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A current bottleneck that prevents Machine Learning (ML) from being successful outside of a few restricted fields such as chess playing and highway driving is its impairment in ap- propriately using context to infer the meaning of what it is observing. This paper describes techniques to allow ML systems to derive meaning from context, derived from how the human cortex works. In particular, this paper shows how the multiplication a horizontal vector representing a Sparse Distributed Representation (SDR) of patterns in sensory data by a vertical vector representing a SDR of patterns in context data followed by a pattern recognition operation on the result- ing matrix results in the integration of relevant context and in the output of data containing meaning.

In this paper, the author examines the differences in handling complexity between the human brain and artificial intelligences. It will be shown how the current modularity and tendency to reduce complexity of most current AIs is a limit to their potential to reach artificial general intelligence (AGI). Finally, techniques to address these limitations and to properly address complexity are ad- dressed.