This is an old idea dating back to the earliest algorithmic information theory work in the '50s/60s like Ray Solomonoff on universal induction: given a powerful

This is an old idea dating back to the earliest algorithmic information theory work in the '50s/60s like Ray Solomonoff on universal induction: given a powerful learning algorithm, create AI by encoding all possible data as a binary stream (with no further preprocessing or engineering), and simply predict each successive bit. This leads to AIXI, the compression-as-intelligence paradigm (and online learning / meta-learning), and most recently, the success of self-supervised learning & generative modeling. See for example Burfoot https://arxiv.org/abs/1104.5466 https://arxiv.org/abs/1104.5466 or Schmidhuber https://arxiv.org/abs/1511.09249#schmidhuber https://arxiv.org/abs/1511.09249#schmidhuber + https://arxiv.org/abs/1802.08864#schmidhuber https://arxiv.org/abs/1802.08864#schmidhuber (among many others) or (if I may shill my own work) https://gwern.net/aunn https://gwern.net/aunn