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 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.
Feed it a racist dataset, and the AI will be racist. Feed it misinformation, and the AI will only reproduce misinformation.
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.
guyrocket@kbin.social 10 months ago
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.
bionicjoey@lemmy.ca 10 months ago
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.
dustyData@lemmy.world 10 months ago
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.
snooggums@kbin.social 10 months ago
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.
Acamon@lemmy.world 10 months ago
That’s my take as well, I would just like to know more about the weighting/bias.
bionicjoey@lemmy.ca 10 months ago
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.