Is Winter Coming? (2024)

Summary

Is Winter Coming? Thoughts on articifial intelligence and lofty expectations. Spring 2024 In the 1960s, AI researchers tried - and failed - to deliver machine translation of Russian to English. Overly confident researchers, lazy journalists and far too optimistic tech utopianists all built expectations that couldn't be met. Eventually, the lack of results put a stop to the previously generous funding. The project was halted and interest in AI faltered. This was the first "AI winter". Much more recently, a colleague of mine wanted to show some photos he'd taken of a huge peanut spill on a countryside road. A truck carrying a large amount of peanuts had apparently encountered some malfunction, dumped its cargo, and left it there for grabs. He tried searching his smartphone photo library for "peanuts", but no images materialized. I suggested to instead try searching for "pebbles" and, lo and behold, we were immediately met with pictures of a sea of peanuts. The image recognition feature on smartphones is one of the things that now fall under the "AI" umbrella: a piece of software trained on a very large number of photos in order to classify and recognize what they depict. The problem is that even in a very large selection of photos, seas of peanuts are extremely rare. To find a photo of one, a human must step in and do something AI software is so far incapable of: knowing how the software was trained and use that knowledge creatively - such as coming up with the visual similarity between peanuts and pebbles. This practice is called prompt engineering. Prompt engineering is not only a rather silly name, it's also a paradoxical practice in the world of AI. The whole selling point of the latest AI hype is to make computers behave more like humans. It's what end users expect, and it's what all the major players are using to drive the current hype. Type in (or speak!) a question in normal conversational English and get an answer back that reads (or speaks!) in a similar sty...