A New Theory of Data Processing: Applying Artificial Intelligence to Cognition and Humanity

Abstract

The traditional data processing uses machine as a passive feature detector or classifier for a given fixed dataset. However, we contend that this is not how humans understand and process data from the real world. Based on active inference, we propose a neural network model that actively processes the incoming data using predictive processing and actively samples the inputs from the environment that conforms to its internal representations. The model we adopt is the Helmholtz machine, a perfect parallel for the hierarchical model of the brain and the forward-backward connections of the cortex, thus available a biologically plausible implementation of the brain functions such as predictive processing, hierarchical message passing, and predictive coding under a machine-learning context. Besides, active sampling could also be incorporated into the model via the generative end as an interaction of the agent with the external world. The active sampling of the environment directly resorts to environmental salience and cultural niche construction. By studying a coupled multi-agent model of constructing a “desire path” as part of a cultural niche, we find a plausible way of explaining and simulating various problems under group flow, social interactions, shared cultural practices, and thinking through other minds.