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Artificial Intelligence Cleveland

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Artificial Intelligence Cleveland

Artificial Intelligence Cleveland

Artificial Intelligence Cleveland

Artificial Intelligence, which commenced publication in 1970, is now the generally accepted premier international forum for the publication of results of current research in this field. The journal welcomes foundational and applied papers describing mature work involving computational accounts of aspects of intelligence. Specifically, it welcomes papers on:

  • Artificial Intelligence and Philosophy
  • Automated reasoning and inference
  • Case-based reasoning
  • Cognitive aspects of AI
  • Commonsense reasoning
  • Constraint processing
  • Heuristic search
  • High-level computer vision
  • Intelligent interfaces
  • Intelligent robotics
  • Knowledge representation
  • Machine learning
  • Multiagent systems
  • Natural language processing
  • Planning and theories of action
  • Reasoning under uncertainty or imprecision

The journal reports results achieved; proposals for new ways of looking at AIproblems must include demonstrations of effectiveness. Papers describing systems or architectures integrating multiple technologies are welcomed. Artificial Intelligence (AIJ) also invites papers on applications, which should describe a principled solution, emphasize its novelty, and present an in-depth evaluation of the AI techniques being exploited. The journal publishes an annual issue devoted to survey articles and also hosts a “competition section” devoted to reporting results from AI competitions. From time to time, there are special issues devoted to a particular topic; such special issues always have open calls.

Artificial Intelligence caters to a broad readership. Papers that are heavily mathematical in content are welcome but should be preceded by a less technical introductory section that is accessible to a wide audience. Papers that are only mathematics, without demonstrated applicability to Artificial Intelligence problems may be returned.

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Artificial intelligence (AI, also machine intelligenceMI) is intelligence displayed by machines, in contrast with the natural intelligence (NI) displayed by humans and other animals. In computer science AI research is defined as the study of “intelligent agents“: any device that perceives its environment and takes actions that maximize its chance of success at some goal.[1] Colloquially, the term “artificial intelligence” is applied when a machine mimics “cognitive” functions that humans associate with other human minds, such as “learning” and “problem solving”.[2] See glossary of artificial intelligence.

The scope of AI is disputed: as machines become increasingly capable, tasks considered as requiring “intelligence” are often removed from the definition, a phenomenon known as the AI effect, leading to the quip “AI is whatever hasn’t been done yet.”[3] For instance, optical character recognition is frequently excluded from “artificial intelligence”, having become a routine technology.[4] Capabilities generally classified as AI as of 2017 include successfully understanding human speech,[5] competing at a high level in strategic gamesystems (such as chess and Go[6]), autonomous cars, intelligent routing in content delivery networksmilitary simulations, and interpreting complex data, including images and videos.

Artificial intelligence was founded as an academic discipline in 1956, and in the years since has experienced several waves of optimism,[7][8] followed by disappointment and the loss of funding (known as an “AI winter“),[9][10] followed by new approaches, success and renewed funding.[8][11] For most of its history, AI research has been divided into subfields that often fail to communicate with each other.[12] These sub-fields are based on technical considerations, such as particular goals (e.g. “robotics” or “machine learning”),[13] the use of particular tools (“logic” or “neural networks”), or deep philosophical differences.[14][15][16] Subfields have also been based on social factors (particular institutions or the work of particular researchers).[12]

The traditional problems (or goals) of AI research include reasoningknowledgeplanninglearningnatural language processingperception and the ability to move and manipulate objects.[13] General intelligence is among the field’s long-term goals.[17] Approaches include statistical methodscomputational intelligence, and traditional symbolic AI. Many tools are used in AI, including versions of search and mathematical optimizationneural networks and methods based on statistics, probability and economics. The AI field draws upon computer sciencemathematicspsychologylinguisticsphilosophyneuroscienceartificial psychology and many others.

The field was founded on the claim that human intelligence “can be so precisely described that a machine can be made to simulate it”.[18] This raises philosophical arguments about the nature of the mind and the ethics of creating artificial beings endowed with human-like intelligence, issues which have been explored by mythfiction and philosophy since antiquity.[19] Some people also consider AI a danger to humanity if it progresses unabatedly.[20] Others believe that it is primarily a risk to employment: a frequently cited paper by Michael Osborne and Carl Benedikt Frey found that almost half of U.S. jobs are at risk to automation due to AI.[21]

In the twenty-first century, AI techniques have experienced a resurgence following concurrent advances in computer power, large amounts of data, and theoretical understanding; and AI techniques have become an essential part of the technology industry, helping to solve many challenging problems in computer science.[22][11]

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