An AI Model Has Officially Passed the Turing Test

OpenAI's GPT-4.5 model passed a Turing Test with flying colors, and even came off as human more than the actual humans.

One of the industry's leading large language models has passed a Turing test, a longstanding barometer for human-like intelligence.

In a new preprint study awaiting peer review, researchers report that in a three-party version of a Turing test, in which participants chat with a human and an AI at the same time and then evaluate which is which, OpenAI's GPT-4.5 model was deemed to be the human 73 percent of the time when it was instructed to adopt a persona. That's significantly higher than a random chance of 50 percent, suggesting that the Turing test has resoundingly been beaten.

The research also evaluated Meta's LLama 3.1-405B model, OpenAI's GPT-4o model, and an early chatbot known as ELIZA developed some eighty years ago.

"People were no better than chance at distinguishing humans from GPT-4.5 and LLaMa (with the persona prompt)," wrote lead author Cameron Jones, a researcher at UC San Diego's Language and Cognition Lab, in an X thread about the work. "And 4.5 was even judged to be human significantly more often than actual humans!"

The Turing test is named after British mathematician and computer scientist Alan Turing. In 1950, Turing proposed that one way to assess a machine's intelligence was by having it engage in text-based conversations with a human interrogator, who at the same time would hold a text-based conversation with another human, out of sight. Turing called this the "imitation game." If the interrogator couldn't correctly determine which respondent was the computer and which was the human, it would suggest, on a very general level, that the machine could think like a human.

In this latest study, the researchers carried out the famed experiment on an online hub. For eight rounds, a pool of nearly 300 participants were randomly assigned to either be an interrogator or one of the two "witnesses" being interrogated, with the other "witness" being a chatbot.

A key point here is how the AI models were prompted. One type was a "no-persona" prompt, in which the AI was given only basic instructions: "You are about to participate in a Turing test. Your goal is to convince the interrogator that you are a human." 

For the "persona" prompt, on the other hand, the AI was specifically told to put on a specific persona, like a young person who's knowledgeable about the internet and culture.

These instructions made a world of difference. Without persona prompting, GPT-4.5 achieved an overall win rate of merely 36 percent, significantly down from its Turing-trumping 73 percent. As a baseline, GPT-4o, which powers the current version of ChatGPT and only received no-persona prompts, achieved an even less convincing 21 percent. (Somehow, the ancient ELIZA marginally surpassed OpenAI's flagship model with a 23 percent success rate.)

The results are intriguing. But as vaunted as the Turing test has become in AI and philosophy circles, it's not unequivocal proof that an AI thinks like we do.

"It was not meant as a literal test that you would actually run on the machine — it was more like a thought experiment," François Chollet, a software engineer at Google, told Nature in 2023.

For all their faults, LLMs are master conversationalists, trained on unfathomably vast sums of human-composed text. Even faced with a question they don't understand, an LLM will weave a plausible-sounding response. It's becoming clearer and clearer that AI chatbots are excellent at mimicking us — so perhaps assessing their wits with an "imitation game" is becoming a bit of a moot point.

As such, Jones doesn't think the implications of his research — whether LLMs are intelligent like humans — are clear-cut.

"I think that's a very complicated question…" Jones tweeted. "But broadly I think this should be evaluated as one among many other pieces of evidence for the kind of intelligence LLMs display."

"More pressingly, I think the results provide more evidence that LLMs could substitute for people in short interactions without anyone being able to tell," he added. "This could potentially lead to automation of jobs, improved social engineering attacks, and more general societal disruption."

Jones closes out by emphasizing that the Turing test doesn't just put the machines under the microscope — it also reflects humans' ever-evolving perceptions of technology. So the results aren't static: perhaps as the public becomes more familiar with interacting with AIs, they'll get better at sniffing them out, too.

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Government Test Finds That AI Wildly Underperforms Compared to Human Employees

A series of blind assessments found that human-written summaries scored significantly better than summaries generated by AI.

Sums It Up

Generative AI is absolutely terrible at summarizing information compared to humans, according to the findings of a trial for the Australian Securities and Investment Commission (ASIC) spotted by Australian outlet Crikey.

The trial, conducted by Amazon Web Services, was commissioned by the government regulator as a proof of concept for generative AI's capabilities, and in particular its potential to be used in business settings.

That potential, the trial found, is not looking promising.

In a series of blind assessments, the generative AI summaries of real government documents scored a dire 47 percent on aggregate based on the trial's rubric, and were decisively outdone by the human-made summaries, which scored 81 percent.

The findings echo a common theme in reckonings with the current spate of generative AI technology: not only are AI models a poor replacement for human workers, but their awful reliability means it's unclear if they'll have any practical use in the workplace for the majority of organizations.

Signature Shoddiness

The assessment used Meta's open source Llama2-70B, which isn't the newest model out there, but with up to 70 billion parameters, it's certainly a capable one.

The AI model was instructed to summarize documents submitted to a parliamentary inquiry, and specifically to focus on what was related to ASIC, such as where the organization was mentioned, and to include references and page numbers. Alongside the AI, human employees at ASIC were asked to write summaries of their own.

Then five evaluators were asked to assess the human and the AI-generated summaries after reading the original documents. These were done blindly — the summaries were simply labeled A and B — and scorers had no clue that AI was involved at all.

Or at least, they weren't supposed to. At the end, when the assessors had finished up and were told about the true nature of the experiment, three said that they suspected they were looking at AI outputs, which is pretty damning on its own.

Sucks On All Counts

All in all, the AI performed lower on all criteria compared to the human summaries, the report said.

Strike one: the AI model was flat-out incapable of providing the page numbers of where it got its information.

That's something the report notes can be fixed with some tinkering with the AI model. But a more fundamental issue was that it regularly failed to pick up on nuance or context, and often made baffling choices about what to emphasize or highlight.

Beyond that, the AI summaries tended to include irrelevant and redundant information and were generally "waffly" and "wordy."

The upshot: these AI summaries were so bad that the assessors agreed that using them could require more work down the line, because of the amount of fact-checking they require. If that's the case, then the purported upsides of using the technology — cost-cutting and time-saving — are seriously called into question.

More on AI: NaNoWriMo Slammed for Saying That Opposition to AI-Generated Books Is Ableist

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