Which agencies are leading when it comes to AI capabilities? They’re really out front because they don’t have just big data — they have huge data. One mission alone for NASA might generate 25 terabytes of data per day. In the past, we weren’t able to handle and process that amount of data, but now we can. One project that NASA’s working on is creating an AI robot doctor to treat people living in Mars. This robot doctor will not only be able to treat conditions that are known today, it will also be able to learn on its own unique conditions that might develop while living on Mars.
One of the lessons learned from
It takes a leader or a set of leaders willing to come up with a vision, rally people around it, and challenge people to do something that’s never been done before. We call it shattering your own constraints because if that team didn’t believe that living on Mars was even possible in the first place, they certainly wouldn’t spend their careers building an AI doctor to treat people once they’re there. Nancy Potok, [former] Chief Operating Officer of Census, and her entire team at Census challenged themselves to apply artificial intelligence to the 2020 census. For the 2020 census they will have a new mobile app, driven by machine intelligence. It’s going to predict when people will be home, what order to go in [to] houses, and give them advice on how to perform their routes and their data collections most efficiently.
Is there a tool or capability agencies use
Zutavern: Any kind of analytics. This also includes agencies that are using statistics or other types of models that forecast. The difference with amazon database machine intelligence or artificial intelligence is the ability for the algorithms to learn on their own. All agencies are using some type of analytics. If they’re using simulation modeling — even Excel type calculation — that’s the first step along the way.
Are there special contract requirements for procuring AI capabilities?
Zutavern: There are definitely some special procurement another is the need for diverse teams with diverse skillsets requirements. One is open source. Within artificial intelligence, the open source world is so important. It’s very robust, and agencies want to have open source components and considerations to their overall solution. [They] don’t want to get locked into proprietary stuff. Whether it’s a full open source solution, or open source is one component of it, requiring that expertise, is huge, especially in the AI world where open tg data source has led to a lot of the breakthroughs that have occurred.
A second one is requiring an experimentation type approach.
With AI, you don’t build a model once and it works. You iterate many times, making improvements along the way until you get the accuracy that you’re looking for. If you think about most government contracting requirements, they like to lay out a well-defined process. AI solutions [are] not clean, well-defined process[es]. It’s an experimentation type approach, so we’ve got to figure out a way to build that in so there’s enough transparency and visibility.