There was a time in the not too distant past--say, nine months ago--when the Turing test seemed like a pretty stringent detector of machine intelligence. Chances are you're familiar with how it works: Human judges hold text conversations with two hidden interlocutors, one human and one computer, and try to determine which is which. If the computer manages to fool at least 30 percent of the judges, it passes the test and is pronounced capable of thought. For 70 years, it was hard to imagine how a computer could pass the test without possessing what AI researchers now call artificial general intelligence, the entire range of human intellectual capacities. Then along came large language models such as GPT and Bard, and the Turing test suddenly began seeming strangely outmoded. OK, sure, a casual user today might admit with a shrug, GPT-4 might very well pass a Turing test if you asked it to impersonate a human.

Before that, he co-founded DeepMind, one of the world's leading artificial intelligence companies. AI systems are increasingly everywhere and are becoming more powerful almost by the day. But how can we know if a machine is truly "intelligent"? For decades this has been defined by the Turing test, which argues that an AI that's able to replicate language convincingly enough to trick a human into thinking it was also human should be considered intelligent. But there's now a problem: the Turing test has almost been passed--it arguably already has been.

But there's now a problem: the Turing test has almost been passed--it arguably already has been. The latest generation of large language models, systems that generate text with a coherence that just a few years ago would have seemed magical, are on the cusp of acing it. So where does that leave AI? And more important, where does it leave us? The truth is, I think we're in a moment of genuine confusion (or, perhaps more charitably, debate) about what's really happening.

In 1950, when Alan Turing conceived "The Imitation Game" as a test of computer behavior, it was unimaginable that humans of the future would spend most hours of their day glued to a screen, inhabiting the world of machines more than the world of people. That is the Copernican Shift in AI. "I propose to consider the question, 'Can machines think?'" Buried in the controversy this summer about Google's LaMDA language model, which an engineer claimed was sentient, is a hint about a big change that's come over artificial intelligence since Alan Turing defined the idea of the "Turing Test" in an essay in 1950. Turing, a British mathematician who laid the groundwork for computing, offered what he called the "Imitation Game." Two entities, one a person, one a digital computer, are asked questions by a third entity, a human interrogator.

Our idea is to evaluate each area step by step. As long as each feature is designed to look like it is part of the same body (same gender, age and so on), then if an eye and mouth can individually pass the test then they should also pass it together. This would allow a robot builder to assess progress as they go to ensure each body part is indistinguishable from a that of human and to prevent ending up with something that falls into the uncanny valley.

AI is changing work but work will change AI too.Getty What does it mean for a machine to be intelligent? For decades, the common answer to that question has been to pass the "Turing test." This test, named after famed mathematician Alan Turing, says that if a machine can carry on a conversation with a human via a textual interface such that the human can not tell the difference between a human and machine, then the machine is intelligent.