This is encouraging. If you're going to work on artificial general intelligence, a reasonable context in which to work on it is game NPCs. They have to operate in a world, interact with others, survive, and accomplish goals. Simulator technology is now good enough that you can do quite realistic worlds. Imagine The Sims, with a lot more internal smarts and real physics, as a base for work.
Robotics has the same issues, but you spend all your time fussing with the mechanical machinery. Carmack is a game developer; he can easily connect whatever he's doing to some kind of game engine.
(Back in the 1990s, I was headed in that direction, got stuck because physics engines were no good, made some progress on physics engines, and sold off that technology. Never got back to the AI part. I'd been headed in a direction we now think is a dead end, anyway. I was trying to use adaptive model-based control as a form of machine learning. You observe a black box's inputs and outputs and try to predict the black box. The internal model has delays, multipliers, integrators, and such. All of these have tuning parameters. You try to guess at the internal model, tune it, see what it gets wrong, try some permutations of the model, keep the winners, dump the losers, repeat. It turns out that the road to machine learning is a huge number of dumb nodes, not a small number of complicated ones. Oh well.)
Robotics has the same issues, but you spend all your time fussing with the mechanical machinery. Carmack is a game developer; he can easily connect whatever he's doing to some kind of game engine.
(Back in the 1990s, I was headed in that direction, got stuck because physics engines were no good, made some progress on physics engines, and sold off that technology. Never got back to the AI part. I'd been headed in a direction we now think is a dead end, anyway. I was trying to use adaptive model-based control as a form of machine learning. You observe a black box's inputs and outputs and try to predict the black box. The internal model has delays, multipliers, integrators, and such. All of these have tuning parameters. You try to guess at the internal model, tune it, see what it gets wrong, try some permutations of the model, keep the winners, dump the losers, repeat. It turns out that the road to machine learning is a huge number of dumb nodes, not a small number of complicated ones. Oh well.)