Zuzanna Stamirowska

Of course, first of all, we do it.

There are no reasons actually for it not to scale and like scaling laws are inherited from Transformer.

But there's also no big need to scale.

This is not the game of scaling of more parameters and more data because this is kind of not where the value is to come from.

The value is to come from faster learning how to solve problems that haven't been seen in the training data.

I like this.

This is where we want to get to.

And actually, if we can show better learning out of smaller data, well, this is the kind of value that...

that we want to prove.

So actually, I hope that very quickly, you know, we'll be more looking at models that are very small, but capable of producing results comparable to the big ones.

Love that.

That's awesome.

We're not looking at scale and root for scaling.

We're looking at this getting better at puzzle solving and reasoning and hopefully, you know, in as general way as possible to get it closer to the way that humans

reason, work, and ultimately innovate.

Because if you look at a real innovator, like the best ones that I know, because I kind of have them on the team, right?

It's not about seeing what's there, but it's seeing what's not there and what could be there.

We don't know, but I don't think those limits work in this way.

I don't think, I mean, right now we do have the number of neurons that you may get there.

You may imagine models where you would be adding them.