Turing Test

The Turing test is a measure of machine intelligence first described by Alan Turing in his 1950 paper "Computing Machinery and Intelligence." Inspired by a parlor game called the imitation game, the test places a human evaluator in conversation with both a human and a machine through a text-only channel. If the evaluator cannot reliably tell which respondent is the machine, the machine is said to have passed the test.

Turing predicted that by the year 2000 a computer would be able to fool an average interrogator at least 30% of the time after five minutes of questioning. For decades no system came close to this standard. Early chatbots like ELIZA (1966) could mimic conversation through pattern matching but failed under sustained interrogation. The test became a cultural touchstone for debating whether machines can truly "think" or merely simulate thinking.

The rise of large language models has renewed interest in the Turing test. In 2025, researchers at UC San Diego published a study in which OpenAI's GPT-4.5 was mistaken for a human 73% of the time, outperforming actual human participants who were correctly identified only 67% of the time. While this result suggests modern LLMs can pass the original test as Turing defined it, critics argue the test measures conversational mimicry rather than genuine understanding, and that more rigorous benchmarks are needed to evaluate true machine intelligence.