Multiscale Causal Learning

Biological intelligence is more sample-efficient than artificial intelligence (AI), learning from fewer examples. Here we answer why. Given data, there can be many policies which seem "correct" because they perfectly fit the data. However, only one correct policy could have actually caused the data. Sample-efficiency requires a means of discerning which. Previous work showed sample efficiency is maximised by weak-policy-optimisation (WPO); preferring policies that more weakly constrain what is considered to be correct, given finite resources. Biology's sample-efficiency demonstrates it is better at WPO. Established theories, such as active inference, do not explain how. To understand how, we formalise the multiscale-competency-architecture (MCA) observed in biological systems, as a sequence of nested agentic-abstraction-layers. We show that WPO at low levels enables synthesis of weaker policies at high. We call this multiscale-causal-learning, and argue this is how we might construct more scale-able, sample-efficient and reliable AI. Furthermore, a sufficiently weak policy at low levels is a precondition of collective policy at higher levels. The higher level collective "identity" is lost if lower levels use an insufficiently weak policy (e.g. cells become isolated from the collective informational structure and revert to primitive behaviour). This has implications for biology, AI-safety, and philosophy.