Artificial intelligence
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The term Artificial Intelligence (AI) was first used by John McCarthy who considers it to mean "the science and engineering of making intelligent machines".[1] It can also refer to intelligence as exhibited by an artificial (man-made, non-natural, manufactured) entity. The terms strong and weak AI can be used to narrow the definition for classifying such systems. AI is studied in overlapping fields of computer science, psychology, neuroscience and engineering, dealing with intelligent behavior, learning and adaptation and usually developed using customized machines or computers.
Research in AI is concerned with producing machines to automate tasks requiring intelligent behavior. Examples include control, planning and scheduling, the ability to answer diagnostic and consumer questions, handwriting, natural language, speech and facial recognition. As such, the study of AI has also become an engineering discipline, focused on providing solutions to real life problems, knowledge mining, software applications, strategy games like computer chess and other video games.
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[edit] History
The field of artificial intelligence truly dawned in the 1950s, since then there have been many achievements in the History of artificial intelligence, some of the more notable moments include:
| Year | Development |
|---|---|
| 1950 | Alan Turing introduces the Turing test intended to test a machine's capability to participate in human-like conversation. |
| 1951 | The first working AI programs were written to run on the Ferranti Mark I machine of the University of Manchester: a checkers-playing program written by Christopher Strachey and a chess-playing program written by Dietrich Prinz. |
| 1956 | John McCarthy coined the term "artificial intelligence" as the topic of the Dartmouth Conference. |
| 1958 | John McCarthy invented the Lisp programming language. |
| 1965 | Joseph Weizenbaum built ELIZA, an interactive program that carries on a dialogue in English language on any topic. |
| 1965 | Edward Feigenbaum initiated DENDRAL, a 10-yr effort to develop software to deduce the molecular structure of organic compounds using scientific instrument data. It was the first expert system. |
| 1966 | Machine Intelligence workshop at Edinburgh - the first of an influential annual series organized by Donald Michie and others. |
| 1968 | HAL 9000 made its appearance in the science fiction movie 2001: A Space Odyssey. |
| 1972 | The Prolog programming language was developed by Alain Colmerauer. |
| 1973 | Edinburgh Freddy Assembly Robot: a versatile computer-controlled assembly system. |
| 1974 | Ted Shortliffe's PhD dissertation on the MYCIN program (Stanford) demonstrated a very practical rule-based approach to medical diagnoses, even in the presence of uncertainty. While it borrowed from DENDRAL, its own contributions strongly influenced the future of expert system development, especially commercial systems. |
| 1997 | The Deep Blue chess program (IBM) beats the world chess champion, Garry Kasparov. |
| 1999 | Sony introduces the AIBO, an artificially intelligent pet. |
| 2004 | DARPA introduces the DARPA Grand Challenge requiring competitors to produce autonomous vehicles for prize money. |
During the 1970s and 1980s AI development experienced an AI winter due to failure to achieve expectations and lack of governmental funding.
During the 1990s and 2000s AI has become very influenced by probability theory and statistics. Bayesian networks are the focus of this movement, providing links to more rigorous topics in statistics and engineering such as Markov models and Kalman filters, and bridging the divide between 'neat' and 'scruffy' approaches. This new school of AI is sometimes called 'machine learning'. The last few years have also seen a big interest in game theory applied to AI decision making. After the September 11, 2001 attacks there has been much renewed interest and funding for threat-detection AI systems, including machine vision research and data-mining.
[edit] Mechanisms
Generally speaking AI systems are built around automated inference engines. Based on certain conditions ("if") the system infers certain consequences ("then"). AI applications are generally divided into two types, in terms of consequences: classifiers ("if shiny then diamond") and controllers ("if shiny then pick up"). Controllers do however also classify conditions before inferring actions and therefore classification form a central part of most AI systems.
Classifiers make use of pattern recognition for condition matching. In many cases this does not imply absolute, but rather the closest match. Techniques to achieve this divides roughly into two schools of thought: Conventional AI and Computational intelligence (CI)[citation needed]
[edit] Classifiers
Classifiers are functions that can be tuned based on examples, which make them very attractive for use in AI. These examples are known as observations or patterns. In supervised learning, each pattern belongs to a certain predefined class. A class can be seen as a decision that has to be made. All the observations combined with their class labels are known as a data set.
When a new observation is received, the observation is classified based on previous experience. A classifier can be trained in various ways, there are mainly statistical and machine learning approaches.
A wide range of classifiers are available, each with its strengths and weaknesses. Classifier performance depends greatly on the characteristics of the data to be classified. There is no single classifier that works best on all given problems, this is also referred to as the 'No free lunch theorem'. Various empirical tests have been performed to compare classifier performance and to find the characteristics of data that determine classifier performance. Determining a suitable classifier for a given problem is however still more an art than a science.
The most widely used classifiers are the neural network (multi-layer perceptron), support vector machines, k-nearest neighbors, Gaussian mixture model, Gaussian, naive Bayes, decision trees and radial basis functions. Van der Walt and Barnard[2] investigated very specific artificial data sets to determine conditions under which certain classifiers perform better and worse than others.[original research?]
[edit] Conventional AI
Conventional AI mostly involves methods now classified as machine learning, characterized by formalism and statistical analysis. This is also known as symbolic AI, logical AI, neat AI and Good Old Fashioned Artificial Intelligence (GOFAI). (Also see semantics.) Methods include:
- Expert systems: apply reasoning capabilities to reach a conclusion. An expert system can process large amounts of known information and provide conclusions based on them.
- Case based reasoning: stores a set of problems and answers in an organized data structure called cases. A Case Based Reasoning system upon being presented with a problem finds a case in its knowledge base that is most closely related to the new problem and presents its solutions as an output with suitable modifications.[3]
- Bayesian networks
- Behavior based AI: a modular method building AI systems by hand.
[edit] Computational intelligence
Computational intelligence involves iterative development or learning (e.g. parameter tuning e.g. in connectionist systems). Learning is based on empirical data and is associated with non-symbolic AI, scruffy AI and soft computing. Methods mainly include:
- Neural networks: 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.
- 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. 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.
[edit] Research challenges
The DARPA Grand Challenge is a race for a $2 million prize where cars drive themselves across several hundred miles of challenging desert terrain without any communication with humans, using GPS, computers and a sophisticated array of sensors. In 2005 the winning vehicles completed all 132 miles of the course in just under 7 hours.
A popular challenge amongst AI research groups is the RoboCup and FIRA annual international robot soccer competitions.
In the post-dot com boom era, some search engine websites have sprung using a simple form of AI to provide answers to questions entered by the visitor. Questions such as "What is the tallest building?" can be entered into the search engine's input form and a list of answers will be returned.
[edit] AI in other disciplines
AI is not only seen in computer science and engineering. It is studied and applied in various different sectors.
[edit] Philosophy
The strong AI vs. weak AI debate ("can a man-made artifact be conscious?") is still a hot topic amongst AI philosophers. This involves philosophy of mind and the mind-body problem. Most notably Roger Penrose in his book The Emperor's New Mind and John Searle with his "Chinese room" thought experiment argue that true consciousness cannot be achieved by formal logic systems, while Douglas Hofstadter in Gödel, Escher, Bach and Daniel Dennett in Consciousness Explained argue in favour of functionalism. In many strong AI supporters’ opinion, artificial consciousness is considered as the holy grail of artificial intelligence. Edsger Dijkstra famously opined that the debate had little importance: "The question of whether a computer can think is no more interesting than the question of whether a submarine can swim."
Epistemology, the study of knowledge, also makes contact with AI, as engineers find themselves debating similar questions to philosophers about how best to represent and use knowledge and information. (e.g. semantic networks).
[edit] Psychology
[edit] 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).[4] A medical clinic can use artificial intelligence systems to organize bed schedules, make a staff rotation, and to 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 robots in the world. In 1995, 700,000 robots were in use worldwide; over 500,000 of which were from Japan.[5].
[edit] Fiction
In science fiction AI — almost always strong AI — is commonly portrayed as an upcoming power trying to overthrow human authority as in HAL 9000, Skynet, Colossus and The Matrix or as service humanoids like C-3PO, Marvin, Data, KITT from Knight Rider, the Bicentennial Man, the Mechas in A.I., Cortana from the Halo series, and Sonny in I, Robot.
A notable exception is Mike in Robert A. Heinlein's The Moon Is a Harsh Mistress: a supercomputer that becomes aware and aids humans in a local revolution to overthrow the authority of other humans. A careful reading of Arthur C. Clarke's version of 2001 suggests that the HAL 9000 found himself/itself in a similar position of divided loyalties. On one hand, HAL needed to take care of the astronauts, on the other the humans who created HAL entrusted him with a secret to be withheld from the astronauts.
The inevitability of world domination by out-of-control AI is also argued by some writers like Kevin Warwick. In works such as the Japanese manga Ghost in the Shell, the existence of intelligent machines questions the definition of life as organisms rather than a broader category of autonomous entities, establishing a notional concept of systemic intelligence. See list of fictional computers and list of fictional robots and androids.
Some writers, such as Vernor Vinge and Ray Kurzweil, have also speculated that the advent of strong AI is likely to cause abrupt and dramatic societal change. The period of abrupt change is sometimes referred to as "the Singularity".
Author Frank Herbert explored the idea of a time when mankind might ban clever machines entirely. His Dune series makes mention of a rebellion called the Butlerian Jihad in which mankind defeats the smart machines of the future and then imposes a death penalty against any who would again create thinking machines. Often quoted from the fictional Orange Catholic Bible, "Thou shalt not make a machine in the likeness of a human mind." A similar idea is also explored in Battlestar Galactica, where artificial intelligence research is seen as controversial due to the mistake of creating the rebellious Cylons.
[edit] List of applications
- Typical problems to which AI methods are applied
- Other fields in which AI methods are implemented
- Lists of researchers, projects & publications
[edit] See also
- Main list: List of basic artificial intelligence topics
[edit] References
- ^ WHAT IS ARTIFICIAL INTELLIGENCE? by John McCarthy[1]
- ^ C.M. van der Walt and E. Barnard,“Data characteristics that determine classifier performance”, in Proceedings of the Sixteenth Annual Symposium of the Pattern Recognition Association of South Africa, pp.160-165, 2006[2][original research?]
- ^ Hammond J, Kristian. Case-based planning: viewing planning as a memory task. Academic Press Perspectives In Artificial Intelligence; Vol 1. Pages: 277. 1989. ISBN 0-12-322060-2
- ^ Robots beat humans in trading battle. BBC News, Business. The British Broadcasting Corporation (August 8, 2001). Retrieved on 2006-11-02.
- ^ "Robot," Microsoft® Encarta® Online Encyclopedia 2006 [3]
[edit] External links
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- AI at the Open Directory Project (suggest site)
- AI-Tools, the Open Source AI community homepage
- Artificial Intelligence Directory, a directory of Web resources related to artificial intelligence
- The Association for the Advancement of Artificial Intelligence
- Freeview Video 'Machines with Minds' by the Vega Science Trust and the BBC/OU
- Heuristics and artificial intelligence in finance and investment
- John McCarthy's frequently asked questions about AI
- Generation5 - Large artificial intelligence portal with articles and news.
- Mindmakers.org, an online organization for people building large scale A.I. systems
- Ray Kurzweil's website dedicated to AI including prediction of future development in AI
