人工智能：拥有不同于人类思维的机器

人工智能：拥有不同于人类思维的机器

Artificial intelligence: The machines with alien minds

Our smartest machines look nothing like we predicted – has the field lost its way, or do we need to rethink what AI actually means, asks Tom Chatfield.

Would modern artificial intelligence live up to the dreams of the field’s founders? Perhaps not. But in many ways, the smartest machines we have built are entities they never could have imagined.

In 1956, attendees of a research camp at Dartmouth College in New Hampshire coined the phrase "artificial intelligence" to describe its efforts to “find how to make machines use language, form abstractions and concepts, solve kinds of problems now reserved for humans, and improve themselves.”

Compare that with the AI project that Facebo ok announced this month. Under one of the world’s most prominent experts, it will “do world-class artificial-intelligence research using all of the knowledge that people have shared on Facebook” –with potential gains including building “services that are much more natural to interact with.”

What does that mean? Like much of modern AI, they will be training algorithms to sift and analyse unimaginably vast amounts of data in the hope that smart answers will emerge. Google, IBM and many others are now using this technique –called machine learning –to great commercial success, and the “intelligence” they create underpins everything from your internet searches to online language translation. Since the calculations of these machines involves making statistical correlations within huge caches of data, their reasoning can be unfathomable to the human mind – often these systems provide apparently intelligent answers, but nobody has any idea how they came to their conclusions.

Yet even the most advanced forms of m achine intelligence cannot hope to pass for a human in Turing’s famous test –let alone use natural language or develop concepts themselves, as the pioneers hoped. More than half a century of research has brought us a far more sophisticated grasp of what machine intelligence looks like – but has it lost its way? Or do we need to reframe our ideas about what the term AI actually means?

One person who believes progress in AI has fallen short in many ways is the author and academic Douglas Hoftstadter – most famous for his Pulitzer-Prize-winning 1979 book Gödel, Escher, Bach – who in a recentprofile for The Atlantic magazine emphasized his disillusionment with the current direction of AI.

For Hoftstadter, the label “intelligence” is simply inappropriate for d escribing insights drawn by brute computing power from massive data sets – because, from his perspective, the fact that results appear smart is irrelevant if the process underlying them bears no resemblance to intelligent thought. As he put it to interview er James Somers, “I don’t want to be involved in passing off some fancy program’s behaviour for intelligence when I know that it has nothing to do with intelligence. And I don’t know why more people aren’t that way.”

Cheap tricks

By Hofstadter’s standards, iconic computational achievements like beating the world’s best players at chess or Jeopardy are rendered trivial by the “trickery” involved: by the fact that the winning computer has done little more than weigh the relative benefits of several billion possibilities, without at any point knowing anything about the nature of the game being played.