Notes

1. Anderson C., Wired Magazine, www.wired.com/science/discoveries/magazine/16-07/ pb_intro.

2. C. D. Borgman, Big Data, Little Data, No Data: Scholarship in the Networked World (Cambridge, MA: MIT Press; 2016).

3. R. D. King, K. E. Whelan, F. M. Jones, P. G. Reiser, C. H. Bryant, S. H. Muggleton, et al., “Functional Genomic Hypothesis Generation and Experimentation by a Robot Scientist,” Nature 427:247-252, 2004.

4. Viktor Mayer-Schbnberger and Kenneth Cukier, Big Data (Boston: Houghton-Mifflin; 2013). This book, especially chapters 1-4, is an extensive, readable account of the Big Data Mindset.

5. “Epic Failure of Google Flu Trends”: https://www.wired.com/2015/10/can-learn-epic- failure-google-flu-trends/.

6. Jeremy Ginsberg, Matthew H. Mohebbi, Rajan S. Patel, Lynnette Brammer, Mark S. Smolinski, and Larry Brilliant, “Detecting Influenza Epidemics Using Search Engine Query Data,” Nature 457:1012-1015, 2009.

7. D. Lazer, R. Kennedy, G. King, and A. Vespignani, “Big Data. The Parable of Google Flu: Traps in Big Data Analysis,” Science 343:1203-1205, 2014. doi: 10.1126/sci- ence.1248506. See also Steve Lohr, “Google Flu Trends: The Limits of Big Data,” New York Times, March 28, 2014 Business, Innovation, Technology, Society.

8. https://en.wikipedia.org/wiki/Bias-variance_tradeoff.

9. D. Butler, “Web Data Predict Flu,” Nature 456:2887-2888, 2008.

10. The A. M. Turing Award is an annual prize given by the Association for Computing Machinery (ACM) to an individual selected for contributions “of lasting and major technical importance to the computer field.” The Turing Award is generally recog­nized as the highest distinction in computer science and the “Nobel Prize of com­puting”; see https://en.wikipedia.org/wiki/Turing_Award and https://amturing.acm. org/byyear.cfm.

11. Judea Pearl, “An Introduction to Causal Inference,” The International Journal

of Biostatistics 6(2), article 7, 1-59,2010.

ijb.2010.6.2.1203.xml). See also

Kenneth A. Bollen and Judea Pearl, “Eight Myths About Causality and Structural Equation Models,” in Stephen L Morgan (Ed.), Handbook of Causal Analysis for Social Research (Dordrecht, The Netherlands: Springer; 2013), pp. 301-328.

12. Diagram of simple Bayesian network and calculations, Wikipedia, courtesy of AnAj—Own work (original text: self-made), Public Domain, https://commons. wikimedia.org/w/index.php?curid=19734596.

13. On May 11, 1997, an IBM computer named Deep Blue defeated the world chess champion, Garry Kasparov; see http://www-03.ibm.com/ibm/history/ibm100/us/ en/icons/deepblue/.

14. Over the course of three nights in February 2011, an IBM computer named Watson beat human Jeopardy Champions Ken Jennings and Brad Rutter: http://www. nytimes.com/2011/02/17/science/17jeopardy-watson.html?pagewanted=all.

15. D. Lee, “IBM's Computer Argues, Pretty Convincingly, with Humans,” https://www. bbc.com/news/technology-44531132.

16. Neural networks; https://en.wikipedia.org/wiki/Neural_network. See M. Taylor and M. Koenig, The Math of Neural Networks Kindle Edition (Amazon Digital Services LLC/Blue Windmill Media; 2017) for a very basic introduction.

17. Robot Journalists of the Associated Press: https://www.theverge.com/2016/7/4/ 12092768/ap-robot-journalists-automated-insights-minor-league-baseball.

18. Deep learning: https://en.wikipedia.org/wiki/Deep_learning. See also Deep Learning Symposium Stanford University, https://www.youtube.com/watch?v= czLI3oLDe8M.

19. Yilun Wang and Michael Kosinski, “Deep Neural Networks Are More Accurate Than Humans at Detecting Sexual Orientation from Facial Images,” Journal of Personality and Social Psychology 114: 246-257, 2018

20. European Data Protection Regulation; https://www.eugdpr.org/. Especially Section 4: Right to Object and Automated Individual Decision Making; Article 21: Right to Object; Article 22: Automated Individual Decision-Making, Including Profiling.

21. See Cliff Kuangnov, “Can A. I. Be Taught to Explain Itself?” an excellent article on “ex­plainable AI” at https://www.nytimes.com/2017/11/21/magazine/can-ai-be-taught- to-explain-itself.html?hp&action=click&pgtype=Homep age&clickSource=st ory-heading&module=first-column-region&region=top-news&WT.nav=top-news.

22. Lilian Edwards and Michael Veale, “Slave to the Algorithm? Why a ‘Right to an Explanation' Is Probably Not the Remedy You Are Looking For,” Duke Law and Technology Review 16:18-84, 2018.

23. R. D. King, J. Rowland, S. G. Oliver, et al., “The Automation of Science,” Science 324:85-89,2009.

24. L. N. Soldatova and A. Rzhetsky, “Representation of Research Hypotheses,” Journal of Biomedical Semantics Suppl 2:S9, 2011.

25. Adam makes extensive use of “auxotrophic” mutant strains of yeast, meaning that each strain lacks the capacity to make a certain nutrient that is necessary for growth; see https://en.wikipedia.org/wiki/Auxotrophy.

26. Soldatova and Rzhetsky, “Representation of Research Hypotheses,” p. 1.

27. King, Rowand, et al., ibid.

28. I. Douven, “Abduction,” in Edward N. Zalta (Ed.), The Stanford Encyclopedia of Philosophy, Summer 2017 ed., https://plato.stanford.edu/archives/sum2017/entries/ abduction/.

29. King, Rowland, et al., ibid.