Thomas Ahle: evaluation

Transcript context

…Very cool, very cool. I mean, what's really exciting to me is that it's possible to build chips that can do certain types of things orders of magnitude faster. And and that's why I wanna talk a little bit about thermodynamic computing. Right? So, you know, so apparently, instead of forcing transistors to settle at 0 or 1, you let noise do a random walk and bias it, so the chip is a stochastic differential equation. Right? I mean, that sounds crazy. Like, how does that work? Yeah. I mean, this is 1 of the things that really that got me to normal in the 1st place. Before normal I was at Facebook as we call them, in the research group that does probabilistic computing. Like we were doing like these spatial neural networks where you assume like probability distributions or all your weights and you try and infer like the posterior from like the prior data that you look at. But a lot of these techniques were kind of slow because you had to do, you had to like either try and do lots and lots of repetitions with different random seeds or you were trying to do it analytically. I mean that kind of maybe takes a little bit of the point out of the probability. But then you could also just, but then it's funny because you have these chips and like the chip manufacturers, they spend so much time like getting out every single little piece of noise out of their systems and like having these extremely sharp margins for everything like so much you know, precision is probably like the most precise business in the world. And then what do we do with them? We just like add randomness everywhere. And yeah, so why not try and build a chip that's just inherently random. Like I mean, yeah, I think the brain probably has bunch of randomness. But here the 1st chip we made was then this, you basically, you have this array of capacitors and you have certain resistances between them you can program and then you infuse all of this noise and it will start to behave according to these stochastic differential equations. And then you think, okay, what can we do with that? It's sort of, it's a new computational paradigm that we've found very interesting to explore where you can say 1 of the things, for example, you could do with it is it actually turns out that the matrix that you put onto the chip in the weights differential or the stochastic differential equation. It actually behaves sort of according to the inverse of that matrix. Yeah. And so then we could try and capture and average it out. And when we spoke about this on the phone, said something very interesting. Because, you know, we often talk a good game about this. Was talking with Michael Jordan the other day and, you know, like, yeah, we need uncertainty quantification, we need adaptive computation…

Stored transcript either side of the excerpt. The highlighted words are the published quote; the surrounding text is unedited source, never generated.