The U.S. Air Force’s latest project involving artificial intelligence is set for launch, and it seeks to fuse nearly every potential source of information including text, video, and other sources of data.
The objective of the project is to change the way commanders, airmen, thinking machines make decisions.
Called “Data to Decision,” or D2D, the program was launched last year but is set for major expansion in 2018, according to Air Force officials.
The goal is to change the way every commander, airman, and even thinking machine makes decisions. It’s a program called Data to Decision, or D2D. Launched last year, the program will expand significantly in 2018, service officials say.
The purpose of the program is to create “a complete cycle of understanding, from predicting what we expect to encounter, prescribe what can be done to help understand the environment, then find, fix, track, target, engage, assess, anything, anytime, anywhere in any domain” Mark Tapper, the Air Force’s Champion for Data 2 Decision, an experimentation campaign underpinning the Air Superiority 2030 flight plan, told American media. “Our mantra is improve every decision – those made by airmen or by machines.”
That will include decisions about where bits of information and data should be sent next.
“How you aggregate all the data, take the pieces that matter for a mission, move it where you need to move it (perhaps for different purposes) then think about where it needs to move and why” will be key, Tapper said.
The program is grand in its ambitions to use a wide variety of data, extending well beyond traditional aerial surveillance footage to potentially include, well, everything: social media posts, live-streaming diagnostic data off of jets, drones, and other aircraft, attainable whether data, pilot biophysical data from soldier-worn sensors, and more.
It’s not the first time the military has experimented with fusing a wide variety of data for better decision making. A 2014 Defense Advanced Research Projects Agency, or DARPA, program called Insight also sought to create a fuller picture of the battlespace by combining and crunching data. It used multiple neural nets to populate a larger engine that then used Bayesian statistical methods to output predictions and probability. [source]
Analysis: “Neural nets and deep learning approaches have proven effective in chaotic, unstructured environments and problems” like helping self-driving cars navigate crowded city streets or helping algorithms identify objects in YouTube videos, the American media source noted.
But here’s the biggest drawback to AI: “The biggest part of the problem of artificial intelligence is: they build these incredibly long algorithms with all of these gates to go through. They push all of this machine learning and data through it. Frankly, we are not entirely sure how all of that works, all the time,” said Dale Ormond, the principal director for research in the Office of the Assistant Secretary of Defense (research and engineering).
“If someone sabotages your data that your feeding your algorithm for learning, we’re not entirely sure what’s going to come out of the other end. It’s going to take a while to figure out how to organize these things to be successful,” Ormond continued.
This suggests that one way to defeat AI is to corrupt the data feed the system is using to ‘learn.’ This helps explain why AI and cyberwarfare may go hand-in-hand.