^ I'm still trying to figure out how this can be incorporated into a GPT-2/ IGPT. It's like an Internet of Things. It's prediction, and it uses patterns, but it doesn't seem like it can answer questions. Yet my brain can solve the tests. Ok I gave it thought. Some of the tests are not physics based, hence useless, for example the cycadelic pattern fill in, that isn't a sequence prediction or even object repair, just art repair... There is probably a maze test, and that and the laser test seems to be a tree search/video prediction than a static prediction. Good for predicting a string threaded through or a video of a man escaping a cave system. You can ask it to de-noise or rotate or summarize or translate objects (cat2dog). There's probably a stack/group test. I'm not sure how these helps answer big questions like GPT-2 "can". It seems like the dynamics of the net are controllable and hence many of the tasks can be useless, some rarely used, and some often used. Is it confusing to anyone else? How often do you rotate objects or solves mazes in GPT-2??? Rarely, right? And what's the stacking for, I know an invention may stack memory cells in rows, I guess the word "row" can be, in vision, actually modifying the old object, ex. GPT-2 may write the apple turned brown and was cut in half and stacked, then melted in an oven. Generating video would require morphing/ re-arranging the object. But that's based on data/objects's relative locations fed to it.
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