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- Definition of Artificial Intelligence
- Key Milestones in the Growth of Artificial Intelligence
- Components of Artificial Intelligence
- Conceptual Model of Artificial Intelligence
- Practical Applications of Artificial Intelligence
- The Future of Artificial Intelligence
- Future of Artificial Intelligence. Retrieved on 15th March 2012 from
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Artificial intelligence is a rapidly growing field of computer science and engineering with a definite intention of building machines able to act and think like human beings. Previously, artificial intelligence was concerned with developing programs for theorem attesting and game playing. Contemporary artificial intelligence includes diverse tools and procedures for human like reasoning, learning, planning, language, and pattern recognition (Kumar, 2009). Studies show that artificial intelligence is one of the most successful branches of the broad area of computing.
The procedure involved in developing intelligent systems is not straightforward; rather it is one of the most difficult, intricate, and also the most challenging job ever undertaken by human beings (Kumar, 2009). Kumar (2009) says that “artificial intelligence is the most challenging job because human is trying to prototype his own mind” (p. 3). Artificial intelligence systems include people, procedures, hardware, software, data and knowledge required to come up with the computer systems and machines that exhibit features of intelligence.
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Definition of Artificial Intelligence
Artificial intelligence is defined as the study of how to make computers do things at which, at the moment, people are better (Ertel, 2011). Artificial intelligence can be defined as the intelligence which is created by human beings by applying various scientific and engineering techniques (Kumar, 2009). From the two definitions, artificial intelligence is thus concerned with coming up with intelligent computer systems which exhibit the features we associate with intelligence in human actions. While coming up with the concept of artificial intelligence, the man’s accomplishments were directed towards making his tasks easier by using other means and developing procedures which will replace him in performing the duties which require a lot of labor and time.
Nath (2009) indicated that, in the wider sense, artificial intelligence is aimed at designing programs that kindle the real processes that human beings go through in their intelligent behavior. These simulation programs are deliberated as the theories unfolding and explaining human performance. In addition, cognitive scientists are interested in employing artificial intelligence techniques to inform us about how human beings do intelligent tasks (Nath, 2009).
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Artificial intelligence includes rational tasks, such as understanding English language, identifying scenes, and making sense out of different situations. The procedures that artificial intelligence applies to resolve problems are the representation and inference methods for handling the significant knowledge and search-based problem solving methods for exploiting that knowledge (Nath, 2009).
Key Milestones in the Growth of Artificial Intelligence
The graph below shows the history of the various artificial intelligence areas. The width of the bars indicates the prevalence of the method used.
Artificial intelligence portrays many ancient scientific achievements. Ertel indicated that artificial intelligence as a practical, goal-driven science searched for a way to merge neural networks with logical rules or the knowledge of human experts, which in the beginning had huge difficulties (2011). Researchers desired to unite logic’s ability to unequivocally characterize knowledge with neural networks strengths of handling improbability. Ertel (2011) noted that many diagnostic and expert systems have been built up for problems of everyday reasoning using Bayesian networks. The success of Bayesian networks widely used in artificial intelligence stems from their intuitive unambiguousness, the clean semantics of restrictive probability and from mathematically grounded probability theory (Ertel, 2011).
During 1990’s, data mining was developed as a sub-discipline of artificial intelligence in the area of statistical data analysis for extraction of knowledge from huge databases. Ertel (2011) says that “data mining introduces the requirement of using large databases to gain explicit knowledge” (p. 8). In the artificial intelligence problem-solving tactic, it is essential to choose a depiction scheme to incarcerate essential features of a problem domain and make that information available to a problem solving procedure (Kumar, 2009). The representation language must allow the programmer to express that knowledge.
Components of Artificial Intelligence
There are several components of artificial intelligence. Kumar (2009) says that the first component is the physical symbol hypothesis which defines an illustration language used to characterize all forms of knowledge, skill, purpose, and causality. This capacity to formalize symbolic model is necessary to the representation of intelligence as a running computer program and it can be added to the prescribed systems (Kumar, 2009). The second component of an artificial intelligence problem solution is search. Kumar (2009) noted that search is the systematic assessment of instances within the figurative framework looking for solutions, sub-problem goals, or any other aspect of the problem that is under consideration (Kumar, 2009).
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The third component of artificial intelligence is heuristics. Kumar (2009) says that a heuristic is a method for organizing search across the alternatives offered by a specific representation. Heuristics search techniques are designed to overcome the intricacy of an in-depth search which acts as a barrier to useful solutions for many classes of interesting problems (Kumar, 2009).
Artificial intelligence is capable of handling uncertain situations because the real world is full of events which are tentative. Kumar (2009) says that humans have an excellent capacity to use their perception to handle the uncertain situations which artificial intelligence machines can also handle. Kumar (2009) says that artificial intelligence deals with theorem proving, using mathematical formalizations to prove the existing theorems. Game playing is an interesting component of AI which analyzes the concepts behind game playing. Artificial intelligence develops automated computer systems which can play certain games, normally considered as brain games.
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Artificial intelligence systems possessing intelligence are being built and several types of these are out to perform. Kumar (2009) established that artificial intelligence has been through a long journey, enthusiastic as well as fascinating. This implies that whatever has been attained over the years is now used for measuring its capabilities. Artificial intelligence is being tested to actually make believe that whatever is still left is not unachievable. Kumar (2009) says that the military put artificial intelligence based hardware to the test in war during Desert Storm. Since then, artificial intelligence-based technologies have been used in missile systems, heads- up displays, and other developments.
Artificial intelligence has made tremendous changes in our homes. These changes are in line with the popularity of the computer having intelligence increasing, the attention of the man has also grown (Kumar, 2009). Applications for the Apple Macintosh and IBM-compatible computer, such as voice and character recognition have become available (Kumar, 2009). In addition, artificial intelligence technology has made steadying camcorders simple in using a fuzzy logic. Kumar (2009) established that the use of an expert system in automobile industries and others is increasing each day for enhancing efficiency and economy.
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Conceptual Model of Artificial Intelligence
Artificial intelligence is a broad field that encompasses various specialty areas, such as expert systems, robotics, vision systems, natural language processing, learning systems, and neural networks (Stair & Reynolds, 2005). The diagram 2.0 below shows the conceptual model of artificial intelligence.
Robotics involves the development of mechanical or computer devices that carry out tasks requiring a high degree of accuracy or that are monotonous or hazardous for humans. Stair & Reynolds (2005) articulated that although most of today’s robots are limited in their capabilities, future robots will find wider applications in banks, restaurants, homes, and offices.
The third area of artificial intelligence involves vision systems. Stair & Reynolds (2005) commented that vision systems include hardware and software that allows computers to capture, store, and manipulate visual images. They are efficient in identifying people based on their facial features.
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The fourth area of artificial intelligence is natural language processing and voice recognition. Stair & Reynolds (2005) indicated that natural language processing allows a computer to understand and react to statements and commands made in a natural language, such as English.
The fifth area of artificial intelligence is expert systems. Expert systems consist of hardware and software that stores knowledge and makes inferences similar to those of human beings (Stair & Reynolds, 2005). An expert system behaves similarly to an individual expert in a particular field. Stair & Reynolds (2005) indicated that computerized expert systems like individual experts use heuristics or rules of thumb to arrive at conclusions or make suggestions. The software for expert systems development has evolved greatly since 1990 from traditional programming languages to the expert system shells as shown in the diagram 3.0 below.
Practical Applications of Artificial Intelligence
AI was used in the development of an autonomous vehicle. Kumar (2009) says that a DARPA-funded onboard computer system from Carnegie Mellon University drove a van all but 52 of the 2849 miles from Washington, DC to San Diego, averaging 63 miles per hour day and night, rain or shine.
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AI was used in the development of computer chess. Deep Blue, a chess computer built by IBM researchers, defeated the world champion Gary Kasparov in a landmark performance. AI was used in mathematical theorem proving. A computer system at Argonne National Laboratories proved a long-standing conjecture about algebra using a method that would be considered creative if done by humans.
AI is applied in the development of advanced user interfaces. Kumar (2009) says that PEGASUS is a spoken language interface connected to the American Airlines EAASY SABRE reservation system which allows subscribers to obtain flight information and make flight reservations via a large, online dynamic database accessed through their personal computer over the telephone.
Ertel (2011) indicated that systems like CART, ID3, and C4.5 could quickly and automatically build very accurate decision trees which can represent propositional logic concepts and then be used as expert systems.
Search algorithms are a classical and well-developed part of artificial intelligence. This is because they are the basis of learning and problem solving. Artificial intelligence is being used in problem solving and planning (Ertel, 2011).
The difficulty of artificial intelligence is more than compensated for by the impending rewards both realistic and intellectual. Whitby (2009) noted that in practical terms, artificial intelligence has already justified itself in its applications. Applications of artificial intelligence and its spin-offs are already influencing our technology and society and will progressively continue to do so in the future.
The Future of Artificial Intelligence
The future of artificial intelligence is bright as a result of the advancements made by learning institutions and technology companies, such as IBM, Microsoft, HP, and Dell in the fields of nanotechnology and molecular computing. The graph below shows the exponential growth of artificial intelligence. This can be expressed in terms of its accelerating pace.
Future of Artificial Intelligence. Retrieved on 15th March 2012 from
In the current trend, the computational power for a modern desktop computer doubles every two years. This certainly leads to a logarithmic graph improving towards a computer with a near-infinite computational capacity. The table and graph below estimates the exponential growth of artificial intelligence from the 20th century to the 21st century.
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The graph below shows the exponential growth of artificial intelligence. This can be expressed in terms of its accelerating pace.
The intelligent systems offered by artificial intelligence today are not a universal formula, but a workshop with a manageable number of tools for very different tasks. Being one of the most successful and rapidly growing fields in computer science, the selection of the right tool and its sensible use in each individual case is left to the artificial intelligence system developer or knowledge engineer. It is important to note that artificial intelligence is interdisciplinary because it represents interesting discoveries from such various fields as logic, operations research, statistics, control engineering, image processing, and linguistics.