Comment by roundabout-host
12 hours ago
It also cannot do tasks it wasn't trained for. It can extend texts, read images and click on a desktop, but only because it's made for that.
12 hours ago
It also cannot do tasks it wasn't trained for. It can extend texts, read images and click on a desktop, but only because it's made for that.
I don't think that's strictly true, as I can give it a new gui or tui program it wasn't trained on and it will learn it. Unless you're talking about general abilities like sight, but the same is somewhat true of humans.
If you consider the data on which an LLM was trained on to be points on a very highly multidimensional object, the claim is that the LLM can interpolate a convex hull spanned by those points, therefore recovering a subset of consequences attainable from those points. Obviously this hull includes completely novel points that were not present in the initial data set, so the output of the LLM goes beyond its initial training. And yet, there are clearly points outside a convex hull spanned by any finite number of points, such that we can imagine not all possible outputs are attainable using this method.
The claim is furthermore that truly original thinking, the infamous leaps in understanding and creativity, happen by attaining points outside such a convex hull.
It's hard to rigorously verify or disprove this claim. Hopefully this helps build an intuition of why the claim is not as shallow and obviously wrong as it may seem initially.
I'd call it interpolation on a high dimensional manifold. Convex hull is too simple a shape.
But yes, metaphorically I think that's right.
This makes me realize there is a higher bar we need to achieve with AI still. The ability for the model to evolve through interactions more on a hourly or daily basis. The models are accelerating but inference doesn’t modify the model.
This is false, unless you think the same for humans
Humans cannot do tasks they are not "trained" for.
speak for yourself