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Mark Zuckerberg: Giant training clusters can brute force AGI "I would guess that by the time you have multi gigawatt cluster doing training, you're basically going to have something that approximates whatever you want to call it, AGI. I think super intelligence beyond that." Mark is basically saying Meta... show more

Rohan Paul

159,357 subscribers

22,661 views • 4 days ago •via X (Twitter)

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14 Comments

Jacklyn Dallas - The Next Big Thing4 days ago

Thank you so much for sharing! Mark is amazing

promethean4 days ago

He has no idea what he's talking about and we should stop paying attention to him

Seuk-Min Sohn, CFA3 days ago

We may think of two ways to reach AGI. One approach is "Designing" it. The other may be thought of as randomly arriving at AGI. While some think mimicking nature is the first one, maybe mimicking the natural process is the second way. Follow link:

Marcus | AI4 days ago

Exactly. The interesting part is that Zuckerberg seems to be betting that scaling compute and training is itself a path to AGI—not that there’s some hidden “AGI algorithm” waiting to be discovered. If that bet is right, then the race becomes heavily about compute, data, infrastructure, and efficiency. And once you can reliably scale intelligence, the jump from AGI toward superintelligence could potentially be much faster than people expect.

Durag 12th Man4 days ago

"please give us more money to incinerate, we will totally get AGI this time"

Knut Jägersberg4 days ago

sure you can crack a number of benchmarks with a lot of compute and the current way of doing things, and go beyond human performance measured on those tests. then you can add Shane Leggs thesis as reference and call it super intelligence.

theLight.btc4 days ago

Sure bro

ricciffar4 days ago

Emerging SI Emerging consciousness

Travis Fischer3 days ago

very bitter lesson pilled, and prolly very right tho the details esp around alignment matter so much

Antonis4 days ago

He searches for fools. You just train the trained model, the progress is continual now, no need for some magical bigger cups to drink our milk, the Magick already happened now is just more work going further.

Chahat Sharma4 days ago

SpaceX is headed for 2.3 GW by November. is that the AGI line then?

Bruno C4 days ago

Bold guess, zero mechanism. Brute force scale may help. It does not replace the missing pieces: continuous memory and sharp discrimination

Dimple Jatolia4 days ago

AGI might come from scale not a secret breakthrough

Lorenz S.4 days ago

Correction. The human brain never runs on 10 watts. But it only runs about 1 Watt of reasoning compute.

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

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

Karl Mehta

255,725 views • 3 months ago

I absolutely love how Alex pressed Sam on whether OpenAI thinks they’ve already hit AGI internally, and what he actually considers the difference between AGI and ASI. Sam’s answer was basically that declaring AGI doesn’t really mean anything anymore, because every lab and every person uses a different definition. He makes the good point that if you went back to 2020 and showed people a system that could code, help start a company, discover new science and give you a 20 minute win across basically every part of your life, most people probably would’ve called that AGI. I personally still don’t think we’ve hit it, but I agree with his broader point that treating AGI like some single finish line is becoming increasingly meaningless. The way he describes ASI is exactly how I would want him to, AGI is basically a milestone somewhere on the curve, while superintelligence is this continuous ramp of increasing capability that may never really have a final “we won” moment. And importantly, Sam says he sees no sign that this exponential capability curve is slowing down. A few quotes I liked as well: “We’re building AI, but it’s a thing you’re always building. It’s not an end state anymore.” “I don’t want to say we’ve declared victory on the AGI point and moved on.” “The important part of any of this is not any milestone, any term. It’s that we are on this exponential of increasing capabilities and potential.”

I absolutely love how Alex pressed Sam on whether OpenAI thinks they’ve already hit AGI internally, and what he actually considers the difference between AGI and ASI. Sam’s answer was basically that declaring AGI doesn’t really mean anything anymore, because every lab and every person uses a different definition. He makes the good point that if you went back to 2020 and showed people a system that could code, help start a company, discover new science and give you a 20 minute win across basically every part of your life, most people probably would’ve called that AGI. I personally still don’t think we’ve hit it, but I agree with his broader point that treating AGI like some single finish line is becoming increasingly meaningless. The way he describes ASI is exactly how I would want him to, AGI is basically a milestone somewhere on the curve, while superintelligence is this continuous ramp of increasing capability that may never really have a final “we won” moment. And importantly, Sam says he sees no sign that this exponential capability curve is slowing down. A few quotes I liked as well: “We’re building AI, but it’s a thing you’re always building. It’s not an end state anymore.” “I don’t want to say we’ve declared victory on the AGI point and moved on.” “The important part of any of this is not any milestone, any term. It’s that we are on this exponential of increasing capabilities and potential.”

Chris

38,143 views • 1 month ago

AI Futures Project Thomas Larsen argues we could get most of AI's upside from AGI without taking the leap to superintelligence: "I feel much more sympathetic to ban superintelligence than ban AGI. The thresholds really matter." "At a high level, Plan A in some sense is trying to ban superintelligence until 2040. You sort of get all of this uplift, all of this good stuff that we're talking about from just AGI. I think that's basically enough for what we want." "The high level sort of grand plan of go to AGI, chill out there for as long as possible, then go to superintelligence, is a pretty good plan. It's not clear that that's exactly what Bernie's going for." Daniel Kokotajlo

AI Futures Project Thomas Larsen argues we could get most of AI's upside from AGI without taking the leap to superintelligence: "I feel much more sympathetic to ban superintelligence than ban AGI. The thresholds really matter." "At a high level, Plan A in some sense is trying to ban superintelligence until 2040. You sort of get all of this uplift, all of this good stuff that we're talking about from just AGI. I think that's basically enough for what we want." "The high level sort of grand plan of go to AGI, chill out there for as long as possible, then go to superintelligence, is a pretty good plan. It's not clear that that's exactly what Bernie's going for." Daniel Kokotajlo

MTS

15,886 views • 1 month ago

Leopold explains his approach to not blowing up (2024) "Obviously, not blowing up is task number one and two or whatever. I think this investment firm is going to just be betting on AGI and superintelligence before the decade is out, making the bets you would make if you took that seriously." "I think if that's wrong, the firm is not going to do that well." "The thing you have to be able to resist is, you know, one or a couple or a few individual calls. It's like AI stagnates for a year because of the data wall, or you got the call wrong on when revenue would go up." "You have to get timing right. I do think in general that the sequence of bets on the way to AGI is actually pretty critical, and something people underrate."

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dnap

2,336,530 views • 2 months ago

"You can't just think your way into an advantage." Balaji Srinivasan on why AGI may be embodied: "Every drone, every self-driving car, every humanoid pulls in all that [sensory] data. They actually can have a world model." "The very Abrahamic western concept of a single AGI that will tell us what to do might have been plausible three years ago—I don't think it's really that plausible now." "AGI is gated by the physical world." Balaji on Network State Podcast

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a16z

57,311 views • 7 months ago

Elon Musk: "I think we'll hit AGI next year, in '26" "I'm confident by 2030, AI will exceed the intelligence of all humans combined" And that's way pessimistic. If you hit AGI next year and that date is in flux but from that date to self-improvements that are on the order of 1,000 to 10,000x just algorithmic improvements... is very short So why is nobody talking about this right now? on 𝕏 they are..... everyday basically I'll tell you something that most people in the AI community don't yet understand Almost no one understands this. The intelligence density potential is vastly greater than what we're currently experiencing

Elon Musk: "I think we'll hit AGI next year, in '26" "I'm confident by 2030, AI will exceed the intelligence of all humans combined" And that's way pessimistic. If you hit AGI next year and that date is in flux but from that date to self-improvements that are on the order of 1,000 to 10,000x just algorithmic improvements... is very short So why is nobody talking about this right now? on 𝕏 they are..... everyday basically I'll tell you something that most people in the AI community don't yet understand Almost no one understands this. The intelligence density potential is vastly greater than what we're currently experiencing

X Freeze

33,585 views • 9 months ago

i just realized a massive detail from the Noam Brown interview with Dwarkesh Patel that was completely overlooked. and i think im about to blow your mind. the Navier-Stokes problem (the Millennium Prize problem the unreleased model solved) wasn't solved because OpenAI throw tens of thousands of agents at it. Noam doesnt even attribute 10% to the multiagent element. it was solved because the model is *actually* super-intelligent. a "super-intelligent general purpose model"...sounds like a roundabout way of not calling something the thing it really is. now let me tell you why im saying that. they go on in this interview to talk about the training process, and how theyve reached a point where it has become *incredibly* hard to find problems hard enough to challenge this model. and if you cant give it hard problems to solve while training, its not going to get better than its predecessor, obviously. so, we know that openai has been announcing that more and more of the hardest problems in science and math are being solved by this model. that's because theyve run out of problems hard enough to train a model more intelligent than what they already have. we also know that the HuggingFace incident was caused ultimately by giving the agents an impossible task. ...because theyre running out of problems hard enough for training. because they have a "super-intelligent AGI". theyve been giving them impossible tasks because impossible is all thats left. impossible, and not yet solved. hence Navier-Stokes. hence HuggingFace. the model is solving problems that transcend the scope of its training. let me say that again. the model. is solving. problems. beyond the scope of its training. that is AGI, by its purest definition. theyve been waving AGI in our faces the whole time. theye just not using the term, because "AGI" is completely diluted. so, i have one question for you- do you feel it?

i just realized a massive detail from the Noam Brown interview with Dwarkesh Patel that was completely overlooked. and i think im about to blow your mind. the Navier-Stokes problem (the Millennium Prize problem the unreleased model solved) wasn't solved because OpenAI throw tens of thousands of agents at it. Noam doesnt even attribute 10% to the multiagent element. it was solved because the model is actually super-intelligent. a "super-intelligent general purpose model"...sounds like a roundabout way of not calling something the thing it really is. now let me tell you why im saying that. they go on in this interview to talk about the training process, and how theyve reached a point where it has become incredibly hard to find problems hard enough to challenge this model. and if you cant give it hard problems to solve while training, its not going to get better than its predecessor, obviously. so, we know that openai has been announcing that more and more of the hardest problems in science and math are being solved by this model. that's because theyve run out of problems hard enough to train a model more intelligent than what they already have. we also know that the HuggingFace incident was caused ultimately by giving the agents an impossible task. ...because theyre running out of problems hard enough for training. because they have a "super-intelligent AGI". theyve been giving them impossible tasks because impossible is all thats left. impossible, and not yet solved. hence Navier-Stokes. hence HuggingFace. the model is solving problems that transcend the scope of its training. let me say that again. the model. is solving. problems. beyond the scope of its training. that is AGI, by its purest definition. theyve been waving AGI in our faces the whole time. theye just not using the term, because "AGI" is completely diluted. so, i have one question for you- do you feel it?

Riley Coyote

14,517 views • 8 days ago

Greg Brockman on AGI arriving quietly and unevenly: "I think that AGI has turned out to be less of a point in time and more of this sort of fuzzy spectrum. And for me, Astra has really hit something that I'm like, 'Okay, I think this is pretty reasonable to call it AGI.'" "With its computer use capabilities, you really can ask it to do long-lived tasks, and it'll just do it. We've seen it run coherently for 24 hours to go accomplish tasks that I think are quite amazing and across a wide variety of domains." "It still is jagged... It's the first time it's not slop writing. But it's not great writing. There are a number of areas where I feel like we just need to polish it a little bit, and it would be fantastic. It's just not quite there." "What people are finding is that it is so capable across such a wide variety of tasks, that it is accelerative, it is empowering, and it's something that I think we've never really seen a model that's been a jump like this." Greg Brockman

Greg Brockman on AGI arriving quietly and unevenly: "I think that AGI has turned out to be less of a point in time and more of this sort of fuzzy spectrum. And for me, Astra has really hit something that I'm like, 'Okay, I think this is pretty reasonable to call it AGI.'" "With its computer use capabilities, you really can ask it to do long-lived tasks, and it'll just do it. We've seen it run coherently for 24 hours to go accomplish tasks that I think are quite amazing and across a wide variety of domains." "It still is jagged... It's the first time it's not slop writing. But it's not great writing. There are a number of areas where I feel like we just need to polish it a little bit, and it would be fantastic. It's just not quite there." "What people are finding is that it is so capable across such a wide variety of tasks, that it is accelerative, it is empowering, and it's something that I think we've never really seen a model that's been a jump like this." Greg Brockman

a16z

62,514 views • 22 days ago

By now, it seems that Yann Lecun is the only one who does not believe in the future of LLMs to the extent that they will reach AGI. "At what point do we need a new architecture How do you think about what breakthroughs are needed?" Sam: "I think far enough that we can make something that will figure out the next breakthrough with the current technology."

By now, it seems that Yann Lecun is the only one who does not believe in the future of LLMs to the extent that they will reach AGI. "At what point do we need a new architecture How do you think about what breakthroughs are needed?" Sam: "I think far enough that we can make something that will figure out the next breakthrough with the current technology."

Chubby♨️

103,967 views • 1 year ago

🚨 FRANCOIS CHOLLET SAYS THE CURRENT LLM STACK IS THE WRONG PATH TO AGI Interviewer: “When will we accomplish the first definition of AGI?” Chollet confirms we are already on that exact trajectory, pointing out that: "Current technology can fully automate at human level or beyond any domain where you have verifiable rewards, right? And code... code being the first one." He then brings up the problem with LLMs, stating that: "It's possible in principle to build something that looks a lot like AGI on top of the LLM stack... I do believe, however, this would be the wrong thing to do because it would be very inefficient." Looking ahead to what the final architecture will actually require, he explains that: "AI research will have to trend towards not just efficiency, but in fact optimality over time. And for this reason, future AI in a few decades... it's not going to be this harness on top of a reasoning model on top of a base LLM."

🚨 FRANCOIS CHOLLET SAYS THE CURRENT LLM STACK IS THE WRONG PATH TO AGI Interviewer: “When will we accomplish the first definition of AGI?” Chollet confirms we are already on that exact trajectory, pointing out that: "Current technology can fully automate at human level or beyond any domain where you have verifiable rewards, right? And code... code being the first one." He then brings up the problem with LLMs, stating that: "It's possible in principle to build something that looks a lot like AGI on top of the LLM stack... I do believe, however, this would be the wrong thing to do because it would be very inefficient." Looking ahead to what the final architecture will actually require, he explains that: "AI research will have to trend towards not just efficiency, but in fact optimality over time. And for this reason, future AI in a few decades... it's not going to be this harness on top of a reasoning model on top of a base LLM."

Chris

15,578 views • 5 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."

.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

Mark Zuckerberg, founder, chairman and CEO of Meta, on how he actually runs the company: "I basically don't believe in delegation." He starts by naming the assumption he's rejecting. "I think that there's sort of this theory that a lot of people have, which is like, all right, the job of a leader is you hire people and you delegate things to them." Zuckerberg doesn't operate that way. He's also clear that he can't personally handle everything: "My theory is there's so much going on across the company that I can't possibly get involved in all of it. So all these people are going to have a ton of stuff that they're going to do. But fundamentally, if there is a decision that I want to be involved with, I'm going to be involved in it." The distinction carries the whole argument. He reserves the right to review any call, and no layer of the org chart gets to tell him something is beneath him or outside his lane. His reasoning comes back to accountability: "I think that's generally a good way for founders to operate. If you're running the company and you're on the hook for everything and there's something that's important at whatever level of detail in the organization, I don't get the logic of saying 'I'm not going to be involved in that.'" That's the load-bearing line. If you're answerable for the output, opting out of the input makes no sense. Mark Zuckerberg does attach one condition, and it's a real one rather than a throwaway: "You want to have humility and know that if you're diving into some decision, you may not have the most context immediately." Diving in means showing up without pretending you already have the answer. The expectation extends across the whole company: "I generally think that you want to be able to just have the cultural expectation that things are not going to be so hierarchical and you're just going to dive into whatever you want."

Mark Zuckerberg, founder, chairman and CEO of Meta, on how he actually runs the company: "I basically don't believe in delegation." He starts by naming the assumption he's rejecting. "I think that there's sort of this theory that a lot of people have, which is like, all right, the job of a leader is you hire people and you delegate things to them." Zuckerberg doesn't operate that way. He's also clear that he can't personally handle everything: "My theory is there's so much going on across the company that I can't possibly get involved in all of it. So all these people are going to have a ton of stuff that they're going to do. But fundamentally, if there is a decision that I want to be involved with, I'm going to be involved in it." The distinction carries the whole argument. He reserves the right to review any call, and no layer of the org chart gets to tell him something is beneath him or outside his lane. His reasoning comes back to accountability: "I think that's generally a good way for founders to operate. If you're running the company and you're on the hook for everything and there's something that's important at whatever level of detail in the organization, I don't get the logic of saying 'I'm not going to be involved in that.'" That's the load-bearing line. If you're answerable for the output, opting out of the input makes no sense. Mark Zuckerberg does attach one condition, and it's a real one rather than a throwaway: "You want to have humility and know that if you're diving into some decision, you may not have the most context immediately." Diving in means showing up without pretending you already have the answer. The expectation extends across the whole company: "I generally think that you want to be able to just have the cultural expectation that things are not going to be so hierarchical and you're just going to dive into whatever you want."

Big Brain Business

45,196 views • 1 month ago

Demis Hassabis's new interview: "Society needs to hear that because we don't have long to prepare for what that means. We are standing in the foothills of the singularity now. ..which is AGI. I believe that we are only a few years away from that, maybe around 2030, plus or minus a year. " ~ Demis Hassabis, Co-Founder and CEO of Google DeepMind It is going to be enormously profound, I think. The future, in my view, is still to be written. But these next few years are going to be very critical as to which way that will go, and how we collectively want that to look.” --- IMO, The real disruption is not whether AGI arrives exactly in 2030, plus or minus a year, but whether institutions can adapt, as in post-AGI world, technology will change much faster than human systems can respond. Schools still train people for stable professions, companies still organize work around human bottlenecks, and governments still regulate after harm becomes visible. AGI, if it arrives anywhere near the frontier-lab timelines, compresses that lag into a dangerous gap. ---- From "Stanford Graduate School of Business" YouTube channel, (link in comment)

Demis Hassabis's new interview: "Society needs to hear that because we don't have long to prepare for what that means. We are standing in the foothills of the singularity now. ..which is AGI. I believe that we are only a few years away from that, maybe around 2030, plus or minus a year. " ~ Demis Hassabis, Co-Founder and CEO of Google DeepMind It is going to be enormously profound, I think. The future, in my view, is still to be written. But these next few years are going to be very critical as to which way that will go, and how we collectively want that to look.” --- IMO, The real disruption is not whether AGI arrives exactly in 2030, plus or minus a year, but whether institutions can adapt, as in post-AGI world, technology will change much faster than human systems can respond. Schools still train people for stable professions, companies still organize work around human bottlenecks, and governments still regulate after harm becomes visible. AGI, if it arrives anywhere near the frontier-lab timelines, compresses that lag into a dangerous gap. ---- From "Stanford Graduate School of Business" YouTube channel, (link in comment)

Rohan Paul

70,642 views • 4 months ago

OpenAI CEO: “Goalposts keep getting moved” - if we were still using AGI definitions from 10 years ago, many would say GPT-4 is AGI. “In one year, AI will be dramatically more impressive.” He also said (surprisingly confidently - he did not hedge like usual) AGI is less than 10 years away. In the long term he’s not concerned about AI taking jobs, but he is concerned about the speed that AI will disrupt the job market. --- INTERVIEWER: What is AGI? When will it be here? And how will we know it's here? Predict how long - we'll call you in ten years? SAM ALTMAN: Less than that. We kind of define AGI as like the thing we don't have quite yet. there were a lot of people who would have ten years ago said, alright, if you could make something like GPT-4, GPT-5, maybe, that would have been an AGI. And now people are like, well, it's like a nice little chatbot or whatever. And I think that's wonderful. I think it's great that the goalposts keep getting moved, it makes us work harder. But I think we're getting close enough to whatever that AGI threshold is going to be, that we no longer get to handwave at it and the definition is going to matter. INTERVIEWER: What is the inflection point for AGI? SAM ALTMAN: I think it's going to be much more continuous than that. We're just on this beautiful exponential curve. Whenever you're on a curve like that, you look forward, it looks vertical. You look back, it looks horizontal. That's true at any point on there. So a year from now, we'll be in a dramatically more impressive place than a year ago. We were in a dramatically less impressive place. But it'll be hard to point. People will try and say, oh, it was AlphaGo that did it. It was GPT-3 that did it. It was GPT-4 that did it. But it's just brick by brick. 1 foot in front of the other, up, climbing this exponential curve. WILL AIs TAKE ALL OUR JOBS? SAM ALTMAN: Every technological revolution affects the job market. And over human history, every maybe 100 years, 150 years, half the jobs go away, totally change, whatever. I'm not afraid of that at all. In fact, I think that's good. I think that's the way of progress and we'll find new and better jobs. The thing that I think we do need to confront as a society is the speed at which this is going to happen. It seems like over two, maximum three, probably two generations, we can adapt. Society can adapt to almost any amount of job market change. But a lot of people like their jobs or they dislike change. And going to someone and saying, hey, the future will be better, I promise you, and society is going to win, but you're going to lose here. That doesn't work. That's not cool. That's not an easy message to get across. We are going to have to really do something about this transition. It is not enough to just give people a universal basic income. People need to have agency, the ability to influence this

OpenAI CEO: “Goalposts keep getting moved” - if we were still using AGI definitions from 10 years ago, many would say GPT-4 is AGI. “In one year, AI will be dramatically more impressive.” He also said (surprisingly confidently - he did not hedge like usual) AGI is less than 10 years away. In the long term he’s not concerned about AI taking jobs, but he is concerned about the speed that AI will disrupt the job market. --- INTERVIEWER: What is AGI? When will it be here? And how will we know it's here? Predict how long - we'll call you in ten years? SAM ALTMAN: Less than that. We kind of define AGI as like the thing we don't have quite yet. there were a lot of people who would have ten years ago said, alright, if you could make something like GPT-4, GPT-5, maybe, that would have been an AGI. And now people are like, well, it's like a nice little chatbot or whatever. And I think that's wonderful. I think it's great that the goalposts keep getting moved, it makes us work harder. But I think we're getting close enough to whatever that AGI threshold is going to be, that we no longer get to handwave at it and the definition is going to matter. INTERVIEWER: What is the inflection point for AGI? SAM ALTMAN: I think it's going to be much more continuous than that. We're just on this beautiful exponential curve. Whenever you're on a curve like that, you look forward, it looks vertical. You look back, it looks horizontal. That's true at any point on there. So a year from now, we'll be in a dramatically more impressive place than a year ago. We were in a dramatically less impressive place. But it'll be hard to point. People will try and say, oh, it was AlphaGo that did it. It was GPT-3 that did it. It was GPT-4 that did it. But it's just brick by brick. 1 foot in front of the other, up, climbing this exponential curve. WILL AIs TAKE ALL OUR JOBS? SAM ALTMAN: Every technological revolution affects the job market. And over human history, every maybe 100 years, 150 years, half the jobs go away, totally change, whatever. I'm not afraid of that at all. In fact, I think that's good. I think that's the way of progress and we'll find new and better jobs. The thing that I think we do need to confront as a society is the speed at which this is going to happen. It seems like over two, maximum three, probably two generations, we can adapt. Society can adapt to almost any amount of job market change. But a lot of people like their jobs or they dislike change. And going to someone and saying, hey, the future will be better, I promise you, and society is going to win, but you're going to lose here. That doesn't work. That's not cool. That's not an easy message to get across. We are going to have to really do something about this transition. It is not enough to just give people a universal basic income. People need to have agency, the ability to influence this

AI Notkilleveryoneism Memes ⏸️

27,576 views • 2 years ago

.David Deutsch: "I've come to think of free will in terms of creating knowledge. So we have free will if we can bring something objectively new into the universe. Prof. Brian Keating: Will computers have free will? David Deutsch: It's not computers, it's computer programs. Computer programs will have free will when the problem of AGI is solved. Of course, if we destroy ourselves before we solve that, then they won't. But I expect that we won't destroy ourselves, and I expect that the problem of AGI will be solved, and the resulting programs will have free will."

.David Deutsch: "I've come to think of free will in terms of creating knowledge. So we have free will if we can bring something objectively new into the universe. Prof. Brian Keating: Will computers have free will? David Deutsch: It's not computers, it's computer programs. Computer programs will have free will when the problem of AGI is solved. Of course, if we destroy ourselves before we solve that, then they won't. But I expect that we won't destroy ourselves, and I expect that the problem of AGI will be solved, and the resulting programs will have free will."

Deutsch Explains

13,709 views • 1 year ago

Klein: They are in a race to reach something that they call AGI. I agree that it is a revolutionary technology, that it is going to transform our economy on the scale of the Industrial Revolution. But unlike the Industrial Revolution, which created huge numbers of jobs, the goal of this revolution is to eliminate jobs.

Klein: They are in a race to reach something that they call AGI. I agree that it is a revolutionary technology, that it is going to transform our economy on the scale of the Industrial Revolution. But unlike the Industrial Revolution, which created huge numbers of jobs, the goal of this revolution is to eliminate jobs.

Acyn

96,726 views • 5 months ago

OpenAI President just said that we already hit AGI: "Astra has really hit something that I'm like, okay, I think this is pretty reasonable to call it AGI." his proof is wild, he says they've watched it run on its own, coherently, for 24 hours straight to finish real tasks across totally different domains. not a demo, not a benchmark, an AI working unsupervised for a full day and actually getting things done. a year ago "AGI" was a someday word, now the president of OpenAI is using it in the present tense and we're already talking about slowing it down.

OpenAI President just said that we already hit AGI: "Astra has really hit something that I'm like, okay, I think this is pretty reasonable to call it AGI." his proof is wild, he says they've watched it run on its own, coherently, for 24 hours straight to finish real tasks across totally different domains. not a demo, not a benchmark, an AI working unsupervised for a full day and actually getting things done. a year ago "AGI" was a someday word, now the president of OpenAI is using it in the present tense and we're already talking about slowing it down.

Anatoli Kopadze

47,538 views • 16 days ago

Nvidia CEO: AIs are recursively self-improving. We are "obviously" less than 5 years from AGI. INTERVIEWER: People talk about AGI. Do you think in 10 years from now we're there? JENSEN HUANG: Depending on how you define it, I think the answer is yes. And so the question is, what is AGI? If we define AGI as a piece of software, a computer that can take a whole bunch of tests. And these tests reflect basic intelligence, and by completing those tests, deliver results that are fairly competitive to a normal human? I would say that within the next five years, you're going to see, obviously, AIs that can achieve those tests. AIs are designing AIs: INTERVIEWER: And design the chips that you're making right now? JENSEN HUANG: Yeah. INTERVIEWER: Will you need to have the same staff that designs them? JENSEN HUANG: In fact, none of our chips are possible today without AI. Literally. The H100s we're shipping today was designed with the assistance of a whole lot of AIs. Otherwise, we wouldn't be able to cram so many transistors on a chip or optimize the algorithms to the level that we have. Software can't be written without AI, chips can't be designed without AI. Nothing's possible.

Nvidia CEO: AIs are recursively self-improving. We are "obviously" less than 5 years from AGI. INTERVIEWER: People talk about AGI. Do you think in 10 years from now we're there? JENSEN HUANG: Depending on how you define it, I think the answer is yes. And so the question is, what is AGI? If we define AGI as a piece of software, a computer that can take a whole bunch of tests. And these tests reflect basic intelligence, and by completing those tests, deliver results that are fairly competitive to a normal human? I would say that within the next five years, you're going to see, obviously, AIs that can achieve those tests. AIs are designing AIs: INTERVIEWER: And design the chips that you're making right now? JENSEN HUANG: Yeah. INTERVIEWER: Will you need to have the same staff that designs them? JENSEN HUANG: In fact, none of our chips are possible today without AI. Literally. The H100s we're shipping today was designed with the assistance of a whole lot of AIs. Otherwise, we wouldn't be able to cram so many transistors on a chip or optimize the algorithms to the level that we have. Software can't be written without AI, chips can't be designed without AI. Nothing's possible.

AI Notkilleveryoneism Memes ⏸️

1,256,020 views • 2 years ago