Mazviita Chirimuuta

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.

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.

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,