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Friday, January 05, 2018

Machine learning: The good, the bad and the ugly |

Follow on Twitter as @Matt_Lnrd
"While machine learning might be the enabling technology of the future, for the Intelligence Advanced Research Projects Activity, it's old school" summarizes Matt Leonard, reporter/producer at GCN.

Brain in a circuit board
Photo: Sergey Tarasov/

Founded in 2006 to drive research and innovation within the federal government’s intelligence agencies, IARPA has been researching machine learning from the beginning.
“Machine learning has been a priority research area since we were created 10 years ago,” IARPA Director Jason Matheny said. “In fact, most of our first programs were in machine learning.”

Some of these early efforts include the Biometrics Exploitation Science and Technology program, which developed tools for facial recognition that have since been widely adopted.  Aladdin Video, for example, was an effort to identify actions in streaming video. It could "tell whether this is a video of a birthday party, or a video of someone break dancing, or a video of somebody describing how to build an explosive device,” Matheny said. Other programs focused on natural-language processing.

These projects laid the foundation for the use of machine learning in more complex applications, such as predicting  cyberattacks based on chatter in hacker forums and the market price of malware; forecasting military mobilization and terrorism; and developing accurate 3-D models of buildings or entire cities from satellite imagery.

But IARPA is also researching ways of improving the fundamental architecture upon which machine learning is built: the neural network, “a very rough approximation of how we thought the brain worked in the 1950s," Matheny explained. "Our machine learning approaches, in general, haven’t caught up with neuroscience.”

One effort to close this gap is the ongoing Machine Intelligence from Cortical Networks program that seeks to reverse engineer the algorithms of the brain. In its first year, MICrONS has developed the largest dataset of wiring diagrams of the circuits responsible for learning in animal brains, which, at this point,  are much better than machines at learning.