The term artificial intelligence is also used to describe an attribute of machines or programs: the intelligence that the system demonstrates.
AI research uses tools and insights from many fields, including computer science, psychology, philosophy, neuroscience, cognitive science, probability, linguistics, economics, control theory, optimization, operations research and logic.
AI research also overlays with tasks such as robotics, control systems, scheduling, data mining, logistics, speech recognition, facial recognition and many others.
Computational intelligence Computational intelligence involves iterative development or learning (e.g., parameter tuning in connectionist systems).
Subjects in computational intelligence mainly include: Neural networks: trainable systems with very strong pattern recognition capabilities. 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, especially the integration of symbolic and connectionist models.
Artificial Intelligence techniques are increasingly extending and enriching decision support through such means as coordinating data delivery, analyzing data trends, providing forecasts, developing data consistency, quantifying uncertainty, anticipating the user’s data needs, providing information to the user in the most appropriate forms, and suggesting courses of action.