ASK THE RIGHT QUESTIONS


PAINTING A PICTURE WITH AI


The electronic warfare team from 3rd Armored Brigade Combat Team, 1st Cavalry Division (3/1 CAV), along with members of the Rapid Equipping Force, prepare the Electronic Warfare Tactical Vehicle for operation in January at the National Training Center at Fort Irwin, California. RCCTO is seeking new technologies that apply AI and machine learning to paint a picture of the electromagnetic spectrum. (U.S. Army photo by Capt. Scott Kuhn, 3/1 CAV)


intelligence of an average human being” would manifest within 10 years. Te field has cycled through similar peaks of optimism that give way to failure since then—and has yet to produce a machine that can achieve the heights that Minsky predicted. Tough recent advances in computer processors and sensors have enabled a leap in maturity, the technology is not fully mature. Computers still have difficulty classifying objects that are not the norm, and unintended errors can cause mistakes as well. It is not possible to predict all corner cases (situations outside of normal operating parameters), and misclassification of data can lead to fatal errors.


In March 2018, an Uber experimental autonomous vehicle oper- ating in Tempe, Arizona, struck and killed a woman who was walking her bicycle outside of a crosswalk in a poorly illumi- nated area. Te vehicle’s sensors detected an object six seconds before the crash and determined an emergency braking maneu- ver was necessary; it did not engage the brakes. Te National


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Transportation Safety Board report on the incident, published in May 2018, noted: “According to Uber, emergency braking maneuvers are not enabled while the vehicle is under computer control, to reduce the potential for erratic vehicle behavior. Te vehicle operator is relied on to intervene and take action. Te system is not designed to alert the operator.”


In 2017, National Science Foundation researchers built an algo- rithm to determine what changes to an object would confuse an AI classification program (like a driverless car program of the kind Uber was testing in Arizona). Te algorithm gener- ated two different attacks: a stop sign with graffiti on it and a stop sign with stickers strategically placed on it. (See Figure 1.) In both cases, the AI program misclassified the stop sign as a 45 mph speed limit sign. “Adversarial attacks” with subtly altered images, sounds or objects that normally would not fool humans are able to fool AI programs.


Army AL&T Magazine


Summer 2019


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