A theme emerged when Apple's director of artificial intelligence research outlined results from several of the company's recent AI projects on the sidelines of a major conference Friday. Each involved giving software capabilities needed for self-driving cars. Ruslan Salakhutdinov addressed roughly 200 AI experts who had signed up for a free lunch and peek at how Apple uses machine learning, a technique for analyzing large stockpiles of data. He discussed projects using data from cameras and other sensors to spot cars and pedestrians on urban streets, navigate in unfamiliar spaces, and build detailed 3-D maps of cities. The talk offered new insight into Apple's secretive efforts around autonomous-vehicle technology.
In a study they plan to present at the Privacy Enhancing Technology Symposium in Germany this July, a group of researchers from the University of Washington and the University of California at San Diego found that they could "fingerprint" drivers based only on data they collected from internal computer network of the vehicle their test subjects were driving, what's known as a car's CAN bus. "With very limited amounts of driving data we can enable very powerful and accurate inferences about the driver's identity," says Miro Enev, a former University of Washington researcher who worked on the study before taking a job as a machine-learning engineer at Belkin. With very limited amounts of driving data we can enable very powerful and accurate inferences about the driver's identity. Using the full collection of the car's sensors--including how the driver braked, accelerated and angled the steering wheel--the researchers found that their algorithm could distinguish each of the drivers, with 100 percent accuracy, based on only 15 minutes of the driving data.