Yeah. I'm coming from the same background, but with some experience orchestrating these systems in production. AFAIK a bunch of the submissions to the various "

Yeah. I'm coming from the same background, but with some experience orchestrating these systems in production. AFAIK a bunch of the submissions to the various "AGI" awards like Alexa prize use ensembles of models and some way of weighing each one based on a context in order to choose which classifier to trust in a particular scenario. E.g. MILABOT. There is so much more than one monolithic NN since those are easy to saturate in terms of precision and recall with enough training data and features, but are not enough to provide good UX in any complex domain/ontology. So it makes more sense to have many different models trained on each subdomain/taxonomy so that each can be specialized and then combined orthogonally. Then the question becomes "how do we orchestrate them?" Well, there is a lot of research from the 80s and 90s that kinda got left by the wayside due to hype cycles (see the last "AI winter"). My faves are Collagen and Ravenclaw. And there is a lot of literature around topic frame stack modelling, which can be combined with various expert systems or other logics. I am currently using CLIPS (PyKnow) with custom Ravenclaw implementation. I believe b4.ai is doing something similar without the logic/rules engine, and actually applying ML to topic selection as well. My systems are goal oriented so I like to give them a teleology for business reasons, which would not suffice for AGI ambitions. TLDR data science isn't enough on its own. We need engineers to architect things properly to solve problems.