Prologue
An early list of examples of machine learning’s impact on daily life can be found in “Behind-the-scenes data mining,” by George John (SIGKDD Explorations, 1999), which was also the inspiration for the “day-in-the-life” paragraphs of the prologue. Eric Siegel’s book Predictive Analytics (Wiley, 2013) surveys a large number of machine-learning applications. The term big data was popularized by the McKinsey Global Institute’s 2011 report Big Data: The Next Frontier for Innovation, Competition, and Productivity. Many of the issues raised by big data are discussed in Big Data: A Revolution That Will Change How We Live, Work, and Think, by Viktor Mayer-Schönberger and Kenneth Cukier (Houghton Mifflin Harcourt, 2013). The textbook I learned AI from is Artificial Intelligence,* by Elaine Rich (McGraw-Hill, 1983). A current one is Artificial Intelligence: A Modern Approach, by Stuart Russell and Peter Norvig (3rd ed., Prentice Hall, 2010). Nils Nilsson’s The Quest for Artificial Intelligence (Cambridge University Press, 2010) tells the story of AI from its earliest days.
Chapter One
Nine Algorithms That Changed the Future, by John MacCormick (Princeton University Press, 2012), describes some of the most important algorithms in computer science, with a chapter on machine learning. Algorithms,* by Sanjoy Dasgupta, Christos Papadimitriou, and Umesh Vazirani (McGraw-Hill, 2008), is a concise introductory textbook on the subject. The Pattern on the Stone, by Danny Hillis (Basic Books, 1998), explains how computers work. Walter Isaacson recounts the lively history of computer science in The Innovators (Simon & Schuster, 2014).
“Spreadsheet data manipulation using examples,”* by Sumit Gulwani, William Harris, and Rishabh Singh (Communications of the ACM, 2012), is an example of how computers can program themselves by observing users. Competing on Analytics, by Tom Davenport and Jeanne Harris (HBS Press, 2007), is an introduction to the use of predictive analytics in business. In the Plex, by Steven Levy (Simon & Schuster, 2011), describes at a high level how Google’s technology works. Carl Shapiro and Hal Varian explain the network effect in Information Rules (HBS Press, 1999). Chris Anderson does the same for the long-tail phenomenon in The Long Tail (Hyperion, 2006).
The transformation of science by data-intensive computing is surveyed in The Fourth Paradigm, edited by Tony Hey, Stewart Tansley, and Kristin Tolle (Microsoft Research, 2009). “Machine science,” by James Evans and Andrey Rzhetsky (Science, 2010), discusses some of the different ways computers can make scientific discoveries. Scientific Discovery: Computational Explorations of the Creative Processes,* by Pat Langley et al. (MIT Press, 1987), describes a series of approaches to automating the discovery of scientific laws. The SKICAT project is described in “From digitized images to online catalogs,” by Usama Fayyad, George Djorgovski, and Nicholas Weir (AI Magazine, 1996). “Machine learning in drug discovery and development,”* by Niki Wale (Drug Development Research, 2001), gives an overview of just that. Adam, the robot scientist, is described in “The automation of science,” by Ross King et al. (Science, 2009).
Sasha Issenberg’s The Victory Lab (Broadway Books, 2012) dissects the use of data analysis in politics. “How President Obama’s campaign used big data to rally individual votes,” by the same author (MIT Technology Review, 2013), tells the story of its greatest success to date. Nate Silver’s The Signal and the Noise (Penguin Press, 2012) has a chapter on his poll aggregation method.
Robot warfare is the theme of P. W. Singer’s Wired for War (Penguin, 2009). Cyber War, by Richard Clarke and Robert Knake (Ecco, 2012), sounds the alarm on cyberwar. My work on combining machine learning with game theory to defeat adversaries, which started as a class project, is described in “Adversarial classification,”* by Nilesh Dalvi et al. (Proceedings of the Tenth International Conference on Knowledge Discovery and Data Mining, 2004). Predictive Policing, by Walter Perry et al. (Rand, 2013), is a guide to the use of analytics in police work.
Chapter Two
The ferret brain rewiring experiments are described in “Visual behaviour mediated by retinal projections directed to the auditory pathway,” by Laurie von Melchner, Sarah Pallas, and Mriganka Sur (Nature, 2000). Ben Underwood’s story is told in “Seeing with sound,” by Joanna Moorhead (Guardian, 2007), and at www.benunderwood.com. Otto Creutzfeldt makes the case that the cortex is one algorithm in “Generality of the functional structure of the neocortex” (Naturwissenschaften, 1977), as does Vernon Mountcastle in “An organizing principle for cerebral function: The unit model and the distributed system,” in The Mindful Brain, edited by Gerald Edelman and Vernon Mountcastle (MIT Press, 1978). Gary Marcus, Adam Marblestone, and Tom Dean make the case against in “The atoms of neural computation” (Science, 2014).
“The unreasonable effectiveness of data,” by Alon Halevy, Peter Norvig, and Fernando Pereira (IEEE Intelligent Systems, 2009), argues for machine learning as the new discovery paradigm. Benoît Mandelbrot explores the fractal geometry of nature in the eponymous book* (Freeman, 1982). James Gleick’s Chaos (Viking, 1987) discusses and depicts the Mandelbrot set. The Langlands program, a research effort that seeks to unify different subfields of mathematics, is described in Love and Math, by Edward Frenkel (Basic Books, 2014). The Golden Ticket, by Lance Fortnow (Princeton University Press, 2013), is an introduction to NP-completeness and the P = NP problem. The Annotated Turing,* by Charles Petzold (Wiley, 2008), explains Turing machines by revisiting Turing’s original paper on them.
The Cyc project is described in “Cyc: Toward programs with common sense,”* by Douglas Lenat et al. (Communications of the ACM, 1990). Peter Norvig discusses Noam Chomsky’s criticisms of statistical learning in “On Chomsky and the two cultures of statistical learning” (http://norvig.com/chomsky.html). Jerry Fodor’s The Modularity of Mind (MIT Press, 1983) summarizes his views on how the mind works. “What big data will never explain,” by Leon Wieseltier (New Republic, 2013), and “Pundits, stop sounding ignorant about data,” by Andrew McAfee (Harvard Business Review, 2013), give a flavor of the controversy surrounding what big data can and can’t do. Daniel Kahneman explains why algorithms often beat intuitions in Chapter 21 of Thinking, Fast and Slow (Farrar, Straus and Giroux, 2011). David Patterson makes the case for the role of computing and data in the fight against cancer in “Computer scientists may have what it takes to help cure cancer” (New York Times, 2011).