A.I. Researchers See Danger of Haves and Have-Nots


Since their “Green A.I.” paper was published in July, their message has resonated with many in the research community.

Henry Kautz, a professor of computer science at the University of Rochester, noted that accuracy is “really only one dimension we care about in theory and in practice.” Others, he said, include how much energy is used, how much data is required and how much skilled human effort is needed for A.I. technology to work.

A more multidimensional view, Mr. Kautz added, could help level the playing field between academic researchers and computer scientists at the big tech companies, if research projects relied less on raw computing firepower.

Big tech companies are pursuing greater efficiency in their data centers and their artificial intelligence software, which they say will make computing power more available to the outside developers and academics.

John Platt, a distinguished scientist in Google’s artificial intelligence division, points to its recent development of deep-learning models, EfficientNets, which are 10 times smaller and faster than conventional ones. “That democratizes use,” he said. “We want these models to be trainable and accessible by as many people as possible.”

The big tech companies have given universities many millions over the years in grants and donations, but some computer scientists say they should do more to close the gap between the A.I. research haves and have-nots. Today, they say, the relationship that tech giants have to universities is largely as a buyer, hiring away professors, graduate students and even undergraduates.

The companies would be wise to also provide substantial support for academic research including much greater access to their wealth of computing — so the competition for ideas and breakthroughs extends beyond corporate walls, said Ed Lazowska, a professor at the University of Washington.

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