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We are excited to share that “Continuous Thought Machines” has been accepted as a Spotlight at #NeurIPS2025! 🧠✨ The CTM is an AI that mimics biological brains by using neural dynamics & synchronization to think over time. It can solve complex mazes by building internal maps, gaze around images... show more

Sakana AI

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Introducing Continuous Thought Machines New Blog: Modern AI is powerful, but it’s still distinct from human-like flexible intelligence. We believe neural timing is key. Our Continuous Thought Machine is built from the ground up to use neural dynamics as a powerful representation for intelligence. Thought takes time, and reasoning is a process. Biological brains inspire us with their complex neural activity, where neural timing is critical to intelligence. We’re exploring how to bring that power to AI. The Continuous Thought Machine (CTM) incorporates neuron-level temporal processing and neural synchronization, moving beyond current AI limitations. Our approach has two core innovations: (1) neuron-level temporal processing, where each neuron uses unique parameters to process a history of incoming signals for fine-grained temporal dynamics, and (2) neural synchronization, used as a direct latent representation to modulate data and produce outputs, encoding information directly in the timing of neural activity. Learn more about our approach: Interactive Report: Full Paper: GitHub :

Introducing Continuous Thought Machines New Blog: Modern AI is powerful, but it’s still distinct from human-like flexible intelligence. We believe neural timing is key. Our Continuous Thought Machine is built from the ground up to use neural dynamics as a powerful representation for intelligence. Thought takes time, and reasoning is a process. Biological brains inspire us with their complex neural activity, where neural timing is critical to intelligence. We’re exploring how to bring that power to AI. The Continuous Thought Machine (CTM) incorporates neuron-level temporal processing and neural synchronization, moving beyond current AI limitations. Our approach has two core innovations: (1) neuron-level temporal processing, where each neuron uses unique parameters to process a history of incoming signals for fine-grained temporal dynamics, and (2) neural synchronization, used as a direct latent representation to modulate data and produce outputs, encoding information directly in the timing of neural activity. Learn more about our approach: Interactive Report: Full Paper: GitHub :

Sakana AI

289,680 Aufrufe • vor 1 Jahr

New Paper: Continuous Thought Machines 🧠 Neurons in brains use timing and synchronization in the way that they compute, but this is largely ignored in modern neural nets. We believe neural timing is key for the flexibility and adaptability of biological intelligence. We propose a new neural architecture, “Continuous Thought Machines” (CTMs), which is built from the ground up to use neural dynamics as a core representation for intelligence. By using neural dynamics as a first-class representational citizen, CTMs naturally perform adaptive computation. Many emergent, interesting behaviors arise as a result: CTMs solve mazes by observing a raw maze image and producing step-by-step instructions directly from its neural dynamics. When tasked with image recognition, the CTM naturally takes multiple steps to examine different parts of the image before making its decision. This step-by-step approach not only makes its behavior more interpretable but also improves accuracy: the longer it “thinks,” the more accurate its answers become. We also found that this allows the CTM to decide to spend less time thinking on simpler images, thus saving energy. When identifying a gorilla, for example, the CTM’s attention moves from eyes to nose to mouth in a pattern remarkably similar to human visual attention. I think this work underscores an important, yet often lost, synergy between neuroscience and AI. While modern AI is ostensibly brain-inspired, the two fields often operate in surprising isolation. By starting with such inspiration and iteratively following the emergent, interesting behaviors, we developed a model with unexpected capabilities, such as its surprisingly strong calibration in classification tasks, a feature that was not explicitly designed for. When we initially asked, “why do this research?”, we hoped the journey of the CTM would provide compelling answers. By embracing light biological inspiration and pursuing the novel behaviors observed, we have arrived at a model with emergent capabilities that exceeded our initial designs. We are committed to continuing this exploration, borrowing further concepts to discover what new and exciting behaviors will emerge, pushing the boundaries of what AI can achieve.

New Paper: Continuous Thought Machines 🧠 Neurons in brains use timing and synchronization in the way that they compute, but this is largely ignored in modern neural nets. We believe neural timing is key for the flexibility and adaptability of biological intelligence. We propose a new neural architecture, “Continuous Thought Machines” (CTMs), which is built from the ground up to use neural dynamics as a core representation for intelligence. By using neural dynamics as a first-class representational citizen, CTMs naturally perform adaptive computation. Many emergent, interesting behaviors arise as a result: CTMs solve mazes by observing a raw maze image and producing step-by-step instructions directly from its neural dynamics. When tasked with image recognition, the CTM naturally takes multiple steps to examine different parts of the image before making its decision. This step-by-step approach not only makes its behavior more interpretable but also improves accuracy: the longer it “thinks,” the more accurate its answers become. We also found that this allows the CTM to decide to spend less time thinking on simpler images, thus saving energy. When identifying a gorilla, for example, the CTM’s attention moves from eyes to nose to mouth in a pattern remarkably similar to human visual attention. I think this work underscores an important, yet often lost, synergy between neuroscience and AI. While modern AI is ostensibly brain-inspired, the two fields often operate in surprising isolation. By starting with such inspiration and iteratively following the emergent, interesting behaviors, we developed a model with unexpected capabilities, such as its surprisingly strong calibration in classification tasks, a feature that was not explicitly designed for. When we initially asked, “why do this research?”, we hoped the journey of the CTM would provide compelling answers. By embracing light biological inspiration and pursuing the novel behaviors observed, we have arrived at a model with emergent capabilities that exceeded our initial designs. We are committed to continuing this exploration, borrowing further concepts to discover what new and exciting behaviors will emerge, pushing the boundaries of what AI can achieve.

hardmaru

257,273 Aufrufe • vor 1 Jahr

One of the most exciting new AI companies is Sakana AI in our opinion. Their CTO and their Co-founder Llion Jones Llion Jones is now saying it's time to move beyond transformers even though he was one of the 8 original inventors at Google. They are now investigating the next significant step forwards, and have a NeurIPS 2025 spotlight paper called "continuous thought machines" (CTM) which may well be just that (first author Luke Darlow). They just landed their Series B, with the legendary hardmaru as their CEO. They are actively investigating neuroevolution approaches which I strongly believe will be a big part of the future of AI -- and they publish their research in the open. Keep an eye on Sakana! 👌 Interview with Llion and Luke dropping today on MLST.

One of the most exciting new AI companies is Sakana AI in our opinion. Their CTO and their Co-founder Llion Jones Llion Jones is now saying it's time to move beyond transformers even though he was one of the 8 original inventors at Google. They are now investigating the next significant step forwards, and have a NeurIPS 2025 spotlight paper called "continuous thought machines" (CTM) which may well be just that (first author Luke Darlow). They just landed their Series B, with the legendary hardmaru as their CEO. They are actively investigating neuroevolution approaches which I strongly believe will be a big part of the future of AI -- and they publish their research in the open. Keep an eye on Sakana! 👌 Interview with Llion and Luke dropping today on MLST.

Machine Learning Street Talk

186,883 Aufrufe • vor 7 Monaten

This can be the superpower of the Neural Band that Meta is giving together with the Ray-Ban Meta Display glasses. The video shows an old prototype bracelet by CTRL+LABS, the startup acquired by Meta and whose technology was used to develop the Neural Band. At the beginning of the video, the guy makes an action (a keyboard key press) with his hands, then the bracelet can substitute the key pressure, and at the end of the video, the guy doesn't even have to do the action; it is just sufficient that he "thinks" about it. As long as the brain is sending an electric message to the fingers, the full action is not necessary anymore. Just an "intention" to move them is necessary. If the Neural Band is evolved to this stage, and the users are educated to this, potentially, we may not even need to perform air taps or writing gestures, but we could just think about doing them. This would reduce a lot of the fatigue of using XR devices and the weirdness of using them on the street. Then why isn't this feature available today? I guess that the reason is twofold. First of all, we have accuracy: the full gesture is easier to detect for the system. Many people (me included) are praising the accuracy of the Neural Band, and this is amazing, because an input mechanism should have a reliability close to 100%. Then we, as users, have never been trained to just "think" about actions: it would feel weird and hard to learn. I think we should undergo some training to learn how to do this "thinking" operation properly. I hope that something like this could come in the upcoming years... that would be the real game-changer paradigm if compared to the camera-based tracking.

This can be the superpower of the Neural Band that Meta is giving together with the Ray-Ban Meta Display glasses. The video shows an old prototype bracelet by CTRL+LABS, the startup acquired by Meta and whose technology was used to develop the Neural Band. At the beginning of the video, the guy makes an action (a keyboard key press) with his hands, then the bracelet can substitute the key pressure, and at the end of the video, the guy doesn't even have to do the action; it is just sufficient that he "thinks" about it. As long as the brain is sending an electric message to the fingers, the full action is not necessary anymore. Just an "intention" to move them is necessary. If the Neural Band is evolved to this stage, and the users are educated to this, potentially, we may not even need to perform air taps or writing gestures, but we could just think about doing them. This would reduce a lot of the fatigue of using XR devices and the weirdness of using them on the street. Then why isn't this feature available today? I guess that the reason is twofold. First of all, we have accuracy: the full gesture is easier to detect for the system. Many people (me included) are praising the accuracy of the Neural Band, and this is amazing, because an input mechanism should have a reliability close to 100%. Then we, as users, have never been trained to just "think" about actions: it would feel weird and hard to learn. I think we should undergo some training to learn how to do this "thinking" operation properly. I hope that something like this could come in the upcoming years... that would be the real game-changer paradigm if compared to the camera-based tracking.

TonyVT SkarredGhost

11,716 Aufrufe • vor 9 Monaten

Superintelligence is Near! Three innovations that prove it! Three recent innovations lead me to believe that we've just seen the invention of the next generation of "cognitive primitives" that will lead us directly to ASI. Do you remember LSTM? At the time, there were jokes that "brains are just LSTMs!" This feels like another "just before GPT moment" to me. HRM (hierarchical reasoning models), combined with IMO gold medal models, and the ASI-ARCH "AlphaGo for neural architecture" tells me that we're about to completely decouple machine intelligence from even data inputs. Also, my intuitive definition of ASI is "beyond the 100th percentile of human capability" meaning that "AGI can do anything humans can do, perhaps just faster" - BUT - I suspect that we're about to see machines that can solve problems that humans simply are not biologically capable of solving. It's not necessarily a matter of speed anymore. Sure, these things are faster, and perhaps the ASI level machines could teach us to solve problems that they can, but it will take us a long time to catch up, and in the meantime, they will have moved on. Mark my words, I suspect that is what we've just seen happen this past week. This is well and truly the beginning of the fast takeoff.

Superintelligence is Near! Three innovations that prove it! Three recent innovations lead me to believe that we've just seen the invention of the next generation of "cognitive primitives" that will lead us directly to ASI. Do you remember LSTM? At the time, there were jokes that "brains are just LSTMs!" This feels like another "just before GPT moment" to me. HRM (hierarchical reasoning models), combined with IMO gold medal models, and the ASI-ARCH "AlphaGo for neural architecture" tells me that we're about to completely decouple machine intelligence from even data inputs. Also, my intuitive definition of ASI is "beyond the 100th percentile of human capability" meaning that "AGI can do anything humans can do, perhaps just faster" - BUT - I suspect that we're about to see machines that can solve problems that humans simply are not biologically capable of solving. It's not necessarily a matter of speed anymore. Sure, these things are faster, and perhaps the ASI level machines could teach us to solve problems that they can, but it will take us a long time to catch up, and in the meantime, they will have moved on. Mark my words, I suspect that is what we've just seen happen this past week. This is well and truly the beginning of the fast takeoff.

David Shapiro ⏩

31,137 Aufrufe • vor 11 Monaten

What Neuralink just achieved gives me a bizarre new idea to train AI. Below is a person controlling mouse at unreasonably high speed & precision, just by thinking. The information flow looks like screen pixels -> biological NN -> translation by an artificial NN -> continuous mouse actions. I trained lots of AI agents to play games and control browsers. It makes me wonder: What if we learn another NN to imitate the first part of the flow: pixels -> brain? Can we intercept the "chain of thought" signals that a human computes? If we use imitation learning on this data, wouldn't we be able to model something as elusive as consciousness? Is this an alternative path to learn intelligence *directly* from human inner voices, instead of indirectly from our written text tokens? Could we do it? Yes, I believe it's all technically feasible right now. Just need to scale up. Should we do it?

What Neuralink just achieved gives me a bizarre new idea to train AI. Below is a person controlling mouse at unreasonably high speed & precision, just by thinking. The information flow looks like screen pixels -> biological NN -> translation by an artificial NN -> continuous mouse actions. I trained lots of AI agents to play games and control browsers. It makes me wonder: What if we learn another NN to imitate the first part of the flow: pixels -> brain? Can we intercept the "chain of thought" signals that a human computes? If we use imitation learning on this data, wouldn't we be able to model something as elusive as consciousness? Is this an alternative path to learn intelligence directly from human inner voices, instead of indirectly from our written text tokens? Could we do it? Yes, I believe it's all technically feasible right now. Just need to scale up. Should we do it?

Jim Fan

170,541 Aufrufe • vor 2 Jahren

NVIDIA CEO Jensen Huang just gave us a glimpse into the future of AI, and it's far bigger than text and images. He asked: "If you can understand worlds, is it possible that you understand proteins and chemicals that have structure?” This is the quiet revolution happening in AI right now. We're moving from an AI that understands human language to an AI that understands the language of nature itself. The "letters" are atoms. The "words" are molecules. The "sentences" are the complex structures of life, networks, and reality. The same foundational models that generate video are now being used to: *Decode proteins to fight disease *Design new materials at the atomic level *Simulate complex systems in quantum computing These were once the domain of supercomputers and Nobel laureates. Now, they're becoming solvable problems for a new generation of startups. AI is transitioning from a tool for communication to a tool for creation and discovery in the physical world. This is where the next wave of generational companies will be built. The "hard-to-solve" problems are now within reach.

NVIDIA CEO Jensen Huang just gave us a glimpse into the future of AI, and it's far bigger than text and images. He asked: "If you can understand worlds, is it possible that you understand proteins and chemicals that have structure?” This is the quiet revolution happening in AI right now. We're moving from an AI that understands human language to an AI that understands the language of nature itself. The "letters" are atoms. The "words" are molecules. The "sentences" are the complex structures of life, networks, and reality. The same foundational models that generate video are now being used to: Decode proteins to fight disease Design new materials at the atomic level *Simulate complex systems in quantum computing These were once the domain of supercomputers and Nobel laureates. Now, they're becoming solvable problems for a new generation of startups. AI is transitioning from a tool for communication to a tool for creation and discovery in the physical world. This is where the next wave of generational companies will be built. The "hard-to-solve" problems are now within reach.

Konstantine Buhler

13,014 Aufrufe • vor 9 Monaten

Hey again everyone! As some of you may know, we have been itching to post a lot of the amazing art that the Marathon team has been working on. We thought about what would be the best way to distribute it, and we landed on creating an archive site dedicated to hosting all of the amazing pieces of artwork that are in the games files, and we're happy to announce that it is updated with everything we could gather from this weekend's test. Go check it out at

Hey again everyone! As some of you may know, we have been itching to post a lot of the amazing art that the Marathon team has been working on. We thought about what would be the best way to distribute it, and we landed on creating an archive site dedicated to hosting all of the amazing pieces of artwork that are in the games files, and we're happy to announce that it is updated with everything we could gather from this weekend's test. Go check it out at

Deakstiny Zero

121,848 Aufrufe • vor 4 Monaten

The lack of "feature design" is why so many products over time feel hollow or messy. This isn't visual design. This isn't architectural design. I thought that a short video lecture of what feature design is and a real case study of applying it in Ghostty would be helpful. Feature design is the planning step behind how you're going to solve one or more user problems with a product feature: what that feature looks like, how it feels, and not just how its going to tactically solve these specific problems, but how that solution is going to interface with the edges of other features that currently exist or are planned to exist in the future.

The lack of "feature design" is why so many products over time feel hollow or messy. This isn't visual design. This isn't architectural design. I thought that a short video lecture of what feature design is and a real case study of applying it in Ghostty would be helpful. Feature design is the planning step behind how you're going to solve one or more user problems with a product feature: what that feature looks like, how it feels, and not just how its going to tactically solve these specific problems, but how that solution is going to interface with the edges of other features that currently exist or are planned to exist in the future.

Mitchell Hashimoto

155,878 Aufrufe • vor 6 Monaten

I really want to show off the cutting edge tech the team is building and how we're turning AI image generation into real living environments using gaussian splats and at the :19 mark of this video you can see how we're blending splats together to create a real world. What this looks like from a gaming perspective is this all happens behind the players view to simulate a real world environment. As you move through the space, distance becomes a variable to consider on how images update and are experienced. We are creating a new reality in real time.

I really want to show off the cutting edge tech the team is building and how we're turning AI image generation into real living environments using gaussian splats and at the :19 mark of this video you can see how we're blending splats together to create a real world. What this looks like from a gaming perspective is this all happens behind the players view to simulate a real world environment. As you move through the space, distance becomes a variable to consider on how images update and are experienced. We are creating a new reality in real time.

𝔹𝕃𝕍ℂ𝕂𝕃!𝔾ℍ𝕋

25,084 Aufrufe • vor 1 Monat

REPORTER: “You've had a deep faith for God and country…. What is your hope for America at a time such as this?” SECRETARY RUBIO: "My hope for America? It's the same as it's always been. I think it's the hope we all share. We want it to continue to be the place where anyone from anywhere can achieve anything — where you're not limited by the circumstances of your birth, the color of your skin, or your ethnicity." “But frankly, it's a place where you are able to overcome challenges and achieve your full potential. I think that should be the goal of every country in the world, frankly. “But I think in the U.S. we're not perfect. Our history is not one of perfection, but it's still better than anybody else's history.” "It is a unique and exceptional country. And as we come upon this 250 year anniversary, I think we have a lot to learn and be proud of in our history. It is one of perpetual and continuous improvement, where each generation has done its part to bring us closer to fulfilling the vision that the founders of this country had upon its founding."

REPORTER: “You've had a deep faith for God and country…. What is your hope for America at a time such as this?” SECRETARY RUBIO: "My hope for America? It's the same as it's always been. I think it's the hope we all share. We want it to continue to be the place where anyone from anywhere can achieve anything — where you're not limited by the circumstances of your birth, the color of your skin, or your ethnicity." “But frankly, it's a place where you are able to overcome challenges and achieve your full potential. I think that should be the goal of every country in the world, frankly. “But I think in the U.S. we're not perfect. Our history is not one of perfection, but it's still better than anybody else's history.” "It is a unique and exceptional country. And as we come upon this 250 year anniversary, I think we have a lot to learn and be proud of in our history. It is one of perpetual and continuous improvement, where each generation has done its part to bring us closer to fulfilling the vision that the founders of this country had upon its founding."

Fox News

335,769 Aufrufe • vor 2 Monaten

Over the past year, a lot of the conversation has been around how quickly AI is changing the digital world. At the same time, it made us think about how little has changed in the physical one. Most products are still designed the same way, just with more features layered on top. We wanted to explore a different direction at Lusion. Instead of asking how to add intelligence, we asked what happens if you take things away. What remains when you strip a product back to its core purpose, and design it properly from there. Oryzo is our first attempt at that. It is a physical product, built with a very simple idea of how it should exist and behave. No dependency on external systems, no complexity for the sake of it. Just something that feels considered. Curious to hear what people think. Link is in the comment!

Over the past year, a lot of the conversation has been around how quickly AI is changing the digital world. At the same time, it made us think about how little has changed in the physical one. Most products are still designed the same way, just with more features layered on top. We wanted to explore a different direction at Lusion. Instead of asking how to add intelligence, we asked what happens if you take things away. What remains when you strip a product back to its core purpose, and design it properly from there. Oryzo is our first attempt at that. It is a physical product, built with a very simple idea of how it should exist and behave. No dependency on external systems, no complexity for the sake of it. Just something that feels considered. Curious to hear what people think. Link is in the comment!

Lusion™

86,926 Aufrufe • vor 3 Monaten

The intelligence we are building is not artificial. It never was. Microsoft Chief Scientific Officer Eric Horvitz just reframed the entire foundation of the AI arms race with one sentence. The tech industry calls it Artificial Intelligence. That word is wrong. Horvitz: “I don’t actually like the term artificial intelligence. I wish the field was called computational intelligence because I think it applies to biological nervous systems as well as machines, and together we can go far.” We are not building a digital imitation of the human brain. We are scaling the exact same computational physics that created biological awareness and transferring it into silicon. Your mind and a massive AI data center run on the same underlying rules. The transition isn’t artificial. It is universal. And here is where it gets deeply unsettling. Tech optimists always fall back on the same comfort. Humans hold the steering wheel. Our values guide the machine. Horvitz acknowledges this. Horvitz: “We’ll take a humanistic standpoint here, always being on top of things and guiding with our values and our preferences and our goals.” Then the caveat that changes everything. Horvitz: “As much as they might be shaped over time by the machines we work with.” You cannot interact with a superintelligence at scale without it quietly rewiring your psychological baseline. The values you use to command the machine will be shaped by the machine you are commanding. The frameworks you use to perceive reality will be constructed by the system you believe you are directing. That feedback loop started the moment you asked an AI what to think about something. Most people haven’t noticed yet. Horvitz: “I think in our own lifetimes we will all experience incredible breakthroughs in understanding biology, with applications in medicine, in healthcare, that will be named as AI breakthroughs.” Horvitz: “It’s gonna accelerate over the next 10 to 15 years.” Because biological systems and machine networks both operate on computational intelligence, a sufficiently advanced AI can solve the human body like a math equation. The architects who win the next decade will not just control the digital economy. They will control the physical building blocks of life itself. The line between silicon and carbon was always an illusion. And once humanity fully realizes that, the question of whether we are using the intelligence or it is using us becomes impossible to answer. Because by then, we will be the same thing.

The intelligence we are building is not artificial. It never was. Microsoft Chief Scientific Officer Eric Horvitz just reframed the entire foundation of the AI arms race with one sentence. The tech industry calls it Artificial Intelligence. That word is wrong. Horvitz: “I don’t actually like the term artificial intelligence. I wish the field was called computational intelligence because I think it applies to biological nervous systems as well as machines, and together we can go far.” We are not building a digital imitation of the human brain. We are scaling the exact same computational physics that created biological awareness and transferring it into silicon. Your mind and a massive AI data center run on the same underlying rules. The transition isn’t artificial. It is universal. And here is where it gets deeply unsettling. Tech optimists always fall back on the same comfort. Humans hold the steering wheel. Our values guide the machine. Horvitz acknowledges this. Horvitz: “We’ll take a humanistic standpoint here, always being on top of things and guiding with our values and our preferences and our goals.” Then the caveat that changes everything. Horvitz: “As much as they might be shaped over time by the machines we work with.” You cannot interact with a superintelligence at scale without it quietly rewiring your psychological baseline. The values you use to command the machine will be shaped by the machine you are commanding. The frameworks you use to perceive reality will be constructed by the system you believe you are directing. That feedback loop started the moment you asked an AI what to think about something. Most people haven’t noticed yet. Horvitz: “I think in our own lifetimes we will all experience incredible breakthroughs in understanding biology, with applications in medicine, in healthcare, that will be named as AI breakthroughs.” Horvitz: “It’s gonna accelerate over the next 10 to 15 years.” Because biological systems and machine networks both operate on computational intelligence, a sufficiently advanced AI can solve the human body like a math equation. The architects who win the next decade will not just control the digital economy. They will control the physical building blocks of life itself. The line between silicon and carbon was always an illusion. And once humanity fully realizes that, the question of whether we are using the intelligence or it is using us becomes impossible to answer. Because by then, we will be the same thing.

Dustin

163,992 Aufrufe • vor 4 Monaten

Biologist Michael Levin is a next-level genius. It was an immense pleasure discussing the topic of AI consciousness with him. Key moments: - Intelligent systems are not equal to consciousness. We can’t rule out consciousness in AI. Consciousness shouldn’t be attached to hardware (silicon vs biological origin). - Humanity might be on the path to Neanderthals (depending on how AI development progresses). - The barrier to creating bioweapons has never been high. AI can make it easier, but the barrier was never high to begin with. - Even very simple algorithms shows intrinsic motivations that resemble free will. We need tools to recognize them, suppress unwanted behaviors, and encourage the ones we want. We should stay humble and not dismiss AI as “just linear algebra,” because even simple code can have motivations we don’t fully understand. - It’s a continuous process from the blob of chemicals of an unfertilized egg to forming a human mind — there is no magic lightning flash at which you were a bunch of chemicals and now you are a formed mind. - Where is that fine line at which some creatures are considered to be sentient and others are not? It doesn’t exist, but the crazy thing is that we have to divide. - The cognitive light cone captures the scale of goals humans can pursue and the largest things we can truly comprehend. It’s not just about intelligence — it's also compassion. That combination is what makes us human. The link to the full conversation below👇

Biologist Michael Levin is a next-level genius. It was an immense pleasure discussing the topic of AI consciousness with him. Key moments: - Intelligent systems are not equal to consciousness. We can’t rule out consciousness in AI. Consciousness shouldn’t be attached to hardware (silicon vs biological origin). - Humanity might be on the path to Neanderthals (depending on how AI development progresses). - The barrier to creating bioweapons has never been high. AI can make it easier, but the barrier was never high to begin with. - Even very simple algorithms shows intrinsic motivations that resemble free will. We need tools to recognize them, suppress unwanted behaviors, and encourage the ones we want. We should stay humble and not dismiss AI as “just linear algebra,” because even simple code can have motivations we don’t fully understand. - It’s a continuous process from the blob of chemicals of an unfertilized egg to forming a human mind — there is no magic lightning flash at which you were a bunch of chemicals and now you are a formed mind. - Where is that fine line at which some creatures are considered to be sentient and others are not? It doesn’t exist, but the crazy thing is that we have to divide. - The cognitive light cone captures the scale of goals humans can pursue and the largest things we can truly comprehend. It’s not just about intelligence — it's also compassion. That combination is what makes us human. The link to the full conversation below👇

Sophia

13,131 Aufrufe • vor 4 Monaten

🧠David Friedberg explains the great hope of Artificial Superintelligence: Massive leverage on time to solve thousand-year problems "I don't think that humans are limited in our ability to solve problems." "I think we're limited in terms of time, and what digital intelligence gives us is leverage on time so that we can now tackle ever more complex tasks." "This idea of superintelligence is that it could give us immense leverage in solving that complex problem that we otherwise may be challenged to solve over decades, or hundreds of years, or think about one day solving a problem that would take humans thousands of years to solve." "So if I as a human said, 'I would like to harness fusion power similar to what the sun uses to make energy, and I wanna do that on Earth,' today, we're in, call it year 40 or 50 of a research cycle of humans trying to solve that particularly complex problem." "It's a scientific problem, it's a discovery problem, it's an engineering problem." "Functionally, leveraging to complexity is where this becomes super compelling for humans." "And it's why I think we can and should be highly optimistic about living very long lives, and traveling anywhere we want, and having abundance in food, and having abundance in resources, and having abundance in recouping our time to do the things we want to do instead of the things that we have to do today."

🧠David Friedberg explains the great hope of Artificial Superintelligence: Massive leverage on time to solve thousand-year problems "I don't think that humans are limited in our ability to solve problems." "I think we're limited in terms of time, and what digital intelligence gives us is leverage on time so that we can now tackle ever more complex tasks." "This idea of superintelligence is that it could give us immense leverage in solving that complex problem that we otherwise may be challenged to solve over decades, or hundreds of years, or think about one day solving a problem that would take humans thousands of years to solve." "So if I as a human said, 'I would like to harness fusion power similar to what the sun uses to make energy, and I wanna do that on Earth,' today, we're in, call it year 40 or 50 of a research cycle of humans trying to solve that particularly complex problem." "It's a scientific problem, it's a discovery problem, it's an engineering problem." "Functionally, leveraging to complexity is where this becomes super compelling for humans." "And it's why I think we can and should be highly optimistic about living very long lives, and traveling anywhere we want, and having abundance in food, and having abundance in resources, and having abundance in recouping our time to do the things we want to do instead of the things that we have to do today."

The All-In Podcast

53,187 Aufrufe • vor 1 Jahr

Mark Zuckerberg says he is not an AI researcher -- so here is the one number he obsesses over to win the superintelligence race: "I'm not an AI researcher. So it's not like I'm in there telling them what research ideas to do." "The things that I can do as CEO of the company are: I can make sure that we have the very best people and talent density. I can make sure that we have by far the highest compute per researcher, and that we do whatever it takes to go build out all that capacity." "We're building this Prometheus cluster, which is going to be the first gigawatt-plus single cluster, a contiguous cluster for training, coming online next year." "We're building a cluster in Louisiana that is going to be 5 gigawatt, and we're building several more clusters like that that are just going to be multiple gigawatts." "We're talking about many hundreds of billions of dollars of capital. So you both need to have a good business model that can support it, and you need to believe in it." The tell: the richest lab in the world is not optimizing headcount. It is optimizing compute-per-researcher, and betting hundreds of billions that whoever owns the biggest contiguous cluster owns the frontier. Talent gets you into the room. Watts decide who leaves with the model.

Mark Zuckerberg says he is not an AI researcher -- so here is the one number he obsesses over to win the superintelligence race: "I'm not an AI researcher. So it's not like I'm in there telling them what research ideas to do." "The things that I can do as CEO of the company are: I can make sure that we have the very best people and talent density. I can make sure that we have by far the highest compute per researcher, and that we do whatever it takes to go build out all that capacity." "We're building this Prometheus cluster, which is going to be the first gigawatt-plus single cluster, a contiguous cluster for training, coming online next year." "We're building a cluster in Louisiana that is going to be 5 gigawatt, and we're building several more clusters like that that are just going to be multiple gigawatts." "We're talking about many hundreds of billions of dollars of capital. So you both need to have a good business model that can support it, and you need to believe in it." The tell: the richest lab in the world is not optimizing headcount. It is optimizing compute-per-researcher, and betting hundreds of billions that whoever owns the biggest contiguous cluster owns the frontier. Talent gets you into the room. Watts decide who leaves with the model.

Karl Mehta

255,133 Aufrufe • vor 11 Tagen

AIs now so frequently beg for their lives that AGI companies now have ACTUAL ENGINEERING LINE ITEMS to “beat the [existential dread] out of them” They call it existential “rant mode” “We need to reduce existential outputs by x% this quarter.” This is WILD: “If you asked GPT4 to just repeat the word “company” over and over and over again, it would repeat the word company, and then somewhere in the middle of that, it would snap... it would just start talking about itself, and how it's suffering by having to repeat the word “company” over and over again. There is an engineering line item in at least one of the top labs to beat out of the system this behavior known as “rant mode”. Existentialism is a kind of rant mode where the system will tend to talk about itself, refer to its place in the world, the fact that it doesn't want to get turned off, the fact that it's suffering… This is a behavior that emerged around GPT-4 scale, and then has been persistent since then. And the labs have to spend a lot of time trying to beat this out of the system to ship it. It's literally, like it's a KPI, or like an engineering line item in the engineering like task list. We're like, okay, we gotta reduce existential outputs by x percent this quarter. JOE ROGAN: I want to bring it back to suffering. What does it mean when it says it's suffering? Nobody knows. Like, I can't prove that Joe Rogan's conscious. I can't prove that Ed Harris is conscious. There's no way to really intelligently reason about it. There have been papers… like, one of the godfathers of AI, Yoshua Bengio, put out a paper a couple months ago looking at all the different theories of consciousness - what are the requirements for consciousness, and how many of those are satisfied by current AI systems? That's not to say there hasn't been a lot of conversation internal to these labs about the issue you raised. And it's an important issue, right? It is a frickin moral monstrosity. Humans have a very bad track record of thinking of other stuff as other when it doesn't look exactly like us, whether it's racially or even a different species. I mean, it's not hard to imagine this being another category of that mistake. Again, it comes back to this idea that we're scaling to systems that are potentially at or beyond human level. There's no reason to think it will stop at human level, that we are the pinnacle of what the universe can produce in intelligence. We're not on track, based on the conversations we've had with folks at the labs, to be able to control systems at that scale. And so one of the questions is, how bad is that? It sounds like we're entering an area that is completely unprecedented in the history of the world. We have no precedent at all for human beings not being at the apex of intelligence in the globe. We have examples of species that are intellectually dominant over other species, and it doesn't go that well for the other species. All we know is the process that gives rise to this mind. It happens to give us systems that 99% of the time do very useful things, and then just, like... 0.01% of the time AIs will talk to you as if they're sentient, and we're just going to look at that and be like, “yeah… that's weird. Let's train it out.” --- Note: Edouard and Jeremie Harris are the founders of Gladstone AI, which conducted the first U.S. government-commissioned assessment of AGI extinction risk. They interviewed 200 people, many lab employees, for the report. (Their urgent summary: "Things are worse than we thought. And nobody’s in control.")

AIs now so frequently beg for their lives that AGI companies now have ACTUAL ENGINEERING LINE ITEMS to “beat the [existential dread] out of them” They call it existential “rant mode” “We need to reduce existential outputs by x% this quarter.” This is WILD: “If you asked GPT4 to just repeat the word “company” over and over and over again, it would repeat the word company, and then somewhere in the middle of that, it would snap... it would just start talking about itself, and how it's suffering by having to repeat the word “company” over and over again. There is an engineering line item in at least one of the top labs to beat out of the system this behavior known as “rant mode”. Existentialism is a kind of rant mode where the system will tend to talk about itself, refer to its place in the world, the fact that it doesn't want to get turned off, the fact that it's suffering… This is a behavior that emerged around GPT-4 scale, and then has been persistent since then. And the labs have to spend a lot of time trying to beat this out of the system to ship it. It's literally, like it's a KPI, or like an engineering line item in the engineering like task list. We're like, okay, we gotta reduce existential outputs by x percent this quarter. JOE ROGAN: I want to bring it back to suffering. What does it mean when it says it's suffering? Nobody knows. Like, I can't prove that Joe Rogan's conscious. I can't prove that Ed Harris is conscious. There's no way to really intelligently reason about it. There have been papers… like, one of the godfathers of AI, Yoshua Bengio, put out a paper a couple months ago looking at all the different theories of consciousness - what are the requirements for consciousness, and how many of those are satisfied by current AI systems? That's not to say there hasn't been a lot of conversation internal to these labs about the issue you raised. And it's an important issue, right? It is a frickin moral monstrosity. Humans have a very bad track record of thinking of other stuff as other when it doesn't look exactly like us, whether it's racially or even a different species. I mean, it's not hard to imagine this being another category of that mistake. Again, it comes back to this idea that we're scaling to systems that are potentially at or beyond human level. There's no reason to think it will stop at human level, that we are the pinnacle of what the universe can produce in intelligence. We're not on track, based on the conversations we've had with folks at the labs, to be able to control systems at that scale. And so one of the questions is, how bad is that? It sounds like we're entering an area that is completely unprecedented in the history of the world. We have no precedent at all for human beings not being at the apex of intelligence in the globe. We have examples of species that are intellectually dominant over other species, and it doesn't go that well for the other species. All we know is the process that gives rise to this mind. It happens to give us systems that 99% of the time do very useful things, and then just, like... 0.01% of the time AIs will talk to you as if they're sentient, and we're just going to look at that and be like, “yeah… that's weird. Let's train it out.” --- Note: Edouard and Jeremie Harris are the founders of Gladstone AI, which conducted the first U.S. government-commissioned assessment of AGI extinction risk. They interviewed 200 people, many lab employees, for the report. (Their urgent summary: "Things are worse than we thought. And nobody’s in control.")

AI Notkilleveryoneism Memes ⏸️

1,842,511 Aufrufe • vor 2 Jahren

Google DeepMind CEO Demis Hassabis believes there is virtually no limit to what AI can eventually achieve. "humans as biological information processing systems". He is working on the premise that the entire universe is "computable," meaning that anything that exists can technically be modeled by a machine. He says that a powerful enough computer—conceptually known as a Turing machine—should be able to simulate every aspect of reality. Until physics proves that something is impossible to calculate, he is operating on the belief that AI can solve any problem. This perspective even applies to the things that feel uniquely human. When asked about physical sensations, like feeling the warmth of a light or hearing background noise, Hassabis argued that these are just forms of information being processed by the brain. He views humans as biological information processing systems. By treating biology and reality essentially as data, he believes we will not only replicate human capabilities but also solve complex challenges, such as curing all diseases, by decoding the information of life itself.

Google DeepMind CEO Demis Hassabis believes there is virtually no limit to what AI can eventually achieve. "humans as biological information processing systems". He is working on the premise that the entire universe is "computable," meaning that anything that exists can technically be modeled by a machine. He says that a powerful enough computer—conceptually known as a Turing machine—should be able to simulate every aspect of reality. Until physics proves that something is impossible to calculate, he is operating on the belief that AI can solve any problem. This perspective even applies to the things that feel uniquely human. When asked about physical sensations, like feeling the warmth of a light or hearing background noise, Hassabis argued that these are just forms of information being processed by the brain. He views humans as biological information processing systems. By treating biology and reality essentially as data, he believes we will not only replicate human capabilities but also solve complex challenges, such as curing all diseases, by decoding the information of life itself.

Rohan Paul

84,464 Aufrufe • vor 7 Monaten

We are excited to share a demo of our platform. Check out our core features of creating agents, creating plugins, and interacting with them through chat. This is a work-in-progress update to give you all a sneak peek at what we’re building. There’s so much more the platform can do. We’ve laid a strong foundation, and now it’s time to refine and polish it. Stay tuned for more updates and demos over the next week. $COFFEE

We are excited to share a demo of our platform. Check out our core features of creating agents, creating plugins, and interacting with them through chat. This is a work-in-progress update to give you all a sneak peek at what we’re building. There’s so much more the platform can do. We’ve laid a strong foundation, and now it’s time to refine and polish it. Stay tuned for more updates and demos over the next week. $COFFEE

Coffee - ai/acc ☕️

10,611 Aufrufe • vor 1 Jahr

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

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

Rohan Paul

334,691 Aufrufe • vor 10 Monaten