[dreamcompiler]

My conjecture is about e.g. the Rete algorithm for rule search and the likelihood that it could be made more scalable on multicore and distributed hardware with modern functional data structures allowing easy rule updates.

I don't know whether rule search or logic unification could be mapped onto GPU or TPU operations; I suspect not, but it's worth looking into.

[jeremyjh]

You realize someone has to write all those rules - and correctly - right? Are you saying that we literally didn't have the computing power to run all the rules we could actually write? I think you need to support this idea that symbolic approaches failed to lack of computing power.

[YeGoblynQueenne]

Like I say in another comment, machine learning took off in part as a way to avoid having to hand-craft rules for expert systems' rule bases (although machine learning existed as a discipline from the early days of AI). So a lot of work on machine learning in the '80s and '90s went to learning rules.

For instance (also in another comment) Decision Tree learners basically learn a set of If-Then-Else rules. They're one type of symbolic machine learning and there's more where they came from (e.g. Ross Quinlan's FOIL, for First-Order Inductive Learner, which is basically a first-order version of decision trees; Inductive Logic Programming which I study for my PhD; and many, many more). This work has dwindled, but it's still going.

So, no, you don't have to write rules by hand, anymore than you need to set the weights of a neural net by hand. You can just learn them.