Technology

They're talking more about Expert Systems or Inference Engines, which were some of the earlier forms of applications used in AI research. In terms of software development, they are closer to databases than traditional software. That is, the system is built up by defining a repository of base facts and logical relationships, and the engine can use that to return answers to questions based on formal logic.

So they are bringing this up as a good use-case for AI because it has been quite successful. The thing is that it is generally best implemented for specific domains to make it easier for experts to access information that they can properly assess. The "one tool for everything in the hands of everybody" is naturally going to be a poor path forward, but that's what modern LLMs are trying to be (at least, as far as investors are concerned).

(Assuming you meant "you" instead of "I" for the 3rd word)

Yeah, it fits more with the older definition of AI from before NNs took the spotlight, when it meant more of a normal program that acted intelligent.

The learning part is being able to add new branches or leaf nodes to the tree, where the program isn't learning on its own but is improving based on the expeirences of the users.

It could also be encoded as a series of probability multiplications instead of a tree, where it checks on whatever issue has the highest probability using the checks/questions that are cheapest to ask but afffect the probability the most.

Which could then be encoded as a NN because they are both just a series of matrix multiplications that a NN can approximate to an arbitrary %, based on the NN parameters. Also, NNs are proven to be able to approximate any continuous function that takes some number of dimensions of real numbers if given enough neurons and connections, which means they can exactly represent any disctete function (which a decision tree is).

It's an open question still, but it's possible that the equivalence goes both ways, as in a NN can represent a decision tree and a decision tree can approximate any NN. So the actual divide between the two is blurrier than you might expect.

Which is also why I'll always be skeptical that NNs on their own can give rise to true artificial intelligence (though there's also a part of me that wonders if we can be represented by a complex enough decision tree or series of matrix multiplications).