[9q9]

Short answer: no!

Friston's work is (in)famous for being so vague as to be completely untestable. He has been criticised over the extreme vagueness of his ideas many times, and he has never given a good answer. Sometimes some of his followers try to make it testable as neuroscience or useful for AI. Both have failed so far. As far as I can see, leading working neuroscientists don't take this Friston / free-engery stuff seriously. He's even got a parody Twitter account now: https://twitter.com/farlkriston

He now claims to have the best COVID model based on free-energy. Can I please see the code and form my own opinion? Has anybody seen this code?

[ruralman]

See https://www.fil.ion.ucl.ac.uk/spm/covid-19/. At the bottom:

"The figures in these manuscripts can be reproduced using annotated (MATLAB/Octave) code that is available as part of the free and open source academic software SPM. The routines are called by a demonstration script that can be invoked by typing DEM_COVID or DEM_COVID_X at the MATLAB prompt. At the time of writing, these routines are available in the development version of the next SPM release."

In my view Friston's ideas are hardly vague. Hard to understand sometimes, yes, but when I have put in the effort to understand them I have always been rewarded.

[9q9]

I cannot see the "these routines [being] available in the development version of the next SPM release". The development version is a 111 MB zip file [1]. When I uncompress the file I get a big flat directory with 100s of files. Which of those is is the software used in the paper? I have a bad feeling about this. I don't see how the authors are displaying intellectual integrity by not releasing, concurrently with the paper, software for such an important problem public health issue.

   ideas are hardly vague. 
   Hard to understand 

The core intuition is easy to understand: brain predicts its observations including observations about itself (proprioception) and acts in a way to minimise surprise. This can be seen as a form of self-supervised learning in the terminology of contemporary machine learning. Lots of people have said somewhat similar things before at a similar level of vagueness. Nobody disagrees that "somehow" the brain learns about the world by prediction and interaction. The interesting question is to go beyond this vagueness: what exactly is the brain doing? Where exactly is the brain minimising 'free energy'? Can I have a testable prediction please?

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.

[1] https://www.fil.ion.ucl.ac.uk/spm/covid-19/#software

[uoaei]

Your challenges to the core intuition are predicated on a simplistic and uncharitable interpretation.

Gambling, watching sports, and speed dating all have secondary motivations (earning money, tribal success, potential to spread your genes, respectively), but what's more is that these are all arenas of controlled and quite specific surprise. You know exactly the type of surprise that you are going to get, and the satisfaction you get from being right or the post-rationalization you perform for being wrong are both useful to the human. Contrast this to the "surprise" of a global pandemic, or massive social unrest. No one knows what's going to happen next and so you have a large contingent of people who are desperately trying to enact conservatism of the "move things back to normal" flavor. This is a stress response, and the stress is induced by not knowing what kind of surprises lay ahead.

The latter is the kind of surprise that is being minimized in the free-energy framework.

[9q9]

I have explicitly stated that I am using a simplistic interpretation.

I am neither seeing that Friston has (A) produced anything even remotely resembling a testable framework this "kind of surprise that is being minimized in the free-energy framework" and (B) pointed to any plausible mechanisms in the brain that should that this is in fact "the kind of surprise that is being minimized". He just handwaves.

What clearcut evidence can you give me that humans minimise this "kind of surprise"? What evidence would you accept as falsifying this? Where does Friston make clear that "secondary motivations" don't count? Also making a super vague, unquantified statement like "large contingent of people who are desperately trying to enact conservatism ..." in defense of Friston / free-energy doesn't give me a lot of confidence in the social milieu that this theory comes from. All the more so, since my OPs explicitly criticised Friston for vagueness.

[ruralman]

In the zip file, see the files toolbox/DEM/spm_COVID*

What follows is my understanding.

> what exactly is the brain doing?

This is outside my area of expertise, but it is updating brain states (whatever that turns out to mean, neural mass activity, individual neural activity), and parameters, likely candidates being neurotransmitters. The mechanism has been proposed to be message passing among hierarchical regions of the cortex.

> Where exactly is the brain minimising 'free energy'?

It is a global effect, but whenever a state or parameter are updated (again whatever those are found to be) the free energy decreases. If these turn out to be localized then that would be the (context dependent) "where".

> Can I have a testable prediction please?

The one I am most interested in is, since generative models are the core of active inference, if active inference is true then we should expect to be able to identify such models and setup conditions under which they update according to the FEP, including actions.

This is a difficult task and I suspect it will be shown in a simple biological system like C-elegans first. My own interest is in cyber-physical systems.

> If read literally, the core intuition is also false because people regularly and deliberately expose themselves to surprise, e.g. gambling, watching sports. Now there are various ad-hoc fixes to save free-energy-minimisation, but then which of them many conflicting ad-hoc fixes?

This is the dark-room argument, which as you suggest has been beat to death. I admit to not understanding what the problem is. If a system has an internal model that keeps it from exploring then it would die (of starvation). What states are surprising is all about the priors (that are designed by evolution presumably) and experience. I think it is also important to be clear that surprise is used in a very technical statistical sense.

[9q9]

Gambling etc is not the dark-room argument, I've explicitly left out the dark-room.

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.

[1] K. Friston et al, Free-Energy Minimization and the Dark-Room Problem https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3347222/

[ruralman]

> there are many ways you can average. Which one is it?

I think it is pretty clear from the paper that the average is over time.

The most detailed (and certainly not vague) description is given in "A Free Energy Principle for Biological Systems", Entropy 2012

> How for example do the 302 neurons of C elegans average?

They don't. An agent can optimize an objective without computing it explicitly.

> Free Energy does not make interesting predictions for neuroscience

What is your objection to my proposed prediction above, that we will find brain models with observable activity that follows the FEP?

Friston also has many papers that explain known facts in terms of FEP. I will grant you those are not predictions but they are consistency arguments.

> none of the progress in AI/ML has come from the Free Energy millieu either

Free energy is a dynamical version of variational Bayes, which has had enormous impact in ML/AI.

[9q9]

Regarding average: which average in the sense of: average over what time window? Any specific choice here needs to be justified as happening in the brain.

Regarding "we will find brain models with observable activity that follows the FEP?": abstractly you are saying that your prediction for theory T is that we will eventually confirm T. This does not exclude anything, I can state this for any theory T whatsoever. (For fun, try to instantiate T with outlandish theories, e.g. with "We will eventually find weapons of mass destruction in Irak", or with plausible theories that have failed so far, e.g. "We will eventuallly see supersymmetry". Does your prediction rule anything out?)

Regarding variational Bayes, that was not invented by the Free Energy millieu.

[jotm]

Ah, that's the word I've been trying to recall while reading the article.

Lots of big words, with nothing specific, nothing actionable. Vague as it can be.

Doesn't help that the phrase "free energy" drives most people with any expertise either mad or catatonic. Conspiracy theorist have ruined it.

[9q9]

   expertise either mad or catatonic.

Yes.

Unfortunately it still works really well with students, who are not yet knowledgable enough to be able to recognise the Fristonian ideas for what they are--hot air--and who waste their time with active learning before realising that it's currently mostly hot air. I've seen this play out several times by now with my (current and former) students.

[sampo]

Here is some article, and there is a software note in the end with some links. I didn't really check it out, though. https://arxiv.org/abs/2004.04463

[9q9]

Thanks.

I looked at that link but could not find the code that lets me reproduce the results in the paper. Maybe I didn't look hard enough?