9q9

That's also why I worked with his team and read a lot of his papers for a while. His principal idea was originally that neurons perform free energy minimisation. This idea makes a lot of sense, once you understand what free energy means. But, to the best of my knowledge, it has not at all been empirically verified for neurons (I'd be delighted to be proven wrong in this belief). So he went the route of generalising the free energy principle: "the free energy principle asserts that any “thing” that attains a nonequilibrium steady state can be construed as performing an elemental sort of Bayesian inference". Terms "can be construed" and "elemental sort of Bayesian inference" do a lot of work here. Updating and generalising one's research hypothesis is legitimate (albeit one could be more explicit about this), but it weakens the claim being made. Anyway, under a charitable interpretation of those terms, I agree that this is true, but, at the same time doesn't say much. Indeed, under the charitable interpretation it basically equates doing free energy minimisation with existence. Friston has lately said that the FEP is not falsifiable. Take it from the horse's mouth (i.e. a Verses employee): "the free energy principle just applies to stones, it applies to birds, it applies to any kinds of animals" on Machine Learning Street Talk [1].

Here is my current position: from a mathematical principle this general one cannot derive scalable ML algorithms!

> he seems to have joined only in 2022, they were 4 years old at the time.

The company founders have a cryptocurrency and (later) metaverse background.

"We are the next DeepMind" is easy to say ... The DeepMind founders had a stellar predigee in computer games, AI and neuroscience, the Verses founders have a cryptocurrency background. Verses also released [1] last month. What both the Atari and the Mastermind announcements have in common is the lack of details, including code. Why do they not show their code? How do we know their figures are real? We've just had the OpenAI vs FrontierMath discussion [2, 3]. Presumably, being able to play Pong, a 1972 computer game, is unlikely to be their moat ...

Interesting also their 2024 MLST presentation [4]. Does that inspire confidence? It was that video that made my priors on Friston having had a breakthrough in ML change downwards dramatically ... But do not take my word for it, please make up your own mind.

- The CEO is an "International Bestselling Author" [1].

- The company blog states that Friston has "successfully [decoded] the underlying mechanisms of intelligence as it functions in the brain and biological systems" [2].

But they got $10M investment from G42, an Emirati VC [3]. Note that G42 have also invested in Cerebras and OpenAI [4]. So their PR works.

Coincidentally, Friston's treatment [1] of the dark room is not convincing, but it nicely illustrates Friston's tendency to make ad-hoc adjustments, for example in [1] he talks about "average" surprise, but there are many ways you can average. Which one is it? How for example do the 302 neurons of C elegans average? Saying this is a difficult task is correct given our understanding of neurons in 2020, but the fact that Friston seems to think Free Energy accomodates all possibilities means it in "not even wrong" territory. In it's current shape, Free Energy does not make interesting predictions for neuroscience, and none of the progress in AI/ML has come from the Free Energy millieu either.

If "surprise is used in a very technical statistical sense" means something concrete, precise, for example minimising KL-divergence of states, the question becomes: show me that this is what the brain does. Or build an AI that does something that is competitive with other forms of contemporary AI.

   ideas are hardly vague. 
   Hard to understand 

If read literally, Friston's core intuition is false: people regularly and deliberately expose themselves to surprise, e.g. gambling, watching sports, speed dating. Now there are various ad-hoc fixes to save free-energy-minimisation, which should make the theory more testable, but Friston then has to state clearly which of the many conflicting ad-hoc fixes are in place, and explain how they manifest themselves in the brain! Friston has been confronted with those problems many times, but he basically ignores them.