Lectures: Mon. & Wed. Robertson 016, (WWS)
Mon 2/2 Broad Overview of the Course by Course Participants. Presentation of the framework of human-machine interaction in a problem solving environment.
Wed 2/4 Models of Human Information Processing -- A. Kornhauser Skill-rule-and- knowledge-base approaches, semiotic interpretation of human acts, mental models of aggregation, abstraction and analogy.
Mon 2/9. The Mind-Body Problem: Dualism. Descartes' argument for two distinct substances, body and mind. Various forms of dualism---substance, events, properties, phenomena. Possible relations between two distinct realms: dualistic interaction, epiphenomenalism. Rejections of dualism: idealism, physicalism.
Wed 2/11. Mind as a Computer Program. Mind not a substance but a certain functional organization of matter. Computers as thinking machines. Computers as aids in thinking.
Readings: Eric B. Baum, Chapter 1, “Introduction” from What is Thought, MIT Press, 2004 Class Notes
Mon 2/16. Deductions by Machines - P. N. Johnson-Laird. How do we get machines to think? One answer: get them to think logically. Formal logic can be implemented in various computer programs. Another answer: get machines to use rules with specific contents. Expert systems. The problems of these approaches: intractability, and lack of decision procedure, and need to make inferences that undo previous conclusions.
Wed 2/18. Deductions by Humans - P. Johnson-Laird. Are human beings rational? Do they make deductions in the same way as machines, i.e. by deriving conclusions using rules of inference? Demonstrations of typical patterns of performance in deductive reasoning, including illusory inferences that everyone gets wrong. How human reasoning is semantic rather than a syntactic process; it appears to depend on constructing mental models of situations.
Readings: Johnson-Laird, P.N. (2003) Mental models and reasoning. In Leighton, J.P., and Sternberg, R.J. (Eds.) The Nature of Reasoning. Cambridge: Cambridge University Press. Pp. 169-204. Class Notes
Feedback on Homework #3
Mon 2/23. Probabilistic Thinking by Humans and Machines - P. Johnson-Laird. Representing uncertainty: the advantages of the probability calculus. Extensional vs. nonextensional reasoning about probabilities. Common errors in human reasoning about probabilities. Bayes’s theorem in expert systems, and in human thinking. A theory of naive probabilistic reasoning.
Readings: Sections 14.2 to 14.6 of Ch 14. Uncertainty, on S. J. Russell and P. Norvig, Artificial Intelligence: A Modern Approach. Englewood Cliffs, NJ: Prentice-Hall, 1995, pp. 420-433. Class Notes
Wed 2/25. Creativity in Humans and Machines - P. Johnson-Laird. Can machines be creative? A working definition of creativity. A taxonomy of creative processes: three computational architectures. Non-determinism. Some algorithms for creativity in science and art.
Readings: Ch.'s 13-15, and Appendices 1 and 2 of P. McCorduck, Aaron's Code: Meta-Art, Artificial Intelligence, and the work of Harold Cohen. New York: Freeman,1991. Pp. 85-110; 199-208 Class Notes
Mon 3/1. Basic Principles of Statistical Learning. Pattern recognition, function estimation, probability, noise, criteria. Balance error against complexity, parameters, capacity of a set of functions, VC dimension, shattering.
Wed 3/3. Methods of Machine Learning. Nearest neighbor: curse of dimensionality. Perceptron learning, linear separations, multi-layer nets, problem of local minima. Support vector machines, transduction. Giving up classical philosophy of science.
Mon 3/8. Integration of the First Half of the course – A. L. Kornhauser, P. Johnson-Laird & G. H. Harman
Readings: Review of the Readings, Lectures and Class Notes
Wed 3/10. MID-TERM HOURLY EXAM (covers everything through Monday 3/8, Segments 1-3)
Mon 3/22. Computers in the Social Environment - J. Cooper. Principles of social interaction, e.g., social comparison, social influence. The computer as a participant in the social system.
Lepper & Malone, "Making Learning Fun: A Taxonomy of Intrinsic Motivation for learning," in Aptitude Learning and Instruction, edited by Snow and Farr, 1987, Vol. III, Ch. 10, p 223-253 and
Chapters 1 & 2 Cooper, J. and K. Weaver Gender and Computers: Understanding the Digital Divide, Lawrence Erlbaum Associates (2003) Class Notes (cover week 7 & 8)
Wed 3/24. Motivational Issues in Computer Education for Children -- J. Cooper. Achievements in learning from computers. Intrinsic motivation: wanting to learn more in computer education.
Readings: Chapters 3 & 4 Cooper, J. and K. Weaver Gender and Computers: Understanding the Digital Divide, Lawrence Erlbaum Associates (2003)
Mon 3/29. Gender and the Computer - J. Cooper. Understanding anxiety and motivation as a function of gender. How do males and females differ in their approach to avoidance of computers? Are gender differences a function of software, of hardware? To what extent are gender differences in computing a function of social content?
Readings Chapters 5 Cooper, J. and K. Weaver Gender and Computers: Understanding the Digital Divide, Lawrence Erlbaum Associates (2003)
Wed 3/31. Personality differences in Computing – J. Cooper Study of research findings on personality differences in computing.
Readings: Chapters 6 & 7 Cooper, J. and K. Weaver Gender and Computers: Understanding the Digital Divide, Lawrence Erlbaum Associates (2003)
Mon 4/5 Views of Viewing: The Anatomy of Vision and the Modeling of Visual Cognition -- A. Kornhauser. A look at the human vision system; its anatomy, its operation and the modeling of the system. Overview of the human anatomy of the retina and the visual pathways and comparison with the vision system of the Frog. Focus on the “information processing structure” of the retina and the visual cortex. Computational models of low-level and high-level vision. Approaches to the modeling of the human vision system and visual cognition. Computational models of low-level and high-level vision.
Readings: Lettvin, J.Y., et al “what the Frog’s Eye Tells the Frog’s Brain”, Proc. Of the IRE,Nov. 1959 pp 1940-1951
from J.H. Schwartz, Principles of Neural Science,
Ch 27, "The Retina and Phototransduction,"
Ch 28, "Anatomy of the Central Visual Pathways." Class Notes
Wed 4/7 Models of Visual Cognition -- A. Kornhauser.. Computational models of low-level and high-level vision. Approaches to the modeling of the human vision system and visual cognition. Computational models of low-level and high-level vision.
Readings: From D. L. Osherson, Visual Cognition and Action, Vol 2, Ch 1, "Computational Theories of Low-Level Vision," Ch 2, "High-Level Vision," Ch 3 "Mental Imagery". Class Notes
Mon 4/12 Learning with Machines and Artificial Neural Networks – A. Kornhauser. Foundations of artificial neural systems. Comparison of real ans\d artificial neural systems. The evolution of highly parallel distributed processing models known as neural networks. Presentation of various mathematical frameworks, different approaches to learning; choices of training sets. Specific examples using back-propagation networks.
Wed 4/14 Helping Humans Make Better Everyday Decisions -- A. Kornhauser. With vast amounts of real-time information available, what kind of machines will help the individual make better decisions? What are the communication, computing and interface requirements? How will the supporting information be gathered and distributed. What about quality? A pragmatic example: getting from A to B, how to navigate, guide and control. Class Notes
The project combines a term paper with a brief visual presentation. Your plan for the project should be discussed by one or two of the faculty in this course well in advance of Monday 4/19.
Mon 4/19. Princeton Engineering Anomalies Research. Purpose, history, style, agenda. Human/Machine Experiments I:
Readings Jahn and Dunne, Margins of Reality, Section II; Two Decades of PEAR: An Anthology of Selected Publications, Articles #14 ("Correlations of Random Binary Sequences with Pre-Stated Operator Intention"), #6 ("Experiments in Remote Human/Machine Interactions"), #8 ("Count Population Profiles in Engineering Anomalies Experiments"), #10 ("Series Position Effects in Random Event Generator Experiments"), and #11 ("Gender Differences in Human/Machine Anomalies"). Class Notes
Wed 4/21. Princeton Engineering Anomalies Research (continued). Human/Machine Experiments II: Field Applications. Information Acquisition and Remote Perception.
Readings Margins of Reality, Section III; Two Decades of PEAR: An Anthology of Selected Publications, Article #7 ("FieldREG II: Consciousness Field Effects, Replications and Explorations"); Information and Uncertainty in Remote Perception Research (Available on-line). Class Notes Part A; Class Notes Part B
Mon 4/26. Theoretical Modeling and Review of Science History.
Wed 4/28. Theoretical Modelling II: Quantum Mechanics of Consciousness; A Modular Model of Mind/Matter Interactions, Sensors, Filters and the Source of Reality.
Readings Margins of Reality, Section IV; Two Decades of PEAR: An Anthology of Selected Publications, Article #2 ("On the Quantum Mechanics of Consciousness, with Application to Anomalous Phenomena"); "A Modular Model of Mind/Matter Interactions" (Available on-line). Class Notes
Thursday evening 4/29 Room 016 WWS SECOND-HOURLY EXAM (covers weeks 7-12)
Formal oral presentation of term project 6 minutes per student Use of visual aids is highly recommended. Students are REQUIRED to attend ALL presentations in their group in order to contribute to the evaluation of the presentations in their group!
Picnic at Kornhauser’s immediately afterwards ( - weather permitting!)