Hybrid Active Inference

TL;DR

This paper proposes a hybrid cognitive framework combining human and machine cognition through hierarchical active inference, enabling adaptive, integrated processing that enhances both human functions and artificial intelligence capabilities.

Contribution

It introduces a novel hybrid active inference model that integrates human and machine cognition, utilizing probabilistic deep learning and brain interfacing for self-supervised, hierarchical processing.

Findings

Increased predictability of brain signals with training.

Potential for autonomous cognitive process development.

Framework applicable with invasive and non-invasive sensors.

Abstract

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.