I hear people saying things like “chatgpt is basically just a fancy predictive text”. I’m certainly not in the “it’s sentient!” camp, but it seems pretty obvious that a lot more is going on than just predicting the most likely next word.
Even if it’s predicting word by word within a bunch of constraints & structures inferred from the question / prompt, then that’s pretty interesting. Tbh, I’m more impressed by chatgpt’s ability to appearing to “understand” my prompts than I am by the quality of the output. Even though it’s writing is generally a mix of bland, obvious and inaccurate, it mostly does provide a plausible response to whatever I’ve asked / said.
Anyone feel like providing an ELI5 explanation of how it works? Or any good links to articles / videos?
It is literally the same exact kind of algorithm that predicts the next word you will type on your phone based on what’s already been typed. The differences are that it has a much larger training dataset, which means more accurate predictions, it processes based on the entire body of text that has already been given (including the hidden prompt and previous messages), and that it doesn’t always predict whole words, but instead clusters of characters.
If you want a more general overview of how machine learning works in general, this is a good video series to watch: https://www.youtube.com/watch?v=aircAruvnKk
If you want to see some evidence that it doesn’t truly understand what it says, try having it generate and explain some jokes or riddles that rely on wordplay. It will completely shatter the illusion.
Me:
ChatGPT 4:
Edit: I prodded it a little, and I actually quite like the fourth one below.
Me:
ChatGPT:
All of those jokes are plagiarized. It doesn’t actually understand the jokes, it’s just repeating ones that it’s seen before. Ask it to explain why some of these are funny.
Seriously. I’ve literally heard all of those jokes before. It may have even stolen them all from a single website.
Great video! Thanks for posting that
I agree, that was good.
My major takeaway is that neutral networks, and AI in general, are mostly pattern recognition with a little bias and weighting thrown in to improve accuracy.
And that is why I question all the supposedly amazing things people seem to think it will do and many of the applications of AI.
That’s my take as well, I would just like to know more about the weighting/bias.
Weighting and bias are based on the training dataset. And the training dataset of ChatGPT is mostly internet content, literature, social media discussions, articles, etc.
So the inherent biases are going to be limited in the same way. For example, ChatGPT is not good at generating or interpreting code written in Malbolge, despite the fact that this language is meant to be relatively easy to understand for a machine yet difficult for a human to understand. Because it isn’t processing like a machine, it is processing text like a person.
It also is bad at understanding wordplay like puns since wordplay requires a simultaneous understanding of the meaning of a word as well as the linguistics that underly that word. It is decent at generating puns which already exist and are out in the world, but it can’t creatively generate new ones or interpret novel puns or other wordplay, since that would require a deeper understanding of the language.
Looking at the things it is bad at can give a great insight into its limitations, and in turn into how it works.
That’s exactly right. It is a statistical model that is based on some training dataset. The quality of the predictions is only as good as the completeness and bias of the training set.
And it is one of the major issues with giving AI and the corporations who make them free reign to “think” and inform decision making. Feed it a racist dataset, and the AI will be racist. Feed it misinformation, and the AI will only reproduce misinformation.
The proof that AI is just garbage in and garbage out is that AI always does this while some people are able to be anti-racist and anti-misinformation as a response even if most people fall for it.