Paul Christiano: uncertainty

Transcript context

…But what are the ODS you think you’ll succeed? I mean, it depends a little bit what you mean by succeed. But if you, say, get explanations that are great and accurately reflect reality and work for all of these applications that we’re imagining or that we are optimistic about, like kind of the best case success, I don’t know, like 1020 percent something. And then there’s like a higher probability of various intermediate results that provide value or insight without being, like, the whole dream. But I think the probability of succeeding in the sense of realizing the whole dream is quite low. Yeah in terms of what explanations look like physically or like the most ambitious plan, the most optimistic plan is that you are searching for explanations in parallel with searching for neural networks. So you have a parameterization of your space of explanations which mirrors the parameterization of your space of neural networks. Or you should think of as kind of similar to what is a neural network? It’s some simple architecture where you fill in a trillion numbers and that specifies how it behaves. So to you should expect an explanation to be like a pretty flexible general skeleton that’s saying pretty flexible general skeleton which just has a bunch of numbers you fill in. And what you are doing to produce an explanation is primarily just filling in these floating point numbers. When we conventionally think of explanations if you think of the explanation for why the universe moves this way it wouldn’t be something that you could discover on some smooth evolutionary surface where you can climb up the hill towards the laws of physics. These are the laws of physics. You kind of just derive them from reverse principles. But in this case it’s not like just a bunch of correlations between the orbits of different planets or something. Maybe the word explanation has a different I didn’t even ask the question but maybe you can just speak to that.…

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