Dwarkesh Patel: prediction

Transcript context

…I think it’s worth thinking about why you expect diminishing returns and how well that argument actually applies in practice. An analogy I like is thinking about going to an event, like a wedding, and you go to the dessert buffet. They’ve put out thirty desserts. Naturally, what people do is the best desserts go first. We don’t quite have a well-ordered preference there, so maybe there’s some difference, but human beings are fairly similar, so the best desserts will go first. This is an argument for why you expect diminishing returns in a lot of different fields. If it’s relatively easy to see what’s available and people have similar preferences, then the best stuff goes first and it just gets worse and worse after that. If you look at a very static snapshot in time of scientific progress, maybe there’s some truth to that. But if somebody is standing behind the dessert table and is replenishing and restocking the desserts and keeps adding new ones in, it may turn out that a little bit later, much better desserts appear, and you’re going to go and eat those instead. Scientific progress has a little bit of that flavor. We go through these funny time periods. Computer science is a great example, where computer science basically arose as a side effect of some pretty abstruse questions in the philosophy of mathematics and logic. You’ve got these people trying to attack these rather esoteric questions that seem quite high up in exploration, and they discover this fundamental new field, and all of a sudden there’s an explosion there. The diminishing returns argument just didn’t apply there. We just weren’t able to see what was there. This has been the case over and over again. New fields arrive and all of a sudden, and boom, it’s easy to make progress again. Young people flood in because you can be twenty-one and make major breakthroughs rather than having to spend twenty-five years mastering everything that’s been done before. It’s obviously very attractive. I’m not sure anybody understands very well the dynamics of that, or how to think about why the structure of knowledge is that way, where these new fields keep opening up. But it does seem empirically to be the case. Despite the fact that that is the case… Take deep learning. Obviously, this is an example of a new field where twenty-one-year-olds can make progress and it’s relatively new. Fifteen years or so since it got back into high gear. But already we’re in a stage where you need billions, tens of billions, or hundreds of billions of dollars to keep making progress at the frontier. There are a couple ways to understand that. One is that it actually is harder than the kinds of things the ancients had to do, or is more intensive at least. Second is it might not have been, but because our civilizational resources are so large, the amount of people is so large, the amount of money is so large, we can basically make the kind of progress it would have taken the ancients forever to make almost immediately. We notice something is productive and immediately dump in all the resources. But it’s also weird that there’s not that many of them. I feel like deep learning is notable because it is one big exception to the fact that it’s hard to think of other examples. I think that’s a consequence of the architecture of attention. At any given time, there’s always a most successful thing. If deep learning wasn’t a thing, maybe you’d be talking about CRISPR. Maybe we wouldn’t think about solving the protein structure prediction problem as a success of AI. Maybe we would have figured out how to do it with curve fitting, more broadly construed, and we’d just be like, “Wow, that took a lot of computing resources.” But protein structure prediction might be an enormously important thing. There is always our biggest thing. What you’re pointing at is more a consequence of the way in which attention gets centralized. It’s basically fashion, is what I’m saying. It’s not just fashion, but there is some dynamic there.…

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