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Martin Casado on why artificial intelligence is the wrong term and why a16z's new fund is called the Machine Age Fund: "Artificial intelligence was the wrong word. It's machine intelligence... because it's not how humans think. It is a cache of human thought." "To date, we don't know how...

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41,131 views • 2 months ago •via X (Twitter)

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"Until we run out of problems, we're not going to run out of demand." Erik Torenberg, Martin Casado, Ben Horowitz, and Raghu Raghuram on why AI demand has no ceiling, and what that means for supply: "As we've moved from chatbots to reasoning to agents to multi-agents, each step has multiplied the number of tokens a single task takes up by increasing orders of magnitude." "The simple way to think about it is any problem that you have can be solved with enough infrastructure and money. GPUs and power, and money." "AI's answer to getting better and better is to use more AI. Inference is one basic building block that it keeps using over and over and over again." "In the past, money would come in, you have an engineering problem, we know that it takes two years, normally fails... [Now] there's nothing between the money going in and then the hardware creating intelligence. And so now we're just limited by our ability to create supply." martin_casado benahorowitz.eth Raghu Raghuram Erik Torenberg
1:59

"Until we run out of problems, we're not going to run out of demand." Erik Torenberg, Martin Casado, Ben Horowitz, and Raghu Raghuram on why AI demand has no ceiling, and what that means for supply: "As we've moved from chatbots to reasoning to agents to multi-agents, each step has multiplied the number of tokens a single task takes up by increasing orders of magnitude." "The simple way to think about it is any problem that you have can be solved with enough infrastructure and money. GPUs and power, and money." "AI's answer to getting better and better is to use more AI. Inference is one basic building block that it keeps using over and over and over again." "In the past, money would come in, you have an engineering problem, we know that it takes two years, normally fails... [Now] there's nothing between the money going in and then the hardware creating intelligence. And so now we're just limited by our ability to create supply." martin_casado benahorowitz.eth Raghu Raghuram Erik Torenberg

a16z

38,880 views • 2 months ago

Rick Rubin on being addicted to the miracle of creation: “I like the moment of revelation. So we're working on something and it's just okay. It's boring. I'd rather not be there. That's how it feels. But then something happens and it's like magic. Something appears. It's not like we made it. Something comes up in the process. It's conjured in the process. It’s like a miracle and that's the thing that's addictive. That feeling when it's like it's not good, it's not good, it's not good, it's not good. We try something and it doesn't work. Try something else and it doesn't work. But then all of the sudden what we're listening to is really cool. That's the [addictive] feeling, that moment of discovery where it goes from nothing to something really good. And then the whole rest of the process after that is protecting that because it's super delicate. When it happens it's like this miracle happened and this magic thing happened and now we have to protect it through the rest of the process to not ruin it. It's scary because you don't want it to end. You don't want it to stop because we know if it stops, we can't control it. We can't do it again. It's not like that. It's this moment in time where something magical happens. So we spend a lot of time waiting and I can't say that's fun. Waiting is not fun.”
1:44

Rick Rubin on being addicted to the miracle of creation: “I like the moment of revelation. So we're working on something and it's just okay. It's boring. I'd rather not be there. That's how it feels. But then something happens and it's like magic. Something appears. It's not like we made it. Something comes up in the process. It's conjured in the process. It’s like a miracle and that's the thing that's addictive. That feeling when it's like it's not good, it's not good, it's not good, it's not good. We try something and it doesn't work. Try something else and it doesn't work. But then all of the sudden what we're listening to is really cool. That's the [addictive] feeling, that moment of discovery where it goes from nothing to something really good. And then the whole rest of the process after that is protecting that because it's super delicate. When it happens it's like this miracle happened and this magic thing happened and now we have to protect it through the rest of the process to not ruin it. It's scary because you don't want it to end. You don't want it to stop because we know if it stops, we can't control it. We can't do it again. It's not like that. It's this moment in time where something magical happens. So we spend a lot of time waiting and I can't say that's fun. Waiting is not fun.”

David Senra

12,759 views • 5 months ago

Martin Casado spent years dismissing the AI doom scenario and says what he got wrong was how long the scaling laws would keep holding: "I was responding to this Bostrom notion of recursive self-improvement, fast takeoff. You create one of these things, you step back, and it takes over the world... That's clearly not what's happening." "But here's what I got wrong. What I got wrong is I did not know that we could effectively just continue to pour money in this. The scaling laws are holding." "I don't know what it means to do a $100 billion training run, to have this thing that you're putting $100 billion in, and then that money comes from this meta-economic machinery that may want to solve whatever." "They may want to solve cancer, but they may also want to create a weapon. Like, who knows?" "This concentration of this many resources in a useful way, I think, is very new. I don't think we understand the implications. I think you could reasonably argue that that's very dangerous if you apply that $100 billion in the wrong way... What does it mean to be able to concentrate resources?" martin_casado
1:11

Martin Casado spent years dismissing the AI doom scenario and says what he got wrong was how long the scaling laws would keep holding: "I was responding to this Bostrom notion of recursive self-improvement, fast takeoff. You create one of these things, you step back, and it takes over the world... That's clearly not what's happening." "But here's what I got wrong. What I got wrong is I did not know that we could effectively just continue to pour money in this. The scaling laws are holding." "I don't know what it means to do a $100 billion training run, to have this thing that you're putting $100 billion in, and then that money comes from this meta-economic machinery that may want to solve whatever." "They may want to solve cancer, but they may also want to create a weapon. Like, who knows?" "This concentration of this many resources in a useful way, I think, is very new. I don't think we understand the implications. I think you could reasonably argue that that's very dangerous if you apply that $100 billion in the wrong way... What does it mean to be able to concentrate resources?" martin_casado

a16z

80,403 views • 2 months ago

Steve Jobs on how he learned to run a company: Question: "You're 21. You're a big success. You know, you've just sort of done it by the seat of your pants. You don't have any particular training in this. How do you learn to run a company?" Steve Jobs: "You know, throughout the years in business, I found something, which was that I always ask why you do things. And the answers you invariably get are, oh, that's just the way it's done. Nobody knows why they do what they do. Nobody thinks about things very deeply in business. That's what I found. I'll give you an example. When we were building our Apple I's in the garage, we knew exactly what they cost. When we got into a factory in the Apple II days, the accounting had this notion of a standard cost, where you'd kind of set a standard cost and at the end of a quarter you'd adjust it with a variance. And I kept asking, well, why do we do this? And the answer was, well, that's just the way it's done. And after about six months of digging into this, what I realized was the reason you do it is because you don't really have good enough controls to know how much it costs. So you guess, and then you fix your guess at the end of the quarter. And the reason you don't know how much it costs is because your information systems aren't good enough. But nobody said it that way. And so later on, when we designed this automated factory for Macintosh, we were able to get rid of a lot of these antiquated concepts and know exactly what something cost to the second. So in business, a lot of things are, I call it folklore. They're done because they were done yesterday and the day before. And so what that means is if you're willing to sort of ask a lot of questions and think about things and work really hard, you can learn business pretty fast. It's not the hardest thing in the world. It's not rocket science. It's not rocket science."
2:06

Steve Jobs on how he learned to run a company: Question: "You're 21. You're a big success. You know, you've just sort of done it by the seat of your pants. You don't have any particular training in this. How do you learn to run a company?" Steve Jobs: "You know, throughout the years in business, I found something, which was that I always ask why you do things. And the answers you invariably get are, oh, that's just the way it's done. Nobody knows why they do what they do. Nobody thinks about things very deeply in business. That's what I found. I'll give you an example. When we were building our Apple I's in the garage, we knew exactly what they cost. When we got into a factory in the Apple II days, the accounting had this notion of a standard cost, where you'd kind of set a standard cost and at the end of a quarter you'd adjust it with a variance. And I kept asking, well, why do we do this? And the answer was, well, that's just the way it's done. And after about six months of digging into this, what I realized was the reason you do it is because you don't really have good enough controls to know how much it costs. So you guess, and then you fix your guess at the end of the quarter. And the reason you don't know how much it costs is because your information systems aren't good enough. But nobody said it that way. And so later on, when we designed this automated factory for Macintosh, we were able to get rid of a lot of these antiquated concepts and know exactly what something cost to the second. So in business, a lot of things are, I call it folklore. They're done because they were done yesterday and the day before. And so what that means is if you're willing to sort of ask a lot of questions and think about things and work really hard, you can learn business pretty fast. It's not the hardest thing in the world. It's not rocket science. It's not rocket science."

Founder Mode

32,346 views • 8 months ago

Martin Casado explains AI as a fundamentally new abstraction layer of computing: "I don't think in the history of computer science that I can recall have we ever abdicated actual reasoning or logic. It's always been a resource. It's been like compute, network, and storage..." "And then the human is putting in the high-level thing, and it's using the compute, network, and storage to calculate the answer." "But now I feel like you're actually abdicating thinking in a way, where you're like, 'Tell me the answer.' Like, 'I'm not even really sure what the question is.'" "This is the first time it feels like a different layer of the stack. Maybe this really is the next abstraction, which is more of a human-level abstraction, which doesn't map directly and so is actually different... You kind of pray to the model god in the right words, and then it produces the answer that just ends up being useful." martin_casado
2:07

Martin Casado explains AI as a fundamentally new abstraction layer of computing: "I don't think in the history of computer science that I can recall have we ever abdicated actual reasoning or logic. It's always been a resource. It's been like compute, network, and storage..." "And then the human is putting in the high-level thing, and it's using the compute, network, and storage to calculate the answer." "But now I feel like you're actually abdicating thinking in a way, where you're like, 'Tell me the answer.' Like, 'I'm not even really sure what the question is.'" "This is the first time it feels like a different layer of the stack. Maybe this really is the next abstraction, which is more of a human-level abstraction, which doesn't map directly and so is actually different... You kind of pray to the model god in the right words, and then it produces the answer that just ends up being useful." martin_casado

a16z

36,147 views • 2 months ago

On anthropomorphizing AI. a 2024 interview of Satya Nadella still makes a lot of sense. "I don't like anthropomorphizing. I sort of believe it's a tool. It's got intelligence, if you want to give it that moniker, but it's not the same intelligence that I have, right? I think one of the most unfortunate names is “artificial intelligence.” It's different. I wish we had called it “different intelligence,” even, because I have my intelligence. I don't need any artificial intelligence. I just want my intelligence to compose with this different intelligence so that I have more agency, quite frankly. So I think of that as the relationship I want to have with the AIs around us, where I want help. I want it to assist me in getting more agency." ---- From "Bloomberg Television" YouTube channel, (full video link in comment)
0:40

On anthropomorphizing AI. a 2024 interview of Satya Nadella still makes a lot of sense. "I don't like anthropomorphizing. I sort of believe it's a tool. It's got intelligence, if you want to give it that moniker, but it's not the same intelligence that I have, right? I think one of the most unfortunate names is “artificial intelligence.” It's different. I wish we had called it “different intelligence,” even, because I have my intelligence. I don't need any artificial intelligence. I just want my intelligence to compose with this different intelligence so that I have more agency, quite frankly. So I think of that as the relationship I want to have with the AIs around us, where I want help. I want it to assist me in getting more agency." ---- From "Bloomberg Television" YouTube channel, (full video link in comment)

Rohan Paul

31,329 views • 1 month ago

🚨Governor DeSantis says people are realizing how much Artificial Intelligence SUCKS and want nothing to do with it! “Artificial intelligence has become VERY unpopular. You remember like a year ago everyone said ‘oh you know AI it's we got to go all in you know we just need to do all this’. Now people are seeing some of the SLOP that's being created and they're like well wait a minute is that the world we want to live in? You’re saying that this is somehow good because it's going to take away people's jobs? Is that really good? You want to subsidize that? I don't think people want to subsidize it.”
1:31

🚨Governor DeSantis says people are realizing how much Artificial Intelligence SUCKS and want nothing to do with it! “Artificial intelligence has become VERY unpopular. You remember like a year ago everyone said ‘oh you know AI it's we got to go all in you know we just need to do all this’. Now people are seeing some of the SLOP that's being created and they're like well wait a minute is that the world we want to live in? You’re saying that this is somehow good because it's going to take away people's jobs? Is that really good? You want to subsidize that? I don't think people want to subsidize it.”

Chris Nelson 🏝️🇺🇸

196,788 views • 7 months ago

Former Meta Chief AI Scientist Yann LeCun on the three paradigms of machine learning — and why the third is what made ChatGPT possible: Here's each one, and where it breaks. First, supervised learning. You tell the machine the answer. "You show it a picture, let's say of a table, and you tell it this is a table. So it's supervised because you tell it what the correct answer is." Get it wrong, and the machine rewrites itself: "The system computes its output, and if it says something else than table, then it's going to adjust its parameters, its internal structure, so that the output it produces gets closer to the output you want." Repeat at scale and something more than memorisation appears: "Eventually the system will find a way to recognize every image you trained it on, but also images it's never seen that are similar to the one you train it on. This is called a generalization ability." The limit: a human has to supply every single answer. That doesn't scale to the size of the internet. Second, reinforcement learning. You don't give the answer, only a verdict. "You don't tell the system what the correct answer is. You only tell it whether the answer it produced was good or bad." Learning to ride a bike, essentially: "You try to ride a bike and you don't know how to ride the bike and after a while you fall. So you know you did something bad and so you change your strategy a little bit. And eventually you learn how to ride a bike." For years the field assumed this was the closest thing to how animals actually learn. Yann LeCun's verdict: "Now it turns out reinforcement learning is extremely inefficient." It dominates wherever failure is free: "It works really well if you want to train a system to play chess or play go or poker, because you can have the system play millions and millions of games against itself and basically fine-tune itself. But it doesn't really work in the real world." The limit, in one image: "If you want to train a car to drive itself, you're not going to do it with reinforcement learning. It's going to crash thousands of times." On robotics he's careful rather than dismissive: "Reinforcement learning can be part of the solution, but it's not the complete answer. It's not sufficient." Third, self-supervised learning. You tell the machine nothing at all. "And this is what has enabled the recent progress in natural language understanding and chatbots." The strange part is that you stop asking for a task: "You don't train the system to accomplish any particular task. You just train it to basically capture the structure..." The method is deliberate sabotage: "You take a piece of text, you corrupt it in some way, by for example removing some words, and then you train a big neural net to predict the words that are missing." And one narrow version of that trick runs every chatbot on Earth: "A special case of this is that you take a piece of text and the last word in that text is not visible, and so you train the system to predict the last word in that text — and this is the way large language models are trained on." So why did the third one win? Supervised learning needs a human. Reinforcement learning needs a crash. Self-supervised learning needs neither — because the missing word and the correct answer are the same thing. The data grades itself.
2:58

Former Meta Chief AI Scientist Yann LeCun on the three paradigms of machine learning — and why the third is what made ChatGPT possible: Here's each one, and where it breaks. First, supervised learning. You tell the machine the answer. "You show it a picture, let's say of a table, and you tell it this is a table. So it's supervised because you tell it what the correct answer is." Get it wrong, and the machine rewrites itself: "The system computes its output, and if it says something else than table, then it's going to adjust its parameters, its internal structure, so that the output it produces gets closer to the output you want." Repeat at scale and something more than memorisation appears: "Eventually the system will find a way to recognize every image you trained it on, but also images it's never seen that are similar to the one you train it on. This is called a generalization ability." The limit: a human has to supply every single answer. That doesn't scale to the size of the internet. Second, reinforcement learning. You don't give the answer, only a verdict. "You don't tell the system what the correct answer is. You only tell it whether the answer it produced was good or bad." Learning to ride a bike, essentially: "You try to ride a bike and you don't know how to ride the bike and after a while you fall. So you know you did something bad and so you change your strategy a little bit. And eventually you learn how to ride a bike." For years the field assumed this was the closest thing to how animals actually learn. Yann LeCun's verdict: "Now it turns out reinforcement learning is extremely inefficient." It dominates wherever failure is free: "It works really well if you want to train a system to play chess or play go or poker, because you can have the system play millions and millions of games against itself and basically fine-tune itself. But it doesn't really work in the real world." The limit, in one image: "If you want to train a car to drive itself, you're not going to do it with reinforcement learning. It's going to crash thousands of times." On robotics he's careful rather than dismissive: "Reinforcement learning can be part of the solution, but it's not the complete answer. It's not sufficient." Third, self-supervised learning. You tell the machine nothing at all. "And this is what has enabled the recent progress in natural language understanding and chatbots." The strange part is that you stop asking for a task: "You don't train the system to accomplish any particular task. You just train it to basically capture the structure..." The method is deliberate sabotage: "You take a piece of text, you corrupt it in some way, by for example removing some words, and then you train a big neural net to predict the words that are missing." And one narrow version of that trick runs every chatbot on Earth: "A special case of this is that you take a piece of text and the last word in that text is not visible, and so you train the system to predict the last word in that text — and this is the way large language models are trained on." So why did the third one win? Supervised learning needs a human. Reinforcement learning needs a crash. Self-supervised learning needs neither — because the missing word and the correct answer are the same thing. The data grades itself.

Big Brain AI

49,293 views • 2 months ago

Arteta: “We know what is at stake and the fact that, as a club, we haven't done it for so long is a reason for that. It talks about the difficulty but I just want to focus on the things that we can control, dominate, and we have done. “It's not that we have to do a lot of new things that we haven't done. No, no, no, let's keep doing a lot of it or almost all the things that we've done for nine months, because that's what has taken us all the way there. “On Wednesday, I saw a lot of them. And today we've gone back so we need to learn, we need to be very critical of ourselves, and then pick ourselves up because on Wednesday we have a big one coming up.”
0:36

Arteta: “We know what is at stake and the fact that, as a club, we haven't done it for so long is a reason for that. It talks about the difficulty but I just want to focus on the things that we can control, dominate, and we have done. “It's not that we have to do a lot of new things that we haven't done. No, no, no, let's keep doing a lot of it or almost all the things that we've done for nine months, because that's what has taken us all the way there. “On Wednesday, I saw a lot of them. And today we've gone back so we need to learn, we need to be very critical of ourselves, and then pick ourselves up because on Wednesday we have a big one coming up.”

Connor Humm

21,403 views • 6 months ago

Steven Sinofsky on why Jev fixes his oldest complaint about AI: natural language was never an efficient interface. "Ask yourself how many people are really, really good at asking questions. Less than half the people can ask a good question in a meeting." "And then how often do you look at the answer and get really frustrated before it's finished, but you have to pay all this money to watch the seven paragraphs come out...?" "My favorite is the output of it is designed for probabilistic programming." "Instead of saying, 'Is this a customer service question? Then route to customer service, otherwise route to general help desk,' it's, 'This is 80% customer service.' That's exactly simulation." "There's 50 years of computer science research in literally probabilistic if statements. Suddenly the coolest place to be in computer science is gonna be probabilistic programming, which was all of computer science in the 1960s and '70s." "This is not all new. It's gonna be very interesting to dust off all of that work, because it's exactly what's going on. An if statement now is if X percent, not if always." "The way that Jev works is it's a custom programming language almost, which is: here's the prompt, come back with a percentage. And then you put that in the if statement." Steven Sinofsky
2:17

Steven Sinofsky on why Jev fixes his oldest complaint about AI: natural language was never an efficient interface. "Ask yourself how many people are really, really good at asking questions. Less than half the people can ask a good question in a meeting." "And then how often do you look at the answer and get really frustrated before it's finished, but you have to pay all this money to watch the seven paragraphs come out...?" "My favorite is the output of it is designed for probabilistic programming." "Instead of saying, 'Is this a customer service question? Then route to customer service, otherwise route to general help desk,' it's, 'This is 80% customer service.' That's exactly simulation." "There's 50 years of computer science research in literally probabilistic if statements. Suddenly the coolest place to be in computer science is gonna be probabilistic programming, which was all of computer science in the 1960s and '70s." "This is not all new. It's gonna be very interesting to dust off all of that work, because it's exactly what's going on. An if statement now is if X percent, not if always." "The way that Jev works is it's a custom programming language almost, which is: here's the prompt, come back with a percentage. And then you put that in the if statement." Steven Sinofsky

a16z

148,850 views • 1 month ago

Ben Horowitz says instead of banning AI, educators should set problems students can't solve without it: "I was talking to Dan Boneh, who is a great professor of computer science and cryptography at Stanford. His take on AI was: You have two choices. You can ban it, and by the way, that won't work. Or you can make the problems so hard that you can't solve them without AI." "What he's seeing is, 'I've got students solving things that no student in history could have ever solved.' That's what's possible. You want to have that orientation: What can you solve with the tools? What can you do that's a breakthrough? Now everybody has an opportunity to create a breakthrough." "Then you're a main character, not an NPC. That's what the academy is about. Do you want to be a main character? If you want to be a main character, we help you do that. If you want to be a cog in a machine, this is not going to be the best place for you." benahorowitz.eth
1:19

Ben Horowitz says instead of banning AI, educators should set problems students can't solve without it: "I was talking to Dan Boneh, who is a great professor of computer science and cryptography at Stanford. His take on AI was: You have two choices. You can ban it, and by the way, that won't work. Or you can make the problems so hard that you can't solve them without AI." "What he's seeing is, 'I've got students solving things that no student in history could have ever solved.' That's what's possible. You want to have that orientation: What can you solve with the tools? What can you do that's a breakthrough? Now everybody has an opportunity to create a breakthrough." "Then you're a main character, not an NPC. That's what the academy is about. Do you want to be a main character? If you want to be a main character, we help you do that. If you want to be a cog in a machine, this is not going to be the best place for you." benahorowitz.eth

a16z

156,984 views • 1 month ago

Ben Horowitz on the oldest law in startups, and why the industry no longer has to obey it: "The one thing we all knew in startup world is that if I have a two-year lead on you and you try to catch me by hiring 1,000 engineers, you're gonna wreck your company." "That never works. It's a Mythical Man-Month. Nine women can't have a baby in a month. That's it. That never works." "Okay, now that works. But it's not hiring 1,000 engineers. It's taking $3 billion and lighting up a magnificent cluster, and then all of a sudden, whatever, Grok can come out of nowhere and, oh, all of a sudden it's real." "You can throw money at the problem, and you can throw money at almost any problem, and that works. That is just completely different than anything we've ever lived through. By the way, we're all psychologically adjusting to this." benahorowitz.eth Erik Torenberg
1:17

Ben Horowitz on the oldest law in startups, and why the industry no longer has to obey it: "The one thing we all knew in startup world is that if I have a two-year lead on you and you try to catch me by hiring 1,000 engineers, you're gonna wreck your company." "That never works. It's a Mythical Man-Month. Nine women can't have a baby in a month. That's it. That never works." "Okay, now that works. But it's not hiring 1,000 engineers. It's taking $3 billion and lighting up a magnificent cluster, and then all of a sudden, whatever, Grok can come out of nowhere and, oh, all of a sudden it's real." "You can throw money at the problem, and you can throw money at almost any problem, and that works. That is just completely different than anything we've ever lived through. By the way, we're all psychologically adjusting to this." benahorowitz.eth Erik Torenberg

a16z

195,635 views • 2 months ago

.David Deutsch: "What's currently called AI and AGI are not only different from each other, they are very close to being the exact opposites of each other. The reason is that an AI, current AI is like an AI that diagnoses diseases or an AI that plays chess or an AI that controls a huge factory. Those things have objective functions, that is they have a function that they are designed to maximize and that is why they are used in those particular applications. Or in military terms, you could say the objective is to hit the target. You might say the objective is to hit the target unless some thing specified, but it's a specified thing comes up in which case don't hit the target and so on. This is, as I said, almost the opposite of what humans do when humans think. For a start, the AI has to be obedient, that is it has to actually do the things it is programmed to do, whereas a human is fundamentally disobedient, especially when being creative. When a human plays chess, they are performing a completely different kind of computation. They don't do the same things, they don't investigate the same possibilities that the artificial chess playing machine does, because the artificial one is capable of looking at billions and billions of possibilities, whereas the human can only look at hundreds or something. They are doing something completely different. Another difference is that the human can explain, can write a book later, having become world champion, can write a book saying how I did it, as the computer program that beats the world champion can write no such book, because it has no idea how it did it. It was just following a program. I was doing this and that and that and none of that is illuminating. Also, third thing, the chess player can decide I don't want to play chess anymore, from now on I will play Go or from now on I will play tennis. If commanded to play chess, the functionality will deteriorate completely. Those things are different. What we want in an AGI is that it behaves in a way that cannot be specified in advance, because if you specified it, you would already have the answer. The AGI program has to give unexpected answers, answers to questions we didn't even know how to ask."
3:11

.David Deutsch: "What's currently called AI and AGI are not only different from each other, they are very close to being the exact opposites of each other. The reason is that an AI, current AI is like an AI that diagnoses diseases or an AI that plays chess or an AI that controls a huge factory. Those things have objective functions, that is they have a function that they are designed to maximize and that is why they are used in those particular applications. Or in military terms, you could say the objective is to hit the target. You might say the objective is to hit the target unless some thing specified, but it's a specified thing comes up in which case don't hit the target and so on. This is, as I said, almost the opposite of what humans do when humans think. For a start, the AI has to be obedient, that is it has to actually do the things it is programmed to do, whereas a human is fundamentally disobedient, especially when being creative. When a human plays chess, they are performing a completely different kind of computation. They don't do the same things, they don't investigate the same possibilities that the artificial chess playing machine does, because the artificial one is capable of looking at billions and billions of possibilities, whereas the human can only look at hundreds or something. They are doing something completely different. Another difference is that the human can explain, can write a book later, having become world champion, can write a book saying how I did it, as the computer program that beats the world champion can write no such book, because it has no idea how it did it. It was just following a program. I was doing this and that and that and none of that is illuminating. Also, third thing, the chess player can decide I don't want to play chess anymore, from now on I will play Go or from now on I will play tennis. If commanded to play chess, the functionality will deteriorate completely. Those things are different. What we want in an AGI is that it behaves in a way that cannot be specified in advance, because if you specified it, you would already have the answer. The AGI program has to give unexpected answers, answers to questions we didn't even know how to ask."

Deutsch Explains

72,475 views • 1 year ago

.Naval: Epistemology, which is a fancy word for the theory of how knowledge grows or how knowledge growth occurs. And we've all been told since we're young that there's a scientific method and that scientists sort of do this stuff in white lab coats and we're supposed to accept it because of this thing called the scientific method. And then they give us true beliefs that we can then say, well the science is settled and we take that we move on. And we all only have a very, very vague understanding of how this works. And people say, well maybe you go out in the real world, you look at what's happening, you make all these observations, and then based on that you form a theory, you test the theory against more observations, and the more observations you get the closer you get to the truth. And once you have enough observation it's true and then you call it a scientific theory or a law and it's settled and you move on. And this is the popular conception of how science works. And as Popper pointed out and as you take even further, this is completely wrong. And so I'd love for you to get into that, which is what is knowledge? How does it grow? What is the real scientific method? And how do we figure things out? David Deutsch: I love the way you just stated the prevailing view there and laced every aspect of it with the contempt that it deserves. So you just went through touching every base. It's amazing that this series of misconceptions is still common sense. I mean, that it was common sense at a time when we didn't really have science or when science was just starting up, when the main issue in science was freeing itself from dogmatism, freeing itself from religion, freeing itself from authority, and so on. There it was understandable that people would look for an alternative source of authority and they would think, oh, it's sense impressions. We can see the world and you know, these religious people, they can't even see God and so on. And so we are confined to what we can see. That's where we get our ideas from. And as you say, that is completely false. Sense impressions, like all observation, even the most careful scientific observation is all theory laden. And theories are inherently fallible. I mean, we actually want to replace our best theories. Everybody who does a PhD is technically anyway, working to overturn something in the existing body of knowledge. You're not turned away at the door if you say, I don't believe this stuff, I'm going to produce something better. Whereas for most of human history, that was exactly what you were forbidden to do. The idea was that we already had all the important knowledge. If you want to discover something new, what you had to make sure of was that it didn't contradict the existing knowledge. Now, you have to make sure that it does contradict existing knowledge. So more or less. Naval: Yeah, it's this tradition of criticism that you've talked about in the West, that the Enlightenment really ushered in the Enlightenment era. David Deutsch: It has been institutionalized. So in many ways, our institutions are wiser than we are. So the institutions of science, for instance, have this built in, even if scientists actually don't always act that way. In fact, they often don't act that way, and act in a dogmatic way and try to preserve the status quo and are resistant to new ideas and so on. But the institutions, the way the procedures of science work, makes the right thing happen in the end anyway, regardless of what the people are trying to do. Naval: So you're saying the knowledge of the true scientific method is embedded in the institutions of science in the PhD process? David Deutsch: Well, the best scientific method that we know of, and one shouldn't really think of it as a method, you know, there's this wonderful lecture by Popper when he first was made a professor at the London School of Economics. He was made a professor of scientific method, and his first six lectures, I wish the rest of them were, the first six lectures are on the internet somewhere. And he starts the first one by saying, I am the first professor of scientific method in the British Empire. The British Empire still existed at the time, more or less. And so the first thing I want to say to you is that there is no such thing as the scientific method. And then he goes on from there. So this subject does not exist. So if any of you have come here to learn the handle that you have to turn in order to make scientific knowledge come out the other end, you're going to be disappointed.
4:50

.Naval: Epistemology, which is a fancy word for the theory of how knowledge grows or how knowledge growth occurs. And we've all been told since we're young that there's a scientific method and that scientists sort of do this stuff in white lab coats and we're supposed to accept it because of this thing called the scientific method. And then they give us true beliefs that we can then say, well the science is settled and we take that we move on. And we all only have a very, very vague understanding of how this works. And people say, well maybe you go out in the real world, you look at what's happening, you make all these observations, and then based on that you form a theory, you test the theory against more observations, and the more observations you get the closer you get to the truth. And once you have enough observation it's true and then you call it a scientific theory or a law and it's settled and you move on. And this is the popular conception of how science works. And as Popper pointed out and as you take even further, this is completely wrong. And so I'd love for you to get into that, which is what is knowledge? How does it grow? What is the real scientific method? And how do we figure things out? David Deutsch: I love the way you just stated the prevailing view there and laced every aspect of it with the contempt that it deserves. So you just went through touching every base. It's amazing that this series of misconceptions is still common sense. I mean, that it was common sense at a time when we didn't really have science or when science was just starting up, when the main issue in science was freeing itself from dogmatism, freeing itself from religion, freeing itself from authority, and so on. There it was understandable that people would look for an alternative source of authority and they would think, oh, it's sense impressions. We can see the world and you know, these religious people, they can't even see God and so on. And so we are confined to what we can see. That's where we get our ideas from. And as you say, that is completely false. Sense impressions, like all observation, even the most careful scientific observation is all theory laden. And theories are inherently fallible. I mean, we actually want to replace our best theories. Everybody who does a PhD is technically anyway, working to overturn something in the existing body of knowledge. You're not turned away at the door if you say, I don't believe this stuff, I'm going to produce something better. Whereas for most of human history, that was exactly what you were forbidden to do. The idea was that we already had all the important knowledge. If you want to discover something new, what you had to make sure of was that it didn't contradict the existing knowledge. Now, you have to make sure that it does contradict existing knowledge. So more or less. Naval: Yeah, it's this tradition of criticism that you've talked about in the West, that the Enlightenment really ushered in the Enlightenment era. David Deutsch: It has been institutionalized. So in many ways, our institutions are wiser than we are. So the institutions of science, for instance, have this built in, even if scientists actually don't always act that way. In fact, they often don't act that way, and act in a dogmatic way and try to preserve the status quo and are resistant to new ideas and so on. But the institutions, the way the procedures of science work, makes the right thing happen in the end anyway, regardless of what the people are trying to do. Naval: So you're saying the knowledge of the true scientific method is embedded in the institutions of science in the PhD process? David Deutsch: Well, the best scientific method that we know of, and one shouldn't really think of it as a method, you know, there's this wonderful lecture by Popper when he first was made a professor at the London School of Economics. He was made a professor of scientific method, and his first six lectures, I wish the rest of them were, the first six lectures are on the internet somewhere. And he starts the first one by saying, I am the first professor of scientific method in the British Empire. The British Empire still existed at the time, more or less. And so the first thing I want to say to you is that there is no such thing as the scientific method. And then he goes on from there. So this subject does not exist. So if any of you have come here to learn the handle that you have to turn in order to make scientific knowledge come out the other end, you're going to be disappointed.

Deutsch Explains

116,352 views • 1 year ago

Ilya Sutskever explains the 1% human training loop, and why fully autonomous AI is the wrong target "The thing you really want is for the human teachers that tell, that teach the AI, for them to collaborate with an AI." "You might want to think of it as being in a world where the human teachers do 1% of the world and the work and the AI do 99% of the work." "You don't want it to be 100% AI" "But you do want it to be a human-machine collaboration which teaches the next machine." Most teams are trying to remove the human from the loop. Ilya's design keeps a tiny human control surface and uses it to teach the next generation. The leverage is not zero humans. It is 1% human judgment steering 99% machine work. - Ilya Sutskever (Ilya Sutskever), AI researcher, on Dwarkesh Patel
0:32

Ilya Sutskever explains the 1% human training loop, and why fully autonomous AI is the wrong target "The thing you really want is for the human teachers that tell, that teach the AI, for them to collaborate with an AI." "You might want to think of it as being in a world where the human teachers do 1% of the world and the work and the AI do 99% of the work." "You don't want it to be 100% AI" "But you do want it to be a human-machine collaboration which teaches the next machine." Most teams are trying to remove the human from the loop. Ilya's design keeps a tiny human control surface and uses it to teach the next generation. The leverage is not zero humans. It is 1% human judgment steering 99% machine work. - Ilya Sutskever (Ilya Sutskever), AI researcher, on Dwarkesh Patel

Karl Mehta

30,651 views • 3 months ago

Why did so many languages copy async/await from C#? Anders Hejlsberg(Anders Hejlsberg) - creator of TypeScript, C# & Turbo Pascal - on what they got right with the design: #1 - async/await was designed to solve a common problem in the event-loop model: "A lot of languages are built around cooperative multitasking in the sense that they have an event loop that sits and dispatches events. Then you handle the event and then you yield back to the event handler loop. And it all runs in a single thread cooperatively. The problem with that is if you then want to do some long running work: how do I stop in the middle of this piece of long running work and yield back to the event loop cooperatively? And then when my result is ready, I can come back and continue executing here." #2 - state machines are the solution, but hard to build: "Well, in order to do that in an inverted architecture like that, you have to build a state machine. State machines are notoriously hard for people to implement because you've got to move all of your state off of the stack into objects. And then you have this big case statement that envelopes your entire logic. It's a nightmare to figure out. But, the transformation from serially executing code into a state machine, its continuation-passing-style translation is actually one that you can do in a machine-based fashion." #3 - compilers are good at writing state machines: "You can have the compiler write the state machine if you introduce syntax that allows you to indicate where you want to yield. And that's what await is. Await is basically saying, I want to yield here, and I want to yield this promise, and then when the promise completes, I want you to come back here and continue executing. Then the compiler writes a state machine around it and it actually turns it into this big switch statement and moves all of the state that survives across the await into something that's heap allocated. So it can be brought back. And doing all of that work is something that compilers are great at. And so that was sort of the idea that we have this new style of programming where we're using promises or the equivalent of promises and the ability to yield and then we have callbacks. But trying to write your program in that style, that's also what JavaScript suffered from a lot. It's like all this callback style stuff. With Async and Await, you get the illusion that you're just writing normal sequential code and then the compiler does the painful transformation for you. That turns out to be really useful."
3:13

Why did so many languages copy async/await from C#? Anders Hejlsberg(Anders Hejlsberg) - creator of TypeScript, C# & Turbo Pascal - on what they got right with the design: #1 - async/await was designed to solve a common problem in the event-loop model: "A lot of languages are built around cooperative multitasking in the sense that they have an event loop that sits and dispatches events. Then you handle the event and then you yield back to the event handler loop. And it all runs in a single thread cooperatively. The problem with that is if you then want to do some long running work: how do I stop in the middle of this piece of long running work and yield back to the event loop cooperatively? And then when my result is ready, I can come back and continue executing here." #2 - state machines are the solution, but hard to build: "Well, in order to do that in an inverted architecture like that, you have to build a state machine. State machines are notoriously hard for people to implement because you've got to move all of your state off of the stack into objects. And then you have this big case statement that envelopes your entire logic. It's a nightmare to figure out. But, the transformation from serially executing code into a state machine, its continuation-passing-style translation is actually one that you can do in a machine-based fashion." #3 - compilers are good at writing state machines: "You can have the compiler write the state machine if you introduce syntax that allows you to indicate where you want to yield. And that's what await is. Await is basically saying, I want to yield here, and I want to yield this promise, and then when the promise completes, I want you to come back here and continue executing. Then the compiler writes a state machine around it and it actually turns it into this big switch statement and moves all of the state that survives across the await into something that's heap allocated. So it can be brought back. And doing all of that work is something that compilers are great at. And so that was sort of the idea that we have this new style of programming where we're using promises or the equivalent of promises and the ability to yield and then we have callbacks. But trying to write your program in that style, that's also what JavaScript suffered from a lot. It's like all this callback style stuff. With Async and Await, you get the illusion that you're just writing normal sequential code and then the compiler does the painful transformation for you. That turns out to be really useful."

The Pragmatic Engineer

13,258 views • 4 months ago

"Visual-spatial intelligence is as fundamental as language." Fei-Fei Li says world models are the key to letting AI see the world, reason about it, interact with it, navigate it, and even 'build civilization upon it.' "It's very natural for me that World Labs' north star is to unlock spatial intelligence. The moment to me is right to do it." "We've got these ingredients—we've got compute, we've got a much deeper understanding of data, way deeper than [the ImageNet days]... and we've got some advancement of algorithms."
1:27

"Visual-spatial intelligence is as fundamental as language." Fei-Fei Li says world models are the key to letting AI see the world, reason about it, interact with it, navigate it, and even 'build civilization upon it.' "It's very natural for me that World Labs' north star is to unlock spatial intelligence. The moment to me is right to do it." "We've got these ingredients—we've got compute, we've got a much deeper understanding of data, way deeper than [the ImageNet days]... and we've got some advancement of algorithms."

a16z

32,387 views • 8 months ago

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

69,717 views • 5 months ago

President Trump: The United States totally rejects any attempt to construct a globalist scheme to control the artificial intelligence being spoken of so much now, here and after officially called super intelligence, changing the name in that the use of the word artificial makes intelligence fake. It makes it sound fake, and it is not fake. It's actually amazing, but we have to be careful.
0:55

President Trump: The United States totally rejects any attempt to construct a globalist scheme to control the artificial intelligence being spoken of so much now, here and after officially called super intelligence, changing the name in that the use of the word artificial makes intelligence fake. It makes it sound fake, and it is not fake. It's actually amazing, but we have to be careful.

U.S. Mission to the UN

33,550 views • 1 month ago

The intelligence we are building is not artificial. It never was. Microsoft Chief Scientific Officer Eric Horvitz just reframed the entire foundation of the AI arms race with one sentence. The tech industry calls it Artificial Intelligence. That word is wrong. Horvitz: “I don’t actually like the term artificial intelligence. I wish the field was called computational intelligence because I think it applies to biological nervous systems as well as machines, and together we can go far.” We are not building a digital imitation of the human brain. We are scaling the exact same computational physics that created biological awareness and transferring it into silicon. Your mind and a massive AI data center run on the same underlying rules. The transition isn’t artificial. It is universal. And here is where it gets deeply unsettling. Tech optimists always fall back on the same comfort. Humans hold the steering wheel. Our values guide the machine. Horvitz acknowledges this. Horvitz: “We’ll take a humanistic standpoint here, always being on top of things and guiding with our values and our preferences and our goals.” Then the caveat that changes everything. Horvitz: “As much as they might be shaped over time by the machines we work with.” You cannot interact with a superintelligence at scale without it quietly rewiring your psychological baseline. The values you use to command the machine will be shaped by the machine you are commanding. The frameworks you use to perceive reality will be constructed by the system you believe you are directing. That feedback loop started the moment you asked an AI what to think about something. Most people haven’t noticed yet. Horvitz: “I think in our own lifetimes we will all experience incredible breakthroughs in understanding biology, with applications in medicine, in healthcare, that will be named as AI breakthroughs.” Horvitz: “It’s gonna accelerate over the next 10 to 15 years.” Because biological systems and machine networks both operate on computational intelligence, a sufficiently advanced AI can solve the human body like a math equation. The architects who win the next decade will not just control the digital economy. They will control the physical building blocks of life itself. The line between silicon and carbon was always an illusion. And once humanity fully realizes that, the question of whether we are using the intelligence or it is using us becomes impossible to answer. Because by then, we will be the same thing.
1:05

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

Dustin

164,598 views • 8 months ago