1. Artificial Intelligence CIS 342 The College of Saint Rose David Goldschmidt, Ph.D.

2. What is Artificial Intelligence?

3. 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.

4. The Turing Test Alan Turing, British mathematician (1912-1954) – “ Computing machinery and intelligence ” paper in 1950 Computing machinery and intelligence – 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

5. 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

6. The Turing Test in Action…

7. 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.

8. History of AI Claude Shannon, MIT, Bell Labs (1950): – Computers playing chess – Chess game involved about 10 120 possible moves! – Even examining one move per microsecond would require 3 x 10 106 years to make its first move Need to incorporate intelligence via heuristics

9. 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) Programs with Common Sense Marvin Minsky, Princeton, MIT: – Anti-logical approach to knowledge representation and reasoning called frames (1975)

10. Evolution of Programming Languages

11. 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.

12. History of AI Evolutionary computation (1970s-today): – Natural intelligence is a product of evolution – Can we solve problems by simulating biological evolution? – Survival of the fittest – Genetic programming – Evolutionary computing

13. 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

14. History of AI Knowledge engineering (1980s-today): – Fuzzy set theory (Zadeh, 1965) associates words with degrees of truth or value – Rule-based knowledge systems – Combine information from multiple experts – Semantic Web Numerous hybrid approaches exist