Because of the incredible flexibility of the artificial intelligence process, AI software can be tailored to any problem requiring its special qualities. For example, there are all kind of problems with no algorithmic solution. Non-numerical problems often don’t yield to the algorithmic process. Neither do problems with uncertainty and ambiguity, but both are readily solved with AI techniques. With algorithmic software, the problem is guaranteed to be solved; with AI, there can be partial or even no solution. As a result, AI often fits the disorganized, imperfect real world better than conventional software because it can deal with shades of gray.
The first applications of AI were games playing and puzzles solving. Researchers felt that the ability of a computer to play a game was an excellent demonstration of human intelligence, and it is, in a controlled and limited way. AI programs to play chess, checkers, backgammon, tic-tac-toe and others were written, tested and improved. AI programs were also written to solve puzzles and riddles. The first applications proved the symbolic processing concepts of AI, and games and puzzles are still used as examples in teaching.
However, AI researchers discovered limitations of computers in initial applications of AI. Huge memory and processing power was needed to store the knowledge base. Fortunately, this problem was solved by recent development of Computer technology. Today, mini-computers routinely run AI software.
Although games are interesting, they are not particularly useful. Computer users are looking for programs that will increase productivity and performance, and artificial intelligence has found some specific niches in which it excels. These applications are given in below picture and some of them are discussed in detail as the article progresses.
General Problem Solving
General problem solving is something AI does well, and that ability can be applied to a very wide range of subjects. For people with tough problems to solve or problems that don’t fit algorithmic processes, AI offers a fresh alternative.
Some early attempts were made to create a general purpose AI problem-solving program. Although limited success was achieved, the approach was just not suitable for all types and sizes of problems. As a result, virtually all AI software is custom designed for the application. The best AI software is designed and optimized for the specific job.
In the 1990s, computer speech recognition reached a practical level for limited purposes. Thus United Airlines has replaced its keyboard tree for flight information by a system using speech recognition of flight numbers and city names. It is quite convenient. On the the other hand, while it is possible to instruct some computers using speech, most users have gone back to the keyboard and the mouse as still more convenient.
Understanding Natural Language
Just getting a sequence of words into a computer is not enough. Parsing sentences is not enough either. The computer has to be provided with an understanding of the domain the text is about, and this is presently possible only for very limited domains.
The world is composed of three-dimensional objects, but the inputs to the human eye and computers’ TV cameras are two dimensional. Some useful programs can work solely in two dimensions, but full computer vision requires partial three-dimensional information that is not just a set of two-dimensional views. At present there are only limited ways of representing three-dimensional information directly, and they are not as good as what humans evidently use.
A “knowledge engineer” interviews experts in a certain domain and tries to embody their knowledge in a computer program for carrying out some task. How well this works depends on whether the intellectual mechanisms required for the task are within the present state of AI. When this turned out not to be so, there were many disappointing results. One of the first expert systems was MYCIN in 1974, which diagnosed bacterial infections of the blood and suggested treatments. It did better than medical students or practicing doctors, provided its limitations were observed. Namely, its ontology included bacteria, symptoms, and treatments and did not include patients, doctors, hospitals, death, recovery, and events occurring in time. Its interactions depended on a single patient being considered. Since the experts consulted by the knowledge engineers knew about patients, doctors, death, recovery, etc., it is clear that the knowledge engineers forced what the experts told them into a predetermined framework. In the present state of AI, this has to be true. The usefulness of current expert systems depends on their users having common sense.
One of the most feasible kinds of expert system given the present knowledge of AI is to put some information in one of a fixed set of categories using several sources of information. An example is advising whether to accept a proposed credit card purchase. Information is available about the owner of the credit card, his record of payment and also about the item he is buying and about the establishment from which he is buying it (e.g., about whether there have been previous credit card frauds at this establishment).
Fuzzy logic controllers have been developed for automatic gearboxes in automobiles. For example, the 2006 Audi TT, VW Touareg and VW Caravell feature the DSP transmission which utilizes Fuzzy Logic. A number of Škoda variants (Škoda Fabia) also currently include a Fuzzy Logic-based controller.
Today’s cars now have AI-based driver assist features such as self-parking and advanced cruise controls. AI has been used to optimize traffic management applications, which in turn reduces wait times, energy use, and emissions by as much as 25 percent. In the future, fully autonomous cars will be developed. AI in transportation is expected to provide safe, efficient, and reliable transportation while minimizing the impact on the environment and communities. The major challenge to developing this AI is the fact that transportation systems are inherently complex systems involving a very large number of components and different parties, each having different and often conflicting objectives.
Various tools of artificial intelligence are also being widely deployed in homeland security, speech and text recognition, data mining, and e-mail spam filtering. Applications are also being developed for gesture recognition (understanding of sign language by machines), individual voice recognition, global voice recognition (from a variety of people in a noisy room), and facial expression recognition for interpretation of emotion and non-verbal cues. Other applications are robot navigation, obstacle avoidance, and object recognition.
Complete List of applications
Typical problems to which AI methods are applied
- Optical character recognition
- Handwriting recognition
- Speech recognition
- Face recognition
- Artificial creativity
- Computer vision, Virtual reality and Image processing
- Diagnosis (artificial intelligence)
- Game theory and Strategic planning
- Game artificial intelligence and Computer game bot
- Natural language processing, Translation and Chatterbots
- Nonlinear control and Robotics
Other fields in which AI methods are implemented
- Artificial life
- Automated reasoning
- Biologically inspired computing
- Concept mining
- Data mining
- Knowledge representation
- Semantic Web
- E-mail spam filtering
- Behavior-based robotics
- Developmental robotics (Epigenetic)
- Evolutionary robotics
- Hybrid intelligent system
- Intelligent agent
- Intelligent control
- Crash course in AI and expert systems by Louis E. Frenzel, Jr
- image intelligent transport system