Compression is a component of general intelligence. A few years ago I was very sceptical of machine learning ever leading to general intelligence. I've since changed my mind. There are a lot of parallels to this work and the concept of "embeddings" in machine learning. Intelligence requires the ability to generalize. A prerequisite for generalization is the ability to take something high-dimensional and reduce it to…
"Towards conceptual generalization in the embedding space" https://arxiv.org/abs/1906.01873
I still think the approach outlined in the paper (using embeddings to map the physical world) is sound especially for the field of self-driving which is in dire need of generalization, but I've since changed my mind and currently do not believe we can achieve AGI (ever).
While embeddings are a great tool for compressing information, they do not provide inherent mechanisms for manipulating the information stored in order to generalize and infer outcomes in new, unseen situations.
And even if we would start producing embeddings in a way where they would have some basic understanding of the physical world, we could never achieve it to the level of detail necessary - because physical world is not a discrete function. Otherwise we would be creating a perfect simulation (within a simulation?). And the last time I was playing God, was in "Populous".