This paradigm prevailed until 1988, year zero for modern machine translation, when a team of IBM’s speech-­recognition researchers presented a new approach. What these computer scientists proposed was that Warren Weaver’s insight about cryptography was essentially correct — but that the computers of the time weren’t nearly powerful enough to do the job. “Our approach,” they wrote, “eschews the use of an intermediate mechanism (language) that would encode the ‘meaning’ of the source text.” All you had to do was load reams of parallel text through a machine and compute the statistical likelihood of ­matches across languages. If you train a computer on enough material, it will come to understand that 99.9 percent of the time, “the butterfly” in an English text corresponds to “le papillon” in a parallel French one. One researcher quipped that his system performed incrementally better each time he fired a linguist. Human collaborators, preoccupied with shades of “meaning,” could henceforth be edited out entirely.

Though some researchers still endeavor to train their computers to translate Dante with panache, the brute-force method seems likely to remain ascendant. This statistical strategy, which supports Google Translate and Skype Translator and any other contemporary system, has undergone nearly three decades of steady refinement. The problems of semantic ambiguity have been lessened — by paying pretty much no attention whatsoever to semantics. The English word “bank,” to use one frequent example, can mean either “financial institution” or “side of a river,” but these are two distinct words in French. When should it be translated as “banque,” when as “rive”? A probabilistic model will have the computer examine a few of the other words nearby. If your sentence elsewhere contains the words “money” or “robbery,” the proper translation is probably “banque.” (This doesn’t work in every instance, of course — a machine might still have a hard time with the relatively simple sentence “A Parisian has to have a lot of money to live on the Left Bank.”) Furthermore, if you have a good probabilistic model of what standard sentences in a language do and don’t look like, you know that the French equivalent of “The box is in the ink-­filled writing implement” is encountered approximately never.

Contemporary emphasis is thus not on finding better ways to reflect the wealth or intricacy of the source language but on using language models to smooth over garbled output. A good metaphor for the act of translation is akin to the attempt to answer the question “What player in basketball corresponds to the quarterback?” Current researchers believe that you don’t really need to know much about football to answer this question; you just need to make sure that the people who have been drafted to play basketball understand the game’s rules. In other words, knowledge of any given source language — and the universal cultural encyclopedia casually encoded within it — is growing ever more irrelevant.

Many computational linguists continue to claim that, after all, they are interested only in “the gist” and that their duty is to find inexpensive and fast ways of trucking the gist across languages. But they have effectively arrogated to themselves the power to draw a bright line where “the gist” ends and “style” begins. Human translators think it’s not so simple. The machinist’s attitude is that when someone’s mother is trapped under a house, it’s fussy and self-­important to worry too much about nuance. They see the redundancy and allusiveness of natural languages as a matter not of intricacy but of confusion and inefficiency. Most valuable utterances revert to the mean of statistical probability. If this makes them unpopular with poets and fanciers of language, so be it. “Go to the American Translators Association convention,” one marathon attendee told me, “and you’ll see — they hate us.”

This is to some extent true. As the translator Susan Bernofsky put it to me, “They create the impression that translation is not an art.” (A widely admired literary translator, who wished to remain anonymous, admitted that although she worries about machine translation’s mission creep, she thinks Google Translate is a wonderful tool for writing notes to the woman who cleans her house.)

What mostly annoys human translators isn’t the arrogance of machines but their appropriation of the work of forgotten or anonymous humans. Machine translation necessarily supervenes on previous human effort; otherwise there wouldn’t be the parallel corpora that the machines need to do their work. I mentioned to an Israeli graduate student that I had been reading the Wikipedia page of Yehoshua Bar-­Hillel and had found out that his granddaughter, Gili, is a minor celebrity in Israel as the translator of the “Harry Potter” books. He hadn’t heard of her and didn’t seem interested in the process by which a publisher paid to import books about magic for children. But we would have no such tools as Google Translate for the Hebrew-­English language pair if Bar-­Hillel had not hand-­translated, with care, more than 4,000 pages of an extremely useful parallel corpus. In a sense, their machines aren’t actually translating; they’re just speeding along tracks set down by others. This is the original sin of machine translation: The field would be nowhere without the human translators they seek, however modestly, to supersede.

Perhaps to paper over the associated guilt, the group in Urbana-Champaign cultivated a minor resentment toward their human counterparts. More than once I heard someone at the marathon refer to the fact that human translators are finicky and inconsistent and prone to complaint. Quality control is impossible. As one attendee explained to me, “If you show a translator an unidentified version of his own translation of a text from a year ago, he’ll look it over and tell you it’s terrible.”