DARPA making AI to explain why NextBigFuture.com

The field of AI has made great strides in the last several years, thanks to developments in machine learning algorithms and deep learning systems based on artificial neural networks (ANNs). Researchers have found that vast sets of example data are the way to train up such systems to produce the desired results, whether that is picking out a face from a photograph or recognizing speech input. But the resultant systems often turn out to operate as an inscrutable "black box" and even their developers find themselves unable to explain why it arrived at a particular decision. That may soon prove unacceptable in areas where an AI's decisions could have an impact on people's lives, such as employment, mortgage lending, or self-driving vehicles. The value of so-called explainable AI was called into question recently by Google research director Peter Norvig, who noted that humans are not very good at explaining their decision-making either, and claimed that the performance of an AI system could be gauged simply by observing its outputs over time.