Grant Harvey

My concern with that approach, though, is that the current language models, they have a limit to their context, right?

So you must be using something else, or you can tell me what you think about this.

If you have this giant database of all this different molecular data, how do you make sure that it's considering absolutely everything when it's going to work here?

Do you get what I'm saying?

It's considering everything in terms of.

Like basically how do you prevent loss from happening with the context window when you're running an agent through this data?

I guess what I'm wondering.

And that makes sense because when you're doing a more specific run, you have a more constrained problem space.

So you're like, okay, we know it needs to focus in this area because we're looking for this chemical property.

That makes sense.

Yeah, I guess that was the thing that was throwing me off is like, I know LLMs have a context limit of a million.

So you can't necessarily put all the chemical data in the world in LLM and expect to get that.

Yeah, yeah, yeah, yeah, yeah.

That's cool.

What about Avatar?

Because what I thought was really interesting about Avatar, which is the other AI tool that you use, is it's actually tracking the battery life cycle.

And you mentioned safety.

So I'm curious if you could talk a little bit about that and why that's a big deal.

Because...

Batteries are living chemistry, and being able to track them is really important in how they're performing.