Technology

Hehe. I’m imagining sitting 100 human test subjects down in a lab setting and asking them what 2+2 is, and then telling them they’re wrong when they answer 4. I don’t know how many of them would guess again but I know it’s not zero. Meanwhile, GPT can probably give a better answer to any advanced math or science query than the majority of humans.

I’m a writer and a language nerd and I watch people all the time use words incorrectly because they think they know what they mean, but they really don’t. They’re just regurgitating them in what they think is the same situation they heard them. They don’t “understand” the word and are just guessing and churning out crap.

I don’t have a dog in this race but I think it’s interesting how people judge artificial intelligence with too much credit given to what goes on with human intelligence. Most people who say it’s “just a next word predictor” read that phrase somewhere and are regurgitating it, not at all dissimilarly to what LLMs do. They use phrases like “it doesn’t actually understand” without being able to define, with any clarity or precision, and without resorting to examples, what would actually impress them as real intelligence.

ChatGPT doesn't learn like that though, does it? I thought it was "static" with its training data.

Toronto is Canadian New York. It wants to be the capital and probably should be but it doesn't speak enough French.

I kinda get why I'm getting downvoted, honestly. The ChatGPT fanboys definitely give off an "NFT-grindset" kind of vibe, and they can be loud and overzealous with their prognosticating. It feels cathartic to make fun of the thing they've adopted as a centerpiece of their personality

None of that changes what is objectively the very real and very unexpected improvement these models are displaying, and we're still not sure what it is they're doing behind the curtain. "Predicting the next most likely word" is simply not a sufficient explanation for how these models seem to correctly interpret intent and apply factual knowledge stored in its dataset in abstract ways.

People want to squabble over anthropomorphic word choices and debate 'consiousness', and fair enough, its an interesting question. But that doesn't really come close to what's really interesting about the models gaining functionality when by all accounts they should only be 'guessing the next most likely word'.

I'm not really interested in debating people who are performatively unimpressed by these products, but it bothers me that those people continue rolling their eyes when significant advancements are made. Like sure, it's not new that ML algorithms can decode keystrokes from an audio recording, but it's a big deal when those models can be run on consumer grade hardware and not just a super computer run by a three letter agency.