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|The origins of
"artificial intelligence" programming sought to
replicate the way humans think and solve problems.
first program that received recognition was written by
Herbert Simon and Allen Newell in 1955 it was called The
continued in this area and programming languages such as
LISP and PROLOGUE were born.
systems were developed that stored conditional rules and
information that could guide the user to an answer by
asking appropriate questions.
Over time other methods
of solving non-linear problems were also included in the
artificial intelligence fold. These additions tended to focus
on the "mechanics" of problem solving rather than on
the "human element" in logic that the very early
works tried to achieve.
logic introduced flexibility and non-linear responses
to rule based systems.
networks are a type of adaptive learning
programs. They combine a network of numerical processing
units each with its own weight, or importance, to act
together. Through trial and error they search for
the answer to the problem. Neural networks do not
need to have a set of rules to solve a problem, they
surmise the rules from a set of historical data.
- Genetic algorithms are
another type of problem solving program. They work in a
manner similar to the way nature allows for "survival
of the fittest". They allow hundreds or thousands of
possible solutions to compete with each other to find the
best solution. Unlike neural networks, you must have a
deeper understanding of the nature of the problem you are
trying to solve, in order to apply the genetic algorithms.
There is a distinct difference between
the way humans think and the way most "artificial
intelligence" programs solve problems. The stock
market problem is not just about economics or technical
analysis, it is about humans making decisions. It is
the perception of what other investors are about to do. Solving
the stock market problem is solving two problems. One is
trying to extrapolate the non-linear response to market
stimuli and the other is trying to replicate human response.
Our model takes both options into consideration.
To believe with certainty, we must begin by doubting.---