Dr. Qichao Hu

the a thousand parameters from the AI, you are not able to explain those things.

It's like a different language, not meant for us human species to understand, but they are- Does that mean, are they real?

Because we see, so when the AI model fits, we see a thousand different parameters, but then they're in the form of zero ones, zero ones.

We can't really interpret them.

We can't really give them physical meaning, but these, and then if you were to ask the human scientists to find patterns based on that 20 parameters, the patterns are weaker and not as strong as when you ask the AM model to find patterns based on a thousand plus parameters.

And then when you ask the AI model to predict end of life just with a beginning performance, it's much more accurate.

So even though we're not able to assign it physical meanings, it works.

It's like a different set of laws that we're not able to comprehend, but it works.

That is so cool.

About three years since on the material side.

And I think going forward, now that the approach really works, we really need to expand this.

So a lot of the high throughput robots and then the computing, we do need to expand those so we can actually map it much faster.

Yeah.

Basically, the more data you give it, the smarter it gets.

And I will say the biggest difference is once you've reached a sufficient amount of data that you teach it, then the model is able to give you results and forecasts that's almost spot on.

So then you can really save a lot of effort.

But you really have to teach a sufficient amount of data.

So when we have the raw data, we don't really teach that to a large language model.

We use a foundation model because the large language models are really good when the data is in text format, but when it's in like Excel numbers, it's not as good.

So we use that to teach.