Finding Similar Objects and Active Inference for Surprise in Numenta Neocortex Model

TL;DR

This paper introduces algorithms for the Numenta neocortex model to identify object similarities and perform active inference of surprises, combining the thousand-brains system with the free-energy principle for advanced AI capabilities.

Contribution

It proposes novel algorithms for similarity detection and active inference within the thousand-brains system, integrating the free-energy principle for brain-inspired AI.

Findings

Algorithms successfully identify object similarities.

Active inference of surprises is achieved using Bayesian methods.

The approach offers a fundamental perspective on neural association and inference.

Abstract

Jeff Hawkins and his colleagues in Numenta have proposed the thousand-brains system. This is a model of the structure and operation of the neocortex and is under investigation as a new form of artificial intelligence. In their study, learning and inference algorithms running on the system are proposed, where the prediction is an important function. The author believes that one of the most important capabilities of the neocortex in addition to prediction is the ability to make association, that is, to find the relationships between objects. Similarity is an important example of such relationships. In our study, algorithms that run on the thousand-brains system to find similarities are proposed. Although the setting for these algorithms is restricted, the author believes that the case it covers is fundamental. Karl Friston and his colleagues have studied the free-energy principle that explains how the brain actively infers the cause of a Shannon surprise. In our study, an algorithm is proposed for the thousand-brains system to make this inference. The problem of inferring what is being observed from the sensory data is a type of inverse problem, and the inference algorithms of the thousand-brains system and free-energy principle solve this problem in a Bayesian manner. Our inference algorithms can also be interpreted as Bayesian or non-Bayesian updating processes.