Richard Socher

doesn't it?

Yeah.

What is it?

The Eureka machine, it's not the official term.

We definitely will use moving forward, but I do think it's a great analogy.

It's essentially an AI that got very good at inventing scientific advances.

And we'll start with scientific advances on the science of intelligence and artificial intelligence itself,

but eventually believe that that will lead to a machine that will then get even better at doing all kinds of and making all other kinds of inventions because it got very good at ideating, implementing and validating ideas.

Yeah, so recursive superintelligence is essentially an AI that improves itself.

That means over time, it also gets better at improving itself.

So it's a self-reinforcing loop that compounds with essentially no ceiling.

Like most of AI, we have like one specific objective function and you get these very sort of spiky,

intelligence capabilities.

Here, we believe this could get much, much smoother because what we're asking the AI to do is to automate knowledge work itself.

So automate the discovery of different kinds of knowledge.

And so that's why we think it will actually lead to this discovery engine, this Eureka machine.

And why is this possible now?

It's mostly because AI is code and AI can code.

And so you can actually loop that capability onto itself if you figure out a lot of other tricky things.

You know, people often say ideas are cheap and it's sort of true, right?