Know Your AI Before You Unleash It

I used my one free online article to read Ted Chiang’s February 9, 2023 article in The New Yorker, “ChatGPT Is a Blurry Jpeg of the Web.” It’s well worth the price of an issue (if they include it in one), one of those mind-expanding explanations of technology that ignites the imagination and shifts how one looks at a problem.

Ted Chiang, my favorite science fiction author, is a master of the pithy analogy. He describes the current crop of large learning models—GPT-3 (ChatGPT), DALL*E, etc. —in terms of data compression and decompression. These may well be the same terms in which AI practitioners understand these problems, but Chiang articulates the connection so well, it’s hard not to see learning, both machine and human, in these terms. It feels revelatory.

If you’re already versed in compression algorithms, do read Ted Chiang’s article. It’s wonderful. But for my friends and family who aren’t in technology, with whom I’ve been discussing these text-and-art generating systems, I have a simplified walkthrough—my own stab at pithy analogies.

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The Wines of GPT-3

When I read the news that scientists had found chemicals on Venus that could be the product of microbial life, I joyfully tweeted a tasting note of a Venusian wine.

The wines of Venus have high acidity, but the atmospheric terroir crushes all tannic structure. They theoretically pair well with rich foods, but are instantly lethal in the smallest quantities.

@RajivMote on Twitter

This was noticed by science fiction writer and technologist James Yu, who has been playing with Open AI’s GPT-3 project, a machine learning system trained on a vast spread of text from the Web, and does a spooky-good job of generating text similar to prompts it is fed. GPT-3 fleshed out more wines from the solar system. The following are all machine-generated wine notes

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