This paper reviews the state of artificial intelligence (AI) and the quest to create general AI with human-like cognitive capabilities. This review argues that improvements in current AI using mathematical or logical techniques are unlikely to lead to general AI. Instead, the AI community should incorporate neuroscience discoveries about the neocortex. It further explains the limitations of current AI techniques and focuses on the biologically constrained Thousand Brains Theory describing the neocortex’s computational principles.
This paper approaches spatial mapping as a problem of learning graphs of environment parts. We show that hippocampal modules may dynamically create graphs representing spatial arrangements, and this proposed fast-relation-graph-learning algorithm can expand to incorporate many spatial and non-spatial tasks.
This paper demonstrates how the application of Numenta’s brain-inspired, sparse algorithms achieves more than 100x speed-up on inference tasks compared to dense networks with no loss of accuracy.
Recent proposals suggest that the brain might use similar mechanisms to understand the structure of objects in diverse sensory modalities, including vision. In machine vision, object recognition given a sequence of sensory samples of an image is a challenging problem when the sequence does not follow a consistent, fixed pattern – yet this is something humans do naturally and effortlessly. We explore how grid cell-based path integration in a cortical network can support reliable recognition of objects given an arbitrary sequence of inputs.
This paper shows that a set of grid cell modules, each with only 2D responses, can generate unambiguous and high-capacity representations of variables in much higher-dimensional spaces.