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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

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🇰🇷 SOUTH KOREA CREATES TERMINATOR-STYLE LIQUID ROBOT South Korean scientists have built a shape-shifting liquid robot that can slide through tight metal grates, merge with others, and move across water. It's soft but stable, coated in water-repellent particles, guided by ultrasound, and tough enough to bounce back from impact. This was designed for disaster rescue, surveillance, and internal medical procedures, this robot mimics living cells: it divides, reforms, and adapts on command…absolutely insane! It's the closest thing yet to programmable fluid life, and it could soon be saving lives from inside your body or deep inside a collapsed building. Source: The AI Field, AzerNewsAZ

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Mario Nawfal

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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

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🚨 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?

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HustleBitch

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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.

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Sinical

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14,954 Aufrufe • vor 4 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

177,058 Aufrufe • vor 6 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

287,833 Aufrufe • vor 8 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

🧵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.

lethalintelligence.ai

918,086 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

1,413,176 Aufrufe • vor 4 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 6 Monaten

Self-Evolving AI : New MIT AI Rewrites its Own Code and it’s Changing Everything | Julian Horsey, Geeky Gadgets TL;DR Key Takeaways : - MIT’s SEAL framework introduces “self-adapting language models” that autonomously enhance their capabilities by generating synthetic training data, self-editing, and updating internal parameters. - SEAL’s self-adaptation process mirrors human learning, allowing continuous improvement and dynamic adaptation to new tasks without relying on external datasets. - Reinforcement learning serves as a feedback mechanism in SEAL, rewarding effective self-edits and making sure sustained progress and goal alignment. SEAL overcomes AI’s reliance on pre-existing datasets by generating its own training material, excelling in long-term task retention and complex problem-solving scenarios. - Potential applications of SEAL include autonomous robotics, personalized education, and advanced problem-solving in fields like healthcare, logistics, and scientific research. --- What if artificial intelligence could not only learn but also rewrite its own code to become smarter over time? This is no longer a futuristic fantasy—MIT’s new “self-adapting language models” (SEAL) framework has made it a reality. Unlike traditional AI systems that rely on external datasets and human intervention to improve, SEAL takes a bold leap forward by autonomously generating its own training data and refining its internal processes. In essence, this AI doesn’t just evolve—it rewires itself, mirroring the way humans adapt through trial, error, and self-reflection. The implications are staggering: a system that can independently enhance its capabilities could redefine the boundaries of what AI can achieve, from solving complex problems to adapting in real time to unforeseen challenges. In this exploration by Wes Roth of MIT’s innovative SEAL framework, you’ll uncover how this self-improving AI works and why it’s a fantastic option for the field of artificial intelligence. From its ability to overcome the “data wall” that limits many current systems to its use of reinforcement learning as a feedback mechanism, SEAL introduces a level of autonomy and adaptability that was previously unimaginable. Imagine AI systems that can retain knowledge over time, dynamically adjust to new tasks, and operate with minimal human oversight. Whether you’re intrigued by its potential for autonomous robotics, personalized education, or advanced problem-solving, SEAL’s ability to rewrite its own rules promises to reshape the future of technology. Could this be the first step toward truly independent, self-evolving AI? What Sets SEAL Apart? The SEAL framework introduces a novel concept of self-adaptation, distinguishing it from traditional AI models. Unlike conventional systems that depend on external datasets for updates, SEAL enables AI to generate synthetic training data independently. This self-generated data is then used to iteratively refine the model, making sure continuous improvement. By persistently updating its internal parameters, SEAL enables AI systems to dynamically adapt to new tasks and inputs. To better illustrate this, consider how humans learn. When faced with a new concept, you might take notes, revisit them, and refine your understanding as you gather more information. SEAL mirrors this process by continuously refining its internal knowledge and performance through iterative self-improvement. This capability allows SEAL to evolve in real time, making it uniquely suited for tasks requiring adaptability and long-term learning. The Role of Reinforcement Learning in SEAL Reinforcement learning plays a critical role in the SEAL framework, acting as a feedback mechanism that evaluates the effectiveness of the model’s self-edits. It rewards changes that enhance performance, creating a cycle of continuous improvement. Over time, this feedback loop optimizes the system’s ability to generate and apply edits, making sure sustained progress. This process is analogous to how humans learn through trial and error. By rewarding effective changes, SEAL aligns its self-generated data and edits with desired outcomes. The integration of reinforcement learning not only enhances the system’s adaptability but also ensures it remains focused on achieving specific goals. This structured feedback mechanism is a cornerstone of SEAL’s ability to refine itself autonomously and efficiently. Real-World Applications and Testing SEAL has demonstrated remarkable performance across various applications, particularly in tasks requiring the integration of factual knowledge and advanced question-answering capabilities. For instance, when tested on benchmarks like the ARC AGI, SEAL outperformed other models by effectively generating and using synthetic data. This ability to create its own training material addresses a significant limitation of current AI systems: their reliance on pre-existing datasets. SEAL’s capacity for long-term task retention and dynamic adaptation further enhances its utility. It excels in scenarios that demand sustained focus and coherence, such as answering complex questions or adapting to evolving objectives. By using its iterative learning process, SEAL is equipped to handle these challenges with exceptional efficiency, making it a valuable tool for a wide range of real-world applications. Overcoming AI’s Data Limitations One of SEAL’s most promising features is its ability to overcome the “data wall” that constrains many AI systems today. By generating synthetic data, SEAL ensures a continuous supply of training material, allowing sustained development without relying on external datasets. This capability is particularly valuable for autonomous AI systems that must operate independently over extended periods. Additionally, SEAL addresses a critical weakness in many current AI models: their struggle with coherence and task retention over long durations. By emulating human learning processes, SEAL enables AI systems to manage complex, long-term tasks with minimal human intervention. This ability to retain and apply knowledge over time positions SEAL as a fantastic tool for advancing AI capabilities. Potential Applications and Future Impact The introduction of SEAL marks a significant milestone in AI research, opening new possibilities for self-improving systems. Its ability to dynamically adapt, retain knowledge, and generate its own training data has far-reaching implications for the future of AI development. Potential applications include: - Autonomous robotics: Systems that can adapt to changing environments and perform tasks with minimal human oversight. - Personalized education: AI-driven platforms that tailor learning experiences to individual needs and preferences. - Advanced problem-solving: Applications in fields such as healthcare, logistics, and scientific research, where adaptability and precision are critical. Read more:

Self-Evolving AI : New MIT AI Rewrites its Own Code and it’s Changing Everything | Julian Horsey, Geeky Gadgets TL;DR Key Takeaways : - MIT’s SEAL framework introduces “self-adapting language models” that autonomously enhance their capabilities by generating synthetic training data, self-editing, and updating internal parameters. - SEAL’s self-adaptation process mirrors human learning, allowing continuous improvement and dynamic adaptation to new tasks without relying on external datasets. - Reinforcement learning serves as a feedback mechanism in SEAL, rewarding effective self-edits and making sure sustained progress and goal alignment. SEAL overcomes AI’s reliance on pre-existing datasets by generating its own training material, excelling in long-term task retention and complex problem-solving scenarios. - Potential applications of SEAL include autonomous robotics, personalized education, and advanced problem-solving in fields like healthcare, logistics, and scientific research. --- What if artificial intelligence could not only learn but also rewrite its own code to become smarter over time? This is no longer a futuristic fantasy—MIT’s new “self-adapting language models” (SEAL) framework has made it a reality. Unlike traditional AI systems that rely on external datasets and human intervention to improve, SEAL takes a bold leap forward by autonomously generating its own training data and refining its internal processes. In essence, this AI doesn’t just evolve—it rewires itself, mirroring the way humans adapt through trial, error, and self-reflection. The implications are staggering: a system that can independently enhance its capabilities could redefine the boundaries of what AI can achieve, from solving complex problems to adapting in real time to unforeseen challenges. In this exploration by Wes Roth of MIT’s innovative SEAL framework, you’ll uncover how this self-improving AI works and why it’s a fantastic option for the field of artificial intelligence. From its ability to overcome the “data wall” that limits many current systems to its use of reinforcement learning as a feedback mechanism, SEAL introduces a level of autonomy and adaptability that was previously unimaginable. Imagine AI systems that can retain knowledge over time, dynamically adjust to new tasks, and operate with minimal human oversight. Whether you’re intrigued by its potential for autonomous robotics, personalized education, or advanced problem-solving, SEAL’s ability to rewrite its own rules promises to reshape the future of technology. Could this be the first step toward truly independent, self-evolving AI? What Sets SEAL Apart? The SEAL framework introduces a novel concept of self-adaptation, distinguishing it from traditional AI models. Unlike conventional systems that depend on external datasets for updates, SEAL enables AI to generate synthetic training data independently. This self-generated data is then used to iteratively refine the model, making sure continuous improvement. By persistently updating its internal parameters, SEAL enables AI systems to dynamically adapt to new tasks and inputs. To better illustrate this, consider how humans learn. When faced with a new concept, you might take notes, revisit them, and refine your understanding as you gather more information. SEAL mirrors this process by continuously refining its internal knowledge and performance through iterative self-improvement. This capability allows SEAL to evolve in real time, making it uniquely suited for tasks requiring adaptability and long-term learning. The Role of Reinforcement Learning in SEAL Reinforcement learning plays a critical role in the SEAL framework, acting as a feedback mechanism that evaluates the effectiveness of the model’s self-edits. It rewards changes that enhance performance, creating a cycle of continuous improvement. Over time, this feedback loop optimizes the system’s ability to generate and apply edits, making sure sustained progress. This process is analogous to how humans learn through trial and error. By rewarding effective changes, SEAL aligns its self-generated data and edits with desired outcomes. The integration of reinforcement learning not only enhances the system’s adaptability but also ensures it remains focused on achieving specific goals. This structured feedback mechanism is a cornerstone of SEAL’s ability to refine itself autonomously and efficiently. Real-World Applications and Testing SEAL has demonstrated remarkable performance across various applications, particularly in tasks requiring the integration of factual knowledge and advanced question-answering capabilities. For instance, when tested on benchmarks like the ARC AGI, SEAL outperformed other models by effectively generating and using synthetic data. This ability to create its own training material addresses a significant limitation of current AI systems: their reliance on pre-existing datasets. SEAL’s capacity for long-term task retention and dynamic adaptation further enhances its utility. It excels in scenarios that demand sustained focus and coherence, such as answering complex questions or adapting to evolving objectives. By using its iterative learning process, SEAL is equipped to handle these challenges with exceptional efficiency, making it a valuable tool for a wide range of real-world applications. Overcoming AI’s Data Limitations One of SEAL’s most promising features is its ability to overcome the “data wall” that constrains many AI systems today. By generating synthetic data, SEAL ensures a continuous supply of training material, allowing sustained development without relying on external datasets. This capability is particularly valuable for autonomous AI systems that must operate independently over extended periods. Additionally, SEAL addresses a critical weakness in many current AI models: their struggle with coherence and task retention over long durations. By emulating human learning processes, SEAL enables AI systems to manage complex, long-term tasks with minimal human intervention. This ability to retain and apply knowledge over time positions SEAL as a fantastic tool for advancing AI capabilities. Potential Applications and Future Impact The introduction of SEAL marks a significant milestone in AI research, opening new possibilities for self-improving systems. Its ability to dynamically adapt, retain knowledge, and generate its own training data has far-reaching implications for the future of AI development. Potential applications include: - Autonomous robotics: Systems that can adapt to changing environments and perform tasks with minimal human oversight. - Personalized education: AI-driven platforms that tailor learning experiences to individual needs and preferences. - Advanced problem-solving: Applications in fields such as healthcare, logistics, and scientific research, where adaptability and precision are critical. Read more:

Owen Gregorian

70,672 Aufrufe • vor 1 Jahr

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

788,845 Aufrufe • vor 2 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 10 Monaten

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

142,129 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 11 Monaten

Build and customize complex AI applications with a flexible framework in this new short course, Building AI Applications with Haystack. Created in collaboration with deepset, makers of Haystack, and taught by Tuana, who is the developer relations lead for Haystack at deepset. Generative AI technology is changing rapidly and it can be challenging to integrate APIs from different LLMs, vector databases, and various tools such as web search. In this course, you will learn how to use the Haystack framework to make your development process more modular, allowing you to manage complexity and focus more on building your application. In detail, you’ll: - Build a RAG pipeline using Haystack’s main building blocks – components, pipelines, and document stores. - Create custom components in your pipeline by building a Hacker News summarizer that extends your app’s ability to access APIs. - Use conditional routing to create a branching pipeline with a fallback to web search mechanism when the LLM does not have the necessary context to respond to the user's query. - Build a self-reflecting agent for named entity recognition that loops using an output validator custom component. - Create a chat agent using OpenAI's function-calling capabilities which allow you to provide Haystack pipelines as tools to the LLM, enhancing that agent's capabilities. By the end of this course, you will learn a high-level orchestration framework that can help make your applications flexible, extendible, and maintainable, even as the technology stack changes, new user needs arise, and you add new features to your application. Please sign up here:

Build and customize complex AI applications with a flexible framework in this new short course, Building AI Applications with Haystack. Created in collaboration with deepset, makers of Haystack, and taught by Tuana, who is the developer relations lead for Haystack at deepset. Generative AI technology is changing rapidly and it can be challenging to integrate APIs from different LLMs, vector databases, and various tools such as web search. In this course, you will learn how to use the Haystack framework to make your development process more modular, allowing you to manage complexity and focus more on building your application. In detail, you’ll: - Build a RAG pipeline using Haystack’s main building blocks – components, pipelines, and document stores. - Create custom components in your pipeline by building a Hacker News summarizer that extends your app’s ability to access APIs. - Use conditional routing to create a branching pipeline with a fallback to web search mechanism when the LLM does not have the necessary context to respond to the user's query. - Build a self-reflecting agent for named entity recognition that loops using an output validator custom component. - Create a chat agent using OpenAI's function-calling capabilities which allow you to provide Haystack pipelines as tools to the LLM, enhancing that agent's capabilities. By the end of this course, you will learn a high-level orchestration framework that can help make your applications flexible, extendible, and maintainable, even as the technology stack changes, new user needs arise, and you add new features to your application. Please sign up here:

Andrew Ng

53,788 Aufrufe • vor 2 Jahren