It Began with Babbage (37 page)

Shannon described a simple version of what would later be called a
minmax strategy
in game play.
⁶³
He did not actually write a program to play chess; rather, he explored possible strategies that would lead to the development of a practical, “virtual” chess-playing machine to play what, in chess is called, the middle game.
⁶⁴

It is worth noting that in deciding on a move to make using some kind of minmax strategy, Shannon was advocating the construction of a plan that would consider alternative moves, anticipate the opponent's move in response, up to a certain “depth” of look-ahead, and then decide on the actual move to play. This was precisely what Craik had envisioned in his discussion on the nature of thought in
The Nature of Explanation
(1943) (see Section II, this chapter).

Shannon's 1950 article was (probably) the first publication on the possibility of a chess-playing program. In addition, it is fair to say that the article marked the
beginning of a distinct branch of the emerging computer science later called
artificial intelligence
.

But why should a computer capable of playing chess of the same level of skill as a good human chess player be deemed “intelligent” (in the ordinary human sense) in contrast to a computer capable of solving differential equations of the sort Stanley Gill explored for his PhD dissertation (see
Chapter 10
, Section IV)? After all, the rules of chess are quite simple and most people can learn to play chess, at least at a basic level, whereas one has to have considerable mathematical acumen to learn to solve differential equations. In what
significant
sense is the problem of playing chess (or other similar board games, such as checkers) superior to the problem of solving differential equations?

Shannon addressed this issue, albeit briefly. For the kinds of problems he identified at the beginning of his 1950 article—machines that could design, translate, make decisions, perform logical deductions, and so on—the procedures performed entailed making judgments, trying out something to see if it works, and trying something else if it does not. The solutions of such problems were never just right or wrong, but rather spanned a spectrum of possibilities, from the very best to the very worst and several shades in between. Thus, a solution might be one that is acceptably good rather than the very best.
⁶⁵

These are significant insights, anticipating much that will follow in this story. What strikes us most immediately is that problems such as chess entail ingredients of ordinary thought that humans negotiate on an everyday basis—with all the uncertainties, propensity for error, limited rationality, and subjectivity attendant on such thinking. These were the challenges Shannon broached on—and that Alan Turing would boldly confront.

Perhaps it was no coincidence that among the possible things Shannon believed the computer could be programmed to do was create a machine that could translate between languages.
⁶⁶
Automatic translation was much in the mind of Shannon's coauthor on information theory, Warren Weaver. In 1949, Weaver wrote a memorandum simply titled
Translation
, in which, referring to himself somewhat archly in the third person, he remembered how his wartime experience with computing machines had led him to think about automatic translation.
⁶⁷

Even more than Shannon's deliberations on a chess-playing machine, Weaver's memorandum reveals the optimism, bordering on brashness, that attended the thinking of early scientists concerned with the application of the digital computer. In the realm of translation, we have previously witnessed Herman Goldstine and von Neumann grapple with the problem of coding (programming) as an act of translation—mapping from a mathematical culture to machine culture—and producing a computational text from mathematical text (see
Chapter 9
, Section III). We have witnessed David Wheeler invent an artificial symbolic language (“assembly language”) to write programs in and a means
(an “assembler”) to translate such programs to machine-executable form (“machine language”; see
Chapter 9
, Section VI).

What Weaver was contemplating was of a different qualitative order altogether—translating text from one
natural
language to another using the computer. As it turned out, Weaver's memorandum of 1949 marked the beginning of a discipline straddling linguistics and computing called
machine translation
.
⁶⁸

We have also previously noted that translating literary text is a creative process involving not only the conversion of words in one language to another, but a mapping of one linguistic culture to another (see
Chapter 9
, Section III), wherein the translation enacts a complicated interweaving of understanding and interpretation.
⁶⁹
The prospect of
machine
translation, in the literary sense of translation, thus seems still more formidable.

But Weaver was not alone in this contemplation. Across the Atlantic, at Birbeck College, London University, Andrew D. Booth (1918–2009), a physicist working in Desmond Bernal's famed laboratory—and like many scientists of the time, drawn into computers and computing through his particular brand of research—was also dwelling on the possibility of machine translation.
⁷⁰
However, Booth (who may have influenced Weaver, who visited the Englishman in 1948
⁷¹
) was, at the time, concerned with mechanizing a dictionary.
⁷²
Weaver had more vaulting ambitions, in which such language issues as polysemy (that is, the phenomenon of multiple meanings of words) and word order would enter the frame.

Weaver was neither a linguist nor a literary translator. As a mathematician-turned-science policy shaper (employed by the Rockefeller Foundation), as Shannon's collaborator on a mathematical theory of communication, Weaver was drawn to cryptography as a source of analogical insight to the problem of machine translation. Airily and rather extraordinarily, he confessed to being “tempted” to propose that a book written in Chinese is nothing but a book written in English but using Chinese code.
⁷³
For Weaver, the act of translation became a problem of deciphering—a position that would surely make translators, translation theorists, and literary scholars wince. Possible methods of cryptology would become, he said, when properly interpreted, “useful methods of translation.”
⁷⁴

These “useful methods of translation” used in cryptography could have an interesting attribute. Deciphering a message was a process that made use of “frequencies of letters, letter combinations, interval between letters and letter combinations, letter patterns, etc.”
⁷⁵
So translation, from this point of view, was a process that entailed finding certain statistical regularities in texts. But these regularities, Weaver continued, were—broadly speaking—language independent.
⁷⁶
This meant, according to him, that among all the languages invented by humankind, there were certain properties that were statistically invariant across the languages.
⁷⁷

Languages, in other words (as Weaver saw them), had, deep down, certain
invariant
characteristics. This suggested to Weaver a way of tackling the problem of machine translation. Rather than translate directly from one natural language to another—say,
from Russian to Portuguese—perhaps the proper strategy is to translate “down” from the source language to the shared base language, to an as-yet-undiscovered “universal language,” and then translate “up” from the latter to the target language.
⁷⁸

Weaver's conception of a “universal language” is striking because it seems to anticipate, in a general sort of way, the idea of “linguistic universals”—linguistic features common to (most) languages, such as the presence of the categories nouns and verbs, patterns of word order—which would be discussed by linguists during the 1960s.
⁷⁹
At any rate, it is at the level of a universal language that Weaver believed that human communication takes place, and it would be at this level that the process of machine translation should begin. How this would happen he did not elaborate. He recognized that much work on the logical structure of language would have to be done before automatic translation could be tackled effectively. However, in any case, regardless of whether the approach he was advocating led to success in machine translation, it would surely produce “much useful knowledge about the general nature of communication.”
⁸⁰

Machine translation has to do with natural language, the most human of human characteristics, one that separates humans from nonhumans. Yet, strangely enough, machine translation never quite penetrated into the innards of artificial intelligence in the way computer chess would; rather, it became an enterprise and a research tradition of its own. But, like the computer chess project, the machine translation enterprise as imagined by Weaver turned out to be a far more difficult problem than early machine translation researchers had anticipated. In any case, within a decade of the Weaver memorandum, the study of the structure of language itself would be turned topsy-turvy by a young linguist named Noam Chomsky.

Shannon was by no means the only person at the time who was thinking about thinking machines. Indeed, soon after the ENIAC seeped into public consciousness, the term
electronic brain
began to appear in the popular press. No less a personage than Lord Louis Mountbatten (1900–1979) used the term to describe the ENIAC in a talk he delivered to the (British) Institute of Radio Engineers (IRE) in 1946.
⁸¹
As Sir Maurice Wilkes recollected in 1985, this reference to computers as electronic brains excited much debate in the British press.
⁸²
An American participant in the early development of the commercial computer, Edmund C. Berkeley (1909–1988), a founding member of the ACM in 1947 (see
Chapter 8
, Section XVI) and its first secretary, published in 1949 a book called
Giant Brains, or Machines That Think
.
⁸³
So the climate was already in place for serious discussions of thinking machines in the later 1940s.

Indeed, well before Shannon's manuscript on computer chess was submitted for publication, Turing had dwelt on the topic. Like Shannon, he was much interested in the uses of the digital computer. As early as 1945, in his definitive report on the ACE being
developed at the NPL in Teddington (see
Chapter 8
, Section IX), Turing asked whether a machine could play chess.
⁸⁴
Three years later, he submitted a report to the NPL, then still his employer, titled
Intelligent Machinery
.
⁸⁵

However, Turing's thoughts on thinking machines came into public notice most famously—insofar as an article in one of England's most august philosophical journals could be said to excite “public” notice—with an article titled “Computing Machinery and Intelligence” in the October 1950 issue of
Mind
.
⁸⁶

Turing began with the question: Can machines think? But, wishing to avoid the pitfalls of defining such terms as
machine
and
think
, he proposed a thought experiment that he called the “imitation game.” Imagine, first, he wrote, a man (
M
), a woman (
W
), and an interrogator (
I
) who may be of either sex.
I
is placed in a room separate from the room occupied by
M
and
W
. The purpose of the game is for
I
to put questions to
M
and
W
(without knowing which one of them is the man and which is the woman) in such a fashion that
I
can determine, from the answers, which is the man and which is the woman. The answers, of course, from
M
and
W
must be given in written or typewritten form to mask the sex of the answerer.

Turing then suggested replacing either
M
or
W
by a computer C. In this situation,
I
offers questions to either a human
H
or a computer
C
, and the task for
I
is to ascertain from the answers which is a human and which is the machine.

The original question, “Can machines think?” could now be reformulated as: Are there imaginable digital computers that would do well in the imitation game?
⁸⁷
The former question Turing dismissed as too meaningless. As for the latter, he predicted that, within 50 years, computers with an adequate memory capacity (implying, it would seem, that this was the crucial factor) would be able to play the imitation game successfully and pass his test criterion.
⁸⁸
Indeed, he further predicted that, by the end of the 20th century, the idea of thinking machines would be deemed commonplace.
⁸⁹

We are reminded here of Austrian–British philosopher of science Sir Karl Popper (1902–1994) famously insisting that science progresses through a succession of “bold conjectures” followed by attempted refutations of the conjectures.
⁹⁰
Turing defended his prediction precisely as such a bold conjecture, arguing that conjectures are so often the means for pursuing promising paths of research.
⁹¹

Turing's imitation game, in which the machine's responses to the interrogator's questions might fool the latter into thinking that the machine is the human, has come to be called the
Turing test
. Any machine that can fool the interrogator at least 30% of the time would, in Turing's view, be deemed an intelligent or thinking machine. The essence of the game was, of course, that the interrogator could ask
any
question whatsoever, spanning the whole range of human experience.