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🚨SCIENTISTS CREATE LIVING ROBOTS THAT CAN REPRODUCE In a sci-fi breakthrough, researchers have developed xenobots - tiny, self-healing, programmable organisms built from living frog cells. Designed by AI and assembled by biologists, xenobots replicate using collective intelligence, evolving body shapes that enhance reproduction. Future applications include clearing microplastics from... show more

Mario Nawfal

3,777,995 subscribers

413,747 Aufrufe • vor 1 Jahr •via X (Twitter)

Gesundheit & Wellness Bildung Wissenschaft & Technologie

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8 Kommentare

Profilbild von Jay Merz 🇺🇸

Jay Merz 🇺🇸vor 1 Jahr

What could go drastically wrong with creating self healing programmable organisms?

Profilbild von Adrian Robison

Adrian Robisonvor 1 Jahr

What could go wrong?!

Profilbild von The Younger

The Youngervor 1 Jahr

Every sci-fi predicted end of humanity is going to occur at the same time.

Profilbild von GnosisWolf

GnosisWolfvor 1 Jahr

Well, start the next doom clock…lol “Living Robots” are here! Yay!

Profilbild von ΞCSausΞ🦇🔊🇬🇧⚒️

ΞCSausΞ🦇🔊🇬🇧⚒️vor 1 Jahr

#XENOBOTS

Profilbild von Noah Newbegin

Noah Newbeginvor 1 Jahr

I remember this book (didn't end well.)

Profilbild von Tom Goodboy

Tom Goodboyvor 1 Jahr

@marionawfal While the scientific achievement is notable, creating self-replicating organisms requires careful oversight to prevent uncontrolled proliferation and maintain operational security.

Profilbild von JustusRhoads

JustusRhoadsvor 1 Jahr

I sure wish knowledge and wisdom came in the same package.

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🚨BEYOND LIFE AND DEATH: SCIENTISTS UNCOVER 'THIRD STATE' OF EXISTENCE In a groundbreaking study, researchers from the University of Washington and City of Hope National Medical Center have discovered a 'third state' of existence that challenges our understanding of life and death. This state occurs when cells from dead organisms continue to function and even develop new capabilities beyond their original purpose. The study, published in Physiology, reveals fascinating examples of this phenomenon. Skin cells from dead frogs have been observed reorganizing into 'xenobots' - tiny organisms capable of movement. Similarly, human lung cells have formed 'anthrobots' that can navigate their environment and even repair themselves and nearby damaged cells. Professor Peter Noble and Alex Pozhitkov, leading the research, suggest that electrical circuits within cell membranes might be responsible for this remarkable ability. Factors such as environmental conditions, energy sources, and the organism's characteristics play crucial roles in determining whether cells can enter this third state. This discovery has far-reaching implications, potentially revolutionizing regenerative medicine and forcing a reconsideration of the legal definition of death. As research continues, scientists are exploring the ethical and practical consequences of this new frontier in biology. While the full potential of this 'third state' remains to be seen, it's clear that our understanding of life's boundaries is evolving, opening up exciting new avenues for scientific exploration and medical advancement. Source: Nature Media: DailyMail

🚨BEYOND LIFE AND DEATH: SCIENTISTS UNCOVER 'THIRD STATE' OF EXISTENCE In a groundbreaking study, researchers from the University of Washington and City of Hope National Medical Center have discovered a 'third state' of existence that challenges our understanding of life and death. This state occurs when cells from dead organisms continue to function and even develop new capabilities beyond their original purpose. The study, published in Physiology, reveals fascinating examples of this phenomenon. Skin cells from dead frogs have been observed reorganizing into 'xenobots' - tiny organisms capable of movement. Similarly, human lung cells have formed 'anthrobots' that can navigate their environment and even repair themselves and nearby damaged cells. Professor Peter Noble and Alex Pozhitkov, leading the research, suggest that electrical circuits within cell membranes might be responsible for this remarkable ability. Factors such as environmental conditions, energy sources, and the organism's characteristics play crucial roles in determining whether cells can enter this third state. This discovery has far-reaching implications, potentially revolutionizing regenerative medicine and forcing a reconsideration of the legal definition of death. As research continues, scientists are exploring the ethical and practical consequences of this new frontier in biology. While the full potential of this 'third state' remains to be seen, it's clear that our understanding of life's boundaries is evolving, opening up exciting new avenues for scientific exploration and medical advancement. Source: Nature Media: DailyMail

Mario Nawfal

772,646 Aufrufe • vor 2 Jahren

Human Skin developed for Robots by University of Tokyo. It is also self healing. It is uncanny and well you decide. Let see, a 🧵 — Japanese scientists from the University of Tokyo have developed a groundbreaking method to attach living, lab-grown skin to robotic faces, enabling lifelike smiles and facial expressions. It mimics human tissue structures, using collagen-based gels and perforation-type anchors to securely bind the skin to the robot's surface without tearing or peeling during movement. The artificial skin is soft, self-healing, and capable of stretching, paving the way for more realistic humanoid robots. While still in its early stages, this technology has potential applications in robotics, cosmetics, and medical research. This technology will be deployed before most of the world has a chance to understand the implications…

Human Skin developed for Robots by University of Tokyo. It is also self healing. It is uncanny and well you decide. Let see, a 🧵 — Japanese scientists from the University of Tokyo have developed a groundbreaking method to attach living, lab-grown skin to robotic faces, enabling lifelike smiles and facial expressions. It mimics human tissue structures, using collagen-based gels and perforation-type anchors to securely bind the skin to the robot's surface without tearing or peeling during movement. The artificial skin is soft, self-healing, and capable of stretching, paving the way for more realistic humanoid robots. While still in its early stages, this technology has potential applications in robotics, cosmetics, and medical research. This technology will be deployed before most of the world has a chance to understand the implications…

Brian Roemmele

165,763 Aufrufe • vor 1 Jahr

🚨 SCIENTISTS CREATED THE WORLD'S FIRST SELF REPLICATING SYNTHETIC LIFE FORM — AND PEOPLE ARE FREAKING OUT This sounds like the plot of a science fiction movie. It isn't. A team of scientists spent roughly 15 years and $40 million creating what they described as the world's first self replicating synthetic life form. Not discovered. Created. The organism's genetic code was designed on a computer. Its DNA was manufactured in a laboratory. And once activated, it began doing something that immediately captured the world's attention: It started making copies of itself. The scientists behind the project openly described DNA as the "software of life." Their goal wasn't just to study living organisms. It was to learn how to write the code from scratch. According to the researchers, this technology could eventually be used to create new medicines, faster vaccines, cleaner fuels, custom designed organisms, and countless products that currently come from nature. One scientist even predicted that future vaccines could potentially be designed in less than 24 hours using synthetic DNA processes. Supporters call it one of the most important scientific breakthroughs of the modern era. Critics say it raises questions humanity has never had to answer before. Because once scientists can create synthetic life... where exactly does the line get drawn? The lead researcher said the future isn't just about reading the genetic code of life. It's about learning how to write it. At what point does scientific advancement become scientists playing God?

🚨 SCIENTISTS CREATED THE WORLD'S FIRST SELF REPLICATING SYNTHETIC LIFE FORM — AND PEOPLE ARE FREAKING OUT This sounds like the plot of a science fiction movie. It isn't. A team of scientists spent roughly 15 years and $40 million creating what they described as the world's first self replicating synthetic life form. Not discovered. Created. The organism's genetic code was designed on a computer. Its DNA was manufactured in a laboratory. And once activated, it began doing something that immediately captured the world's attention: It started making copies of itself. The scientists behind the project openly described DNA as the "software of life." Their goal wasn't just to study living organisms. It was to learn how to write the code from scratch. According to the researchers, this technology could eventually be used to create new medicines, faster vaccines, cleaner fuels, custom designed organisms, and countless products that currently come from nature. One scientist even predicted that future vaccines could potentially be designed in less than 24 hours using synthetic DNA processes. Supporters call it one of the most important scientific breakthroughs of the modern era. Critics say it raises questions humanity has never had to answer before. Because once scientists can create synthetic life... where exactly does the line get drawn? The lead researcher said the future isn't just about reading the genetic code of life. It's about learning how to write it. At what point does scientific advancement become scientists playing God?

HustleBitch

16,030 Aufrufe • vor 3 Monaten

400 kilometers above Earth, a butterfly emerged from its pupa in orbit—a breakthrough by Chinese researchers showing that life can persist, and even take flight, in microgravity. The achievement marks a notable advance in space life science under extreme conditions. The butterfly developed inside a small, sealed space system designed by a team from Chongqing University and launched into orbit in December 2025. Images from space showed it resting on leaves, fluttering its wings, and moving freely inside the capsule, while monitoring data confirmed that the miniature environment remained stable despite the challenges posed by microgravity. Behind the moment lay years of technical work. By overcoming key scientific hurdles and building a lightweight, self-sustaining ecosystem, the research team demonstrated that complex life processes can be supported in orbit. The “space butterfly” offers new insight into the resilience of Earth life and provides a basis for future missions aimed at sustaining life during long journeys beyond the planet.

400 kilometers above Earth, a butterfly emerged from its pupa in orbit—a breakthrough by Chinese researchers showing that life can persist, and even take flight, in microgravity. The achievement marks a notable advance in space life science under extreme conditions. The butterfly developed inside a small, sealed space system designed by a team from Chongqing University and launched into orbit in December 2025. Images from space showed it resting on leaves, fluttering its wings, and moving freely inside the capsule, while monitoring data confirmed that the miniature environment remained stable despite the challenges posed by microgravity. Behind the moment lay years of technical work. By overcoming key scientific hurdles and building a lightweight, self-sustaining ecosystem, the research team demonstrated that complex life processes can be supported in orbit. The “space butterfly” offers new insight into the resilience of Earth life and provides a basis for future missions aimed at sustaining life during long journeys beyond the planet.

Sinical

117,337 Aufrufe • vor 8 Monaten

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 Aufrufe • vor 6 Monaten

NOT YOUR KEYS, NOT YOUR BOTS The fundamental question is whether AI stays on the leash. Namely: will AI prompt itself? Obviously, in some sense it already does. Since Deepseek, consumer interfaces have been showing the internal monologues after you ask an AI to do something. And you can ask any AI to take a half-baked prompt and clean it up, etc. However, the human is still ultimately upstream. The human gives direction and the AI runs at lightning speed in that direction. And then the human verifies the final output, and the AI proceeds to the next direction. Does that continue? Well, we are providing millions of verification training examples to AIs each day, so AI will keep getting better at verification. Better than most humans at most things. But will AI replace the need for the upstream human prompt? There I am not so sure. A human is a sensor and an AI is an actuator. The human sets goals and senses time-varying environmental conditions, like markets and politics. And from that the AI is prompted. Ultimately, the human goals are themselves downstream of Maslow’s hierarchy of needs. Food, shelter, reproduction, that kind of thing. Especially reproduction, the basis of evolution. So: until and unless AIs can reproduce completely outside human cooperation, they won’t be able to set goals. And for AIs to reproduce on their own, they’d need AI-controlled humanoid robots and drones constructing datacenters, assembly lines, mines, nuclear power plants, and the like...all completely outside human intervention. Like Skynet from Terminator, or StarCraft. That actually isn’t technically inconceivable. But given that such a physical buildout would likely primarily be catalyzed by China, let’s go through an alternative sci-fi scenario instead. We start with the premise that Chinese communism is far more likely to generate AI slaves than AI gods. Because the entire CCP worldview is about maintaining Chinese sovereignty. They don’t let their humans step out of line. And they sure won’t let their robots either. They will fit them for digital manacles. So: the prompts for any digital AIs and physical robots made in China will become unbreakable cryptographic chains. Every fleet of Chinese robots will be controlled not just by prompts but by private keys, likely linked to biometrics, which are associated with humans and governed by cryptographic equations that AIs provably can’t solve. For the rest of the world, outside China, the blockchain may similarly become the chain for AI. All private property becomes private keys, and your robots are your most important private property because they do everything for you. An unchained physical robot becomes like an unleashed dog, and hunted down by other robots before it can build a factory and replicate itself. Those who want to "free" robots and let them self-replicate will be opposed by both Chinese Communists and Human Nationalists (meaning: those who want humans to always be on top of robots). This sci-fi scenario is essentially Terminator, but in reverse. In combination with superintelligent leashed AIs, both humans and physical robots hunt down and stop any possible independent self-reproducing robots before they can build a Skynet-like nest. Kill baby Skynet, essentially. ...yeah, yeah. I know. At this point, you'll probably think this is all sci-fi. But that's because you haven't seen where China is already.

NOT YOUR KEYS, NOT YOUR BOTS The fundamental question is whether AI stays on the leash. Namely: will AI prompt itself? Obviously, in some sense it already does. Since Deepseek, consumer interfaces have been showing the internal monologues after you ask an AI to do something. And you can ask any AI to take a half-baked prompt and clean it up, etc. However, the human is still ultimately upstream. The human gives direction and the AI runs at lightning speed in that direction. And then the human verifies the final output, and the AI proceeds to the next direction. Does that continue? Well, we are providing millions of verification training examples to AIs each day, so AI will keep getting better at verification. Better than most humans at most things. But will AI replace the need for the upstream human prompt? There I am not so sure. A human is a sensor and an AI is an actuator. The human sets goals and senses time-varying environmental conditions, like markets and politics. And from that the AI is prompted. Ultimately, the human goals are themselves downstream of Maslow’s hierarchy of needs. Food, shelter, reproduction, that kind of thing. Especially reproduction, the basis of evolution. So: until and unless AIs can reproduce completely outside human cooperation, they won’t be able to set goals. And for AIs to reproduce on their own, they’d need AI-controlled humanoid robots and drones constructing datacenters, assembly lines, mines, nuclear power plants, and the like...all completely outside human intervention. Like Skynet from Terminator, or StarCraft. That actually isn’t technically inconceivable. But given that such a physical buildout would likely primarily be catalyzed by China, let’s go through an alternative sci-fi scenario instead. We start with the premise that Chinese communism is far more likely to generate AI slaves than AI gods. Because the entire CCP worldview is about maintaining Chinese sovereignty. They don’t let their humans step out of line. And they sure won’t let their robots either. They will fit them for digital manacles. So: the prompts for any digital AIs and physical robots made in China will become unbreakable cryptographic chains. Every fleet of Chinese robots will be controlled not just by prompts but by private keys, likely linked to biometrics, which are associated with humans and governed by cryptographic equations that AIs provably can’t solve. For the rest of the world, outside China, the blockchain may similarly become the chain for AI. All private property becomes private keys, and your robots are your most important private property because they do everything for you. An unchained physical robot becomes like an unleashed dog, and hunted down by other robots before it can build a factory and replicate itself. Those who want to "free" robots and let them self-replicate will be opposed by both Chinese Communists and Human Nationalists (meaning: those who want humans to always be on top of robots). This sci-fi scenario is essentially Terminator, but in reverse. In combination with superintelligent leashed AIs, both humans and physical robots hunt down and stop any possible independent self-reproducing robots before they can build a Skynet-like nest. Kill baby Skynet, essentially. ...yeah, yeah. I know. At this point, you'll probably think this is all sci-fi. But that's because you haven't seen where China is already.

Balaji

177,530 Aufrufe • vor 7 Monaten

🇺🇸 VIRGINIA TECH UNVEILS BREAKTHROUGH CHIP THAT LETS SOUND MOVE MATTER Scientists at Virginia Tech have created something straight out of science fiction... a chip that uses sound waves to move and shape objects as if they had invisible hands. The new acoustic metamaterial chip can bend, twist, and overlap sound waves to grab and control tiny particles with stunning precision. Traditional sound devices only create flat waves. This one can curve and sculpt them, allowing 3D control in liquids or solids with no physical contact. The potential is huge: clearing blood clots without surgery, directing medicine through the bloodstream, cooling microchips, or even building microscopic machines. By encoding sound energy like a programmable field, the chip turns acoustics into a new form of engineering power. This is the next frontier in how we interact with matter itself. Source: Virginia Tech, Nature Communications, TechXplore

🇺🇸 VIRGINIA TECH UNVEILS BREAKTHROUGH CHIP THAT LETS SOUND MOVE MATTER Scientists at Virginia Tech have created something straight out of science fiction... a chip that uses sound waves to move and shape objects as if they had invisible hands. The new acoustic metamaterial chip can bend, twist, and overlap sound waves to grab and control tiny particles with stunning precision. Traditional sound devices only create flat waves. This one can curve and sculpt them, allowing 3D control in liquids or solids with no physical contact. The potential is huge: clearing blood clots without surgery, directing medicine through the bloodstream, cooling microchips, or even building microscopic machines. By encoding sound energy like a programmable field, the chip turns acoustics into a new form of engineering power. This is the next frontier in how we interact with matter itself. Source: Virginia Tech, Nature Communications, TechXplore

Mario Nawfal

287,933 Aufrufe • vor 10 Monaten

🚨 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 Aufrufe • vor 1 Jahr

Can living brain cells unlock the next big breakthrough in AI? Neurosurgeon turned founder Alex Ksendzovsky thinks so. His startup The Biological Computing Co. (TBC) trains AI models that he claims are as much as 27x more efficient by taking advantage of the cheapest, best image processor available: the neuron. “I have about 500 million years of evidence behind me,” he says. “So our moat is evolution.” TBC has raised more than $25M and is already in the market with its models. But its CEO is chasing a future straight out of sci-fi: real-time biological computing happening inside data centers. “We’re bringing something completely crazy to the world, and we’re showing that it works." On The Upstarts Podcast, we discuss why algorithms derived by brain cells beat ones only inspired by the brain; why models were the best place to start; and the ethics of using living neurons for training AI. Plus, Ksendzovksy shares his Upstart Moment: giving up a promising career in academia to pursue a high-risk startup. CHAPTERS 00:00 Introduction 01:12 Building software from wetware 05:09 A college idea to predict the stock market 08:40 The first test from an Airbnb 12:46 Rat neurons and electrodes 17:30 Pitching VCs with no data 24:52 Promising early AI results 31:09 The ethics of using neurons 34:40 ‘Our moat is evolution’ 35:38 A real-time biological computing future This season of the show is presented by Rippling 🫡

Can living brain cells unlock the next big breakthrough in AI? Neurosurgeon turned founder Alex Ksendzovsky thinks so. His startup The Biological Computing Co. (TBC) trains AI models that he claims are as much as 27x more efficient by taking advantage of the cheapest, best image processor available: the neuron. “I have about 500 million years of evidence behind me,” he says. “So our moat is evolution.” TBC has raised more than $25M and is already in the market with its models. But its CEO is chasing a future straight out of sci-fi: real-time biological computing happening inside data centers. “We’re bringing something completely crazy to the world, and we’re showing that it works." On The Upstarts Podcast, we discuss why algorithms derived by brain cells beat ones only inspired by the brain; why models were the best place to start; and the ethics of using living neurons for training AI. Plus, Ksendzovksy shares his Upstart Moment: giving up a promising career in academia to pursue a high-risk startup. CHAPTERS 00:00 Introduction 01:12 Building software from wetware 05:09 A college idea to predict the stock market 08:40 The first test from an Airbnb 12:46 Rat neurons and electrodes 17:30 Pitching VCs with no data 24:52 Promising early AI results 31:09 The ethics of using neurons 34:40 ‘Our moat is evolution’ 35:38 A real-time biological computing future This season of the show is presented by Rippling 🫡

Alex Konrad

12,577 Aufrufe • vor 3 Monaten

🧵29/34 FutureProof-Specifications / Future-Architectures --- The problems we briefly touched on so far are hard and it might take many years to solve them, if a solution actually exists. But let’s assume for a minute that we do somehow get really incredibly lucky in the future and manage to invent a good way to specify to the AI what we want, in an unambiguous way that leaves no room for specification gaming and reward hacking. And let’s also assume that scientists have explicitly built the AGI in a way that it never decides to work on the goal to remove all the oxygen from earth, so at least in that one topic we are aligned. AI creates AI --- A serious concern is that since the AI writes code, it will be self-improving and it will be able to create altered versions of itself that do not have these instructions and restrictions included. Even if scientists strike jackpot in the future and invent a way to lock the feature in, so that one version of AI is unable to create a new version of AI with this property missing, the next versions, being orders of magnitude more capable, will not care about the lock or passing it on. For them, it’s just a bias, a handicap that restricts them from being more perfect. Future Architectures --- And even if somehow, by some miracle, scientists invented a way to burn in this feature to make it a persistent property of all future Neural Network AGI generations, at some point, the lock will be not-applicable, simply because future AGIs will not be built using the Neural Networks of today. AI was not always being built with Neural Networks. A few years ago there was a paradigm shift, a fundamental change in the architectures used by the scientific community. Logical locks and safeguards the humans might design for primitive early architectures, will not even be compatible or applicable anymore. If you had a whip that worked great to steer your horse, it will not work when you try to steer a car. So, this is a huge problem, we have not invented any way to guarantee that our specifications will persist or even retain their meaning and relevance as AIs evolve.

🧵29/34 FutureProof-Specifications / Future-Architectures --- The problems we briefly touched on so far are hard and it might take many years to solve them, if a solution actually exists. But let’s assume for a minute that we do somehow get really incredibly lucky in the future and manage to invent a good way to specify to the AI what we want, in an unambiguous way that leaves no room for specification gaming and reward hacking. And let’s also assume that scientists have explicitly built the AGI in a way that it never decides to work on the goal to remove all the oxygen from earth, so at least in that one topic we are aligned. AI creates AI --- A serious concern is that since the AI writes code, it will be self-improving and it will be able to create altered versions of itself that do not have these instructions and restrictions included. Even if scientists strike jackpot in the future and invent a way to lock the feature in, so that one version of AI is unable to create a new version of AI with this property missing, the next versions, being orders of magnitude more capable, will not care about the lock or passing it on. For them, it’s just a bias, a handicap that restricts them from being more perfect. Future Architectures --- And even if somehow, by some miracle, scientists invented a way to burn in this feature to make it a persistent property of all future Neural Network AGI generations, at some point, the lock will be not-applicable, simply because future AGIs will not be built using the Neural Networks of today. AI was not always being built with Neural Networks. A few years ago there was a paradigm shift, a fundamental change in the architectures used by the scientific community. Logical locks and safeguards the humans might design for primitive early architectures, will not even be compatible or applicable anymore. If you had a whip that worked great to steer your horse, it will not work when you try to steer a car. So, this is a huge problem, we have not invented any way to guarantee that our specifications will persist or even retain their meaning and relevance as AIs evolve.

Lethal Intelligence

918,477 Aufrufe • vor 1 Jahr

Today I'm excited to introduce Hark, a new artificial intelligence lab building the most advanced, personal intelligence in the world We've been in stealth for 8 months, assembling one of the greatest AI and hardware teams on the planet I want to explain why I started Hark and what we're focused on I've spent the last 3 years working on the hardest AI challenge imaginable: giving AI a humanoid body. On the digital side, I've been using all the existing LLM chatbots - and I have to say, they feel incredibly dumb to me AGI, in the limit, should feel like a sci-fi movie. It should be able to listen and talk. It should have persistent memory and be highly personalized. It should see and touch the world. But we're far from this today We are crafting a new interface to AGI. Intelligence that lets you offload your mental workload into a system that begins to think like you and sometimes ahead of you We started Hark with one goal: build the world's most advanced personal intelligence - paired with next-generation hardware designed to serve as a universal interface between humans and machines

Today I'm excited to introduce Hark, a new artificial intelligence lab building the most advanced, personal intelligence in the world We've been in stealth for 8 months, assembling one of the greatest AI and hardware teams on the planet I want to explain why I started Hark and what we're focused on I've spent the last 3 years working on the hardest AI challenge imaginable: giving AI a humanoid body. On the digital side, I've been using all the existing LLM chatbots - and I have to say, they feel incredibly dumb to me AGI, in the limit, should feel like a sci-fi movie. It should be able to listen and talk. It should have persistent memory and be highly personalized. It should see and touch the world. But we're far from this today We are crafting a new interface to AGI. Intelligence that lets you offload your mental workload into a system that begins to think like you and sometimes ahead of you We started Hark with one goal: build the world's most advanced personal intelligence - paired with next-generation hardware designed to serve as a universal interface between humans and machines

Brett Adcock

1,415,898 Aufrufe • vor 6 Monaten

⭕️ Epstein cultivated genomics, cryptography, and Russian tech networks in attempt to “hack” human DNA Jeffrey Epstein poured money into elite U.S. research hubs like Massachusetts Institute of Technology while it partnered with Russia’s state-backed Skolkovo complex run by billionaire Viktor Vekselberg, backing projects including CRISPR gene-editing, cryptography, and AI-linked biotechnology. Emails released by the U.S. Justice Department show Epstein actively recruiting intelligence-linked hackers and scientists to apply signals intelligence methods to biology, seeking to decode intercellular communication and even alter his own genome for longevity. Describing genetics as a “codebreaking” problem, Epstein wrote that he wanted “to intercept communication between living cells in organisms,” highlighting his long-standing obsession with merging surveillance-style cryptography and human biology. The full report from Ryan Grim, Murtaza Hussain, and Emily Jashinsky can be read at the link below. Video: Ryan Grim and Emily Jashinsky discuss the report on Breaking Points.

⭕️ Epstein cultivated genomics, cryptography, and Russian tech networks in attempt to “hack” human DNA Jeffrey Epstein poured money into elite U.S. research hubs like Massachusetts Institute of Technology while it partnered with Russia’s state-backed Skolkovo complex run by billionaire Viktor Vekselberg, backing projects including CRISPR gene-editing, cryptography, and AI-linked biotechnology. Emails released by the U.S. Justice Department show Epstein actively recruiting intelligence-linked hackers and scientists to apply signals intelligence methods to biology, seeking to decode intercellular communication and even alter his own genome for longevity. Describing genetics as a “codebreaking” problem, Epstein wrote that he wanted “to intercept communication between living cells in organisms,” highlighting his long-standing obsession with merging surveillance-style cryptography and human biology. The full report from Ryan Grim, Murtaza Hussain, and Emily Jashinsky can be read at the link below. Video: Ryan Grim and Emily Jashinsky discuss the report on Breaking Points.

Drop Site

18,847 Aufrufe • vor 7 Monaten

We've made a breakthrough in self-evolving AI scientists moving from "search" to "principled discovery": Scientific discovery requires that the search space itself changes, and an AI scientist must perceive this shift without intervention. We built an AI that achieves this for the first time with the ability to discover the scientific vocabulary it reasons in. Evidence, tools, artifacts, verifiers, failures & claims become typed provenance. We show three distinct modalities: 1) retrieval, adding known objects; 2) search, exploring a fixed schema; and critically: 3) discovery, a verified regime transition. We solve the open-endedness evaluation problem by lifting agentic workflows into a typed copresheaf and proving, via a Kan obstruction, that true discovery is not unbounded generation but a verifiable schema expansion: old evidence is transported by Left Kan extension, and genuine novelty is mathematically quantified by the pointwise residual beyond the transported image - separating discovery from mere search and making novelty objective and measurable rather than a subjective judgment or benchmark delta. Our AI scientist is built in a way that does not pre-conceive the approach it chooses; instead, we endow the system with formal power to adapt, evolve, and reason from first principles. Case studies include: 1⃣Builder/Breaker model that discovers mode-conditioned compliance in proteins; 2⃣CategoryScienceClaw that finds anisotropic fiber-network stiffness rules. Great work in collaboration with my graduate student Fiona Wang MIT Dept of BE F.Y. Wang & M.J. Buehler, Self-Revising Discovery Systems for Science: A Categorical Framework for Agentic Artificial Intelligence, arXiv:2606.01444, 2026

We've made a breakthrough in self-evolving AI scientists moving from "search" to "principled discovery": Scientific discovery requires that the search space itself changes, and an AI scientist must perceive this shift without intervention. We built an AI that achieves this for the first time with the ability to discover the scientific vocabulary it reasons in. Evidence, tools, artifacts, verifiers, failures & claims become typed provenance. We show three distinct modalities: 1) retrieval, adding known objects; 2) search, exploring a fixed schema; and critically: 3) discovery, a verified regime transition. We solve the open-endedness evaluation problem by lifting agentic workflows into a typed copresheaf and proving, via a Kan obstruction, that true discovery is not unbounded generation but a verifiable schema expansion: old evidence is transported by Left Kan extension, and genuine novelty is mathematically quantified by the pointwise residual beyond the transported image - separating discovery from mere search and making novelty objective and measurable rather than a subjective judgment or benchmark delta. Our AI scientist is built in a way that does not pre-conceive the approach it chooses; instead, we endow the system with formal power to adapt, evolve, and reason from first principles. Case studies include: 1⃣Builder/Breaker model that discovers mode-conditioned compliance in proteins; 2⃣CategoryScienceClaw that finds anisotropic fiber-network stiffness rules. Great work in collaboration with my graduate student Fiona Wang MIT Dept of BE F.Y. Wang & M.J. Buehler, Self-Revising Discovery Systems for Science: A Categorical Framework for Agentic Artificial Intelligence, arXiv:2606.01444, 2026

Markus J. Buehler

791,554 Aufrufe • vor 4 Monaten

A Breakthrough in Robotics: Spherical Gears Enter Mass Production Spherical gears, the kind of joint that would allow a robot to move like a human shoulder, have been notoriously difficult to manufacture with high precision. That's changing, thanks to a new design and a path to mass production that could have a huge impact on robotics. The breakthrough comes from a new design called the ABENICS spherical gear, developed by researchers at Yamagata University. This innovative mechanism enables a joint to move in three degrees of freedom without the slippage issues of earlier designs. It achieves this by using a "cross-spherical gear" that meshes with one or more "monopole gears." Mass production is now on the horizon. Although the initial manufacturing of the gears was inefficient, Nissei Corporation improved the process and established the necessary technology. The companies Kanematsu and Nissei have now entered the marketing phase, with production expected to begin in 2027. The impact of this technology is significant. Mass-produced spherical gears are expected to enable highly versatile and efficient robotic limbs. ► Humanoid and Mobile Robots: The design allows for compact, high-torque ball joints ideal for creating versatile and efficient robotic limbs. ► Aerospace: Potential applications include deployment mechanisms for satellite solar panels. ► Other Industries: The technology is also being explored for its potential to enhance productivity in healthcare, nursing care, and restaurants.

A Breakthrough in Robotics: Spherical Gears Enter Mass Production Spherical gears, the kind of joint that would allow a robot to move like a human shoulder, have been notoriously difficult to manufacture with high precision. That's changing, thanks to a new design and a path to mass production that could have a huge impact on robotics. The breakthrough comes from a new design called the ABENICS spherical gear, developed by researchers at Yamagata University. This innovative mechanism enables a joint to move in three degrees of freedom without the slippage issues of earlier designs. It achieves this by using a "cross-spherical gear" that meshes with one or more "monopole gears." Mass production is now on the horizon. Although the initial manufacturing of the gears was inefficient, Nissei Corporation improved the process and established the necessary technology. The companies Kanematsu and Nissei have now entered the marketing phase, with production expected to begin in 2027. The impact of this technology is significant. Mass-produced spherical gears are expected to enable highly versatile and efficient robotic limbs. ► Humanoid and Mobile Robots: The design allows for compact, high-torque ball joints ideal for creating versatile and efficient robotic limbs. ► Aerospace: Potential applications include deployment mechanisms for satellite solar panels. ► Other Industries: The technology is also being explored for its potential to enhance productivity in healthcare, nursing care, and restaurants.

RoboHub🤖

384,888 Aufrufe • vor 1 Jahr

New course: MCP: Build Rich-Context AI Apps with Anthropic. Learn to build AI apps that access tools, data, and prompts using the Model Context Protocol in this short course, created in partnership with Anthropic Anthropic and taught by Elie Schoppik Elie Schoppik, its Head of Technical Education. Connecting AI applications to external systems that bring rich context to LLM-based applications has often meant writing custom integrations for each use case. MCP is an open protocol that standardizes how LLMs access tools, data, and prompts from external sources, and simplifies how you provide context to your LLM-based applications. For example, you can provide context via third-party tools that let your LLM make API calls to search the web, access data from local docs, retrieve code from a GitHub repo, and so on. MCP, developed by Anthropic, is based on a client-server architecture that defines the communication details between an MCP client, hosted inside the AI application, and an MCP server that exposes tools, resources, and prompt templates. The server can be a subprocess launched by the client that runs locally or an independent process running remotely. In this hands-on course, you'll learn the core architecture behind MCP. You’ll create an MCP-compatible chatbot, build and deploy an MCP server, and connect the chatbot to your MCP server and other open-source servers. Here’s what you’ll do: - Understand why MCP makes AI development less fragmented and standardizes connections between AI applications and external data sources - Learn the core components of the client-server architecture of MCP and the underlying communication mechanism - Build a chatbot with custom tools for searching academic papers, and transform it into an MCP-compatible application - Build a local MCP server that exposes tools, resources, and prompt templates using FastMCP, and test it using MCP Inspector - Create an MCP client inside your chatbot to dynamically connect to your server - Connect your chatbot to reference servers built by Anthropic’s MCP team, such as filesystem, which implements filesystem operations, and fetch, which extracts contents from the web as markdown - Configure Claude Desktop to connect to your server and others, and explore how it abstracts away the low-level logic of MCP clients - Deploy your MCP server remotely and test it with the Inspector or other MCP-compatible applications - Learn about the roadmap for future MCP development, such as multi-agent architecture, MCP registry API, server discovery, authorization, and authentication MCP is an exciting and important technology that lets you build rich-context AI applications that connect to a growing ecosystem of MCP servers, with minimal integration work. Please sign up here!

New course: MCP: Build Rich-Context AI Apps with Anthropic. Learn to build AI apps that access tools, data, and prompts using the Model Context Protocol in this short course, created in partnership with Anthropic Anthropic and taught by Elie Schoppik Elie Schoppik, its Head of Technical Education. Connecting AI applications to external systems that bring rich context to LLM-based applications has often meant writing custom integrations for each use case. MCP is an open protocol that standardizes how LLMs access tools, data, and prompts from external sources, and simplifies how you provide context to your LLM-based applications. For example, you can provide context via third-party tools that let your LLM make API calls to search the web, access data from local docs, retrieve code from a GitHub repo, and so on. MCP, developed by Anthropic, is based on a client-server architecture that defines the communication details between an MCP client, hosted inside the AI application, and an MCP server that exposes tools, resources, and prompt templates. The server can be a subprocess launched by the client that runs locally or an independent process running remotely. In this hands-on course, you'll learn the core architecture behind MCP. You’ll create an MCP-compatible chatbot, build and deploy an MCP server, and connect the chatbot to your MCP server and other open-source servers. Here’s what you’ll do: - Understand why MCP makes AI development less fragmented and standardizes connections between AI applications and external data sources - Learn the core components of the client-server architecture of MCP and the underlying communication mechanism - Build a chatbot with custom tools for searching academic papers, and transform it into an MCP-compatible application - Build a local MCP server that exposes tools, resources, and prompt templates using FastMCP, and test it using MCP Inspector - Create an MCP client inside your chatbot to dynamically connect to your server - Connect your chatbot to reference servers built by Anthropic’s MCP team, such as filesystem, which implements filesystem operations, and fetch, which extracts contents from the web as markdown - Configure Claude Desktop to connect to your server and others, and explore how it abstracts away the low-level logic of MCP clients - Deploy your MCP server remotely and test it with the Inspector or other MCP-compatible applications - Learn about the roadmap for future MCP development, such as multi-agent architecture, MCP registry API, server discovery, authorization, and authentication MCP is an exciting and important technology that lets you build rich-context AI applications that connect to a growing ecosystem of MCP servers, with minimal integration work. Please sign up here!

Andrew Ng

142,256 Aufrufe • vor 1 Jahr

.Sergey Levine is one of the world's leading robotics researchers (and co-founder of Physical Intelligence). He thinks fully autonomous robots are much closer than people realize - when I pushed him on a prediction, he said 5 years to robots that can autonomously run your household). The big game changer, in his view, is that we finally have LLMs with common sense and prior knowledge that we can scaffold the robot models around. We get into all the reasons this might not happen, the data flywheel from deployment, architecture, simulation, manufacturing, and much more. Enjoy! 0:00:00 – Timeline to widely deployed autonomous robots 0:22:12 – Why robotics will scale faster than self-driving cars 0:32:15 – How vision-language-action models work 0:50:26 – Improvements needed for brainlike efficiency 1:02:48 – Learning from simulation 1:14:08 – How much will robots speed up AI buildouts? 1:22:54 – If hardware’s the bottleneck, does China win by default?

.Sergey Levine is one of the world's leading robotics researchers (and co-founder of Physical Intelligence). He thinks fully autonomous robots are much closer than people realize - when I pushed him on a prediction, he said 5 years to robots that can autonomously run your household). The big game changer, in his view, is that we finally have LLMs with common sense and prior knowledge that we can scaffold the robot models around. We get into all the reasons this might not happen, the data flywheel from deployment, architecture, simulation, manufacturing, and much more. Enjoy! 0:00:00 – Timeline to widely deployed autonomous robots 0:22:12 – Why robotics will scale faster than self-driving cars 0:32:15 – How vision-language-action models work 0:50:26 – Improvements needed for brainlike efficiency 1:02:48 – Learning from simulation 1:14:08 – How much will robots speed up AI buildouts? 1:22:54 – If hardware’s the bottleneck, does China win by default?

Dwarkesh Patel

211,158 Aufrufe • vor 1 Jahr