Chief Scientist at Baidu confident hardware will push AI further
Advances in computer hardware will help drive innovations in artificial intelligence (AI) even further, according to Andrew Ng, chief scientist at Baidu Research. He regards increases in computing power as the catalyst to expanded uses of AI, with deep-learning becoming more accurate, as well as being used in developments such as generating and parsing language. Ng said the roadmaps laid out by hardware vendors were "credible" and expected to see "more computational power and faster networks" in the future.
Andrew Ng, chief scientist at Baidu Research, and a major figure in the field of machine learning and AI, says improvements in computer processor design will keep performance advances and breakthroughs coming for the foreseeable future. “Multiple [hardware vendors] have been kind enough to share their roadmaps,” Ng says. “I feel very confident that they are credible and we will get more computational power and faster networks in the next several years.”

The field of AI has gone through phases of rapid progress and hype in the past, quickly followed by a cooling in investment and interest, often referred to as “AI winters.” The first chill occurred in the 1970s, as progress slowed and government funding dried up; another struck in the 1980s as the latest trends failed to have the expected commercial impact.

Then again, there’s perhaps been no boom to match the current one, propelled by rapid progress in training machines to do useful tasks. Artificial intelligence researchers are now offered huge wages to perform fundamental research, as companies build research teams on the assumption that commercially important breakthroughs will follow.

Andrew Ng, chief scientist at Baidu Research.

The advances seen in recent years have come thanks to the development of powerful “deep learning” systems (see “10 Breakthrough Technologies 2013: Deep Learning”). Starting a few years ago, researchers found that very large, or deep, neural networks could be trained, using labeled examples, to recognize all sorts of things with human-like accuracy. This has led to stunning advances in image and voice recognition and elsewhere.

Ng says these systems will only become more powerful. This might not only increase the accuracy of existing deep learning tools, but also allow the technique to be leveraged in new areas, such as parsing and generating language.

What’s more, Ng says, hardware advances will provide the fuel required to make emerging AI techniques feasible.

“There are multiple experiments I’d love to run if only we had a 10-x increase in performance,” Ng adds. For instance, he says, instead of having various different image-processing algorithms, greater computer power might make it possible to build a single algorithm capable of doing all sorts of image-related tasks.

The world’s leading AI experts convened in Barcelona this week for a prominent event called the Neural Information Processing Systems conference. The scale of the gathering, which has grown from several hundred people a few years ago to more than 6,000 this year, offers some sense of the huge interest there is in artificial intelligence.