  We describe a framework of hybrid cognition by formulating a hybrid cognitive
agent that performs hierarchical active inference across a human and a machine
part. We suggest that, in addition to enhancing human cognitive functions with
an intelligent and adaptive interface, integrated cognitive processing could
accelerate emergent properties within artificial intelligence. To establish
this, a machine learning part learns to integrate into human cognition by
explaining away multi-modal sensory measurements from the environment and
physiology simultaneously with the brain signal. With ongoing training, the
amount of predictable brain signal increases. This lends the agent the ability
to self-supervise on increasingly high levels of cognitive processing in order
to further minimize surprise in predicting the brain signal. Furthermore, with
increasing level of integration, the access to sensory information about
environment and physiology is substituted with access to their representation
in the brain. While integrating into a joint embodiment of human and machine,
human action and perception are treated as the machine's own. The framework can
be implemented with invasive as well as non-invasive sensors for environment,
body and brain interfacing. Online and offline training with different machine
learning approaches are thinkable. Building on previous research on shared
representation learning, we suggest a first implementation leading towards
hybrid active inference with non-invasive brain interfacing and state of the
art probabilistic deep learning methods. We further discuss how implementation
might have effect on the meta-cognitive abilities of the described agent and
suggest that with adequate implementation the machine part can continue to
execute and build upon the learned cognitive processes autonomously.
