Zuzanna Stamirowska

Well, the point is, you use it more, it becomes stronger.

I mean, this is just a principle.

And it's only positive activation, so there's no positive and negative.

It's only positive, it gets stronger.

Something is not used, over time it will start fading.

But journey speaking, the connections that were useful become stronger.

And this is kind of it.

And then, I mean, this structure is, you know, it's actually very efficient because it's like a brain.

So it's computationally efficient.

It distributes nicely.

It gives so many nice properties that unlock a number of things, you know, that then for us, even from the engineering standpoint, in terms of how it scales, how it distributes, how you can run it on many machines.

et cetera, et cetera.

But it's like a scale-free, so this is sorry, super geeky, but it's a scale-free graph structure.

So point is, even if we go beyond the scales that we've seen in data and tests, we scientifically know how it will behave.

It's very different from the Transformer, at least as we see it now, because for Transformer, it hasn't been studied.

It would probably be difficult to study.

For this, because we know how the emergence works, we know that, I mean, yeah, it's scale-free.

Same laws we'll be holding, you know, above what we've seen in tests and kind of data until now.

Yes, in a way.

So specifically, we do see very precisely neural activity.