Right, we have not even solved games in the non-real time environment, so why bother adding additional constraints like “on real hardware in real time”. This is exactly like Tesla trying to switch from LIDAR to cameras before self driving is even solved. It’s avoiding the real harder challenge and going off on inane tangents. John is essentially bike shedding.

In this case, John is going off on this inane tangent because of his prior experience with hardware and video games instead of challenging himself to solve the actual hard and open problems.

I’m going to predict how this plays out for the inevitable screenshot in one to two years. John picks some existing RL algo and optimizes it to run in real time on real hardware. While he’s doing this the field moves on to better and new algorithms and architectures. John finally achieves his goal and posts a vid of some (now ancient) RL algo playing some Atari game in real time. Everyone says “neat” and moves on. John gets to feel validated yet all his work is completely useless.

for systems that learn in real-time, is there a way for the humans to know/understand how/why the system came to the conclusion it has? there are examples of where humans ran experiments that came to a conclusion for the wrong reasons. if an AI system thinks it knows the answer for the wrong reason, wouldn't that then poison its reasoning later as well? can an AI system learn its reasoning is wrong and then update it when provided better evidence? that seems to be something a vast majority of humans cannot do.

And this is different from the argument that's being pooh-poed and downvoted how? You are effectively saying "Carmack is smart and is working on cool stuff that'll most likely be useless" in different words.

Oh darn I used the word just now everything I said is invalidated! That’s too bad!

FWIW, I’m aware of that heuristic too which is why I intentionally use the word “just” as a meta heuristic to filter a certain type of person.