Dr. Emilia Javorsky

So in that domain, AI does very well when it has the data to work with and that data is sufficiently representative of the phenomenon that we would like to study.

One area we're hearing a lot about is AI being able to predict whether a new drug is going to be toxic or non-toxic.

And that's because we have extensive libraries of existing compounds that we know whether or not those cause problems or adverse events when they were put into people.

So the AI can take a look at the new compound and say, OK, based on all of my knowledge of everything else that's either safe or unsafe, what do I think this will be?

Do I predict this to be more likely to be safe or unsafe?

And that's called computational toxicology.

And AI is doing a great job at that.

We're hearing a lot about AI for drug design, right?

Being able to really just lean into the chemistry part of biology, even more so than biology itself, to design new molecules, to design new drugs.

So that also is, I'd say, an area that's quite exciting.

And then there's clinical AI.

So AIs that are actually being used in the operating room when they're excising tumors, right?

And trying to figure out if they have a margin or not.

And that's because there's imaging databases of what a margin looks like, right?

That an AI can look at and say, okay, I think we've got it or we haven't gotten it.

I would just highlight those three examples.

And each of those are not being developed within large companies.

They're all being developed either by small startups or even academic institutions.

Whereas the ASI promise is saying, let's just digest everything.

Let's take all knowledge and put it into one big giant model and see what insights it can derive from that model, right?