Two of the smartest people I follow in the AI world recently sat down to check in on how the field is going.

One was François Chollet, creator of the widely used Keras library and author of the ARC-AGI benchmark, which tests if AI has reached “general” or broadly human-level intelligence. Chollet has a reputation as a bit of an AI bear, eager to deflate the most boosterish and over-optimistic predictions of where the technology is going. But in the discussion, Chollet said his timelines have gotten shorter recently. Researchers had made big progress on what he saw as the major obstacles to achieving artificial general intelligence, like models’ weakness at recalling and applying things they learned before.

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Chollet’s interlocutor — Dwarkesh Patel, whose podcast has become the single most important place for tracking what top AI scientists are thinking — had, in reaction to his own reporting, moved in the opposite direction. While humans are great at learning continuously or “on the job,” Patel has become more pessimistic that AI models can gain this skill any time soon.

“[Humans are] learning from their failures. They’re picking up small improvements and efficiencies as they work,” Patel noted. “It doesn’t seem like there’s an easy way to slot this key capability into these models.”

All of which is to say, two very plugged-in, smart people who know the field as well as anyone else can come to perfectly reasonable yet contradictory conclusions about the pace of AI progress.

In that case, how is someone like me, who’s certainly less knowledgeable than Chollet or Patel, supposed to figure out who’s right?

The forecaster wars, three years in

One of the most promising approaches I’ve seen to resolving — or at least adjudicating — these disagreements comes from a small group called the Forecasting Research Institute.

In the summer of 2022, the institute began what it calls the Existential........

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