The New Yorker:

The C.E.O. of OpenAI helped usher artificial intelligence into public life. Now, as fears and fortunes mount, his own transformation is just beginning.

By Erin Neil

In 2017, soon after Google researchers invented a new kind of neural network called a transformer, a young OpenAI engineer named Alec Radford began experimenting with it. What made the transformer architecture different from that of existing A.I. systems was that it could ingest and make connections among larger volumes of text, and Radford decided to train his model on a database of seven thousand unpublished English-language books—romance, adventure, speculative tales, the full range of human fantasy and invention. Then, instead of asking the network to translate text, as Google’s researchers had done, he prompted it to predict the most probable next word in a sentence.

The machine responded: one word, then another, and another—each new term inferred from the patterns buried in those seven thousand books. Radford hadn’t given it rules of grammar or a copy of Strunk and White. He had simply fed it stories. And, from them, the machine appeared to learn how to write on its own. It felt like a magic trick: Radford flipped the switch, and something came from nothing.

His experiments laid the groundwork for ChatGPT, released in 2022. Even now, long after that first jolt, text generation can still provoke a sense of uncanniness. Ask ChatGPT to tell a joke or write a screenplay, and what it returns—rarely good, but reliably recognizable—is a sort of statistical curve fit to the vast corpus it was trained on, every sentence containing traces of the human experience encoded in that data.

Go to link