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Are brains computational? Can AI be conscious? Roger Penrose, alongside Max Tegmark and Sabrina Gonzalez Pasterski, unpacks the deepest questions at the intersection of neuroscience, mathematics, and physics, challenging whether minds are just biological computers, or something fundamentally beyond computation. Tap to watch in full.

Institute of Art and Ideas

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13,271 次观看 • 5 个月前 •via X (Twitter)

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"It’s not AI; it’s AC; Artificial Cleverness!" Sir Roger Penrose explains why current "Artificial Intelligence" is missing the most critical ingredient: consciousness. By delving into the limits of mathematical computability, he argues that while computers are masters of "computational mathematics," true intelligence requires a conscious understanding that exists beyond any algorithm. At the fundamental level, what we call AI is just high-speed calculation. To Penrose, intelligence and consciousness are inseparable; and until a machine can "understand," it’s simply being clever.

"It’s not AI; it’s AC; Artificial Cleverness!" Sir Roger Penrose explains why current "Artificial Intelligence" is missing the most critical ingredient: consciousness. By delving into the limits of mathematical computability, he argues that while computers are masters of "computational mathematics," true intelligence requires a conscious understanding that exists beyond any algorithm. At the fundamental level, what we call AI is just high-speed calculation. To Penrose, intelligence and consciousness are inseparable; and until a machine can "understand," it’s simply being clever.

Cosmos Archive

82,999 次观看 • 4 个月前

Adding more GPUs will never make a machine conscious. Nobel Prize-winning physicist Roger Penrose just dismantled the entire AI race’s core assumption. Right now, the industry operates on one belief. Build massive data centers. Scale the models. AGI will just “wake up.” Penrose destroys this completely. Penrose: “There is this sort of view that once you make a computer complicated enough or something, it suddenly becomes aware. I just don’t believe that. There’s no reason to believe that.” A machine can compute better than any human alive. But computation is not awareness. Penrose: “There is something quite different involved in understanding things, in being aware of things, of feeling things, which is not part of computations.” We’re confusing rule-following with actual intelligence. Penrose: “The keyword is the word ‘understanding.’ You can follow rules alright, but we don’t understand what we’re doing. The understanding is the key point.” Models today are exceptional at processing data. At mimicking logic. But true understanding requires consciousness. Penrose: “It doesn’t make sense to say of a device that it understands something if it’s not even aware of it. There is something much more profound in being conscious of something.” And here’s what should terrify every AI lab on earth. Penrose: “I believe that the brain is following the laws of physics, sure. We don’t have a good picture of the laws of physics.” Penrose: “Quantum mechanics is not an answer to the way the universe operates. It’s a partial answer. It’s incomplete.” We’re trying to engineer synthetic consciousness using classical computation. While biological consciousness likely operates on physics we haven’t even discovered yet. The race to AGI isn’t just an engineering problem. It’s a frontier science problem. The labs are hiring engineers. The problem might require physicists who don’t exist yet.

Adding more GPUs will never make a machine conscious. Nobel Prize-winning physicist Roger Penrose just dismantled the entire AI race’s core assumption. Right now, the industry operates on one belief. Build massive data centers. Scale the models. AGI will just “wake up.” Penrose destroys this completely. Penrose: “There is this sort of view that once you make a computer complicated enough or something, it suddenly becomes aware. I just don’t believe that. There’s no reason to believe that.” A machine can compute better than any human alive. But computation is not awareness. Penrose: “There is something quite different involved in understanding things, in being aware of things, of feeling things, which is not part of computations.” We’re confusing rule-following with actual intelligence. Penrose: “The keyword is the word ‘understanding.’ You can follow rules alright, but we don’t understand what we’re doing. The understanding is the key point.” Models today are exceptional at processing data. At mimicking logic. But true understanding requires consciousness. Penrose: “It doesn’t make sense to say of a device that it understands something if it’s not even aware of it. There is something much more profound in being conscious of something.” And here’s what should terrify every AI lab on earth. Penrose: “I believe that the brain is following the laws of physics, sure. We don’t have a good picture of the laws of physics.” Penrose: “Quantum mechanics is not an answer to the way the universe operates. It’s a partial answer. It’s incomplete.” We’re trying to engineer synthetic consciousness using classical computation. While biological consciousness likely operates on physics we haven’t even discovered yet. The race to AGI isn’t just an engineering problem. It’s a frontier science problem. The labs are hiring engineers. The problem might require physicists who don’t exist yet.

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Roger Penrose, Nobel Prize-winning physicist and mathematician, explains why we should stop calling it AI and start calling it "artificial cleverness": He believes the entire field is mislabelled, and the label itself is doing damage. His objection is simple but cuts deep: "The name is wrong. It's not artificial intelligence. It's not intelligence. Intelligence would involve consciousness. Well, if it's a machine, it's not conscious." For Penrose, people have confused raw computing power with genuine understanding. "People have lost the plot. They've lost it in the power of computing. The thing is that computers have got so powerful that they've lost the thread of what they're doing. But I think consciousness is something different. It's not computational." He believes the term itself has hypnotized people into a category error: "People are so hypnotized. The trouble is that AI is a bad term. It means artificial intelligence. Now intelligence in my view is conscious. That's what intelligence is about." So he proposes a rename. Artificial Cleverness. AC instead of AI. To illustrate the distinction, Penrose draws on his experience teaching mathematics: "You have mathematics students. Some of them understand what they're doing. Some are just clever. They can repeat what they've learned. They know how to do it very cleverly. They can calculate very well, but they don't necessarily understand what they're doing." That gap, between calculating well and actually understanding, is the gap Penrose sees between today's machines and genuine intelligence. Cleverness can be manufactured. Consciousness, in his view, cannot. So the question worth sitting with: when we call a system "intelligent," are we describing what it does, or quietly assuming something about what it is?

Roger Penrose, Nobel Prize-winning physicist and mathematician, explains why we should stop calling it AI and start calling it "artificial cleverness": He believes the entire field is mislabelled, and the label itself is doing damage. His objection is simple but cuts deep: "The name is wrong. It's not artificial intelligence. It's not intelligence. Intelligence would involve consciousness. Well, if it's a machine, it's not conscious." For Penrose, people have confused raw computing power with genuine understanding. "People have lost the plot. They've lost it in the power of computing. The thing is that computers have got so powerful that they've lost the thread of what they're doing. But I think consciousness is something different. It's not computational." He believes the term itself has hypnotized people into a category error: "People are so hypnotized. The trouble is that AI is a bad term. It means artificial intelligence. Now intelligence in my view is conscious. That's what intelligence is about." So he proposes a rename. Artificial Cleverness. AC instead of AI. To illustrate the distinction, Penrose draws on his experience teaching mathematics: "You have mathematics students. Some of them understand what they're doing. Some are just clever. They can repeat what they've learned. They know how to do it very cleverly. They can calculate very well, but they don't necessarily understand what they're doing." That gap, between calculating well and actually understanding, is the gap Penrose sees between today's machines and genuine intelligence. Cleverness can be manufactured. Consciousness, in his view, cannot. So the question worth sitting with: when we call a system "intelligent," are we describing what it does, or quietly assuming something about what it is?

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Sir Roger Penrose argues that current AI is a misnomer because it lacks the one ingredient essential for true intelligence: Understanding. By applying the lens of mathematical logic, he explains that while machines are masters of following rules, the ability to understand why those rules work is a non-computational process. To Penrose, the leap from calculation to comprehension requires more than just better code; it requires Consciousness. At the fundamental level, intelligence isn't just data processing; it's the conscious awareness that gives meaning to the math. Credit: TheInstituteOfArtAndIdeas

Sir Roger Penrose argues that current AI is a misnomer because it lacks the one ingredient essential for true intelligence: Understanding. By applying the lens of mathematical logic, he explains that while machines are masters of following rules, the ability to understand why those rules work is a non-computational process. To Penrose, the leap from calculation to comprehension requires more than just better code; it requires Consciousness. At the fundamental level, intelligence isn't just data processing; it's the conscious awareness that gives meaning to the math. Credit: TheInstituteOfArtAndIdeas

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Is math something humans invent—or something we discover? And why does it describe the universe so uncannily well? In this episode of Uncommon Knowledge, Peter Robinson sits down with mathematicians David Berlinski, Sergiu Klainerman, and Stephen C. Meyer to explore one of the deepest mysteries in science and philosophy: the reality of mathematics. From the simple certainty that 2 + 2 = 4 to the mind-bending mathematics behind black holes and quantum physics, the conversation asks why abstract numbers—created in the human mind—map so perfectly onto the physical world. Is mathematics purely logical, or does it point to a deeper structure of reality that isn't material at all? Along the way, the panel explores beauty in science, the "unreasonable effectiveness" of math, and whether the concept of materialism can really explain the world we live in. This wide-ranging discussion blends mathematics, physics, philosophy, and metaphysics into a fascinating conversation about truth, beauty, and the nature of reality itself. Watch the full episode of Uncommon Knowledge on X:

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On net, is our biological hardware a help or hindrance to our intelligence? Are GPUs are to brains what fingers are to robot motors, and there’s a bunch of cognitive dexterity that our brain is uniquely good at implementing? Ala Morevac’s paradox, evolution has spent way more time optimizing nanotech than the semiconductor industry has. Or maybe it's something structural: the brain can do unstructured sparsity, and can colocate memory and compute. Or is it the other way around, because with digital computers, we can get rid of all these biological handicaps: The brain operates on a 20 W budget (and consequently runs at only 200 Hz speed in order to accommodate small voltage gaps). And it needs to do all this crazy intense molecular machinery in order to do things that take trivial amounts of code in computers: for example, update this parameter to this value. Net net, does current biological or digital computing hardware win out? I asked Adam Marblestone:

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“One thing’s for certain. No matter where you are around the globe, energy will be central to the next stage of the AI revolution,” says CFR energy and climate expert Varun Sivaram. Returning from the World Economic Forum in Davos, Sivaram unpacks two risks and one opportunity at the intersection of energy and AI.

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Roger Penrose just broke the most expensive assumption in human history. Every major AI lab operates on one belief. Scale it far enough and consciousness just appears. Penrose: “There is this sort of view that once you make a computer complicated enough or something, it suddenly becomes aware. I just don’t believe that. There’s no reason to believe that.” Nobel Prize in Physics. Proved black holes are inevitable from general relativity alone. And he’s calling the foundational thesis of the entire AI industry a baseless assertion. Not flawed. Not premature. Baseless. No one proved that complexity produces consciousness. No experiment. No paper. No mathematical proof. The most technologically advanced industry built its core thesis on an assumption indistinguishable from faith. Penrose: “The keyword is the word ‘understanding.’ You can follow rules alright, but we don’t understand what we’re doing. The understanding is the key point.” These models don’t understand anything. Not the questions. Not the answers. Not a single word they generate. They process. They predict. They output. But understanding requires something no architecture has ever produced. Awareness. Penrose: “It doesn’t make sense to say of a device that it understands something if it’s not even aware of it. There is something much more profound in being conscious of something.” They built the most sophisticated language machines in history. Not one has ever experienced a single moment of its own existence. Penrose: “There is something quite different involved in understanding things, in being aware of things, of feeling things, which is not part of computations.” Awareness is not a software update. Consciousness is not a scaling problem. You cannot build what you cannot define. And no one alive has defined consciousness. Penrose: “I believe that the brain is following the laws of physics, sure. We don’t have a good picture of the laws of physics.” Penrose: “Quantum mechanics is not an answer to the way the universe operates. It’s a partial answer. It’s incomplete.” The physics that lets you read this sentence and know that you’re reading it might not exist in any textbook on Earth. We are not just trying to build machines that think. We are trying to engineer something we experience every second of our lives and cannot explain. You are proof of that right now. You are conscious. You know you exist. You feel yourself thinking. No one alive can tell you what that is. That is what the largest investment in human history is trying to build.

Roger Penrose just broke the most expensive assumption in human history. Every major AI lab operates on one belief. Scale it far enough and consciousness just appears. Penrose: “There is this sort of view that once you make a computer complicated enough or something, it suddenly becomes aware. I just don’t believe that. There’s no reason to believe that.” Nobel Prize in Physics. Proved black holes are inevitable from general relativity alone. And he’s calling the foundational thesis of the entire AI industry a baseless assertion. Not flawed. Not premature. Baseless. No one proved that complexity produces consciousness. No experiment. No paper. No mathematical proof. The most technologically advanced industry built its core thesis on an assumption indistinguishable from faith. Penrose: “The keyword is the word ‘understanding.’ You can follow rules alright, but we don’t understand what we’re doing. The understanding is the key point.” These models don’t understand anything. Not the questions. Not the answers. Not a single word they generate. They process. They predict. They output. But understanding requires something no architecture has ever produced. Awareness. Penrose: “It doesn’t make sense to say of a device that it understands something if it’s not even aware of it. There is something much more profound in being conscious of something.” They built the most sophisticated language machines in history. Not one has ever experienced a single moment of its own existence. Penrose: “There is something quite different involved in understanding things, in being aware of things, of feeling things, which is not part of computations.” Awareness is not a software update. Consciousness is not a scaling problem. You cannot build what you cannot define. And no one alive has defined consciousness. Penrose: “I believe that the brain is following the laws of physics, sure. We don’t have a good picture of the laws of physics.” Penrose: “Quantum mechanics is not an answer to the way the universe operates. It’s a partial answer. It’s incomplete.” The physics that lets you read this sentence and know that you’re reading it might not exist in any textbook on Earth. We are not just trying to build machines that think. We are trying to engineer something we experience every second of our lives and cannot explain. You are proof of that right now. You are conscious. You know you exist. You feel yourself thinking. No one alive can tell you what that is. That is what the largest investment in human history is trying to build.

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The Economist

50,484 次观看 • 1 个月前

A NOBEL WINNING PHYSICIST ARGUED THAT NO AI, NO MATTER HOW POWERFUL, WILL EVER TRULY UNDERSTAND A SINGLE THING IT SAYS. HIS REASON IS NOT COMPUTE OR DATA -- IT IS A MATH THEOREM FROM THE 1930s THAT SAYS SOME TRUTHS CAN BE SEEN BUT NEVER COMPUTED 81 minutes with Roger Penrose -- the Oxford physicist who won a Nobel for his work on black holes and general relativity. -> His claim: whatever consciousness is, it is not a computation. A machine following rules can imitate understanding, but never actually have it. He builds it on Gödel: a human can just "see" that certain statements are true, even though no algorithm can ever prove them. That seeing, he argues, is non-computational. If he is right, understanding is not something you scale into. You can stack a trillion parameters and still have zero awareness underneath. Which cuts straight through the AI moment. Everyone assumes bigger models will eventually "wake up". Penrose says that is a category error -- more computation is still just computation. You thought intelligence and understanding were the same thing. This is the conversation that pulls them apart. Save this. It is the sharpest case against the hype ↓

A NOBEL WINNING PHYSICIST ARGUED THAT NO AI, NO MATTER HOW POWERFUL, WILL EVER TRULY UNDERSTAND A SINGLE THING IT SAYS. HIS REASON IS NOT COMPUTE OR DATA -- IT IS A MATH THEOREM FROM THE 1930s THAT SAYS SOME TRUTHS CAN BE SEEN BUT NEVER COMPUTED 81 minutes with Roger Penrose -- the Oxford physicist who won a Nobel for his work on black holes and general relativity. -> His claim: whatever consciousness is, it is not a computation. A machine following rules can imitate understanding, but never actually have it. He builds it on Gödel: a human can just "see" that certain statements are true, even though no algorithm can ever prove them. That seeing, he argues, is non-computational. If he is right, understanding is not something you scale into. You can stack a trillion parameters and still have zero awareness underneath. Which cuts straight through the AI moment. Everyone assumes bigger models will eventually "wake up". Penrose says that is a category error -- more computation is still just computation. You thought intelligence and understanding were the same thing. This is the conversation that pulls them apart. Save this. It is the sharpest case against the hype ↓

slash1s

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🚨 Chamath explains Grok 4's breakthrough and how Elon leapfrogged the competition in AI: Elon Musk and the team at xAI understood "The Bitter Lesson" by Richard Sutton Chamath Palihapitiya: "(The Bitter Lesson) basically says in a nutshell, that you're always better off, when you're trying to solve an AI problem, taking a general learning approach that can scale with computation. Because it ultimately proves to be the most effective." "The alternative would be something that's much more human labored and human involved that requires human knowledge." "So the first method, what it essentially allows you to do is view any problem as an endless, scalable search or learning task." "As it's turned out, whether it's chess, or Go, or speech recognition, or computer vision, whenever there was two competing approaches, one that used general computation and one that used human knowledge, the general computation problem always won." " And so it creates this bitter lesson for humans that want to think that we are at the center of all of this critical learning and all of these leaps, and what these results show is a general computational approach that doesn't require as much human labeling can actually get to the answer and better answers faster."

🚨 Chamath explains Grok 4's breakthrough and how Elon leapfrogged the competition in AI: Elon Musk and the team at xAI understood "The Bitter Lesson" by Richard Sutton Chamath Palihapitiya: "(The Bitter Lesson) basically says in a nutshell, that you're always better off, when you're trying to solve an AI problem, taking a general learning approach that can scale with computation. Because it ultimately proves to be the most effective." "The alternative would be something that's much more human labored and human involved that requires human knowledge." "So the first method, what it essentially allows you to do is view any problem as an endless, scalable search or learning task." "As it's turned out, whether it's chess, or Go, or speech recognition, or computer vision, whenever there was two competing approaches, one that used general computation and one that used human knowledge, the general computation problem always won." " And so it creates this bitter lesson for humans that want to think that we are at the center of all of this critical learning and all of these leaps, and what these results show is a general computational approach that doesn't require as much human labeling can actually get to the answer and better answers faster."

The All-In Podcast

597,321 次观看 • 1 年前