The "general" in "general intelligence" is when you can hack together a picture of other domains by stitching together objects in your own domain. Our reasoning

The "general" in "general intelligence" is when you can hack together a picture of other domains by stitching together objects in your own domain. Our reasoning system only evolved to talk about 3 physical space dimensions and 1 time dimension and Newtonian physics and euclidean geometry. Things like trades and ownership and train lines aren't basic objects in our domain and the abstract rules governing their relations are completely unlike those rules of object properties like heavy and bright and loud. Can an intelligence stuck in a different substrate also come up with abstractions and generalize its intelligence? Maybe >The logical extreme of your argument---if I understand it correctly---is that all machine learners are equally valuable. That's not quite it. My argument is that general intelligence can in principle be grounded in the semantics of pretty much any baseline of objects and relations. The specific base line properties such as size, shape, color etc is orthogonal to the property of intelligence. We don't need to give the machine familiar sensory input for general intelligence, and general intelligence would not imply that it can do well at conversation in our domain (merely that it can talk about our domain in a very contorted way if it had to). I'll admit if the goal is to make an intelligence "in our image" then sensory modalities will get us there faster. It will also lead people to erroneously believe that the key ingredient to general intelligence is physical grounding in our reality. My counter example to that belief is general intelligence embedded in a pure language model, which is perfectly recognizable as "in our image" AGI just so long as you stick to topics like transit lines and chess and whatever else can be grounded in words alone.