[GarnetFloride]

You would think we would have to take a statistical approach to AGI.

Look how we learned physics. Aristotelian physics was "An object in motion tends to come to a stop." That looked right most of the time a bowling ball on sand, grass, or even dirt comes to a stop pretty fast. But once you have a nice smooth marble floor the ball goes a lot further.

Newtonian physics solved that and several other issues and works fine, most of the time, but has corner cases when going very fast or getting near a high gravity location. Then relativity and the rest.

We need to build a system that we can teach like we do children that lets them reason that something is true under certain circumstances but may not hold generally so have to update what true is. And that looks like statistics.

[desertrider12]

The Cyc project basically achieved what you're talking about, even without approaching AGI. They manually programmed concepts and relationships between things into a huge knowledge graph. Then they had heuristics for choosing the appropriate version of facts for a given context (e.g. level of rigor). It was arguably able to use a library of abstractions similarly to what Chollet is talking about, but couldn't learn new ones automatically through exploration or play.