AI is not a new thing, and machine learned logic definitely counts as AI.
replyFor those that have experience with ML, yes. For those that have recently become acquainted with it (more on business side) they seem to really struggle with this in my experience. '
replyYeah, and don’t forget Eliza!
replyML is part of AI, and has always been. AI is not equal to chatgpt and AI wasn't coined/conceived in November 2022.
replyIs a LLM logic in weights derived from machine learning?
replyWell, yes. That's literally what it is.
replyWhat what is? The article has nothing to do with LLMs. It even explicitly says they don’t use LLMs.
reply> Is a LLM logic in weights derived from machine learning?
replyI was just answering this question. LLM logic in weights is fundamentally from machine learning, so yes. Wasn't really saying anything about the article.
Good one… but Is a DB query filter AI? I forgot to say though is sounds like a really cool thing to do
replyStrictly speaking, expert systems are AI as well, as in, an expert comes up with a bunch of if/else rules. So yes technically speaking even if they didn’t acquire the weights using ML and hand-coded them, it could still be called AI.
replyIt is 100% valid to label an algorithm that plays tic-tac-toe as "AI"
replyMuch of the early AI research was spent on developing various algorithms that could play board games.
Didn't even need computers, one early AI was MENACE [1], a set of 304 matchboxes which could learn how to play noughts and crosses.
[1] https://en.wikipedia.org/wiki/Matchbox_Educable_Noughts_and_...
Yup this is exactly my point, in the 80s there were plenty of “AI” companies and “fuzzy logic” was the buzzword of the day.
replyI built the Matchbox for Hexapawn, detailed in National Geographic Kids!
replyI didn't know what a Jujube was, but I got the idea.
Calling it "AI" is marketing sugar. It is closer to an inference-only state machine where gradient descent did the wiring instead of an engineer, and the annoying part is that once the detector setup or noise profile moves, retraining and redeploy stop being normal ML chores and turn into hardware respins, validation, and a lot of waiting. That distinction stops sounding pedantic the first time a bug fix means touching silicon instead of pushing to a repo.
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