Reaching the technological singularity is almost certainly going to involve AI that is able to improve itself. Google may have now taken a small step along this path by creating AI that can build AI.
Speaking at the company’s annual I/O developer conference, CEO Sundar Pichai announced a project called AutoML that can automate one of the hardest parts of designing deep learning software: choosing the right architecture for a neural network. The Google researchers created a machine learning system that used reinforcement learning—the trial and error approach at the heart of many of Google’s most notable AI exploits—to figure out the best architectures to solve language and image recognition tasks. Not only did the results rival or beat the performance of the best human-designed architectures, but the system made some unconventional choices that researchers had previously considered inappropriate for those kinds of tasks.