Machine Learning Street Talk (MLST)
Abstraction & Idealization: AI's Plato Problem [Mazviita Chirimuuta]
23 Jan 2026
Chapter 1: What is the problem of generalizing neuroscience?
What should we say as philosophers about the relationship between neuroscience and philosophy of mind? So how much of our ideas about how the mind works can we read off from the results that neuroscience is telling us?
Chapter 2: How does abstraction differ from idealization in science?
The results you get in the lab can be well established and fine, there's nothing wrong with those data, but there's more of a problem of generalising from what you learn in the lab to outside of the lab with neuroscience.
For cognition in the real world, it's precisely all of that complexity and all of that interactivity that is really important to how, for example, animals are able to negotiate their environment. It's not an argument that AI is impossible so much as why does it seem so possible, so inevitable to people?
If you look at the history of the development of the life sciences of psychology, there are certain shifts towards a much more mechanistic understanding of both what life is and what the mind is, which are very congenial to thinking that whatever is going on in animals like us, in terms of the processes which lead to cognition, they're just mechanisms anyway.
So why couldn't you put them into an actual machine and have that actual machine do what we do?
Yes, but anyway, Mashwita, welcome to MLST.
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Chapter 3: What lessons can we learn from the reflex theory's oversimplification?
It's amazing to have you here.
Thanks so much for having me along.
So you wrote this book, The Brain Abstracted. It's an amazing book. Folks at home should definitely buy this book.
Chapter 4: How does haptic realism change our understanding of knowledge?
It's really, really good. Tell me about this book.
It was quite a few years in the making. I think officially I started writing it maybe 2018 and it came out in 2024. But it was really based on ideas that I'd been working on maybe since 2008. 2014, I started publishing some philosophy of science papers about computational explanation in neuroscience.
Then going back beyond that, some of my own experiences when I was doing training in neuroscience on the visual system and I was using computational models of the era before there was deep learning or anything that fancy.
thinking about really what does understanding the brain through this lens of computation by saying that we have models which not only simulate the brain as a biological simulation using computers and all kinds of things or weather simulations such and so forth but actually kind of alleged to duplicate the function of cells in the brain which is this kind of additional claim which is made of about computational
modeling when it's applied to the brain as this unique structure which is not only a biological organ but also a kind of computer itself.
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Chapter 5: Is the brain really comparable to a computer?
The arc of your book is we have this problem with simplification because as scientists we want to build legible theories about how the world works and
A lot of philosophy of science in recent years has picked up this topic of abstraction and idealization. So abstraction is sort of quite a general word, which can just mean sort of ignoring details which are there in concrete real life situations.
So it would be familiar to you from doing Newtonian problems and physics where your teacher tells you, well, there's always friction in real life, but we'll pretend that the friction isn't there. So you're leaving out a detail which is known to be there in the concrete system. Idealization means attributing properties to the system that you're modeling in science, which are known to be false.
So for example, in genetics modeling, the assumption is made of infinite populations. These kinds of idealizations often make the calculations more tractable, but of course, there's no such thing as an infinite population in real life.
Chapter 6: What are the implications of agency and causal powers in cognitive science?
In some way, an abstraction is also always a false representation, always an idealization. So sometimes the difference between the two can be subtle. How I put this in the book is that an idealization kind of points us to the thought that when we have a scientific representation, we're kind of presenting something which is kind of cleaner and better than the thing in real life.
When we talk about someone being idealistic, it's like they have a view of how things should be. And unfortunately, reality does not live up to that. So idealization in science is often to do with sort of representing things mathematically in a way which is kind of cleaner and neater than could be possible in real life.
And on abstraction, you said in your book that there's the lofty philosophical version of abstraction, which is, you know, upstairs in the heavens of Plato, I think you said, or even Galileo. There's this idea that these natural forms exist, which are disconnected entirely from the sort of the spatial, the temporal realms.
And then there's the more deflationary view of abstraction, which is simply that we just ignore details. Now, I'm speaking with my good friend Francois Chollet again tomorrow.
Chapter 7: How does Heidegger's philosophy relate to technology and human finitude?
He's releasing the new version of the Arc Challenge. And I think he does have this, and many AI researchers do, they have this Platonistic idea. He calls it the kaleidoscope effect, which is that the universe basically is written in code. And what we see is like a kaleidoscope, when all of the rules of the universe just get composed together in different ways.
And all we need to do as AI researchers is kind of decompose back into the rules. What could possibly go wrong?
So I watched some of the videos with Francois. I found it really fascinating, precisely this kaleidoscope hypothesis, because seeing that as a philosopher, I thought, that's Plato. Because Francois precisely says, we have the world of appearance. It's complicated. It looks intractable. It's messy. But underlying that real reality is neat and mathematical decomposable.
This is precisely this sort of contrast between the world of forms and the world of being, sort of eternal stable truth, and the world of becoming, appearance, messy, flowing, complicated reality. And so it goes back thousands of years in philosophy. It's really interesting that this is an assumption not only that AI researchers make often,
But it runs through science as a kind of justification for the pursuit of mathematical representations, even when they sort of depart from known facts about the concrete physical systems in reality. The idea that the mathematical representation is getting you more to the truth, the underlying truth of how things are, as opposed to what I call the down-to-earth view of what
abstraction is and mathematical representation is that it's something that we do because of our cognitive limitations.
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Chapter 8: What are the potential impacts of technology on future generations?
So instead of thinking that the abstraction gets you like the higher level of reality, just saying that we do abstraction because we're finite knowers, there's limits to how much complexity any individual person or group of people can actually encompass in their modeling strategies or representations.
And actually, it's only by pretending things that are more simple than they actually are that we get some traction. So that's like the down to earth mundane explanation of why abstraction is so much used in science.
Yeah, it's so pervasive in the deep learning world. I mean, I also interviewed the folks who pioneered this geometric deep learning blueprint. And that's the same idea, basically, that the world is described with geometry. And all we need to do is imbue these geometric or inductive priors into deep learning models.
And then they can, essentially, by reducing the degrees of freedom to ones which are aligned with how the universe works, then we get where we want to go.
I think the notion of patterns and real patterns, to invoke Dennett's term there, is a helpful one. So one thing that you could say is going on here is that, yes, there's lots of complexity there in the natural world.
apparent in the data, but if you just denoise the data a bit, underlying there, there's a real pattern and we don't have to be like Platonists and weird about it, but there's just regularity that is sometimes masked by noise. that doesn't seem like too metaphysically problematic.
But one of the questions that I posed to that as a challenge to that very moderate view, and I say this frequently in the book, is when you're saying that some of the apparent dysregularity in the data is irrelevant, That's your decision as a scientist. It's not relevant to you at the moment, but it could be relevant to someone else.
It could be really important to how that system works in the natural world for reasons that you're not aware of. So when we sort of classify the signal versus noise in our data sets, we shouldn't ignore the fact that those are decisions that we're bringing to bear on our investigation.
We shouldn't assume that we're just reading off the signal, the real pattern that is there in reality, and that there aren't very many other significant real patterns there. And to the extent that we're probably also kind of creating pattern through the very denoising process that we bring about.
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