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).
Buddahriffic@lemmy.world 2 days ago
(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).
_g_be@lemmy.world 1 day ago
could be a great idea if people could be trusted to correctly interpret things that are not in their scope of expertise. The parallel I’m thinking of is IT, where people will happily and repeatedly call a monitor “the computer”. Imagine telling the AI your heart hurts when it’s actually muscle spasms or indigestion.
The value in medical professionals is not just the raw knowledge but the practice of objective assessment or deduction of symptoms, in a way that I didn’t foresee a public-facing system being able to replicate
Buddahriffic@lemmy.world 1 day ago
Over time, the more common mistakes would be integrated into the tree. If some people feel indigestion as a headache, then there will be a probability that “headache” is caused by “indigestion” and questions to try to get the user to differentiate between the two.
And it would be a supplement to doctors rather than a replacement. Early questions could be handled by the users themselves, but at some point a nurse or doctor will take over and just use it as a diagnosis helper.
_g_be@lemmy.world 1 day ago
As a supplement to doctors that sounds like a fantastic use of AI. Then it’s an encyclopedia you engage in conversation