Talk Abstract
The Biological Path Towards Strong AI
Today’s wave of AI technology is still being driven by the ANN neuron
pioneered decades ago. Hierarchical Temporal Memory (HTM) is a realistic
biologically-constrained model of the pyramidal neuron reflecting
today’s most recent neocortical research. This talk will describe and
visualize core HTM concepts like sparse distributed representations,
spatial pooling and temporal memory. Strong AI is a common goal of many
computer scientists. So far, machine learning techniques have created
amazing results in narrow fields, but haven’t produced something we
could all call “intelligent”. Given recent advances in neuroscience
research, we know a lot more about how neurons work together now than we
did when ANNs were created. We believe systems with a more realistic
neuronal model will be more likely to produce Strong AI. Hierarchical
Temporal Memory is a theory of intelligence based upon neuroscience
research. The neocortex is the seat of intelligence in the brain, and it
is structurally homogeneous throughout. This means a common algorithm is
processing all your sensory input, no matter which sense. We believe we
have discovered some of the foundational algorithms of the neocortex,
and we’ve implemented them in software. I’ll show you how they work with
detailed dynamic visualizations of Sparse Distributed Representations,
Spatial Pooling, and Temporal Memory.
Matthew Taylor • HTM Community Leader