1. What is Artificial Intelligence? –Depends on your perspective... Philosophical: a method for modeling intelligence Psychological: a method for studying human cognition Engineering: a set of methodologies for building complex programs

2. Philosophical Perspective: a method for modeling intelligence –How do you define “intelligence”? ideal: syllogisms...laws of thought...logic need for embodiment? mind-body duality (Descartes) physical brain required? Chinese Room experiment Symbol Systems Hypothesis (Simon and Newell) grounding, mechanization, novelty, adaptiveness, free will? soul? animal intelligence? –operational definition: acting intelligent Turing test; what is the correct measuring stick? –no contact, response characteristics, news, humor, gender... Eliza, chatter bots, Loebner prize, Deep Blue

3. Psychological Perspective: a method for studying human cognition –strengths: perception, ambiguity, interpretation ill-formed problems; weighing multiple criteria judgement, common-sense, expertise “insight”, analogy, Eureka effect –weaknesses: calculations limited, selective, and maleable memory confirmation bias; role of emotions? –behaviorism vs. info-processing metaphor (I/O, internal rep) –connection to language Sapir-Whorf hypothesis (verbal representations) concepts, intension/meaning, maps, skills, automation

4. Engineering Perspective: a method for building complex programs –need more than just C++ or java (or OOP) –search algorithms, inference techniques, methods for dealing with uncertainty... –knowledge-based programming Feigenbaum: “In the knowledge is the power” high-level programming languages, expressiveness expert systems, logic, McCarthy real-world knowledge bases, ontologies, Cyc –intelligent agents: decision-making –control theory, “cybernetics,” Kalman filters –decision theory: Bayes, Markov

5. Relationships of AI to other fields: –Economics rational decision-making; satisficing (Simon) –Mathematics computability; Godel’s Incompleteness Theorem; logic and number theory; Leibnitz –Neuroscience neural networks, Minsky connectionism, distributed representations, grounding