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Physical Intelligence (Physical Intelligence) has raised $400 million in a new round at a $2 billion valuation. Physical Intelligence (Physical Intelligence), San Francisco, California, United States, was founded in 2024 by Adnan Esmail (Adnan Esmail), Brian Ichter (Brian Ichter), Karol Hausman (Karol Hausman), Lachy Groom (Lachy Groom), and Sergey... show more

Silicon Valley Investclub

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13,627 次观看 • 1 年前 •via X (Twitter)

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Lachy Groom1 年前

@physical_int @adnan_esm @brian_ichter wrong font on the π symbol 😩

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We profiled the most hyped robotics company in the world. Inside Physical Intelligence with Co-founder Lachy Groom Exclusive interview in the Physical Intelligence robotics lab, who’re backed by top investors to build robots which work in the real world. Not just scripted environments. We cover: 0:00 So…What is Physical Intelligence? 1:41 Why can’t we solve Marovec’s Paradox? 2:33 Live Demo: Meet the Machines 4:17 Why robots still struggle today… 6:14 Meet PI’s Investors (Thrive Capital) 7:19 Teaming up with the mega-brilliant co-founder group of Karol Haussman, Sergey Levine, Chelsea Finn, Brian Ichter, Adnan Esmail & Quan Vuong 9:20 PI’s Robot Capabilities 11:37 How are they making intelligent robots? 13:43 The Future Impact of Everyday Robots 15:49 Jeff Bezos Investment in Physical Intelligence 16:43 What will the next 1-3 years look like? 17:54 Revealing PI’s Work Culture 19:23 Outro

We profiled the most hyped robotics company in the world. Inside Physical Intelligence with Co-founder Lachy Groom Exclusive interview in the Physical Intelligence robotics lab, who’re backed by top investors to build robots which work in the real world. Not just scripted environments. We cover: 0:00 So…What is Physical Intelligence? 1:41 Why can’t we solve Marovec’s Paradox? 2:33 Live Demo: Meet the Machines 4:17 Why robots still struggle today… 6:14 Meet PI’s Investors (Thrive Capital) 7:19 Teaming up with the mega-brilliant co-founder group of Karol Haussman, Sergey Levine, Chelsea Finn, Brian Ichter, Adnan Esmail & Quan Vuong 9:20 PI’s Robot Capabilities 11:37 How are they making intelligent robots? 13:43 The Future Impact of Everyday Robots 15:49 Jeff Bezos Investment in Physical Intelligence 16:43 What will the next 1-3 years look like? 17:54 Revealing PI’s Work Culture 19:23 Outro

Sachin Shah

119,486 次观看 • 6 个月前

Alphabet (Google's parent company) acquired a stake in Physical Intelligence. San Francisco-based Physical Intelligence, an AI and robotics startup, has secured $600 million in fresh funding, pushing its post-money valuation to $5.6 billion. - The round was led by Alphabet's independent growth fund CapitalG. - Existing backers who participated in this round include Lux Capital, Thrive Capital, and Jeff Bezos. - New participants include Index Ventures and T. Rowe Price, among others. Physical Intelligence is building general-purpose AI foundation models and learning algorithms designed to act as the "brain" for robots. Here's their latest π*0.6 model in actio

Alphabet (Google's parent company) acquired a stake in Physical Intelligence. San Francisco-based Physical Intelligence, an AI and robotics startup, has secured $600 million in fresh funding, pushing its post-money valuation to $5.6 billion. - The round was led by Alphabet's independent growth fund CapitalG. - Existing backers who participated in this round include Lux Capital, Thrive Capital, and Jeff Bezos. - New participants include Index Ventures and T. Rowe Price, among others. Physical Intelligence is building general-purpose AI foundation models and learning algorithms designed to act as the "brain" for robots. Here's their latest π*0.6 model in actio

The Humanoid Hub

244,972 次观看 • 8 个月前

New Unsupervised Learning with Karol Hausman & Danny Driess (Physical Intelligence) on building generalist robotics foundation models and: - What’s next in AI x robotics - Biggest outstanding questions - How they 10x’d model training speed - Open sourcing π 0 - Breakthroughs in generalization Spotify: Apple: YouTube:

New Unsupervised Learning with Karol Hausman & Danny Driess (Physical Intelligence) on building generalist robotics foundation models and: - What’s next in AI x robotics - Biggest outstanding questions - How they 10x’d model training speed - Open sourcing π 0 - Breakthroughs in generalization Spotify: Apple: YouTube:

Jacob Effron

13,961 次观看 • 1 年前

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

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 🦊

27,871 次观看 • 5 个月前

Ten years ago I started Stord because nobody had built the infrastructure for what happens after a consumer clicks buy. Today we raised $250M at a $3 billion valuation. Commerce now has the physical intelligence layer that merchants deserve. Full piece:

Ten years ago I started Stord because nobody had built the infrastructure for what happens after a consumer clicks buy. Today we raised $250M at a $3 billion valuation. Commerce now has the physical intelligence layer that merchants deserve. Full piece:

Sean Henry

2,329,802 次观看 • 2 个月前

The threat of physical attacks by Iran on people living in the UK has increased 'significantly' since 2022, according to a new report by parliament's intelligence watchdog. Jason Farrell reports ⬇️

The threat of physical attacks by Iran on people living in the UK has increased 'significantly' since 2022, according to a new report by parliament's intelligence watchdog. Jason Farrell reports ⬇️

Sky News

56,962 次观看 • 1 年前

Physical AI is here. 🤝 Grounded in physics. Driven by intelligence. Together, Dassault Systèmes and NVIDIA are pioneering a new era of virtual twins that learn, simulate, and evolve. 🔗

Physical AI is here. 🤝 Grounded in physics. Driven by intelligence. Together, Dassault Systèmes and NVIDIA are pioneering a new era of virtual twins that learn, simulate, and evolve. 🔗

NVIDIA

53,023 次观看 • 6 个月前

On Bloomberg last night, I was asked why Amazon drives more than a third of U.S. e-commerce. It is not because of their storefront. It is not because of their payments. It is not even because of product selection. It is because consumers trust their deliveries. That trust is built on physical infrastructure at scale with intelligence layered on top. And that is what Stord is building for every independent merchant. The most defensible companies in the world combine software intelligence with physical execution. Today, 9 of the 10 largest U.S. companies by revenue operate physical networks at scale. Software alone cannot replicate the outcomes created when you control the movement of physical atoms. That is the physical intelligence layer for commerce. And that is what Stord Labs is built to advance. Deploying AI and robotics against real operational complexity, in the real world, across a vertically integrated network operating at scale. Bloomberg TV

On Bloomberg last night, I was asked why Amazon drives more than a third of U.S. e-commerce. It is not because of their storefront. It is not because of their payments. It is not even because of product selection. It is because consumers trust their deliveries. That trust is built on physical infrastructure at scale with intelligence layered on top. And that is what Stord is building for every independent merchant. The most defensible companies in the world combine software intelligence with physical execution. Today, 9 of the 10 largest U.S. companies by revenue operate physical networks at scale. Software alone cannot replicate the outcomes created when you control the movement of physical atoms. That is the physical intelligence layer for commerce. And that is what Stord Labs is built to advance. Deploying AI and robotics against real operational complexity, in the real world, across a vertically integrated network operating at scale. Bloomberg TV

Sean Henry

1,275,331 次观看 • 2 个月前

We raised $130M in Series B funding at a $1.3B valuation to build the computer aided design suite for molecules. The round was led by General Catalyst & Oak HC/FT along with existing investors Thrive Capital, OpenAI, DIMENSION, Neo, Lachy Groom, Menlo Ventures, SV Angel, and Yosemite. We're also joined by new investors including Emerson Collective and Glade Brook.

We raised $130M in Series B funding at a $1.3B valuation to build the computer aided design suite for molecules. The round was led by General Catalyst & Oak HC/FT along with existing investors Thrive Capital, OpenAI, DIMENSION, Neo, Lachy Groom, Menlo Ventures, SV Angel, and Yosemite. We're also joined by new investors including Emerson Collective and Glade Brook.

Chai Discovery

114,420 次观看 • 8 个月前

Chelsea Finn (Chelsea Finn) on building general-purpose robotics, and bringing intelligence into the physical world. At AI Startup School in San Francisco. 00:00 - General Purpose Robots 00:11 - Challenges in Robotics Applications 00:57 - Physical Intelligence: A New Approach 01:47 - Learning from Language Models 02:08 - Data Sources for Training Robots 03:32 - Training with Real-World Data 04:39 - Initial Successes and Challenges 09:10 - Breakthrough in Robot Training 11:03 - Improving Performance 15:43 - Expanding Capabilities 17:34 - Robots in Unseen Environments 25:54 - Handling Open-Ended Prompts 29:36 - Evaluating Robot Performance 30:03 - Future Directions and Challenges 31:27 - Audience Q&A

Chelsea Finn (Chelsea Finn) on building general-purpose robotics, and bringing intelligence into the physical world. At AI Startup School in San Francisco. 00:00 - General Purpose Robots 00:11 - Challenges in Robotics Applications 00:57 - Physical Intelligence: A New Approach 01:47 - Learning from Language Models 02:08 - Data Sources for Training Robots 03:32 - Training with Real-World Data 04:39 - Initial Successes and Challenges 09:10 - Breakthrough in Robot Training 11:03 - Improving Performance 15:43 - Expanding Capabilities 17:34 - Robots in Unseen Environments 25:54 - Handling Open-Ended Prompts 29:36 - Evaluating Robot Performance 30:03 - Future Directions and Challenges 31:27 - Audience Q&A

Y Combinator

129,834 次观看 • 1 年前

UFO whistleblower David Grusch says the United States government has knowledge of at least four different types of non-human intelligence. Some are "bipedal life" with physical bodies. Others are "sentient plasmoid life." A bipedal life form is a two-legged, human-like physical being. A sentient plasmoid life form is a hypothetical intelligent “plasma/energy” entity with no solid body. He says the secrecy began in 1954 under President Eisenhower.

UFO whistleblower David Grusch says the United States government has knowledge of at least four different types of non-human intelligence. Some are "bipedal life" with physical bodies. Others are "sentient plasmoid life." A bipedal life form is a two-legged, human-like physical being. A sentient plasmoid life form is a hypothetical intelligent “plasma/energy” entity with no solid body. He says the secrecy began in 1954 under President Eisenhower.

Shadow of Ezra

166,272 次观看 • 2 个月前

At Physical Intelligence, we teamed up with Weave Robotics and Ultra to stress-test our models in real-world deployments. This was a really fun collaboration that saw our latest pi06 model running in production at Sea Breeze Cleaners and a real warehouse! More below.

At Physical Intelligence, we teamed up with Weave Robotics and Ultra to stress-test our models in real-world deployments. This was a really fun collaboration that saw our latest pi06 model running in production at Sea Breeze Cleaners and a real warehouse! More below.

Sergey Levine

31,910 次观看 • 5 个月前

Base Power (Base Power) is in talks to raise around $1 billion at a $12 billion valuation in a new funding round. Base Power, headquartered in Austin, Texas, United States, was founded in 2023 by Zachary Dell and Justin Lopas (Justin Lopas).

Base Power (Base Power) is in talks to raise around $1 billion at a $12 billion valuation in a new funding round. Base Power, headquartered in Austin, Texas, United States, was founded in 2023 by Zachary Dell and Justin Lopas (Justin Lopas).

Silicon Valley Investclub

71,478 次观看 • 2 个月前

It’s time to go beyond language models. Introducing Odyssey-2 Max, our most powerful world model yet. It materially advances the SOTA in physical accuracy. This is a big step toward models that simulate and interact with the world in real time. A new intelligence entirely!

It’s time to go beyond language models. Introducing Odyssey-2 Max, our most powerful world model yet. It materially advances the SOTA in physical accuracy. This is a big step toward models that simulate and interact with the world in real time. A new intelligence entirely!

Odyssey

2,185,494 次观看 • 4 个月前

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

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

133,833 次观看 • 4 个月前

I find it wild that Physical Intelligence robotics models can work at all given that they only have a 1 second context length. As the robot is moving around a room and cleaning up a table, it only sees the current timestamp of images from the cameras (plus the text description of its task). I asked Sergey Levine was how this model can work at all. If a human had to execute a many minutes long plan in order to accomplish a physical task, but had her memory reset every second, there's no way she could pull it off But somehow just knowing what state the world is literally in this moment is all you need to know to keep a reasonably complication plan going? Sergey's answer calls on Morevac's Paradox, and I thought it was quite interesting. Full episode out tomorrow!

I find it wild that Physical Intelligence robotics models can work at all given that they only have a 1 second context length. As the robot is moving around a room and cleaning up a table, it only sees the current timestamp of images from the cameras (plus the text description of its task). I asked Sergey Levine was how this model can work at all. If a human had to execute a many minutes long plan in order to accomplish a physical task, but had her memory reset every second, there's no way she could pull it off But somehow just knowing what state the world is literally in this moment is all you need to know to keep a reasonably complication plan going? Sergey's answer calls on Morevac's Paradox, and I thought it was quite interesting. Full episode out tomorrow!

Dwarkesh Patel

126,066 次观看 • 11 个月前

OpenAI's Chief Futurist Joshua Achiam breaks with the consensus on takeoff, arguing intelligence hits a physical ceiling and then raw compute decides who wins: "People expect that there's no ceiling for the amount of intelligence that you could have in a model, and they think of recursive self-improvement as this loop that's gonna happen at some point or another. RSI starts happening and model intelligence takes off and it goes to the moon, and they don't see a ceiling." "I think on physical grounds, there's gotta be a maximum amount of computation that you can have per unit volume and energy in the physical universe. And that sort of implies there's a maximum amount of intelligence per unit volume and unit of energy." "If that's the case, eventually, seeing how fast AI model capabilities are increasing right now, eventually everyone hits that saturation point and everyone's got roughly equivalently capable models from a raw intelligence perspective." "The open source frontier lags the closed source frontier by some number of months, but the fact that it's months is crazy. So eventually everyone is probably working with equally maximally capable models, and then it's amount of compute that you're able to throw at a problem that determines who wins." Joshua Achiam

OpenAI's Chief Futurist Joshua Achiam breaks with the consensus on takeoff, arguing intelligence hits a physical ceiling and then raw compute decides who wins: "People expect that there's no ceiling for the amount of intelligence that you could have in a model, and they think of recursive self-improvement as this loop that's gonna happen at some point or another. RSI starts happening and model intelligence takes off and it goes to the moon, and they don't see a ceiling." "I think on physical grounds, there's gotta be a maximum amount of computation that you can have per unit volume and energy in the physical universe. And that sort of implies there's a maximum amount of intelligence per unit volume and unit of energy." "If that's the case, eventually, seeing how fast AI model capabilities are increasing right now, eventually everyone hits that saturation point and everyone's got roughly equivalently capable models from a raw intelligence perspective." "The open source frontier lags the closed source frontier by some number of months, but the fact that it's months is crazy. So eventually everyone is probably working with equally maximally capable models, and then it's amount of compute that you're able to throw at a problem that determines who wins." Joshua Achiam

MTS

60,213 次观看 • 26 天前