A little surprised that neither the paper nor the discussion here cite Francois Chollet's 2019 paper on defining intelligence for machines [1], which talks expl

A little surprised that neither the paper nor the discussion here cite Francois Chollet's 2019 paper on defining intelligence for machines [1], which talks explicitly about visual reasoning and proposed an RPM-like visual task as a benchmark. I found it very useful for thinking about what it means for an AI to be generally intelligent. From what I remember (having not read it in a while), his idea is basically that general intelligence is the efficiency with which you can learn, after appropriately "penalizing" any baked-in priors or knowledge beyond what a typical animal is born with. As an example, Magnus Carlsen's chess skill is a testament to his general intelligence because he became skilled at chess in one human lifetime, starting out with only basic priors about the world like objectness, agentness, and some others I can't remember. In contrast, DeepBlue is not as intelligent, because it started with many more priors specific to chess, and AlphaZero is also not as intelligent, because it acquired its chess skill much less efficiently, requiring the equivalent of thousands of human lifetimes. I don't remember if the paper considers whether 3.5 billion years of "pre-training" counts in this regard, though. [1] https://arxiv.org/abs/1911.01547 https://arxiv.org/abs/1911.01547