this post was submitted on 26 Jan 2024
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This is a classic problem for machine learning systems, sometimes called over fitting or memorization. By analogy, it's the difference between knowing how to do multiplication vs just memorizing the times tables. With enough training data and large enough storage AI can feign higher "intelligence", and that is demonstrably what's going on here. It's a spectrum as well. In theory, nearly identical recall is undesirable, and there are known ways of shifting away from that end of the spectrum. Literal AI 101 content.
Edit: I don't mean to say that machine learning as a technique has problems, I mean that implementations of machine learning can run into these problems. And no, I wouldn't describe these as being intelligent any more than a chess algorithm is intelligent. They just have a much more broad problem space and the natural language processing leads us to anthropomorphize it.
No it is not. What is going on nobody calls intelligence. They train a model to draw this so that is what it does. Nothing here has anything to do with any problems with machine learning
Pick one
Pick a quote by splicing words I said?
Ellipses are used in quotes to remove irrelevant parts without changing the meaning of the sentence. Makes it take less time to quote someone
Apparently you're unfamiliar with basic concepts of the language we're using here
You typically wrap those ellipses in square brackets when making such a change. In fact, you do so with any editorial changes to a quote to make things more clear.
For example, if Mike was quoted about the war in Ukraine as saying “I just think this whole thing is silly, they should stop” you could alter the quote as such: Mike said “I just think […] [Russia] should stop.”
It did change immensely what I said...