CUPERTINO, Calif. — If you aren't currently considering how to use deep neural networks to solve your problems, you almost certainly should be, according to Jeff Dean, a Google senior fellow and leader of the deep learning artificial intelligence research project known as Google Brain.

In a keynote address at the Hot Chips conference here Tuesday (Aug. 22), Dean outlined how deep neural nets are dramatically reshaping computational devices and making significant strides in speech, vision, search, robotics and healthcare, among other areas. He said hardware systems optimized for performing a small handful of specific operations that make up the vast majority of machine learning models would create more powerful neural networks.

"Building specialized computers for the properties that neural nets have makes a lot of sense," Dean said. "If you can produce a system that is really good at doing very specific [accelerated low-precision linear algebra] operations, that's what we want."


Of the 14 Grand Challenges for Engineering in the 21st Century identified by the National Academy of Engineering in 2008, Dean believes that neural networks can play an integral role in solving five — including restoring and improving urban architecture, advancing health informatics engineering better medicines and reverse engineering the human brain. But Dean said neural networks offer the greatest potential for helping to solve the final challenge on the NAE's list: engineering the tools for scientific discovery.

"People have woken up to the idea that we need more computational power for a lot of these problems," Dean said.