“new $240 million center at MIT may help advance the field of artificial intelligence by developing novel devices and materials to power the latest machine-learning algorithms. It could, perhaps, also help IBM reclaim its reputation for doing cutting-edge AI.
The project, announced by IBM and MIT today, will research new approaches in deep learning, a technique in AI that has led to big advances in areas such as machine vision and voice recognition. But it will also explore completely new computing devices, materials, and physical phenomena, including efforts to harness quantum computers—exotic but potentially very powerful new machines—to make AI even more capable.
“A lot of innovation is happening using standard silicon and architectures, but what about the devices and the material science?” says Dario Gil, vice president of AI at IBM Research. “It’s an area no one is touching, and it has the potential for orders-of-magnitude improvements.”
The center will also look at ways that AI can be more effectively deployed in industries like health care and security. And it will study the economic impact of artificial intelligence and automation, a hugely significant issue for society.
The move is significant for MIT. The university was at the forefront of AI research during the 1950s, but the field’s center of gravity has moved westward more recently, with big tech companies like Google, Facebook, Microsoft, and Amazon leading the charge.
The investment also signals a shift for IBM. The company pushed AI forward by developing Deep Blue, a machine that beat the world chess champion, Garry Kasparov, in 1997 (see “How the Chess Was Won”). The Watson supercomputer that won the game show Jeopardy! in 2010 used cutting-edge machine-learning and natural-language processing techniques. In recent years, however, other companies have stolen the limelight in AI research, and the company has sometimes been accused of overhyping the AI services available under the Watson brand.
Focusing on hardware, in particular, may be a good way to reboot. Though there has been dramatic progress in AI in recent years, most of it has come thanks to a handful of algorithms, as well as the growing availability of powerful supercomputers and large quantities of training data. Even as new approaches emerge, novel materials and computing architectures offer huge potential to enhance these AI algorithms.
Most cutting-edge machine learning is done today on conventional computer chips, whether originally designed for graphics processing or custom-made to handle the necessary computations as efficiently as possible. Rethinking chip architectures and the kinds of components used could boost performance significantly. IBM already has a strong research focus on materials science and novel computing devices. “As excited as we all are about AI, the field has multiple decades ahead of it,” Gil says.”