Is There a Smarter Path to Artificial Intelligence? Some Experts Hope So

["Dave Farber" <farber () gmail com>]

> Begin forwarded message:
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> From: Dewayne Hendricks <dewayne () warpspeed com <mailto:dewayne () warpspeed com>>
> Subject: [Dewayne-Net] Is There a Smarter Path to Artificial Intelligence? Some Experts Hope So
> Date: July 17, 2018 at 2:22:36 AM GMT+9
> To: Multiple recipients of Dewayne-Net <dewayne-net () warpspeed com <mailto:dewayne-net () warpspeed com>>
> Reply-To: dewayne-net () warpspeed com <mailto:dewayne-net () warpspeed com>
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> Is There a Smarter Path to Artificial Intelligence? Some Experts Hope So
> By Steve Lohr
> Jun 20 2018
> <https://www.nytimes.com/2018/06/20/technology/deep-learning-artificial-intelligence.html 
> <https://www.nytimes.com/2018/06/20/technology/deep-learning-artificial-intelligence.html>>
>
> 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. 
> Thanks to deep learning, computers can easily identify faces and recognize spoken words, making other forms of 
> humanlike intelligence suddenly seem within reach.
>
> Companies like Google, Facebook and Microsoft have poured money into deep learning. Start-ups pursuing everything 
> from cancer cures to back-office automation trumpet their deep learning expertise. And the technology’s perception 
> and pattern-matching abilities are being applied to improve progress in fields such as drug discovery and 
> self-driving cars.
>
> 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 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 <http://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.”
>
> If the reach of deep learning is limited, too much money and too many fine minds may now be devoted to it, said Oren 
> Etzioni, chief executive of the Allen Institute for Artificial Intelligence. “We run the risk of missing other 
> important concepts and paths to advancing A.I.,” he said.
>
> Amid the debate, some research groups, start-ups and computer scientists are showing more interest in approaches to 
> artificial intelligence that address some of deep learning’s weaknesses. For one, the Allen Institute, a nonprofit 
> lab in Seattle, announced in February that it would invest $125 million over the next three years largely in research 
> to teach machines to generate common-sense knowledge — an initiative called Project Alexandria.
>
> While that program and other efforts vary, their common goal is a broader and more flexible intelligence than deep 
> learning. And they are typically far less data hungry. They often use deep learning as one ingredient among others in 
> their recipe.
>
> “We’re not anti-deep learning,” said Yejin Choi, a researcher at the Allen Institute and a computer scientist at the 
> University of Washington. “We’re trying to raise the sights of A.I., not criticize tools.”
>
> Those other, non-deep learning tools are often old techniques employed in new ways. At Kyndi, a Silicon Valley 
> start-up, computer scientists are writing code in Prolog, a programming language that dates to the 1970s. It was 
> designed for the reasoning and knowledge representation side of A.I., which processes facts and concepts, and tries 
> to complete tasks that are not always well defined. Deep learning comes from the statistical side of A.I. known as 
> machine learning.
>
> Benjamin Grosof, an A.I. researcher for three decades, joined Kyndi in May as its chief scientist. Mr. Grosof said he 
> was impressed by Kyndi’s work on “new ways of bringing together the two branches of A.I.”
>
> Kyndi has been able to use very little training data to automate the generation of facts, concepts and inferences, 
> said Ryan Welsh, the start-up’s chief executive.
>
> [snip]
>
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