Analyzes observations and builds an understanding of objects, motion, interactions, and physical context. ▪️ Generator Tower Uses that understanding to generate future videos and action sequences that obey physical constraints. So Cosmos 3 moves from: Perception → Model A Prediction → Model B Actions → Model C to: Perception + Prediction + Actions → One unified system. The goal is to make robots, autonomous vehicles, and smart environments better at answering three questions: 1. What is happening? 2. What will happen next? 3. What should I do? That's a big shift from today's AI, which mostly focuses on generating text. And check the benchmarks! Physical AI needs to generate decisions that survive contact with the real world. 🚗🤖

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13,836 görüntüleme • 4 ay önce

the micro1 robotics lab: real world data for intelligent models that co-exist in the physical world. we’re in-the-wild across 75 countries in 6,000+ unique environments collecting data. diverse movements, objects, and settings. the future of AI is as human as you can imagine. join us to start training robots today (link in comments).

the micro1 robotics lab: real world data for intelligent models that co-exist in the physical world. we’re in-the-wild across 75 countries in 6,000+ unique environments collecting data. diverse movements, objects, and settings. the future of AI is as human as you can imagine. join us to start training robots today (link in comments).

Ali Ansari

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Elon Musk says Optimus is the infinite money glitch. Not because robots are valuable. Because robots can make more robots. Musk explained that humanoid robots improve through three exponential trends multiplied together: digital intelligence, AI chip capability, and electromechanical dexterity. But the real unlock is that the robot can start making the robots. "So you have a recursive multiplicative exponential," he said. "This is a supernova." Musk's thesis is that once you have a physical robot capable of manufacturing, the production curve stops being linear and becomes self-replicating. Every robot built accelerates the building of the next one. The implication is that the companies that control physical manufacturing will control AI's future, not the ones with the best models. The bottleneck is not intelligence. It is atoms.

Elon Musk says Optimus is the infinite money glitch. Not because robots are valuable. Because robots can make more robots. Musk explained that humanoid robots improve through three exponential trends multiplied together: digital intelligence, AI chip capability, and electromechanical dexterity. But the real unlock is that the robot can start making the robots. "So you have a recursive multiplicative exponential," he said. "This is a supernova." Musk's thesis is that once you have a physical robot capable of manufacturing, the production curve stops being linear and becomes self-replicating. Every robot built accelerates the building of the next one. The implication is that the companies that control physical manufacturing will control AI's future, not the ones with the best models. The bottleneck is not intelligence. It is atoms.

Shruti

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Anthropic's Sholto Douglas describes the concept of a software-only singularity: "It's one where the models are far better at digital tasks than they are at physical ones. And so we see rapid change in the digital world with models, and relatively little change in the physical world. So information and software changes dramatically, and this ends up having some pretty weird effects." "It means that maybe like the drivers of what have been the last couple decades of progress in the economy turn around." "And I think we'll see that flow on into the physical world but at a delay. So you get much better at doing chip design. You get much better at training AI models. AI models get a lot faster. Chips get a lot better. The general economy gets a lot more efficient because the sort of information and message parsing that is much of the rest of the economy ends up becoming much more efficient." "But at the same time you don't yet have robots providing limitless physical abundance. Science probably progresses really fast up to the degree that you need interaction with labs or larger particle colliders or something like this. And then you go, 'Okay well I need to build the robots.'"

Anthropic's Sholto Douglas describes the concept of a software-only singularity: "It's one where the models are far better at digital tasks than they are at physical ones. And so we see rapid change in the digital world with models, and relatively little change in the physical world. So information and software changes dramatically, and this ends up having some pretty weird effects." "It means that maybe like the drivers of what have been the last couple decades of progress in the economy turn around." "And I think we'll see that flow on into the physical world but at a delay. So you get much better at doing chip design. You get much better at training AI models. AI models get a lot faster. Chips get a lot better. The general economy gets a lot more efficient because the sort of information and message parsing that is much of the rest of the economy ends up becoming much more efficient." "But at the same time you don't yet have robots providing limitless physical abundance. Science probably progresses really fast up to the degree that you need interaction with labs or larger particle colliders or something like this. And then you go, 'Okay well I need to build the robots.'"

TBPN

208,090 görüntüleme • 7 ay önce