Thank you, that was very enlightening. I see now where you were coming from.
I still think that some breakthroughs are more -equal- fundamental and some methods are more correct, that is, efficient in seeking the truth. Perhaps attempts to first point out some specific interesting features of human consciousness (or intelligence, or brain) and only then try to analyse and replicate them would meet more success. In that sense logic and neural networks are successful, while bayesian inference is not.
I wonder if you are familiar with TRIZ? It strikes me as positively loony, but it is a not-outright-unsuccessful attempt at a general algorithm for discovering new, uh, counterintuitive implications of known natural laws. Not truths per se, but pretty close.
I’ve read a book on it, as it happens. It seemed quite a useful set of schemas for generating new ideas in industrial design, but of course not a complete algorithm.
I’ve peeked at your profile and the linked page. See, I’m currently enrolled into linguistics program, and I was considering dedicating some time to The Art of Prolog, so I’ve researched what Prolog software there is and wasn’t especially impressed. Could I maybe ask you for advice as to what kind of side project Prolog is suited for? I’m familiar with Lisp and C and I’ve dabbled with Haskell and Coq, and I would really really like to write something at least marginally useful.
I think Prolog, like Lisp, is mainly useful for being a different way of thinking about computation. The only practical industrial uses of Prolog I’ve ever heard of are some niche expert systems, a tool for exploring Unix systems for security vulnerabilities, and an implementation of part of the Universal Plug and Play protocol.
Thank you, that was very enlightening. I see now where you were coming from.
I still think that some breakthroughs are more -equal- fundamental and some methods are more correct, that is, efficient in seeking the truth. Perhaps attempts to first point out some specific interesting features of human consciousness (or intelligence, or brain) and only then try to analyse and replicate them would meet more success. In that sense logic and neural networks are successful, while bayesian inference is not.
I wonder if you are familiar with TRIZ? It strikes me as positively loony, but it is a not-outright-unsuccessful attempt at a general algorithm for discovering new, uh, counterintuitive implications of known natural laws. Not truths per se, but pretty close.
double tildas mean strike-through
I’ve read a book on it, as it happens. It seemed quite a useful set of schemas for generating new ideas in industrial design, but of course not a complete algorithm.
I’ve peeked at your profile and the linked page. See, I’m currently enrolled into linguistics program, and I was considering dedicating some time to The Art of Prolog, so I’ve researched what Prolog software there is and wasn’t especially impressed. Could I maybe ask you for advice as to what kind of side project Prolog is suited for? I’m familiar with Lisp and C and I’ve dabbled with Haskell and Coq, and I would really really like to write something at least marginally useful.
I think Prolog, like Lisp, is mainly useful for being a different way of thinking about computation. The only practical industrial uses of Prolog I’ve ever heard of are some niche expert systems, a tool for exploring Unix systems for security vulnerabilities, and an implementation of part of the Universal Plug and Play protocol.