The test results would not depend on the machine's ability to give correct answers to questions, only on how closely its answers resembled those a human would give. If the evaluator could not reliably tell the machine from the human, the machine would be said to have passed the test. The conversation would be limited to a text-only channel, such as a computer keyboard and screen, so the result would not depend on the machine's ability to render words as speech. The evaluator would be aware that one of the two partners in conversation was a machine, and all participants would be separated from one another.
Turing proposed that a human evaluator would judge natural language conversations between a human and a machine designed to generate human-like responses. As AI migrates more and more into everyday life, we should worry if systems without common sense are making decisions where common sense is needed.The Turing test, originally called the imitation game by Alan Turing in 1950, is a test of a machine's ability to exhibit intelligent behaviour equivalent to, or indistinguishable from, that of a human. He identifies a possible mechanism behind common sense and the capacity to call on background knowledge: the ability to represent objects of thought symbolically. “If our goal is to understand intelligent behavior, we had better understand the difference between making it and faking it,” he observes. He argues that a computer program that passes the famous Turing Test could be a mindless zombie, and he proposes another way to test for intelligence-the Winograd Schema Test, developed by Levesque and his colleagues. Levesque considers the role of language in learning.
This kind of artificial intelligence is equipped to handle situations that depart from previous patterns-as we do in real life, when, for example, we encounter a washed-out bridge or when the barista informs us there's no more soy milk.
In this book, Hector Levesque shifts the conversation to “good old fashioned artificial intelligence,” which is based not on heaps of data but on understanding commonsense intelligence. This is what powers a driverless car, for example. AI is all the rage, and the buzziest AI buzz surrounds adaptive machine learning: computer systems that learn intelligent behavior from massive amounts of data. What can artificial intelligence teach us about the mind? If AI's underlying concept is that thinking is a computational process, then how can computation illuminate thinking? It's a timely question. What artificial intelligence can tell us about the mind and intelligent behavior.
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