programming-for-dummies

Machine Learning 653
With LISP, every command is also considered data that can be manipulated.
So a LISP program can literally rewrite its own commands and data while it
runs. Prolog works in a similar way. Basically a Prolog program consists of
rules and data, so Prolog programs can modify both the rules that they
follow and their data.
Both languages make self-modifying programs possible for mimicking
machine learning, but using an artificial intelligence language alone doesn’t
make your program any smarter. Instead, programmers also need specific
methods for mimicking intelligence in a computer.
Bayesian probability
One simple example of machine learning is based on Bayes’ theorem, after
Thomas Bayes. This theorem deals with probabilities. Put into practical
application, many junk e-mail (spam) filters use Bayesian filtering, which
basically examines junk e-mail and compares it to valid e-mail.
Based on this comparison, a spam filter based on Bayes’ theorem can gradually
assign probabilities that new messages are either junk or valid messages. The
more junk and valid e-mail the Bayesian filter can examine, the “smarter” it gets
in recognizing and sorting e-mail into their proper categories. Essentially an
anti-spam filter’s “intelligence” is stored in its growing database of characteristics
that identify spam.
Neural networks
One problem with machine learning is organizing information so that the computer
can modify its own data. Although languages — like LISP and Prolog —
allow self-modifying programs, computer scientists have created a way to
model the human brain using ordinary data structures, such as graphs with
each node mimicking a neuron in a brain. This entire connection of interlocking
nodes, or mimicking neurons, is a neural network, as shown in Figure 4-2.
Book VII
Chapter 4
Artificial
Intelligence
Neurons
Figure 4-2:
A neural
network.