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Dario Amodei

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2024-11-11
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2024-11-11
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  1. “Ways that we kind of try to steer but are not fully able to steer. And so there's never quite that exact equivalence where the only thing you're changing is intelligence. We always try and improve other things and some things change without us knowing or measuring. So it's very much an exact science. In many ways, the manner and personality of these models is more an art than it is the science.”

    2024-11-11 · Lex Fridman Podcast · #452 – Dario Amodei: Anthropic CEO on Claude, AGI & the Future of AI & Humanity · IDENTIFIED FROM THE TRANSCRIPT · source

  2. “And our thinking then was, well, each new generation of models should shift that trade-off curve. So when we release Sonnet 3.5, it has the same roughly the same cost and speed as the Sonnet 3 model, but it increased its intelligence to the point where it was smarter than the original Opus 3 model, especially for code, but also just in general. Now, we've shown results for Haiku 3.5, and I believe Haiku 3.5, the smallest new model, is about as good as Opus 3, the largest old model. So basically the aim here is to shift the curve, and then at some point there's going to be an Opus 3.5. Now, every new generation of models has its own thing. They use new data. Their personality changes in”

    2024-11-11 · Lex Fridman Podcast · #452 – Dario Amodei: Anthropic CEO on Claude, AGI & the Future of AI & Humanity · IDENTIFIED FROM THE TRANSCRIPT · source

  3. “Or, you know, we have plenty of companies that are just like, you know, I want to do autocomplete on my IDE or something. And for all of those things, you want to act fast and you want to use the model very broadly. So we wanted to serve that whole spectrum of needs. So we ended up with this, you know, this kind of poetry theme. And so what's a really short poem? It's a haiku. And so haiku is the small, fast, cheap model that is, you know, was at the time was released surprisingly intelligent for how fast and cheap it was. Sonnet is a medium-sized poem, right? A couple paragraphs. And so Sonnet was the middle model. It is smarter, but also a little bit slower, a little bit more expensive. And Opus, like a magnum opus, is a large work. Opus was the largest, smartest model at the time. So that was the original kind of thinking behind it.”

    2024-11-11 · Lex Fridman Podcast · #452 – Dario Amodei: Anthropic CEO on Claude, AGI & the Future of AI & Humanity · IDENTIFIED FROM THE TRANSCRIPT · source

  4. “Yeah, so let's go back to March when we first released these three models. So our thinking was different companies produce kind of large and small models better and worse models. We felt that there was demand both for a really powerful model. That might be a little bit slower that you'd have to pay more for, and also for fast cheap models that are as smart as they can be for how fast and cheap, right? Whenever you want to do some kind of like, you know, difficult analysis, like if I, you know, I want to write code, for instance, or, you know, I want to, I want to brainstorm ideas or I want to do creative writing, I want the really powerful model. Then there's a lot of practical applications in a business sense where it's like I'm interacting with a website. I'm like doing my taxes or I'm talking to like a legal advisor and I want to analyze a contract.”

    2024-11-11 · Lex Fridman Podcast · #452 – Dario Amodei: Anthropic CEO on Claude, AGI & the Future of AI & Humanity · IDENTIFIED FROM THE TRANSCRIPT · source

  5. “Has a strong personality. It has these kind of like obsessive interests. You know, we can all think of someone who's like obsessed with something. So it does make it feel somehow a bit more human.”

    2024-11-11 · Lex Fridman Podcast · #452 – Dario Amodei: Anthropic CEO on Claude, AGI & the Future of AI & Humanity · IDENTIFIED FROM THE TRANSCRIPT · source

  6. “Somehow these interventions on the model where you kind of adjust its behavior somehow emotionally made it seem more human. Than any other version of the model”

    2024-11-11 · Lex Fridman Podcast · #452 – Dario Amodei: Anthropic CEO on Claude, AGI & the Future of AI & Humanity · IDENTIFIED FROM THE TRANSCRIPT · source

  7. “I'm amazed at how clean it's been. I'm amazed at things like induction heads. I'm amazed at things like, you know, that we can use sparse autoencoders to find these directions within the networks and that the directions correspond to these very clear concepts. We demonstrated this a bit with the Golden Gate Bridge claud. So this was an experiment where we found a direction inside one of the neural networks layers that corresponded to the Golden Gate Bridge. And we just turned that way up. And so we released this model as a demo. It was kind of half a joke for a couple days, but it was illustrative of the method we developed. And you could take the Golden Gate, you could take the model, you could ask it about anything. It would be like you could say, how is your day? And anything you asked because this feature was activated would connect the Golden Gate Bridge. So it would say.”

    2024-11-11 · Lex Fridman Podcast · #452 – Dario Amodei: Anthropic CEO on Claude, AGI & the Future of AI & Humanity · IDENTIFIED FROM THE TRANSCRIPT · source

  8. “Trying to. I mean, I think we're still early in terms of our ability to see things, but I've been surprised at how much we've been able to look inside these systems and understand what we see, right? Unlike with the scaling laws where it feels like there's some law that's deriving these models to perform better, on the inside, the models aren't, you know, there's no reason why they should be designed for us to understand them, right? They're designed to operate. They're designed to work just like the human brain or human biochemistry. They're not designed for a human to open up the hatch, look inside, and understand them. But we have found, and you can talk in much more detail about this to Chris, that when we open them up, when we do look inside them, we find things that are surprisingly interesting.”

    2024-11-11 · Lex Fridman Podcast · #452 – Dario Amodei: Anthropic CEO on Claude, AGI & the Future of AI & Humanity · IDENTIFIED FROM THE TRANSCRIPT · source

  9. “Takes away our competitive advantage because it's like, oh, now others are doing it as well, but it's good for the broader system. And so we have to invent some new thing that we're doing that others aren't doing as well. And the hope is to basically bid up the importance of doing the right thing. And it's not about us in particular, right? It's not about having one particular good guy. Other companies can do this as well. If they join the race to do this, that's the best news ever, right? It's just about kind of shaping the incentives to point upward instead of shaping the incentives.”

    2024-11-11 · Lex Fridman Podcast · #452 – Dario Amodei: Anthropic CEO on Claude, AGI & the Future of AI & Humanity · IDENTIFIED FROM THE TRANSCRIPT · source

  10. “Today, we're doing some early betas with it, and probably it will eventually. But, you know, this is a very, very long research bed and one in which we've built in public and shared our results publicly. And we did this because we think it's a way to make models safer. An interesting thing is that as we've done this, other companies have started doing it as well. In some cases, because they've been inspired by it, in some cases because they're worried that, you know, if other companies are doing this that look more responsible, they want to look more responsible too. No one wants to look like the irresponsible actor. And so they adopt this, they adopt this as well when folks come to anthropic, interpretability is often a draw, and I tell them, the other places you didn't go, tell them why you came here. And then you see soon that there's interpretability teams elsewhere as well. And in a way that...”

    2024-11-11 · Lex Fridman Podcast · #452 – Dario Amodei: Anthropic CEO on Claude, AGI & the Future of AI & Humanity · IDENTIFIED FROM THE TRANSCRIPT · source

  11. “Yeah, so I want to separate out a couple things, right? So, you know, Anthropics mission is to kind of try to make this all go well, right? And, you know, we have a theory of change called race to the top, right? Race to the top is about trying to push the other players to do the right thing by setting an example. It's not about being the good guy. It's about setting things up so that all of us can be the good guy. I'll give a few examples of this. Early in the history of anthropic, one of our co-founders, Chris Ola, who I believe you're interviewing soon, he's the co-founder of the field of mechanistic interpretability, which is an attempt to understand what's going on inside AI models. So we had him and one of our early teams focus on this area of interpretability, which we think is good for making models safe and transparent. For three or four years, that had no commercial application whatsoever. It still doesn't.”

    2024-11-11 · Lex Fridman Podcast · #452 – Dario Amodei: Anthropic CEO on Claude, AGI & the Future of AI & Humanity · IDENTIFIED FROM THE TRANSCRIPT · source

  12. “Level math, physics, and biology from models like OpenAIs 01. So if we just continue to extrapolate this, right, in terms of skill that we have, I think if we extrapolate the straight curve within a few years, we will get to these models being above the highest professional level in terms of humans. Now, will that curve continue? You've pointed to and I've pointed to a lot of reasons why possible reasons why that might not happen. But if the extrapolation curve continues, that is the trajectory we're on.”

    2024-11-11 · Lex Fridman Podcast · #452 – Dario Amodei: Anthropic CEO on Claude, AGI & the Future of AI & Humanity · IDENTIFIED FROM THE TRANSCRIPT · source

  13. “Powerful AI happening so fast is just that if you extrapolate the next few points on the curve, we're very quickly getting towards human level ability, right? Some of the new models that we developed, some reasoning models that have come from other companies, they're starting to get to what I would call the PhD or professional level, right? If you look at their coding ability, the latest model we released, Sonnet 3.5, the new or updated version, it gets something like 50% on SuiBench. And SuiBench is an example of a bunch of professional real-world software engineering tasks. At the beginning of the year, I think the state of the art was three or four percent. So in 10 months, we've gone from 3% to 50% on this task. And I think in another year, we'll probably be at 90%. I mean, I don't know, but might even be less than that. We've seen similar things in graduate.”

    2024-11-11 · Lex Fridman Podcast · #452 – Dario Amodei: Anthropic CEO on Claude, AGI & the Future of AI & Humanity · IDENTIFIED FROM THE TRANSCRIPT · source

  14. “So, right now, I think most of the frontier model companies, I would guess, are operating roughly $1 billion scale plus or minus a factor of three, right? Those are the models that exist now or are being trained now. I think next year we're going to go to a few billion and then 2026 we may go to above 10 billion and probably by 2027 their ambitions to build $100 billion clusters. And I think all of that actually will happen. There's a lot of determination to build the compute to do it within this country. And I would guess that it actually does happen. Now, if we get to $100 billion, that's still not enough compute. That's still not enough scale, then either we need even more scale or we need to develop some way of doing it more efficiently, of shifting the curve. I think between all of these, one of the reasons I'm bullish about”

    2024-11-11 · Lex Fridman Podcast · #452 – Dario Amodei: Anthropic CEO on Claude, AGI & the Future of AI & Humanity · IDENTIFIED FROM THE TRANSCRIPT · source

  15. “Method or some new technique we need to unblock things. I've seen no evidence of that so far, but if things were to slow down that perhaps could be one reason.”

    2024-11-11 · Lex Fridman Podcast · #452 – Dario Amodei: Anthropic CEO on Claude, AGI & the Future of AI & Humanity · IDENTIFIED FROM THE TRANSCRIPT · source

  16. “Chain of thought and stop to think and reflect on their own thinking. In a way, that's another kind of synthetic data coupled with reinforcement learning. So my guess is with one of those methods, we'll get around the data limitation, or there may be other sources of data that are available. We could just observe that even if there's no problem with data, as we start to scale models up, they just stop getting better. It seemed to be a reliable observation that they've gotten better. That could just stop at some point for a reason we don't understand. The answer could be that we need to invent some new architecture. There have been problems in the past with, say, numerical stability of models where it looked like things were leveling off, but actually when we found the right unblocker, they didn't end up doing so. So perhaps there's some new options.”

    2024-11-11 · Lex Fridman Podcast · #452 – Dario Amodei: Anthropic CEO on Claude, AGI & the Future of AI & Humanity · IDENTIFIED FROM THE TRANSCRIPT · source

  17. “So, a few things now we're talking about hitting the limit before. Hundreds of trillions of words on the internet, but a lot of it is repetitive or it's search engine search engine optimization drivel or maybe in the future it'll even be text generated by AIs itself. And so I think there are limits to what can be produced in this way. That said, we, and I would guess other companies are working on ways to make data synthetic, where you can use the model to generate more data of the type that you have already, or even generate data from scratch. If you think about what was done with DeepMind's AlphaGo Zero, they managed to get a bot all the way from NOAA ability to play Go whatsoever to above human level just by playing against itself. There was no example data from humans required in the alpha go zero version of it. The other direction, of course, is these reasoning models that do”

    2024-11-11 · Lex Fridman Podcast · #452 – Dario Amodei: Anthropic CEO on Claude, AGI & the Future of AI & Humanity · IDENTIFIED FROM THE TRANSCRIPT · source

  18. “Yeah, I think in many cases, in theory, technology could change very fast. For example, all the things that we might invent with respect to biology. But remember, there's a clinical trial system that we have to go through to actually administer these things to humans. I think that's a mixture of things that are unnecessary and bureaucratic and things that kind of protect the integrity of society and the whole challenge is that it's hard to tell. It's hard to tell what's going on. It's hard to tell which is which, right? My view is definitely, I think, in terms of drug development. My view is that we're too slow and we're too conservative. But certainly if you get these things wrong, it's possible to risk people's lives by being too reckless. And so at least some of these human institutions are in fact protecting people. So it's all about finding the balance. I strongly...”

    2024-11-11 · Lex Fridman Podcast · #452 – Dario Amodei: Anthropic CEO on Claude, AGI & the Future of AI & Humanity · IDENTIFIED FROM THE TRANSCRIPT · source

  19. “Of folks trying to study the immune system or metabolic pathways. And each person understands only a tiny bit part of it, specializes, and they're struggling to combine their knowledge with that of other humans. And so I have an instinct that there's a lot of room at the top for AIs to get smarter. If I think of something like materials in the physical world or like addressing conflicts between humans or something like that, I mean, it may be there's only some of these problems are not intractable but much harder. And it may be that there's only so well you can do at some of these things, right? Just like with speech recognition, there's only so clear I can hear your speech. So I think in some areas there may be ceilings that are very close to what humans have done. In other areas, those ceilings may be very far away. And I think we'll only find.”

    2024-11-11 · Lex Fridman Podcast · #452 – Dario Amodei: Anthropic CEO on Claude, AGI & the Future of AI & Humanity · IDENTIFIED FROM THE TRANSCRIPT · source

  20. “I don't think any of us knows the answer to that question. My strong instinct would be that there's no ceiling below the level of humans, right? We humans are able to understand these various patterns. And so that makes me think that if we continue to scale up these models to kind of develop new methods for training them and scaling them up, that will at least get to the level that we've gotten to with humans. There's then a question of how much more is it possible to understand than humans do? How much is it possible to be smarter and more perceptive than humans? I would guess the answer has got to be domain dependent. If I look at an area like biology, and I wrote this essay, Machines of Loving Grace, it seems to me that humans are struggling to understand the complexity of biology, right? If you go to Stanford or Harvard or to Berkeley, you have whole departments.”

    2024-11-11 · Lex Fridman Podcast · #452 – Dario Amodei: Anthropic CEO on Claude, AGI & the Future of AI & Humanity · IDENTIFIED FROM THE TRANSCRIPT · source

  21. “Exactly. If you have a small network, you only get the common stuff. If I take a tiny neural network, it's very good at understanding that a sentence has to have, you know, verb, adjective, noun, right? But it's terrible at deciding what those verb adjective and nouns should be and whether they should make sense. If I make it just a little bigger, it gets good at that. Then suddenly it's good at the sentences, but it's not good at the paragraphs. And so these rarer and more complex patterns get picked up as I add more capacity to the network.”

    2024-11-11 · Lex Fridman Podcast · #452 – Dario Amodei: Anthropic CEO on Claude, AGI & the Future of AI & Humanity · IDENTIFIED FROM THE TRANSCRIPT · source

  22. “That long tail of other patterns is really smooth, like it is with the one over F noise in physical processes like resistors, then you can imagine as you make the network larger, it's kind of capturing more and more of that distribution. And so that smoothness gets reflected in how well the models are at predicting and how well they perform. Language is an evolved process, right? We've developed language. We have common words and less common words. We have common expressions and less common expressions. We have ideas, clich ⁇ s that are expressed frequently. And we have novel ideas. And that process has developed, has evolved with humans over millions. And so the guess, and this is pure speculation, would be that there's some kind of long-tail distribution of the distribution of these ideas.”

    2024-11-11 · Lex Fridman Podcast · #452 – Dario Amodei: Anthropic CEO on Claude, AGI & the Future of AI & Humanity · IDENTIFIED FROM THE TRANSCRIPT · source

  23. “That are produced by some natural process that has a lot of different scales, right? Not a Gaussian, which is kind of narrowly distributed, but if I look at kind of like large and small fluctuations that lead to electrical noise, they have this decaying one over x distribution. And so now I think of like patterns in the physical world, right? Or in language. If I think about the patterns in language, there are some really simple patterns. Some words are much more common than others, like the. Then there's basic noun verb structure. Then there's the fact that nouns and verbs have to agree. They have to coordinate. And there's the higher level sentence structure. Then there's the thematic structure of paragraphs. And so the fact that there's this regressing structure, you can imagine that as you make the networks larger, first they capture the really simple correlations, the really simple patterns, and there's this long tail of other patterns.”

    2024-11-11 · Lex Fridman Podcast · #452 – Dario Amodei: Anthropic CEO on Claude, AGI & the Future of AI & Humanity · IDENTIFIED FROM THE TRANSCRIPT · source

  24. “So, in my previous career as a biophysicist, so I did physics undergrad and then biophysics in grad school. So I think back to what I know as a physicist, which is actually much less than what some of my colleagues had anthropic have in terms of expertise in physics, there's this concept called the one over f noise and one over x distributions, where often, you know, just like if you add up a bunch of natural processes, you get a Gaussian. If you add up a bunch of kind of differently distributed natural processes, if you like, if you like take a probe and hook it up to a resistor, the distribution of the thermal noise in the resistor goes as one over the frequency. It's some kind of natural convergent distribution. And I think what it amounts to is that if you look at a lot of things that are”

    2024-11-11 · Lex Fridman Podcast · #452 – Dario Amodei: Anthropic CEO on Claude, AGI & the Future of AI & Humanity · IDENTIFIED FROM THE TRANSCRIPT · source

  25. “Yeah, we've documented scaling laws in lots of domains other than language, right? So initially the paper we did that first showed it was in early 2020 where we first showed it for language. There was then some work late in 2020.”

    2024-11-11 · Lex Fridman Podcast · #452 – Dario Amodei: Anthropic CEO on Claude, AGI & the Future of AI & Humanity · IDENTIFIED FROM THE TRANSCRIPT · source

  26. “Yes In particular, linear scaling up of bigger networks, bigger training times, and more data. So all of these things, almost like a chemical reaction. You know, you have three ingredients in the chemical reaction, and you need to linearly scale up the three ingredients. If you scale up one, not the others, you run out of the other reagents and the reaction stops. But if you scale up everything in series, then the reaction can proceed.”

    2024-11-11 · Lex Fridman Podcast · #452 – Dario Amodei: Anthropic CEO on Claude, AGI & the Future of AI & Humanity · IDENTIFIED FROM THE TRANSCRIPT · source

  27. “Really believe that probably the scaling is going to continue and that there's some magic to it that we haven't really explained on a theoretical basis yet.”

    2024-11-11 · Lex Fridman Podcast · #452 – Dario Amodei: Anthropic CEO on Claude, AGI & the Future of AI & Humanity · IDENTIFIED FROM THE TRANSCRIPT · source

  28. “And, you know, when I first heard them, honestly, I thought probably I'm the one who's wrong and, you know, all these experts in the field are right. They know the situation better than I do, right? There's, you know, the Chomsky argument about like you can get syntactics, but you can't get semantics. There was this idea, oh, you can make a sentence make sense, but you can't make a paragraph make sense. The latest one we have today is, you know, we're going to run out of data or the data is in high quality enough or models can't reason. And each time every time we manage to either find a way around or scaling just is the way around. Sometimes it's one, sometimes it's the other. And so I'm now at this point. I still think, you know, it's always quite uncertain we have nothing but inductive inference to tell us that the next few years are going to be like the next, the last 10 years. But I've seen the movie enough times. I've seen the story happen for enough times to.”

    2024-11-11 · Lex Fridman Podcast · #452 – Dario Amodei: Anthropic CEO on Claude, AGI & the Future of AI & Humanity · IDENTIFIED FROM THE TRANSCRIPT · source

  29. “Trillions of words of language data we can train on them and the models we were trained in those days were tiny. You could train them on one to eight GPUs, whereas now we train jobs on tens of thousands, soon going to hundreds of thousands of GPUs. And so when I saw those two things together, and there were a few people like Ilya Sutzkiver, who you've interviewed who had somewhat similar views, right? He might have been the first one, although I think a few people came to similar views around the same time, right? There was Rich Sutton's bitter lesson. There was Guerren wrote about the scaling hypothesis. But I think somewhere between 2014 and 2017 was when it really clicked for me, when I really got conviction that, hey, we're going to be able to do these incredibly wide cognitive tasks if we just scale up the models. And at every stage of scaling, there are always arguments.”

    2024-11-11 · Lex Fridman Podcast · #452 – Dario Amodei: Anthropic CEO on Claude, AGI & the Future of AI & Humanity · IDENTIFIED FROM THE TRANSCRIPT · source

  30. “Neural networks, and I said, I don't know. What if you make them bigger and give them more layers? And what if you scale up the data along with this? I just saw these as like independent dials that you could turn. And I noticed that the model started to do better and better as you gave them more data, as you as you made the models larger, as you trained them for longer. And I didn't measure things precisely in those days, but along with colleagues, we very much got the informal sense that the more data and the more compute and the more training you put into these models, the better they perform. And so initially my thinking was, hey, maybe that is just true for speech recognition systems, right? Maybe that's just one particular quirk, one particular area. I think it wasn't until 2017 when I first saw the results from GPT-1 that it clicked for me that language is probably the area in which we can do this.”

    2024-11-11 · Lex Fridman Podcast · #452 – Dario Amodei: Anthropic CEO on Claude, AGI & the Future of AI & Humanity · IDENTIFIED FROM THE TRANSCRIPT · source

  31. “So, I can only describe it as it relates to kind of my own experience, but I've been in the AI field for about 10 years. And it was something I noticed very early on. So I first joined the AI world when I was working at Baidu with Andrew Ng in late 2014, which is almost exactly 10 years ago now. And the first thing we worked on was speech recognition systems. And in those days, I think deep learning was a new thing. It had made lots of progress, but everyone was always saying we don't have the algorithms we need to succeed. You know, we're not, we're only matching a tiny, tiny fraction. There's so much we need to kind of discover algorithmically. We haven't found the picture of how to match the human brain. And when in some ways it was fortunate, I was kind of, you know, you can have almost beginner's luck, right? I was like a newcomer to the field. And, you know, I looked at the neural net that we were using for speech, the recurrent.”

    2024-11-11 · Lex Fridman Podcast · #452 – Dario Amodei: Anthropic CEO on Claude, AGI & the Future of AI & Humanity · IDENTIFIED FROM THE TRANSCRIPT · source