Tuesday, November 20, 2018

Fundamentals of the Artificial Intelligence - Notes - Toshinori Munakata

Book by Munakata is available with me.

Important Topics

What is artificial intelligence?

The Industrial Revolution, which started in England around 1760, has replaced human muscle power with the machine. Artificial intelligence (AI) aims at replacing human intelligence with the machine. The work on artificial intelligence started in the early 1950s, and the term  was coined in 1956.

AI can be more broadly defined as "the study of making computers do things that the human needs intelligence to do." This extended definition not only includes the first, mimicking human thought processes, but also covers the technologies that make the computer achieve intelligent tasks even if they do not necessarily simulate human thought processes.

But what is intelligent computation (AI) and what is not AI? 

Purely numeric computations, such as adding and multiplying numbers with incredible speed, are not AI. The category of pure numeric computations includes engineering problems such as solving a system of linear equations, numeric differentiation and integration, statistical analysis, and so on. Similarly, pure data recording and information retrieval are not AI. So processing of most business data and file processing, simple word processing and database handling are not AI.  

Two types of AI: A computer performing symbolic integration of (sin^2x)(e^-x)  is intelligent. 
Classes of problems requiring intelligence include inference based on knowledge,  reasoning with uncertain or incomplete information, various forms of perception and learning, and applications to problems such as control, prediction, classification, and optimization. 

A second type of intelligent computation is based on the mechanisms for biological processes used to arrive at a solution. The primary examples of this type or category are neural networks and genetic algorithms. These techniques are being used to compute many complex things using computers even though  the techniques do not appear intelligent, 

Although much practical AI is still best characterized as advanced computing rather than "intelligence," applications in everyday commercial and industrial settings have grown, especially since 1990.

As mentioned above, there are two fundamentally different major approaches in the field of AI. One is traditional symbolic AI. It is characterized by a high level of abstraction and a macroscopic view. Knowledge engineering systems and logic programming fall in this category. Symbolic AI covers areas such as knowledge based systems, logical reasoning, symbolic machine learning, search techniques, and natural language processing. 

The second approach is based on low level, microscopic biological models and other computation procedures.  Neural networks and genetic algorithms are the prime examples of this latter approach.  These new evolving areas have shown application potential  from which many people expect significant practical applications in the future. There are relatively new AI techniques which include fuzzy systems, rough set theory, and chaotic systems or chaos for short. 

Neural networks:   A artificial neural network has neurons as the basic unit.  Neurons are interconnected by edges, forming a neural network. Similar to the brain, the network receives input, internal processes take place such as activations of the neurons, and the network yields output. 

Genetic algorithms: Computational models based on genetics and evolution theory and processes. The three basic ingredients are selection of solutions based on their fitness, reproduction of genes, and occasional mutation. The computer finds better and better solutions to problems mimicking the   
species evolution process.  

Fuzzy systems: It coverts discrete objects techniques like sets into continuous objects. In ordinary logic, proposition is either true or false, with nothing between, but fuzzy logic allows truthfulness in various degrees and truth a continuous variable. 

Rough Sets:  "Rough" sets means approximation sets. Given a set of elements and attribute values associated with these elements, some of which can be imprecise or incomplete, the theory is suitable 
to reasoning and discovering relationships in the data. 

Chaos: Nonlinear deterministic dynamical systems that exhibit sustained irregularity and extreme sensitivity to initial conditions. 

Further Reading 

For practical applications of AI, both in traditional and newer areas, the following 
five special issues provide a comprehensive survey. 

T. Munakata (Guest Editor), Special Issue on "Commercial and Industrial AI," 
Communications of the ACM, Vol. 37, No. 3, March, 1994. 

T. Munakata (Guest Editor), Special Issue on "New Horizons in Commercial and 
Industrial AI," Communications of the ACM, Vol. 38, No. 11, Nov., 1995. 

U. M. Fayyad, et al. (Eds.), Data Mining and Knowledge Discovery in Databases, 
Communications of the ACM, Vol. 39, No. 11, Nov., 1996. 

T. Munakata (Guest Editor), Special Section on "Knowledge Discovery," 
Communications of the ACM, Vol. 42, No. 11, Nov., 1999. 

U. M. Fayyad, et al. (Eds.), Evolving Data Mining into Solutions for Insights, , 
Communications of the ACM, Vol. 45, No. 8, Aug., 2002. 

Four books on traditional AI (Symbolic AI) 

G. Luger, Artificial Intelligence: Structures and Strategies for Complex Problem 
Solving, 5th Ed., Addison-Wesley; 2005. 

S. Russell and P. Norvig, Artificial Intelligence: Modern Approach, 2nd Ed., 
Prentice-Hall, 2003. 

E. Rich and K. Knight, Artificial Intelligence, 2nd Ed., McGraw-Hill, 1991. 

P.H. Winston, Artificial Intelligence, 3rd Ed., Addison-Wesley, 1992. 

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