A self-driving car has a split second to determine whether the image it sees is a person and whether that person has stepped on or off a curb. A mistake, of course, would have deadly consequences. Such is the increasingly challenging world of artificial intelligence and machine learning with demand for computers and robots that can instantly analyze images, reason and solve problems. One of the biggest challenges in AI is that computers simply can't do the computations fast and accurately enough, especially in regards to understanding fast-changing images. Washington State University researchers have recently developed a computer architecture that achieves similar accuracy as conventional graphical processing units (GPUs) but works more than 50 times faster.

Washington State University researchers are creating the first-ever "IQ test" for artificial intelligence (AI) systems that would score systems on how well they learn and adapt to new, unknown environments. Diane Cook, Regents Professor and Huie-Rogers Chair Professor, and Larry Holder, professor in the School of Electrical Engineering and Computer Science, received a grant of just over $1 million from the Defense Advanced Research Projects Agency (DARPA) to create a framework to test the "intelligence" of AI systems. "Previously, research on measuring intelligence in AI systems has been mostly theoretical," Holder said. Holder and Cook will design a test that will grade AI systems based on the difficulty of problems that they can solve. Creating methods to rank problems on their difficulty will be one of the major parts of the research.