ABDUCTIVE HUMANISM: COMPARATIVE ADVANTAGES OF ARTIFICIAL INTELLIGENCE AND HUMAN COGNITION ACCORDING TO LOGICAL INFERENCE By WILLIAM JOSEPH LITTLEFIELD II Submitted in partial fulfillment of the requirements for the degree of Master of Arts World Literature CASE WESTERN RESERVE UNIVERSITY May, 2019 We hereby approve the thesis/dissertation of William Joseph Littlefield II candidate for the degree of Master of Arts. ​ ​ Committee Chair Florin Berindeanu Committee Member Richard Buchanan Committee Member Mark Turner Date of Defense April 3, 2019 *We also certify that written approval has been obtained for any proprietary material contained therein. 2 Table of Contents Abstract 4 1. Introduction 5 2. Logical AI 6 a. Deductive Methods b. Inductive Methods c. Comparative Advantages of AI 3. Big Data 16 a. Epistemic Superiority b. Computational Creativity 4. Abduction 19 a. Charles Sanders Peirce b. Abduction & Discovery 5. Abductive Humanism 23 a. Comparative Advantages of Human Cognition b. A Future of Abduction 6. Conclusion 31 Bibliography 33 3 Abductive Humanism: Comparative Advantages of Artificial Intelligence and Human Cognition According to Logical Inference Abstract By WILLIAM JOSEPH LITTLEFIELD II Speculation about artificial intelligence and big data has become commonplace. Foremost among these discussions is the potential for these technologies to displace the value of human labor. However, these discussions have become untethered from the history and development of the technologies in question. This paper analyzes paradigms of artificial intelligence to reveal that they are closely tied to different types of logical inference. The properties of computing machinery provide them with an advantage at deductive and inductive tasks when compared with human cognition. But, the understudied method of Peircean abduction poses several problems for computing machinery. Moreover, Peircean abduction, also called “creative abduction,” bears resemblance to important themes in the history of humanism. Human cognition handles the issues of Peircean abduction with remarkable ease, suggesting that humans will maintain a comparative advantage at this type of logical inference for the foreseeable future. 4 “Imagination is more robust in proportion as reasoning power is weak.”1 ​ - Giambattista Vico, The New Science ​ 1. Introduction Two great chimeras of science fiction are proliferating: one represents an existential crisis, the other, epistemological. I speak, of course, of artificial intelligence and big data, and like most crises, their horror is in the questions that they pose. What is the value of human labor once we have outsourced reason? What is the value of knowledge when we have outsourced memory? Machine learning algorithms can be trained to see what we do not; brute force computation can outrun us. Our favorite applications now know us better than our friends or family do.2 Still, there are particular tasks where human cognition will possess a comparative advantage for the foreseeable future — this paper will argue that such tasks are centered around the human capacity for abductive reasoning. Meanwhile, as these two technologies continue to develop, their comparative 1 Vico, Giambattista, et al. The New Science of Giambattista Vico: Revised Translation of the ​ ​ Third Edition (1744). Cornell University Press, 1968. ​ 2 Youyou, Wu, et al. “Computer-Based Personality Judgments Are More Accurate than Those ​ Made by Humans.” Proceedings of the National Academy of Sciences, vol. 112, no. 4, 2015, pp. ​ ​ 1036–1040., doi:10.1073/pnas.1418680112. 5 advantage at inductive and deductive reasoning will only improve.3 Therefore, I advocate for a new humanism, simply referred to as abductive humanism. ​ To this end, this paper begins by reviewing some paradigms in the history of artificial intelligence and how they are representative of the aforementioned types of logical inference. The speed, inerrability, and indefatigability of computing machines has rendered them as superior tools for such inference. Additional consideration is given to the epistemic advantages which big data provides and the consequences of those advantages for computational creativity. Ultimately, though, a revisiting of the foundations of humanism is combined with outstanding problems in logical artificial intelligence (logical AI), problems which are well handled by abductive reasoning, to conclude as Selmer Bringsjord quipped, that, “computation, among other things, is beneath us.”4 2. Logical AI 3 A disclaimer regarding methodology: there are some who may object to the idea of broadly periodizing the history of artificial intelligence according to the underlying logic employed. Furthermore, scholars of logical AI have been grappling with the problems of abduction, defeasible reasoning, and nonmonotonic logic since the emergence of the field. Suggesting that the technologies of the past, or of today, are strictly deductive, inductive, or abductive in their approach could be denounced as an oversimplification. The author readily concedes both of those points. Instead, this paper considers which approaches have generally materialized in ​ ​ artificial intelligence empirically, and which have generally eluded success, to describe ​ ​ macroscale trends in the history and philosophy of technology. Legitimacy of method lies in the fact that this paper intends to respond to scholarly and popular thought of a similar ilk. 4 John McCarthy provides the following definition of logical AI: “logical AI involves representing knowledge of an agent's world, its goals and the current situation by sentences in logic.” McCarthy, John. “Concept of Logical AI.” Logic-Based Artificial Intelligence, by Jack Minker, ​ ​ Kluwer Academic Publishers, 2000, pp. 37–56. This witticism by Bringsjord dates from a 1994 refutation of Searle’s arguments against cognition as computation. Bringsjord, Selmer. ​ “Computation, among Other Things, Is beneath Us.” Minds and Machines, vol. 4, no. 4, 1994, pp. ​ ​ 469–488., doi:10.1007/bf00974171. 6 Much of the speculation and hysteria regarding AI arises from generalizations about its seemingly infinite applications. Once more, predictions of a leisure society seem reasonable, if not inevitable.5 However, by contextualizing this conversation within the history of logical AI, the limitations of these technologies become much clearer.6 What is more, the history of AI, in practice, maps to different strategies of logical inference. As will be seen, abductive reasoning is mostly aspirational within logical AI, which is a telling contrast to the achievements in inductive and deductive reasoning. Deductive Methods Deductive and inductive reasoning both comprise large fields of study within philosophy. Despite the many historical interpretations of induction and deduction, it can be safely said that induction is a kind of “bottom-up” reasoning while deduction is “top-down.” The most reprinted illustration of deduction is likely the following syllogism: All men are mortal. Socrates is a man. 5 See the work of Yuval Noah Harari, whose arguments about the future of artificial intelligence are among those which this paper intends to refute. Harari, Yuval Noah. “The Meaning of Life in a World without Work.” The Guardian, Guardian News and Media, 8 May 2017, ​ ​ www.theguardian.com/technology/2017/may/08/virtual-reality-religion-robots-sapiens-book. For a ​ book length exposition, see Homo Deus: A Brief History of Tomorrow. Harari, Yuval N. Homo ​ ​ ​ Deus: A Brief History of Tomorrow. Harper Perennial, 2018. ​ 6 Something very similar occurred with early academic speculation about “cyberspace.” After the word was coined by William Gibson in the 1984 novel, Neuromancer, a flurry of papers, ​ ​ conferences, and books emerged that imagined a digital future which was increasingly untethered from reality. Those working more closely with the technology and policy of the era, such as John Perry Barlow, offered much more accurate prognostications. 7 Therefore, Socrates is mortal. Bertrand Russell provides a memorable example of induction in the chicken that is fed daily by a farmer, inferring that the following day, they will be fed once more (only to have their neck wrung).7 As can be seen, deduction operates by inspecting whether a particular case falls within the domain of an established rule. Some of the most visible examples of early AI were chess-playing programs, often called “chess engines.” The approach first employed in the development of chess engines was to survey chess grandmasters about how to play chess, aggregate their answers, and create a set of logical rules that corresponded to their strategies. During gameplay, if a situation fell under an established rule, then the program would execute a particular strategy in accordance with that rule.8 This is very typical of the dominant paradigm in early AI. In fact, such programs are often referred to as “GOFAI,” or Good Old-Fashioned Artificial Intelligence, a name coined by the philosopher John Haugeland (1945-2010).9 But, in academic terms, this technology is referred to as “symbolic AI.” Deep Blue, the chess engine developed by IBM, and which first defeated world champion Gary Kasparov in 1996, relied primarily upon symbolic AI.10 7 Russell, Bertrand, and John Skorupski. The Problems of Philosophy. OUP Oxford, 2014. Here, ​ ​ Russell is of course explaining the infamous “problem of induction.” 8 Such programs rely upon a system of conditional if-then statements, a basic fundamental of computer science.