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"**

In 1986 Paul Smolensky
modified the Boltzmann Machine into what became known as the “Restricted Boltzmann
machine”, which lends itself to easier computation. This network is restricted
to one visible layer and one hidden layer, with units in each layer never
connected to units in the same layer. By the end of the 1980s,
neural networks had established themselves as a viable computing technology,
and a serious alternative to expert systems as a mechanical approximation of
the brain. The probabilistic approach to neural network design had won out. “Learning” is reduced to the
classic statistical problem of finding the best model to fit the data. There
are two main ways to go about this. A generative model is a full probabilistic
model of the problem, a model of how the data are actually generated (for example, a table of frequencies of
English word pairs can be used to generate a “likely” sentence). Discriminative
algorithms, instead, classify data without providing a model of how the data
are actually generated. Discriminative models are inherently supervised.
Traditionally, neural networks were discriminative algorithms. In 1996 the developmental
psychologist Jenny Saffran showed that babies use probability theory to learn
about the world, and they do learn very quickly a lot of facts. So Bayes had stumbled on to an important
fact about the way the brain works, not just a cute mathematical theory. Back to the beginning of the chapter "Connectionism and Neural Machines" | Back to the index of all chapters |