Dario Amodei: belief

Transcript context

…The following is a conversation with Dario Amodei, CEO of Anthropic, the company that created Claude, that is currently and often at the top of most LLM benchmark leader boards. On top of that, Dario and the Anthropic team have been outspoken advocates for taking the topic of AI safety very seriously. And they have continued to publish a lot of fascinating AI on this and other topics. I’m also joined afterwards by two other brilliant people from Anthropic. First Amanda Askell, who is a researcher working on alignment and fine-tuning of Claude, including the design of Claude’s character and personality. A few folks told me she has probably talked with Claude more than any human at Anthropic. So she was definitely a fascinating person to talk to about prompt engineering and practical advice on how to get the best out of Claude. After that, Chris Olah stopped by for a chat. He’s one of the pioneers of the field of mechanistic interpretability, which is an exciting set of efforts that aims to reverse engineering neural networks, to figure out what’s going on inside, inferring behaviors from neural activation patterns inside the network. This is a very promising approach for keeping future super-intelligent AI systems safe. For example, by detecting from the activations when the model is trying to deceive the human it is talking to. This is the Lex Fridman podcast. To support it, please check out our sponsors in the description. And now, dear friends, here’s Dario Amodei. Let’s start with a big idea of scaling laws and the scaling hypothesis. What is it? What is its history, and where do we stand today? So I can only describe it as it relates to 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. We are only matching a tiny fraction. There’s so much we need to discover algorithmically. We haven’t found the picture of how to match the human brain.” And in some ways it was fortunate, you can have almost beginner’s luck. I was like a newcomer to the field. And I looked at the neural net that we were using for speech, the recurrent 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 independent dials that you could turn. And I noticed that the models started to do better and better as you gave them more data, 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. 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. We can get 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 Sudskever who you’ve interviewed, who had somewhat similar views. He might’ve 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, Gwern 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 these incredibly wide cognitive tasks if we just scale up the models. ” where 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 these incredibly wide cognitive tasks if we just scale up the models.” And at every stage of scaling, there are always arguments. And when I first heard them honestly, I thought, “Probably I’m the one who’s wrong and all these experts in the field are right. They know the situation better than I do, right?” There’s the Chomsky argument about, “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, “We’re going to run out of data, or the data isn’t 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 it’s always quite uncertain. We have nothing but inductive inference to tell us that the next two years are going to be like the last 10 years. But I’ve seen the movie enough times, I’ve seen the story happen for enough times to 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.…

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