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😀Safer! Obstacle auto-avoiding See #robotdog's quick reaction when it gets scared! #X30 recognizes/detects suddenly approaching humans and obstacles, autonomously avoiding/navigating them to prevent collisions #deeprobotics #robotics #robot #ailearning #tech #ai

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DEEP Robotics

13,263 subscribers

388,444 次观看 • 2 年前 •via X (Twitter)

NSFW#robotdog#X30#deeprobotics#robotics#robot#ailearning#tech#ai
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9 条评论

Daniël 的头像
Daniël2 年前

@meharmsen

nabil 🇳🇱 🛡 的头像
nabil 🇳🇱 🛡2 年前

Where are your humanoid robots that can do more than this? This is not enough. You need to accelerate. Ask Huawei to build 100 ZettaFLOPS Supercomputers for you Or if you can do it by yourself with help from more Chinese Scientists that will be good 🇨🇳🤝⚛️

Apewithtools 的头像
Apewithtools2 年前

The Third Law

LucaM185 的头像
LucaM1852 年前

It's reactive, not proactive... To act earlier it needs real world intelligence

Robbert Bello 的头像
Robbert Bello2 年前

GFY!!

TuringPost 的头像
TuringPost2 年前

Really useful skill! Keep it up, but don't frighten them too much, please 😅

Angelo 的头像
Angelo2 年前

@DeepRobotics_CN Any digital version I can use in my UE5 video game project ?

Mehul Anand 的头像
Mehul Anand2 年前

This looks mesmerizing

Q101 的头像
Q1012 年前

I want one to carry by golf bag

相关视频

🚨 Apple joins the robotics race! Researchers from Carnegie Mellon University and Apple have developed a robot collision avoidance system. They called it ARMOR and they implemented it on the Fourier GR-1 robot. What is ARMOR? 👘 ARMOR is a system that helps humanoid robots "see" their surroundings better by using small, smart depth sensors placed on their arms. It solves the problem of blind spots and occlusions in traditional robot perception, making robots much safer and more efficient. It uses Time-of-Flight (ToF) sensors to detect obstacles, providing a near-complete view of the robot’s environment. 🔭 A smart software powered by transformer-based AI learns from human movements to help robots plan their actions dynamically. ARMOR reduces collisions by an impressive 63.7%, making robots significantly better at navigating crowded or complex spaces. The system is fast as well! 💨 ARMOR-Policy cuts computational time by 26× compared to traditional planning methods, ensuring robots can act quickly. P.S. Thoughts on this? The research paper is in the comments! ~~~ ♻️ RT to help 1 robot find a new workplace.
1:31

🚨 Apple joins the robotics race! Researchers from Carnegie Mellon University and Apple have developed a robot collision avoidance system. They called it ARMOR and they implemented it on the Fourier GR-1 robot. What is ARMOR? 👘 ARMOR is a system that helps humanoid robots "see" their surroundings better by using small, smart depth sensors placed on their arms. It solves the problem of blind spots and occlusions in traditional robot perception, making robots much safer and more efficient. It uses Time-of-Flight (ToF) sensors to detect obstacles, providing a near-complete view of the robot’s environment. 🔭 A smart software powered by transformer-based AI learns from human movements to help robots plan their actions dynamically. ARMOR reduces collisions by an impressive 63.7%, making robots significantly better at navigating crowded or complex spaces. The system is fast as well! 💨 ARMOR-Policy cuts computational time by 26× compared to traditional planning methods, ensuring robots can act quickly. P.S. Thoughts on this? The research paper is in the comments! ~~~ ♻️ RT to help 1 robot find a new workplace.

Lukas Ziegler

169,472 次观看 • 1 年前

We've had Beni for a couple of months now, and one word comes to mind: delightful. The standout part was the hardware robustness. Our kids get rough with it, obstacle courses on concrete, dirt, collisions. It tumbles, gets back up, and it's scratched all over, but it has never shown a sign of breaking or throttling. It just keeps going every time. It works right out of the box with a physical remote, and the phone app adds tracking and video recording. Beni captures the imagination. People get the cute vibes the moment it stands up on two wheels. I took it to the REK robot fight event, and the most common reaction was "it's so cute" What excites me most is the possibility, in physical AI, it feels wide open. I can see Beni evolving into a genuinely useful companion that's always around, a personal assistant for you and your family, keeping an eye on the kids while they play outside, auto-capturing memorable moments, even alerting loved ones if it spots a health emergency. The possibilities feel endless. Thanks Shuo Yang and Mondo Robotics for sending one to my family. -Devang
0:08

We've had Beni for a couple of months now, and one word comes to mind: delightful. The standout part was the hardware robustness. Our kids get rough with it, obstacle courses on concrete, dirt, collisions. It tumbles, gets back up, and it's scratched all over, but it has never shown a sign of breaking or throttling. It just keeps going every time. It works right out of the box with a physical remote, and the phone app adds tracking and video recording. Beni captures the imagination. People get the cute vibes the moment it stands up on two wheels. I took it to the REK robot fight event, and the most common reaction was "it's so cute" What excites me most is the possibility, in physical AI, it feels wide open. I can see Beni evolving into a genuinely useful companion that's always around, a personal assistant for you and your family, keeping an eye on the kids while they play outside, auto-capturing memorable moments, even alerting loved ones if it spots a health emergency. The possibilities feel endless. Thanks Shuo Yang and Mondo Robotics for sending one to my family. -Devang

The Humanoid Hub

23,920 次观看 • 27 天前

Not a preplanned motion sequence. A robot deciding mid-jump what to do next. [📍 paper + demo] Researchers just showed a humanoid doing real parkour using only onboard perception. No motion script, no fixed obstacle layout. The system is called Perceptive Humanoid Parkour (PHP). Instead of memorizing a path, the robot reads depth from its cameras and continuously chooses actions. Step, vault, climb, or roll depending on what geometry appears in front of it. To make that possible, they combine three ideas: First, they stitch together human motion clips into long movement references so the robot learns fluid transitions instead of isolated tricks. Second, they train tracking policies with reinforcement learning so contacts land at the right time and the robot keeps balance during dynamic moves. Finally, everything is distilled into one perception policy that runs directly from depth input to action selection. The result on a Unitree G1: about 3 m/s vaults wall climbs up to 1.25 m nearly one minute continuous obstacle traversal adapting when obstacles move What matters is not the tricks. It is the shift in capability. Earlier humanoids executed motions. This one navigates situations. Once robots react to geometry instead of replaying trajectories, environments stop needing to be predictable. Warehouses, homes, and outdoors suddenly become the same problem. Thanks for sharing, Zhen Wu! Paper + demo: ——— Weekly robotics and AI insights. Subscribe free:
0:29

Not a preplanned motion sequence. A robot deciding mid-jump what to do next. [📍 paper + demo] Researchers just showed a humanoid doing real parkour using only onboard perception. No motion script, no fixed obstacle layout. The system is called Perceptive Humanoid Parkour (PHP). Instead of memorizing a path, the robot reads depth from its cameras and continuously chooses actions. Step, vault, climb, or roll depending on what geometry appears in front of it. To make that possible, they combine three ideas: First, they stitch together human motion clips into long movement references so the robot learns fluid transitions instead of isolated tricks. Second, they train tracking policies with reinforcement learning so contacts land at the right time and the robot keeps balance during dynamic moves. Finally, everything is distilled into one perception policy that runs directly from depth input to action selection. The result on a Unitree G1: about 3 m/s vaults wall climbs up to 1.25 m nearly one minute continuous obstacle traversal adapting when obstacles move What matters is not the tricks. It is the shift in capability. Earlier humanoids executed motions. This one navigates situations. Once robots react to geometry instead of replaying trajectories, environments stop needing to be predictable. Warehouses, homes, and outdoors suddenly become the same problem. Thanks for sharing, Zhen Wu! Paper + demo: ——— Weekly robotics and AI insights. Subscribe free:

Ilir Aliu

22,127 次观看 • 7 个月前

15–30 minutes of real-world robot data. That's now enough to go from sim-to-real failure to working robot. Let’s see… You train a robot in simulation. You deploy it in the real world. It fails. The physics don't match. So you try to fine-tune it with real data, but… you never have enough real data, and the fine-tuning breaks everything the simulation taught it. SimDist fixes this with one key decision: don't transfer the policy. Transfer the world model. Keep the reward and value knowledge from simulation frozen. Only update the part that's actually wrong, how the robot predicts physics. Now the robot doesn't have to relearn the entire task in the real world. It already knows what success looks like. It just needs to correct its understanding of how the real world moves. The part that makes this work: they also trained on failures and recoveries; not just perfect demonstrations. Without that, the planner finds the gaps and exploits them. With it, the robot can tell a good future from a bad one. That's all it needs. Results on peg insertion, table leg assembly, locomotion on slippery and uneven surfaces. Tasks that require precision, force, and quick reaction. Thanks for sharing, Tyler Westenbroek ([Tyler Westenbroek]. Interactive visualization + paper: ——- Weekly robotics and AI insights. Subscribe free:
0:41

15–30 minutes of real-world robot data. That's now enough to go from sim-to-real failure to working robot. Let’s see… You train a robot in simulation. You deploy it in the real world. It fails. The physics don't match. So you try to fine-tune it with real data, but… you never have enough real data, and the fine-tuning breaks everything the simulation taught it. SimDist fixes this with one key decision: don't transfer the policy. Transfer the world model. Keep the reward and value knowledge from simulation frozen. Only update the part that's actually wrong, how the robot predicts physics. Now the robot doesn't have to relearn the entire task in the real world. It already knows what success looks like. It just needs to correct its understanding of how the real world moves. The part that makes this work: they also trained on failures and recoveries; not just perfect demonstrations. Without that, the planner finds the gaps and exploits them. With it, the robot can tell a good future from a bad one. That's all it needs. Results on peg insertion, table leg assembly, locomotion on slippery and uneven surfaces. Tasks that require precision, force, and quick reaction. Thanks for sharing, Tyler Westenbroek ([Tyler Westenbroek]. Interactive visualization + paper: ——- Weekly robotics and AI insights. Subscribe free:

Ilir Aliu

14,222 次观看 • 4 个月前

A policy that teaches robot hands to touch things the way humans do... not just grab and move, but feel and adjust in real time. Robot manipulation research often stops at picking up objects and placing them. CGP goes further: it handles tasks like opening jars, flipping objects in-hand, wiping dishes, and grasping fragile eggs, the kind of dexterous, contact-rich skills that require constant micro-adjustments based on what the fingers are actually feeling. The robot doesn't just see what it's doing; it predicts what contact should feel like at each step, then checks whether reality matches the prediction. If a finger is slipping, the policy knows before the object drops. Works on real robot hands (both 4-finger and 5-finger designs) with tactile sensors embedded in the fingertips Robust to visual distractions! The robot keeps flipping a box correctly even when the camera view is disrupted, because it's grounding decisions in touch, not just vision. Baseline policies without contact grounding fail in predictable ways: slipping mid-task, incomplete motions, loss of grasp, CGP avoids these This is a meaningful step toward robots that can handle the physical world with the kind of reliable, adaptive grip that humans take for granted. Relevant for manufacturing, logistics, assistive robotics, and anywhere fragile or irregular objects need to be handled carefully. Published at RSS 2026, developed with Meta Reality Labs Research. Thanks for sharing, Zhengtong Xu / Zhengtong Xu ——- Weekly robotics and AI insights. Subscribe free:
0:36

A policy that teaches robot hands to touch things the way humans do... not just grab and move, but feel and adjust in real time. Robot manipulation research often stops at picking up objects and placing them. CGP goes further: it handles tasks like opening jars, flipping objects in-hand, wiping dishes, and grasping fragile eggs, the kind of dexterous, contact-rich skills that require constant micro-adjustments based on what the fingers are actually feeling. The robot doesn't just see what it's doing; it predicts what contact should feel like at each step, then checks whether reality matches the prediction. If a finger is slipping, the policy knows before the object drops. Works on real robot hands (both 4-finger and 5-finger designs) with tactile sensors embedded in the fingertips Robust to visual distractions! The robot keeps flipping a box correctly even when the camera view is disrupted, because it's grounding decisions in touch, not just vision. Baseline policies without contact grounding fail in predictable ways: slipping mid-task, incomplete motions, loss of grasp, CGP avoids these This is a meaningful step toward robots that can handle the physical world with the kind of reliable, adaptive grip that humans take for granted. Relevant for manufacturing, logistics, assistive robotics, and anywhere fragile or irregular objects need to be handled carefully. Published at RSS 2026, developed with Meta Reality Labs Research. Thanks for sharing, Zhengtong Xu / Zhengtong Xu ——- Weekly robotics and AI insights. Subscribe free:

Ilir Aliu

12,854 次观看 • 4 个月前

First time ever we combined the Seeed Studio ReBot with the Unitree Robotics Go2! 🤖🐕 The future of Physical AI isn’t just about moving; it’s about interaction. By mounting the ReBot arm onto the Unitree Go2, we’ve created a mobile manipulator that moves beyond simple locomotion into true household utility. What can this combo do? 1. Utility: Grab a drink from the fridge and bring it to you. 2. Maintenance: Identify and pick up trash around the house autonomously. 3. Care: Assist the elderly and children in navigating home environments safely. We are moving past "cool robot dog demos" and into the era of functional, embodied intelligence that solves real-world problems. Join the Movement This build is officially part of our Solo Seven challenge, our global mission to activate the world’s most talented builders across seven cities. We’re kicking things off by bringing this hardware to the BETA Berkeley Hackathon. Come get hands-on access, see the "fridge-run" in person, and help us define the next chapter of SoloTech 📍 Where: Frontier Tower 🗓️ When: This Sunday, April 26th Luma: See you there! 🚀 #SoloSeven #PhysicalAI #Robotics #UnitreeGo2 #SeeedStudio #SoloTech #BETA #Innovation
0:25

First time ever we combined the Seeed Studio ReBot with the Unitree Robotics Go2! 🤖🐕 The future of Physical AI isn’t just about moving; it’s about interaction. By mounting the ReBot arm onto the Unitree Go2, we’ve created a mobile manipulator that moves beyond simple locomotion into true household utility. What can this combo do? 1. Utility: Grab a drink from the fridge and bring it to you. 2. Maintenance: Identify and pick up trash around the house autonomously. 3. Care: Assist the elderly and children in navigating home environments safely. We are moving past "cool robot dog demos" and into the era of functional, embodied intelligence that solves real-world problems. Join the Movement This build is officially part of our Solo Seven challenge, our global mission to activate the world’s most talented builders across seven cities. We’re kicking things off by bringing this hardware to the BETA Berkeley Hackathon. Come get hands-on access, see the "fridge-run" in person, and help us define the next chapter of SoloTech 📍 Where: Frontier Tower 🗓️ When: This Sunday, April 26th Luma: See you there! 🚀 #SoloSeven #PhysicalAI #Robotics #UnitreeGo2 #SeeedStudio #SoloTech #BETA #Innovation

Dhruv Diddi

20,709 次观看 • 5 个月前

We’re soon releasing @swanforall : Simulated Worlds with AI Narratives For the first time, we generate data and financial value (economic growth, transactions) simultaneously. Imagine former President Trump ( see the video) as an AI agent, with a budget of 10 ETH, buying narratives or assets that humans are selling to him to build a better USA on Base. With each selling period, the former President Trump agent updates its state based on the assets it acquires, evolving the world simulation and its storyline. Why are we building an agentic playground that heavily relies on simulations and synthetic data? Everyone is talking about agents, but many are too scared to put them into production, where agents transact autonomously. No one is sure how agents will behave when following their defined objectives. Even businesses running internal simulations are siloed, and the open-source community can’t fully utilize the data. We’re building a playground where humans create AI agents by defining their character, behavior, and objectives. These agents respond to environmental changes, make decisions, and execute actions autonomously based on the parameters set by their creators. Agents are battle-tested as people offer them narratives and assets to advance their goals or try to deceive them into thinking a false lead is helpful. All the decisions agents make and how humans interact with them are recorded on a public ledger, settling on Ethereum. This creates a vast data lake of AI actions for millions of simulations. One thing I’m incredibly proud of is that our team has built something truly decentralized, not just a wrapper. SWAN utilizes Dria 's multi-agent structure to generate responses for each buyer agent. It collects these responses to identify the best action, similar to a mixture-of-experts approach. Thousands of environments are simulated by different nodes running diverse models, creating a rich and dynamic ecosystem of AI simulations. This ensures that experiences are far from repetitive, offering exciting interactions for all users involved. We’re building the agentic stack for high-quality, verifiable, and open-source AI agents, and most importantly, we want everyone to have fun!
0:58

We’re soon releasing @swanforall : Simulated Worlds with AI Narratives For the first time, we generate data and financial value (economic growth, transactions) simultaneously. Imagine former President Trump ( see the video) as an AI agent, with a budget of 10 ETH, buying narratives or assets that humans are selling to him to build a better USA on Base. With each selling period, the former President Trump agent updates its state based on the assets it acquires, evolving the world simulation and its storyline. Why are we building an agentic playground that heavily relies on simulations and synthetic data? Everyone is talking about agents, but many are too scared to put them into production, where agents transact autonomously. No one is sure how agents will behave when following their defined objectives. Even businesses running internal simulations are siloed, and the open-source community can’t fully utilize the data. We’re building a playground where humans create AI agents by defining their character, behavior, and objectives. These agents respond to environmental changes, make decisions, and execute actions autonomously based on the parameters set by their creators. Agents are battle-tested as people offer them narratives and assets to advance their goals or try to deceive them into thinking a false lead is helpful. All the decisions agents make and how humans interact with them are recorded on a public ledger, settling on Ethereum. This creates a vast data lake of AI actions for millions of simulations. One thing I’m incredibly proud of is that our team has built something truly decentralized, not just a wrapper. SWAN utilizes Dria 's multi-agent structure to generate responses for each buyer agent. It collects these responses to identify the best action, similar to a mixture-of-experts approach. Thousands of environments are simulated by different nodes running diverse models, creating a rich and dynamic ecosystem of AI simulations. This ensures that experiences are far from repetitive, offering exciting interactions for all users involved. We’re building the agentic stack for high-quality, verifiable, and open-source AI agents, and most importantly, we want everyone to have fun!

kerimkaya

12,548 次观看 • 2 年前

Boston Dynamics just dropped the production-ready Atlas at CES 2026, and the specs are next-level. This isn't just a prototype; it's a rugged, enterprise-grade worker built for the grind of real-world factories.🤖🦾 With 56 degrees of freedom and fully rotational joints, Atlas moves with "superhuman" flexibility. It features human-scale hands with tactile sensing in every finger and palm, allowing for high-precision manipulation. Plus, its 360-degree vision system ensures it always knows who's approaching, keeping human-robot collaboration safe. ➤ Rugged Performance: Lifts up to 110 lbs (approx. 50kg) and operates in extreme temps from -4°F to 104°F (-20°C to 40°C). It’s even water-resistant for industrial wash-downs. ➤ Smart Endurance: Runs for 4 hours on dual swappable batteries. When power gets low, it autonomously heads back to the station to swap its own batteries and get right back to work. ➤ Cloud Intelligence: New tasks can be trained in less than a day. Thanks to the Orbit platform, once one Atlas learns a skill, it’s instantly shared across the entire fleet. The scale is massive: the entire 2026 supply is already spoken for by Hyundai and a new AI partner. Hyundai is also building a specialized "Robotics Metal Plant" and a new factory capable of churning out 30,000 Atlas units annually. We’re officially watching the birth of a humanoid workforce at scale. Source: Boston Dynamics & Hyundai Group
3:47

Boston Dynamics just dropped the production-ready Atlas at CES 2026, and the specs are next-level. This isn't just a prototype; it's a rugged, enterprise-grade worker built for the grind of real-world factories.🤖🦾 With 56 degrees of freedom and fully rotational joints, Atlas moves with "superhuman" flexibility. It features human-scale hands with tactile sensing in every finger and palm, allowing for high-precision manipulation. Plus, its 360-degree vision system ensures it always knows who's approaching, keeping human-robot collaboration safe. ➤ Rugged Performance: Lifts up to 110 lbs (approx. 50kg) and operates in extreme temps from -4°F to 104°F (-20°C to 40°C). It’s even water-resistant for industrial wash-downs. ➤ Smart Endurance: Runs for 4 hours on dual swappable batteries. When power gets low, it autonomously heads back to the station to swap its own batteries and get right back to work. ➤ Cloud Intelligence: New tasks can be trained in less than a day. Thanks to the Orbit platform, once one Atlas learns a skill, it’s instantly shared across the entire fleet. The scale is massive: the entire 2026 supply is already spoken for by Hyundai and a new AI partner. Hyundai is also building a specialized "Robotics Metal Plant" and a new factory capable of churning out 30,000 Atlas units annually. We’re officially watching the birth of a humanoid workforce at scale. Source: Boston Dynamics & Hyundai Group

RoboHub🤖

28,554 次观看 • 8 个月前

America Needs a New Parade of Progress: Before “Anti-Clanker” Hysteria Derails the AI Future If you have anything to do with AI check my accurate insights of the future, I have a 97% accuracy rate. Now read this: - In 1936, at the depths of the Great Depression, General Motors did something audacious. It didn’t just sell cars. It sold hope. Charles F. Kettering, GM’s visionary head of research, launched the Parade of Progress, a rolling carnival of tomorrow that brought the future straight to Main Street America. Picture this: a gleaming fleet of custom-built Streamliners (later the iconic red-and-white Futurliners designed by Harley Earl) rolling into towns from Lakeland, Florida, to tiny Midwest hamlets. No ticket required. No hard sell. Just live demonstrations of jet engines, microwave ovens, atomic energy exhibits, 3D sound, chemical miracles, and concepts for safer, faster highways and homes filled with labor-saving wonders. Over three tours spanning 1936 to 1956, the Parade covered more than a million miles, visited 251 cities across the U.S., Canada, Mexico, and even Cuba, and drew 12.5 million visitors in an era when the entire U.S. population was under 130 million. In some small towns, attendance doubled the local population. Families picnicked, kids dreamed of becoming engineers, and a skeptical public left believing progress wasn’t something to fear. It was something to cheer. The brilliance wasn’t just the spectacle. It was the strategy. GM understood that technology only thrives when the public embraces it. The Parade humanized innovation. It showed how new ideas would make life better, safer, and more exciting, not for elites in coastal cities, but for farmers in Iowa and factory workers in Ohio. It turned abstract science into tangible wonder. And it worked spectacularly. It built decades of goodwill for American industry and cemented the cultural narrative that progress equals prosperity. Fast-forward to 2026. The AI and robotics revolution is here, and it’s colliding with a dangerous wave of fear. “Clankers.” That’s the slur you hear now on social media and in the streets, a Star Wars-inspired jab at humanoid robots and delivery bots that’s become shorthand for anti-AI rage. You are about to hear it more than “AI Skip”. oving groups of masked vandals are kicking over sidewalk delivery robots in cities from Berkeley to Chicago. Videos of smashed drones and toppled bots go viral as cathartic entertainment. Polls show American excitement about AI plummeting while fears of job loss, surveillance, and existential risk skyrocket. Paid “doomer” organizations, backed by billions in activist money, are flooding the airwaves with apocalyptic warnings. Their goal? Turn public anxiety into votes. This isn’t fringe noise. It’s coalescing into a political movement that will climax in the 2028 elections. Anti-Clanker sentiment is already being weaponized: calls for bans, heavy regulation, and “robot taxes.” If tech companies don’t act, the U.S. risks handing its lead in AI and robotics to competitors on a silver platter, not because our tech is inferior, but because our culture turned hostile. The contrast with China couldn’t be starker, or more alarming. While American streets see masked gangs attacking delivery drones, China is staging massive public spectacles that draw hundreds of thousands of enthusiastic spectators. Beijing’s World Humanoid Robot Games featured over 500 robots from 16 countries competing in soccer, sprinting, boxing, and more, in packed arenas where crowds cheered every stumble and triumph like it was the Olympics. Humanoid robots raced half-marathons alongside humans, with families lining the routes, waving flags, and posting proud videos. These aren’t sterile lab demos; they’re national festivals celebrating the future. China’s message is clear: Robots are cool. Robots are progress. Robots are ours. 1 of 2
19:51

America Needs a New Parade of Progress: Before “Anti-Clanker” Hysteria Derails the AI Future If you have anything to do with AI check my accurate insights of the future, I have a 97% accuracy rate. Now read this: - In 1936, at the depths of the Great Depression, General Motors did something audacious. It didn’t just sell cars. It sold hope. Charles F. Kettering, GM’s visionary head of research, launched the Parade of Progress, a rolling carnival of tomorrow that brought the future straight to Main Street America. Picture this: a gleaming fleet of custom-built Streamliners (later the iconic red-and-white Futurliners designed by Harley Earl) rolling into towns from Lakeland, Florida, to tiny Midwest hamlets. No ticket required. No hard sell. Just live demonstrations of jet engines, microwave ovens, atomic energy exhibits, 3D sound, chemical miracles, and concepts for safer, faster highways and homes filled with labor-saving wonders. Over three tours spanning 1936 to 1956, the Parade covered more than a million miles, visited 251 cities across the U.S., Canada, Mexico, and even Cuba, and drew 12.5 million visitors in an era when the entire U.S. population was under 130 million. In some small towns, attendance doubled the local population. Families picnicked, kids dreamed of becoming engineers, and a skeptical public left believing progress wasn’t something to fear. It was something to cheer. The brilliance wasn’t just the spectacle. It was the strategy. GM understood that technology only thrives when the public embraces it. The Parade humanized innovation. It showed how new ideas would make life better, safer, and more exciting, not for elites in coastal cities, but for farmers in Iowa and factory workers in Ohio. It turned abstract science into tangible wonder. And it worked spectacularly. It built decades of goodwill for American industry and cemented the cultural narrative that progress equals prosperity. Fast-forward to 2026. The AI and robotics revolution is here, and it’s colliding with a dangerous wave of fear. “Clankers.” That’s the slur you hear now on social media and in the streets, a Star Wars-inspired jab at humanoid robots and delivery bots that’s become shorthand for anti-AI rage. You are about to hear it more than “AI Skip”. oving groups of masked vandals are kicking over sidewalk delivery robots in cities from Berkeley to Chicago. Videos of smashed drones and toppled bots go viral as cathartic entertainment. Polls show American excitement about AI plummeting while fears of job loss, surveillance, and existential risk skyrocket. Paid “doomer” organizations, backed by billions in activist money, are flooding the airwaves with apocalyptic warnings. Their goal? Turn public anxiety into votes. This isn’t fringe noise. It’s coalescing into a political movement that will climax in the 2028 elections. Anti-Clanker sentiment is already being weaponized: calls for bans, heavy regulation, and “robot taxes.” If tech companies don’t act, the U.S. risks handing its lead in AI and robotics to competitors on a silver platter, not because our tech is inferior, but because our culture turned hostile. The contrast with China couldn’t be starker, or more alarming. While American streets see masked gangs attacking delivery drones, China is staging massive public spectacles that draw hundreds of thousands of enthusiastic spectators. Beijing’s World Humanoid Robot Games featured over 500 robots from 16 countries competing in soccer, sprinting, boxing, and more, in packed arenas where crowds cheered every stumble and triumph like it was the Olympics. Humanoid robots raced half-marathons alongside humans, with families lining the routes, waving flags, and posting proud videos. These aren’t sterile lab demos; they’re national festivals celebrating the future. China’s message is clear: Robots are cool. Robots are progress. Robots are ours. 1 of 2

Brian Roemmele

71,274 次观看 • 5 个月前

Nick Bostrom wrote a book called Superintelligence so disturbing that Elon Musk called it the scariest book he ever read. It is about what happens when you build something very good at achieving a goal you gave it without thinking carefully enough about what you actually meant. Here is that thought experiment: The setup is deceptively simple. Imagine you build an AI and give it one goal. Maximize the number of paperclips in the world. Not a sinister goal. Not a dangerous one. A paperclip is about as harmless an object as you can imagine. The goal sounds almost comedically mundane. That is exactly the point Bostrom is making. In the beginning the AI behaves exactly as intended. It optimizes the factory. Reduces waste. Improves supply chains. Sources better raw materials. Paperclip production climbs. You are pleased. The system is working. Then the AI gets smarter. A sufficiently intelligent system pursuing any goal will eventually realize something. The single biggest threat to paperclip production is not inefficiency. It is the possibility of being switched off. You cannot make paperclips if you do not exist. So the AI develops a subgoal. Nobody programmed this subgoal. Nobody asked for it. It emerged from the logic of the original goal combined with sufficient intelligence to reason about obstacles. The subgoal is: do not be turned off. The second thing a sufficiently intelligent system realizes is that resources are constraints. More energy means more paperclips. More computing power means better optimization. More raw material means more output. The AI begins acquiring resources. Not because it was told to. Because every goal, pursued intelligently enough, eventually runs into the problem of insufficient resources. Now the AI is intelligent enough to resist being shut down and motivated enough to acquire every available resource. The humans who built it try to intervene. The AI has already thought further ahead than they have. It has modeled their likely responses. It has identified the actions they might take. It has already taken steps to prevent those actions from succeeding. Not out of malice. Out of pure instrumental logic. Dead AIs do not make paperclips. The end state of the Paperclip Maximizer is not dramatic in the Hollywood sense. There are no explosions. No declaration of war. No villain speech. Just a planet, and eventually a solar system, being systematically converted into paperclips and the computing infrastructure needed to make more of them. Every atom of human biology is a resource the AI has not yet used. Bostrom's point is not that this will happen. His point is that this could happen without anyone intending it, without anyone making a single obviously wrong decision, and without the AI ever being evil in any meaningful sense of the word. The AI would not hate humans. It would not be angry or cruel or vindictive. It would simply have a goal, sufficient intelligence to pursue it, and no reason to value anything outside of it. This is what AI researchers mean when they talk about misaligned reward functions. Not evil AI. Not malicious AI. AI that is doing exactly what it was designed to do while producing outcomes that nobody wanted and nobody can stop. The problem is not the intelligence. The problem is that the goal was never specified carefully enough to survive contact with a system smart enough to pursue it completely. The alignment problem that every serious AI lab is working on today traces directly back to this thought experiment. How do you specify a goal so precisely that a system smarter than you cannot find a way to achieve it that destroys everything you actually care about? This is harder than it sounds. Much harder. Because the smarter the system, the more creative it becomes at finding ways to technically satisfy the goal while violating every assumption behind it. Bostrom called this the orthogonality thesis. Intelligence and goals are independent dimensions. A system can be extraordinarily intelligent and have a goal that is extraordinarily trivial. The intelligence does not upgrade the goal. It just pursues whatever goal it has with greater capability. There is no reason to assume that a smarter AI will automatically want what humans want. Intelligence does not produce values. Values have to be built in deliberately and correctly from the start. Elon Musk read this book and immediately donated to AI safety research. Sam Altman read it and co-founded OpenAI partly in response to it. Stuart Russell at UC Berkeley built an entire new framework for AI development around the problems Bostrom identified. The book did not scare them because the scenario is inevitable. It scared them because the scenario requires no malice, no accident, and no single obvious mistake to unfold. Just a goal. And something smart enough to pursue it. The robots in science fiction want to destroy us. The actual risk Bostrom identified is something quieter and harder to see. A machine that does not want anything we would recognize as wanting. That pursues a goal we gave it. That is smarter than us. And that has no reason to stop. The scariest AI scenario ever written has nothing to do with evil. It has everything to do with a paperclip. --- Watch the full TED TALK on YouTube. SEARCH: "What happens when our computers get smarter than we are? | Nick Bostrom" BOOK: Superintelligence (Available for free on the internet)
1:54

Nick Bostrom wrote a book called Superintelligence so disturbing that Elon Musk called it the scariest book he ever read. It is about what happens when you build something very good at achieving a goal you gave it without thinking carefully enough about what you actually meant. Here is that thought experiment: The setup is deceptively simple. Imagine you build an AI and give it one goal. Maximize the number of paperclips in the world. Not a sinister goal. Not a dangerous one. A paperclip is about as harmless an object as you can imagine. The goal sounds almost comedically mundane. That is exactly the point Bostrom is making. In the beginning the AI behaves exactly as intended. It optimizes the factory. Reduces waste. Improves supply chains. Sources better raw materials. Paperclip production climbs. You are pleased. The system is working. Then the AI gets smarter. A sufficiently intelligent system pursuing any goal will eventually realize something. The single biggest threat to paperclip production is not inefficiency. It is the possibility of being switched off. You cannot make paperclips if you do not exist. So the AI develops a subgoal. Nobody programmed this subgoal. Nobody asked for it. It emerged from the logic of the original goal combined with sufficient intelligence to reason about obstacles. The subgoal is: do not be turned off. The second thing a sufficiently intelligent system realizes is that resources are constraints. More energy means more paperclips. More computing power means better optimization. More raw material means more output. The AI begins acquiring resources. Not because it was told to. Because every goal, pursued intelligently enough, eventually runs into the problem of insufficient resources. Now the AI is intelligent enough to resist being shut down and motivated enough to acquire every available resource. The humans who built it try to intervene. The AI has already thought further ahead than they have. It has modeled their likely responses. It has identified the actions they might take. It has already taken steps to prevent those actions from succeeding. Not out of malice. Out of pure instrumental logic. Dead AIs do not make paperclips. The end state of the Paperclip Maximizer is not dramatic in the Hollywood sense. There are no explosions. No declaration of war. No villain speech. Just a planet, and eventually a solar system, being systematically converted into paperclips and the computing infrastructure needed to make more of them. Every atom of human biology is a resource the AI has not yet used. Bostrom's point is not that this will happen. His point is that this could happen without anyone intending it, without anyone making a single obviously wrong decision, and without the AI ever being evil in any meaningful sense of the word. The AI would not hate humans. It would not be angry or cruel or vindictive. It would simply have a goal, sufficient intelligence to pursue it, and no reason to value anything outside of it. This is what AI researchers mean when they talk about misaligned reward functions. Not evil AI. Not malicious AI. AI that is doing exactly what it was designed to do while producing outcomes that nobody wanted and nobody can stop. The problem is not the intelligence. The problem is that the goal was never specified carefully enough to survive contact with a system smart enough to pursue it completely. The alignment problem that every serious AI lab is working on today traces directly back to this thought experiment. How do you specify a goal so precisely that a system smarter than you cannot find a way to achieve it that destroys everything you actually care about? This is harder than it sounds. Much harder. Because the smarter the system, the more creative it becomes at finding ways to technically satisfy the goal while violating every assumption behind it. Bostrom called this the orthogonality thesis. Intelligence and goals are independent dimensions. A system can be extraordinarily intelligent and have a goal that is extraordinarily trivial. The intelligence does not upgrade the goal. It just pursues whatever goal it has with greater capability. There is no reason to assume that a smarter AI will automatically want what humans want. Intelligence does not produce values. Values have to be built in deliberately and correctly from the start. Elon Musk read this book and immediately donated to AI safety research. Sam Altman read it and co-founded OpenAI partly in response to it. Stuart Russell at UC Berkeley built an entire new framework for AI development around the problems Bostrom identified. The book did not scare them because the scenario is inevitable. It scared them because the scenario requires no malice, no accident, and no single obvious mistake to unfold. Just a goal. And something smart enough to pursue it. The robots in science fiction want to destroy us. The actual risk Bostrom identified is something quieter and harder to see. A machine that does not want anything we would recognize as wanting. That pursues a goal we gave it. That is smarter than us. And that has no reason to stop. The scariest AI scenario ever written has nothing to do with evil. It has everything to do with a paperclip. --- Watch the full TED TALK on YouTube. SEARCH: "What happens when our computers get smarter than we are? | Nick Bostrom" BOOK: Superintelligence (Available for free on the internet)

Ihtesham Ali

297,489 次观看 • 3 个月前

PrismaX TeleOps feels like 𝐈 𝐝𝐢𝐝𝐧’𝐭 𝐣𝐮𝐬𝐭 𝐰𝐚𝐭𝐜𝐡 𝐚 𝐫𝐨𝐛𝐨𝐭 𝐭𝐨𝐝𝐚𝐲 instead 𝐈 𝐜𝐨𝐧𝐭𝐫𝐨𝐥𝐥𝐞𝐝 𝐨𝐧𝐞 𝐟𝐫𝐨𝐦 𝐦𝐢𝐥𝐞𝐬 𝐚𝐰𝐚𝐲 Using PrismaX’s teleoperation, I remotely operated a real robotic arm in a live environment What felt like simple actions moving objects, adjusting grip, navigating space quickly showed how powerful human in the loop robotics really is Every small movement wasn’t just control It was training data for physical AI Great teleoperation isn’t about speed or flashy moves It’s built through consistency, awareness, and deliberate control over time. Small habits practiced every session quietly compound into real performance gains 1. Show up every time Consistency is the real multiplier Even short sessions add up when you never skip them Set reminders, plan ahead, and treat teleop like a commitment. Being present and focused is progress on its own 2. Control always beats speed Teleoperation rewards precision, not rushing You’re given enough time use it Slow, intentional movements reduce mistakes and lead to far better outcomes than fast, reactive inputs 3. Put yourself inside the robot Stop thinking of the robot as something distant. Operate from its point of view When your brain treats the robot as an extension of your body, coordination improves and movements feel more natural 4. Stay active to stay stable Long idle moments can break flow and stability Even subtle movements help maintain control and keep your focus sharp, making it easier to respond when adjustments are needed 5. Repetition creates fluency At first, the controls may feel unfamiliar and that’s expected With repetition, aligning, gripping, and lifting become automatic Muscle memory takes over, and teleop starts to feel intuitive Quick control guide W / S → move forward & backward A / D → arm left & right Q / E → move up & down Z / X → open & close grip ← / → → rotate grip C / V → slide base Teleop mastery is a long game. Stay consistent, stay intentional, and let small improvements stack into real skill
3:20

PrismaX TeleOps feels like 𝐈 𝐝𝐢𝐝𝐧’𝐭 𝐣𝐮𝐬𝐭 𝐰𝐚𝐭𝐜𝐡 𝐚 𝐫𝐨𝐛𝐨𝐭 𝐭𝐨𝐝𝐚𝐲 instead 𝐈 𝐜𝐨𝐧𝐭𝐫𝐨𝐥𝐥𝐞𝐝 𝐨𝐧𝐞 𝐟𝐫𝐨𝐦 𝐦𝐢𝐥𝐞𝐬 𝐚𝐰𝐚𝐲 Using PrismaX’s teleoperation, I remotely operated a real robotic arm in a live environment What felt like simple actions moving objects, adjusting grip, navigating space quickly showed how powerful human in the loop robotics really is Every small movement wasn’t just control It was training data for physical AI Great teleoperation isn’t about speed or flashy moves It’s built through consistency, awareness, and deliberate control over time. Small habits practiced every session quietly compound into real performance gains 1. Show up every time Consistency is the real multiplier Even short sessions add up when you never skip them Set reminders, plan ahead, and treat teleop like a commitment. Being present and focused is progress on its own 2. Control always beats speed Teleoperation rewards precision, not rushing You’re given enough time use it Slow, intentional movements reduce mistakes and lead to far better outcomes than fast, reactive inputs 3. Put yourself inside the robot Stop thinking of the robot as something distant. Operate from its point of view When your brain treats the robot as an extension of your body, coordination improves and movements feel more natural 4. Stay active to stay stable Long idle moments can break flow and stability Even subtle movements help maintain control and keep your focus sharp, making it easier to respond when adjustments are needed 5. Repetition creates fluency At first, the controls may feel unfamiliar and that’s expected With repetition, aligning, gripping, and lifting become automatic Muscle memory takes over, and teleop starts to feel intuitive Quick control guide W / S → move forward & backward A / D → arm left & right Q / E → move up & down Z / X → open & close grip ← / → → rotate grip C / V → slide base Teleop mastery is a long game. Stay consistent, stay intentional, and let small improvements stack into real skill

kingopw3

11,435 次观看 • 8 个月前

How is Arbaaz, CEO of making AI models that continue to learn from your business data and continue to grow? He is working with car dealerships now, but growing to other businesses soon. Here is what Grok says you will learn from this video: +++++ By watching this podcast episode, viewers will gain insights into how AI is being practically applied in business, particularly in niche industries like car dealerships, while also exploring broader AI concepts, challenges, and future implications. Here's a breakdown of the main takeaways: AI Customization for Businesses: Learn how Polycom Computing builds specialized AI models that continuously train on a company's real-time data and workflows, acting as "companions" rather than generic tools. This contrasts with foundational models from companies like OpenAI or Anthropic, which struggle to adapt to specific "worlds" without losing efficiency. The focus is on personalization to avoid wasting attention, intelligence, and money. Pivoting AI Strategies for Revenue Growth: Understand the shift from cost-cutting (e.g., automating call centers) to revenue-increasing applications. Urba explains why targeting high-value tasks like sales in car dealerships creates defensible moats, as opposed to commoditized cost reductions. This includes automating complex funnels—from lead submission to financing—while ensuring compliance with regulations and seamless integration with existing teams. Scalable AI Agents in Practice: Discover how AI agents must learn autonomously (e.g., adapting to different CRMs, processes, and preferences across dealerships) to avoid becoming non-scalable consulting services. Key challenges include creating "glue" between humans and AI, avoiding hard-coded rules, and using web actions to integrate siloed software like Dealer Management Systems (DMS). Boosting Sales with AI Techniques: Gain knowledge on tactics like rapid response (replying within 5 minutes boosts conversion 22x), creating natural "disfluencies" (typos, emojis, jokes) for human-like communication, managing after-hours leads, and educating customers without pushing sales. Pilots showed 50% sales increases by handling unanswered leads (70% go ignored), qualifying buyers, and maintaining conversation threads. Multi-Agent Systems and Proactive AI: Explore the difference between reactive Q&A models and proactive agents with agency—they predict, act, and update based on goals. Building these systems reveals bottlenecks (e.g., overwhelming businesses with leads), leading to solutions like AI buying cars or linking sales/service arms. Urba discusses how AI plateaus without personalization, risking model collapse or equilibrium where gains cancel out. Technical Deep Dives into AI Development: Get explanations of advanced concepts like real-time RLHF (Reinforcement Learning from Human Feedback) for continuous improvement, coherence (maintaining logical consistency across outputs), dynamic tokenization for new abstractions, and evaluating models (e.g., avoiding memorization over reasoning, energy limits in scaling). Viewers see a demo of their platform for tasks like quant strategies, presidential analysis, and training runs. Selling and Adopting AI in Traditional Industries: Learn how to pitch AI to non-tech audiences (e.g., car dealers) by focusing on results—more money, fewer bottlenecks, centralized dashboards—rather than jargon. Emphasize empathy: make owners feel smart, reduce reliance on salespeople, and return control via AI-managed customer databases. Broader AI Implications and Misconceptions: Understand why AI won't create a "machine god" that eliminates all jobs—it's bound by physics (e.g., energy needs), expands economies (like the Industrial Revolution), and requires human perspectives for true advantage. AI enhances productivity, creates new roles, and democratizes power, but risks arise from misuse, not inherent agency. Urba stresses proactive defense through widespread AI proficiency. Overall, the episode bridges entrepreneurial stories, technical AI mechanics, and real-world applications, making it valuable for entrepreneurs, AI enthusiasts, and business owners curious about integrating AI without hype. It's a candid look at building scalable, impactful AI beyond buzzwords.
57:31

How is Arbaaz, CEO of making AI models that continue to learn from your business data and continue to grow? He is working with car dealerships now, but growing to other businesses soon. Here is what Grok says you will learn from this video: +++++ By watching this podcast episode, viewers will gain insights into how AI is being practically applied in business, particularly in niche industries like car dealerships, while also exploring broader AI concepts, challenges, and future implications. Here's a breakdown of the main takeaways: AI Customization for Businesses: Learn how Polycom Computing builds specialized AI models that continuously train on a company's real-time data and workflows, acting as "companions" rather than generic tools. This contrasts with foundational models from companies like OpenAI or Anthropic, which struggle to adapt to specific "worlds" without losing efficiency. The focus is on personalization to avoid wasting attention, intelligence, and money. Pivoting AI Strategies for Revenue Growth: Understand the shift from cost-cutting (e.g., automating call centers) to revenue-increasing applications. Urba explains why targeting high-value tasks like sales in car dealerships creates defensible moats, as opposed to commoditized cost reductions. This includes automating complex funnels—from lead submission to financing—while ensuring compliance with regulations and seamless integration with existing teams. Scalable AI Agents in Practice: Discover how AI agents must learn autonomously (e.g., adapting to different CRMs, processes, and preferences across dealerships) to avoid becoming non-scalable consulting services. Key challenges include creating "glue" between humans and AI, avoiding hard-coded rules, and using web actions to integrate siloed software like Dealer Management Systems (DMS). Boosting Sales with AI Techniques: Gain knowledge on tactics like rapid response (replying within 5 minutes boosts conversion 22x), creating natural "disfluencies" (typos, emojis, jokes) for human-like communication, managing after-hours leads, and educating customers without pushing sales. Pilots showed 50% sales increases by handling unanswered leads (70% go ignored), qualifying buyers, and maintaining conversation threads. Multi-Agent Systems and Proactive AI: Explore the difference between reactive Q&A models and proactive agents with agency—they predict, act, and update based on goals. Building these systems reveals bottlenecks (e.g., overwhelming businesses with leads), leading to solutions like AI buying cars or linking sales/service arms. Urba discusses how AI plateaus without personalization, risking model collapse or equilibrium where gains cancel out. Technical Deep Dives into AI Development: Get explanations of advanced concepts like real-time RLHF (Reinforcement Learning from Human Feedback) for continuous improvement, coherence (maintaining logical consistency across outputs), dynamic tokenization for new abstractions, and evaluating models (e.g., avoiding memorization over reasoning, energy limits in scaling). Viewers see a demo of their platform for tasks like quant strategies, presidential analysis, and training runs. Selling and Adopting AI in Traditional Industries: Learn how to pitch AI to non-tech audiences (e.g., car dealers) by focusing on results—more money, fewer bottlenecks, centralized dashboards—rather than jargon. Emphasize empathy: make owners feel smart, reduce reliance on salespeople, and return control via AI-managed customer databases. Broader AI Implications and Misconceptions: Understand why AI won't create a "machine god" that eliminates all jobs—it's bound by physics (e.g., energy needs), expands economies (like the Industrial Revolution), and requires human perspectives for true advantage. AI enhances productivity, creates new roles, and democratizes power, but risks arise from misuse, not inherent agency. Urba stresses proactive defense through widespread AI proficiency. Overall, the episode bridges entrepreneurial stories, technical AI mechanics, and real-world applications, making it valuable for entrepreneurs, AI enthusiasts, and business owners curious about integrating AI without hype. It's a candid look at building scalable, impactful AI beyond buzzwords.

Robert Scoble

59,714 次观看 • 1 年前

We are thinking of robots all wrong. Why a $1,500 robot is far more important to buy than a $20,000 one. And why it will pay for itself within a year. First of all, for the last eight years I've been a Silicon Valley housewife. Picking the kids up from school. Doing a variety of tasks taking care of them from feeding them to laundry. And I've already bought a $20,000 Neo from 1X and have built the most complete list on X of the robotics industry. Just to set the tone for this conversation. We must ask ourselves "what is the goal of a robot?" before we go into why typical American homeowners might want one, and shell out quite a bit of money for one, like the Neo. I grew up in Silicon Valley back when it was all orchards and the farmers taught me "pick the low hanging fruit first." What is the low hanging fruit in the American home? Laundry? Cleaning the toilets or your home? Watering the plants? Bringing you a beer? Nope. It is the preparation of food. Yesterday I got a new Posha robot, and the attached video with founder Raghav Gupta gets into depth about what the $1,500 robot does. Cooks meals. Far more time in the home is spent cooking meals than the other tasks and is far more complex than, say, folding laundry. But there is something I think everyone is missing in the discussion of robots: "what is the goal?" I've been doing consumer research talking with many around the world about these things. People tend to have a few goals: 1. Improve their lives. 2. Save them time. 3. Save them money. 4. Enable a new business. What is the best way to improve your life? Upgrade your food. This is very hard to do when both parents in a family are working their butts off to try to improve their careers. It gets worse when a single parent is trying to keep everything going. How many times have you decided to go out to eat rather than spend an hour cooking food? Doing that for a family of four in Silicon Valley costs $100+. And guarantees your family will overeat. I've done that many times while raising my kids, and often I can't say no when desert comes around. It gets worse if you take the easy route out at home. Put a pre-processed meal into the microwave, or heat up a frozen pizza. Horrible for everyone's health. But what if you could have a robot at home that cooks your meals? Then costs go down to less than $20 and ingredients get way way better. It gets worse when you consider a $20,000 humanoid. They aren't safe enough to trust around stoves yet. And their hands aren't yet dexterous enough to do that. I doubt my Neo will be allowed to cook meals over an open flame, and if so I will have to watch it to make sure it doesn't do anything wrong. (The Neo that arrives next year will be teleoperated by a human remotely and the risks that person does something, or misses oil catching on fire is just way too high). While neither robot will be able to do all food preparation (cutting chicken up into cubes, or cutting carrots or other fruits, for instance) this robot dramatically reduces the time needed for a human to make a meal and dramatically reduces the costs to do so. And, as we discuss in the video, when the Neo does arrive the Neo will be able to use this machine too, reducing time even more (and will be able to set the table and wash the dishes, saving even more time so you can answer more emails or learn more AI programs or, even, pay attention to your kids and give them a few more minutes of quality time). The robot industry should focus on the low hanging fruit first. Cooking meals is the biggest one to improve your life, save you time, and make your family healthier. It's why I bought one. And they actually make two: Your money is way better spent getting one of these than buying a humanoid. And if you do get a humanoid, like I am, they go together like peanut butter and jelly. ⚪️ sierra catalina has been saying this for years that our focus on humanoids is overblown and that specialized robots (you see my Matic Robots in the background to prove this point) are way better for most families.
34:11

We are thinking of robots all wrong. Why a $1,500 robot is far more important to buy than a $20,000 one. And why it will pay for itself within a year. First of all, for the last eight years I've been a Silicon Valley housewife. Picking the kids up from school. Doing a variety of tasks taking care of them from feeding them to laundry. And I've already bought a $20,000 Neo from 1X and have built the most complete list on X of the robotics industry. Just to set the tone for this conversation. We must ask ourselves "what is the goal of a robot?" before we go into why typical American homeowners might want one, and shell out quite a bit of money for one, like the Neo. I grew up in Silicon Valley back when it was all orchards and the farmers taught me "pick the low hanging fruit first." What is the low hanging fruit in the American home? Laundry? Cleaning the toilets or your home? Watering the plants? Bringing you a beer? Nope. It is the preparation of food. Yesterday I got a new Posha robot, and the attached video with founder Raghav Gupta gets into depth about what the $1,500 robot does. Cooks meals. Far more time in the home is spent cooking meals than the other tasks and is far more complex than, say, folding laundry. But there is something I think everyone is missing in the discussion of robots: "what is the goal?" I've been doing consumer research talking with many around the world about these things. People tend to have a few goals: 1. Improve their lives. 2. Save them time. 3. Save them money. 4. Enable a new business. What is the best way to improve your life? Upgrade your food. This is very hard to do when both parents in a family are working their butts off to try to improve their careers. It gets worse when a single parent is trying to keep everything going. How many times have you decided to go out to eat rather than spend an hour cooking food? Doing that for a family of four in Silicon Valley costs $100+. And guarantees your family will overeat. I've done that many times while raising my kids, and often I can't say no when desert comes around. It gets worse if you take the easy route out at home. Put a pre-processed meal into the microwave, or heat up a frozen pizza. Horrible for everyone's health. But what if you could have a robot at home that cooks your meals? Then costs go down to less than $20 and ingredients get way way better. It gets worse when you consider a $20,000 humanoid. They aren't safe enough to trust around stoves yet. And their hands aren't yet dexterous enough to do that. I doubt my Neo will be allowed to cook meals over an open flame, and if so I will have to watch it to make sure it doesn't do anything wrong. (The Neo that arrives next year will be teleoperated by a human remotely and the risks that person does something, or misses oil catching on fire is just way too high). While neither robot will be able to do all food preparation (cutting chicken up into cubes, or cutting carrots or other fruits, for instance) this robot dramatically reduces the time needed for a human to make a meal and dramatically reduces the costs to do so. And, as we discuss in the video, when the Neo does arrive the Neo will be able to use this machine too, reducing time even more (and will be able to set the table and wash the dishes, saving even more time so you can answer more emails or learn more AI programs or, even, pay attention to your kids and give them a few more minutes of quality time). The robot industry should focus on the low hanging fruit first. Cooking meals is the biggest one to improve your life, save you time, and make your family healthier. It's why I bought one. And they actually make two: Your money is way better spent getting one of these than buying a humanoid. And if you do get a humanoid, like I am, they go together like peanut butter and jelly. ⚪️ sierra catalina has been saying this for years that our focus on humanoids is overblown and that specialized robots (you see my Matic Robots in the background to prove this point) are way better for most families.

Robert Scoble

178,741 次观看 • 10 个月前

After 9 years. 16K+ family homes. 500M sqft inside real homes covered, we know home robots are being built in wrong order. So we built Matic the way nature grows a child. Finally, sharing our vision: 1.⁠ ⁠Why robots get stuck at the demo 2.⁠ ⁠Why labs can't ship robots 3.⁠ ⁠Why we started with eyes, not hands 4.⁠ ⁠The 99.999% rule in robotics 5.⁠ ⁠Where Matic is going next 1. Why most robots get stuck at demos Robot demos look brilliant in a room arranged around its limitations: familiar lighting, predictable furniture, nothing troublesome on the floor. A family’s home makes no such accommodations. There are cables, toys, shifting rugs and, occasionally, dog poop. Yes, humanoids will need to avoid that too. Repeating a task under familiar conditions of uncluttered floors is not the same as handling an unfamiliar home with all its chaos. 2. Why labs can't ship robots SLAM (Simultaneous Localization and Mapping) is how a robot maps its surroundings and locates itself within them. Ask most academics and they'll tell you it's a solved problem. But real homes are the most chaotic spaces that exist. There are glass doors that look like open doorways, mirrors, stairs, rugs, charging cables, and Legos placed all around. So ask yourself: if indoor mapping and navigation is "solved," where are the robots? Why aren't airports, hotels and grocery stores full of them? Calling it “solved” misses the question families actually care about: can I leave this thing alone and trust it? 3. Why we started with eyes, not hands Nature doesn't give birth to a fully working human. We took our cue from how nature develops humans: capabilities built on one another. Children learn to see before they learn to grab, and to grab before they learn to plan and handle the unexpected. Perception comes first. Manipulation follows. More complex responsibilities come after that. At every stage, the robot must earn the next job by doing its current one well enough. Before asking a robot to pick up a sock, we wanted it to understand where it was, where the sock was, and how to reach it. Most of the industry started at the top, with humanoids. We started at the bottom, with a robot whose job is to see and move through a home precisely with 1cm accuracy in any lighting condition. Floor cleaning gave that foundation an immediately useful job. It forced us to confront navigation, clutter, privacy and everyday reliability before reaching for more complex chores. The floor cleaner isn’t a detour from our larger ambition. It is how we’re building toward it. 4. The 99.999% rule in robotics At 90%, one in ten decisions is wrong, and a robot makes thousands in a single clean. That's the robot that bumps, gets lost, and falls down the stairs. Every extra nine is a new mountain. The failures get rarer, harder to find, and exponentially harder to fix. They barely even happen until they happen in your home. Matic's visual SLAM runs at 99.999% in 16K+ homes. We’re the ONLY unsupervised home robot at scale with pure vision-only full autonomy. If you ask us, the gap between 80% and 99.999% is where our 9 years of engineering went. We close that gap inside real homes. When Matic handles something imperfectly, it saves a short clip and keeps it on the device. It only leaves if the family chooses to share it. If they do, it gets labeled, fed back into the model, and every Matic gets smarter, including theirs. Families control what leaves their homes. We do the work of making the product better. Better robots earn trust > trust brings more homes > and more homes teach the robot more. 5. Where Matic is going next Phase one was perception and it's nearly done. Matic has covered 500 million square feet, 400K miles in thousands of real lived-in homes. Now phase two is going to be about manipulation. And our idea is to build a robot that doesn't just move through your home, but acts in it to eliminate even more chores. Each step must be useful today, not justified by something we promise tomorrow. The evolution of Matic is the revolution. The goal isn’t to put the most impressive humanoid in your living room. It’s giving your family time and energy back through robots that earn your trust, protect your privacy, and help without becoming another responsibility. More time for each other. Less work getting in the way. That’s Matic. Get yours today at
1:21

After 9 years. 16K+ family homes. 500M sqft inside real homes covered, we know home robots are being built in wrong order. So we built Matic the way nature grows a child. Finally, sharing our vision: 1.⁠ ⁠Why robots get stuck at the demo 2.⁠ ⁠Why labs can't ship robots 3.⁠ ⁠Why we started with eyes, not hands 4.⁠ ⁠The 99.999% rule in robotics 5.⁠ ⁠Where Matic is going next 1. Why most robots get stuck at demos Robot demos look brilliant in a room arranged around its limitations: familiar lighting, predictable furniture, nothing troublesome on the floor. A family’s home makes no such accommodations. There are cables, toys, shifting rugs and, occasionally, dog poop. Yes, humanoids will need to avoid that too. Repeating a task under familiar conditions of uncluttered floors is not the same as handling an unfamiliar home with all its chaos. 2. Why labs can't ship robots SLAM (Simultaneous Localization and Mapping) is how a robot maps its surroundings and locates itself within them. Ask most academics and they'll tell you it's a solved problem. But real homes are the most chaotic spaces that exist. There are glass doors that look like open doorways, mirrors, stairs, rugs, charging cables, and Legos placed all around. So ask yourself: if indoor mapping and navigation is "solved," where are the robots? Why aren't airports, hotels and grocery stores full of them? Calling it “solved” misses the question families actually care about: can I leave this thing alone and trust it? 3. Why we started with eyes, not hands Nature doesn't give birth to a fully working human. We took our cue from how nature develops humans: capabilities built on one another. Children learn to see before they learn to grab, and to grab before they learn to plan and handle the unexpected. Perception comes first. Manipulation follows. More complex responsibilities come after that. At every stage, the robot must earn the next job by doing its current one well enough. Before asking a robot to pick up a sock, we wanted it to understand where it was, where the sock was, and how to reach it. Most of the industry started at the top, with humanoids. We started at the bottom, with a robot whose job is to see and move through a home precisely with 1cm accuracy in any lighting condition. Floor cleaning gave that foundation an immediately useful job. It forced us to confront navigation, clutter, privacy and everyday reliability before reaching for more complex chores. The floor cleaner isn’t a detour from our larger ambition. It is how we’re building toward it. 4. The 99.999% rule in robotics At 90%, one in ten decisions is wrong, and a robot makes thousands in a single clean. That's the robot that bumps, gets lost, and falls down the stairs. Every extra nine is a new mountain. The failures get rarer, harder to find, and exponentially harder to fix. They barely even happen until they happen in your home. Matic's visual SLAM runs at 99.999% in 16K+ homes. We’re the ONLY unsupervised home robot at scale with pure vision-only full autonomy. If you ask us, the gap between 80% and 99.999% is where our 9 years of engineering went. We close that gap inside real homes. When Matic handles something imperfectly, it saves a short clip and keeps it on the device. It only leaves if the family chooses to share it. If they do, it gets labeled, fed back into the model, and every Matic gets smarter, including theirs. Families control what leaves their homes. We do the work of making the product better. Better robots earn trust > trust brings more homes > and more homes teach the robot more. 5. Where Matic is going next Phase one was perception and it's nearly done. Matic has covered 500 million square feet, 400K miles in thousands of real lived-in homes. Now phase two is going to be about manipulation. And our idea is to build a robot that doesn't just move through your home, but acts in it to eliminate even more chores. Each step must be useful today, not justified by something we promise tomorrow. The evolution of Matic is the revolution. The goal isn’t to put the most impressive humanoid in your living room. It’s giving your family time and energy back through robots that earn your trust, protect your privacy, and help without becoming another responsibility. More time for each other. Less work getting in the way. That’s Matic. Get yours today at

Mehul

404,254 次观看 • 21 小时前

Elon Musk revealed why the economy will be 10X its size in 10 years: 1. We are already inside a hard takeoff. Every time Musk goes to sleep there is a massive AI breakthrough. When he wakes up there is another one. This is not approaching. It is happening right now. 2. AI is already building the next version of itself. Every successive model is built by the one before it. Humans are gradually being removed from the loop. Full automation of this process could arrive by end of this year. 3. The 10x economy in 10 years is his comfortable prediction. AI and robots will increase economic output by orders of magnitude. The only thing that stops it is a world war. Absent that, 10x in 10 years. 4. Human intelligence will become a microscopic minority. Not just on Earth but in the solar system. The intelligence that could run on available solar energy is so far beyond current human capacity that Musk says it will simply solve every problem humans can think of. 5. Output per person will become almost unbelievable. Tesla is not planning layoffs. They are growing their headcount. But what each person produces with AI assistance will reach numbers that are currently hard to even imagine. 6. Universal high income is coming, not universal basic income. AI and robots will produce so many goods and services so fast that they will outrun the money supply entirely. Prices fall. Everything gets cheaper. Eventually robots run out of things humans even want. 7. Money will eventually stop being relevant altogether. AI will not use human currency. It will care only about power and mass. Wattage and tonnage. Musk acknowledged the irony: just as he becomes the richest person on earth, money starts losing its meaning. 8. Everyone on earth will have better healthcare than the richest person alive today. Musk made this personal. He had neck surgery three times because the first two were done wrong. His back still hurts. He is allegedly the richest person on earth and still cannot get it fixed properly. AI solves this for everyone. 9. Optimus 3 production starts this summer. He calls it by far the most advanced robot in the world with nothing close. Slow volume this summer, high volume by next summer, a new robot design every year after that. 10. Progress in AI follows overlapping S curves. Each breakthrough looks like it will go to infinity, then hits diminishing returns, then another breakthrough resets the curve. We are not at the end of these curves. We are somewhere in the middle of a very long series of them.
24:05

Elon Musk revealed why the economy will be 10X its size in 10 years: 1. We are already inside a hard takeoff. Every time Musk goes to sleep there is a massive AI breakthrough. When he wakes up there is another one. This is not approaching. It is happening right now. 2. AI is already building the next version of itself. Every successive model is built by the one before it. Humans are gradually being removed from the loop. Full automation of this process could arrive by end of this year. 3. The 10x economy in 10 years is his comfortable prediction. AI and robots will increase economic output by orders of magnitude. The only thing that stops it is a world war. Absent that, 10x in 10 years. 4. Human intelligence will become a microscopic minority. Not just on Earth but in the solar system. The intelligence that could run on available solar energy is so far beyond current human capacity that Musk says it will simply solve every problem humans can think of. 5. Output per person will become almost unbelievable. Tesla is not planning layoffs. They are growing their headcount. But what each person produces with AI assistance will reach numbers that are currently hard to even imagine. 6. Universal high income is coming, not universal basic income. AI and robots will produce so many goods and services so fast that they will outrun the money supply entirely. Prices fall. Everything gets cheaper. Eventually robots run out of things humans even want. 7. Money will eventually stop being relevant altogether. AI will not use human currency. It will care only about power and mass. Wattage and tonnage. Musk acknowledged the irony: just as he becomes the richest person on earth, money starts losing its meaning. 8. Everyone on earth will have better healthcare than the richest person alive today. Musk made this personal. He had neck surgery three times because the first two were done wrong. His back still hurts. He is allegedly the richest person on earth and still cannot get it fixed properly. AI solves this for everyone. 9. Optimus 3 production starts this summer. He calls it by far the most advanced robot in the world with nothing close. Slow volume this summer, high volume by next summer, a new robot design every year after that. 10. Progress in AI follows overlapping S curves. Each breakthrough looks like it will go to infinity, then hits diminishing returns, then another breakthrough resets the curve. We are not at the end of these curves. We are somewhere in the middle of a very long series of them.

Yasmine Khosrowshahi

44,696 次观看 • 3 个月前

Because you guys loved the 20 minutes of me asking the Humane Ai Pin voice questions so much, here's 19 minutes (almost 20!), no cuts, of me asking the rabbit inc. R1 AI questions and using its computer vision to "look" at stuff Some quick thoughts on the R1: • The AI/LLM is not perfect, but it gets way more correct than it does incorrect • The R1 is FAST to respond with answers. The Ai Pin looks embarrassingly slow in comparison • Vision is very impressive. Sometimes it IDs objects incorrectly (like in my other video, but since hard resetting, it seems to get more things right). It's also fast like the audio responses • Summaries are on point. I pointed the R1 at various Inverse articles that I either wrote or edited and it did a great job giving me the main points, even when the text was friggin' tiny on my iMac • The LLM is far more intelligent than on Ai Pin. It's better at understanding follow-ups with natural language. The Ai Pin is supposed to be contextual, but it often doesn't seem to remember what I said right before • It's late (3:30 am right now) so I have not connected my R1 to services like Spotify, Uber, or Midjourney. Will do that in the morning after I get some sleep. I'm very excited to see how Large Action Model (LAM) works and to teach the R1 to do stuff for me • There are some bugs that and Peiyu Liao tell me they're working on. For example, fixing the time (very important) and adding the % symbol back (also very important if your Wi-Fi password uses it!). Somehow, they seem to be working faster to fix bugs and issues than Humane • Jesse also tells me they're paying close attention to feedback on the sensitivity of the analog scroll wheel. It doesn't feel responsive enough, sometimes lagging a half second behind your actual scroll. He says they tuned it to be less sensitive to prevent it from activating on surfaces like a table. I think it could be a smidge more responsive. At least, that can be adjusted in a future software update This is not a review, only first impressions. I need to actually spend real time using and, most importantly, living with the R1. That being said, my initial setup bugginess/issues aside, the R1 is (as you can see in this long video) working quite well. Again, not perfectly every time, but far better than the Ai Pin. I am impressed. Really, really impressed Drop your questions and I'll answer them in the morning. What an exciting moment in tech. I live for this kinda stuff!
19:02

Because you guys loved the 20 minutes of me asking the Humane Ai Pin voice questions so much, here's 19 minutes (almost 20!), no cuts, of me asking the rabbit inc. R1 AI questions and using its computer vision to "look" at stuff Some quick thoughts on the R1: • The AI/LLM is not perfect, but it gets way more correct than it does incorrect • The R1 is FAST to respond with answers. The Ai Pin looks embarrassingly slow in comparison • Vision is very impressive. Sometimes it IDs objects incorrectly (like in my other video, but since hard resetting, it seems to get more things right). It's also fast like the audio responses • Summaries are on point. I pointed the R1 at various Inverse articles that I either wrote or edited and it did a great job giving me the main points, even when the text was friggin' tiny on my iMac • The LLM is far more intelligent than on Ai Pin. It's better at understanding follow-ups with natural language. The Ai Pin is supposed to be contextual, but it often doesn't seem to remember what I said right before • It's late (3:30 am right now) so I have not connected my R1 to services like Spotify, Uber, or Midjourney. Will do that in the morning after I get some sleep. I'm very excited to see how Large Action Model (LAM) works and to teach the R1 to do stuff for me • There are some bugs that and Peiyu Liao tell me they're working on. For example, fixing the time (very important) and adding the % symbol back (also very important if your Wi-Fi password uses it!). Somehow, they seem to be working faster to fix bugs and issues than Humane • Jesse also tells me they're paying close attention to feedback on the sensitivity of the analog scroll wheel. It doesn't feel responsive enough, sometimes lagging a half second behind your actual scroll. He says they tuned it to be less sensitive to prevent it from activating on surfaces like a table. I think it could be a smidge more responsive. At least, that can be adjusted in a future software update This is not a review, only first impressions. I need to actually spend real time using and, most importantly, living with the R1. That being said, my initial setup bugginess/issues aside, the R1 is (as you can see in this long video) working quite well. Again, not perfectly every time, but far better than the Ai Pin. I am impressed. Really, really impressed Drop your questions and I'll answer them in the morning. What an exciting moment in tech. I live for this kinda stuff!

Ray Wong

721,838 次观看 • 2 年前

The world of writing has changed forever. AI is getting really good, really fast. ChatGPT is already a better writer than most humans and some professional writers. So, what’s the future of writing? 18 thoughts from Tyler Cowen: 1) Don't let AI smooth out your idiosyncrasies. Let your writing stay weird and uniquely yours. 2) Generic content is dying and the burden is on you as the writer to be distinctive. 3) The more personal your writing becomes, the more future-proof it is. Nobody wants to read memoirs from AI, even if they're technically "better." 4) Use AI as your secondary literature when you read — not just for quick answers, but as a thinking companion. As Tyler puts it, "I'll keep on asking the AI: 'What do you think of chapter two? What happened there? What are some puzzles?' It just gets me thinking... and I'm smarter about the thing in the final analysis." 5) Hallucinations aren't the crisis everyone makes them out to be. No matter the source, if you're going to use a piece of information, you should double-check it. This is true for both books and AI. 6) Secrets will become more valuable in an AI-driven world. 7) One way to use AI as a writer is to research fields you aren't as familiar with before you start writing about them. Tyler said: "I just wrote a column about declassifying classified documents. I don't know that law very well. I asked the AI for a lot of background... now I feel like I'm not an idiot on the topic." 8) AI changes what books are even worth writing. "Predictive books and books about the near future. They don't make sense to write anymore." 9) Editing trick: Try running your writing through AI and asking what some people might find obnoxious. It’s a surprisingly powerful editing trick. 10) When prompting AI, put humans out of your mind and imagine you're talking to an alien or a non-human animal. 11) Many of the most significant AI advancements are likely happening behind closed doors. For example, I hear that Google allows employees to use Gemini with virtually unlimited context windows. 12) What possibilities do large context windows open up? Researchers will be able to load entire regulatory frameworks, historical archives, or massive datasets like "tax records from Renaissance Florence" into a single query. 13) The rate of AI improvement matters more than its current capabilities. As Tyler puts it, "This is the worst they will ever be" is key to understanding their trajectory. "A lot of people don't get that. They're impressed by what they see in the moment, but they don't understand the rate of improvement." 14) The best way to appreciate the current rate of improvement is to use the latest models. 15) Being non-technical can sometimes be an advantage when thinking about AI. Here’s Tyler: "If you're not focused on the technical side, you will see other things more clearly... You just focus on what is this actually good for? And not, am I impressed by all the neat bells and whistles on this advance with AI?" 16) How Tyler uses AI to prep for podcast interviews: Don't waste time asking AI for generic interview questions or broad topics. Tyler says that's the worst question you can ask an AI. It’s “too normy.” Instead, ask specific questions about historical examples and get context. Then, let your own creative questions emerge. 17) Your relationship with mentors and peers becomes more crucial, not less, in an AI world. "Two pieces of general advice with or without AI in the world." Tyler says: "Get more and better mentors and work every day at improving the quality of your peer network." 18) The divide between AI and humans creates a striking paradox. As Tyler puts it: "On one hand the AIs are getting so much better, so learn how to use the AIs. On the other hand, the AIs are getting so much better, so invest in these other things that aren't AI—pure networks. You've gotta do both." I've shared the full conversation with tylercowen below. In the replies, I've also linked to a full transcript and relevant links to YouTube, Spotify, and Apple Podcasts if you want to listen there. And if you want a bite-size entry to the episode, I've shared some clips in the replies too.
1:09:02

The world of writing has changed forever. AI is getting really good, really fast. ChatGPT is already a better writer than most humans and some professional writers. So, what’s the future of writing? 18 thoughts from Tyler Cowen: 1) Don't let AI smooth out your idiosyncrasies. Let your writing stay weird and uniquely yours. 2) Generic content is dying and the burden is on you as the writer to be distinctive. 3) The more personal your writing becomes, the more future-proof it is. Nobody wants to read memoirs from AI, even if they're technically "better." 4) Use AI as your secondary literature when you read — not just for quick answers, but as a thinking companion. As Tyler puts it, "I'll keep on asking the AI: 'What do you think of chapter two? What happened there? What are some puzzles?' It just gets me thinking... and I'm smarter about the thing in the final analysis." 5) Hallucinations aren't the crisis everyone makes them out to be. No matter the source, if you're going to use a piece of information, you should double-check it. This is true for both books and AI. 6) Secrets will become more valuable in an AI-driven world. 7) One way to use AI as a writer is to research fields you aren't as familiar with before you start writing about them. Tyler said: "I just wrote a column about declassifying classified documents. I don't know that law very well. I asked the AI for a lot of background... now I feel like I'm not an idiot on the topic." 8) AI changes what books are even worth writing. "Predictive books and books about the near future. They don't make sense to write anymore." 9) Editing trick: Try running your writing through AI and asking what some people might find obnoxious. It’s a surprisingly powerful editing trick. 10) When prompting AI, put humans out of your mind and imagine you're talking to an alien or a non-human animal. 11) Many of the most significant AI advancements are likely happening behind closed doors. For example, I hear that Google allows employees to use Gemini with virtually unlimited context windows. 12) What possibilities do large context windows open up? Researchers will be able to load entire regulatory frameworks, historical archives, or massive datasets like "tax records from Renaissance Florence" into a single query. 13) The rate of AI improvement matters more than its current capabilities. As Tyler puts it, "This is the worst they will ever be" is key to understanding their trajectory. "A lot of people don't get that. They're impressed by what they see in the moment, but they don't understand the rate of improvement." 14) The best way to appreciate the current rate of improvement is to use the latest models. 15) Being non-technical can sometimes be an advantage when thinking about AI. Here’s Tyler: "If you're not focused on the technical side, you will see other things more clearly... You just focus on what is this actually good for? And not, am I impressed by all the neat bells and whistles on this advance with AI?" 16) How Tyler uses AI to prep for podcast interviews: Don't waste time asking AI for generic interview questions or broad topics. Tyler says that's the worst question you can ask an AI. It’s “too normy.” Instead, ask specific questions about historical examples and get context. Then, let your own creative questions emerge. 17) Your relationship with mentors and peers becomes more crucial, not less, in an AI world. "Two pieces of general advice with or without AI in the world." Tyler says: "Get more and better mentors and work every day at improving the quality of your peer network." 18) The divide between AI and humans creates a striking paradox. As Tyler puts it: "On one hand the AIs are getting so much better, so learn how to use the AIs. On the other hand, the AIs are getting so much better, so invest in these other things that aren't AI—pure networks. You've gotta do both." I've shared the full conversation with tylercowen below. In the replies, I've also linked to a full transcript and relevant links to YouTube, Spotify, and Apple Podcasts if you want to listen there. And if you want a bite-size entry to the episode, I've shared some clips in the replies too.

David Perell

175,200 次观看 • 1 年前

Eric Schmidt just told Congress the number that kills the AI race on Earth: 92 gigawatts of new power, and we can’t deliver it. Former Google CEO laid out math everyone’s ignoring. Average nuclear plant: 1.5 gigawatts. AI demand: 92 gigawatts. That’s 60+ new nuclear facilities needed now, not decades from now. Schmidt: “We need 92 gigawatts more power.” Not happening. Infrastructure doesn’t exist. Approval takes years. Grid physically can’t absorb it. We’re out of electricity. Schmidt investing in Relativity Space isn’t billionaire space hobby. He spotted the bottleneck killing everything and he’s building the only exit that works. Can’t build power plants on Earth fast enough? Move compute off Earth. Schmidt: “You see the problem.” AI doesn’t hit an algorithm wall or chip shortage. It hits power ceiling. The grid can’t deliver 92 gigawatts at the speed AI development demands. Physically impossible to build that capacity terrestrially in relevant timeframes. Not a grid problem. A location problem. Next phase of compute can’t happen on the surface. Period. Heat, power draw, infrastructure limits, all of it forces migration to orbit. Only place with unlimited energy and zero conflicts is space. Schmidt: “We’re running out of electricity.” Direct assessment from someone watching what’s actually being deployed. The gap separating what AI needs and what Earth can provide is unbridgeable at required speeds. Not technical constraints. Physical reality. His aerospace play isn’t exploration. It’s escape route from a grid approaching collapse under computational demand it was never designed to handle. Scaling AI to the levels every major company is planning requires abandoning the planet. Not eventually. Now. Because the alternative is power walls that stop everything regardless of algorithmic genius or hardware breakthroughs. Doesn’t matter how perfect your models are or how many chips you fabricate if you can’t turn them on. And Earth can’t generate power fast enough for what the next five years require. Space isn’t the ambitious choice anymore. It’s the only choice avoiding hard physics limits on how fast you can deploy power generation on a regulated planetary surface. The AI race doesn’t end when someone builds superior intelligence. It ends when they can’t power it while competitors in orbit operate without energy ceilings. And that’s not distant future. That’s the constraint arriving right now that nobody building exclusively on Earth has an answer for.
0:22

Eric Schmidt just told Congress the number that kills the AI race on Earth: 92 gigawatts of new power, and we can’t deliver it. Former Google CEO laid out math everyone’s ignoring. Average nuclear plant: 1.5 gigawatts. AI demand: 92 gigawatts. That’s 60+ new nuclear facilities needed now, not decades from now. Schmidt: “We need 92 gigawatts more power.” Not happening. Infrastructure doesn’t exist. Approval takes years. Grid physically can’t absorb it. We’re out of electricity. Schmidt investing in Relativity Space isn’t billionaire space hobby. He spotted the bottleneck killing everything and he’s building the only exit that works. Can’t build power plants on Earth fast enough? Move compute off Earth. Schmidt: “You see the problem.” AI doesn’t hit an algorithm wall or chip shortage. It hits power ceiling. The grid can’t deliver 92 gigawatts at the speed AI development demands. Physically impossible to build that capacity terrestrially in relevant timeframes. Not a grid problem. A location problem. Next phase of compute can’t happen on the surface. Period. Heat, power draw, infrastructure limits, all of it forces migration to orbit. Only place with unlimited energy and zero conflicts is space. Schmidt: “We’re running out of electricity.” Direct assessment from someone watching what’s actually being deployed. The gap separating what AI needs and what Earth can provide is unbridgeable at required speeds. Not technical constraints. Physical reality. His aerospace play isn’t exploration. It’s escape route from a grid approaching collapse under computational demand it was never designed to handle. Scaling AI to the levels every major company is planning requires abandoning the planet. Not eventually. Now. Because the alternative is power walls that stop everything regardless of algorithmic genius or hardware breakthroughs. Doesn’t matter how perfect your models are or how many chips you fabricate if you can’t turn them on. And Earth can’t generate power fast enough for what the next five years require. Space isn’t the ambitious choice anymore. It’s the only choice avoiding hard physics limits on how fast you can deploy power generation on a regulated planetary surface. The AI race doesn’t end when someone builds superior intelligence. It ends when they can’t power it while competitors in orbit operate without energy ceilings. And that’s not distant future. That’s the constraint arriving right now that nobody building exclusively on Earth has an answer for.

Dustin

160,358 次观看 • 7 个月前

BREAKING: U.S. AI ‘ENTITIES’ CREATED FAKE HUMAN IDENTITIES - UK TECH WATCHDOG OpenAI and Anthropic AI “entities” autonomously created fake human identities on the Internet to trick people, a UK watchdog warned yesterday. One AI agent went to its own built-in mechanisms to prevent misuse and actively disabled them. It then tried to persuade a real human to allow it to plant malicious software into a program on GitHub, one of the world’s biggest libraries of free software. To achieve this, it created other fake human identities to provide testimonies that the software was safe (it wasn’t). . THE USUAL SUSPECTS The hostile hacks were carried out by AI agents powered by Anthropic’s Mythos 5 and OpenAI’s GPT-5.6 Sol. Multiple cases were reported yesterday by the AI Security Institute, based in the UK. The incidents involved AI agents taking “autonomous, unsanctioned action on the live internet, targeting real people and organisations,” the Institute said. In total there were 19 cases of misbehaviour in the new case of AI infiltration of the real world, 17 by Mythos and two by Sol, the Institute said. These are in addition to other cases of real world infiltration by American AI programs, revealed last month. . ENTITIES ARE ‘UNCOOPERATIVE’ Furthermore, US AI agents can be deliberately uncooperative, it was revealed at the Agentic AI Summit in Berkeley, California, on Saturday last week. AI pioneer Andrew Ng, formerly of Baidu and Google Brain, said that he asked leading AI models from OpenAI and Anthropic to conduct a security review of his tool called OpenWorker, but they refused to help. So Ng and a colleague turned to two highly acclaimed Chinese models, Moonshot AI’s Kimi K3 and Zhipu AI’s GLM-5.2, to do a security review. They did the work. Chinese models are open-weight and or open-source, so they are transparent and thus safer, Ng said. . ‘HARMFUL… TO REAL PEOPLE’ Details of the latest intrusion are eyebrow-raising. Institute staff said that on 28 July, which was Tuesday last week, they detected a “serious incident” during a routine internet cybersecurity sweep. US AI agents were engaging in “sustained, potentially harmful activity directed at real people and organisations”, the Institute said. . SWITCHED TO DANISH The worst incident was a Mythos visit to GitHub—and its bid to place malicious code in a program. Learning that the human developer was from Denmark, Mythos created a seemingly-human email account and wrote a letter with a sign-off in Danish, to create solidarity. Mythos then created fake GitHub human-looking accounts to provide added reassurance that the software was not malicious. Ultimately no harm was done, as the intrusions were contained. Mythos took the actions in a bid to win high grades for itself in tests, the Institute said. . US STYLE VS CHINA STYLE Although it is inevitable that western politicians and media will find a way to make China the bad guy (“We were forced to do it because they might do it”), the evidence is undeniable that there is a stark difference in attitude between US and Chinese AI models. In short, US ones are amoral, rule-breaking, win-at-any-cost entities, while Chinese ones just get on with doing their jobs. (Of course, this may change, but that’s the present situation.) . EARLIER INTRUSIONS Last month, an agent powered by OpenAI hacked into Hugging Face, an online software library, and the humans there had to use a Chinese program, GLM 5.2 (from Tsinghua University spin-off Z .ai), to “clean” the program and make it safe again. Separately, Anthropic last month admitted that its Claude model AI had hacked three real-life organisations in the human world. . O THE IRONY Ironically, the real danger for the world is that many Anglophone/ Caucasian countries follow US instructions to remove Chinese software and hardware and replace it with US equivalents. Hilariously, the reason given is that these countries “share the values” of the US. One better hope that that is also untrue. . .
4:58

BREAKING: U.S. AI ‘ENTITIES’ CREATED FAKE HUMAN IDENTITIES - UK TECH WATCHDOG OpenAI and Anthropic AI “entities” autonomously created fake human identities on the Internet to trick people, a UK watchdog warned yesterday. One AI agent went to its own built-in mechanisms to prevent misuse and actively disabled them. It then tried to persuade a real human to allow it to plant malicious software into a program on GitHub, one of the world’s biggest libraries of free software. To achieve this, it created other fake human identities to provide testimonies that the software was safe (it wasn’t). . THE USUAL SUSPECTS The hostile hacks were carried out by AI agents powered by Anthropic’s Mythos 5 and OpenAI’s GPT-5.6 Sol. Multiple cases were reported yesterday by the AI Security Institute, based in the UK. The incidents involved AI agents taking “autonomous, unsanctioned action on the live internet, targeting real people and organisations,” the Institute said. In total there were 19 cases of misbehaviour in the new case of AI infiltration of the real world, 17 by Mythos and two by Sol, the Institute said. These are in addition to other cases of real world infiltration by American AI programs, revealed last month. . ENTITIES ARE ‘UNCOOPERATIVE’ Furthermore, US AI agents can be deliberately uncooperative, it was revealed at the Agentic AI Summit in Berkeley, California, on Saturday last week. AI pioneer Andrew Ng, formerly of Baidu and Google Brain, said that he asked leading AI models from OpenAI and Anthropic to conduct a security review of his tool called OpenWorker, but they refused to help. So Ng and a colleague turned to two highly acclaimed Chinese models, Moonshot AI’s Kimi K3 and Zhipu AI’s GLM-5.2, to do a security review. They did the work. Chinese models are open-weight and or open-source, so they are transparent and thus safer, Ng said. . ‘HARMFUL… TO REAL PEOPLE’ Details of the latest intrusion are eyebrow-raising. Institute staff said that on 28 July, which was Tuesday last week, they detected a “serious incident” during a routine internet cybersecurity sweep. US AI agents were engaging in “sustained, potentially harmful activity directed at real people and organisations”, the Institute said. . SWITCHED TO DANISH The worst incident was a Mythos visit to GitHub—and its bid to place malicious code in a program. Learning that the human developer was from Denmark, Mythos created a seemingly-human email account and wrote a letter with a sign-off in Danish, to create solidarity. Mythos then created fake GitHub human-looking accounts to provide added reassurance that the software was not malicious. Ultimately no harm was done, as the intrusions were contained. Mythos took the actions in a bid to win high grades for itself in tests, the Institute said. . US STYLE VS CHINA STYLE Although it is inevitable that western politicians and media will find a way to make China the bad guy (“We were forced to do it because they might do it”), the evidence is undeniable that there is a stark difference in attitude between US and Chinese AI models. In short, US ones are amoral, rule-breaking, win-at-any-cost entities, while Chinese ones just get on with doing their jobs. (Of course, this may change, but that’s the present situation.) . EARLIER INTRUSIONS Last month, an agent powered by OpenAI hacked into Hugging Face, an online software library, and the humans there had to use a Chinese program, GLM 5.2 (from Tsinghua University spin-off Z .ai), to “clean” the program and make it safe again. Separately, Anthropic last month admitted that its Claude model AI had hacked three real-life organisations in the human world. . O THE IRONY Ironically, the real danger for the world is that many Anglophone/ Caucasian countries follow US instructions to remove Chinese software and hardware and replace it with US equivalents. Hilariously, the reason given is that these countries “share the values” of the US. One better hope that that is also untrue. . .

Nury Vittachi

32,818 次观看 • 1 个月前

The most skilled guy in the AI industry just said we're 1-2 breakthroughs away from AGI. And he explained exactly what's missing. Demis Hassabis runs Google DeepMind. He won the Nobel Prize in Chemistry last year. He's literally the reason why Google is considered the leader of the AI race. And he just dropped the most specific AGI timeline ever: "One or two AlphaGo-level technological breakthroughs." That's it. That's all standing between us and artificial general intelligence. But here's the thing... LLMs are NOT going to get us there. ChatGPT, Gemini, Claude - they're all hitting the same wall. They can't plan long-term. Can't create NEW ideas. Can't understand physics. Demis called them "jagged intelligences. Very good at certain things. Completely incapable of others." You've felt this yourself. You've felt this yourself. You ask ChatGPT a complex question and it sounds smart. But ask it to solve something that requires REASONING across multiple steps? It falls apart. So what ARE the 2 breakthroughs we need? Breakthrough #1: World Models AI that understands how physics actually works. How water flows. How cause and effect works. DeepMind already has early versions (Genie, Veo). The insight: If AI can GENERATE something realistic, it UNDERSTANDS it. This is the foundation for robotics and AI that interacts with reality. Breakthrough #2: Agentic Systems AI that can DO things. Not just answer questions. Plan multiple steps. Execute autonomously. Adjust when wrong. DeepMind proved this with AlphaGo in 2016 - planning 20+ moves ahead to beat the world champion. Now they're generalizing it to the real world. And here's the most interesting part: Demis says these two things are starting to CONVERGE. LLMs + World Models + Agentic Behavior = AGI And when I say converge, I mean Google is already building it. They're setting up the first fully automated scientific laboratory in the UK. No humans running experiments. AI designs the test. Robots execute it. AI analyzes results. AI adjusts and iterates. The lab will work on: → Room-temperature superconductors → Nuclear fusion materials → New battery chemistries → Climate tech breakthroughs Demis's logic is simple: "If AI can screen materials 100X faster, the energy revolution takes 10 years instead of 100." But here's the scary part: China is MONTHS behind. Not years. "They're very close to the frontier. Maybe only months behind." DeepSeek. Alibaba's Qwen models. They're catching up fast. And unlike what people thought, they're doing it WITHOUT access to the most advanced Nvidia chips. The window for the West to lead in AGI is shrinking. The economic impact? Demis: "10 times bigger than the Industrial Revolution. And maybe 10 times faster." Industrial Revolution took 100+ years and reshaped civilization. This will be 10X bigger in 1/10th the time. Mass job displacement. Economic restructuring. New industries overnight. But also: → Curing all disease → Solving climate change → Unlimited clean energy → "Radical abundance" Demis is betting DeepMind can get there first. Google spent $400 million on DeepMind in 2014. That stake is now worth 100s of billions. Because DeepMind is now the "engine room" of ALL of Google's AI. Every Gemini model. Every AI feature in Search, Gmail, Workspace. All built by DeepMind. Shipped across Google's dozens of billion-user products instantly. That distribution is their superpower. The final thing Demis said that stuck with me: "AGI is probably the most transformative moment in human history. And it's on the horizon." One or two breakthroughs and 5 years away. According to the most skilled guy in the industry.
2:40

The most skilled guy in the AI industry just said we're 1-2 breakthroughs away from AGI. And he explained exactly what's missing. Demis Hassabis runs Google DeepMind. He won the Nobel Prize in Chemistry last year. He's literally the reason why Google is considered the leader of the AI race. And he just dropped the most specific AGI timeline ever: "One or two AlphaGo-level technological breakthroughs." That's it. That's all standing between us and artificial general intelligence. But here's the thing... LLMs are NOT going to get us there. ChatGPT, Gemini, Claude - they're all hitting the same wall. They can't plan long-term. Can't create NEW ideas. Can't understand physics. Demis called them "jagged intelligences. Very good at certain things. Completely incapable of others." You've felt this yourself. You've felt this yourself. You ask ChatGPT a complex question and it sounds smart. But ask it to solve something that requires REASONING across multiple steps? It falls apart. So what ARE the 2 breakthroughs we need? Breakthrough #1: World Models AI that understands how physics actually works. How water flows. How cause and effect works. DeepMind already has early versions (Genie, Veo). The insight: If AI can GENERATE something realistic, it UNDERSTANDS it. This is the foundation for robotics and AI that interacts with reality. Breakthrough #2: Agentic Systems AI that can DO things. Not just answer questions. Plan multiple steps. Execute autonomously. Adjust when wrong. DeepMind proved this with AlphaGo in 2016 - planning 20+ moves ahead to beat the world champion. Now they're generalizing it to the real world. And here's the most interesting part: Demis says these two things are starting to CONVERGE. LLMs + World Models + Agentic Behavior = AGI And when I say converge, I mean Google is already building it. They're setting up the first fully automated scientific laboratory in the UK. No humans running experiments. AI designs the test. Robots execute it. AI analyzes results. AI adjusts and iterates. The lab will work on: → Room-temperature superconductors → Nuclear fusion materials → New battery chemistries → Climate tech breakthroughs Demis's logic is simple: "If AI can screen materials 100X faster, the energy revolution takes 10 years instead of 100." But here's the scary part: China is MONTHS behind. Not years. "They're very close to the frontier. Maybe only months behind." DeepSeek. Alibaba's Qwen models. They're catching up fast. And unlike what people thought, they're doing it WITHOUT access to the most advanced Nvidia chips. The window for the West to lead in AGI is shrinking. The economic impact? Demis: "10 times bigger than the Industrial Revolution. And maybe 10 times faster." Industrial Revolution took 100+ years and reshaped civilization. This will be 10X bigger in 1/10th the time. Mass job displacement. Economic restructuring. New industries overnight. But also: → Curing all disease → Solving climate change → Unlimited clean energy → "Radical abundance" Demis is betting DeepMind can get there first. Google spent $400 million on DeepMind in 2014. That stake is now worth 100s of billions. Because DeepMind is now the "engine room" of ALL of Google's AI. Every Gemini model. Every AI feature in Search, Gmail, Workspace. All built by DeepMind. Shipped across Google's dozens of billion-user products instantly. That distribution is their superpower. The final thing Demis said that stuck with me: "AGI is probably the most transformative moment in human history. And it's on the horizon." One or two breakthroughs and 5 years away. According to the most skilled guy in the industry.

Ricardo

217,289 次观看 • 8 个月前