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Ex-OpenAI Psyho says ARC-AGI is selling a "completely false narrative" about AI capabilities: "Generally, games are quite well-designed. By far the worst part about ARC-AGI is the narrative that they're trying to push. The official ARC Prize Twitter pinned post says all of the AI models are getting below...

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🚨🇨🇳 China could crash the U.S. stock market Not with tariffs With AI MODEL DUMPING The same playbook they used in SOLAR and EVs Make it almost as good Sell it much cheaper Take the market Now they are doing it with AI Open models are getting close to U.S. frontier models But some run at just a fraction of the cost If a company can cut a $100M AI bill to a few million It will switch And that is the real problem OpenAI and Anthropic are not priced like normal companies They are priced like they have unlimited PRICING POWER China is trying to destroy that The AI BUBBLE was never about whether AI works It was about how much companies can charge for it

Leshka.eth ⛩

11,737 просмотров • 1 месяц назад

Mark Zuckerberg reveals what ‘The Social Network’ got completely wrong about how he started Facebook “I only saw it once but I took our whole company to see it, I figured everyone at the company was gonna see it anyway so we might as well just take everyone to go see it” “It was weird man, cause they got all these very specific details of like what I was wearing, the specific things correct, but then the whole narrative arc around my motivations and all this stuff were like completely wrong” “the whole arc is like I’m somehow motivated by trying to find a girlfriend, it’s like I was dating Priscilla before I started Facebook” “It’s an unfortunate part of the internet how people make up a lot of the founding mythology, people cast it as if Facemash, that prank thing that I made, was the precursor to Facebook, I guess because they have face in the name, but Facemash was a prank, completely separate from Facebook” “To this day, 21 years later, largely because of the movie, people are like oh yeah you started this as a service to rate the attractiveness of people” “it’s like no, I was just a kid doing a random different thing, Facebook is a completely different and much more interesting and ambitious project than that”
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Mark Zuckerberg reveals what ‘The Social Network’ got completely wrong about how he started Facebook “I only saw it once but I took our whole company to see it, I figured everyone at the company was gonna see it anyway so we might as well just take everyone to go see it” “It was weird man, cause they got all these very specific details of like what I was wearing, the specific things correct, but then the whole narrative arc around my motivations and all this stuff were like completely wrong” “the whole arc is like I’m somehow motivated by trying to find a girlfriend, it’s like I was dating Priscilla before I started Facebook” “It’s an unfortunate part of the internet how people make up a lot of the founding mythology, people cast it as if Facemash, that prank thing that I made, was the precursor to Facebook, I guess because they have face in the name, but Facemash was a prank, completely separate from Facebook” “To this day, 21 years later, largely because of the movie, people are like oh yeah you started this as a service to rate the attractiveness of people” “it’s like no, I was just a kid doing a random different thing, Facebook is a completely different and much more interesting and ambitious project than that”

Third

1,320,683 просмотров • 2 месяцев назад

Travis Kalanick: “AGI is not here yet, and it's silly for folks to say it is.” “When it comes to big companies, I think the big thing about, let's call it the autonomous enterprise, is change management. And change management is about all the people that already work there, the middle managers, the technocrats, the bureaucrats. The change management is a human thing. And it's very tricky with very complex processes, many of which are not even documented. And in theory, it's just all going to happen real fast. But in practice, that's hard. So that's part one. Part two, what I'm seeing with true tech companies, they are fired up about the development, the productivity, the deployment schedule, and the new features they're able to roll out much, much, much faster because they've pivoted their culture, sort of very pro-AI development. Now, there are folks selling their book that are like, ‘Oh, we're at AGI,’ and all this. Anybody who has worked with these agents and done the AI dev stuff, there's a lot of good stuff, but they're not that smart yet. They're just not that smart.”
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Travis Kalanick: “AGI is not here yet, and it's silly for folks to say it is.” “When it comes to big companies, I think the big thing about, let's call it the autonomous enterprise, is change management. And change management is about all the people that already work there, the middle managers, the technocrats, the bureaucrats. The change management is a human thing. And it's very tricky with very complex processes, many of which are not even documented. And in theory, it's just all going to happen real fast. But in practice, that's hard. So that's part one. Part two, what I'm seeing with true tech companies, they are fired up about the development, the productivity, the deployment schedule, and the new features they're able to roll out much, much, much faster because they've pivoted their culture, sort of very pro-AI development. Now, there are folks selling their book that are like, ‘Oh, we're at AGI,’ and all this. Anybody who has worked with these agents and done the AI dev stuff, there's a lot of good stuff, but they're not that smart yet. They're just not that smart.”

The All-In Podcast

112,830 просмотров • 4 месяцев назад

ARC-AGI-3 is built different, it has dumbfounded almost all regular attempts so far because it's so much harder than anything that came before. It has no rules, it's agentic and has no explicit goals, they need to be discovered. Tufalabs won the first milestone of ARC Prize > There is no language built into the benchmark, but these guys "put the language back in", because in their view - it's the best way to climb up the notional "abstraction mountain" and effectively use many of the abstractions which have evolved over millions of years of language evolution. > They built a novel harness "The Duck" around a 27B open weights model (Qwen 3.6) to solve extremely challenging and novel reasoning problems that require abstraction. > This is the launch video of their winning agentic harness, "The Duck". We have also released an exclusive interview with them on MLST, just dropped. > The million dollar question is: what will François Chollet think about how they've done it, and is this a step towards AGI?
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ARC-AGI-3 is built different, it has dumbfounded almost all regular attempts so far because it's so much harder than anything that came before. It has no rules, it's agentic and has no explicit goals, they need to be discovered. Tufalabs won the first milestone of ARC Prize > There is no language built into the benchmark, but these guys "put the language back in", because in their view - it's the best way to climb up the notional "abstraction mountain" and effectively use many of the abstractions which have evolved over millions of years of language evolution. > They built a novel harness "The Duck" around a 27B open weights model (Qwen 3.6) to solve extremely challenging and novel reasoning problems that require abstraction. > This is the launch video of their winning agentic harness, "The Duck". We have also released an exclusive interview with them on MLST, just dropped. > The million dollar question is: what will François Chollet think about how they've done it, and is this a step towards AGI?

Machine Learning Street Talk

35,121 просмотров • 2 месяцев назад

.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."
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.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 просмотров • 1 год назад

“When LeBron went back to Cleveland, he talked about Miami like college…I learned how to be a champion—So if this is the final chapter, to make it a new team that he's never been part of doesn't feel right in terms of the narrative arc. Miami has a little bit of it.” @howardbeck.bsky.social
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“When LeBron went back to Cleveland, he talked about Miami like college…I learned how to be a champion—So if this is the final chapter, to make it a new team that he's never been part of doesn't feel right in terms of the narrative arc. Miami has a little bit of it.” @howardbeck.bsky.social

Dru

44,031 просмотров • 1 месяц назад

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."
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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 просмотров • 3 месяцев назад

🚨 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."
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🚨 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 просмотров • 5 месяцев назад

Ansem says $VVV is one of the sleeper consumer app play in crypto "VVV is a sleeper consumer app play onchain. I think VVV is one of the sleeper plays in crypto" "They have like 4 million users right now. I think they just hit like $100 million annualized revenue" "VVV is one of the applications that has found breakout success and consumer success, which is extremely hard to do with customers that aren't even really crypto-native" "I think the narrative for them as uncensored AI is just great generally going forward" "For the same reason that Bitcoin made a ton of sense, like uncensored money, you should have uncensored access to AI, especially if AI is going to continue to get a lot more impressive over the years" "It's a great trade. It was a really strong leader this year. It went from $1 to $20" "Their numbers are fucking through the roof. They're crushing it" "I think it's rare that a crypto app is crushing it outside of crypto and also has a token attached to it. And the team is focused on delivering value to the token as well" "I like Venice a lot. There's not really much that they're competing with in the AI space right now in crypto"
1:42

Ansem says $VVV is one of the sleeper consumer app play in crypto "VVV is a sleeper consumer app play onchain. I think VVV is one of the sleeper plays in crypto" "They have like 4 million users right now. I think they just hit like $100 million annualized revenue" "VVV is one of the applications that has found breakout success and consumer success, which is extremely hard to do with customers that aren't even really crypto-native" "I think the narrative for them as uncensored AI is just great generally going forward" "For the same reason that Bitcoin made a ton of sense, like uncensored money, you should have uncensored access to AI, especially if AI is going to continue to get a lot more impressive over the years" "It's a great trade. It was a really strong leader this year. It went from $1 to $20" "Their numbers are fucking through the roof. They're crushing it" "I think it's rare that a crypto app is crushing it outside of crypto and also has a token attached to it. And the team is focused on delivering value to the token as well" "I like Venice a lot. There's not really much that they're competing with in the AI space right now in crypto"

Shadooow

17,144 просмотров • 10 дней назад

Andrej Karpathy called the current AI models as "slop" "I do think that overall the models are not there yet. I feel like the industry is making too big of a jump and trying to pretend that this is amazing, but it's not. It's slop, and I think they are not coming to terms with it. Maybe they are trying to fundraise or something like that, I'm not sure what's going on. We are at this intermediate stage. The models are amazing, but they still need a lot of work for now. Autocomplete is my sweet spot." --- On the latest Dwarkesh Patel podcast.
0:30

Andrej Karpathy called the current AI models as "slop" "I do think that overall the models are not there yet. I feel like the industry is making too big of a jump and trying to pretend that this is amazing, but it's not. It's slop, and I think they are not coming to terms with it. Maybe they are trying to fundraise or something like that, I'm not sure what's going on. We are at this intermediate stage. The models are amazing, but they still need a lot of work for now. Autocomplete is my sweet spot." --- On the latest Dwarkesh Patel podcast.

Rohan Paul

33,331 просмотров • 10 месяцев назад

Elon Musk: It shouldn't be up to a few media oligarchs to steer the narrative. “The biggest point of manipulation that occurs in the media is not when the media says something that's false or misleading – they do that – but their choice of narrative. What are they going to talk about? By choosing to focus on one thing and not focus on another thing, they're steering what information the public receives. Whereas, I think the right thing is that the narratives that are important should be the narratives that the people as a whole think are important, not what a few media oligarchs think is important. But they sure like controlling information.” Harrisburg, Pennsylvania, October 19, 2024
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Elon Musk: It shouldn't be up to a few media oligarchs to steer the narrative. “The biggest point of manipulation that occurs in the media is not when the media says something that's false or misleading – they do that – but their choice of narrative. What are they going to talk about? By choosing to focus on one thing and not focus on another thing, they're steering what information the public receives. Whereas, I think the right thing is that the narratives that are important should be the narratives that the people as a whole think are important, not what a few media oligarchs think is important. But they sure like controlling information.” Harrisburg, Pennsylvania, October 19, 2024

ELON CLIPS

2,288,898 просмотров • 1 год назад

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 просмотров • 29 дней назад

Milla Jovovich opens up about how her divorce impacted the final cut of Joan of Arc "Joan of Arc could have been an amazing movie. Unfortunately, my relationship with Luc at the time maybe skewed his vision of what he wanted the movie to be" "we were going through a divorce at the time, it was tough on him" "when I saw the finished product, I felt like he was mad at me" "we had so many different levels, from 1 to 100, and it felt like because of the emotional involvement, the edit ended up being like 100 the whole way" "I didn't want Joan to be so unhinged" "I felt bad about that, because I felt like that was one of the greatest performances I had ever given in my career" "I wish that things had been different with that movie because it had everything in it to be a classic, and it just didn't hit that mark" Feat: GQVidoes || Milla Jovovich
1:08

Milla Jovovich opens up about how her divorce impacted the final cut of Joan of Arc "Joan of Arc could have been an amazing movie. Unfortunately, my relationship with Luc at the time maybe skewed his vision of what he wanted the movie to be" "we were going through a divorce at the time, it was tough on him" "when I saw the finished product, I felt like he was mad at me" "we had so many different levels, from 1 to 100, and it felt like because of the emotional involvement, the edit ended up being like 100 the whole way" "I didn't want Joan to be so unhinged" "I felt bad about that, because I felt like that was one of the greatest performances I had ever given in my career" "I wish that things had been different with that movie because it had everything in it to be a classic, and it just didn't hit that mark" Feat: GQVidoes || Milla Jovovich

Mue Deborah

310,076 просмотров • 28 дней назад

AGI might be the culmination of human purpose. Lomez: "I've been trying to talk myself out of the inevitability of AGI for a while. It's not even my preference, but that doesn't matter." "There's a teleology of humanness that almost is designed to produce AGI. We're fulfilling almost some kind of Calvinist, religious project." "This is the culmination of human intelligence and ingenuity. I just don't see any way around that." "There's something beautiful about it. It fits. There's some harmony in the idea that humans are designed to do this... to produce an intelligence greater than itself."
2:19

AGI might be the culmination of human purpose. Lomez: "I've been trying to talk myself out of the inevitability of AGI for a while. It's not even my preference, but that doesn't matter." "There's a teleology of humanness that almost is designed to produce AGI. We're fulfilling almost some kind of Calvinist, religious project." "This is the culmination of human intelligence and ingenuity. I just don't see any way around that." "There's something beautiful about it. It fits. There's some harmony in the idea that humans are designed to do this... to produce an intelligence greater than itself."

MTS

69,952 просмотров • 4 месяцев назад

🚨 Jensen Huang says everyone panicked about the AI data when MOST training data was never REAL to begin with. Ilya Sutskever told the industry pre-training was over. "Ilya said, 'We're out of data,' or something like that. 'Pre-training is over,' or something like that," Huang says. "The industry panicked, you know, that this is the end of AI." "And of course, of course that's obviously not true. We're gonna keep on scaling the amount of data that we have to train with." "A lot of that data is probably gonna be synthetic." That's where the panic came from — synthetic data sounds like cheating. "Most of the data that we are training, that we teach each other with, inform each other with, is synthetic." "It's synthetic because it didn't come out of nature." "You created it. I'm consuming it. I modify it, augment it, I regenerate it, somebody else consumes it." The textbook in your hand is synthetic. The post you're reading is synthetic. The lecture you took is synthetic. Nature didn't make any of it. Humans did. AI just learned to do the same thing — faster. "Training is now limited by compute," Huang says. "Data is now limited by compute." The data wall wasn't a wall. It was a mirror. If you're new here, follow @AiEvolutio for the latest on ChatGPT, Claude, and the AI tools shaping how we work and create. — Jensen Huang ( NVIDIA ), NVIDIA CEO, on Lex Fridman's ( Lex Fridman ) podcast
1:29

🚨 Jensen Huang says everyone panicked about the AI data when MOST training data was never REAL to begin with. Ilya Sutskever told the industry pre-training was over. "Ilya said, 'We're out of data,' or something like that. 'Pre-training is over,' or something like that," Huang says. "The industry panicked, you know, that this is the end of AI." "And of course, of course that's obviously not true. We're gonna keep on scaling the amount of data that we have to train with." "A lot of that data is probably gonna be synthetic." That's where the panic came from — synthetic data sounds like cheating. "Most of the data that we are training, that we teach each other with, inform each other with, is synthetic." "It's synthetic because it didn't come out of nature." "You created it. I'm consuming it. I modify it, augment it, I regenerate it, somebody else consumes it." The textbook in your hand is synthetic. The post you're reading is synthetic. The lecture you took is synthetic. Nature didn't make any of it. Humans did. AI just learned to do the same thing — faster. "Training is now limited by compute," Huang says. "Data is now limited by compute." The data wall wasn't a wall. It was a mirror. If you're new here, follow @AiEvolutio for the latest on ChatGPT, Claude, and the AI tools shaping how we work and create. — Jensen Huang ( NVIDIA ), NVIDIA CEO, on Lex Fridman's ( Lex Fridman ) podcast

AI Evolution

15,565 просмотров • 3 месяцев назад

Jensen Huang just called out every CEO using AI as an excuse to fire people. He says layoffs tied to AI “don’t make any sense” because “AI just arrived.” “The narrative that connects AI to job loss… it is just too lazy.” “AI has just arrived. How is it possible they’re already losing jobs?” “How is it possible that AI became productive and useful only 6 months ago, and they were somehow laying people off two years ago because of AI?” “It doesn’t make any sense. It was just a way for them to sound smart. And I REALLY HATE that.” A recent survey of nearly 6,000 executives found that 89% of companies have seen no measurable productivity gains from AI. Here we have the CEO of the most valuable AI company on earth, NVIDIA, telling you that the layoffs you’re seeing are not about AI. They’re about CEOs who found a convenient story and ran with it. And he hates it.
0:42

Jensen Huang just called out every CEO using AI as an excuse to fire people. He says layoffs tied to AI “don’t make any sense” because “AI just arrived.” “The narrative that connects AI to job loss… it is just too lazy.” “AI has just arrived. How is it possible they’re already losing jobs?” “How is it possible that AI became productive and useful only 6 months ago, and they were somehow laying people off two years ago because of AI?” “It doesn’t make any sense. It was just a way for them to sound smart. And I REALLY HATE that.” A recent survey of nearly 6,000 executives found that 89% of companies have seen no measurable productivity gains from AI. Here we have the CEO of the most valuable AI company on earth, NVIDIA, telling you that the layoffs you’re seeing are not about AI. They’re about CEOs who found a convenient story and ran with it. And he hates it.

The Vigilant Fox 🦊

130,953 просмотров • 3 месяцев назад

Phillip: Is that really acceptable to Republicans like you? Moynihan: What are they lying about? Phillip: They said that the police instigated the attack. Is that true or false? Moynihan: Look, I think. Is it a lot of people…. Phillip: Are these things that they're putting on the website—Is it true or is it false? Moynihan: Look, I think most people…. Phillip: The white house website today says Pence was a coward… You're good with that? Moynihan: I think I understand where the trump base is coming from because they feel frustrated…
2:36

Phillip: Is that really acceptable to Republicans like you? Moynihan: What are they lying about? Phillip: They said that the police instigated the attack. Is that true or false? Moynihan: Look, I think. Is it a lot of people…. Phillip: Are these things that they're putting on the website—Is it true or is it false? Moynihan: Look, I think most people…. Phillip: The white house website today says Pence was a coward… You're good with that? Moynihan: I think I understand where the trump base is coming from because they feel frustrated…

Acyn

256,915 просмотров • 7 месяцев назад

This is the best take I have heard around the existing AI landscape today Fantastic explanation around the constraints at OpenAI and Anthropic, focus on enterprise business models, and the doomerism narrative in AI From former AI czar David Sacks "Even though Anthropic's revenue has followed this exponential graph very predictably, it cannot do that ever. The reason is that as you hit new levels of scale, you encounter new problems. You are simply going to run out of compute, electricity, data centers, and infrastructure. There are limits to the physical world you are going to hit. And there is already evidence that Anthropic is hitting some of those limits."
4:27

This is the best take I have heard around the existing AI landscape today Fantastic explanation around the constraints at OpenAI and Anthropic, focus on enterprise business models, and the doomerism narrative in AI From former AI czar David Sacks "Even though Anthropic's revenue has followed this exponential graph very predictably, it cannot do that ever. The reason is that as you hit new levels of scale, you encounter new problems. You are simply going to run out of compute, electricity, data centers, and infrastructure. There are limits to the physical world you are going to hit. And there is already evidence that Anthropic is hitting some of those limits."

Boring_Business

236,448 просмотров • 4 месяцев назад

🚨David Friedberg: AI is starting to identify and solve problems on its own “I'll give you a science corner example: there's this Evo 2 model that they publish at the Arc Institute, which Patrick Collison, you know, is the main funder and chairman.” “So that Evo 2 model, they just ingested all the DNA data they could find in the world.” “Trillions and trillions of base paired data that they ingested and then they looked at patterns in DNA. And that's it.” “They had no context for what the DNA represented, they had no context for the concept of genes, none of the structured understanding of what that DNA does, what it is, and you know what it did?” “They fed in the BRCA gene variant and the thing output a warning saying, ‘I think that this is a pathogenic variant to DNA,’ without having any context.” “This is the breast cancer allele.” “And it didn't have any knowledge and it wasn't trained on that at all.” “It had no knowledge that there are pathogenic variants for cancer, and it identified that this was a genetic variant that can cause some sort of pathogenic outcome in the organism.” “That's a great example where there's a lack of understanding at the human level on what really drives some of the patterns in nature, the patterns in society, the patterns in behavior that are kind of emergent phenomena perhaps, that these AI models are starting to identify.”
1:03

🚨David Friedberg: AI is starting to identify and solve problems on its own “I'll give you a science corner example: there's this Evo 2 model that they publish at the Arc Institute, which Patrick Collison, you know, is the main funder and chairman.” “So that Evo 2 model, they just ingested all the DNA data they could find in the world.” “Trillions and trillions of base paired data that they ingested and then they looked at patterns in DNA. And that's it.” “They had no context for what the DNA represented, they had no context for the concept of genes, none of the structured understanding of what that DNA does, what it is, and you know what it did?” “They fed in the BRCA gene variant and the thing output a warning saying, ‘I think that this is a pathogenic variant to DNA,’ without having any context.” “This is the breast cancer allele.” “And it didn't have any knowledge and it wasn't trained on that at all.” “It had no knowledge that there are pathogenic variants for cancer, and it identified that this was a genetic variant that can cause some sort of pathogenic outcome in the organism.” “That's a great example where there's a lack of understanding at the human level on what really drives some of the patterns in nature, the patterns in society, the patterns in behavior that are kind of emergent phenomena perhaps, that these AI models are starting to identify.”

The All-In Podcast

79,718 просмотров • 1 год назад

Most AI models feel like assistants. MiniMax M3 felt like a teammate. I used Cursor + the MiniMax M3 API to build TweetGen — a tool that generates tweets instantly for any topic. What surprised me wasn’t just the speed, but how naturally M3 handled the whole workflow. It scaffolded the repo, wrote clean code, corrected itself, and kept context across iterations. I wasn’t nudging it line by line — it was committing changes like a real collaborator. Instead of “AI that helps you code,” it felt like “AI that codes with you.” That shift is huge.
1:04

Most AI models feel like assistants. MiniMax M3 felt like a teammate. I used Cursor + the MiniMax M3 API to build TweetGen — a tool that generates tweets instantly for any topic. What surprised me wasn’t just the speed, but how naturally M3 handled the whole workflow. It scaffolded the repo, wrote clean code, corrected itself, and kept context across iterations. I wasn’t nudging it line by line — it was committing changes like a real collaborator. Instead of “AI that helps you code,” it felt like “AI that codes with you.” That shift is huge.

Mr. Jason💡

12,304 просмотров • 2 месяцев назад