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In GPT-4, a word vector has thousands of dimensions, which describe its shades of similarity to and difference from every other word. During training, a large language model tweaks a word’s coördinates whenever it makes a prediction error; words that appear in texts together are nudged closer in space. This produces an incredibly dense representation of usages and meanings, in which analogy becomes a matter of geometry. In a classic example, if you take the word vector for “Paris,” subtract “France,” and then add “Italy,” the nearest other vector will be “Rome.”
The Case That A.I. Is Thinking | the New Yorker
James Somers
The statistical transformations of AI are the latest iteration in this process of rendering the world ready for algorithmic governance.
Resisting AI
Dan McQuillan
Was I kind to others? It was hard to nail down an answer. I worried that if I did turn out to have a personality, it would be one of the unkind ones.
Conversations With Friends
Sally Rooney
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