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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...

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Lachy Groomvor 1 Jahr

@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
21:38

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

119,403 Aufrufe • vor 5 Monaten

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
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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

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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:
0:31

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:

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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:
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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

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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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The goal isn't to tell a robot to fold your t-shirt. You should be able to prompt it “run my household” once, and have it cook, clean, shop, and adapt for months. Physical Intelligence co-founder Sergey Levine says once these robots start collecting real-world experience, the learning flywheel begins. Single digit years looks realistic.
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The goal isn't to tell a robot to fold your t-shirt. You should be able to prompt it “run my household” once, and have it cook, clean, shop, and adapt for months. Physical Intelligence co-founder Sergey Levine says once these robots start collecting real-world experience, the learning flywheel begins. Single digit years looks realistic.

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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 ⬇️
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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. 🔗
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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

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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.
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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

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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.
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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.

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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).
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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).

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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!

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2,183,089 Aufrufe • vor 2 Monaten

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

133,833 Aufrufe • vor 3 Monaten

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!
1:36

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

125,151 Aufrufe • vor 10 Monaten

TRM Labs has raised USD 70 million in Series C funding — at a USD 1 billion valuation. This raise fuels our core blockchain intelligence platform and accelerates our investment in AI solutions to disrupt criminal networks and national security threats. We're proud to be backed by leading investors — and to support institutions on the frontlines in 50+ countries. Read more ➡️
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TRM Labs has raised USD 70 million in Series C funding — at a USD 1 billion valuation. This raise fuels our core blockchain intelligence platform and accelerates our investment in AI solutions to disrupt criminal networks and national security threats. We're proud to be backed by leading investors — and to support institutions on the frontlines in 50+ countries. Read more ➡️

TRM Labs

75,632 Aufrufe • vor 5 Monaten