(p. B1) For the past five years, the hottest thing in artificial intelligence has been a branch known as deep learning. The grandly named statistical technique, put simply, gives computers a way to learn by processing vast amounts of data.
. . .
But now some scientists are asking whether deep learning is really so deep after all.
In recent conversations, online comments and a few lengthy essays, a growing number of A.I. experts are warning that the infatuation with deep learning may well breed myopia and overinvestment now — and disillusionment later.
“There is no real intelligence there,” said Michael I. Jordan, a professor at the University of California, Berkeley, and the author of an essay published in April intended to temper the lofty expectations surrounding A.I. “And I think that trusting these brute force algorithms too much is a faith misplaced.”
The danger, some experts warn, is (p. B4) that A.I. will run into a technical wall and eventually face a popular backlash — a familiar pattern in artificial intelligence since that term was coined in the 1950s. With deep learning in particular, researchers said, the concerns are being fueled by the technology’s limits.
Deep learning algorithms train on a batch of related data — like pictures of human faces — and are then fed more and more data, which steadily improve the software’s pattern-matching accuracy. Although the technique has spawned successes, the results are largely confined to fields where those huge data sets are available and the tasks are well defined, like labeling images or translating speech to text.
The technology struggles in the more open terrains of intelligence — that is, meaning, reasoning and common-sense knowledge. While deep learning software can instantly identify millions of words, it has no understanding of a concept like “justice,” “democracy” or “meddling.”
Researchers have shown that deep learning can be easily fooled. Scramble a relative handful of pixels, and the technology can mistake a turtle for a rifle or a parking sign for a refrigerator.
In a widely read article published early this year on arXiv.org, a site for scientific papers, Gary Marcus, a professor at New York University, posed the question: “Is deep learning approaching a wall?” He wrote, “As is so often the case, the patterns extracted by deep learning are more superficial than they initially appear.”
For the full story, see:
Steve Lohr. “Researchers Seek Smarter Paths to A.I.” The New York Times (Thursday, June 21, 2018): B1 & B4.
(Note: ellipses added.)
(Note: the online version of the story has the date June 20, 2018, and has the title “Is There a Smarter Path to Artificial Intelligence? Some Experts Hope So.” The June 21st date is the publication date in my copy of the National Edition.)
The essay by Jordan, mentioned above, is:
Jordan, Michael I. “Artificial Intelligence – the Revolution Hasn’t Happened Yet.” Medium.com, April 18, 2018.
The manuscript by Marcus, mentioned above, is:
Marcus, Gary. “Deep Learning: A Critical Appraisal.” Jan. 2, 2018.