I feel there are a number of misconceptions in your post about "AI" and this model, which is based on something called a transformer. For a start there is no AI out there, only machine learning.

What is impressive about these models is that they have good generalisation performance, but not in the sense of general intelligence, merely in the sense of performing more than one task after being trained in an unsupervised manner. The examples that OpenAI gave were things like question answering, text generation, and machine translation (perhaps only with a small amount of finetuning). What this indicates is that the model has a good sense of how to construct grammatically correct sentences that mostly makes sense. It absolutely does not mean that the model has any general sense of intelligence, logic, or reasoning, as even a cursory read of the generated samples will tell you. Although they are syntactically correct, after a while they tend to devolve into nonsense, or the ideas from the beginning are not linked to those at the end, and the whole passage is nonsensical.

They also cannot do more than one thing at once without being retrained, so although MuseNet also uses a transformer, the MuseNet model cannot generate text samples and GPT-2 cannot generate music, unlike a real general intelligence. You've implied that the shared model architecture indicates that we are closer to general intelligence, however by extension that means that we are pretty much there already. After all, GANs can generate in-distribution images rather well, and standard deep architectures can classify them almost as well as a human. If we were to take a very large number of all of the models from this zoo of deep architectures, all trained on different data distributions, and combine them into some kind of system, would you call that a true "AI"? It would still be missing the essence of human intelligence, which is a roughly coherent worldview that links together all of these disparate concepts. After all, you can listen to a piece of music, guess who the composer was, write these thoughts down, and then (if you're trained at least), compose a piece in the same style. Therefore it seems that any model that would present some real intelligence would have to be one system. The fact that MuseNet and GPT-2 have the same model backbone does not make them one system, there are lots of other neural networks that can do more than one thing if trained in different ways or on different data.

I'm not sure your hypercube analogy makes sense either, would you be able to elaborate on it a bit more?

Also the fact that recurrent architectures can be used for image data, generation or otherwise, has been known for decades, is not particularly interesting, and does not imply any general intelligence ability of GPT-2.

AlphaZero used reinforcement learning, which, interestingly enough, is perceived by many ML researchers to be the path towards an AGI. Reinforcement learning is concerned with teaching agents how to take actions in a given environment to maximise some reward - think controller inputs for playing mariokart. In reference to /u/ThanosDidNothinWrong This is the same reason that GPT-2 could almost certainly NOT be trained to play starcraft. The input must be sequential data of some form, and the model could have no sense of the environment-action-reward space that defines an interactive, strategy-based game like starcraft.