Chris Kempes: preference

Transcript context

…Chomsky said to us that deep learning is a bit like, I mean, used the term anything goes. And in his estimation, it didn't kind of, you know, as a theory, it didn't demarcate what something isn't versus what it is. I mean, in your estimation, what makes a good scientific theory? And what does it mean for us to actually understand something? I mean, for me, it's all about compactness and compression, right? So I think the amazing thing about equations is that we can transmit them to each other quickly, easily in small forms. Now there's a huge amount of knowledge that 1 requires to then decode those equations, right, so it's not so simple, like you and I might both have to spend 30 years learning all the mathematics it takes to transmit a certain sort of equation to each other. But once we've done that, once we have that training, this compact form is really an easy way to transmit understanding and knowledge. 1 of my favorite ways to test understanding is to predict the unseen or the unexpected, and I think that's, many theorists get very excited when they have a theory that predicts something that hasn't been seen yet, because it's sort of, I think what Rob Phillips once called it a dangerous prediction, right? There's no data for it yet, There's no way you could have tuned your theory to match some old data. You're making a prediction that will either be right or wrong about something that hasn't been seen yet. But that's a real notion of understanding I think, to say, you know, we've never looked for this, we've never seen this, but if X is true and Y is true, then certainly Z must be out there, let's go find it. And I think that's a real demonstration of understanding. I've just read your paper with David Krakauer, Multiple Paths to Life, and towards the end of the paper you said, we want to have a universal theory of life. Tell me about that.…

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