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Alan Turing's Imitation Game has long been a benchmark for machine intelligence. But what it really measures is deception.
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In 1950, Turing designed a simple test to evaluate whether a computer possessed artificial intelligence comparable to humans; a computer must be able to pass as a human during a series of questions.
Today, Google's text generating deep learning models such as GPT-3 easily pass the Turing test. However, whether these models actually understand their generated output or rather excel at combining human text for specific questions stays up for debate.
This article points out the outdated nature of the Turing test to measure NLP advances which is now evaluated on new benchmarks. The Turing test instead raises ethical concerns for AI and its potential for deceit.
It is also interesting to note that NLP models can pass as humans on specific questions but often fail when applied to questions to new domains. Far from resembling human consciousness, current AI remains very specialized and data powered. This motivates the development of new tests to understand model generalization.