This document provides an overview of artificial intelligence including definitions, history, and common techniques. It discusses genetic algorithms, ant algorithms, neural networks, fuzzy logic, and branches of AI such as machine learning. The history of AI is explored from the 1940s to today. Methods for achieving AI are described as top-down (symbolic) and bottom-up (connectionist). In conclusion, AI is presented as both an initially solvable but ultimately thorny technology, with early promises not fully realized but work continuing today.

1. ARTIFICIAL INTELLIGENCE
Fundamental And Basic About AI
Created By Rohit Jain

2. CONTENT ::--
DEFINITION AND HISTORY OF AI
GENETIC ALGORITHM
ANT ALGORITHMS
NEURAL NETWORKS
ADAPTIVE RESONANCE THEORY
 FUZZY LOGIC
AI TODAY

3. Branches OF AI
 Automatic Programming
 Bayesian Networks
 Constraint Satisfaction
 Knowledge Engineering
 Machine Learning
 Planning
 Search

4. Definition and History Of AI ::--
 Artificial Intelligence Is The Process Of Creating Machines that can act in a
Manner that could be considered by Human to be Intelligent.
 AI has The Potential To Change The World Like No Other Technology.
 1940s- Birth Of Computers
 1950s The Birth OF AI
 1960s The Rise Of AI
 1970s The Fall Of AI
 1980s An AI Boom And Bust
 1990s To today AI Rises Again, Quietly

5. GENETIC ALGORITHM ::--
 The Genetic Algorithm is a search algorithm and an optimization
technique that stimulates the phenomenon of natural evolution.
 The Genetic Algorithm, Instead of trying to optimize a single solution,
works with a population of candidate solution that are encoded as
chromosomes.

6. ANT ALGORITHMS ::--
Ant Algorithms are particularly interesting in that they can be
used to solve not only static problems, but also highly dynamic
problems such as routing problems in changing networks.
The Ant Algorithm models the behavior of ants within their
natural domain to identify optimal paths through landscapes.

7. NEURAL NETWORKS ::--
Neural Networks are very simple implement of local behavior
observed within our own brains.
The brain is composed of neurons, which are the individual processing
elements.
Artificial Neural Networks Attempts to mimic the basic operations of
the brain. Information is passed between the neurons, and based upon
the structures and synapse weights, a network behavior is provided.

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9. FUZZY LOGIC::--
Fuzzy Logic was introduced to allow software to operate in the
domain of degrees of truth.
Instead of binary systems that represents truth or falsity, degrees of
truth operate over the infinite space between 0.0 and 1.0 inclusive.
Complex Application with a multitude of inputs and outputs can be
modeled, and implemented, very simple using Fuzzy Logic.

10. METHODS OF ACHIEVE AI :-
 Top Down :-
The Top Dwon Category is Synonymous with Traditional Symbolic AI
where cognition is high level concept.
 Bottom Up :-
The Bottom Up category is synonymous with connectionist AI closely
the model of our own mammalian brain.

11. AI Today::--
• AI is a classical example of a technology that seems
initially so solvable , and then, on closer examination, so
thorny and difficult.
• The early promises Of AI didn’t work out, which make
predicting its future ambitious at best.

12. Declaration
• All The Data Are Showed On This Presentation in Come up With
some Books and Google.com
• Books Are “AI App Programming” And “Fundamental Of AI”
• Thanks For Paying attention