A Brief Bibliography of Artificial Intelligence

Bostrom, N. (2014). Superintelligence: Paths, dangers, strategies. Oxford, UK: Oxford University Press.

Brown, T., Mann, B., et al. (2020) Language Models are Few-Shot Learners.  arXiv:2005.14165v4 [cs.CL] 22 Jul 2020. https://arxiv.org/abs/2005.14165

Domingos, P. (2012). A few useful things to know about machine learning. Communications of the ACM, 55(10), 78–87. doi:10.1145/2347736.2347755; http://homes.cs.washington.edu/~pedrod/papers/cacm12.pdf

Fernández-Delgado, M., Cernadas, E., Barro, S., and Amorim, D. (2014). Do we need hundreds of classifiers to solve real world classification problems. Journal of Machine Learning Research, 15, 3133–3181.  http://jmlr.org/papers/volume15/delgado14a/delgado14a.pdf

Ferrucci, D., Brown, E., Chu-Carroll, J., Fan, J., Gondek, D., Kalyanpur, A. A., Lally, A., Murdock, J. W., Nyberg, E., Prager, J., Schlaefer, N. & Welty, C. (2010). Building Watson: An Overview of the DeepQA Project. AI Magazine, 31, 59–79.

Grossman, Maura R., and Gordon V. Cormack. “Technology-assisted review in e-discovery can be more effective and more efficient than exhaustive manual review.” Rich. JL & Tech. 17 (2010): 1.

Markoff, J. (2016). Machines of Loving Grace: The Quest for Common Ground Between Humans and Robots. Ecco; Reprint Edition.

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

Roitblat, H. L. (2020).  Algorithms Are Not Enough: Creating General Artificial Intelligence.  Cambridge, MA.  MIT Press.  https://mitpress.mit.edu/books/algorithms-are-not-enough

Roitblat, H. L.,  Kershaw, A. & Oot, P. (2010). Document Categorization in Legal Electronic Discovery: Computer Classification vs. Manual Review.  Journal of the American Society for Information Science and Technology, 61(1):70-80.

Rosenblatt, F. (1958). The perceptron: A probabilistic model for information storage and organization in the brain. Psychological Review, 65, 386–408. doi:10.1037/h0042519

Russell, S., & Norvig, P. (2020) Artificial Intelligence: A Modern Approach, 4th Edition.  Pearson.

Samuel, Arthur L. (1959). Some studies in machine learning using the game of checkers. Computation & intelligence: collected readings. American Association for Artificial Intelligence, USA, 391–414.  http://www2.stat.duke.edu/~sayan/R_stuff/Datamatters.key/Data/samuel_1959_B-95.pdf

Shafer, G. (1976). A mathematical theory of evidence. Princeton, NJ: Princeton University Press.

Turing, A. M. (1965). On computable numbers with an application to the Entscheidungsproblem. In M. Davis (Ed.), The undecidable (pp. 116–154). New York, NY: Raven Press. (Original work published in Proceedings of the London Mathematical Society, Ser. 2, Vol. 42, 1936–7, pp. 230–265; corrections ibid., Vol. 43, 1937, pp. 544–546).

Turing, A. M. (1947). Lecture to the London Mathematical Society on 20 February 1947. Reprinted in D. C. Ince (Ed.) (1992), Collected works of A. M. Turing: Mechanical intelligence (pp. 87–105). Amsterdam, the Netherlands: North Holland.

Turing, A. M. (1950). Computing machinery and intelligence. Mind, 59, 433–460.

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