  Meta-learning aims to develop algorithms that can learn from other learning
algorithms to adapt to new and changing environments. This requires a model of
how other learning algorithms operate and perform in different contexts, which
is similar to representing and reasoning about mental states in the theory of
mind. Furthermore, the problem of uncertainty in the predictions of
conventional deep neural networks highlights the partial predictability of the
world, requiring the representation of multiple predictions simultaneously.
This is facilitated by coordination among neural modules, where different
modules' beliefs and desires are attributed to others. The neural coordination
among modular and decentralized neural networks is a fundamental prerequisite
for building autonomous intelligence machines that can interact flexibly and
adaptively. In this work, several pieces of evidence demonstrate a new avenue
for tackling the problems above, termed Meta Neural Coordination. We discuss
the potential advancements required to build biologically-inspired machine
intelligence, drawing from both machine learning and cognitive science
communities.
