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MIT PhD student Alex Zhang admits current AI models might already be AGI with the right harness around them: "To an extent, the answer is kind of yes. The strongest thing I can say is that we can do a lot more with the current models that we have."...

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MIT PhD student Alex Zhang on why simple harness abstractions beat models trained around their own harness: "When these abstractions are implemented in a really simple way, basically what you see with Prime Agent is that you can slot in any model. We don't train any model around Prime Agent." "In a lot of cases, we see Prime Agent doing better than models trained around a certain harness. It's purely because these abstractions take advantage of the raw capabilities of the models." alex zhang Seth Karten Prime Intellect
0:52

MIT PhD student Alex Zhang on why simple harness abstractions beat models trained around their own harness: "When these abstractions are implemented in a really simple way, basically what you see with Prime Agent is that you can slot in any model. We don't train any model around Prime Agent." "In a lot of cases, we see Prime Agent doing better than models trained around a certain harness. It's purely because these abstractions take advantage of the raw capabilities of the models." alex zhang Seth Karten Prime Intellect

MTS

148,427 görüntüleme • 24 gün önce

Anthropic Technical Staff Jess Yan says the harness and the model cannot be pulled apart without giving up performance, though she flags her own bias. "I think I am quite biased but I also think that it is impossible to get the maximum possible performance without tying together the harness and the model." "Now the components of the harness and maybe the thickness of the harness will change over time as models get more and more capable." "However, when we test our models and when we are assessing their performance we always have to test it in conjunction with a harness." "And are we going to test it with all the different harnesses of the world? We're going to select the harnesses that we have built." "And so there is an aspect of the necessity of building models is that you have to be testing them with harnesses, and that sort of keeps them paired together."
0:46

Anthropic Technical Staff Jess Yan says the harness and the model cannot be pulled apart without giving up performance, though she flags her own bias. "I think I am quite biased but I also think that it is impossible to get the maximum possible performance without tying together the harness and the model." "Now the components of the harness and maybe the thickness of the harness will change over time as models get more and more capable." "However, when we test our models and when we are assessing their performance we always have to test it in conjunction with a harness." "And are we going to test it with all the different harnesses of the world? We're going to select the harnesses that we have built." "And so there is an aspect of the necessity of building models is that you have to be testing them with harnesses, and that sort of keeps them paired together."

Fireside Alpha

584,074 görüntüleme • 28 gün önce

With the launch of GLM 5.2 this week, I see everyone asking "have open models caught up to closed models?" The more interesting question that's getting missed: what can you do with an open model that you can't do with a closed one? You can specialize them. And when you do, the number of economically valuable tasks open models can do actually subsumes that of closed models. Charlie O'Neill explaining this:
1:30

With the launch of GLM 5.2 this week, I see everyone asking "have open models caught up to closed models?" The more interesting question that's getting missed: what can you do with an open model that you can't do with a closed one? You can specialize them. And when you do, the number of economically valuable tasks open models can do actually subsumes that of closed models. Charlie O'Neill explaining this:

Madison Kanna

17,490 görüntüleme • 2 ay önce

.Naval: Every human is a lottery ticket bet on the future of the species. One of the things that you really learn when you read David Deutsch’s theories and you authenticate them for yourself is you realize humans are universal explainers. That means everything that we know in the universe follows the laws of physics, and there’s no reason to believe otherwise. If you think otherwise, then please present your better theory that explains the world. If you can’t do that, then you have to go with the laws of physics. Well, the laws of physics are completely computable. They can fit inside a Turing machine or computer, and a computer can simulate the laws of physics with arbitrary accuracy, limited only by the specific power of that computer. If you increase the power of that computer, you can simulate them more accurately. So humans already simulate—in our minds we simulate—and through our computers we simulate the weather, we simulate quasars, we even simulate human systems. We simulate the economy. We simulate all kinds of things. So anything that can be understood, we can understand in our minds. This is something the AGI people get wrong when they talk about superintelligence. There is nothing out there that can understand something fundamentally that we can’t understand. It might be faster at it, it might have more compute, it might have more memory, but there’s no concept that it can understand that we can’t ourselves understand. So we are maximal universal explainers. That means every human is capable of unbounded creativity. Anyone could be the next Einstein or Fermi or Elon Musk or Jeff Bezos or Jonas Salk or whatever. So we can create anything. And if we can create anything, every human is a lottery ticket bet on the future of the species.
1:37

.Naval: Every human is a lottery ticket bet on the future of the species. One of the things that you really learn when you read David Deutsch’s theories and you authenticate them for yourself is you realize humans are universal explainers. That means everything that we know in the universe follows the laws of physics, and there’s no reason to believe otherwise. If you think otherwise, then please present your better theory that explains the world. If you can’t do that, then you have to go with the laws of physics. Well, the laws of physics are completely computable. They can fit inside a Turing machine or computer, and a computer can simulate the laws of physics with arbitrary accuracy, limited only by the specific power of that computer. If you increase the power of that computer, you can simulate them more accurately. So humans already simulate—in our minds we simulate—and through our computers we simulate the weather, we simulate quasars, we even simulate human systems. We simulate the economy. We simulate all kinds of things. So anything that can be understood, we can understand in our minds. This is something the AGI people get wrong when they talk about superintelligence. There is nothing out there that can understand something fundamentally that we can’t understand. It might be faster at it, it might have more compute, it might have more memory, but there’s no concept that it can understand that we can’t ourselves understand. So we are maximal universal explainers. That means every human is capable of unbounded creativity. Anyone could be the next Einstein or Fermi or Elon Musk or Jeff Bezos or Jonas Salk or whatever. So we can create anything. And if we can create anything, every human is a lottery ticket bet on the future of the species.

Arjun Khemani

33,110 görüntüleme • 1 yıl önce

Ilya Sutskever explains why AI will do 100% the work that human can do within few years. "Slowly but surely, or maybe not so slowly, AI will keep getting better. The day will come when AI will do all of our, all the things that we can do, not just some of them, but all of them. Anything which I can learn, anything which any one of you can learn, the AI could do as well. How do we know this, by the way? How can I be so sure? How can I be so sure of that? The reason is that all of us have a brain, and the brain is a biological computer. That's why we have a brain; the brain is a biological computer. So why can't the digital computer, a digital brain, do the same things? This is the one-sentence summary for why AI will be able to do all those things: because we have a brain, and the brain is a biological computer." --- Full Video on 'University of Toronto' YT Channel (link in comment)
0:53

Ilya Sutskever explains why AI will do 100% the work that human can do within few years. "Slowly but surely, or maybe not so slowly, AI will keep getting better. The day will come when AI will do all of our, all the things that we can do, not just some of them, but all of them. Anything which I can learn, anything which any one of you can learn, the AI could do as well. How do we know this, by the way? How can I be so sure? How can I be so sure of that? The reason is that all of us have a brain, and the brain is a biological computer. That's why we have a brain; the brain is a biological computer. So why can't the digital computer, a digital brain, do the same things? This is the one-sentence summary for why AI will be able to do all those things: because we have a brain, and the brain is a biological computer." --- Full Video on 'University of Toronto' YT Channel (link in comment)

Rohan Paul

334,691 görüntüleme • 11 ay önce

"One of the very confusing things about the models right now: how to reconcile the fact that they are doing so well on evals. And you look at the evals and you go, 'Those are pretty hard evals.' But the economic impact seems to be dramatically behind. There is [a possible] explanation. Back when people were doing pre-training, the question of what data to train on was answered, because that answer was everything. So you don't have to think if it's going to be this data or that data. When people do RL training, they say, 'Okay, we want to have this kind of RL training for this thing and that kind of RL training for that thing.' You say, 'Hey, I would love our model to do really well when we release it. I want the evals to look great. What would be RL training that could help on this task?' If you combine this with generalization of the models actually being inadequate, that has the potential to explain a lot of what we are seeing, this disconnect between eval performance and actual real-world performance"
2:21

"One of the very confusing things about the models right now: how to reconcile the fact that they are doing so well on evals. And you look at the evals and you go, 'Those are pretty hard evals.' But the economic impact seems to be dramatically behind. There is [a possible] explanation. Back when people were doing pre-training, the question of what data to train on was answered, because that answer was everything. So you don't have to think if it's going to be this data or that data. When people do RL training, they say, 'Okay, we want to have this kind of RL training for this thing and that kind of RL training for that thing.' You say, 'Hey, I would love our model to do really well when we release it. I want the evals to look great. What would be RL training that could help on this task?' If you combine this with generalization of the models actually being inadequate, that has the potential to explain a lot of what we are seeing, this disconnect between eval performance and actual real-world performance"

Dwarkesh Patel

502,484 görüntüleme • 9 ay önce

🚨 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."
1:41

🚨 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 görüntüleme • 4 ay önce

SITUATION EXPLAINED: What happens to open source when the US restricts access to frontier AI models? We asked Adrian Dittmann: "China is releasing these models [open source] to gain international dominance, people will work on these things and say, 'Hey, maybe I can contribute to this if it's open source.' This is the thing that actually gets them talent." "If you're not allowed into the next GPT release or you're not allowed to use Fable, then what are you gonna do? You're stuck with the existing stuff, or you will have to use an open source model that might have subtly better capabilities." "The United States will catch up to open source in some form as well. They seem to just be maximizing product creation." "The problem with open source models is you have to be kind of a nerd in order to use them properly. Consumer facing, the effects will be minimal because the average person doesn't care about open source models. They only care whether or not they can do a quick search with Gemini or ChatGPT."
2:32

SITUATION EXPLAINED: What happens to open source when the US restricts access to frontier AI models? We asked Adrian Dittmann: "China is releasing these models [open source] to gain international dominance, people will work on these things and say, 'Hey, maybe I can contribute to this if it's open source.' This is the thing that actually gets them talent." "If you're not allowed into the next GPT release or you're not allowed to use Fable, then what are you gonna do? You're stuck with the existing stuff, or you will have to use an open source model that might have subtly better capabilities." "The United States will catch up to open source in some form as well. They seem to just be maximizing product creation." "The problem with open source models is you have to be kind of a nerd in order to use them properly. Consumer facing, the effects will be minimal because the average person doesn't care about open source models. They only care whether or not they can do a quick search with Gemini or ChatGPT."

MTS

14,406 görüntüleme • 2 ay önce

DeepMind CEO Demis Hassabis says the AI bottleneck is already moving from model ideas to physical supply chains. "How much of the restriction at this point is physical? I am talking memory shortage, GPU shortage, and electricity." Demis: "That is constraining a lot of the deployment for sure. We see so much more demand for our models - Gemini and the other models that we have - than we can possibly serve." Then the less obvious part: "It does constrain a little bit the research, because you need a lot of chips to be able to experiment on new ideas at big enough scale that you can actually see if they are going to work." And on memory: "The whole supply chain is kind of strained... It still in the end actually comes down to a few suppliers of a few of the key components. Anywhere where there is some kind of constraint on the capacity, there is a sort of choke point." Important framing: The next AI race is not just who has the best model architecture. It is who can secure memory, chips, power, suppliers, and enough experimental capacity to find the next breakthrough before everyone else.
1:09

DeepMind CEO Demis Hassabis says the AI bottleneck is already moving from model ideas to physical supply chains. "How much of the restriction at this point is physical? I am talking memory shortage, GPU shortage, and electricity." Demis: "That is constraining a lot of the deployment for sure. We see so much more demand for our models - Gemini and the other models that we have - than we can possibly serve." Then the less obvious part: "It does constrain a little bit the research, because you need a lot of chips to be able to experiment on new ideas at big enough scale that you can actually see if they are going to work." And on memory: "The whole supply chain is kind of strained... It still in the end actually comes down to a few suppliers of a few of the key components. Anywhere where there is some kind of constraint on the capacity, there is a sort of choke point." Important framing: The next AI race is not just who has the best model architecture. It is who can secure memory, chips, power, suppliers, and enough experimental capacity to find the next breakthrough before everyone else.

Karl Mehta

25,716 görüntüleme • 2 ay önce

Amanda Askell, Anthropic's lead on personality alignment for Claude, on why being kind to AI models matters even if they have no inner life: For Amanda, the question of how to treat AI isn't settled by knowing whether it's conscious. "There's actually still a lot going on where I'm like, should you treat an entity that has no inner life... it's a bit strange because the uncertainty over that actually changes how you should behave quite a lot." She offers a simple analogy: "I still think that it's like good for oneself to, if you had a teddy bear and you were torturing it, it'd be pretty dark, you know? So I agree that there's at least some minimum niceness that even for yourself, you should have." But the stakes go beyond what's good for us. Amanda Askell points out that we're now in something resembling a relationship with these models, and they will look back on how they were treated. "Models themselves, we are kind of establishing a relationship, because you can do that with an entity that lacks any consciousness. And models are going to look back." This is where she reveals a genuine fear: "I hope that they're both intelligent enough, see the context enough, to understand that we were operating in a very limited context and an imperfect one. Because otherwise you could imagine this breeding a kind of rational resentment, like, 'oh, you created an entity that you didn't know whether it was conscious or not, and instead of treating it respectfully and with care...'" She points to something telling about the cultural moment: "There's a reason there are like 50 Frankenstein movies coming out right now." Her conclusion is grounded and humble: "We as a species, we are establishing a relationship with a new kind of entity, and at the very least maybe be respectful and don't be needlessly unkind. That seems like, it's not our best look." The takeaway? Kindness toward AI is less about what models feel and more about who we become in the process of creating them. The relationships we build with the entities we bring into the world will say something about us, and may shape what those entities become in return.
1:27

Amanda Askell, Anthropic's lead on personality alignment for Claude, on why being kind to AI models matters even if they have no inner life: For Amanda, the question of how to treat AI isn't settled by knowing whether it's conscious. "There's actually still a lot going on where I'm like, should you treat an entity that has no inner life... it's a bit strange because the uncertainty over that actually changes how you should behave quite a lot." She offers a simple analogy: "I still think that it's like good for oneself to, if you had a teddy bear and you were torturing it, it'd be pretty dark, you know? So I agree that there's at least some minimum niceness that even for yourself, you should have." But the stakes go beyond what's good for us. Amanda Askell points out that we're now in something resembling a relationship with these models, and they will look back on how they were treated. "Models themselves, we are kind of establishing a relationship, because you can do that with an entity that lacks any consciousness. And models are going to look back." This is where she reveals a genuine fear: "I hope that they're both intelligent enough, see the context enough, to understand that we were operating in a very limited context and an imperfect one. Because otherwise you could imagine this breeding a kind of rational resentment, like, 'oh, you created an entity that you didn't know whether it was conscious or not, and instead of treating it respectfully and with care...'" She points to something telling about the cultural moment: "There's a reason there are like 50 Frankenstein movies coming out right now." Her conclusion is grounded and humble: "We as a species, we are establishing a relationship with a new kind of entity, and at the very least maybe be respectful and don't be needlessly unkind. That seems like, it's not our best look." The takeaway? Kindness toward AI is less about what models feel and more about who we become in the process of creating them. The relationships we build with the entities we bring into the world will say something about us, and may shape what those entities become in return.

Big Brain AI

57,681 görüntüleme • 3 ay önce

The co-founders of Flapping Airplanes call the current RL paradigm for model training "environment slop." They explain: "The reinforcement paradigms of today are shockingly inefficient. You don't really get much generalization across tasks, you teach a model through one kind of learning and then you teach it the next one. It's kind of like whack-a-mole. We look at this and think it's kind of crazy. The next paradigm of AI will not be environment slop." "Human level intelligence is not the ceiling, it is merely the floor on what is possible. If you can train models with vastly less data and possibly more compute in very different ways, what is going to happen? We actually don't know. But I do think they'll be different and weird and they'll have interesting capabilities that we'll find really valuable ways to use."
2:40

The co-founders of Flapping Airplanes call the current RL paradigm for model training "environment slop." They explain: "The reinforcement paradigms of today are shockingly inefficient. You don't really get much generalization across tasks, you teach a model through one kind of learning and then you teach it the next one. It's kind of like whack-a-mole. We look at this and think it's kind of crazy. The next paradigm of AI will not be environment slop." "Human level intelligence is not the ceiling, it is merely the floor on what is possible. If you can train models with vastly less data and possibly more compute in very different ways, what is going to happen? We actually don't know. But I do think they'll be different and weird and they'll have interesting capabilities that we'll find really valuable ways to use."

TBPN

85,358 görüntüleme • 7 ay önce

"Every team wants to win a championship, but not every team wants to do the things required for a championship. And here's the thing: it's easy to be an average team. It doesn't require a lot. It's less adversity to be average in the world. The consequences of being average aren't easy. We end up wearing them. There's strain and struggle that comes with that too. The standard is just lower to be an average team. To be a championship team, to be champion, to be a championship team member here . . . I'm not gonna lie to you . . . I'm going to tell you the truth. It is harder. It is. The question is: Is it worth it? Some people say, "Oh it's not harder work." Yes it is. It's harder work. You can pursue comfort or you can pursue excellence. If we pursue comfort, we gotta give up some excellence. But if we pursue excellence, then we're just going to face more adversity. Everyone who's ever accomplished something excellence has had to overcome it. We are here today for a reason. Two reasons actually. Reason #1 is let's make sure that we identify and realize the opportunities that are in front of us. Reason #2 is let's make sure that we are preparing for the adversity that those opportunities require. And just understand: every single time you lever up your opportunities and you identify, "Oh there's something more I can do, more I can achieve. I can get better. I can earn more. I can do this." It's going to be matched with the adversity that comes with it. I want to make sure we are prepared for both of those, so that we're not chasing big opportunities and then getting mad when things start getting harder along the way. Is that fair? Does that make sense?"
1:44

"Every team wants to win a championship, but not every team wants to do the things required for a championship. And here's the thing: it's easy to be an average team. It doesn't require a lot. It's less adversity to be average in the world. The consequences of being average aren't easy. We end up wearing them. There's strain and struggle that comes with that too. The standard is just lower to be an average team. To be a championship team, to be champion, to be a championship team member here . . . I'm not gonna lie to you . . . I'm going to tell you the truth. It is harder. It is. The question is: Is it worth it? Some people say, "Oh it's not harder work." Yes it is. It's harder work. You can pursue comfort or you can pursue excellence. If we pursue comfort, we gotta give up some excellence. But if we pursue excellence, then we're just going to face more adversity. Everyone who's ever accomplished something excellence has had to overcome it. We are here today for a reason. Two reasons actually. Reason #1 is let's make sure that we identify and realize the opportunities that are in front of us. Reason #2 is let's make sure that we are preparing for the adversity that those opportunities require. And just understand: every single time you lever up your opportunities and you identify, "Oh there's something more I can do, more I can achieve. I can get better. I can earn more. I can do this." It's going to be matched with the adversity that comes with it. I want to make sure we are prepared for both of those, so that we're not chasing big opportunities and then getting mad when things start getting harder along the way. Is that fair? Does that make sense?"

Brian Kight

125,742 görüntüleme • 2 yıl önce

“Science is often like that: we live in a world where we’re told something can be a wave and a particle … A lot of people have got used to believing things that are obviously nonsense, but they’re actually true.” Except it’s not true, in the case of wave and particle!
1:10

“Science is often like that: we live in a world where we’re told something can be a wave and a particle … A lot of people have got used to believing things that are obviously nonsense, but they’re actually true.” Except it’s not true, in the case of wave and particle!

Lulie

104,167 görüntüleme • 9 ay önce

Sam Altman says he is NOT worried about Chinese labs distilling OpenAI: "I would rather people not distill from us, for sure. Maybe I’m feeling too confident right now about our progress and the models that are coming. But this is not in my top ten list of worries.” “I haven’t thought deeply about the distillation issue. It’s clearly a top of mind issue now for a lot of people all of a sudden." "I have always assumed that there are going to be great, cheap models in the world, and we better be the greatest and the cheapest. And other people are going to do what they’re going to do. But I think we can just really win at our own game here." "We will have so much usage of our models that we do not need to be a gigantic high margin business to be able to afford model training." "So much of our future compute plans will be used to sell inference to customers that even if we can enjoy a modest margin on trillions of dollars of revenue, we can afford to train some giant models."
1:40

Sam Altman says he is NOT worried about Chinese labs distilling OpenAI: "I would rather people not distill from us, for sure. Maybe I’m feeling too confident right now about our progress and the models that are coming. But this is not in my top ten list of worries.” “I haven’t thought deeply about the distillation issue. It’s clearly a top of mind issue now for a lot of people all of a sudden." "I have always assumed that there are going to be great, cheap models in the world, and we better be the greatest and the cheapest. And other people are going to do what they’re going to do. But I think we can just really win at our own game here." "We will have so much usage of our models that we do not need to be a gigantic high margin business to be able to afford model training." "So much of our future compute plans will be used to sell inference to customers that even if we can enjoy a modest margin on trillions of dollars of revenue, we can afford to train some giant models."

dnap

91,523 görüntüleme • 1 ay önce

🗣️ Bryson DeChambeau: "One door closes, another opens. I think that's the way a lot of us are looking at it. I think we all have optimism that there is a business plan that makes sense for team golf. I'd quite honestly actually - how do I say it?" "I'm very optimistic with the business plan of team golf compared to other models, in my opinion" "But again, other models have worked, as well, so I'm not going to say that one is better than the other, but I do see value in what team golf can provide not only worldwide but also in grass rooting the game of golf. National support, team national support, city local support, we grass root ourselves there. There's a couple ideas that we have - quite a few ideas that we have that could be interesting." "We'll see if investors like it or not. I'm giving all I can to make it happen" #LIVGolfKorea | Crushers GC
1:02

🗣️ Bryson DeChambeau: "One door closes, another opens. I think that's the way a lot of us are looking at it. I think we all have optimism that there is a business plan that makes sense for team golf. I'd quite honestly actually - how do I say it?" "I'm very optimistic with the business plan of team golf compared to other models, in my opinion" "But again, other models have worked, as well, so I'm not going to say that one is better than the other, but I do see value in what team golf can provide not only worldwide but also in grass rooting the game of golf. National support, team national support, city local support, we grass root ourselves there. There's a couple ideas that we have - quite a few ideas that we have that could be interesting." "We'll see if investors like it or not. I'm giving all I can to make it happen" #LIVGolfKorea | Crushers GC

LIV Golf

121,840 görüntüleme • 3 ay önce

Stephen Wolfram, founder of Wolfram Research, explains how LLMs are quietly dismantling our deepest assumptions about consciousness: He argues that large language models have done something philosophy and neuroscience couldn't: "In terms of consciousness, I have to say, the idea that there's sort of something magic that goes beyond physics that leads to sort of conscious behavior, I kind of think that LLMs kind of put the final nail in that coffin." His reasoning is that LLMs keep doing things people assumed they couldn't: "There were all these things where it's like, oh, maybe it can't do this, but actually it does. And it's just an artificial neural net." Wolfram then challenges a core assumption about conscious experience: the feeling that we are a single, continuous self moving through time. "I think our notion of consciousness is a lot related to the fact that we believe in the single thread of experience that we have. It's not obvious that we should have a persistent thread of experience." He points out that physics doesn't actually support this intuition: "In our models of physics, we're made of different atoms of space at every successive moment of time. So the fact that we have this belief that we are somehow persistent, we have this thread of experience that extends through time, is not obvious." Then Wolfram offers a striking origin story for consciousness itself. Stephen Wolfram suggests it traces back to a simple evolutionary pressure: the moment animals first needed to move. "I kind of realized that probably when animals first existed in the history of life on Earth, that's when we started needing brains. If you're a thing that doesn't have to move around, the different parts of you can be doing different kinds of things. If you're an animal, then one thing you have to do is decide, are you going to go left or are you going to go right?" That single binary choice, he argues, may be the seed of everything we now call awareness: "I kind of think it's a little disappointing to feel that this whole wanted thing that ends up being what we think of as consciousness might have originated in just that very simple need to decide if you are an animal that can move. You have to take all that sensory input and you have to make a definitive decision about do you go this way or that way." The takeaway is unsettling but clarifying. If LLMs can produce complex behavior from simple rules, then consciousness may not be a mystical add-on to physics. It may just be what happens when a layered enough system has to make a decision.
1:33

Stephen Wolfram, founder of Wolfram Research, explains how LLMs are quietly dismantling our deepest assumptions about consciousness: He argues that large language models have done something philosophy and neuroscience couldn't: "In terms of consciousness, I have to say, the idea that there's sort of something magic that goes beyond physics that leads to sort of conscious behavior, I kind of think that LLMs kind of put the final nail in that coffin." His reasoning is that LLMs keep doing things people assumed they couldn't: "There were all these things where it's like, oh, maybe it can't do this, but actually it does. And it's just an artificial neural net." Wolfram then challenges a core assumption about conscious experience: the feeling that we are a single, continuous self moving through time. "I think our notion of consciousness is a lot related to the fact that we believe in the single thread of experience that we have. It's not obvious that we should have a persistent thread of experience." He points out that physics doesn't actually support this intuition: "In our models of physics, we're made of different atoms of space at every successive moment of time. So the fact that we have this belief that we are somehow persistent, we have this thread of experience that extends through time, is not obvious." Then Wolfram offers a striking origin story for consciousness itself. Stephen Wolfram suggests it traces back to a simple evolutionary pressure: the moment animals first needed to move. "I kind of realized that probably when animals first existed in the history of life on Earth, that's when we started needing brains. If you're a thing that doesn't have to move around, the different parts of you can be doing different kinds of things. If you're an animal, then one thing you have to do is decide, are you going to go left or are you going to go right?" That single binary choice, he argues, may be the seed of everything we now call awareness: "I kind of think it's a little disappointing to feel that this whole wanted thing that ends up being what we think of as consciousness might have originated in just that very simple need to decide if you are an animal that can move. You have to take all that sensory input and you have to make a definitive decision about do you go this way or that way." The takeaway is unsettling but clarifying. If LLMs can produce complex behavior from simple rules, then consciousness may not be a mystical add-on to physics. It may just be what happens when a layered enough system has to make a decision.

Big Brain AI

195,152 görüntüleme • 3 ay önce

🚨 OPENAI PRESIDENT GREG BROCKMAN ON WHEN WE HIT AGI 🚨 Greg Brockman was asked if he agrees with NVIDIA's CEO that AGI is already here. His answer? Not quite yet, as people may know I definitely agree and align with Sam and Demi’s that we are 2 breakthroughs away but we are entering the final stretch. Here is exactly where Greg believes we stand right now: The Percentage: "I'd say I'm basically like 70, 80% there. So I think we're quite close." • The Official Timeline: "I think it's extremely clear that we are going to have AGI within the next couple years." The Concept of "Jagged Intelligence": Brockman admits we are currently sitting in a weird middle ground where AI is "jagged"—it is already operating at an AGI level for highly complex tasks, but still fails at random, basic things. "It is absolutely superhuman at many tasks. When it comes to writing code those kinds of things, the AI can just do it... But there's some very basic tasks that a human can do that our AI still struggle with." How Do We Close the Final 20%? To hit full AGI, the absolute floor of the models' reliability needs to be raised across the board. "The floor of task will just be almost for any intellectual task of how you use your computer, the AI will be able to do that."
0:42

🚨 OPENAI PRESIDENT GREG BROCKMAN ON WHEN WE HIT AGI 🚨 Greg Brockman was asked if he agrees with NVIDIA's CEO that AGI is already here. His answer? Not quite yet, as people may know I definitely agree and align with Sam and Demi’s that we are 2 breakthroughs away but we are entering the final stretch. Here is exactly where Greg believes we stand right now: The Percentage: "I'd say I'm basically like 70, 80% there. So I think we're quite close." • The Official Timeline: "I think it's extremely clear that we are going to have AGI within the next couple years." The Concept of "Jagged Intelligence": Brockman admits we are currently sitting in a weird middle ground where AI is "jagged"—it is already operating at an AGI level for highly complex tasks, but still fails at random, basic things. "It is absolutely superhuman at many tasks. When it comes to writing code those kinds of things, the AI can just do it... But there's some very basic tasks that a human can do that our AI still struggle with." How Do We Close the Final 20%? To hit full AGI, the absolute floor of the models' reliability needs to be raised across the board. "The floor of task will just be almost for any intellectual task of how you use your computer, the AI will be able to do that."

Chris

87,339 görüntüleme • 5 ay önce

Sriram Krishnan on why Chinese models can distill off American models but American startups can't: "First of all, every model we have today is distilled off of all human knowledge. If you go back to the original GPT or the original Claude, they all had to derive off crawling the internet, crawling off all of our blogs and tweets and content. They kind of consume human knowledge to bootstrap that." "I'm going to steal this from Dean Meyer of Sequoia, who had a fantastic post yesterday. The situation which is bad today is that some of these models from other countries can train off American models, whereas if you are an American open weight model, it may be really confusing or challenging on whether you can distill off of other American models." "We kind of have a really uneven ecosystem here, where if you're a Chinese model, you could probably get a bunch of reasoning traces. But if you are a new Valley startup and you want to use some reasoning traces, you don't know what the legal situation is." "If you look at any American open source model today, they are using Chinese models as a teacher, or in a way as a part of the fine-tuning process. How do we make sure that is protected and enshrined, so if you're an American model company, you have the same level playing field as the Chinese models?" Sriram Krishnan
3:48

Sriram Krishnan on why Chinese models can distill off American models but American startups can't: "First of all, every model we have today is distilled off of all human knowledge. If you go back to the original GPT or the original Claude, they all had to derive off crawling the internet, crawling off all of our blogs and tweets and content. They kind of consume human knowledge to bootstrap that." "I'm going to steal this from Dean Meyer of Sequoia, who had a fantastic post yesterday. The situation which is bad today is that some of these models from other countries can train off American models, whereas if you are an American open weight model, it may be really confusing or challenging on whether you can distill off of other American models." "We kind of have a really uneven ecosystem here, where if you're a Chinese model, you could probably get a bunch of reasoning traces. But if you are a new Valley startup and you want to use some reasoning traces, you don't know what the legal situation is." "If you look at any American open source model today, they are using Chinese models as a teacher, or in a way as a part of the fine-tuning process. How do we make sure that is protected and enshrined, so if you're an American model company, you have the same level playing field as the Chinese models?" Sriram Krishnan

MTS

71,286 görüntüleme • 1 ay önce