What is Artificial Intelligence? General definition: AI is the branch of computer science that is concerned with the automation of intelligent behavior. what is intelligent behavior? is intelligent behavior the same for a computer and a human?

at least we have experience with human intelligence possible definition: intelligence is the ability to form plans to achieve goals by interacting with an information-rich environment Tighter definition: AI is the science of making machines do things that would require intelligence if done by people. (Minsky) What is Artificial Intelligence?

What is AI? Intelligence encompasses abilities such as: understanding language perception learning reasoning

Self-defeating definition: AI is the science of automating intelligent behaviors currently achievable by humans only. this is a common perception by the general public as each problem is solved, the mystery goes away and it's no longer "AI" successes go away, leaving only unsolved problems What is AI?

AI ranges across many disciplines computer science, engineering, cognitive science, logic, … research often defies classification, requires a broad context Self-fulfilling definition: AI is the collection of problems and methodologies studied by AI researchers. What is AI?

Pre-history of AI the quest for understanding & automating intelligence has deep roots 4 th cent. B.C.: Aristotle studied mind & thought, defined formal logic 14 th –16 th cent.: Renaissance thought built on the idea that all natural or artificial processes could be mathematically analyzed and understood 18 th cent.: Descartes emphasized the distinction between mind & brain (famous for "Cogito ergo sum") 19 th cent.: advances is science & understanding nature made the idea of creating artificial life seem plausible Shelley's Frankenstein raised moral and ethical questions Babbage's Analytical Engine proposed a general-purpose, programmable computing machine -- metaphor for the brain 19 th -20 th cent.: saw many advances in logic formalisms, including Boole's algebra, Frege's predicate calculus, Tarski's theory of reference 20 th cent.: advent of digital computers in late 1940's made AI a viable Turing wrote seminal paper on thinking machines (1950)

Pre-history of AI birth of AI occurred when Marvin Minsky & John McCarthy organized the Dartmouth Conference in 1956 brought together researchers interested in "intelligent machines" for next 20 years, virtually all advances in AI were by attendees Minsky (MIT), McCarthy (MIT/Stanford), Newell & Simon (Carnegie),… John McCarthy Marvin Minsky

History of AI the history of AI research is a continual cycle of optimism & hype reality check & backlash refocus & progress … 1950's – birth of AI, optimism on many fronts general purpose reasoning, machine translation, neural computing, … first neural net simulator (Minsky): could learn to traverse a maze GPS (Newell & Simon): general problem-solver/planner, means- end analysis Geometry Theorem Prover (Gelertner): input diagrams, backward reasoning SAINT(Slagle): symbolic integration, could pass MIT calculus exam

History of AI 1960's – failed to meet claims of 50's, problems turned out to be hard! so, backed up and focused on "micro-worlds" within limited domains, success in: reasoning, perception, understanding, … ANALOGY (Evans & Minsky): could solve IQ test puzzle STUDENT (Bobrow & Minsky): could solve algebraic word problems SHRDLU (Winograd): could manipulate blocks using robotic arm, explain self STRIPS (Nilsson & Fikes): problem-solver planner, controlled robot "Shakey" Minsky & Papert demonstrated the limitations of neural nets

History of AI 1970's – results from micro-worlds did not easily scale up so, backed up and focused on theoretical foundations, learning/understanding conceptual dependency theory (Schank) frames (Minsky) machine learning: ID3 (Quinlan), AM (Lenat) practical success: expert systems DENDRAL (Feigenbaum): identified molecular structure MYCIN (Shortliffe & Buchanan): diagnosed infectious blood diseases

History of AI 1980's – BOOM TOWN! cheaper computing made AI software feasible success with expert systems, neural nets revisited, 5 th Generation Project XCON (McDermott): saved DEC ~ $40M per year neural computing: back-propagation (Werbos), associative memory (Hopfield) logic programming, specialized AI technology seen as future

History of AI 1990's – again, failed to meet high expectations so, backed up and focused : embedded intelligent systems, agents, … hybrid approaches: logic + neural nets + genetic algorithms + fuzzy + … CYC (Lenat): far-reaching project to capture common-sense reasoning Society of Mind (Minsky): intelligence is product of complex interactions of simple agents Deep Blue (formerly Deep Thought): defeated Kasparov in Speed Chess in 1997

History of AI

Neural networks

Branches of AI Games - study of state space search, e.g., chess Automated reasoning and theorem proving, e.g., logic theorist Expert/Knowledge-based systems Natural language understanding and semantic modeling Model human cognitive performance Robotics and planning Automatic programming Learning Vision

Textbooks

Nils J. Nillson Problem-Solving Methods in Artificial Intelligence McGraw-Hill, 1971.

Textbooks Nils J. Nillson Principles of Artificial Intelligence Morgan Kaufmann, 1986

Textbooks Nils J. Nillson Artificial Intelligence: a New Synthesis Morgan Kaufmann, 1998

Textbooks Rajan Shinghal Formal Concepts in Artificial Intelligence Chapman & Hall, 1992

Textbooks George F.Luger William A.Stubblefield Ronald L.Rivest Artificial Intelligence and the design of Expert Systems Benjamin/Cummings, 1989

Textbooks Elaine Rich Kevin Knight Artificial Intelligence McGraw-Hill, 1991

Textbooks Judea Pearl Intelligent search strategies for computer problem solving Addison-Wesley, 1984

Textbooks Nirmal.K.Bose Ping Liang Neural Network Fundamentalswith Graphs, Algorithms and Applications McGraw-Hill, 1996

Textbooks Roger Penrose The emperors new mind

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