Fri, Sep 29, 2017 • Events

Strange Loop

Matthew Taylor • HTM Community Leader

Video:

Abstract:

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

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