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Larry Ellison explains how AI-powered surgical robots now surpass even the most skilled human doctors. Unlike humans, these robots have microscopic vision built into their sensors. They can see individual cells without a microscope. This allows them to cut with extreme precision removing cancer without harming healthy tissue. Their... show more

Wes Roth

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257,709 views • 11 months ago •via X (Twitter)

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The All-In Podcast discussed Figure's AI breakthrough this week I believe this AI breakthrough was essential to enabling a billion robots on the planet - allowing humans to communicate with them and have them carry out actions Helix, our internal AI model, powers the Figure 02 robot. The robot can now generalize to objects it has never seen before and can semantically plan responses to spoken commands, such as "put the groceries away where you think they should go" It’s important to note: the robot’s actuators are not the limiting factor. Right now, both dexterity and actuator speed are constrained by the software. In fact, the actuators are capable of operating at more than 5x their current speed, but our software is holding them back. Over time, as Helix improves, the robot will ultimately surpass human speeds I like these guys. Looking forward to having Chamath Palihapitiya at Figure HQ to see these robots in action!

The All-In Podcast discussed Figure's AI breakthrough this week I believe this AI breakthrough was essential to enabling a billion robots on the planet - allowing humans to communicate with them and have them carry out actions Helix, our internal AI model, powers the Figure 02 robot. The robot can now generalize to objects it has never seen before and can semantically plan responses to spoken commands, such as "put the groceries away where you think they should go" It’s important to note: the robot’s actuators are not the limiting factor. Right now, both dexterity and actuator speed are constrained by the software. In fact, the actuators are capable of operating at more than 5x their current speed, but our software is holding them back. Over time, as Helix improves, the robot will ultimately surpass human speeds I like these guys. Looking forward to having Chamath Palihapitiya at Figure HQ to see these robots in action!

Brett Adcock

135,552 views • 1 year ago

Scientists just figured out how to reverse aging using AI. And this is a massive breakthrough. We can now reprogram any human cell back to age 20. Heart cells, brain cells, skin cells, all reset to their biological prime. And here’s the wildest part…the technology to do this, has already existed since 2012 (it won the Nobel Prize). But the real breakthrough wasn’t possible until this year, when they supercharged it with AI. It’s a wild story. So in 2006, scientists discovered Yamanaka factors. They’re proteins that can basically convert any normal cell into a universal stem cell. Now this was a huge deal, because these stem cells are basically like magic healers. If you have torn muscle tissue, you could inject these stem cells into the area and they will turn into the youthful muscle cells you need. So Yamanaka factors were this insane breakthrough, because they allowed any human to turn any cell you already have into these magic healers. But, there was one big problem… It turns out, the original Yamanaka factors weren’t very good at this stem cell conversion. They could do it, but they just weren’t very reliable. Enter OpenAI...and this is where things get crazy. OpenAI designed a special AI model built specifically to create new proteins. Think of it like ChatGPT but for protein engineering. So they took all the Yamanaka research and asked this new AI to go ham on improving it. And get this… Their version was 50x more effective than the original. They tested it on 50 year old cells and it successfully started repairing 30% of their cells in just 7 days. This is just science fiction…it actually happened. And it sounds crazy, but in a few years, humans will be able to take a shot that will literally reverse the age of their cells.

Scientists just figured out how to reverse aging using AI. And this is a massive breakthrough. We can now reprogram any human cell back to age 20. Heart cells, brain cells, skin cells, all reset to their biological prime. And here’s the wildest part…the technology to do this, has already existed since 2012 (it won the Nobel Prize). But the real breakthrough wasn’t possible until this year, when they supercharged it with AI. It’s a wild story. So in 2006, scientists discovered Yamanaka factors. They’re proteins that can basically convert any normal cell into a universal stem cell. Now this was a huge deal, because these stem cells are basically like magic healers. If you have torn muscle tissue, you could inject these stem cells into the area and they will turn into the youthful muscle cells you need. So Yamanaka factors were this insane breakthrough, because they allowed any human to turn any cell you already have into these magic healers. But, there was one big problem… It turns out, the original Yamanaka factors weren’t very good at this stem cell conversion. They could do it, but they just weren’t very reliable. Enter OpenAI...and this is where things get crazy. OpenAI designed a special AI model built specifically to create new proteins. Think of it like ChatGPT but for protein engineering. So they took all the Yamanaka research and asked this new AI to go ham on improving it. And get this… Their version was 50x more effective than the original. They tested it on 50 year old cells and it successfully started repairing 30% of their cells in just 7 days. This is just science fiction…it actually happened. And it sounds crazy, but in a few years, humans will be able to take a shot that will literally reverse the age of their cells.

Whiplash347

68,643 views • 10 months ago

🚨 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.

🚨 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 views • 1 year ago

Researchers at Northwestern University have developed "legged metamachines," a new class of modular robots whose unique physical forms were evolved entirely by artificial intelligence. Rather than following traditional human-engineered blueprints, the researchers used AI to "grow" novel body configurations in a computer simulation, resulting in strange new "species" that move like seals, lizards, or kangaroos. These AI-driven designs bypass standard dog- or human-like structures to create machines that no human engineer would have conceived. The machines are composed of autonomous, Lego-like modules, each containing its own motor, battery, and computer. While individual modules can move on their own, their true agility and near-indestructibility emerge through the AI-evolved architecture that allows them to function as a collective. Because the design is inherently modular, these robots can survive catastrophic damage that would be fatal to traditional machines. Broken parts do not become dead weight; instead, the AI-designed system allows them to keep rolling or crawling independently to rejoin the team. Lead researcher Sam Kriegman notes that these are the first robots to step outdoors after evolving inside a computer. They can survive being cut into pieces, as every separated module remains an individual agent capable of continuing its journey. The study, conducted at the McCormick School of Engineering, was co-authored by Kriegman and PhD students Chen Yu, David Matthews, and Jingxian Wang.

Researchers at Northwestern University have developed "legged metamachines," a new class of modular robots whose unique physical forms were evolved entirely by artificial intelligence. Rather than following traditional human-engineered blueprints, the researchers used AI to "grow" novel body configurations in a computer simulation, resulting in strange new "species" that move like seals, lizards, or kangaroos. These AI-driven designs bypass standard dog- or human-like structures to create machines that no human engineer would have conceived. The machines are composed of autonomous, Lego-like modules, each containing its own motor, battery, and computer. While individual modules can move on their own, their true agility and near-indestructibility emerge through the AI-evolved architecture that allows them to function as a collective. Because the design is inherently modular, these robots can survive catastrophic damage that would be fatal to traditional machines. Broken parts do not become dead weight; instead, the AI-designed system allows them to keep rolling or crawling independently to rejoin the team. Lead researcher Sam Kriegman notes that these are the first robots to step outdoors after evolving inside a computer. They can survive being cut into pieces, as every separated module remains an individual agent capable of continuing its journey. The study, conducted at the McCormick School of Engineering, was co-authored by Kriegman and PhD students Chen Yu, David Matthews, and Jingxian Wang.

Michał Podlewski

14,954 views • 6 months ago

MIT engineers built an AI wristband that controls robots by reading your hand muscles. It works by using an ultrasound to capture images of the muscles and tendons in your wrist. An AI algorithm then translates those images into the exact position of all 5 fingers, tracking 22 different ways your hand can bend, twist, and extend. In demos, wearers directed a robotic hand to play piano just by moving their own fingers. To put the complexity in perspective, it takes: > 34 muscles > 27 joints > 100+ tendons ...Just to scroll your phone This device captures it all from a band the size of a smartwatch. They tested across 8 volunteers with different hand sizes, tracking everything from sign language letters to holding scissors. The team is now collecting data from hundreds more users to train robots in surgical procedures. Something like this could one day become the universal remote for robots and virtual worlds, and we're way closer than most people expect.

MIT engineers built an AI wristband that controls robots by reading your hand muscles. It works by using an ultrasound to capture images of the muscles and tendons in your wrist. An AI algorithm then translates those images into the exact position of all 5 fingers, tracking 22 different ways your hand can bend, twist, and extend. In demos, wearers directed a robotic hand to play piano just by moving their own fingers. To put the complexity in perspective, it takes: > 34 muscles > 27 joints > 100+ tendons ...Just to scroll your phone This device captures it all from a band the size of a smartwatch. They tested across 8 volunteers with different hand sizes, tracking everything from sign language letters to holding scissors. The team is now collecting data from hundreds more users to train robots in surgical procedures. Something like this could one day become the universal remote for robots and virtual worlds, and we're way closer than most people expect.

Rowan Cheung

28,902 views • 5 months ago

QACE Dynamics | VIBR Tech Preview 1 Introducing VIBR, the vision intelligence block that brings real perception to robots. In this first preview, we take a simple vacuum bot, connect it through the QACE dApp, and instantly give it object detection and smarter behavior without touching code. QACE makes it easy by automating the usual workflow: what once took hours of coding in Python and assembling AI models is now reduced to a few clicks. Complex AI vision blocks are created, configured, and ready-to-deploy scripts are generated automatically. Robots can now see, decide, and act in real time. Up next: we will show how these rules and actions evolve into full autonomy modules, with the Qognix Tech Preview dropping this week.

QACE Dynamics | VIBR Tech Preview 1 Introducing VIBR, the vision intelligence block that brings real perception to robots. In this first preview, we take a simple vacuum bot, connect it through the QACE dApp, and instantly give it object detection and smarter behavior without touching code. QACE makes it easy by automating the usual workflow: what once took hours of coding in Python and assembling AI models is now reduced to a few clicks. Complex AI vision blocks are created, configured, and ready-to-deploy scripts are generated automatically. Robots can now see, decide, and act in real time. Up next: we will show how these rules and actions evolve into full autonomy modules, with the Qognix Tech Preview dropping this week.

Qace Dynamics

29,448 views • 1 year ago

Holy…S😳 Xiaomi's New CyberOne is so human-like Although this update features a bionic hand, I was immediately drawn to it. Let's look at the changes in the hand： It can handle industrial precision tasks like turning screws, plus delicate operations such as pinching feathers and throwing balloons. Behind the performance: >Volume cut by 60%:now almost identical in size/shape to a real human hand >Big leap in degrees of freedom (+50% total, +83% active:22-27DOF) >Full-palm tactile sensors over 8200 mm² for precise grip even without vision >150,000+ grip cycles durability (61-hour test) And a major innovation:Smart bionic sweat gland cooling: evaporates water for ~10W active heat dissipation Using tactile gloves to capture real human data, they’re training smoother, human-like grasps with imitation and reinforcement learning. Elon has said that humanoid hands and true AI are the most difficult aspects of building humanoid robots. It seems that Xiaomi is also getting close.

Holy…S😳 Xiaomi's New CyberOne is so human-like Although this update features a bionic hand, I was immediately drawn to it. Let's look at the changes in the hand： It can handle industrial precision tasks like turning screws, plus delicate operations such as pinching feathers and throwing balloons. Behind the performance: >Volume cut by 60%:now almost identical in size/shape to a real human hand >Big leap in degrees of freedom (+50% total, +83% active:22-27DOF) >Full-palm tactile sensors over 8200 mm² for precise grip even without vision >150,000+ grip cycles durability (61-hour test) And a major innovation:Smart bionic sweat gland cooling: evaporates water for ~10W active heat dissipation Using tactile gloves to capture real human data, they’re training smoother, human-like grasps with imitation and reinforcement learning. Elon has said that humanoid hands and true AI are the most difficult aspects of building humanoid robots. It seems that Xiaomi is also getting close.

CyberRobo

111,430 views • 6 months ago

Microsoft's AI can now detect cancer from a $10 tissue sample. For context, every time tumor cells are tested, doctors create a basic microscope slide to study tissue up close. These slides show cell shapes and structures, but they can't reveal which immune cells are actually fighting the cancer. That deeper picture is critical for knowing if a patient will respond to immunotherapy... But the advanced imaging needed costs THOUSANDS. So Microsoft built GigaTIME -- an AI system that generates advanced imaging from the cheap slides hospitals already collect. The system was trained on 40 million cancer cells, then applied to over 14,000 patients across 51 hospitals spanning 24 cancer types. The AI found over 1,200 hidden connections between immune cell behavior and tumor growth that researchers couldn't find before... because the data simply didn't exist at this scale. When validated against 10,000 additional patients from a completely separate database, the results held up. The model is now open source, so any hospital worldwide can use it on samples they already have. I think this is one of the most impactful AI papers I've seen this year!

Microsoft's AI can now detect cancer from a $10 tissue sample. For context, every time tumor cells are tested, doctors create a basic microscope slide to study tissue up close. These slides show cell shapes and structures, but they can't reveal which immune cells are actually fighting the cancer. That deeper picture is critical for knowing if a patient will respond to immunotherapy... But the advanced imaging needed costs THOUSANDS. So Microsoft built GigaTIME -- an AI system that generates advanced imaging from the cheap slides hospitals already collect. The system was trained on 40 million cancer cells, then applied to over 14,000 patients across 51 hospitals spanning 24 cancer types. The AI found over 1,200 hidden connections between immune cell behavior and tumor growth that researchers couldn't find before... because the data simply didn't exist at this scale. When validated against 10,000 additional patients from a completely separate database, the results held up. The model is now open source, so any hospital worldwide can use it on samples they already have. I think this is one of the most impactful AI papers I've seen this year!

Rowan Cheung

43,689 views • 5 months ago

Humanoid robots are cool. But most of them aren’t ready for real-world work. X-Humanoid just launched Embodied Tien Kung 3.0 - a full-size humanoid robot built to actually operate in industrial and commercial environments. What makes it different? • Strong, high-torque joints for heavy tasks • Can move dynamically and stay stable • Handles complex motions (even over obstacles) • Millimeter-level precision for detailed work It runs on the Wise KaiWu AI platform, which allows the robot to see, think, and act in a continuous loop with less human control and support for multi-robot collaboration. It also supports ROS2, MQTT, and TCP/IP, and offers more open hardware + software interfaces to make development easier for teams. The goal is simple: make humanoid robots practical, easier to build on, and ready for real-world use. Visit: Check out the official launch video below to see what it can do! 👇 #Robotics #HumanoidRobot #AI

Humanoid robots are cool. But most of them aren’t ready for real-world work. X-Humanoid just launched Embodied Tien Kung 3.0 - a full-size humanoid robot built to actually operate in industrial and commercial environments. What makes it different? • Strong, high-torque joints for heavy tasks • Can move dynamically and stay stable • Handles complex motions (even over obstacles) • Millimeter-level precision for detailed work It runs on the Wise KaiWu AI platform, which allows the robot to see, think, and act in a continuous loop with less human control and support for multi-robot collaboration. It also supports ROS2, MQTT, and TCP/IP, and offers more open hardware + software interfaces to make development easier for teams. The goal is simple: make humanoid robots practical, easier to build on, and ready for real-world use. Visit: Check out the official launch video below to see what it can do! 👇 #Robotics #HumanoidRobot #AI

Chidanand Tripathi

69,604 views • 7 months ago

Trash isn’t just trash anymore! 🗑️ With vision, sensors, and AI, waste becomes a sorting problem, and a real business opportunity. Here's a robotic system from ZenRobotics - A Terex Brand. Their systems now sort everything from bulky construction debris to high-speed conveyor waste with near-perfect precision. The Heavy Picker handles large, awkward materials like stone and scrap. The Fast Picker operates at high speeds, ideal for lighter materials in material recovery facilities. And behind it all is ZenBrain, an AI trained on over 500 waste categories, making real-time decisions without a human in sight. Great to see robots in waste management industry! ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →

Lukas Ziegler

11,165 views • 3 months ago

With enough data, robots and AI can learn “world models” that let them predict the results of their actions. These models are a way to learn how embodied AI agents can perform a wide variety of useful tasks — but they require a huge amount of data. The team at General Intuition General Intuition has a solution: use data from video games! Games teach movement, problem solving, and complex spatial reasoning, and they come in a staggering diversity of forms, covering a wide variety of problems. What’s more, the captured data is high-quality, without the noise or annotation error that can come from We sat down with Pim de Witte and Adam Jelley from the General Intuition team to learn more about their history, their plans, and their philosophy.

With enough data, robots and AI can learn “world models” that let them predict the results of their actions. These models are a way to learn how embodied AI agents can perform a wide variety of useful tasks — but they require a huge amount of data. The team at General Intuition General Intuition has a solution: use data from video games! Games teach movement, problem solving, and complex spatial reasoning, and they come in a staggering diversity of forms, covering a wide variety of problems. What’s more, the captured data is high-quality, without the noise or annotation error that can come from We sat down with Pim de Witte and Adam Jelley from the General Intuition team to learn more about their history, their plans, and their philosophy.

RoboPapers

85,927 views • 10 months ago

I don't know if you really understand what these people just did... They built an AI that looks at a few casual real-world videos or photos and instantly turns them into a full 3D playground. You can MOVE THE CAMERA HOWEVER YOU WANT, change the lighting, move objects around, and even let physics play out like in the real world. View events from impossible angles because Atlas knows what was there. This is massive! Let's take robotics. Until now training robots meant collecting tons of expensive real-world data. Now a handful of recordings can become thousands of different simulated situations. Robots can practice the same task in new rooms, with different objects and lighting without anyone having to film it all again. For the industry it means we can train robots way faster and cheaper. Home robots, warehouse robots, whatever... they can learn new environments at scale instead of staying stuck in the lab. This is one of the first real bridges between “cool AI video models” and actual physical machines that have to work in the real world. Blog: The team behind World Labs: Fei-Fei Li, Justin Johnson, Ben Mildenhall and Yunzhu Li. The video is from David Pantera. Follow for more insights into robotics and AI. —— Weekly robotics and AI insights. Subscribe free:

I don't know if you really understand what these people just did... They built an AI that looks at a few casual real-world videos or photos and instantly turns them into a full 3D playground. You can MOVE THE CAMERA HOWEVER YOU WANT, change the lighting, move objects around, and even let physics play out like in the real world. View events from impossible angles because Atlas knows what was there. This is massive! Let's take robotics. Until now training robots meant collecting tons of expensive real-world data. Now a handful of recordings can become thousands of different simulated situations. Robots can practice the same task in new rooms, with different objects and lighting without anyone having to film it all again. For the industry it means we can train robots way faster and cheaper. Home robots, warehouse robots, whatever... they can learn new environments at scale instead of staying stuck in the lab. This is one of the first real bridges between “cool AI video models” and actual physical machines that have to work in the real world. Blog: The team behind World Labs: Fei-Fei Li, Justin Johnson, Ben Mildenhall and Yunzhu Li. The video is from David Pantera. Follow for more insights into robotics and AI. —— Weekly robotics and AI insights. Subscribe free:

Ilir Aliu

273,043 views • 25 days ago