Silvana Konermann

then you're spending a few years on checking whether that's the right one, right?

So if you have 40,000 things to pick from, even if you just have to pick a single one, that takes forever, right?

And that's why we haven't cured these diseases yet.

The whole point of it is that it is a universal virtual cell, which means that it needs to learn how to generalize to a new kind of cell or a new state of a cell, a new disease, for example, without having seen data, training data for that new cell type.

So that is a very challenging task, and that's why we're really thinking hard about how to do these studies.

But ultimately, the vision is that this is actually real.

So we have already built our first model that came out eight months ago.

It's not very good.

So, I mean, to be clear, it is state-of-the-art, right?

It's the best model at the time that was published.

But...

it has a really long way to go still to be at the accuracy that I think it needs to be to be really useful.

But an interface that, you know, uses that model that we have today, and so what you can do is you can say, okay, I have this cell that I'm starting with, and then, you know, I want to change this about the cell, and then it spits out different basically changes that you can make to the cell that are most likely to shift it the way you

That's right, yes.

So we have a few ways that we really want people... Thank you.

people to be able to interact with it and also follow along, right?

So one is we're going to be releasing this tool later this year for people to try.

We'll give caveats, like this is not very accurate or this is going to be 20 percent accurate, but also we're going to iterate over the next four years

We're also hosting a virtual cell challenge every year for the whole community.

We had 1,000 teams participating in the first one, and that's really to move the whole field forward to get to where I think we need to be.