Artificial IntelligenceCIS An-Najah National University AR. Natsheh, Ph.D.
Definitions of Intelligence • Essential English Dictionary, Collins, London, 1990: • Ability to understand and learn things • Ability to think and understand instead of doing things by instinct or automatically • Random House Unabridged Dictionary, 2006: • Capacity for learning, reasoning, understanding • Aptitude in grasping truths, relationships, facts, etc.
The Turing Test • Alan Turing, British mathematician (1912-1954) • “Computing machinery and intelligence” paper in 1950 • Can machines think? • The Turing Test (a.k.a. Turing imitation game): • A computer passes the Turing test if human interrogators cannot distinguish the machine from a human based on answers to their questions
The Turing Test • Turing Test • Objective standard view on intelligence • Test is independent of the details of the experiment (i.e. numerous variations) • Provides basis for verification and validation of intelligent systems • A program thought intelligent in some narrow area of expertise is evaluated by comparing its performance to human performance
History of AI • Warren McCulloch & Walter Pitts (1943): • Research on the human central nervous system led to a model of neurons of the brain • Birth of Artificial Neural Networks (ANN) • Binary model • Non-linear model • John von Neumann • ENIAC, EDVAC, etc.
History of AI • Claude Shannon, MIT, Bell Labs (1950): • Computers playing chess • Chess game involved about 10120 possible moves! • Even examining one move per microsecond would require 3 x 10106 years to make its first move • Need to incorporate intelligence via heuristics
History of AI • John McCarthy, Dartmouth, MIT (1950s): • Defined LISP • Only two years after FORTRAN • LISP is based on formal logic • “Programs with Common Sense” paper (1958) • Marvin Minsky, Princeton, MIT: • Anti-logical approach to knowledge representation and reasoning called frames (1975)
History of AI • Great expectations during 1950s and 1960s • But very limited success • Researchers focused too much on all-purpose intelligent machines with goals to learn and reason with human-scale knowledge (and beyond) • Refocus on specific problem domains (1970s) • Domain-specific expert systems with facts, rules, etc. • Analyze chemicals, medical diagnoses, etc.
History of AI • Evolutionary computation (1970s-today): • Natural intelligence is a product of evolution • Can we solve problems by simulatingbiological evolution? • Survival of the fittest • Genetic programming • Evolutionary computing
History of AI • Rebirth of neural networks (1980s-today): • Adaptive resonance theory (Grossberg, 1980) incorporated self-organization principles • Hopfield networks (Hopfield, 1982) introduced neural networks with feedback loops • Back-propagation learning algorithm (Bryson and Ho, 1969) for training multilayer perceptrons
History of AI • Knowledge engineering (1980s-today): • Fuzzy set theory (Zadeh, 1965) associates wordswith degrees of truth or value • Rule-based knowledge systems • Combine information from multiple experts • Semantic Web • Numerous hybrid approaches exist