[y66]

Winning at Jeopardy is just like building a machine that understands what is happening in the global economy and what will happen next. Excerpt from Steve Lohr's interview of David Ferrucci, hedge fund guy famed for contributions to the IBM Watson Jeopardy project:

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Much of artificial intelligence today, he said, focuses on mining vast amounts of data to make predictions. Those predictions are based on statistical probabilities and patterns — a certain symptom is highly correlated with a certain disease, for example.

“But in a purely data-driven approach, I can’t explain my decisions,” Dr. Ferrucci said. “People are so enamored with the data-driven approach that they believe correlation is sufficient.”

The Big Data formula, he noted, has proved to be “incredibly powerful” for tasks like natural-language processing — a central technology behind Google search, for instance.

WatsonPaths, by contrast, builds step-by-step graphs, or paths, that trace possible causes rather than mere statistical correlations. In the case of medicine at the Cleveland Clinic project, for example, the paths go from an observation of symptoms to a conclusion about the diagnosis of a disease and treatment.

That approach is a hybrid of the Big Data tools, which sift through troves of medical literature, and logic tools to identify likely chains of inference — what humans see as logical explanations for the “why” of things. The approach is also a step in the direction of classic artificial intelligence, which relied on knowledge rules and relationships, to create so-called expert systems. The blend combines elements of what Dr. Ferrucci termed “my 30-year journey in A.I.”

[barmar]

Big Data works when the thing it's analyzing inherently follows relatively straightforward rules. Most of nature is like that, which is why the brain has evolved to detect patterns and automatically assume "correlation implies causation". Statistical analysis of data works well in many application domains.

But if a domain is very chaotic, with complex feedback loops, purely statistical analysis can fail. Nate Silver's book explains how this has generally failed in economic analysis. Statistical analysis is how we get nonsensical results like the direction of the economy correlating with the winner of the Super Bowl.

[y66]

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“I’m not too worried about machines replacing cartoonists,” the artist R. Kikuo Johnson says, about his cover for the Money Issue. Johnson may have switched from drawing with ink, brushes, and paper to using a stylus and a digital tablet, but he isn’t worried that computers will take over the rest of his cartooning process. “When robots are advanced enough to be neurotic, then maybe I’ll be concerned,” he said, “though I don’t think too many of us choose this field for job security, anyway.”

[y66]

From Tech Giants Are Paying Huge Salaries for Scarce A.I. Talent by Cade Metz:

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Tech’s biggest companies are placing huge bets on artificial intelligence, banking on things ranging from face-scanning smartphones and conversational coffee-table gadgets to computerized health care and autonomous vehicles. As they chase this future, they are doling out salaries that are startling even in an industry that has never been shy about lavishing a fortune on its top talent.

Typical A.I. specialists, including both Ph.D.s fresh out of school and people with less education and just a few years of experience, can be paid from $300,000 to $500,000 a year or more in salary and company stock, according to nine people who work for major tech companies or have entertained job offers from them. All of them requested anonymity because they did not want to damage their professional prospects.

At the top end are executives with experience managing A.I. projects. In a court filing this year, Google revealed that one of the leaders of its self-driving-car division, Anthony Levandowski, a longtime employee who started with Google in 2007, took home over $120 million in incentives before joining Uber last year through the acquisition of a start-up he had co-founded that drew the two companies into a court fight over intellectual property.

Salaries are spiraling so fast that some joke the tech industry needs a National Football League-style salary cap on A.I. specialists. “That would make things easier,” said Christopher Fernandez, one of Microsoft’s hiring managers. “A lot easier.”

There are a few catalysts for the huge salaries. The auto industry is competing with Silicon Valley for the same experts who can help build self-driving cars. Giant tech companies like Facebook and Google also have plenty of money to throw around and problems that they think A.I. can help solve, like building digital assistants for smartphones and home gadgets and spotting offensive content.

Most of all, there is a shortage of talent, and the big companies are trying to land as much of it as they can. Solving tough A.I. problems is not like building the flavor-of-the-month smartphone app. In the entire world, fewer than 10,000 people have the skills necessary to tackle serious artificial intelligence research, according to Element AI, an independent lab in Montreal.

Man I hope they don't lure away BBO robot programmers. It would really suck to have a car that likes to play in 5 card fits.

[y66]

From The Hidden Automation Agenda of the Davos Elite by Kevin Roose at NYT:

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DAVOS, Switzerland — They’ll never admit it in public, but many of your bosses want machines to replace you as soon as possible.

I know this because, for the past week, I’ve been mingling with corporate executives at the World Economic Forum’s annual meeting in Davos. And I’ve noticed that their answers to questions about automation depend very much on who is listening.

In public, many executives wring their hands over the negative consequences that artificial intelligence and automation could have for workers. They take part in panel discussions about building “human-centered A.I.” for the “Fourth Industrial Revolution” — Davos-speak for the corporate adoption of machine learning and other advanced technology — and talk about the need to provide a safety net for people who lose their jobs as a result of automation.

But in private settings, including meetings with the leaders of the many consulting and technology firms whose pop-up storefronts line the Davos Promenade, these executives tell a different story: They are racing to automate their own work forces to stay ahead of the competition, with little regard for the impact on workers.

All over the world, executives are spending billions of dollars to transform their businesses into lean, digitized, highly automated operations. They crave the fat profit margins automation can deliver, and they see A.I. as a golden ticket to savings, perhaps by letting them whittle departments with thousands of workers down to just a few dozen.

“People are looking to achieve very big numbers,” said Mohit Joshi, the president of Infosys, a technology and consulting firm that helps other businesses automate their operations. “Earlier they had incremental, 5 to 10 percent goals in reducing their work force. Now they’re saying, ‘Why can’t we do it with 1 percent of the people we have?’”

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“That’s the great dichotomy,” said Ben Pring, the director of the Center for the Future of Work at Cognizant, a technology services firm. “On one hand,” he said, profit-minded executives “absolutely want to automate as much as they can.”

“On the other hand,” he added, “they’re facing a backlash in civic society.”

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The choices made by the Davos elite — and the pressure applied on them to act in workers’ interests rather than their own — will determine whether A.I. is used as a tool for increasing productivity or for inflicting pain.

“The choice isn’t between automation and non-automation,” said Erik Brynjolfsson, the director of M.I.T.’s Initiative on the Digital Economy. “It’s between whether you use the technology in a way that creates shared prosperity, or more concentration of wealth.”