AI: Philisophy

AI-Lecture-1.pdf

  1. Searle, J. R. (1980) Minds, Brains, and Programs, Behavioral and Brain Sciences, 3(3), 417–457.

  2. Turing, A. M. (1950) Computing Machinery and Intelligence, Mind, 59(236), 433–460.

  3. Mitchell, M. (2019) Artificial Intelligence: A Guide for Thinking Humans.

  4. Boden, M. A. (2016) AI: Its Nature and Future (2nd ed.)

    https://archive.org/details/aiitsnaturefutur0000bode

  5. Boden, M. A. (1977) Mind as Machine: A History of Cognitive Science. https://archive.org/stream/margaretbodenmindasmachineahistoryofcognitivesciencetwovolumesetoxforduniversitypressusa2006/Margaret Boden - Mind As Machine_ A History of Cognitive Science Two-Volume Set-Oxford University Press%2C USA (2006)_djvu.txt

  6. McCarthy, J., Minsky, M., Rochester, N., & Shannon, C. (1955) A Proposal for the Dartmouth Summer Research Project on Artificial Intelligence. http://jmc.stanford.edu/articles/dartmouth/dartmouth.pdf

Courses:

University of Helsinki AI and Society (MOOC). https://courses.mooc.fi/org/uh-cs/courses/ai-in-society University of Helsinki Elements of AI (MOOC). https://www.elementsofai.com/

Lecture 2.pdf

Books and links:

  1. Hebb, Donald O. The Organization of Behavior: A Neuropsychological Theory. New York: John Wiley & Sons, 1949.
  2. McCulloch, Warren S., and Walter Pitts. “A Logical Calculus of the Ideas Immanent in Nervous Activity.” Bulletin ofMathematical Biophysics 5 (1943): 115–133. https://doi.org/10.1007/BF02478259
  3. Rosenblatt, Frank. The Perceptron: A Perceiving and Recognizing Automaton (Project PARA). Report No. 85-460-1. Buffalo, NY: Cornell Aeronautical Laboratory, 1957.
  4. Rosenblatt, Frank. “The Perceptron: A Probabilistic Model for Information Storage and Organization in the Brain.” Psychological Review 65, no. 6 (1958): 386–408. https://doi.org/10.1037/h0042519
  5. Rosenblatt, Frank. Principles of Neurodynamics: Perceptrons and the Theory of Brain Mechanisms. Washington, DC: Spartan Books, 1962.

Books on machine learning and deep learning: