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We’re very happy to report that we recently published a preprint on AHA, an ‘Artificial Hippocampal Algorithm’ for Episodic Machine Learning. It’s the culmination of a multi-year research project and is a starting point for the next wave of developments. This article describes the motivation for developing AHA and a brief description of the research.…
Artificial neural networks (ANNs) – are conceptually simple; the combination of inputs and weights in a classical ANN can be represented as a single matrix product operation followed by an elementwise nonlinearity. However, as the number of learned parameters increases, it becomes very difficult to train these networks effectively. Most of the tricks that enable…
Reproduced left to right from [8,10,1] Within a 5 day span in October, 4 papers came out that take a significantly different approach to AI hierarchical networks. They are all inspired by biological principles to varying degrees. It’s exciting to see different ways of thinking. Particularly at a time when there is growing skepticism…
Figure 1: Our interpretation of the Thalamocortical system as 3 interacting sub-systems (objective, subjective and executive). The structure of the diagram indicates the dominant direction of information flow in each system. The objective system is primarily concerned with feed-forward data flow, for the purpose of building a representation of the actual agent-world system. The executive…
Figure 1: The physical architecture of general intelligence in the brain, namely the Thalamo-Cortical system. The system comprises a central hub (the Thalamus) surrounded by a surface (the Cortex, shown in blue). The Cortex is made up of a number of functionally-equivalent units called Columns. Each Column is a stack of cell layers. Columns share…