Matthieu Wyart: commitment

Transcript context

…It's so interesting that you're thinking of the constraints as being the algorithm rather than the energy landscape itself. And I get it. I think you're leaning towards there being some kind of a universal learning algorithm. But the way I intuit it is it's almost like the data and the world are more meaningful as constraints. Is that legible? Okay. So I think we will be talking about creativity and then I will also be discussing a lot about constraint, but there will be in my way of thinking in another space. So I started to tell you we are discussing loss landscape and the constraint here was just to feed data. And later on something I'm really interested to discuss is if you think about the world itself, sentences, the data itself, forget about the algorithms that's actually learning it. So data itself is very constrained. All possible sentences are not valid in terms of syntax. So I think thinking of constraint is very useful in both cases but I think of them as very different kind of constraints. And do you still think of yourself as a physicist 1st? I mean, because now we're talking about the physics of learning, we're talking about machine learning.…

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