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Artificial Intelligence:

The origins of "artificial intelligence" programming sought to replicate the way humans think and solve problems. 
  1. The first program that received recognition was written by Herbert Simon and Allen Newell in 1955 it was called The Logic Theorist. 

  2. Research continued in this area and programming languages such as LISP and PROLOGUE were born.

  3. Expert 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. 

  • Fuzzy logic introduced flexibility and non-linear responses to rule based systems.

  • Neural 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.

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