Inquire about purchasing the book | Table of Contents | Annotated Bibliography | Class on Nature of Mind
These are excerpts and elaborations from my book "The Nature of Consciousness"
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In 2006 Hinton (A Fast
Learning Algorithm For Deep Belief Nets) made Deep Belief Networks the talk of
the town, basically a generative algorithm for Restricted BoltzmannMachines which suddenly
relaunched neural networks and led to new, sophisticated applications to
unsupervised learning. Deep Belief Networks are
layered hierarchical architectures that stack Restricted BoltzmannMachines one on top of the
other, each one feeding its output as input to the one immediately higher, with
the two top layers forming an associative memory. The features discovered by one RBM become the training data for
the next one. DBNs are still limited in
one respect: they are static classifiers, i.e. they operate at a fixed
dimensionality. However, speech or images dont come in a fixed dimensionality,
but in a (wildly) variable one. They require sequence recognition, i.e.
dynamic classifiers, that DBNs cannot provide. One method to expand DBNs to
sequential patterns is to combine deep learning with a shallow learning
architecture like the Hidden Markov Model. Meanwhile, in 2006 Osamu
Hasegawa introduced Self-Organising Incremental Neural Network (SOINN), a
self-replicating neural network for unsupervised learning, and in 2011 his team
created a SOINN-based robot that learned functions it was not programmed to do. Back to the beginning of the chapter "Connectionism and Neural Machines" | Back to the index of all chapters |
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