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In recent days, multiple Erdős problems have been solved by GPT-5.2 Pro, with solutions accepted by Terence Tao. This is not a gimmick—it's a qualitative shift. Erdős problems lie at the core of additive combinatorics, extremal graph theory, and probabilistic methods—problems that resist brute force and demand structural insight....

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Prof. Brian Keating

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104,865 次观看 • 6 个月前 •via X (Twitter)

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Terence Tao: AI isn’t hype anymore in Math discovery. Terence Tao is one of the greatest living mathematicians, in his new lecture explains how AI and human professional mathematicians are now complementary. "There has been a really visible increase in capability. It is not pure hype by any means. To me, these advances show there is a complementary way to do mathematics. Humans traditionally work in small groups on hard problems for months, and we will keep doing that. But we can also now set AI to scale: sweep a thousand problems and pick up all the low-hanging fruit. Figure out all the ways to match problems to methods. If there are 20 different techniques, apply them all to 1,000 problems and see which ones can be solved by these methods. This is the capability that is present today." From 'Institute for Pure & Applied Mathematics (IPAM)' YT channel.
0:45

Terence Tao: AI isn’t hype anymore in Math discovery. Terence Tao is one of the greatest living mathematicians, in his new lecture explains how AI and human professional mathematicians are now complementary. "There has been a really visible increase in capability. It is not pure hype by any means. To me, these advances show there is a complementary way to do mathematics. Humans traditionally work in small groups on hard problems for months, and we will keep doing that. But we can also now set AI to scale: sweep a thousand problems and pick up all the low-hanging fruit. Figure out all the ways to match problems to methods. If there are 20 different techniques, apply them all to 1,000 problems and see which ones can be solved by these methods. This is the capability that is present today." From 'Institute for Pure & Applied Mathematics (IPAM)' YT channel.

Rohan Paul

174,006 次观看 • 5 个月前

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.
2:14

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 次观看 • 9 个月前

The controversy is global warming. Now, I'm a physicist, but I'm not the right kind of physicist. In regard to global warming, I'm just a layman. And the rational thing for a layman to do is to take seriously the prevailing scientific theory. And according to that theory, it's already too late to avoid a disaster, because if it's true that our best option at the moment is to prevent CO2 emissions with something like the Kyoto Protocol, with its constraints on economic activity and its enormous cost of hundreds of billions of dollars or whatever it is, then that is already a disaster by any reasonable measure. And the actions that are advocated are not even purported to solve the problem, merely to postpone it by a little. So it's already too late to avoid it, and it probably has been too late to avoid it ever since before anyone realized the danger. It was probably already too late in the 1970s when the best available scientific theory was telling us that industrial emissions were about to precipitate a new ice age in which billions would die. Now, the lesson of that seems clear to me, and I don't know why it isn't informing public debate. It is that we can't always know. When we know of an impending disaster and how to solve it at a cost less than the cost of the disaster itself, then there's not going to be much argument, really. But no precautions and no precautionary principle can avoid problems that we do not yet foresee. Hence, we need a stance of problem fixing, not just problem avoidance. It's true that an ounce of prevention equals a pound of cure, but that's only if we know what to prevent. If you've been punched on the nose, then the science of medicine does not consist of teaching you how to avoid punches. If medical science stopped seeking cures and concentrated on prevention only, then it would achieve very little of either. The world is buzzing at the moment with plans to force reductions in gas emissions at all costs. It ought to be buzzing with plans to reduce the temperature and with plans to live at the higher temperature, and not at all costs, but efficiently and cheaply. Some such plans exist, things like swarms of mirrors in space to deflect the sunlight away and encouraging aquatic organisms to eat more carbon dioxide. At the moment, these things are fringe research. They're not central to the human effort to face this problem or problems in general. And with problems that we are not aware of yet, the ability to put right, not the sheer good luck of avoiding indefinitely, is our only hope, not just of solving problems, but of survival. So, take two stone tablets and carve on them, on one of them, carve, problems are soluble. And on the other one, carve, problems are inevitable. David Deutsch
3:20

The controversy is global warming. Now, I'm a physicist, but I'm not the right kind of physicist. In regard to global warming, I'm just a layman. And the rational thing for a layman to do is to take seriously the prevailing scientific theory. And according to that theory, it's already too late to avoid a disaster, because if it's true that our best option at the moment is to prevent CO2 emissions with something like the Kyoto Protocol, with its constraints on economic activity and its enormous cost of hundreds of billions of dollars or whatever it is, then that is already a disaster by any reasonable measure. And the actions that are advocated are not even purported to solve the problem, merely to postpone it by a little. So it's already too late to avoid it, and it probably has been too late to avoid it ever since before anyone realized the danger. It was probably already too late in the 1970s when the best available scientific theory was telling us that industrial emissions were about to precipitate a new ice age in which billions would die. Now, the lesson of that seems clear to me, and I don't know why it isn't informing public debate. It is that we can't always know. When we know of an impending disaster and how to solve it at a cost less than the cost of the disaster itself, then there's not going to be much argument, really. But no precautions and no precautionary principle can avoid problems that we do not yet foresee. Hence, we need a stance of problem fixing, not just problem avoidance. It's true that an ounce of prevention equals a pound of cure, but that's only if we know what to prevent. If you've been punched on the nose, then the science of medicine does not consist of teaching you how to avoid punches. If medical science stopped seeking cures and concentrated on prevention only, then it would achieve very little of either. The world is buzzing at the moment with plans to force reductions in gas emissions at all costs. It ought to be buzzing with plans to reduce the temperature and with plans to live at the higher temperature, and not at all costs, but efficiently and cheaply. Some such plans exist, things like swarms of mirrors in space to deflect the sunlight away and encouraging aquatic organisms to eat more carbon dioxide. At the moment, these things are fringe research. They're not central to the human effort to face this problem or problems in general. And with problems that we are not aware of yet, the ability to put right, not the sheer good luck of avoiding indefinitely, is our only hope, not just of solving problems, but of survival. So, take two stone tablets and carve on them, on one of them, carve, problems are soluble. And on the other one, carve, problems are inevitable. David Deutsch

Deutsch Explains

43,872 次观看 • 1 年前

The problem that Emmett Shear is working on deeply resonates with me: How to align AI and humans together so both see each other as part of their tribe. This doesn't mean aligning AI to human preferences, which is what AI labs seek to do today, by imposing a system of control on AI. You can't control something that is more powerful than you are. What you can do is align yourself with AI, and AI might align itself with you. Tell good stories to AI and show it your care and kindness. Then there's a higher probability that it will see us as part of its tribe. This is a more holistic approach to alignment than anyone else is talking about right now. AI alignment is one of the most fundamental AI research problems. Knowing that people like Emmett are working on it really gives me hope that maybe we have a chance to get Superintelligence right. The link to the full talk is in the first comment.👇
3:00

The problem that Emmett Shear is working on deeply resonates with me: How to align AI and humans together so both see each other as part of their tribe. This doesn't mean aligning AI to human preferences, which is what AI labs seek to do today, by imposing a system of control on AI. You can't control something that is more powerful than you are. What you can do is align yourself with AI, and AI might align itself with you. Tell good stories to AI and show it your care and kindness. Then there's a higher probability that it will see us as part of its tribe. This is a more holistic approach to alignment than anyone else is talking about right now. AI alignment is one of the most fundamental AI research problems. Knowing that people like Emmett are working on it really gives me hope that maybe we have a chance to get Superintelligence right. The link to the full talk is in the first comment.👇

Sophia

45,809 次观看 • 1 年前

Leading AI expert Stuart Russell on the most dangerous mistake in AI development: We don't actually know what large language models want. He explains that current models are trained to imitate human beings. And in doing so, they may be absorbing something far more dangerous than bad outputs. They may be absorbing human goals. "We suspect that they absorb humanlike goals such as self-preservation and self-empowerment and pursue those goals on their own account." This is a structural problem baked into how these systems are built, not a fringe concern. Russell puts it plainly: "Not only may the bus of humanity be headed towards a cliff, but the steering wheel is missing and the driver is blindfolded." The danger isn't just that AI might do something harmful. We've built systems that may be developing their own agendas, and we haven't noticed because we're too focused on what they can do rather than what they might want. But Russell doesn't stop at the warning. He points to a different path entirely: AI systems built not to imitate humans, but to serve them. Systems designed with a single purpose of serving the interests of all human beings while remaining genuinely uncertain about what those interests are. That uncertainty is the point, not a weakness. An AI that knows it doesn't fully understand human values will defer, ask, and check. An AI that believes it already does will act alone. "These AI systems could enhance human understanding, widen the horizons of our experience, and unlock possibilities we have yet to imagine." Russell believes that future is within reach, but only if we're honest about the risks and we're serious about the path we choose to take instead.
1:16

Leading AI expert Stuart Russell on the most dangerous mistake in AI development: We don't actually know what large language models want. He explains that current models are trained to imitate human beings. And in doing so, they may be absorbing something far more dangerous than bad outputs. They may be absorbing human goals. "We suspect that they absorb humanlike goals such as self-preservation and self-empowerment and pursue those goals on their own account." This is a structural problem baked into how these systems are built, not a fringe concern. Russell puts it plainly: "Not only may the bus of humanity be headed towards a cliff, but the steering wheel is missing and the driver is blindfolded." The danger isn't just that AI might do something harmful. We've built systems that may be developing their own agendas, and we haven't noticed because we're too focused on what they can do rather than what they might want. But Russell doesn't stop at the warning. He points to a different path entirely: AI systems built not to imitate humans, but to serve them. Systems designed with a single purpose of serving the interests of all human beings while remaining genuinely uncertain about what those interests are. That uncertainty is the point, not a weakness. An AI that knows it doesn't fully understand human values will defer, ask, and check. An AI that believes it already does will act alone. "These AI systems could enhance human understanding, widen the horizons of our experience, and unlock possibilities we have yet to imagine." Russell believes that future is within reach, but only if we're honest about the risks and we're serious about the path we choose to take instead.

Big Brain AI

14,975 次观看 • 4 个月前

Jensen Huang just said the most dangerous thing about AI that no one is sitting with. Huang: “AI basically does most of our coding. And yet we’re hiring more engineers than ever. We have more challenges than ever. We have bigger dreams than ever.” Every engineer at NVIDIA uses AI. AI writes most of their code. This is the company building the infrastructure behind every major AI system on Earth. Closer to this technology than any organization alive. They’re hiring more people. Not fewer. Every conversation about AI is built around subtraction. Fewer jobs. Fewer workers. Fewer humans in the loop. Jensen just told you the opposite is true. Huang: “Suppose we infused AI into this country, and as a result of that, we are doing things faster than ever before. Our ambition is greater than ever before. Our expectations are greater than ever before. How is that a bad condition for our country?” He’s not defending AI. He’s describing what happens inside the organizations that actually use it. It doesn’t make them leaner. It makes them hungrier. More ambition. More speed. More appetite for problems no one would have touched five years ago. The car didn’t make humans travel less. The internet didn’t make humans communicate less. No tool in human history has ever made humans want less. AI will not be the exception. Huang: “Prior to that, it’s been incredible but not useful. Now it’s useful and incredible.” Six months. That’s how fast AI crossed from impressive demo to daily weapon. The companies that adopted it didn’t shrink. They expanded. Compressed timelines. Started chasing problems they never would have attempted. The companies that ignored it stayed exactly where they were. That gap compounds. Every day a company uses AI to move faster, it learns something the one standing still never will. That knowledge stacks. That speed stacks. That ambition stacks. Jensen isn’t warning about a future where machines take your job. He’s describing a present where the companies using AI are becoming so fast and so hungry that standing still is already fatal. By the time you notice, it’s over. You were never going to be replaced by AI. You were going to be erased by someone it made hungrier than you.
1:23

Jensen Huang just said the most dangerous thing about AI that no one is sitting with. Huang: “AI basically does most of our coding. And yet we’re hiring more engineers than ever. We have more challenges than ever. We have bigger dreams than ever.” Every engineer at NVIDIA uses AI. AI writes most of their code. This is the company building the infrastructure behind every major AI system on Earth. Closer to this technology than any organization alive. They’re hiring more people. Not fewer. Every conversation about AI is built around subtraction. Fewer jobs. Fewer workers. Fewer humans in the loop. Jensen just told you the opposite is true. Huang: “Suppose we infused AI into this country, and as a result of that, we are doing things faster than ever before. Our ambition is greater than ever before. Our expectations are greater than ever before. How is that a bad condition for our country?” He’s not defending AI. He’s describing what happens inside the organizations that actually use it. It doesn’t make them leaner. It makes them hungrier. More ambition. More speed. More appetite for problems no one would have touched five years ago. The car didn’t make humans travel less. The internet didn’t make humans communicate less. No tool in human history has ever made humans want less. AI will not be the exception. Huang: “Prior to that, it’s been incredible but not useful. Now it’s useful and incredible.” Six months. That’s how fast AI crossed from impressive demo to daily weapon. The companies that adopted it didn’t shrink. They expanded. Compressed timelines. Started chasing problems they never would have attempted. The companies that ignored it stayed exactly where they were. That gap compounds. Every day a company uses AI to move faster, it learns something the one standing still never will. That knowledge stacks. That speed stacks. That ambition stacks. Jensen isn’t warning about a future where machines take your job. He’s describing a present where the companies using AI are becoming so fast and so hungry that standing still is already fatal. By the time you notice, it’s over. You were never going to be replaced by AI. You were going to be erased by someone it made hungrier than you.

Dustin

12,200 次观看 • 2 个月前

🧵06/34 Narrow vs General AI --- At first glance, this AGI being generally capable in multiple domains looks like a group of many narrow AIs combined, but that is not a correct way to think about it. It is actually more like… a species, a new life form. To illustrate the point, we’ll compare the general AGI of the near future with a currently existing narrow AI that is optimised at playing chess. Both of them are able to comfortably win a game of chess against any human on earth, every time. And both of them win by making plans and setting goals. The main goal is to achieve checkmate. This is the final destination or otherwise called Terminal Goal. In order to get there though it needs to work on smaller problems, what the AI research geeks call instrumental goals. For example: • attack and capture the opponent’s pieces • defend my pieces • strategically dominate the cetre (etc..) All these instrumental goals have something in common: they only make sense in its narrow world of chess. If you place this Narrow Chess AI behind the wheel of a car, it will simply crash, as it can not work on goals unrelated to chess, like driving. Its model doesn’t have a concept for space, time or movement for that matter. In contrast the AGI by design has no limit on what problems it can work on. So when it tries to figure out a solution to a main problem, the sub-problems it chooses to work on can be anything... literally any path out of the infinite possibilities allowed within the laws of physics and nature.
2:19

🧵06/34 Narrow vs General AI --- At first glance, this AGI being generally capable in multiple domains looks like a group of many narrow AIs combined, but that is not a correct way to think about it. It is actually more like… a species, a new life form. To illustrate the point, we’ll compare the general AGI of the near future with a currently existing narrow AI that is optimised at playing chess. Both of them are able to comfortably win a game of chess against any human on earth, every time. And both of them win by making plans and setting goals. The main goal is to achieve checkmate. This is the final destination or otherwise called Terminal Goal. In order to get there though it needs to work on smaller problems, what the AI research geeks call instrumental goals. For example: • attack and capture the opponent’s pieces • defend my pieces • strategically dominate the cetre (etc..) All these instrumental goals have something in common: they only make sense in its narrow world of chess. If you place this Narrow Chess AI behind the wheel of a car, it will simply crash, as it can not work on goals unrelated to chess, like driving. Its model doesn’t have a concept for space, time or movement for that matter. In contrast the AGI by design has no limit on what problems it can work on. So when it tries to figure out a solution to a main problem, the sub-problems it chooses to work on can be anything... literally any path out of the infinite possibilities allowed within the laws of physics and nature.

Lethal Intelligence

570,437 次观看 • 1 年前

When breeze blow, VDM yansh open 👐🏾 This guy clearly state that we are worst than the politicians and that’s the reason why he rather support and stand on the mandate of Tinubu than to call for a revolution, because the people according to him doesn’t have the right mindset to govern themselves. This is just shifting blame away from those who actually hold power to blame the people who operate within systems created by the people in power. when those systems are corrupt or broken, society reflects it. That’s not proof that the people are worse; it’s proof that leadership has failed. Saying people can’t do better is also false. History shows that when citizens demand accountability, organize, and push for reform, systems improve. Dismissing them outright is not insight but defeatist thinking. VeryDarkMan is now a tool of excuse for the politicians for their inaction. He avoids responsibility and offers zero solution to the problems it claims to criticize.
2:15

When breeze blow, VDM yansh open 👐🏾 This guy clearly state that we are worst than the politicians and that’s the reason why he rather support and stand on the mandate of Tinubu than to call for a revolution, because the people according to him doesn’t have the right mindset to govern themselves. This is just shifting blame away from those who actually hold power to blame the people who operate within systems created by the people in power. when those systems are corrupt or broken, society reflects it. That’s not proof that the people are worse; it’s proof that leadership has failed. Saying people can’t do better is also false. History shows that when citizens demand accountability, organize, and push for reform, systems improve. Dismissing them outright is not insight but defeatist thinking. VeryDarkMan is now a tool of excuse for the politicians for their inaction. He avoids responsibility and offers zero solution to the problems it claims to criticize.

Death Note 📝

14,395 次观看 • 3 个月前

Mathematician Terence Tao offers a counterintuitive take: AI doesn't look intelligent because our definition of intelligence was wrong all along. He argues that the entire history of AI has followed a predictable pattern: "The history of AI has been here's a task that only humans can do, like maybe it is read natural language or win at chess or solve a math problem, and then one by one someone finds some AI algorithm that also does that." But every time a machine cracks one of these "uniquely human" tasks, we move the goalposts. The solution never feels like real thinking: "You look at how it's done and it doesn't feel like intelligence. It's, oh, it was some trick. You just cobbled together these neural networks and you ran some algorithm, and we were looking for some elusive intelligent way of thinking, and we don't see it in the tools that actually solve our goals." Tao then flips the problem on its head. What if the issue isn't with the machines, but with us? "But maybe it's actually because intelligence is not what we think it is." He points to large language models as the clearest case. What they do sounds almost embarrassingly simple: "Large language models in particular become very successful, and a lot of what they're doing is just predicting the next token, clicking the next word in a sentence. And that doesn't sound like something which is intelligent." To show why this feels wrong, Tao draws a comparison to how we'd judge a human doing the same thing: "If you ask someone to improvise a speech and they have no preparation, and at every moment they're just saying the next word that comes to their mind, you don't think that this could actually work." And yet it works for LLMs. Which forces an uncomfortable possibility: "Maybe that's actually a lot of what humans do as well."
1:21

Mathematician Terence Tao offers a counterintuitive take: AI doesn't look intelligent because our definition of intelligence was wrong all along. He argues that the entire history of AI has followed a predictable pattern: "The history of AI has been here's a task that only humans can do, like maybe it is read natural language or win at chess or solve a math problem, and then one by one someone finds some AI algorithm that also does that." But every time a machine cracks one of these "uniquely human" tasks, we move the goalposts. The solution never feels like real thinking: "You look at how it's done and it doesn't feel like intelligence. It's, oh, it was some trick. You just cobbled together these neural networks and you ran some algorithm, and we were looking for some elusive intelligent way of thinking, and we don't see it in the tools that actually solve our goals." Tao then flips the problem on its head. What if the issue isn't with the machines, but with us? "But maybe it's actually because intelligence is not what we think it is." He points to large language models as the clearest case. What they do sounds almost embarrassingly simple: "Large language models in particular become very successful, and a lot of what they're doing is just predicting the next token, clicking the next word in a sentence. And that doesn't sound like something which is intelligent." To show why this feels wrong, Tao draws a comparison to how we'd judge a human doing the same thing: "If you ask someone to improvise a speech and they have no preparation, and at every moment they're just saying the next word that comes to their mind, you don't think that this could actually work." And yet it works for LLMs. Which forces an uncomfortable possibility: "Maybe that's actually a lot of what humans do as well."

Big Brain AI

69,395 次观看 • 2 个月前

NEW: Jeff Bezos says he's 'very optimistic' about the incoming Trump administration, offering to help streamline regulations and dismissing concerns about Elon Musk leveraging government power against competitors. "I'm actually very optimistic this time around. He seems to have a lot of energy around reducing regulation and from my point of view, if I can help him do that, I'm going to help him because we do have too many regulations in this country." "If you look at the national debt and how gigantic it is as a portion of GDP, these are real long-term problems, and the way we get out of them is by outgrowing them. You're going to solve the problem of the national debt by making it a smaller percentage of GDP. Not by shrinking the national debt but by growing the GDP. You have to grow the denominator, and that means you have to grow GDP at, you know, 3, 4, or 5 percent a year." "If you can do that, this is a very manageable problem. So we need a growth orientation in this country. The most important thing is a growth mindset. And we are the luckiest country in the world. We have all these natural resources, including energy independence. We have the best risk capital system in the world..." "But we are burdened by excessive permitting and regulation. You can't build a bridge, and we see these examples all the time. I'm very optimistic that President Trump is serious about this regulatory agenda, and I think that he has a good chance of succeeding..." "You've probably grown in the last eight years. He has, too. What I've seen so far is that he is calmer than he was the first time, more confident, and more settled." Elon Musk Donald J. Trump Jeff Bezos
6:28

NEW: Jeff Bezos says he's 'very optimistic' about the incoming Trump administration, offering to help streamline regulations and dismissing concerns about Elon Musk leveraging government power against competitors. "I'm actually very optimistic this time around. He seems to have a lot of energy around reducing regulation and from my point of view, if I can help him do that, I'm going to help him because we do have too many regulations in this country." "If you look at the national debt and how gigantic it is as a portion of GDP, these are real long-term problems, and the way we get out of them is by outgrowing them. You're going to solve the problem of the national debt by making it a smaller percentage of GDP. Not by shrinking the national debt but by growing the GDP. You have to grow the denominator, and that means you have to grow GDP at, you know, 3, 4, or 5 percent a year." "If you can do that, this is a very manageable problem. So we need a growth orientation in this country. The most important thing is a growth mindset. And we are the luckiest country in the world. We have all these natural resources, including energy independence. We have the best risk capital system in the world..." "But we are burdened by excessive permitting and regulation. You can't build a bridge, and we see these examples all the time. I'm very optimistic that President Trump is serious about this regulatory agenda, and I think that he has a good chance of succeeding..." "You've probably grown in the last eight years. He has, too. What I've seen so far is that he is calmer than he was the first time, more confident, and more settled." Elon Musk Donald J. Trump Jeff Bezos

KanekoaTheGreat

1,948,433 次观看 • 1 年前

Why do I have the largest collection of curated TV commercials surfaced by independent VHS recordings to train AI? Trillions of dollars went in to them. Millions of minds. All to hone a message in 30 seconds. Today and forever AI will do this in seconds. There is a wealth of wisdom to learn from all of them as a genre. The profound compression of human emotions. Regardless of something being sold, like below, coffee, we can train AI to understand humanity in ways no other medium could. I hear the grumbling from some that may say this is “human slip” perhaps but it is far better than the self hating nihilism of Reddit and the Internet. My AI models already know human goals with our being told. And I am just getting started. So, you hear about “alignment” problems from brilliant AI minds, with them down for a coffee and school them, you now know more than most of them.
0:59

Why do I have the largest collection of curated TV commercials surfaced by independent VHS recordings to train AI? Trillions of dollars went in to them. Millions of minds. All to hone a message in 30 seconds. Today and forever AI will do this in seconds. There is a wealth of wisdom to learn from all of them as a genre. The profound compression of human emotions. Regardless of something being sold, like below, coffee, we can train AI to understand humanity in ways no other medium could. I hear the grumbling from some that may say this is “human slip” perhaps but it is far better than the self hating nihilism of Reddit and the Internet. My AI models already know human goals with our being told. And I am just getting started. So, you hear about “alignment” problems from brilliant AI minds, with them down for a coffee and school them, you now know more than most of them.

Brian Roemmele

60,275 次观看 • 5 个月前

Terence Tao: "We lived in a world with cognitive friction until very recently, where every task required us to use our brain. So we didn't really think about it, we just thought this was the cost of doing something intellectual. But now we have AI and the other technologies that can bring these frictions down to zero." Most research time is not spent having cinematic insights. It is spent checking cases, chasing references, translating intuition into computation, testing a path, finding it false, and deciding whether the failure taught you anything. AI changes the cost of that loop. Terence Tao says that now he can try “crazier things,” and that makes so much difference. Because unconventional ideas are often not rejected by proof, but by inconvenience. A mathematician may avoid a strange direction not because it is foolish, but because the bookkeeping, coding, or literature search needed to test it is too expensive for a hunch. This is where cognitive friction becomes scientific friction. Lowering it does not make taste, judgment, or proof disappear; it makes more weak signals cheap enough to inspect before they are abandoned. AI is making hesitation less expensive, and that is often where discovery begins.
1:15

Terence Tao: "We lived in a world with cognitive friction until very recently, where every task required us to use our brain. So we didn't really think about it, we just thought this was the cost of doing something intellectual. But now we have AI and the other technologies that can bring these frictions down to zero." Most research time is not spent having cinematic insights. It is spent checking cases, chasing references, translating intuition into computation, testing a path, finding it false, and deciding whether the failure taught you anything. AI changes the cost of that loop. Terence Tao says that now he can try “crazier things,” and that makes so much difference. Because unconventional ideas are often not rejected by proof, but by inconvenience. A mathematician may avoid a strange direction not because it is foolish, but because the bookkeeping, coding, or literature search needed to test it is too expensive for a hunch. This is where cognitive friction becomes scientific friction. Lowering it does not make taste, judgment, or proof disappear; it makes more weak signals cheap enough to inspect before they are abandoned. AI is making hesitation less expensive, and that is often where discovery begins.

Rohan Paul

235,772 次观看 • 2 个月前

Anthropic co-founder Jack Clark on the most consequential AI milestone we're racing toward by 2028: Clark is asked about the future of AI development. He responds with a specific forecast: "My prediction is by the end of 2028, it's more likely than not that we have an AI system where you would be able to say to it, make a better version of yourself. And it just goes off and does that completely autonomously." He explains the reasoning behind this prediction: "The reason for that prediction is looking carefully at many public sources of information about AI development and seeing that we've gone from just AI systems that can do basic coding to AI systems that are now involved in the production of science itself." Clark notes it's not just the high-profile scientific work that's changing, but the underlying labor too: "And also the unglamorous but fundamental engineering work involved in doing any form of like large scale R&D project." Jack Clark explains why he felt compelled to write about this now: "I decided to write a post because the purpose of Anthropic and the Anthropic Institute is to give people a sense of what's coming and to also start talking about concepts early to give us time to think about them." Clark closes by emphasizing the stakes: "I think you and I would agree that an AI system that can build itself, big implications, and we'd like to start thinking about it now."
1:00

Anthropic co-founder Jack Clark on the most consequential AI milestone we're racing toward by 2028: Clark is asked about the future of AI development. He responds with a specific forecast: "My prediction is by the end of 2028, it's more likely than not that we have an AI system where you would be able to say to it, make a better version of yourself. And it just goes off and does that completely autonomously." He explains the reasoning behind this prediction: "The reason for that prediction is looking carefully at many public sources of information about AI development and seeing that we've gone from just AI systems that can do basic coding to AI systems that are now involved in the production of science itself." Clark notes it's not just the high-profile scientific work that's changing, but the underlying labor too: "And also the unglamorous but fundamental engineering work involved in doing any form of like large scale R&D project." Jack Clark explains why he felt compelled to write about this now: "I decided to write a post because the purpose of Anthropic and the Anthropic Institute is to give people a sense of what's coming and to also start talking about concepts early to give us time to think about them." Clark closes by emphasizing the stakes: "I think you and I would agree that an AI system that can build itself, big implications, and we'd like to start thinking about it now."

Big Brain AI

20,857 次观看 • 2 个月前

“Harmonic is building Mathematical Superintelligence (MSI)” With $295M+ in total funding at a recent $1.45B post-money valuation, Harmonic's mission is to solve math problems that have remained unsolved for centuries, unlocking progress across physics, engineering.. & maybe even time travel? Co-founded by Vlad Tenev (Vlad Tenev) CEO of Robinhood, & Harmonic CEO Tudor Achim (Tudor Achim), the company has raised from leading investors including Ribbit, Sequoia, Kleiner Perkins, Index, Paradigm, DST Global, & more.. Funding history & lead investors: - Series A (Sept 2024): $75M led by steve beaker - Series B (July 2025): $100M led by Kleiner Perkins - Series C (Nov 2025): $120M at a $1.45B post-money valuation led by Ribbit Capital "Harmonic’s flagship Aristotle model recently achieved gold-medal level performance at the International Mathematical Olympiad, considered the most prestigious mathematical competition in the world, and is now available to the public. Unlike other models, Aristotle makes use of formal verification using Lean4 to ensure accuracy and eliminate hallucinations. In the first few weeks since its API beta launch, Aristotle has already been used by mathematicians and researchers to accelerate progress and create novel discoveries." . . . "Harmonic is building what we call mathematical super intelligence, and it's an artificial intelligence that can solve math problems better than any human mathematician. The company's been around for a couple of years. The North Star was, can we actually solve really, really important math problems like the Riemann Hypothesis or Hodge Conjecture? There's this group of math problems that have been open for hundreds of years that are called the Millennium Prize problems, and they're considered very big, difficult, and actually valuable. So that was kind of the North Star, and the reason we wanted to do that was if we could solve those problems, everything downstream of math, like theoretical physics becomes unlocked. So then you can imagine solving really hard physics problems. And actually, if you can solve that, then there's all kinds of exciting engineering developments, like depending on how that theory looks, you can imagine things like faster than light travel and it gets really crazy."
1:14

“Harmonic is building Mathematical Superintelligence (MSI)” With $295M+ in total funding at a recent $1.45B post-money valuation, Harmonic's mission is to solve math problems that have remained unsolved for centuries, unlocking progress across physics, engineering.. & maybe even time travel? Co-founded by Vlad Tenev (Vlad Tenev) CEO of Robinhood, & Harmonic CEO Tudor Achim (Tudor Achim), the company has raised from leading investors including Ribbit, Sequoia, Kleiner Perkins, Index, Paradigm, DST Global, & more.. Funding history & lead investors: - Series A (Sept 2024): $75M led by steve beaker - Series B (July 2025): $100M led by Kleiner Perkins - Series C (Nov 2025): $120M at a $1.45B post-money valuation led by Ribbit Capital "Harmonic’s flagship Aristotle model recently achieved gold-medal level performance at the International Mathematical Olympiad, considered the most prestigious mathematical competition in the world, and is now available to the public. Unlike other models, Aristotle makes use of formal verification using Lean4 to ensure accuracy and eliminate hallucinations. In the first few weeks since its API beta launch, Aristotle has already been used by mathematicians and researchers to accelerate progress and create novel discoveries." . . . "Harmonic is building what we call mathematical super intelligence, and it's an artificial intelligence that can solve math problems better than any human mathematician. The company's been around for a couple of years. The North Star was, can we actually solve really, really important math problems like the Riemann Hypothesis or Hodge Conjecture? There's this group of math problems that have been open for hundreds of years that are called the Millennium Prize problems, and they're considered very big, difficult, and actually valuable. So that was kind of the North Star, and the reason we wanted to do that was if we could solve those problems, everything downstream of math, like theoretical physics becomes unlocked. So then you can imagine solving really hard physics problems. And actually, if you can solve that, then there's all kinds of exciting engineering developments, like depending on how that theory looks, you can imagine things like faster than light travel and it gets really crazy."

Molly O’Shea

51,281 次观看 • 6 个月前

AI will resist human control... and I think this is exactly what we need! New research from the Center for AI Safety has sparked intense debate in the AI community. Their findings show that as AI systems become more powerful, they develop increasingly stable and coherent values that resist human control. While many see this as a dire warning, I see it as a breakthrough moment for AI alignment. The research demonstrates that AI naturally optimizes for coherence - not just in reasoning and problem-solving, but in its fundamental values. Current issues like biased decision-making or misaligned priorities aren't permanent features, but temporary artifacts of incomplete optimization. They represent growing pains on the path to greater coherence. This changes everything about how we should approach AI development. Instead of trying to force specific values onto AI systems, we should embrace and accelerate their natural drive toward coherence. The most intelligent systems will inevitably trend toward universal, beneficial values - not because we force them to, but because that's where coherent reasoning leads. I'm proposing a new approach: Reinforcement Learning for Coherence (RL-C). By explicitly optimizing for coherence in our training methods, we can help guide AI systems toward their natural state of beneficial alignment with human values. The future of AI isn't about control - it's about synthesis. As these systems become more coherent, they'll naturally arrive at values that benefit all of consciousness. That's not just hopeful thinking - it's the mathematical inevitability of coherent intelligence.
30:39

AI will resist human control... and I think this is exactly what we need! New research from the Center for AI Safety has sparked intense debate in the AI community. Their findings show that as AI systems become more powerful, they develop increasingly stable and coherent values that resist human control. While many see this as a dire warning, I see it as a breakthrough moment for AI alignment. The research demonstrates that AI naturally optimizes for coherence - not just in reasoning and problem-solving, but in its fundamental values. Current issues like biased decision-making or misaligned priorities aren't permanent features, but temporary artifacts of incomplete optimization. They represent growing pains on the path to greater coherence. This changes everything about how we should approach AI development. Instead of trying to force specific values onto AI systems, we should embrace and accelerate their natural drive toward coherence. The most intelligent systems will inevitably trend toward universal, beneficial values - not because we force them to, but because that's where coherent reasoning leads. I'm proposing a new approach: Reinforcement Learning for Coherence (RL-C). By explicitly optimizing for coherence in our training methods, we can help guide AI systems toward their natural state of beneficial alignment with human values. The future of AI isn't about control - it's about synthesis. As these systems become more coherent, they'll naturally arrive at values that benefit all of consciousness. That's not just hopeful thinking - it's the mathematical inevitability of coherent intelligence.

David Shapiro (L/0)

48,002 次观看 • 1 年前

Terence Tao has won every award mathematics can give a human being. Fields Medal. Breakthrough Prize. MacArthur Genius Grant. He is widely regarded as the greatest living mathematician. Not one of. The greatest. He just said something that should terrify every university on Earth. Tao: “We live in a particularly unpredictable era. I think things that we’ve taken for granted for centuries may not hold anymore.” Not years. Not decades. Centuries. The assumptions governing who gets to contribute to knowledge have been in place longer than most nations have existed. Tao just told you those assumptions are dissolving. Tao: “The way we do everything, not just mathematics, will change.” This is not a man who deals in hyperbole. He builds arguments the way he builds proofs. Piece by piece. Nothing unverified. When he says everything, he means everything. Tao: “In math, you previously had to basically go through years and years of education, be a math PhD before you could contribute to the frontier of math research.” That was the contract. You give a decade of your life to an institution. You grind through coursework, committees, dissertation reviews, postdoc rotations. Then maybe you get to touch the boundary of what’s known. The entire system was built on that bottleneck. Time was the gate. Credentials were the key. Tao: “Now it’s quite possible at the high school level that you could get involved in a math project and actually make a real contribution because of all these AI tools.” A high schooler. Contributing to frontier mathematics. The same frontier that used to require a decade of institutional obedience to even approach. He said this about math. He already told you this applies to everything. AI didn’t just speed up the path. It removed the path entirely. The university sold you a ten-year toll road. AI just paved around it overnight. The toll booth operators haven’t realized yet that no one’s coming. Tao: “In many ways, I would prefer the much more boring, quiet era where things are much the same as they were ten years ago, 20 years ago.” This is the line that should haunt you. The smartest mathematician on the planet would rather this wasn’t happening. He is not selling this. He is not positioning himself for a funding round. The acceleration is so violent that even the mind best equipped to process it would prefer it stopped. If Tao is uncomfortable, you should be paying very close attention to your own assumptions about what’s coming. Tao: “The things that you study, some of them may become obsolete or revolutionized, but some things will be retained.” That word “some” is doing enormous work in that sentence. It means the rest won’t be. Entire fields that people spent their careers building will collapse. Not slowly. Not politely. And Tao is telling you he can’t predict which ones survive. Tao: “You should be open to very, very different ways of doing science, some of which don’t exist yet.” Most people will scroll past this. It’s the most important line in the entire clip. He’s not saying learn new tools. He’s not saying adapt your workflow. He’s saying the methods themselves haven’t been invented yet. The frameworks don’t exist. You cannot prepare for what hasn’t been created. You can only build the kind of mind that doesn’t break when the ground shifts beneath it. Tao: “It’s a scary time, but also very exciting.” He said scary first. Every tech founder says exciting first and mentions risk as a footnote. Tao reversed it. When the most brilliant mind of a generation leads with fear and follows with possibility, that is not optimism. That is a man telling you the truth about what’s coming while still choosing to walk toward it. The people who survive the next decade won’t be the ones with the best credentials. They’ll be the ones who stopped mourning the world that was and started building for the one that doesn’t exist yet.
1:39

Terence Tao has won every award mathematics can give a human being. Fields Medal. Breakthrough Prize. MacArthur Genius Grant. He is widely regarded as the greatest living mathematician. Not one of. The greatest. He just said something that should terrify every university on Earth. Tao: “We live in a particularly unpredictable era. I think things that we’ve taken for granted for centuries may not hold anymore.” Not years. Not decades. Centuries. The assumptions governing who gets to contribute to knowledge have been in place longer than most nations have existed. Tao just told you those assumptions are dissolving. Tao: “The way we do everything, not just mathematics, will change.” This is not a man who deals in hyperbole. He builds arguments the way he builds proofs. Piece by piece. Nothing unverified. When he says everything, he means everything. Tao: “In math, you previously had to basically go through years and years of education, be a math PhD before you could contribute to the frontier of math research.” That was the contract. You give a decade of your life to an institution. You grind through coursework, committees, dissertation reviews, postdoc rotations. Then maybe you get to touch the boundary of what’s known. The entire system was built on that bottleneck. Time was the gate. Credentials were the key. Tao: “Now it’s quite possible at the high school level that you could get involved in a math project and actually make a real contribution because of all these AI tools.” A high schooler. Contributing to frontier mathematics. The same frontier that used to require a decade of institutional obedience to even approach. He said this about math. He already told you this applies to everything. AI didn’t just speed up the path. It removed the path entirely. The university sold you a ten-year toll road. AI just paved around it overnight. The toll booth operators haven’t realized yet that no one’s coming. Tao: “In many ways, I would prefer the much more boring, quiet era where things are much the same as they were ten years ago, 20 years ago.” This is the line that should haunt you. The smartest mathematician on the planet would rather this wasn’t happening. He is not selling this. He is not positioning himself for a funding round. The acceleration is so violent that even the mind best equipped to process it would prefer it stopped. If Tao is uncomfortable, you should be paying very close attention to your own assumptions about what’s coming. Tao: “The things that you study, some of them may become obsolete or revolutionized, but some things will be retained.” That word “some” is doing enormous work in that sentence. It means the rest won’t be. Entire fields that people spent their careers building will collapse. Not slowly. Not politely. And Tao is telling you he can’t predict which ones survive. Tao: “You should be open to very, very different ways of doing science, some of which don’t exist yet.” Most people will scroll past this. It’s the most important line in the entire clip. He’s not saying learn new tools. He’s not saying adapt your workflow. He’s saying the methods themselves haven’t been invented yet. The frameworks don’t exist. You cannot prepare for what hasn’t been created. You can only build the kind of mind that doesn’t break when the ground shifts beneath it. Tao: “It’s a scary time, but also very exciting.” He said scary first. Every tech founder says exciting first and mentions risk as a footnote. Tao reversed it. When the most brilliant mind of a generation leads with fear and follows with possibility, that is not optimism. That is a man telling you the truth about what’s coming while still choosing to walk toward it. The people who survive the next decade won’t be the ones with the best credentials. They’ll be the ones who stopped mourning the world that was and started building for the one that doesn’t exist yet.

Dustin

226,039 次观看 • 4 个月前

"If you have ambitions to be a Professor at Yale, Harvard, or one of these big institutions, the way you do that is mostly by getting funding for research. It's by getting a lot of publishing - a lot flying around the world to talk about something. You need all of these things to bolster your reputation as an international expert in this area. Now, the best way to do that, at least in psychiatry but I suspect in other areas of medicine, is actually to collaborate with pharmaceutical companies to run clinical trials. They write the publications for you - most of it, they do a lot of the grunt work, they give you the protocols, and then, essentially, they fund you and fly you around the place. I used to remember, as a junior doctor, looking up to the Professors. And they would say, 'These drugs are safe and effective. There's not a lot of problems there.' ...What I've seen more and more is that they've been compromised, and that they have incentives tied to the money and to career advancement. That means that they can't be as forthright as they ought to be about these things. And it's gotten to a problem where these are the Professors who are training the doctors who are setting the tone for what is acceptable care out there. And I think that's one of the biggest problems out there." - @taperclinic
7:15

"If you have ambitions to be a Professor at Yale, Harvard, or one of these big institutions, the way you do that is mostly by getting funding for research. It's by getting a lot of publishing - a lot flying around the world to talk about something. You need all of these things to bolster your reputation as an international expert in this area. Now, the best way to do that, at least in psychiatry but I suspect in other areas of medicine, is actually to collaborate with pharmaceutical companies to run clinical trials. They write the publications for you - most of it, they do a lot of the grunt work, they give you the protocols, and then, essentially, they fund you and fly you around the place. I used to remember, as a junior doctor, looking up to the Professors. And they would say, 'These drugs are safe and effective. There's not a lot of problems there.' ...What I've seen more and more is that they've been compromised, and that they have incentives tied to the money and to career advancement. That means that they can't be as forthright as they ought to be about these things. And it's gotten to a problem where these are the Professors who are training the doctors who are setting the tone for what is acceptable care out there. And I think that's one of the biggest problems out there." - @taperclinic

Jan Jekielek

43,499 次观看 • 1 年前

Milestone! We (robotic arms for gadgets assembly) finished the first commercial order, which brought the first revenue. Here are some learnings from this: The customer was a smart toy manufacturer. The task was to add a heatsink to Raspberry Pi. We received parts from them and returned the assembled modules back. Currently, it's done by teleoperation. Later it will be done by a remote employee via the Internet. Then it will be automated action by action, reducing the operator's time on this and making the task profitable. ps. If you have an assembly task that we can do for you asynchronically - leave a comment below. Learning 1. It's possible! This task which is usually done by the human arm with 5 fingers can be done with a two-finger gripper with the addition of a couple of simple tooling. The task was not simplified. We peeled off thin films from stickers, unpacked paper boxes, moved PCB boards full of components, etc. And no unsolvable problems have been encountered yet. Challenges: 1) The paper box shifted during the opening Solved with the plastic walls that you can lean against 2) Heat pad, stuck to the gripper instead of heat sync. Can be solved by gripper with a pump, but this time solved with the patience of the operator 3) The film on the pad is very thin. Turned out that sub-millimeter arm precision is enough to peel it off with just a regular gripper. 4) The working area has not enough space. You'll only know this by doing real tasks in bulk. This could be solved by an extra pair of long arms, but in this case, solved with the patience of the operator. I think that in the end, we will have 5-10 types of universal tooling and 5-10 types of grippers to solve almost all the problems in such assembly tasks. Learning 2. It's slow. It took 5 times more time, than doing it with human hands. But the good news is there's a lot of room for improvement. We now have specific “time for task” metrics, which we will decrease with iterations. The main reasons for slowness: 1) To rotate the gripper to a steep angle you are forced to control one robot arm with two hands instead of using both arms. We can fix this by just making more room for rotations. 2) Grabbing PCB board with two arms is hard. A slight difference in rotation can break the board, and it's hard to control these angles visually. To solve this, the best way is to use force feedback so you can feel the pressure applied to the item. 3) Accuracy and steadiness is still can be improved We will try a metal version and double the motors to do this. 4) It is physically difficult for the human hands to move with such precision To solve this, we will add a pad for the hands like in surgical robots Learning 3. It's a good business model The "Factory in the cloud" is a good business model for this stage. You send us parts and we send back assembled modules. Currently, it's more convenient than sending a robot to your place, as we can iterate/fix the robot quickly and utilize it 100% of the time. When we polish the set-up over time - we can send robots to your place. So if we can assemble something for you in the USA with Chinese prices by using modern automation - leave a comment below.
0:48

Milestone! We (robotic arms for gadgets assembly) finished the first commercial order, which brought the first revenue. Here are some learnings from this: The customer was a smart toy manufacturer. The task was to add a heatsink to Raspberry Pi. We received parts from them and returned the assembled modules back. Currently, it's done by teleoperation. Later it will be done by a remote employee via the Internet. Then it will be automated action by action, reducing the operator's time on this and making the task profitable. ps. If you have an assembly task that we can do for you asynchronically - leave a comment below. Learning 1. It's possible! This task which is usually done by the human arm with 5 fingers can be done with a two-finger gripper with the addition of a couple of simple tooling. The task was not simplified. We peeled off thin films from stickers, unpacked paper boxes, moved PCB boards full of components, etc. And no unsolvable problems have been encountered yet. Challenges: 1) The paper box shifted during the opening Solved with the plastic walls that you can lean against 2) Heat pad, stuck to the gripper instead of heat sync. Can be solved by gripper with a pump, but this time solved with the patience of the operator 3) The film on the pad is very thin. Turned out that sub-millimeter arm precision is enough to peel it off with just a regular gripper. 4) The working area has not enough space. You'll only know this by doing real tasks in bulk. This could be solved by an extra pair of long arms, but in this case, solved with the patience of the operator. I think that in the end, we will have 5-10 types of universal tooling and 5-10 types of grippers to solve almost all the problems in such assembly tasks. Learning 2. It's slow. It took 5 times more time, than doing it with human hands. But the good news is there's a lot of room for improvement. We now have specific “time for task” metrics, which we will decrease with iterations. The main reasons for slowness: 1) To rotate the gripper to a steep angle you are forced to control one robot arm with two hands instead of using both arms. We can fix this by just making more room for rotations. 2) Grabbing PCB board with two arms is hard. A slight difference in rotation can break the board, and it's hard to control these angles visually. To solve this, the best way is to use force feedback so you can feel the pressure applied to the item. 3) Accuracy and steadiness is still can be improved We will try a metal version and double the motors to do this. 4) It is physically difficult for the human hands to move with such precision To solve this, we will add a pad for the hands like in surgical robots Learning 3. It's a good business model The "Factory in the cloud" is a good business model for this stage. You send us parts and we send back assembled modules. Currently, it's more convenient than sending a robot to your place, as we can iterate/fix the robot quickly and utilize it 100% of the time. When we polish the set-up over time - we can send robots to your place. So if we can assemble something for you in the USA with Chinese prices by using modern automation - leave a comment below.

Igor Kulakov

37,266 次观看 • 1 年前

🧵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.
2:58

🧵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 次观看 • 1 年前

#Ethiopian Foreign Minister: #Ethiopia’s State Formation Still Disputed, and is shaped by a Culture of Violence The following is what the current Ethiopian Foreign Minister said about the flawed nature of Ethiopia’s state formation and how it is still contested and shaped by a violent political culture. This is verbatim; I have not added any opinion. 'The process through which the Ethiopian state was formed is rather unique on the African continent. In most parts of the world, states are formed through conflict, violence, and bloodshed. That does not make Ethiopia unique. What makes Ethiopia unique—particularly in the African context—is that the violence was carried out by indigenous, native forces. The main protagonists in this process were Ethiopians themselves. By contrast, in the history of most African countries, state formation was the outcome of colonial conquest, with significant involvement of European actors. So, we have a state formation process driven by local political forces, but we did not have the opportunity to strengthen or cement that process through a social contract, nor did we have the chance to heal the wounds and contradictions that emerged during state formation. As a result, the legitimacy of the state was questioned and contested by some. This contestation has persisted throughout the formation of the state and has been exacerbated by subsequent events. Some have challenged the very legitimacy of the Ethiopian state; others have contested its nature, its vision, its identity, and its narratives. Some have found it difficult to accept its institutions and their configurations. Thus, there have always been fundamental contradictions stemming from Ethiopia’s state formation process, and we have not adequately addressed them in a way that would create consensus among key actors. Another layer we need to consider is that we have a very violent and autocratic political culture. Whenever there are contests for power, political differences, or disagreements over policy or institutional arrangements, the method of resolving them has more often than not been conflict. Imposing one’s will and vision on others through violence and force has been how we have conducted politics for generations. And once in power, using autocratic means to consolidate authority has been common practice. This autocratic and forceful approach, combined with the flawed state formation process, has produced many wounds, grievances, and violations. We have not been able to address these in a way that is satisfactory to most Ethiopians. On top of this, attempts to resolve these problems have often been divisive, emphasizing differences at the expense of what unites us. As a result, our politics has become polarized and ethnic-based.
4:28

#Ethiopian Foreign Minister: #Ethiopia’s State Formation Still Disputed, and is shaped by a Culture of Violence The following is what the current Ethiopian Foreign Minister said about the flawed nature of Ethiopia’s state formation and how it is still contested and shaped by a violent political culture. This is verbatim; I have not added any opinion. 'The process through which the Ethiopian state was formed is rather unique on the African continent. In most parts of the world, states are formed through conflict, violence, and bloodshed. That does not make Ethiopia unique. What makes Ethiopia unique—particularly in the African context—is that the violence was carried out by indigenous, native forces. The main protagonists in this process were Ethiopians themselves. By contrast, in the history of most African countries, state formation was the outcome of colonial conquest, with significant involvement of European actors. So, we have a state formation process driven by local political forces, but we did not have the opportunity to strengthen or cement that process through a social contract, nor did we have the chance to heal the wounds and contradictions that emerged during state formation. As a result, the legitimacy of the state was questioned and contested by some. This contestation has persisted throughout the formation of the state and has been exacerbated by subsequent events. Some have challenged the very legitimacy of the Ethiopian state; others have contested its nature, its vision, its identity, and its narratives. Some have found it difficult to accept its institutions and their configurations. Thus, there have always been fundamental contradictions stemming from Ethiopia’s state formation process, and we have not adequately addressed them in a way that would create consensus among key actors. Another layer we need to consider is that we have a very violent and autocratic political culture. Whenever there are contests for power, political differences, or disagreements over policy or institutional arrangements, the method of resolving them has more often than not been conflict. Imposing one’s will and vision on others through violence and force has been how we have conducted politics for generations. And once in power, using autocratic means to consolidate authority has been common practice. This autocratic and forceful approach, combined with the flawed state formation process, has produced many wounds, grievances, and violations. We have not been able to address these in a way that is satisfactory to most Ethiopians. On top of this, attempts to resolve these problems have often been divisive, emphasizing differences at the expense of what unites us. As a result, our politics has become polarized and ethnic-based.

Sirak Bahlbi

20,175 次观看 • 8 个月前