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Why Google’s CEO Is Excited About Automating Artificial Intelligence

screen-shot-2017-05-17-at-1.57.09-pmAI software that can help make AI software could accelerate progress on making computers smarter.

Deep learning teaches software to be smart by passing data through layers of math loosely inspired by biology and known as artificial neural networks. Choosing the right architecture for a neural network’s web of math is a crucial part of making something that works. But it’s not easy to figure out. “We do it by intuition,” says Quoc Le, a machine-learning researcher at Google working on the AutoML project.

Last month, Le and fellow researcher Barret Zoph presented results from experiments in which they tasked a machine-learning system with figuring out the best architecture to use to have software learn to solve language and image-recognition tasks.

On the image task, their system rivaled the best architectures designed by human experts. On the language task, it beat them.

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