Noam Shazeer

I mean, it's a pretty different way of operating than our current Gemini development.

So I think, you know, we will explore these kinds of areas and I think make some progress on them, but we need to sort of really see evidence that it's the right way, you know, that it has a lot of benefits.

Some of those benefits may be improved quality.

Some may be

sort of less concretely measurable, like this ability to have lots of parallel development of different modules.

But that's still a pretty exciting improvement because I think then that would enable us to make faster progress on improving the model's capabilities for lots of different distinct areas.

Like a personal module for you would be useful.

Another thing might be you can use certain data in some settings, but not in other settings.

And maybe we have some YouTube data that's only usable in a YouTube product surface, but not in other settings.

So we can have a module that is trained on that data for that particular purpose.

Yeah.

It's going to be great.

Blob 2.0.

Or maybe they're not even version.

It's just like an incrementally growing blob.

Yeah, I mean, I guess this kind of like notion of an organic, like kind of not quite so carefully mathematically constructed machine learning model is one that's been with you for a little while.

And I feel like

in the development of neural nets, like the biological analog, the artificial neurons, you know, inspiration from biological neurons is a good one and has served us well in the deep learning field.

And we've been able to make a lot of progress with that.

But I feel like we're not necessarily looking at other things that real brains do as much as we perhaps could.