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Lecture 11

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chris wiggins edited this page Mar 20, 2018 · 7 revisions

focus on some of the professional history of computing literature that's on our syllabus.

Starting with Lecture 11, we'll be experiencing how the development of AI -- among mathematicians, cognitive scientists, and the nascent computer science field --
comes to collide with academic and industrial statistics. Useful to that end will be a dive into two works exploring the intellectual and cultural turmoil of bringing explicitly computational techniques, e.g., data assimilation and simulation, into an existing scientific community.

  • Paul Edwards, A Vast Machine (MIT Press, 2010), chs 5-7. NOTE THIS IS A DIFFERENT ASSIGNMENT than on syllabus

  • Peter Galison, ["Computer simulations and the trading zone,"] (http://www.medientheorie.com/doc/galison_simulation.pdf) in The disunity of science: Boundaries, contexts, and power, ed. Peter Galison and David J. Stump (Stanford, CA: Stanford University Press, 1996), 118-157

The Edwards is amazing about data accumulation, modeling, and so forth, for climate data. It's also important and theoretically rich, and will give us more ways to think about data-driven sciences and their infrastructure.

Galison is about monte carlo, and a great follow up to what we've been doing.

(cf. https://data-ppf.slack.com/archives/C3SJQ5FH9/p1491032123318543 ):

For Tuesday, we're moving into more recent streams in machine learning and statistics.

  1. Nilsson, Nils J. The Quest for Artificial Intelligence: A History of Ideas and Achievements. Cambridge ; New York: Cambridge University Press, 2010, Machine Learning, online version: https://ai.stanford.edu/~nilsson/QAI/qai.pdf ; only read these sections:
  • 29.5 Unsupervised learning pp 513-515
  • 29.6 Reinforcement learning pp 515-524
  • 29.7 Enhancements pp 524-527

Nilsson is an AI researcher who contributed, among other things, the "A* algorithm" we discussed briefly on Thursday as a heuristic approximation to the shortest path algorithm.

  1. Breiman, Leo. “Statistical Modeling: The Two Cultures.” Statistical Science 16 (2001): 199–215. http://www.jstor.org/stable/2676681

Breiman is a New Yorker, Columbia alumnus, former merchant marine, pure mathematician, and, later, an evangelist for machine learning among the statisticians.

  1. Cleveland, William S. “Data Science: An Action Plan for Expanding the Technical Areas of the Field of Statistics.” International Statistical Review / Revue Internationale de Statistique 69, no. 1 (April 2001): 21. http://www.jstor.org/stable/1403527 Cleveland was a statistician at Bell Labs and worked closely with Tukey there.

optional:

  1. Tukey's paper after 40 years, Colin Mallows Source: Technometrics, Vol. 48, No. 3 (Aug., 2006), pp. 319-325 https://www.jstor.org/stable/pdf/25471200.pdf is an assessment of the history of applied computational statistics, with particular attention to the paper "Frontiers of data analysis", Tukey 1962, which we read earlier.

  2. For more on Breiman, with plenty of history, see Olshen, Richard. A Conversaton with Leo Breiman. Statist. Sci. 16 (2001), no. 2, 184--198. doi:10.1214/ss/1009213290 , http://projecteuclid.org/euclid.ss/1009213290

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