"A large language model is nothing more than a monumental pile of small numbers. It converts words into numbers, runs those numbers through a numerical pinball game, and turns the resulting numbers back into words. Similar piles are part of the furniture of everyday life. Meteorologists use them to predict the weather. Epidemiologists use them to predict the paths of diseases. Among regular people, they do not usually inspire intense feelings."
"Ellie Pavlick, a computer scientist at Brown, has drawn up a taxonomy of our most common responses. There are the "fanboys," who man the hype wires. They believe that large language models are intelligent, maybe even conscious, and prophesy that, before long, they will become superintelligent. The venture capitalist Marc Andreessen has described A.I. as "our alchemy, our Philosopher's Stone-we are literally making sand think.""
Large language models convert words into numbers, process those numbers through complex statistical computations, and convert the outputs back into words. Comparable numerical models are widely used in fields like meteorology and epidemiology without provoking strong emotions. When these systems began producing fluent language, reactions intensified because language has been seen as a uniquely human capacity. Public responses cluster into groups: enthusiastic proponents who attribute intelligence or consciousness to the models, skeptical critics who dismiss them as clever but empty tricks, and a more agnostic stance that accepts uncertainty. Many observers emphasize that these models function as opaque black boxes.
Read at The New Yorker
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