Cal Newport

I don't think open AI, they're implying it, but they're not saying this.

But I think the feeling we have online is like, well, that's a really hard thing to do.

It's a problem humans tried to solve but couldn't.

Does that mean we have the superhuman intelligence that now can give us super intelligent solutions?

performance, and all sorts of things I might care about, is it time to learn, like Sarah Connor in Terminator 2, how to cock a shotgun with one arm and show off our awesome guns?

By which I mean biceps.

The answer here is likely no as well.

Remember, in a previous AI Reality Check episode of this podcast, I said there's two mental models for thinking about AI capabilities.

The one model is a rising water one.

The capabilities grow

And it's like a water level of capability rising.

And you have mountains that represent problems of different difficulties.

And as the water rises, all mountains of that difficulty are now solvable.

So if the water is high enough to cover the Erdos open problem peaks, then it's covering all these other even easier problems that are relevant to our everyday life.

And therefore, we're about to have really big disruptive impacts.

That, however, is the wrong way to think about AI development.

I said the better way to think about AI capabilities is like you have like a main river and you're exploring tributaries into the river.

And some of those, so each of these tributaries is like another type of problem or application.

And some of these tributaries are able to make a lot of progress.

And other ones, you almost immediately get stuck and it becomes non-navigable.