Artificial intelligence

edit The academic theory of AI

AI divides into two schools of thought. Many definitions describe this segregation differently, but all roughly convey the same idea:

• Conventional AI (symbolic AI / logical AI / neat AI): distinguished by formalism, statistical analysis, definitions and proof. Machine learning has become associated primarily with conventional AI. It includes expert systems and case based reasoning. Also see semantics.
• Computational Intelligence (CI) (non-symbolic AI / scruffy AI, soft computing): recognized for its informal, non-statistical and often trial-and-error approaches. Learning is usually an iterative process of connectionist system parameter tuning, based on empirical data. CI subdivides into 3 main sections: Neural networks, Fuzzy systems and Evolutionary computation. This research overlaps with a-life, cognitive science, cybernetics & robotics. Many hybrid intelligent systems have also appeared. Read more...

edit The philosophy of AI

The debates on weak AI vs. strong AI is still a hot topic amongst AI philosophers. Strong Artificial Intelligence being AI on the human level with a mind & consciousness. 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. Read more...

edit AI in science fiction

In science fiction AI is commonly portrayed as an upcoming power, trying to overthrow human authority as in HAL 9000, The Borg and The Matrix or as service humanoids like C-3PO, Data, AI, Bicentennial Man, I Robot & Marvin. See fictional computers & fictional robots.

The inevitability of AI world domination is also argued by some science writers like Asimov and Warwick.

edit The history of AI

See the work of Turing and his Turing test, Wiener & Minsky of MIT, IBM's Deep Blue, and headings under Artificial intelligence. Read more...

edit AI Publications

Researchers who have made a considerable contribution through multiple significant publications (which are not listed elsewhere) are listed here:

Hinton - McCarthy - Aleksander - Papert - Boden - Selfridge - Pearl - Brooks - Schank - Winograd - Pfeifer - Hendler - Pal - Kasabov

edit Featured article

Artificial neural network

An artificial neural network (ANN), also called a simulated neural network (SNN) (but the term neural network (NN) is grounded in biology and refers to very real, highly complex plexus), is an interconnected group of artificial neurons that uses a mathematical or computational model for information processing based on a connectionist approach to computation. There is no precise agreed definition among researchers as to what a neural network is, but most would agree that it involves a network of highly complex processing elements (neurons), where the global behaviour is determined by the connections between the processing elements and element parameters. Since anything approaching a full appreciation of neuronal function remains a distant dream, and since the factors producing global output result from many non-linear, modulating, and poorly understood real-time feedback signals within a single neuron, the highly linear artificial networks (where 'neurons' are modeled as input/output nodes) are perceived as academic research tools rather than even a distant representation of brain function. Read More...

edit AI News

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edit Did you know...

Portal:Artificial intelligence/Did you know