I don't work in game AI, but my hunch is that part of the reason for any dissonance between state-of-the-art 'AI proper' and state-of-the-art game AI would be t

I don't work in game AI, but my hunch is that part of the reason for any dissonance between state-of-the-art 'AI proper' and state-of-the-art game AI would be the abstraction level of interface permitted by the respective environments each participates in. A lot of the big, breathtaking, (recent[0]) breakthroughs in 'AI proper' have been at the really really high level of abstraction of "how do I parse sense data". Questions like "What kinds of things are in this picture?", "How similar is this sentence to this other sentence?", or "What kind of thing is this thing?" are really asking, "How do I take this untyped, unstructured information and turn it in to something in my ontology?". This is a problem in the real world because photons and soundwaves don't come with type signatures. In a game engine, however, you have powerful hooks directly into the physics of the world you're operating in. Beyond that, you've got hooks into the other agents that you're operating alongside[1]. With a higher-resolution system like this, the work required for seemingly-intelligent behavior becomes less. A lot of that work is offloaded into the higher granularity of the abstraction you're working under. To make a completely unfounded hypothetical, and invert the trope, I'm not sure we'd be as smart as we are if we always could read each other's minds and read and write directly to reality. --- [0] To be fair, a lot of the big push from the GOFAI work in the 60s+ was more in line with the kinds of things useful to game AIs; logic-oriented/planning systems. [1] I know that one way to get more 'compelling' (and less uncanny) AIs in games and in academia is to limit their ability to transgress these boundaries that our intelligence operates under.