I think you are unfairly downvoted for this, probably because I have just as strong opinion in the other direction: I see GPT (especially -4) as a kind of "killer Prolog-style inference engine".
How does Prolog work? You:
* pass it some predicates
* specify rules about what the relationships mean and how various things are computed from the data in predicates
* query a variable
* answer pops out.
How can GPT do this task? You:
* pass it some predicates (structured machine-readable syntax or natural-language sentences)
* specify rules (in natural-language sentences, though it helps to iterate on the wording a bit to make the rules more rigid, and more likely to provide the correct output ~every time) - you don't normally need to specify relationships explicitly because GPT can usually figure it out
* include some additional "massaging" wording to get it reproducibly outputting the kind of result you want
* query a variable (tell it what to find out/infer from the data)
* answer pops out (in human-readable language or structured syntax).
In some ways, they are very much alike. And GPT is much more natural to program with than Prolog.
exactly. PROLOG fails with the non-true inference, and this is why it's inference. otherwise we'd be calling all the high-school made up nonsense inference.
it is absolutely not fair, established or not, to call non-linear regression inference. may be prognosis, maybe prediction, maybe just approximation. but inference is something that actually does logic, based on facts... and probabilistic theory infers only probability, not facts.
GPT is not being programmed, and so is not Prolog. Prolog is being set to follow rules, and infer logical consequences. Prolog is grammar, GPT3 is algebraic structure, which perhaps is also ... differential, so non-discrete.
How does Prolog work? You:
* pass it some predicates * specify rules about what the relationships mean and how various things are computed from the data in predicates * query a variable * answer pops out.
How can GPT do this task? You:
* pass it some predicates (structured machine-readable syntax or natural-language sentences) * specify rules (in natural-language sentences, though it helps to iterate on the wording a bit to make the rules more rigid, and more likely to provide the correct output ~every time) - you don't normally need to specify relationships explicitly because GPT can usually figure it out * include some additional "massaging" wording to get it reproducibly outputting the kind of result you want * query a variable (tell it what to find out/infer from the data) * answer pops out (in human-readable language or structured syntax).
In some ways, they are very much alike. And GPT is much more natural to program with than Prolog.