The 42 chapters of this richly illustrated book describe Turing’s life and contributions from different viewpoints and at different abstraction levels. These essays, written for a (more or less) general audience, frequently show that Turing’s “thought of great originality” was due to his “often going back to first principles for his inspiration” (Peter Hilton). The book consists of eight parts: “Biography” (4 chapters), “The Universal Machine and Beyond” (4 chapters), “Codebreaker” (11 chapters), “Computers After the War” (5 chapters), “Artificial Intelligence and the Mind” (8 chapters), “Biological Growth” (3 chapters), “Mathematics” (5 chapters), and “Finale” (2 chapters).

Sir John Dermot Turing in his very interesting biographical chapter tells us that Alan Turing attended boarding schools from the age of 9 because his father served the Empire in India and his mother, “as a good memsahib, was expected to be with him to run his household,” and that Alan Turing “was able to flourish in environments that ignored his refusal to comply with social norms.” At the age of 23, in 1936, in his classic paper [1], Turing proposed the concept of a stored program computer that was implemented for the first time in the Manchester Baby computer (for which Turing wrote the world’s first programming manual) in 1948. Jack Copeland emphasizes the novel and “daring” approach of Turing’s universal machine that brought machinery into discussions of foundations of mathematics and quotes Max Newman: “It is difficult today to realize how bold an innovation it was to introduce talk about paper tapes and patterns punched in them, into discussions of the foundations of mathematics.” Copeland further notes with regret that “today’s computer science textbooks usually present the universal Turing machine as a purely mathematical entity ... [so that] Turing’s bold innovation has been purified and rendered into conventional coin of mathematics.” Manin’s outstanding text [2] is an important exception: “Whereas before Alan Turing, the most common mental image of mathematical reasoning was related to some form of (written) language, Turing represented computation as the dynamical evolution of an idealized physical system.” Even closer to our programming home, in 1949 Turing presented the concepts of reasoning about a program, program proofs, and assertions: “In order that the man who checks may not have too difficult a task, the programmer should make a number of definite assertions which can be checked individually, and from which the correctness of the whole programme easily follows” ([3], explored in detail in [4] where the 1947 paper by Goldstine and von Neumann about assertions [5] is also discussed). On a related note, Turing with his assistants prepared a large library of routines for a not-yet-existent computer (the term “debugging” was not used by programmers at that time). And in 1951 Turing suggested that Strachey write a “trace” program, so that the machine simulated itself (a debugging tool that was, perhaps independently, used later by various programmers). However, not everything went smoothly from theory to practice: it may be instructive to compare Turing’s papers on a universal machine with Howard Aiken’s statement in 1956 (not included in the book under review!) that “if it should ever turn out that the basic logics of a machine designed for the numerical solution of differential equations coincide with the logics of a machine intended to make bills for a department store, I would regard this as the most amazing coincidence that I have ever encountered.” In this context, a quote from Turing’s 1946 memo (presented by Martin Campbell-Kelly) referring to the “American tradition of solving one’s difficulties by means of much equipment rather than by thought” may be in order.

Some of the 11 chapters on Turing’s codebreaking work at Bletchley Park are reminiscences by his coworkers, and it is worthwhile to observe that already in 1914, Churchill set up a cryptographic bureau staffed with “scholars with minds of their own – the so-called ‘professor types.’ It was an excellent decision” (Mavis Batey, a codebreaker at Bletchley Park). The breaking of the codes of German cipher machines was declassified decades after WWII, and for Tunny, used to encipher the highest-level German army communications, the declassification was done only in 2000. Several chapters by Jack Copeland include both details and conceptual explanations on this decrypting, including a description of Colossus, the first electronic (but not a stored-program) computer developed in 1943, classified for decades and mostly dismantled after the war. Alan Turing, Bill Tutte, and Tommy Flowers were referred to by Jerry Roberts as the three heroes of Bletchley Park without whom “Europe would be a very different place today.” Roberts notes that the tonnages lost by ship sinkings dropped by 77 percent after Turing broke the German Enigma code in 1941, and “if Turing had not managed that, it is almost certain that Britain would have been starved into defeat.” In this context, observe that Turing’s decoding machines built from his design during the WWII effort “worked correctly as soon as they were made” [6].

The eight artificial intelligence-related chapters treat, among others, the Turing test, various Turing-style tests, and the “first moments” of computer chess. Diane Proudfoot discusses whether Turing was a behaviorist and if not, “what was his concept of intelligence,” quoting from Turing’s 1948 and 1952 papers and asking, “does the entity appear intelligent?” Mark Sprevak in his chapter “Turing’s model of the mind” states that “the idea that the human mind is (in some sense) a computer is central to cognitive science. Turing played a key role in developing this idea,” and observes later that in some situations, when humans deliberately arrange their mental processes to work in a rule-governed way, “it seems that our psychological mechanisms approximate those of a Turing machine.” In many (most?) situations, however, I think that the reductionist approach is clearly inadequate, and another excellent book [7], for example, comes to mind.

The part on biological growth includes an interesting chapter by Margaret Boden on Turing’s mathematical theory of embryology and Turing-inspired models of biological self-organization, referring in particular to Waddington’s 1940 work (cited by Turing) on the role of environment in development activities of genes (Waddington is one of the authors of [7]). Boden observes that Turing’s mathematical biology and his ideas on morphogenesis were not influential for decades because “the emphasis of ... molecular biology was more on the actual identity of the molecules (morphogens) than on their actions and interactions. ... Turing’s ideas were out of line with the biological orthodoxy” (perhaps in the same manner as the role of relationships and their semantics has been underestimated in the modeling of business and IT systems). Turing’s theory of morphogenesis as emergence of biological patterns is described in the chapter by Thomas Woolley, Ruth Baker, and Philip Maini where the authors note that “the levels of abstraction and detail in [Turing’s] model were absolutely appropriate at the time he formulated it. [...] The modern ... extensions of his 1952 model ... strongly support his key claim that a complete and physically realistic model of a biological system is not necessary in order to explain specific key phenomena relating to growth.” These observations are in excellent agreement with F. A. Hayek’s chapter, “The Primacy of the Abstract” in [7], that clearly distinguishes between abstract patterns and details.

I would certainly recommend this thought-provoking book.

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