(NYT) Did the Hardest Problem in A.I. Just Get Much Easier?

As A.I. systems have grown increasingly powerful over the past several years, decoding the inner workings of these technologies has only become harder. In 2014, Google’s Inception technology spanned several million “neurons,” the fundamental mathematical calculations that drive an A.I. system. The latest systems span trillions.

The trouble is that no one seems to know what each neuron is doing. When an A.I. system learns a particular concept — like recognizing a car or a cat or a suit tie — it spreads these ideas across multiple neurons in ways that even experts cannot understand. Researchers call this “superposition.” And if they can’t disentangle this phenomenon, the implications could be profound.

In coming years, many businesses, governments and individuals may be reluctant to turn to A.I. technologies because they don’t have a detailed understanding of how these systems work. If they can’t pinpoint why a system made a particular choice, they may not trust it. And if they do put their trust in A.I., they could open themselves up to liability when the technology makes serious mistakes, particularly when A.I. systems are used in sensitive areas, like diagnosing diseases at hospitals or helping the police identify perpetrators in photographs.

When Mr. Cammarata started an interpretability project at OpenAI three years ago, he estimated that less than 2 percent of the neurons inside an A.I. system could be properly understood. Now that he has a new way of looking at the problem — which involves a phenomenon he calls “recursive generalization” — he thinks that he can understand the behavior of more than 90 percent of neurons.

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New York Times @nytimes: Did the Hardest A.I. Problem Just Get Much Easier? – The New York Times. #MachineLearning #AI #aiact https://t.co/nN5DZlk7HL

— Nordic AI Institute (@nordicinst) October 27, 2026