Matt Clifford

But it's worth saying, isn't it, going back to your earlier discussion of the technology, I think one thing that is not obvious to a lot of people is that these are not computer programs in the sense that I think most of us traditionally thought of them.

You can't go and say, well, here are the lines of code.

Why did it do the thing?

One metaphor, and it is a metaphor, but it's quite helpful, is these are computer programs that are grown.

rather than written.

This is a really hard technical problem, even if we just take out the risk question for a second.

Understanding why a large neural network has done a particular thing is just a very hard technical problem.

This thing is too- It's actually a little bit like neuroscience in the sense that one way of thinking about this is what these layers that Yoshua is talking about doing is building representations of ideas which may or may not map to human formulations of those ideas.

There is a field.

within AI called mechanistic interpretability, which is really trying to almost be the neurosurgeon saying like, if we turn this bit off, does the behavior change?

But it's almost at a very, very basic level, right?

It's very primitive.

You mean OpenAI's O1 model, the thinking model?

This is really recent, right?

This is the last year we've had thinking models.

For listeners,

You're the godfather of this.

You're very compelling.

It sounds very scary.

I've worked a lot on these issues, and I also find them troubling, as you know.