https:\/\/www.technologyreview.com\/s\/609318\/the-great-ai-paradox\/<\/p>\n
You\u2019ve probably heard versions of each of\u00a0the following ideas.<\/span><\/p>\n 1. With computers becoming remarkably adept at driving, understanding speech, and other tasks, more jobs could soon be automated than society is prepared to handle.<\/p>\n 2. Improvements in computers\u2019 skills will stack up until machines are far smarter than people. This \u201csuperintelligence\u201d will largely make human labor unnecessary. In fact, we\u2019d better hope that machines don\u2019t eliminate us altogether, either accidentally or on purpose.<\/p>\n This is tricky. Even though the first scenario is already under way, it won\u2019t necessarily lead to the second one. That second idea, despite being an obsession of some very knowledgeable and thoughtful people, is based on huge assumptions. If anything, it\u2019s a diversion from taking more responsibility for the effects of today\u2019s level of automation and dealing with the concentration of power in the technology industry.<\/p>\n To really see what\u2019s going on, we have to be clear on what has been achieved\u2014and what remains far from solved\u2014in artificial intelligence.<\/span><\/p>\n The most stunning developments in computing over the past few years\u2014cars that drive themselves, machines that accurately recognize images and speech, computers that beat the most brilliant human players of complex games like Go\u2014stem from breakthroughs<\/a>\u00a0in a particular branch of AI: adaptive machine learning. As the University of Toronto computer scientist Hector Levesque puts it in his book\u00a0Common Sense, the Turing Test, and the Quest for Real AI<\/em>, the idea behind adaptive machine learning is to \u201cget a computer system to learn some intelligent behavior by training it on massive amounts of data.\u201d<\/p>\n It\u2019s amazing that a machine can detect objects, translate between languages, and even write computer code after being fed examples of those behaviors, rather than having to be programmed in advance. It wasn\u2019t really possible until about a decade ago, because previously there was not sufficient digital data for training purposes, and even if there had been, there wasn\u2019t enough computer horsepower to crunch it all. After computers detect patterns in the data, algorithms in software lead them to draw inferences from these patterns and act on them. That is what\u2019s happening in a car analyzing inputs from multiple sensors and in a machine processing every move in millions of games of Go.<\/p>\n Since machines can process superhuman amounts of data, you can see why they might drive more safely than people in most circumstances, and why they can vanquish Go champions. It\u2019s also why computers are getting even better at things that are outright impossible for people, such as correlating your genome and dozens of other biological variables with the drugs likeliest to cure your cancer.<\/span><\/p>\n Even so, all this is a small part of what could reasonably be defined as real artificial intelligence. Patrick Winston, a professor of\u00a0\u00a0<\/span>AI and computer science at MIT, says it would be more helpful to describe the developments of the past few years as having occurred in \u201ccomputational statistics\u201d rather than in AI. One of the leading researchers in the field,\u00a0Yann LeCun<\/a>, Facebook\u2019s director of AI,\u00a0said<\/a>\u00a0at a Future of Work conference at MIT in November that machines are far from having \u201cthe essence of intelligence.\u201d That includes the ability to understand the physical world well enough to make predictions about basic aspects of it\u2014to observe one thing and then use background knowledge to figure out what other things must also be true. Another way of saying this is that machines don\u2019t have common sense.<\/span><\/p>\nCommon sense<\/strong><\/h3>\n
Things Reviewed<\/h5>\n
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