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Humanoids are an important form factor for developing physical AI models, says Carolina Parada, Google DeepMind's head of robotics, telling Tom Mackenzie their human-like design can make both deployment and learning easier

Bloomberg

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33,184 次观看 • 9 天前 •via X (Twitter)

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New Episode: Carolina Parada leads the robotics team at Google DeepMind. Carolina believes in a future with a broad, rich ecosystem of diverse robot types, where AI is smart enough to embody any robot. We discuss her journey, advancements in Gemini Robotics 1.5, cross-embodiment transfer, RL versus imitation, humanoids, world models, scaling laws, societal concerns, and more. 1:17 Introduction 1:38 Career journey and inspirations 3:50 Reaction to the 2022 ChatGPT launch 4:53 Google DeepMind's robotics mission 9:13 Key upgrades in Gemini Robotics 1.5 14:06 Agentic system's web usage 15:33 Robotics data-gathering methods 16:57 VLA vs. reasoning model training differences 18:22 Convergence of action and reasoning models 19:35 Cross-embodiment transfer 22:34 Learning directly from humans 24:20 Generalization challenges 27:01 Imitation versus reinforcement learning 28:51 Use of world models in robotics 30:31 Applications and testing of Gemini Robotics 1.5 33:04 Do humanoids deserve special focus? 35:06 Home humanoids timeline & limiting factors 37:19 Scaling laws in robotics versus self-driving 38:46 Value of learning classical robotics approaches 40:38 Ethical issues and societal impact

New Episode: Carolina Parada leads the robotics team at Google DeepMind. Carolina believes in a future with a broad, rich ecosystem of diverse robot types, where AI is smart enough to embody any robot. We discuss her journey, advancements in Gemini Robotics 1.5, cross-embodiment transfer, RL versus imitation, humanoids, world models, scaling laws, societal concerns, and more. 1:17 Introduction 1:38 Career journey and inspirations 3:50 Reaction to the 2022 ChatGPT launch 4:53 Google DeepMind's robotics mission 9:13 Key upgrades in Gemini Robotics 1.5 14:06 Agentic system's web usage 15:33 Robotics data-gathering methods 16:57 VLA vs. reasoning model training differences 18:22 Convergence of action and reasoning models 19:35 Cross-embodiment transfer 22:34 Learning directly from humans 24:20 Generalization challenges 27:01 Imitation versus reinforcement learning 28:51 Use of world models in robotics 30:31 Applications and testing of Gemini Robotics 1.5 33:04 Do humanoids deserve special focus? 35:06 Home humanoids timeline & limiting factors 37:19 Scaling laws in robotics versus self-driving 38:46 Value of learning classical robotics approaches 40:38 Ethical issues and societal impact

The Humanoid Hub

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For an industrial robot built for the rigors of factories and power plants, tidying up a living room may seem like a light day at the office for Spot. Yet, this demonstration represents the promise of AI models in robotics. In this case, Google DeepMind's visual-language model (VLM) Gemini Robotics-ER 1.5 was empowering Spot with embodied reasoning. Go behind the scenes and watch the full demo in our blog:

For an industrial robot built for the rigors of factories and power plants, tidying up a living room may seem like a light day at the office for Spot. Yet, this demonstration represents the promise of AI models in robotics. In this case, Google DeepMind's visual-language model (VLM) Gemini Robotics-ER 1.5 was empowering Spot with embodied reasoning. Go behind the scenes and watch the full demo in our blog:

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"AI is an operating system." Lux Capital's Deena Shakir says the biggest opportunities are shifting to application layers like healthcare and robotics, where startups can build on top of foundation models rather than compete with them

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Sam Altman says AI may need a new hardware form factor OpenAI and Jony Ive are exploring a family of devices to make AI easier to use — this will take time (not this year, maybe not next) The Goal is a new kind of computer built around AI, a companion through your life

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The new age of manufacturing is here! We are building a future where anyone can fully express themselves by turning their ideas for physical products into reality. Robotics and AI are the building blocks of this future.

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Simple transfer plate that moves in… two directions. This kind of mechanism can enable flexible 2D movement for boxes or parts without complex robotics. Simple mechanical design. Can move items both X and Y. Easier than full mobile robots in many cases, and useful for storage and sorting systems Sometimes smart mechanics beats complex control. Credit: Festo —- Weekly robotics and AI insights. Subscribe free:

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"Will the robotics industry will be generating trillions of dollars of revenue?", "YES." Dario Amodei says breakthroughs in robotics could emerge in several ways, such as through continual learning or generalization. Once achieved, these models will revolutionize both robot design and control. However, it will likely take another year or two from that point for the technology to diffuse through the economy.

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Are underwater drones the future of Helsing? And what is the acquisition strategy for the next 5 years? "Blue Ocean is an example of a company that we acquired. We've been developing together with them an underwater glider, which can stay underwater for several weeks." Torsten Reil #BloombergTech Tom Mackenzie

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Open models are accelerating development and deployment across agentic AI, physical AI, and a growing range of industries. ✨ “We have billions of dollars of supercomputers in operation, so we can develop our open models,” said NVIDIA CEO Jensen Huang in a recent keynote. “That work is starting to attract attention across industries worldwide, as we build frontier AI models in many different domains.” This includes leading open models from NVIDIA, including: ⚙️ NVIDIA Nemotron for agentic AI ⚙️ NVIDIA Cosmos and GR00T for physical AI ⚙️ NVIDIA Alpamayo for autonomous vehicles ⚙️ NVIDIA Clara for biomedical AI Learn more about open source and AI ➡️

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If a model is 50% reliable, it's 0% useful. Michael Giannangeli, Head of Agentic AI at Amazon Nova, breaks down one of the hardest problems in agentic AI: reliability. Models need to match or beat human performance, or they won't get adopted. To close the gap, teams are building an ecosystem of reinforcement learning "gyms" where AI agents can hone their skills. Think of it like a virtual training ground where agents practice tasks through trial and error, building reliability one rep at a time. 💪

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