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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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a16z

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41,131 次观看 • 7 天前 •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 次观看 • 5 天前

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 次观看 • 3 个月前

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

77,632 次观看 • 10 天前

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

32,290 次观看 • 6 个月前

🚨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 🏝️🇺🇸

196,736 次观看 • 5 个月前

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

21,397 次观看 • 4 个月前

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

193,987 次观看 • 7 天前

.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 次观看 • 1 年前

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 次观看 • 1 个月前

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 次观看 • 2 个月前

"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 次观看 • 6 个月前

Justin Thomas was asked about his group getting put on the clock: "We just didn't really agree with it. ... The hard part to me with the whole pace of play thing is that you, there's so much that goes into golf and there's so much that goes into hole to hole in terms of, are you hitting it close, are you able to tap it in, or you have to mark it, stuff like that, to where, are you holding the group up or are you not, to where it's very hard to make that call. And we just didn't agree with it, to be honest. "But we got taken off, and a hole later we were caught up. So it kind of goes to our point of why we didn't think we should of, but it is what it is. It's a part of it."
1:15

Justin Thomas was asked about his group getting put on the clock: "We just didn't really agree with it. ... The hard part to me with the whole pace of play thing is that you, there's so much that goes into golf and there's so much that goes into hole to hole in terms of, are you hitting it close, are you able to tap it in, or you have to mark it, stuff like that, to where, are you holding the group up or are you not, to where it's very hard to make that call. And we just didn't agree with it, to be honest. "But we got taken off, and a hole later we were caught up. So it kind of goes to our point of why we didn't think we should of, but it is what it is. It's a part of it."

Cameron Jourdan

619,272 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

69,536 次观看 • 3 个月前

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

164,255 次观看 • 6 个月前

Ken Liu Ken Liu on the question we can't avoid anymore: what is consciousness? why certain plants evolved to change our minds, why intelligence ≠ consciousness (AI made this impossible to ignore), and the ethical moment we're racing toward. _-_-_-_-_-_-_-_-_-_-_-_-_-_- "historically in sci-fi, we've imagined that intelligence and consciousness are almost equivalent in the sense that if you have intelligence, you automatically have consciousness. But AI is making us more and more aware of this distinction, right?"So here's an example where consciousness intelligence are decoupled, and it's a very strange feeling to realize that they are fundamentally different things. "In the moment when we have created artificial consciousness, how will we know when that has happened? Because to me, that is an important ethical moment where we have to decide what we can and can't do. Shutting down a model that merely has intelligence is one thing. Shutting down a model that has consciousness is something quite different. "
4:30

Ken Liu Ken Liu on the question we can't avoid anymore: what is consciousness? why certain plants evolved to change our minds, why intelligence ≠ consciousness (AI made this impossible to ignore), and the ethical moment we're racing toward. _-_-_-_-_-_-_-_-_-_-_-_-_-_- "historically in sci-fi, we've imagined that intelligence and consciousness are almost equivalent in the sense that if you have intelligence, you automatically have consciousness. But AI is making us more and more aware of this distinction, right?"So here's an example where consciousness intelligence are decoupled, and it's a very strange feeling to realize that they are fundamentally different things. "In the moment when we have created artificial consciousness, how will we know when that has happened? Because to me, that is an important ethical moment where we have to decide what we can and can't do. Shutting down a model that merely has intelligence is one thing. Shutting down a model that has consciousness is something quite different. "

Sabrina Halper

14,785 次观看 • 7 个月前

Martin Casado on the AI product evolution before Grok Bot really got it right: "We've, as an industry, gone through multiple realizations for how AI enters our lives. Very early on, you add AI to a product and it's like a search bar. And then you do chat with it, and it chats back." "Then OpenClaw showed up... it'll be an extension of you, and it'll share your keys, and it'll know your passwords, and it'll just kind of do stuff that you would do." "What Grok Bot got really right is, how about it's just actually an employee?... It has its own computer, and it has its own browser, and because these are the smartest models in the world, it can do whatever an employee can do." "Now, actually, if I want something done, my first thing I think is like, 'Well, can Grok Bot do it for me?' And often the answer is yes, even if it's something you wouldn't expect." "For example, I'll have it read through my email and do triage. I don't tell it how to do that, but it will know to check with me before actually doing the triage. These things are sophisticated enough that you can give it relatively high-level tasks, and it'll do sophisticated things as a result." martin_casado
1:37

Martin Casado on the AI product evolution before Grok Bot really got it right: "We've, as an industry, gone through multiple realizations for how AI enters our lives. Very early on, you add AI to a product and it's like a search bar. And then you do chat with it, and it chats back." "Then OpenClaw showed up... it'll be an extension of you, and it'll share your keys, and it'll know your passwords, and it'll just kind of do stuff that you would do." "What Grok Bot got really right is, how about it's just actually an employee?... It has its own computer, and it has its own browser, and because these are the smartest models in the world, it can do whatever an employee can do." "Now, actually, if I want something done, my first thing I think is like, 'Well, can Grok Bot do it for me?' And often the answer is yes, even if it's something you wouldn't expect." "For example, I'll have it read through my email and do triage. I don't tell it how to do that, but it will know to check with me before actually doing the triage. These things are sophisticated enough that you can give it relatively high-level tasks, and it'll do sophisticated things as a result." martin_casado

a16z

35,888 次观看 • 7 天前

Cursor CEO Michael Truell: Programming in the future might not mean programming with an AI helper, but in a new language entirely. "There is a future version of the world where the way you interact with AI is a little bit less like it's a human helper that you're delegating work to... and instead, it's a little bit more of an advance in compiler or interpreter technology." "It can lead you to a world where programming languages actually change. They can start to be a little bit more about what you want and a little bit less about how you do it." "There is a world where direct manipulation of the UI starts to play a little bit more into it." Michael Truell with Patrick Collison
1:25

Cursor CEO Michael Truell: Programming in the future might not mean programming with an AI helper, but in a new language entirely. "There is a future version of the world where the way you interact with AI is a little bit less like it's a human helper that you're delegating work to... and instead, it's a little bit more of an advance in compiler or interpreter technology." "It can lead you to a world where programming languages actually change. They can start to be a little bit more about what you want and a little bit less about how you do it." "There is a world where direct manipulation of the UI starts to play a little bit more into it." Michael Truell with Patrick Collison

a16z

92,602 次观看 • 6 个月前

BREAKING: Remote Viewer Just Saw The Future of AI, and it's worrying... Remote Viewer, Edward Riordan, looked at the future of what Joe Rogan would talk about in terms of world events, and what he got back wasn't Joe's podcast... He saw a reality where humans become more and more obsolete to AI: "...they handed over the quote unquote keys to AI... There is... less need for humans is what I was feeling here... The intelligence knows more than you. What can you do... It can do better, faster, cheaper, easier... Billions or trillions of data inputs. Continuous input in real time. You can't keep up." "...there's debate. Debate about it, debate about whether or not this is good. So some are saying yes, some say no. It's underground, the full extent of it, the technological take over a long time in the making. And this was a stage seven...." "...machine learning. But why, I wonder? You don't need humans anymore. The machine AI does. It cheaper, faster and more efficient and most people won't know the difference. This was one of the most important things in this session right here. But you can see the artificial. This was my statement here. It's artificial...." "...people are being conditioned to accept artificial everything to not recognize or consider that what is being presented to them is artificially generated. That's how you that's how you that's how you, douse the spark... It's diabolical..." "...my basic data on this movement order was hive mind. Maybe a event in the sky that every everybody goes. Oh, and it brings the whole world into one mindset."
4:26

BREAKING: Remote Viewer Just Saw The Future of AI, and it's worrying... Remote Viewer, Edward Riordan, looked at the future of what Joe Rogan would talk about in terms of world events, and what he got back wasn't Joe's podcast... He saw a reality where humans become more and more obsolete to AI: "...they handed over the quote unquote keys to AI... There is... less need for humans is what I was feeling here... The intelligence knows more than you. What can you do... It can do better, faster, cheaper, easier... Billions or trillions of data inputs. Continuous input in real time. You can't keep up." "...there's debate. Debate about it, debate about whether or not this is good. So some are saying yes, some say no. It's underground, the full extent of it, the technological take over a long time in the making. And this was a stage seven...." "...machine learning. But why, I wonder? You don't need humans anymore. The machine AI does. It cheaper, faster and more efficient and most people won't know the difference. This was one of the most important things in this session right here. But you can see the artificial. This was my statement here. It's artificial...." "...people are being conditioned to accept artificial everything to not recognize or consider that what is being presented to them is artificially generated. That's how you that's how you that's how you, douse the spark... It's diabolical..." "...my basic data on this movement order was hive mind. Maybe a event in the sky that every everybody goes. Oh, and it brings the whole world into one mindset."

Future Forecasting Group

22,032 次观看 • 5 个月前

LeBron James explains what it takes to make a relationship last “It's not a bed of roses. If you're thinking that the roses don't come with thorns, then you're kidding yourself” “You've got to take the good with the bad, the great with the ugly. It's all part of a relationship and if you want to build with somebody, then you have to be able to do that” “I understand that we all want to be in relationships when we're laughing and we're joking and we're smiling and everybody's happy and everything is easy” “But can you stand the rain? Because it's not going to be a bed of roses. If you can accept that, then you can get through it”
0:35

LeBron James explains what it takes to make a relationship last “It's not a bed of roses. If you're thinking that the roses don't come with thorns, then you're kidding yourself” “You've got to take the good with the bad, the great with the ugly. It's all part of a relationship and if you want to build with somebody, then you have to be able to do that” “I understand that we all want to be in relationships when we're laughing and we're joking and we're smiling and everybody's happy and everything is easy” “But can you stand the rain? Because it's not going to be a bed of roses. If you can accept that, then you can get through it”

Jack

63,547 次观看 • 2 个月前