Well from what I remember from university most of expert systems went into bust because they were promising what ML today promise.
The maintenance of the rules or for you scripts for complex tasks is much more work than anyone is willing to commit to. Also big problem was finding out tacit knowledge and no one was able to code that reliably in.
ML today is promising you won’t have to hand code the rules you just push data and system finds out what the rules are and then can handle new data.
I don’t have to code the rules to check if there is a cat in the picture - that definitely works. Making rules on data that is not so often found on the internet that’s still going to be a hassle. Rules change and world change and for example knowledge cut off is I think still a problem.
In the end yes you can build nice system for some use case where you plugin LLM for classification and you most likely will make money on it. This just won’t be „what was promised” so AGI and we are stuck with this promise and a lot of people won’t accept less than that.
The maintenance of the rules or for you scripts for complex tasks is much more work than anyone is willing to commit to. Also big problem was finding out tacit knowledge and no one was able to code that reliably in.
ML today is promising you won’t have to hand code the rules you just push data and system finds out what the rules are and then can handle new data.
I don’t have to code the rules to check if there is a cat in the picture - that definitely works. Making rules on data that is not so often found on the internet that’s still going to be a hassle. Rules change and world change and for example knowledge cut off is I think still a problem.
In the end yes you can build nice system for some use case where you plugin LLM for classification and you most likely will make money on it. This just won’t be „what was promised” so AGI and we are stuck with this promise and a lot of people won’t accept less than that.