[pedrosorio]

I was not referring to the inconsistent way in which these terms are used to sell products and obtain funding for startups, but to their academic definitions.

While most recent successes in the field of AI have been brought about from advances in the subfield of Machine Learning, even today's most advanced AI systems have components that are not Machine Learning (e.g. Alpha Go still requires tree search techniques from "classic AI" to work).

In the definitions below, AI refers to any "intelligent agent", whereas ML refers to the subset of techniques that achieve this through learning/experience/data.

ML: "Tom M. Mitchell provided a widely quoted, more formal definition of the algorithms studied in the machine learning field: "A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P if its performance at tasks in T, as measured by P, improves with experience E."[0]

AI: "Computer science defines AI research as the study of "intelligent agents": any device that perceives its environment and takes actions that maximize its chance of successfully achieving its goals."

[0] https://en.wikipedia.org/wiki/Machine_learning#Overview [1] https://en.wikipedia.org/wiki/Artificial_intelligence#Defini...

[ska]

Ok, we are bogged down in semantics, but I see where you are coming from. I don't buy the proper subset argument for reasons above (i.e. I don't buy the broading of AI to fit that idea of "intelligent agent", as it includes too many things that don't really fit, imo).

Unless something has changed radically since I stopped paying as much attention there is not actual agreement on these terminologies, at least broadly, in academic circles.

I certainly agree many current systems with a core ML component include other techniques from lots of areas including what you call "classic AI" as well as optimization, etc., but the ML is still the fundamental part of nearly everything recent I've seen. As pretty much every successful system of this type is a hybrid in the sense you mean, I don't find differentiating them from some putative "pure ML" approach very interesting.

There was some good work in very different approaches in the 70s through 80s, but that seems to have tapered off in the 90s really. I'm not very current though and would love to hear of newer interesting things in that vein.