Meat Meets Machine: Multiscale Competency Enables 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. 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 “identity” of the collective is lost if lower levels use an insufficiently weak policy (e.g. cells may become isolated from

the collective informational structure and revert to primitive behaviour). This has implications for biology, machine learning,

AI-safety, and philosophy.