NGA Faces Tech, Policy Hurdles To AI For Target Recognition

WASHINGTON: The National Geospatial-Intelligence Agency (NGA) has developed AI algorithms that could be deployed for ‘target recognition’ — but only if you define “target” recognition in the narrowest manner possible, says Joe Victor, the spy agency’s guru for artificial intelligence and machine learning.

“Can I deploy AI for target recognition? Yes. I can’t get into too much details, but what does that mean though? … If we’re talking about how we’re going to use something to identify something to go action on something, and go tell my DoD partners that they can go off and do the thing that they need to … not yet,” Victor told the Genius Machines forum sponsored by Defense One today.

Victor explained that the issues of using AI to help DoD put steel on steel not only include improving the ability of the algorithms to recognize potential targets — a big job in an of itself that requires much more work on building data sets and training those algorithms — but also policy decisions. (US policy currently forbids a machine from making an autonomous decision to pull the trigger.)

“What is the moral obligation we have and things of that nature? … What is the assurance that we’re doing the right thing there? Those are things we have to employ before we just go off and build Skynet,” he said.

This is not really new news, as best we can tell, but it is significant that it is being said publicly. For example, back in 2012 we reported after confirming it with senior officials that machine-to-machine intelligence tracking already made possible the acquisition of targets and, after human approval, the trigger could be pulled. But training the algorithms requires monstrous amounts of data and that takes time and expertise.

“What we’re looking at here is ‘how do we inform ourselves of what may be of interest, based upon patterns that me as an analyst may be [seeing] as I go through and interrogate data, to highlight things that [I]  may not have realized, or look for trends and patterns’?” Victor said. “AI isn’t the panacea where you go off and just say ‘go tell me what’s coming here and I trust you.’ That’s way far in the future.”

Building those capabilities is one of the key elements of NGA’s first “Technology Strategy,” released May 29. That strategy, in turn, was based on the “2020 Technology Focus Areas” signed off by NGA Director Vice Adm. Robert Sharp in April.

Another key focus is finding and managing that geospatial data and expanding the pool available to NGA. Indeed, the agency recently issued a slew of requests to industry seeking access to a variety of commercial data gathering and management capabilities.

Victor said data is critical as a foundational effort, and that NGA is deeply ensconced in trying to build pools of ‘clean’ data — that is, data that can be trusted and is formatted in ways that allow it to be shared and used.

“What we need is data. And we need that data to be housed in the environment in a manner that’s useful for humans and machines to get to,” he said. “We need to enable our data to be used at a broader scale, and that’s a huge effort to get there. Other agencies also are going through it now, whether in the IC world — intelligence community — or DoD or non-DoD related folks. … The way to get there is to make sure that that data is there, and then start to actually research and develop these exquisite algorithms to start to see how they play.”

The next step, he added, is actually training the AI system so it knows what analysts are interested in, and how to find patterns of activity that have significance.

“That is that is a lot of work to get done and that’s where we have to go,” he summed up.