  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.
