A European Informational Website
learn more
The term Artificial Intelligence (AI) was first used by John McCarthy who used 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. While AI is the generally accepted term others, including both Computational Intelligence and Synthetic Intelligence have been proposed as potentially being "more accurate."[2] 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, philosophy, 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. One of the biggest difficulties with AI is that of comprehension. Many devices have been created that can do amazing things, but critics of AI claim that no actual comprehension by the AI machine has taken place.
The field of artificial intelligence dawned in the 1950s. Since then, there have been many achievements in the history of artificial intelligence; some of the more notable moments include:
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.
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).
Convential AI research focusses on attempts to mimic human intelligence through symbol manipulation and symbolically structured knowledge bases. This approach limits the situations to which conventional AI can be applied. Lofti Zadeh stated that "we are also in possession of computational tools which are far more effective in the conception and design of intelligent systems that the predicate-logic-based methods, which form the core of traditional AI", techniques which has become known as soft computing. These often biologically inspired methods, stand in contrast to conventional AI and compensate for the shortcomings of symbolicism [2].
Classifiers are functions that can be tuned according to examples, making 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 science.
The most widely used classifiers are the neural network, support vector machine, k-nearest neighbor algorithm, Gaussian mixture model, naive Bayes classifier, and decision tree.
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:
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:
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.
AI research has led to many advances in programming languages including the first list processing language by Allen Newell et al., Lisp dialects, Planner, Actors, the Scientific Community Metaphor, production systems, and rule-based languages.
GOFAI TEST research is often done in programming languages such as Prolog or Lisp. Matlab and Lush (a numerical dialect of Lisp) include many specialist probabilistic libraries for Bayesian systems. AI research often emphasise rapid development and prototyping, using such interpreted languages to empower rapid command-line testing and experimentation. Real-time systems are however likely to require dedicated optimized software.
Many expert systems are organized collections of if-then such statements, called productions. These can include stocastic elements, producing intrinsic variation, or rely on variation produced in response to a dynamic environment.
The DARPA Grand Challenge was a race for a $2 million prize where cars had to 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.
This was the first in a series of challenges aimed at a congressional mandate stating that by 2015 one-third of the operational ground combat vehicles of the US Armed Forces should be unmanned . For November 2007, DARPA introduced the DARPA Urban Challenge. The course will involve a 60-mile urban area course. Darpa has secured the prize money for the challenge as $2 million for first place, $1 million for second and 500 thousand for third.
A popular challenge amongst AI research groups is the RoboCup and FIRA annual international robot soccer competitions. Hiroaki Kitano has formulated the International RoboCup Federation challenge: 'In 2050 a team of fully autonomous humanoid robot soccer players shall win the soccer game, comply with the official rule of the FIFA, against the winner of the most recent World Cup' .
In the post-dot com boom era, some search engine websites use 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. <br style="clear:both" />
AI is not only seen in computer science and engineering. It is studied and applied in various different sectors.
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 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).
Notable examples include the languages LISP and Prolog, which were invented for AI research but are now used for non-AI tasks. Hacker culture first sprang from AI laboratories, in particular the MIT AI Lab, home at various times to such luminaries as John McCarthy, Marvin Minsky, Seymour Papert (who developed Logo there) and Terry Winograd (who abandoned AI after developing SHRDLU).
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].
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: The Forbin Project, and The Matrix, or as service humanoids like C-3PO, Marvin, KITT from Knight Rider, the Bicentennial Man, the Mechas in A.I. 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.
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 the re-imagined Battlestar Galactica, where artificial intelligence research is illegal after the Cylons, a species of intelligent machines created by man, had rebelled against their masters and tried to destroy them. The character Dr. Gaius Baltar is known for his controversial view that the ban on research in this area is outmoded and should be lifted.
Artificial intelligence plays a major role in How to Make a Monster, where the fictional character Sol uses his sophisticated AI for the game's monster, which comes to life after the lightning strike.
Golem XIV is an example of highly advanced supercomputer in Stanisław Lem's science-fiction novel Golem XIV. Golem XIV was a military artificial intelligence computer, which was originally invented to lead wars and to win them. Golem stops cooperating with humans on military level, because he considered wars and violence as illogical. His self-developing artificial intelligence refused to execute his primary task. Machine becomes a philosopher greater then any other born on Earth. Golem's intelligence advanced to a lot higher level then human intelligence which lead to conversation and information exchange problems.
Should the promise of strong AI be realized, some futurists such as Vernor Vinge and Ray Kurzweil predict that a period of abrupt and dramatic societal change will ensue. This hypothetical period is sometimes referred to as a technological singularity.
Main list: List of basic artificial intelligence topics
(J)în-kang tì-hūi