Template:Cleanup-priority Template:Redirect Template:AI Template:Portal Artificial intelligence (AI) is defined as intelligence exhibited by an artificial (non-natural, manufactured) entity. Such a system is generally assumed to be a computer.
Although AI has a strong science fiction connotation, it forms a vital branch of computer science, dealing with intelligent behavior, learning and adaptation in machines. Research in AI is concerned with producing useful machines to automate human tasks requiring intelligent behavior. Examples include: answering questions about products for customers, handwriting recognition, speech recognition, and face recognition in CCTV cameras. As such, it has become an engineering discipline, focused on providing solutions to practical problems.
AI methods were used to schedule units in the first Gulf War, and DARPA stated that the costs saved by the efficiency of AI have repaid the US government's entire investment in AI research since the 1950s. AI systems are now in routine use in many businesses, hospitals and military units around the world, as well as being built into many common home computer software applications and video games.
Schools of thought
AI divides roughly into two schools of thought: Conventional AI and Computational Intelligence (CI).
Conventional AI mostly involves methods now classified under Machine learning, characterised by formalism & 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. Clippy the Microsoft Office paperclip is an example.
- Case based reasoning
- Bayesian networks
Computational Intelligence involves iterative learning of connectionist system parameter tuning, based on empirical data. This is also known as non-symbolic AI, scruffy AI or soft computing. Methods are:
- Neural networks: systems with very strong pattern recognition capabilities.
- Fuzzy systems: techniques for reasoning under uncertainty, has 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 such as genetic algorithms and ant algorithms to generate increasingly better solutions to the problem. These methods most notable devides into:
Main article: History of artificial intelligence
Main article: Philosophy of artificial intelligence
The debates on weak AI vs. strong AI is still a hot topic amongst AI philosophers. This involves philosophy of mind and the mind-body problem. Most notably Penrose, in his book The Emperor's New Mind and Searle with his Chinese room exercise, argue that true consciousness can not be achieved by formal logic systems, while Hofstadter in GEB and 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.
In science fiction AI is commonly portrayed as an upcoming power trying to overthrow human authority as in HAL 9000, Skynet, Colossus, or The Matrix or as service humanoids like C-3PO, Data, or the Bicentennial Man.
Typical problems in which AI methods are applied include:
- pattern recognition
- natural language processing & chatterbots
- non-linear control & robotics
- computer vision & image processing
- game theory
- game AI & game bots
Expectations of AI
AI methods are often employed in cognitive science research, which tries to model subsystems of human cognition. Historically, AI researchers aimed for the loftier goal of so-called strong AI—of simulating complete, human-like intelligence. This goal is epitomised by the fictional strong AI computer HAL 9000 in the film 2001: A Space Odyssey. This goal is unlikely to be met in the near future and is no longer the subject of most serious AI research. The label "AI" has something of a bad name due to the failure of these early expectations, and aggravation by various popular science writers and media personalities such as Professor Kevin Warwick whose work has raised the expectations of AI research far beyond its current capabilities. For this reason, many AI researchers say they work in cognitive science, informatics, statistical inference or information engineering. Recent research areas include Bayesian networks and artificial life.
The vision of artificial intelligence replacing human professional judgment has arisen many times in the history of the field, and today in some specialized areas where "expert systems" are routinely used to augment or to replace professional judgment in some areas of engineering and of medicine.
Even though a substantial amount of AI functionality exists in everyday software, some misinformed commentators on computer technology have tried to suggest that a good definition of AI would be "research that has not yet been commercialised". This happens because when AI gets incorporated into an os or application it becomes an understated feature.
AI languages and programming styles
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. Bayesian work often uses Matlab or Lush (a numerical dialect of Lisp). These languages include many specialist probabilistic libraries. Real-life and especially real-time systems are likely to use C++. AI programmers are often academics and emphasise rapid development and prototyping rather than bulletproof software engineering practices, hence the use of interpreted languages to empower rapid command-line testing and experimentation.
The most basic AI program is a single If-Then statement, such as "If A, then B." If you type an 'A' letter, the computer will show you a 'B' letter. Basically, you are teaching a computer to do a task. You input one thing, and the computer responds with something you told it to do or say. All programs have If-Then logic. A more complex example is if you type in "Hello.", and the computer responds "How are you today?" This response is not the computer's own thought, but rather a line you wrote into the program before. Whenever you type in "Hello.", the computer always responds "How are you today?". It seems as if the computer is alive and thinking to the casual observer, but actually it is an automated response. AI is often a long series of If-Then (or Cause and Effect) statements.
A randomizer can be added to this. The randomizer creates two or more response paths. For example, if you type "Hello", the computer may respond with "How are you today?" or "Nice weather" or "Would you like to play a game?" Three responses (or 'thens') are now possible instead of one. There is an equal chance that any one of the three responses will show. This is similar to a pull-cord talking doll that can respond with a number of sayings. A computer AI program can have 1,000s of responses to the same input. This makes it less predictable and closer to how a real person would respond, because a living person would respond unpredictably. When 1,000s of input ("if") are written in (not just "Hello.") and 1,000s of responses ("then") written into the AI program, then the computer can talk (or type) with most people, if those people know the If statement input lines to type.
Many games, like chess and strategy games, use action responses instead of typed responses, so that players can play against the computer. Robots with AI brains would use If-Then statements and randomizers to make decisions and speak. However, the input may be a sensed object in front of the robot instead of a "Hello." line, and the response may be to pick up the object instead of a response line.
AI researchers, research projects and institutions
- AI Consortium
- American Association for Artificial Intelligence
- European Coordinating Committee for Artificial Intelligence
- The Association for Computational Linguistics
- Artificial Intelligence Student Union
- German Research Center for Artificial Intelligence, DFKI
- Association for Uncertainty in Artificial Intelligence
- Singularity Institute for Artificial Intelligence
- The Society for the Study of AI and Simulation of Behaviour
- AGIRI - Artificial General Intelligence Research Institute
- Bio-inspired computing
- Hybrid intelligent system
- Intelligent agent
- Intelligent control
- Raj Reddy's AAAI paper (?) for a comprehensive review of real-world AI systems in deployment today
- University of California at Berkeley AI Resources links to 868 AI resource pages
- Loebner Prize website
- Artificial Intelligence Forum
- AIWiki - a wiki devoted to AI.
- AI web category on Open Directory
- OpenMind CommonSense "Teaching computers the stuff we all know"
- Heuristics and AI in finance and investment
- SourceForge Open Source AI projects - 1139 projects
- Ethical and Social Implications of AI en Computerization
- AI algorithm implementations and demonstrations
- Marvin Minsky's Homepage
- MIT's AI Lab
- AI research group at Information Sciences Institute
- Why Programming is a Good Medium for Expressing Poorly Understood and Sloppily Formulated Ideas
- aiKnow: Cognitive Artificial Intelligence
- What is Artificial Intelligence?
- Stanford Encyclopedia of Philosophy entry on Logic and Artificial Intelligence
- TechBookReport Book reviews related to AI, machine learning and complexity theory
- AI-Junkie: Genetic Algorithm and Neural Network tutorials
- Hello, Are You Human?
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