hmm...I would think of PROLOG or other rule-based system as doing inference, but not of neural-network (which is essentially a mesh of matrix multiplications and functions).
this statement that NNs do inference is not entirely correct IMHO.
this statement that NNs do inference is not entirely correct IMHO.
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.
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.