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Biological intelligence to bootstrap robotic intelligence? If robotics is about to enter its golden age, Mecka is one of the companies feeding it, by sensorizing humans to train robots. The biggest challenge robotics faces is the data flywheel cold start problem. LLMs got a free cold start from the...

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Sonya Huang 🐥

30,623 subscribers

33,030 Aufrufe • vor 6 Tagen •via X (Twitter)

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13 Kommentare

Profilbild von Clementine, like the fruit
Clementine, like the fruitvor 6 Tagen

@MeckaAI @joshavata not a second during this interview where you weren’t smiling. love seeing ceo’s love their work

Profilbild von Sonya Huang 🐥
Sonya Huang 🐥vor 6 Tagen

Full episode on podcast platforms: YouTube: Spotify: Apple:

Profilbild von Anas Biad
Anas Biadvor 6 Tagen

@MeckaAI @joshavata the real question is are we going to have Gundams or not?

Profilbild von Ty
Tyvor 6 Tagen

@MeckaAI Robot Dog Panther Horse FTW!!!

Profilbild von Rushabh Sheth
Rushabh Shethvor 6 Tagen

@MeckaAI The cold start is the crux. Actually putting sensors on people is a moat in itself.

Profilbild von George Burke
George Burkevor 6 Tagen

@MeckaAI Love that they built gloves for the force data. My hunch is the last mile still has to be learned on the robot itself, on the job, since a human hand and a gripper feel that egg very differently.

Profilbild von nabrih77
nabrih77vor 6 Tagen

@MeckaAI Bullish on @MeckaAI Big W @joshavata @jasontheutopian i see mecka funding $8M first and now $128M+ LFG

Profilbild von 大姚
大姚vor 6 Tagen

@MeckaAI Robot data cold-start is the real bottleneck — sensorizing humans is a clever bypass

Profilbild von David Arnal
David Arnalvor 6 Tagen

@MeckaAI First-person video captures intent, but force data seems just as critical. Knowing what a task looks like is very different from knowing how to handle fragile objects safely.

Profilbild von DexorynLabs
DexorynLabsvor 6 Tagen

@MeckaAI Solving cold starts unlocks velocity.

Profilbild von Floryn
Florynvor 6 Tagen

@MeckaAI power boost, we need that too

Profilbild von sasaki kazuya
sasaki kazuyavor 6 Tagen

@MeckaAI Human-generated data could be the missing link for robotics, turning everyday movement into training signals and helping machines learn faster in the real world

Profilbild von vøv △
vøv △vor 6 Tagen

@pythianism @MeckaAI Millions of hours, robot brain finally wakes

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One hundred thousand hours of high-quality egocentric data from Steve Xie and the Lightwheel team. A huge contribution to the community and a big step for embodied AI. it truly seems as if the end of the robotics data shortage is in sight -- which totally changes how we can think about problems vs just a couple years ago. More on RoboPapers ->
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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

Mario Gabriele 🦊

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AlphaGo: two years, human data, humans in the loop. AlphaZero: gave up the human data and outperformed all of it in a few hours. Four hours to beat the best chess AI ever built. David Silver on why Ineffable Intelligence is making the same bet again.
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AlphaGo: two years, human data, humans in the loop. AlphaZero: gave up the human data and outperformed all of it in a few hours. Four hours to beat the best chess AI ever built. David Silver on why Ineffable Intelligence is making the same bet again.

Sequoia Capital

11,242 Aufrufe • vor 13 Tagen

My conversation with Sergey Levine (Sergey Levine). Sergey is the co-founder of Physical Intelligence -- a company building foundation models that can control any robot to do any task in any environment. The company's thesis is that generality is more scalable than specialization, meaning that a model trained across many different robots and tasks will ultimately outperform any system built to do one thing well (eg, just wash dishes). Sergey is a researcher by background, but I think you will appreciate how practical and commercially grounded this conversation is. We discuss: - Why changing a diaper will be the last task a robot masters - The simulation v. real-world data debate - How multimodal LLMs give robots common sense - Moravec's Paradox + Robot Olympics - Why robots can do long-horizon tasks now - A realistic timeline for robots in our homes I should note that I am an investor in Physical Intelligence -- I made the investment because I believe it is one of the most important companies tackling the problem of robotics. Enjoy! Timestamps: 0:00 Intro 2:39 Defining Physical Intelligence 5:19 The Challenge of Building General Models 6:34 The Stakes and Future of General Purpose Robotics 8:15 Pros and Cons of Humanoid Robots 10:12 Historical Milestones in Robotics Research 15:31 Combining Generative AI and Deep RL 21:24 Moravec's Paradox 25:33 Kitchen Robots 29:30 Simulation vs. Real-World Data 30:48 The Robot Olympics 36:31 The Physiological Reality of Embodiment 38:56 Controversies in the Robotics Community 44:18 What Makes a Great Researcher 48:27 How Businesses Should Prepare for Robotics 54:09 Tracking Progress Through Research Papers 57:02 The Next Step: Mid-Level Reasoning 1:02:00 The Kindest Thing
1:15:15

My conversation with Sergey Levine (Sergey Levine). Sergey is the co-founder of Physical Intelligence -- a company building foundation models that can control any robot to do any task in any environment. The company's thesis is that generality is more scalable than specialization, meaning that a model trained across many different robots and tasks will ultimately outperform any system built to do one thing well (eg, just wash dishes). Sergey is a researcher by background, but I think you will appreciate how practical and commercially grounded this conversation is. We discuss: - Why changing a diaper will be the last task a robot masters - The simulation v. real-world data debate - How multimodal LLMs give robots common sense - Moravec's Paradox + Robot Olympics - Why robots can do long-horizon tasks now - A realistic timeline for robots in our homes I should note that I am an investor in Physical Intelligence -- I made the investment because I believe it is one of the most important companies tackling the problem of robotics. Enjoy! Timestamps: 0:00 Intro 2:39 Defining Physical Intelligence 5:19 The Challenge of Building General Models 6:34 The Stakes and Future of General Purpose Robotics 8:15 Pros and Cons of Humanoid Robots 10:12 Historical Milestones in Robotics Research 15:31 Combining Generative AI and Deep RL 21:24 Moravec's Paradox 25:33 Kitchen Robots 29:30 Simulation vs. Real-World Data 30:48 The Robot Olympics 36:31 The Physiological Reality of Embodiment 38:56 Controversies in the Robotics Community 44:18 What Makes a Great Researcher 48:27 How Businesses Should Prepare for Robotics 54:09 Tracking Progress Through Research Papers 57:02 The Next Step: Mid-Level Reasoning 1:02:00 The Kindest Thing

Patrick OShaughnessy

134,397 Aufrufe • vor 6 Monaten

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 Aufrufe • vor 4 Monaten

Releasing the Unfolding Robotics blog! Time to unfold robotics: we trained a robot to fold clothes using 8 bimanual setups, 100+ hours of demonstrations, and 5k+ GPU hours. Flashy robot demos are everywhere. But you rarely see the real story: the data, the failures, the engineering. We’re sharing everything: code, data, and details in the blog →
1:39

Releasing the Unfolding Robotics blog! Time to unfold robotics: we trained a robot to fold clothes using 8 bimanual setups, 100+ hours of demonstrations, and 5k+ GPU hours. Flashy robot demos are everywhere. But you rarely see the real story: the data, the failures, the engineering. We’re sharing everything: code, data, and details in the blog →

LeRobot

283,272 Aufrufe • vor 6 Monaten