Scene: A U.S. adversary is at work on a new type of drone, ship, or aircraft and it's your job to find it, wherever it is. Not long ago, that task would take a massive effort of human, signals, and open-source intelligence collection. But a researcher from AI company Synthetaic has created a tool that will allow users to find virtually any large object that exists in any satellite photo of the Earth within just one day. It's also the sort of capability the National Geospatial-Intelligence Agency is also looking to develop, and it could radically shift strategic advantage on the battlefield. Corey Jaskolski, founder and CEO of Synthetaic, dubbed his satellite image scanning tool Rapid Automatic Image Categorization, or RAIC.

Earlier this month, entrepreneur Corey Jaskolski pulled out a pen and drew his best guess at what the surveillance balloon shot down by a US jet would have looked like from space. Then he fed the sketch and "a gob" of recent satellite images from the area where the balloon was taken down into algorithms developed by his image and video detection startup Synthetatic, and waited. Within two minutes, he says, the algorithms found the 200-foot-tall balloon off the coast of South Carolina. "I couldn't believe it," Jaskolski says. Nor could his wife when he excitedly showed her his results.

The saying "data is the new oil," was reportedly coined by British mathematician and marketing whiz Clive Humby in 2006. Data is the fuel powering modern AI models; without enough of it the performance of these systems will sputter and fail. And like oil, the resource is scarce and controlled by big businesses. What do you do if you're a small computer vision company? You can turn to fake data to train your models, and if you're lucky it might just work.

Synthetaic is a startup working to create data -- specifically images -- that can be used to train artificial intelligence. Founder and CEO Corey Jaskolski's experience includes work with both National Geographic (where he was recently named Explorer of the Year) and a 3D media startup. In fact, he told me that his time with National Geographic made him aware of the need for more data sets in conservation. Well, Jaskolski said that he was working on a project that could automatically identify poachers and endangered animals from camera footage, and one of the major obstacles was the fact that there simply aren't enough existing images of either poachers (who don't generally appreciate being photographed) or certain endangered animals in the wild to train AI to detect them. He added that other companies are trying to create synthetic AI training data through 3D worldbuilding (in other words, "building a replica of the world that you want to have an AI learn in"), but in many cases, this approach is prohibitively expensive.