Abstraction & Idealization: AI's Plato Problem [Mazviita Chirimuuta]
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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?
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.
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.
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.
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.
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.
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Chapters
8 chapters
1
What is the problem of generalizing neuroscience?
0:00–0:15
2
How does abstraction differ from idealization in science?
0:15–1:24
3
What lessons can we learn from the reflex theory's oversimplification?
1:24–1:33
4
How does haptic realism change our understanding of knowledge?
1:33–2:51
5
Is the brain really comparable to a computer?
2:51–3:57
6
What are the implications of agency and causal powers in cognitive science?
3:57–5:12
7
How does Heidegger's philosophy relate to technology and human finitude?
5:12–7:02
8
What are the potential impacts of technology on future generations?
7:02–53:32