Artificial Intelligence And Machine learning

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Different kinds of AI  Cybernetics and brain simulation  Symbolic  Sub-Symbolic  Statistical

Cybernetics and Brain Simulation  In the 1940’s and 50’s a number of researchers explored the connection between Neurology information theory and cybernetics.  some of the researchers used electronic networks to exhibit rudimentary intelligence.  Some examples of this are W. Grey Walters “turtles” and Johns Hopkins “Beast”

Symbolic  In the 1950’s when access to digital computers became possible, AI research began to explore the possibility that human intelligence could be reduced to symbol representation.  Since the 1960’s, symbolic approaches had achieved great success at simulating high-level thinking in small demonstration programs.  Symbolic AI can be broken down into the following categories: Cognitive simulation, Logic-based and knowledge-based

Sub-Symbolic  In the 1980’s the progress in Symbolic AI had stalled and many believed that symbolic systems would never be able to imitate all the processes of human cognition.

Statistical  This type of research was started in the 1990s  It focused on advanced mathematical tools to solve specific problems  The results of these tests were both measurable and variable and have been some of the biggest contributors to many success’s in AI research

Integrating the approaches  Intelligent agents  Multi- agent system

Tools to achieve this  Logic  Probabilistic methods for uncertain reasoning  Classifiers and statistical learning methods

Search and optimization  Many of the problems that AI face can in theory be solved by searching through many possible solutions.  logical proof can be seen as searching for a path that leads from premises to conclusions.  In the 1990s a different kind of search came to prominence, it was based on the mathematical theory of optimization.

Logic  Logic is most commonly used for knowledge representation and problem solving  There are a few different forms of logic used in AI research  Propositional logic, first-order logic, fuzzy logic, subjective logic

Probabilistic methods for uncertain reasoning  Many of the problems faced in AI research require an outside source to operate with incomplete or uncertain information.  Some of the tools used to solve these problems use various methods from probability theory and economics.  A key concept from the science of economics is "utility": a measure of how valuable something is to an intelligent agent, using this an agent can make choices and plan.

Classifiers and statistical learning methods  The two simplest types of AI applications can be divided into Classifiers and controllers.  An example of this is: Classifier (“ if round then ball”) and the controller (“ if round then move”)  One of the most widely used classifiers is neural network

Evaluation of AI  Optimal: it is not possible to preform better.  Strong super-human: preforms better than all humans.  Super-human: preforms better than most humans  Sub-human: preforms worse than most humans

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