Sam Altman

There's things on the internet.

A lot of our work is building a great data set.

Maybe it'd be more fun if it were more.

There's a lot of content in the world, more than I think most people think.

Yeah, I think one thing that is not that well understood about creation of this final product, like what it takes to make GPT-4, the version of it we actually ship out that you get to use inside of ChatGPT, the number of pieces that have to all come together and then we have to figure out either new ideas or just execute existing ideas really well at every stage of this pipeline.

There's quite a lot that goes into it.

Isn't that so remarkable, by the way, that there's like a law of science that lets you predict for these inputs, here's what's going to come out the other end.

Like here's the level of intelligence you can expect.

I'll say it's way more scientific than I ever would have dared to imagine.

Like any new branch of science, we're going to discover new things that don't fit the data and have to come up with better explanations.

And that is the ongoing process of discovering science.

But with what we know now, even what we had in that GPT-4 blog post, I think we should all just be in awe of how amazing it is that we can even predict to this current level.

Well, there's all these different evals that we could talk about.

And... What's an eval?

Oh, like how we measure a model as we're training it, after we've trained it, and say like, you know, how good is this at some set of tasks?

Yeah, I think that'll be really helpful.

But the one that really matters is...

and we pour all of this effort and money and time into this thing.

And then what it comes out with, like how useful is that to people?

How much delight does that bring people?