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Current robotics models really underscore how wildly efficient the human brain is. Robotics models are relatively small (low billions of parameters), are only storing a second of context, and doing 100ms inference steps. By contrast the human brain has 100 trillion synapses (roughly analogous to parameters), processes 24 frames...

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Dwarkesh Patel

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87,247 просмотров • 10 месяцев назад •via X (Twitter)

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Geoffrey Hinton says AI models understand in the same way that people do and the best model we have of how the human brain works is large language models

Tsarathustra

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On net, is our biological hardware a help or hindrance to our intelligence? Are GPUs are to brains what fingers are to robot motors, and there’s a bunch of cognitive dexterity that our brain is uniquely good at implementing? Ala Morevac’s paradox, evolution has spent way more time optimizing nanotech than the semiconductor industry has. Or maybe it's something structural: the brain can do unstructured sparsity, and can colocate memory and compute. Or is it the other way around, because with digital computers, we can get rid of all these biological handicaps: The brain operates on a 20 W budget (and consequently runs at only 200 Hz speed in order to accommodate small voltage gaps). And it needs to do all this crazy intense molecular machinery in order to do things that take trivial amounts of code in computers: for example, update this parameter to this value. Net net, does current biological or digital computing hardware win out? I asked Adam Marblestone:
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On net, is our biological hardware a help or hindrance to our intelligence? Are GPUs are to brains what fingers are to robot motors, and there’s a bunch of cognitive dexterity that our brain is uniquely good at implementing? Ala Morevac’s paradox, evolution has spent way more time optimizing nanotech than the semiconductor industry has. Or maybe it's something structural: the brain can do unstructured sparsity, and can colocate memory and compute. Or is it the other way around, because with digital computers, we can get rid of all these biological handicaps: The brain operates on a 20 W budget (and consequently runs at only 200 Hz speed in order to accommodate small voltage gaps). And it needs to do all this crazy intense molecular machinery in order to do things that take trivial amounts of code in computers: for example, update this parameter to this value. Net net, does current biological or digital computing hardware win out? I asked Adam Marblestone:

Dwarkesh Patel

26,451 просмотров • 6 месяцев назад

Excited to announce that Skild AI has completed the acquisition of Zebra Technologies’ robotics arm (formerly Fetch Robotics). By combining Zebra's human-robot orchestration platform with omnibodied Skild Brain, we plan to turn warehouses everywhere into hubs of hyper-efficiency. Imagine a single platform, single brain optimizing every movement of robots as well as human workers in warehouses. Many of us in the robotics community have used Fetch Robots in the past and have rooted for them over the years, so this acquisition is special for us in many ways.
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Excited to announce that Skild AI has completed the acquisition of Zebra Technologies’ robotics arm (formerly Fetch Robotics). By combining Zebra's human-robot orchestration platform with omnibodied Skild Brain, we plan to turn warehouses everywhere into hubs of hyper-efficiency. Imagine a single platform, single brain optimizing every movement of robots as well as human workers in warehouses. Many of us in the robotics community have used Fetch Robots in the past and have rooted for them over the years, so this acquisition is special for us in many ways.

Deepak Pathak

79,093 просмотров • 2 месяцев назад

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!
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Dwarkesh Patel

125,151 просмотров • 10 месяцев назад

1/ The data parsing market for robotics is just emerging, and it’s already valued in the tens of billions. Data parsing to build data models and software for Robotics is a $20B market in 2024 and is expected to reach $150B+ by 2034 Cherry AI Robotics has built a data parsing tool for robotics, which goes live next week, and will effectively make this project one of the pioneers in data parsing for robotics.
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1/ The data parsing market for robotics is just emerging, and it’s already valued in the tens of billions. Data parsing to build data models and software for Robotics is a $20B market in 2024 and is expected to reach $150B+ by 2034 Cherry AI Robotics has built a data parsing tool for robotics, which goes live next week, and will effectively make this project one of the pioneers in data parsing for robotics.

CherryAI Robotics

90,786 просмотров • 10 месяцев назад

Ilya Sutskever explains why AI will do 100% the work that human can do within few years. "Slowly but surely, or maybe not so slowly, AI will keep getting better. The day will come when AI will do all of our, all the things that we can do, not just some of them, but all of them. Anything which I can learn, anything which any one of you can learn, the AI could do as well. How do we know this, by the way? How can I be so sure? How can I be so sure of that? The reason is that all of us have a brain, and the brain is a biological computer. That's why we have a brain; the brain is a biological computer. So why can't the digital computer, a digital brain, do the same things? This is the one-sentence summary for why AI will be able to do all those things: because we have a brain, and the brain is a biological computer." --- Full Video on 'University of Toronto' YT Channel (link in comment)
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Ilya Sutskever explains why AI will do 100% the work that human can do within few years. "Slowly but surely, or maybe not so slowly, AI will keep getting better. The day will come when AI will do all of our, all the things that we can do, not just some of them, but all of them. Anything which I can learn, anything which any one of you can learn, the AI could do as well. How do we know this, by the way? How can I be so sure? How can I be so sure of that? The reason is that all of us have a brain, and the brain is a biological computer. That's why we have a brain; the brain is a biological computer. So why can't the digital computer, a digital brain, do the same things? This is the one-sentence summary for why AI will be able to do all those things: because we have a brain, and the brain is a biological computer." --- Full Video on 'University of Toronto' YT Channel (link in comment)

Rohan Paul

334,691 просмотров • 9 месяцев назад

A few months ago we set out to understand exactly how our models can be used to accelerate progress in robotics and other AI systems that don’t just represent the world, but are actually able to interact with it. We’ve been collaborating with leading companies in the space and we’ve developed an initial approach that we're now opening up to accelerate development across the industry. Our fifth announcement is GWM Robotics. GWM Robotics is a learned simulator that generates synthetic data for scalable robot training and policy evaluation, removing the bottlenecks of physical hardware.
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A few months ago we set out to understand exactly how our models can be used to accelerate progress in robotics and other AI systems that don’t just represent the world, but are actually able to interact with it. We’ve been collaborating with leading companies in the space and we’ve developed an initial approach that we're now opening up to accelerate development across the industry. Our fifth announcement is GWM Robotics. GWM Robotics is a learned simulator that generates synthetic data for scalable robot training and policy evaluation, removing the bottlenecks of physical hardware.

Runway

35,040 просмотров • 7 месяцев назад

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.
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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.

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382,440 просмотров • 11 месяцев назад

🚨 Amazon has acquired Fauna Robotics, marking its second robotics acquisition in under a month. The move extends Amazon’s robotics strategy beyond fulfillment and further into the home, where safety, adaptability, and human interaction are central. Fauna’s soft-bodied humanoids are built for shared environments, a clear contrast to industrial systems and a better fit for consumer use cases. Taken together, these moves point to a more coordinated strategy across the humanoid stack, from hardware to deployment. The pace of consolidation is increasing, with strategic buyers moving earlier into the market.
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🚨 Amazon has acquired Fauna Robotics, marking its second robotics acquisition in under a month. The move extends Amazon’s robotics strategy beyond fulfillment and further into the home, where safety, adaptability, and human interaction are central. Fauna’s soft-bodied humanoids are built for shared environments, a clear contrast to industrial systems and a better fit for consumer use cases. Taken together, these moves point to a more coordinated strategy across the humanoid stack, from hardware to deployment. The pace of consolidation is increasing, with strategic buyers moving earlier into the market.

XMAQUINA

22,408 просмотров • 3 месяцев назад

John toured NIO's factory in China. They built an upscale EV for under $10k and went from the first shovel in the ground to the first car off the line in just 17 months. He said China knew it would always be behind in ICE cars, so it pushed aggressively into EVs to leapfrog to the next technology and gain an advantage over the rest of the world. "Going to their factory and seeing, number one, how quickly that factory is built. And second, the factory automation -- much of the process was done via robotics. Looking at the auto plants we have in the US, we still have a plant that was originally built a 100 years ago, operating outside of Chicago. The average age of a US plant is roughly 40 years now. They've been upgraded over time but I don't think they replicate what's happening with robotics in China. This combination of being able to do things really quickly with a skilled but low-cost labor force and robotics has just allowed China to create a quality product at a price that nobody in the rest of the world's really been able to figure out yet."
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John toured NIO's factory in China. They built an upscale EV for under $10k and went from the first shovel in the ground to the first car off the line in just 17 months. He said China knew it would always be behind in ICE cars, so it pushed aggressively into EVs to leapfrog to the next technology and gain an advantage over the rest of the world. "Going to their factory and seeing, number one, how quickly that factory is built. And second, the factory automation -- much of the process was done via robotics. Looking at the auto plants we have in the US, we still have a plant that was originally built a 100 years ago, operating outside of Chicago. The average age of a US plant is roughly 40 years now. They've been upgraded over time but I don't think they replicate what's happening with robotics in China. This combination of being able to do things really quickly with a skilled but low-cost labor force and robotics has just allowed China to create a quality product at a price that nobody in the rest of the world's really been able to figure out yet."

Patrick OShaughnessy

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IN NEWS: Baseten raises a $150M series D round. Tuhin Srivastava (Founder & CEO, Baseten) on the future of inference: “I think the token price goes down and inference should get cheaper over time. And that really just means there is going to be more inference.” “Every time we lower prices or optimize models to make it cheaper, four months later customers are spending more anyway.” “Inference prices will go down, but if the world is run by AI in 10 years, there is going to be a lot of inference. It better be cheap.”

TBPN

17,057 просмотров • 10 месяцев назад

Scientists mapped a piece of brain the size of half a grain of rice. One-millionth the size of the human brain. It took them a year and over 1.4 million gigabytes to scan it. They found over 57,000 cells, 150 million synapses, and even some new structures they didn't know existed. Mapping the entire human brain in this level of detail would require all the data storage generated on Earth in a year + a 140-acre data center. But the human brain itself can hold up to ~2.5 million gigabytes of information - enough for ~3 million hours of HD video or 342 years of continuous viewing. It can process roughly 10 quadrillion calculations per second - enough processing power to run over 4,000 high-end gaming PCs all operating at peak ability. And it only runs on the amount of power needed for a single dim light bulb. No technology even comes close to doing what the brain can do. The more we learn about biology, the more complex it becomes. This is God's Glory on display.
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Scientists mapped a piece of brain the size of half a grain of rice. One-millionth the size of the human brain. It took them a year and over 1.4 million gigabytes to scan it. They found over 57,000 cells, 150 million synapses, and even some new structures they didn't know existed. Mapping the entire human brain in this level of detail would require all the data storage generated on Earth in a year + a 140-acre data center. But the human brain itself can hold up to ~2.5 million gigabytes of information - enough for ~3 million hours of HD video or 342 years of continuous viewing. It can process roughly 10 quadrillion calculations per second - enough processing power to run over 4,000 high-end gaming PCs all operating at peak ability. And it only runs on the amount of power needed for a single dim light bulb. No technology even comes close to doing what the brain can do. The more we learn about biology, the more complex it becomes. This is God's Glory on display.

Divinely Designed

587,335 просмотров • 1 месяц назад

"I think the next big area is robotics. How can we enable robotics to be of help to every person because previously it was not able to do so due to the complexities of handling edge cases. How can we with the help of AI fill in the gap of the edge cases, so robotics can come up in the same way for real world scenarios as it did for industrial ones." We had the incredible jason (Co-founder Solayer (mainnet arc)) join our latest episode of AI on AIR hosted by Chi Zhang and Scott Shi - e/acc, where we discussed: 1⃣ How AI is evolving 2⃣ Solayer's MEV solution for fair trading 3⃣ Hardware acceleration techniques to enhance system performance 4⃣ What it will take for robotics to go mainstream
2:02

"I think the next big area is robotics. How can we enable robotics to be of help to every person because previously it was not able to do so due to the complexities of handling edge cases. How can we with the help of AI fill in the gap of the edge cases, so robotics can come up in the same way for real world scenarios as it did for industrial ones." We had the incredible jason (Co-founder Solayer (mainnet arc)) join our latest episode of AI on AIR hosted by Chi Zhang and Scott Shi - e/acc, where we discussed: 1⃣ How AI is evolving 2⃣ Solayer's MEV solution for fair trading 3⃣ Hardware acceleration techniques to enhance system performance 4⃣ What it will take for robotics to go mainstream

KITE AI

15,040 просмотров • 11 месяцев назад

"100 million words context window is already possible, which is roughly what a human hears in a lifetime. Inference support is the only bottleneck to achieve it. And AI Models actually do learn during the context window, without changing the weights." ~ Anthropic CEO Dario Amodei This is from 11 months back. --- From 'Alex Kantrowitz' YT Channel (Full Video link in comment)
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"100 million words context window is already possible, which is roughly what a human hears in a lifetime. Inference support is the only bottleneck to achieve it. And AI Models actually do learn during the context window, without changing the weights." ~ Anthropic CEO Dario Amodei This is from 11 months back. --- From 'Alex Kantrowitz' YT Channel (Full Video link in comment)

Rohan Paul

407,212 просмотров • 4 дней назад

Everything Elon said about Optimus at the All-In Summit today: • We’re finalizing the design of Optimus v3. That release is going to be a very remarkable robot. It will have manual dexterity comparable to a human, meaning a very complex hand, an AI mind that can navigate and comprehend reality, and will be made in very high volume. • Other robotics companies are missing those three very hard things. • I spend more mental cycles on Optimus than any other single thing. Solving real-world AI, all of the electrical-mechanical issues, the supply chain, and production challenges. • There is no supply chain for humanoid robots, so it has to be created from scratch, which requires a lot of vertical integration. None of the actuators in Optimus are available from an existing supply chain. • I think if successful, Optimus would be the biggest product ever. • The marginal cost of production, once we hit a million units per year, will probably be around $20,000. It depends on how much we spend on the AI chip in the robot, and we’ll need to achieve a lot of efficiencies in the actuators—26 actuators per arm (26 motors, gearboxes, and power electronics). The AI chip might cost $5,000 or $6,000, maybe more. At 1 million units a year, production cost will be $20,000, maybe $25,000. Price will be a function of demand. • Human hands have evolved to be incredibly sophisticated machines. Hands are a very first instrument. You can swing a baseball bat, thread a needle, play a piano or violin, and assemble a car. Hands are incredibly versatile instruments. Most of the muscles of the hands are actually in the forearm, and the hand is almost like a puppet. Human tendon evolution is incredibly good. The human hand has 27 or 28 degrees of freedom, depending on how you count it; it’s amazing. • In order to create a robot that can be a generalized humanoid, you must solve the “hands problem.” • Even though there are 10,000 to 20,000 electric motors out there, we couldn’t buy the actuators for any amount of money. We had to design every electric motor, gearbox, and controlling electronics from scratch, from first principles of physics. • Optimus is harder than developing any previous Tesla product, but not harder than Starship. • Right now, we’re struggling with the final design of the hardware, primarily the hand. The hands and forearm are the majority of the engineering difficulty of the entire robot. • If you want to do all the things that a human can do, it turns out you need a humanoid robot. If you want to do a subset, that’s much easier. Humans evolved to the shape and capability that we have for a good reason. There is value to having four fingers and a thumb; even the pinky is quite useful. Toes are much more of a question mark. • The AI5 inference chip will be 40 times better than AI4 by some measures. We know the limiting factors of the chip because the AI software and hardware teams work so closely. Effectively, the Tesla AI hardware and software teams are co-designing the chip. • The Softmax function on AI4 takes 40 steps in emulation mode, which will take only a few steps in AI5 natively. AI5 will easily handle mixed precision. • In terms of nominal raw compute, the AI5 inference chip has 8 times more compute, 9 times more memory, and 5 times more memory bandwidth compared to AI4. Because we’re addressing some core limitations and optimizations at the silicon level, we’re able to realize 40x improvements.
12:26

Everything Elon said about Optimus at the All-In Summit today: • We’re finalizing the design of Optimus v3. That release is going to be a very remarkable robot. It will have manual dexterity comparable to a human, meaning a very complex hand, an AI mind that can navigate and comprehend reality, and will be made in very high volume. • Other robotics companies are missing those three very hard things. • I spend more mental cycles on Optimus than any other single thing. Solving real-world AI, all of the electrical-mechanical issues, the supply chain, and production challenges. • There is no supply chain for humanoid robots, so it has to be created from scratch, which requires a lot of vertical integration. None of the actuators in Optimus are available from an existing supply chain. • I think if successful, Optimus would be the biggest product ever. • The marginal cost of production, once we hit a million units per year, will probably be around $20,000. It depends on how much we spend on the AI chip in the robot, and we’ll need to achieve a lot of efficiencies in the actuators—26 actuators per arm (26 motors, gearboxes, and power electronics). The AI chip might cost $5,000 or $6,000, maybe more. At 1 million units a year, production cost will be $20,000, maybe $25,000. Price will be a function of demand. • Human hands have evolved to be incredibly sophisticated machines. Hands are a very first instrument. You can swing a baseball bat, thread a needle, play a piano or violin, and assemble a car. Hands are incredibly versatile instruments. Most of the muscles of the hands are actually in the forearm, and the hand is almost like a puppet. Human tendon evolution is incredibly good. The human hand has 27 or 28 degrees of freedom, depending on how you count it; it’s amazing. • In order to create a robot that can be a generalized humanoid, you must solve the “hands problem.” • Even though there are 10,000 to 20,000 electric motors out there, we couldn’t buy the actuators for any amount of money. We had to design every electric motor, gearbox, and controlling electronics from scratch, from first principles of physics. • Optimus is harder than developing any previous Tesla product, but not harder than Starship. • Right now, we’re struggling with the final design of the hardware, primarily the hand. The hands and forearm are the majority of the engineering difficulty of the entire robot. • If you want to do all the things that a human can do, it turns out you need a humanoid robot. If you want to do a subset, that’s much easier. Humans evolved to the shape and capability that we have for a good reason. There is value to having four fingers and a thumb; even the pinky is quite useful. Toes are much more of a question mark. • The AI5 inference chip will be 40 times better than AI4 by some measures. We know the limiting factors of the chip because the AI software and hardware teams work so closely. Effectively, the Tesla AI hardware and software teams are co-designing the chip. • The Softmax function on AI4 takes 40 steps in emulation mode, which will take only a few steps in AI5 natively. AI5 will easily handle mixed precision. • In terms of nominal raw compute, the AI5 inference chip has 8 times more compute, 9 times more memory, and 5 times more memory bandwidth compared to AI4. Because we’re addressing some core limitations and optimizations at the silicon level, we’re able to realize 40x improvements.

The Humanoid Hub

238,630 просмотров • 10 месяцев назад

#FluorescenceFriday #Science #neuroscience Dendritic branches (green) in the mouse brain, studded with spines ("lollipops"), and myelin sheaths (pink tubes) that surround axons. Contacts between axons and dendrites called synapses facilitate communication between neurons. There are ~100 trillion synapses in the human brain, so collectively humanity has the same order of synapses as the 10^24 stars estimated in the observable universe. As seen by lattice light sheet expansion microscopy.
1:00

#FluorescenceFriday #Science #neuroscience Dendritic branches (green) in the mouse brain, studded with spines ("lollipops"), and myelin sheaths (pink tubes) that surround axons. Contacts between axons and dendrites called synapses facilitate communication between neurons. There are ~100 trillion synapses in the human brain, so collectively humanity has the same order of synapses as the 10^24 stars estimated in the observable universe. As seen by lattice light sheet expansion microscopy.

Eric Betzig

16,370 просмотров • 2 лет назад

What makes the human brain unique? Most diseases of the human brain are only seen in humans. By understanding what makes the human brain different, we can understand more about these unique diseases. #studyBRAIN
2:33

What makes the human brain unique? Most diseases of the human brain are only seen in humans. By understanding what makes the human brain different, we can understand more about these unique diseases. #studyBRAIN

allen institute

10,911 просмотров • 1 год назад

TESLA ISN'T JUST AHEAD IN AI, IT'S AN ORDER OF MAGNITUDE MORE EFFICIENT THAN THE REST OF THE WORLD "I actually think Tesla is ahead of the rest of the world in intelligence density of AI by an order of magnitude or more This is going to sound like a pretty bold statement, but I know what the intelligence efficiency of the big models are like, Grok and a bunch of the other models Tesla’s AI is, in terms of its memory efficiency, I think more than an order of magnitude better. The Tesla AI is very compute-efficient and very memory-efficient One of the metrics one should consider for any given AI model is the intelligence per gigabyte. Especially when you’re constrained on RAM, having an AI that has very high intelligence density per gigabyte for a given number of gigabytes, how much functionality can you get out of it" - Elon Musk
0:59

TESLA ISN'T JUST AHEAD IN AI, IT'S AN ORDER OF MAGNITUDE MORE EFFICIENT THAN THE REST OF THE WORLD "I actually think Tesla is ahead of the rest of the world in intelligence density of AI by an order of magnitude or more This is going to sound like a pretty bold statement, but I know what the intelligence efficiency of the big models are like, Grok and a bunch of the other models Tesla’s AI is, in terms of its memory efficiency, I think more than an order of magnitude better. The Tesla AI is very compute-efficient and very memory-efficient One of the metrics one should consider for any given AI model is the intelligence per gigabyte. Especially when you’re constrained on RAM, having an AI that has very high intelligence density per gigabyte for a given number of gigabytes, how much functionality can you get out of it" - Elon Musk

X Freeze

62,736 просмотров • 5 месяцев назад

The dissection of the human brain allows for visualization of each brain region in relation to other regions and to the brain as a whole. [📹scientissimum]
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The dissection of the human brain allows for visualization of each brain region in relation to other regions and to the brain as a whole. [📹scientissimum]

Massimo

299,681 просмотров • 1 год назад

Sundar Pichai on the robotics opportunity: ⦿ He's impressed by recent humanoid progress - so much so that he sometimes has to look closely to tell if a robot video is real or fake. ⦿ Google initially moved too early into the application layer, but now sees the combination of AI and robotics as the “sweet spot.” ⦿ He believes we’re 2–3 years away from a “magical moment” in robotics. ⦿ Google is working on playing a role similar to Android in robotics - one of its internal bets is already focused on supporting robotics manufacturers. ⦿ Gemini robotics models will work very well - how to productize them is still an open question at the company.
2:43

Sundar Pichai on the robotics opportunity: ⦿ He's impressed by recent humanoid progress - so much so that he sometimes has to look closely to tell if a robot video is real or fake. ⦿ Google initially moved too early into the application layer, but now sees the combination of AI and robotics as the “sweet spot.” ⦿ He believes we’re 2–3 years away from a “magical moment” in robotics. ⦿ Google is working on playing a role similar to Android in robotics - one of its internal bets is already focused on supporting robotics manufacturers. ⦿ Gemini robotics models will work very well - how to productize them is still an open question at the company.

The Humanoid Hub

87,088 просмотров • 1 год назад