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Neural networks have become a hot topic over the last few years, but evaluating the most efficient way to build one is still more art than science. In fact, it’s more trial and error than art. However, MIT may have solved that problem.

The NAS (Neural Architecture Search, in this context) algorithm they developed “can directly learn specialized convolutional neural networks (CNNs) for target hardware platforms — when run on a massive image dataset — in only 200 GPU hours,” MIT News reports. This is a massive improvement over the 48,000 hours Google reported taking to develop a state-of-the-art NAS algorithm for image classification. The goal of the researchers is to democratize AI by allowing researchers to experiment with various aspects of CNN design without needing enormous GPU arrays to do the front-end work. If finding state of the art approaches requires 48,000 GPU arrays, precious few people, even at large institutions, will ever have the opportunity to try.

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