As for goals, they are indeed better defined in games. But we can create artificial goals attached to the real life: - goal: GPS sensor reports specific position - goal: the camera reports a recognised object in a specific position - goal: a button was pressed (signal reaching the AI e.g. through the cloud)

Reinforcement learning turns out to be fantastically clever at finding really stupid solutions if you give it the tiniest opening to do so. You put a camera in a room to provide feedback for an agent to learn to move an object closer to the camera, and it will happily learn to knock the camera over.

Between these two, some self serving bias and delusion, mission statements etc.. what is real?!

So we can say that's a bad spec if we like, but what's the answer that leads to? I don't need an AI if I can just declare all users must be really good programmers spending their days writing unambiguous specs. That wasn't really the goal of the AI though.

So, the complexities of how the AI is taught to interact ultimately don't matter. It may have a lot of effort to parse the visual of the board to get the perfect information, but the game is defined as one of perfect information.

Wikipedia calls out that there does not seem to be consensus on what this term means for games with chance or concurrent play. https://en.wikipedia.org/wiki/Perfect_information

And yes, we can do our best to turn real life in to a game, but all such models leak pretty badly, and the leaks tend to cause much more fundamental instability.

In the game, you can easily let the AI play against itself countless times. Much, much slower in real life.

Until we have good enough simulators/approximators of reality, AI can't learn fast. However, they can already learn driving in GTA, so who knows?

Yeah, that sounds like it'll end well.