Alexandr (Alex) Wang

So one of the first things we did was help

the DoD with its own data problem to help them be able to train AI systems.

So, you know, one of the first things that we worked on was like, you know, they wanted to, the DoD wanted to do

image recognition on satellite imagery SAR imagery you know other like all forms of overhead imagery but they had this huge data problem you know just like me with the fridge they had the same problem like how you know they need to be able to have data that lets them detect things in all this imagery and so we

The first thing we did was fuel the data sets and data capabilities for the DoD.

That was true for the first few years.

And then more recently, we've been working with them to do large-scale fielding of AI capabilities.

Yeah.

And it's... I mean, mostly it's scalable.

Like...

I mean, the number of satellites in space has like exploded.

So we have so much more sensing today, like way more imagery, way more sensing today than it's even like feasible for humans to work their way through.

Wow.

So that was, yeah, that was like the first problem.

How do you fuel it?

Well, you have to build... So there's two parts.

So first, you have to build effectively like a data foundry.

You have to build a mechanism by which you're able to generate lots and lots of data to fuel these algorithms.

A lot of it...

synthetically so using the algorithms themselves to generate the data but then a lot of it you still need humans to validate and verify so one of the things we did actually for this whole project is um we created a facility in st louis missouri next to uh nga the national geospatial intelligence agency and we produced a