TBH, I didn’t engage much with the paper (I skimmed it beyond the abstract). I just deferred to the results. Here’s extended academic correspondence on this paper between groups of scientists and the authors, if you are interested. I also didn’t read this correspondence, but it doesn’t seem that the critics claim that the work was really a statistical failure.
But supposing the result in their paper is legit, I still don’t know enough neuroscience to understand what’s going on. Like you are saying that it is naturally explained by Active Inference, and that there’s no other easy-to-understand explanation. And you might totally be right about that! But if Steven Byrnes comes up and says that actually the neurons they used had some property that implements reinforcement learning, I would have no way of deciding which of you are right, because I don’t understand the mechanisms involved well enough.
As I wrote in section 4, Steven could come up and say that the neurons actually perform maximum entropy RL (which should also be reward-free, because nobody told the culture to play Pong), because these are indistinguishable at the multi-cellular scale already. But note (again, I trace to the very origin of the argument) that Steven’s argument contra Active Inference was here that there would be more “direct” modes of explanation of the behaviour than Active Inference. For a uniform neuronal culture, there are no intermediate modes of explanation between direct connectome computation, and Active Inference or maximum entropy RL (unlike full-sized brains, where there are high-level structures and mechanisms, identified by neuroscientists).
Now, you may ask, why would we choose to use Active Inference rather than maximum entropy reward-free RL as a general theory of agency (at least, of somewhat sizeable agents, but any agents of interest would definitely have the minimum needed size)? The answer is twofold:
Active Inference provides more interpretable (and, thus, more relatable, and controllable) ontology, specifically of beliefs about the future, which we discuss below. RL doesn’t.
Active Inference is multi-scale composable (I haven’t heard of such work wrt. RL), which means that we can align with AI in a “shared intelligence” style in a principled way.
Why not take the goal to be a utility function instead of the free energy of a probability distribution? Unless there’s something that probability distributions specifically give you, this just seems like mathematical reshuffling of terms that makes things more confusing.
Goal = utility function doesn’t make sense, if you think about it. What your utility function is applied to, exactly? It’s observational data, I suppose (because your brain, as well as any other physical agent, doesn’t have anything else). Then, when you note that real goals of real agents are (almost) never (arguably, just never) “sharp”, you arrive at the same definition of the goal that I gave.
Also note that in the section 4 of the post, in this picture:
There is expected utility theory in the last column. So, utility function (take E[logP(o)]) is recoverable from Active Inference.
Yeah, to me this all just sounds like standard Free Energy Principle/Active Inference obscurantism. Especially if you haven’t even read the paper that you claim is evidence for FEP.
Yeah, to me this all just sounds like standard Free Energy Principle/Active Inference obscurantism.
Again (maybe the last time), I would kindly ask you to point what is obscure in this simple conjecture:
Goal = utility function doesn’t make sense, if you think about it. What your utility function is applied to, exactly? It’s observational data, I suppose (because your brain, as well as any other physical agent, doesn’t have anything else). Then, when you note that real goals of real agents are (almost) never (arguably, just never) “sharp”, you arrive at the same definition of the goal that I gave.
I genuinely want to make my exposition more understandable, but I don’t see anything that wouldn’t be trivially self-evident in this passage.
Especially if you haven’t even read the paper that you claim is evidence for FEP.
DishBrain was not brought up as “evidence for Active Inference” (not FEP, FEP doesn’t need any evidence, it’s just mathematical tool). DishBrain was brought up in the reply to the very first argument by Steven: “I have yet to see any concrete algorithmic claim about the brain that was not more easily and intuitively [from my perspective] discussed without mentioning FEP” (he also should have said Active Inference here).
These are two different things. There is massive evidence for Active Inference in general, as well as RL in the brain and other agents. The (implied) Steven’s argument was like, “I haven’t seen a real-world agent, barring AIs explicitly engineered as Active Inference agents, for which Active Inference would be “first line” explanation through the stack of abstractions”.
This is sort of a niche argument that doesn’t even necessarily need a direct reply, because, as I wrote in the post and in other comments, there are other reasons to use Active Inference (precisely because it’s a general, abstract, high-level theory). Yet, I attempted to provide such an example. Even if this example fails (at least in your eyes), it couldn’t invalidate all the rest of the arguments in the post, and says nothing about obscurantism of the definition of the “goal” that we discuss above (so, I don’t understand your use of “especially”, connecting the two sentences of your comment).
Your original post asked me to put a lot of effort into understanding a neurological study. This study may very well be a hoax, which you hadn’t even bothered checking despite including it in your post.
I’m not sure how much energy I feel like putting into processing your post, at least until you’ve confirmed that you’ve purged all the hoaxy stuff and the only bits remaining are good.
“Unless you can demonstrate that it’s easy” was not an ask of Steven (or you, or any other reader of the post) to demonstrate this, because regardless of whether DishBrain is hoax or not, that would be large research project worth of work to demonstrate this: “easiness” refers anyway to the final result (“this specific model of neuronal interaction easily explains the culture of neurons playing pong”), not to the process of obtaining this result.
So, I thought it is clear that this phrase is a rhetorical interjection.
And, again, as I said above, the entire first argument by Steven is niche and not central (as well as our lengthy discussion of my reply to it), so feel free to skip it.
TBH, I didn’t engage much with the paper (I skimmed it beyond the abstract). I just deferred to the results. Here’s extended academic correspondence on this paper between groups of scientists and the authors, if you are interested. I also didn’t read this correspondence, but it doesn’t seem that the critics claim that the work was really a statistical failure.
As I wrote in section 4, Steven could come up and say that the neurons actually perform maximum entropy RL (which should also be reward-free, because nobody told the culture to play Pong), because these are indistinguishable at the multi-cellular scale already. But note (again, I trace to the very origin of the argument) that Steven’s argument contra Active Inference was here that there would be more “direct” modes of explanation of the behaviour than Active Inference. For a uniform neuronal culture, there are no intermediate modes of explanation between direct connectome computation, and Active Inference or maximum entropy RL (unlike full-sized brains, where there are high-level structures and mechanisms, identified by neuroscientists).
Now, you may ask, why would we choose to use Active Inference rather than maximum entropy reward-free RL as a general theory of agency (at least, of somewhat sizeable agents, but any agents of interest would definitely have the minimum needed size)? The answer is twofold:
Active Inference provides more interpretable (and, thus, more relatable, and controllable) ontology, specifically of beliefs about the future, which we discuss below. RL doesn’t.
Active Inference is multi-scale composable (I haven’t heard of such work wrt. RL), which means that we can align with AI in a “shared intelligence” style in a principled way.
More on this in “Designing Ecosystems of Intelligence from First Principles” (Friston et al., Dec 2022).
Goal = utility function doesn’t make sense, if you think about it. What your utility function is applied to, exactly? It’s observational data, I suppose (because your brain, as well as any other physical agent, doesn’t have anything else). Then, when you note that real goals of real agents are (almost) never (arguably, just never) “sharp”, you arrive at the same definition of the goal that I gave.
Also note that in the section 4 of the post, in this picture:
There is expected utility theory in the last column. So, utility function (take E[logP(o)]) is recoverable from Active Inference.
Yeah, to me this all just sounds like standard Free Energy Principle/Active Inference obscurantism. Especially if you haven’t even read the paper that you claim is evidence for FEP.
Again (maybe the last time), I would kindly ask you to point what is obscure in this simple conjecture:
I genuinely want to make my exposition more understandable, but I don’t see anything that wouldn’t be trivially self-evident in this passage.
DishBrain was not brought up as “evidence for Active Inference” (not FEP, FEP doesn’t need any evidence, it’s just mathematical tool). DishBrain was brought up in the reply to the very first argument by Steven: “I have yet to see any concrete algorithmic claim about the brain that was not more easily and intuitively [from my perspective] discussed without mentioning FEP” (he also should have said Active Inference here).
These are two different things. There is massive evidence for Active Inference in general, as well as RL in the brain and other agents. The (implied) Steven’s argument was like, “I haven’t seen a real-world agent, barring AIs explicitly engineered as Active Inference agents, for which Active Inference would be “first line” explanation through the stack of abstractions”.
This is sort of a niche argument that doesn’t even necessarily need a direct reply, because, as I wrote in the post and in other comments, there are other reasons to use Active Inference (precisely because it’s a general, abstract, high-level theory). Yet, I attempted to provide such an example. Even if this example fails (at least in your eyes), it couldn’t invalidate all the rest of the arguments in the post, and says nothing about obscurantism of the definition of the “goal” that we discuss above (so, I don’t understand your use of “especially”, connecting the two sentences of your comment).
Your original post asked me to put a lot of effort into understanding a neurological study. This study may very well be a hoax, which you hadn’t even bothered checking despite including it in your post.
I’m not sure how much energy I feel like putting into processing your post, at least until you’ve confirmed that you’ve purged all the hoaxy stuff and the only bits remaining are good.
“Unless you can demonstrate that it’s easy” was not an ask of Steven (or you, or any other reader of the post) to demonstrate this, because regardless of whether DishBrain is hoax or not, that would be large research project worth of work to demonstrate this: “easiness” refers anyway to the final result (“this specific model of neuronal interaction easily explains the culture of neurons playing pong”), not to the process of obtaining this result.
So, I thought it is clear that this phrase is a rhetorical interjection.
And, again, as I said above, the entire first argument by Steven is niche and not central (as well as our lengthy discussion of my reply to it), so feel free to skip it.