Artificial Intelligence

The modern definition of artificial intelligence (or AI) is "the study and design of intelligent agents" where an intelligent agent is a system that perceives its environment and takes actions which maximizes its chances of success.

• John McCarthy, who coined the term in 1956, defines it as "the science and engineering of making intelligent machines." Other names for the field have been proposed, such as computational intelligence, synthetic intelligence or computational rationality.
• The term artificial intelligence is also used to describe a property of machines or programs: the intelligence that the system demonstrates.
• AI research uses tools and insights from many fields, including computer science, psychology, philosophy, neuroscience, cognitive science, linguistics, operations research, economics, control theory, probability, optimization and logic.
• AI research also overlaps with tasks such as robotics, control systems, scheduling, data mining, logistics, speech recognition, facial recognition and many others. Computational intelligence Computational intelligence involves iterative development or learning (e.g., parameter tuning in connectionist systems).
• Learning is based on empirical data and is associated with non-symbolic AI, scruffy AI and soft computing. Subjects in computational intelligence as defined by IEEE Computational Intelligence Society mainly include: Neural networks: trainable systems with very strong pattern recognition capabilities.
  
• Fuzzy systems: techniques for reasoning under uncertainty, have been widely used in modern industrial and consumer product control systems; capable of working with concepts such as 'hot', 'cold', 'warm' and 'boiling'. Evolutionary computation: applies biologically inspired concepts such as populations, mutation and survival of the fittest to generate increasingly better solutions to the problem. These methods most notably divide into evolutionary algorithms (e.g., genetic algorithms) and swarm intelligence (e.g., ant algorithms). With hybrid intelligent systems, attempts are made to combine these two groups.
• Expert inference rules can be generated through neural network or production rules from statistical learning such as in ACT-R or CLARION. It is thought that the human brain uses multiple techniques to both formulate and cross-check results.
• Thus, systems integration is seen as promising and perhaps necessary for true AI, especially the integration of symbolic and connectionist models.

General limitations

There are three general limitations in AI, commonly stated as stupidity, ignorance, and laziness.[citation needed] Most real-world problems have one or more of these factors.

• * Stupidity: One does not always know how to compute a perfect solution.

• * Ignorance: One does not always have the necessary information to compute a perfect solution.

• * Laziness: One does not always have the time to compute a perfect solution.

• The solution to laziness is generally a utility heuristic - e.g. in chess, one can take a guess at how likely a certain move is to result in a win or a loss even without having fully computed its outcomes, based on generalized ideas such as defensive positions, numeric piece values, etc.

Applications of artificial intelligence


Business

• Banks use artificial intelligence systems to organize operations, invest in stocks, and manage properties. In August 2001, robots beat humans in a simulated financial trading competition (BBC News, 2001). A medical clinic can use artificial intelligence systems to organize bed schedules, make a staff rotation, and provide medical information. Many practical applications are dependent on artificial neural networks, networks that pattern their organization in mimicry of a brain's neurons, which have been found to excel in pattern recognition. Financial institutions have long used such systems to detect charges or claims outside of the norm, flagging these for human investigation. Neural networks are also being widely deployed in homeland security, speech and text recognition, medical diagnosis (such as in Concept Processing technology in EMR software), data mining, and e-mail spam filtering.

• Robots have become common in many industries. They are often given jobs that are considered dangerous to humans. Robots have proven effective in jobs that are very repetitive which may lead to mistakes or accidents due to a lapse in concentration and other jobs which humans may find degrading. General Motors uses around 16,000 robots for tasks such as painting, welding, and assembly. Japan is the leader in using and producing robots in the world. In 1995, 700,000 robots were in use worldwide; over 500,000 of which were from Japan.

Toys and games

• The 1990s saw some of the first attempts to mass-produce domestically aimed types of basic Artificial Intelligence for education, or leisure. This prospered greatly with the Digital Revolution, and helped introduce people, especially children, to a life of dealing with various types of AI, specifically in the form of Tamagotchis and Giga Pets, the Internet (example: basic search engine interfaces are one simple form), and the first widely released robot, Furby. A mere year later an improved type of domestic robot was released in the form of Aibo, a robotic dog with intelligent features and autonomy.

The Future of A.I.

• Artificial intelligence in the 90's is centered around improving conditions for humans. But is that the only goal in the future? Research is focusing on building human-like robots.
  
• This is because scientists are interested in human intelligence and are fascinated by trying to copy it. If A.I. machines can be capable of doing tasks originally done by humans, then the role of humans will change.

• Robots have already begun to replace factory workers. They are acting as surgeons, pilots, astronauts, etc. According to Crevier, a computer scientist, robots will take over clerical workers, the middle managers and on up.
  
• Eventually what society will be left with are machines working at every store and humans on every beach. As Moravec puts it, we'll all be living as millionaires.

• The visions of some of these scientists seem like unrealistic utopian views. Other scientists and theorists envision a more negative takeover.
  
• "The thinking power of silicon 'brains' will be so formidable that 'if we are lucky, they will keep us as pets'"(Postman). But what if these visions become a reality? Will humans have to worry about their futures if artificial intelligence takes over?