On a brutally hot day this summer, my friend Max met up with his family at a playground. For some reason, a sprinkler for kids was switched off, and Max’s wife had promised everyone that her husband would fix it. Confronted by red-faced six- and seven-year-olds, Max entered a utility shed hoping to find a big, fat “On” switch. Instead, he found a maze of ancient pipes and valves. He was about to give up when, on a whim, he pulled out his phone and fed a photo into ChatGPT-4o, along with a description of his problem. The A.I. thought for a second, or maybe didn’t think, but all the same it said that he was looking at a backflow-preventer system typical of irrigation setups. Did he see that yellow ball valve toward the bottom? That probably controlled the flow. Max went for it, and cheers rang out across the playground as the water turned on.
But the moral case against A.I. may ultimately be stronger than the technical one. “The ‘stochastic parrot’ thing has to be dead at some point,” Samuel J. Gershman, a Harvard cognitive scientist who is no A.I. hype man, told me. “Only the most hardcore skeptics can deny these systems are doing things many of us didn’t think were going to be achieved.” Jonathan Cohen, a cognitive neuroscientist at Princeton, emphasized the limitations of A.I., but argued that, in some cases, L.L.M.s seem to mirror one of the largest and most important parts of the human brain. “To a first approximation, your neocortex is your deep-learning mechanism,” Cohen said. Humans have a much larger neocortex than other animals, relative to body size, and the species with the largest neocortices—elephants, dolphins, gorillas, chimpanzees, dogs—are among the most intelligent.
In 2003, the machine-learning researcher Eric B. Baum published a book called “What Is Thought?” (I stumbled upon it in my college’s library stacks, drawn by the title.) The gist of Baum’s argument is that understanding is compression, and compression is understanding. In statistics, when you want to make sense of points on a graph, you can use a technique called linear regression to draw a “line of best fit” through them. If there’s an underlying regularity in the data—maybe you’re plotting shoe size against height—the line of best fit will efficiently express it, predicting where new points could fall. The neocortex can be understood as distilling a sea of raw experience—sounds, sights, and other sensations—into “lines of best fit,” which it can use to make predictions. A baby exploring the world tries to guess how a toy will taste or where food will go when it hits the floor. When a prediction is wrong, the connections between neurons are adjusted. Over time, those connections begin to capture regularities in the data. They form a compressed model of the world.
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