Enlarge / An artist’s impression of a human and a robot talking. (credit: Getty Images | Benj Edwards)
In a preprint research paper titled “Does GPT-4 Pass the Turing Test?”, two researchers from UC San Diego pitted OpenAI’s GPT-4 AI language model against human participants, GPT-3.5, and ELIZA to see which could trick participants into thinking it was human with the greatest success. But along the way, the study, which has not been peer-reviewed, found that human participants correctly identified other humans in only 63 percent of the interactions—and that a 1960s computer program surpassed the AI model that powers the free version of ChatGPT.
Even with limitations and caveats, which we’ll cover below, the paper presents a thought-provoking comparison between AI model approaches and raises further questions about using the Turing test to evaluate AI model performance.
British mathematician and computer scientist Alan Turing first conceived the Turing test as “The Imitation Game” in 1950. Since then, it has become a famous but controversial benchmark for determining a machine’s ability to imitate human conversation. In modern versions of the test, a human judge typically talks to either another human or a chatbot without knowing which is which. If the judge cannot reliably tell the chatbot from the human a certain percentage of the time, the chatbot is said to have passed the test. The threshold for passing the test is subjective, so there has never been a broad consensus on what would constitute a passing success rate.
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