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Comment by elcritch | Hacker News Reader

Anybody familiar with this method? It looks very intriguing as it looks to be combinging Bayesian posteriors with the neural activation function. Haven’t had time to dig into myself, and it would be nice to know an outside perspective on it.

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A few years back I spent some time reading and following the equations in Friston's papers, maybe understanding like 90% of it. you have to know dynamical systems, differential equations, multivariate matrix stuff, etc. Basically stuff physicists are good at. Seemed that the theory wasn't detailed enough to inspire the next deep NN revoluation, basically the form of the generative model Friston used is very general. My impression was that the coded matlab examples reqired you specify known quantities, like velocity or position. But that requires a human to input, not like a NN where you can just point it at some data and it learns. Would love to be shown otherwise...

I'd be interested to hear any advice you might have on understanding the papers. Particularly, how do you understand the "priors" that specify goals/preferences/set-points in active inference?

Nice! Finally could put my physicist training to use... I skimmed the cited papers but they seemed very generalized as you mentioned. I’ll have to find the matlab code examples to understand how a complete system model would look. The linked post seems to deal with just a single perceptron/activation function AFAICT. The embedded constants don’t bother me too much as both physics models have embedded constants and I presume evolved neurobiological systems would have been tuned over time to incorporate the appropriate constants for dealing with useful quantities (force, mass, etc). Could be fun to port the matlab code to Julia!

OK, here's what I was referring to:

https://www.fil.ion.ucl.ac.uk/spm/software/spm12/

in this package there are (was?) some scripts for running dynamic expectation maximization. Cheers

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