Azalia Mirhoseini, a research scientist at Google Brain, is using artificial intelligence itself  to make better chips for artificial intelligence.

Many microchips that are used for AI weren’t specifically built for it. Most are repurposed from hardware used in video and gaming. As a result, these older, human-engineered designs leave much to be desired in terms of energy efficiency, cost, and functionality. 

Mirhoseini’s system—which trained itself using trial and error, based on the AI concept of reinforcement learning—can produce chip designs in just a few hours. (The world’s top experts need several weeks.) Her AI-designed methods allow for chips that are as good as or better than those designed by human engineers: they’re faster and more energy efficient, and their total internal wire length, and therefore cost, is much lower.

Reinforcement learning is one of AI’s most promising frameworks. Software that uses it essentially teaches itself how to accomplish a task, rather than being programmed, step by step, by  a human. Now, Mirhoseini says, “it’s time to use machine learning and AI to develop better computers and close the loop.”