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Robotics doesn't have a model problem. It has a data problem. And underneath that, a deployment problem. Physical AI progresses through real-world interaction. Robots act, fail, recover, and adapt. Without shared standards, every team relearns the same lessons in isolation. Deployment standards determine whether learning compounds or resets. PrismaX...

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15,330 görüntüleme • 4 ay önce •via X (Twitter)

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Tolani4 ay önce

The Service Layer For Physical AI

Julz profil fotoğrafı
Julz4 ay önce

How will you scale real world data?

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FAHD4 ay önce

So Bullishhhhh 🔥

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M𝖚𝖘𝖙y 🪽4 ay önce

@Julzcrypt gPrisma 🦾

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What's the next chapter in decentralizing the service layer???

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gPrisma

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kingopw34 ay önce

Most awaited post ! gprisma intern

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Dibbyte4 ay önce

Excited for the service layer era

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kinndao4 ay önce

Waiting

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👻MaLeEk🔥4 ay önce

@Julzcrypt How soon 👀

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bigwil4 ay önce

Lots of cool little examples and experiments... It's time to scale now!

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GENTLE 🦹‍♂️4 ay önce

@Julzcrypt “physical AI only improves when robots learn from real-world deployment” Gg PrismaX

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Aj Kanchan 🏴‍☠️ 🚢4 ay önce

This is exactly what robotics needs.

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Li🥳🥳 🦇 (✱,✱)4 ay önce

Let's go, that's the best news!

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Trong Hatachi4 ay önce

service layer sounds right, but compounding only happens if data is portable, open standard or prisma-only and who owns the ops data?

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Ãyø Mî Dê4 ay önce

gprisma 1

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This is awesome.

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When TGE ?

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oussail4 ay önce

Lfg

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Hosam 🟣🟢4 ay önce

Gprisma 🦾

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The Service Layer For Physical AI >>>

OnchainVibe.eth ❖,❖ 🇮🇳🇮🇳 profil fotoğrafı
OnchainVibe.eth ❖,❖ 🇮🇳🇮🇳4 ay önce

Great information gprisma

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The Service Layer For Physical AI

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New future PrismaX

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LFG 🔥

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Victoriou$ Victor4 ay önce

Beautiful to see how @PrismaXai is shaping the future of robotics

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@0xmusty It's time

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In PrismaX we trust!

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That's great PrismaXai

𝙋𝘼𝙒𝘼𝙉  profil fotoğrafı
𝙋𝘼𝙒𝘼𝙉 4 ay önce

Physical AI scales through real world data not just models 🤖

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Sean Waples Dexter(❖,❖)4 ay önce

🥲

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Gprisma 🦾

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Adel Bucetta4 ay önce

the reason most robotics projects fall short is they're trying to shortcut the feedback loop by relying on simulations instead of actual world interaction. that's a data problem, but it's also an opportunity for shared knowledge and standards

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Nana | VN (❖,❖)4 ay önce

That's great PrismaXai

Benzer Videolar

Today Mecka is announcing $60M in funding to become the data and deployment layer for physical AI This raise will allow us to scale our data infrastructure, invest into new verticals, and deploy robots into the real world
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Today Mecka is announcing $60M in funding to become the data and deployment layer for physical AI This raise will allow us to scale our data infrastructure, invest into new verticals, and deploy robots into the real world

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1/7 Physical AI has a data problem. More compute alone won’t teach a robot how to move, grasp, react, or recover from mistakes. LLMs had the entire internet to learn from. Robots need something different: huge amounts of real action data. That’s where Axis Robotics gets interesting. $AXIS is building a data engine for Physical AI, turning robot training into something people can contribute to directly through a browser. The loop is straightforward: Generate → Collect → Process → Deploy → Correct → Repeat. What looks simple on the surface could become something much bigger underneath: a scalable way to produce the training data robots need to become genuinely useful in the physical world. The real unlock for Physical AI might not be another bigger model. It might be better data.

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German robotics company NEURA Robotics has created NEURA Gym: a large-scale, physical AI gym and training ground where hundreds of robots, including the humanoid 4NE-1, learn through real-world interactions. The company is collaborating with the Neuraverse ecosystem to create the world's largest repository of physical training data, promoting the application of humanoid robots.

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Explore where physical AI is headed next with the people shaping it. On Feb. 10 at 9 a.m. PT, join Ming‑Yu, Vice President of Research at NVIDIA, and Chubby♨️ from Superintelligence, for a conversation on how NVIDIA Cosmos reframes AI for a world of robots, AVs, and vision-driven systems. We'll dig into: ✅ Cosmos' origins and designing models for the physical world ✅ The scientific ideas and architectures behind Cosmos and its open models ✅ How open, reasoning-capable physical AI is changing how teams prototype, deploy, and iterate ✅ What a future of AI systems that understand space, time, and causality could unlock If you’re thinking into real‑world AI, this is the conversation to catch ➡️
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🎤 Overheard at #CES2026: Physical AI is moving intelligence from the cloud into the real world, and AMD is powering that shift with a full portfolio that enables robots to sense, decide, and act in real time across industries like healthcare, agriculture, and manufacturing.

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Karol Hausman is the co-founder and CEO of Physical Intelligence, a robotics company building a general-purpose “AI brain for the physical world.” The company has raised more than $1 billion in funding to develop foundation models that allow robots to operate across many machines, environments, and tasks rather than being programmed for a single purpose. In our conversation, we explore: • The moment a lecture from Sergey Levine convinced him to abandon his PhD research direction and pivot fully to deep learning • The case for building a general “AI brain” for the physical world rather than a single specialized robot • The role of real-world data in training robots, the limits of simulation, and how deployment could create a powerful data flywheel • The unique challenges of physical intelligence and why robots must operate with far higher reliability than language models Thank you to the partners who make this possible - Brex: The intelligent finance platform: - Granola: The app that might actually make you love meetings: Timestamps (00:00) Intro (04:05) Karol’s early fascination with robots (18:21) Karol’s entry point to robotics and PhD program (25:49) Combining robotics with LLMs: The Taylor Swift demo (30:48) The 1970s SHRDLU AI experiment (39:40) How research shapes what Physical Intelligence builds (49:07) The return of reinforcement learning in robotics (1:00:00) NVIDIA’s simulation engines (1:07:31) Compensating for missing senses
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Karol Hausman is the co-founder and CEO of Physical Intelligence, a robotics company building a general-purpose “AI brain for the physical world.” The company has raised more than $1 billion in funding to develop foundation models that allow robots to operate across many machines, environments, and tasks rather than being programmed for a single purpose. In our conversation, we explore: • The moment a lecture from Sergey Levine convinced him to abandon his PhD research direction and pivot fully to deep learning • The case for building a general “AI brain” for the physical world rather than a single specialized robot • The role of real-world data in training robots, the limits of simulation, and how deployment could create a powerful data flywheel • The unique challenges of physical intelligence and why robots must operate with far higher reliability than language models Thank you to the partners who make this possible - Brex: The intelligent finance platform: - Granola: The app that might actually make you love meetings: Timestamps (00:00) Intro (04:05) Karol’s early fascination with robots (18:21) Karol’s entry point to robotics and PhD program (25:49) Combining robotics with LLMs: The Taylor Swift demo (30:48) The 1970s SHRDLU AI experiment (39:40) How research shapes what Physical Intelligence builds (49:07) The return of reinforcement learning in robotics (1:00:00) NVIDIA’s simulation engines (1:07:31) Compensating for missing senses

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Robots can already fold laundry, make espresso, clean kitchens, and assemble things. The harder problem is getting them to do those tasks reliably, for long periods of time, without a human babysitting them. At Startup School 2026, Physical Intelligence cofounder Chelsea Finn explains what it takes to build general-purpose robots that work in the real world. She shares how reinforcement learning pushed robot throughput up 2x, how their systems can run autonomously for hours, and why she believes robotics is entering its GPT era: moving from specialized models toward general-purpose systems that can work across tasks, robots, and environments. 00:00 — The State of Physical Intelligence 01:23 — What It Takes to Make Robots Useful 05:11 — The Reliability Problem 07:43 — Reinforcement Learning for Robotics 09:35 — Learning From Failures 12:43 — Training Robots to Improve Themselves 14:21 — Can a Robot Work for 13 Hours Straight? 17:36 — Why Robots Need Memory 21:22 — Building a General-Purpose Robot 25:02 — From Fine-Tuning to Out-of-the-Box Models 27:35 — Training on All the Data 30:20 — One Model That Beats the Specialists 31:21 — Compositional Generalization 37:49 — The GPT Era of Robotics 39:49 — Q&A
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Robots can already fold laundry, make espresso, clean kitchens, and assemble things. The harder problem is getting them to do those tasks reliably, for long periods of time, without a human babysitting them. At Startup School 2026, Physical Intelligence cofounder Chelsea Finn explains what it takes to build general-purpose robots that work in the real world. She shares how reinforcement learning pushed robot throughput up 2x, how their systems can run autonomously for hours, and why she believes robotics is entering its GPT era: moving from specialized models toward general-purpose systems that can work across tasks, robots, and environments. 00:00 — The State of Physical Intelligence 01:23 — What It Takes to Make Robots Useful 05:11 — The Reliability Problem 07:43 — Reinforcement Learning for Robotics 09:35 — Learning From Failures 12:43 — Training Robots to Improve Themselves 14:21 — Can a Robot Work for 13 Hours Straight? 17:36 — Why Robots Need Memory 21:22 — Building a General-Purpose Robot 25:02 — From Fine-Tuning to Out-of-the-Box Models 27:35 — Training on All the Data 30:20 — One Model That Beats the Specialists 31:21 — Compositional Generalization 37:49 — The GPT Era of Robotics 39:49 — Q&A

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🚨 Breaking News: Solo Tech won at CES 2026. 🦾🏆 In a 10×10 booth, we showcased the highest density of working robots on the floor proving that the future of AI is physical, performant, and deployable today. Up close with real systems. No slides. No simulations. Just Physical AI in action. Alongside leaders shaping what’s next NVIDIA Robotics , Sharpa , and Boston Dynamics the message was clear: This is the ChatGPT moment for robotics. And it won’t happen behind screens. It will happen in Robotics Gyms spaces around the world where machines train, models adapt, and builders learn by doing. The era of AI that acts has begun. #BreakingNews #CES2026 #SoloTech #PhysicalAI #RoboticsGym #EdgeAI

Dhruv Diddi

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Jensen Huang says agentic AI is not a chatbot. It is a "digital robot" -- and the next bottleneck is not the model. It is first-person data. "Agentic AI is just a digital robot. It understands, it reasons, it plans, and it acts and uses tools." Then he makes the bigger jump: "The world's video data is from a third person, not first person. And so agentic systems, robotic systems, physical AI -- the data is the hardest problem." That is the hidden constraint in robotics. LLMs got the internet. Physical AI needs demonstrations, synthetic worlds, reinforcement learning, and first-person experience. This is why Jensen keeps calling it a stack: robots are not waiting for one magical model. They need the data engine, the simulator, the reward system, the runtime, and the computer. The next wave of AI is not just software that talks. It is software that sees, plans, acts, and eventually moves through the world.
2:32

Jensen Huang says agentic AI is not a chatbot. It is a "digital robot" -- and the next bottleneck is not the model. It is first-person data. "Agentic AI is just a digital robot. It understands, it reasons, it plans, and it acts and uses tools." Then he makes the bigger jump: "The world's video data is from a third person, not first person. And so agentic systems, robotic systems, physical AI -- the data is the hardest problem." That is the hidden constraint in robotics. LLMs got the internet. Physical AI needs demonstrations, synthetic worlds, reinforcement learning, and first-person experience. This is why Jensen keeps calling it a stack: robots are not waiting for one magical model. They need the data engine, the simulator, the reward system, the runtime, and the computer. The next wave of AI is not just software that talks. It is software that sees, plans, acts, and eventually moves through the world.

Karl Mehta

14,140 görüntüleme • 3 ay önce

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

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2:22

Announcing Axol: a dual-arm robot designed for teams working with physical AI. Made in America. Axol is for builders who believe robots should work, in the real world, not just staged environments, and that the future of physical AI should be open, not closed. #robotics #automation #ai #hardware

Almond Robotics

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Tiff In Tech (600K+ on YouTube) just dropped a new video: Robots Are ‘Doomed to Succeed’ and That’s a Problem. Excited to see OpenMind featured in the conversation, highlighting the missing gaps in physical AI we solve: - Robot understanding of human intent - Building real social intelligence in robots - Operating systems that work across all robot manufacturers and form-factors Check out the full video below👇
2:08

Tiff In Tech (600K+ on YouTube) just dropped a new video: Robots Are ‘Doomed to Succeed’ and That’s a Problem. Excited to see OpenMind featured in the conversation, highlighting the missing gaps in physical AI we solve: - Robot understanding of human intent - Building real social intelligence in robots - Operating systems that work across all robot manufacturers and form-factors Check out the full video below👇

OpenMind

11,197 görüntüleme • 5 ay önce

Unreal Engine 5 is becoming the go-to platform for robotics teams, not just as a simulator but as a synthetic data factory powering the next generation of robotics systems🦾 From synthetic data to sim-to-real transfer, read our piece on how UE5 is quietly reshaping how robots learn to see, move, and act in the real world:
0:36

Unreal Engine 5 is becoming the go-to platform for robotics teams, not just as a simulator but as a synthetic data factory powering the next generation of robotics systems🦾 From synthetic data to sim-to-real transfer, read our piece on how UE5 is quietly reshaping how robots learn to see, move, and act in the real world:

Unreal Engine

61,187 görüntüleme • 5 ay önce