Jeff Beck: preference

Transcript context

…Yeah. Was a similar thing with constraint satisfaction. In the 1970s, there was that Lighthill report, people said Symbolic AI will never work, and they wrote it off. Apparently, just that there are all of these empirical methods that have been discovered in the last 20 years that just make it massively more scalable and tractable. And is it the same thing here? Are there some specific techniques that have dramatically improved the tractability of active inference? Well I would just sort of, I would lump it all into the into the Bayesian inference category. There have been a number of developments over the last, I would say 8. 8. Yeah, 8 years or so, that have made Bayesian inference significantly more tractable than it used to be. Some of it had to do with, you know, work in the sort of Gaussian process space. My my my current favorite trick is is, you know, normalizing flows, which is a great way of ensuring that you have, like, access to sophisticated likelihoods, but nonetheless result in tractable probability distributions. There's the work, you know, I mean, I've been using I've been using like natural gradient methods for a very long time, which allow you to like massively speed up gradient inference and in some situations completely eliminate the need to do gradient inference and instead like, you know, do coordinate descent and allowing you to take massive jumps in parameter space and not actually lose the the ability to do learning of sophisticated model in in a sophisticated modeling scenario. I also like the fact that like the the natural gradient stuff has been getting some great acronyms recently like Bayesian online natural gradient for bong for short. I just think these guys these guys get me every time. I I wish I was that clever, honestly, but, like, it's it's but there have been a lot of developments in that space as well. You know, making you know, in addition additionally, there's been a lot of developments in like rapid sampling methods, conditional sampling methods, constrained methods like that that that that have really improved things. And I think that, like, 1 of the problems, again, with the active awareness community, you know, historically that that I think is now starting to change has been a hesitance to use these sort of a certain approximate methods. There's been this this focus on, like, straight up old school message passing. And you know, as soon as you sort of you know you know, if you relax the desire to be as Bayesian as possible, it opens up a lot more possibilities for for scaling this stuff up. When we're now talking about agents that are interacting with the world around them and that still presumably needs a lot of data.…

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