Next month, however, a team of MIT researchers will be presenting a so-called “Proxyless neural architecture search” algorithm that can speed up the AI-optimized AI design process by 240 times or more. That would put faster and more accurate AI within practical reach for a broad class of image recognition algorithms and other related applications.

“There are all kinds of tradeoffs between model size, inference latency, accuracy, and model capacity,” says Song Han, assistant professor of electrical engineering and computer science at MIT. Han adds that:

“[These] all add up to a giant design space. Previously people had designed neural networks based on heuristics. Neural architecture search tried to free this labor intensive, human heuristic-based exploration [by turning it] into a learning-based, AI-based design space exploration. Just like AI can [learn to] play a Go game, AI can [learn how to] design a neural network.”