Artificial Intelligence

• Artificial intelligence versus computational intelligence

• Capabilities and limitations: for example, learning to identify human emotions, evaluation of living things and machines (intuition, prior knowledge, judgment)

• AI techniques: searching, pattern recognition, face recognition, speech recognition

• Fuzzy logic

• Machine learning: can machines become independent?

• Natural language communication and translators

• Neural networks: similarity to biological systems

• Expert Systems: An expert system is a computer system that emulates the decision-making ability of a human expert, e.g. for medical diagnosis

Neural networks (for more information http://www.youtube.com/watch?v=1eE_DTWoOKka)
traditionally used to refer to a network of biological neurons. The term neural network is used to refer to artificial neural networks.

· Biological neural networks consist of real biological neurons that are connected to the central nervous system.

· Artificial neural networks consist of interconnecting artificial neurons (they mimic the properties of biological neurons). These networks may be used for solving AI problems without creating a model of a real biological system.

Expert Systems (the main article of this Unit is Larichev et al, 2001)

http://www.raai.org/library/papers/Larichev/Larichev_Asanov_Naryzhny_Strahov_2001.pdf)
An expert system is a computer system that emulates the decision-making ability of a human expert. The first expert systems were created in the 1970s and then proliferated in the 1980s. Expert systems were among the first truly successful forms of artificial intelligence software.
Expert systems are often used to advise non-experts in situations where a human expert is not available e.g. medical diagnosis, fault diagnosis (mechanics), financial management advice etc.