Far from a restrictive act of copying, a translator restores the meaning of a text by means of an elaborate process that requires imagination, ingenuity, and freedom.
—Jhumpa Lahiri, “In Praise of Echo” [T]rust is a hard commodity to build, in any interpersonal communication, and all too easy to ruin. No one likes taking another person’s word, and yet in translation, that is literally what the reader is asked to do.
—Mark Polizzotti, Sympathy for the Traitor
The project of machine translation was already in its fifth decade when the search engine AltaVista introduced Babel Fish, at the end of 1997. Named after the “leech-like” creature that functions as a universal translator in Douglas Adams’s The Hitchhiker’s Guide to the Galaxy (1979), it broke new ground by offering translation for free online. Previously, machine translation (MT) was something for which you had to pay and wait, since humans generally intervened to tidy up what the machine produced. AltaVista promised instant results—no human lag required—whether you wished to have an entire Webpage translated (without altering the graphics) or input an inscrutable chunk of foreign text. Here, in other words, was real-time translation of and on the Web. Digital Equipment Corporation, AltaVista’s parent company, declared that Babel Fish had “broken the Internet language barrier.”11xQuoted in Victoria Shannon, “The End User: The Power of Babel—Technology,” New York Times, May 3, 2006; https://www.nytimes.com/2006/05/03/technology/03iht-ptend04.1654790.html.
“Barely breached” would be more accurate. Initially, Babel Fish could translate English text only into German, French, Spanish, Italian, and Portuguese, and vice versa (but not, say, French to Spanish), though its capabilities would expand in the ensuing years. Even within that circumscribed domain, Babel Fish often stumbled, especially with names, technical terms, and idiomatic expressions. Its hold on grammar, too, was shaky. First-time users raved, but professional translators balked. Mischief-makers played a game—called “round-tripping”—in which a translated text is rendered back into its original tongue to see what distortions arise. In this regard, Babel Fish was very obliging.
Shortly after the service’s debut, Umberto Eco, the novelist, semiotician, and satirist, spotted the invitation on AltaVista’s homepage and took the bait, asking Babel Fish to translate English sites into his native Italian. His “first shock,” he reported a few months later in his “La Bustina di Minerva” column in the Italian newsmagazine L’Espresso, came when he saw a page titled “Gli impianti di Shakespeare”—or “Shakespeare’s Plants.”22xUmberto Eco, “La vera storia dei pali del Papa,” L’Espresso, January 15, 1998. The rules-based system that AltaVista employed (created by the Parisian company Systran) had latched on to the wrong sense of the English “works.” The Italian translation should have been opere, the “works” of an artist; instead, it selected impianti, the “works” of an industrialist. Eco noticed other errors: An author was credited with having many ventilatori (the whirring kind of “fans”); a publisher was referred to as Harcourt “Support” (i.e., “Brace”); the Polish people (i polacchi) were reduced to poles (i pali). In a subsequent column, Eco reported on round-tripping, using the opening lines of Dante’s Inferno as a test case. Round-trip Dante, he explained, and you get proof that the machine poses no threat to “il divin poeta.” Send Dante’s lines through several more permutations, and you get modern poetry.33xUmberto Eco, “Trionfante ritorno a Babele. Como giocare seriamente con Altavista,” L’Espresso, February 19, 1998. I am grateful to the good people at L’Espresso for responding promptly to my request for a scan of this column.
Eco kept playing with Babel Fish, and in Mouse or Rat?, his 2003 book on “translation as negotiation,” he again brought up its shortcomings to illustrate the qualities of effective translation. To begin with, Eco pointed out, translation does not consist of mechanical synonym-swapping. If that were the case, Babel Fish’s execution would have been flawless. The words we rely on most have multiple senses, and to determine the specific one invoked on any given occasion, the translator must decipher contextual clues. This Babel Fish could not do—even when given the larger window of multiple sentences. Inputting the opening verses of the Authorized Version’s Genesis, Eco was amused to find the English “spirit of God” transformed into the Spanish “el alcohol de dios.”44xUmberto Eco, Mouse or Rat? Translation as Negotiation (London, England: Phoenix Paperback 2004), 14. Moving between languages, furthermore, the translator needs to honor the grammatical and syntactic mores of both. No competent English user would say “divide waters of waters” (as Babel Fish rendered “aguas de aguas”) or begin a sentence, “In the God who began created heaven…” (as Babel Fish did in multiple languages). Even contextual and grammatical wherewithal could get the translator only so far, however. To decide whether “works” ought to be opere or impianti, the translator needed to know a simple fact that Babel Fish lacked—“that Shakespeare was a poet and a playwright and not an industrial tycoon.”55xIbid., 13.
The machine’s miscues clarified that translation depends on more than a large vocabulary and grammatical proficiency in two tongues. The translator must also possess extensive “world knowledge.”66xIbid., 18. Only one so equipped can undertake the multiple negotiations—with languages, with the author, with the imagined reader, with a very real publisher—that translation entails. Babel Fish might have its uses (and amusements), Eco granted, but it could never handle all that. Real-time machine translation was not ready for the world.
New Techniques, Old Ambitions
Is it now? Within a decade of its release, Babel Fish began to sink to the bottom of the Web, along with AltaVista, DEC, and its later owner, Yahoo, all doomed by the ascendance of Google. Google Translate (launched in 2006) demonstrated that a corpus-based statistical approach was superior to Babel Fish’s often cross-wired rules, though it too was prone to embarrassing gaffes, leading Google to implement a neural net upgrade in 2016. Generative AI has now shaken up the field again, initial results being so promising that some within the industry are speaking of machine translation as “almost a solved problem” (to echo a recent Economist headline).77xMachine Translation Is Almost a Solved Problem,” The Economist, December 11, 2024; https://www.economist.com/science-and-technology/2024/12/11/machine-translation-is-almost-a-solved-problem. Once again, we hear rumors of a forthcoming Babel fish. “Apple Is Turning Its AirPods Into the Babel Fish From Hitchhiker’s Guide to the Galaxy,” the news site Quartz reports.88xEce Yildirim, “Apple Is Turning Its AirPods Into the Babel Fish From Hitchhiker’s Guide to the Galaxy,” Quartz, March 14, 2025; https://qz.com/apple-airpods-translate-ios-19-1851770018. “Meta’s New Translation AI Is Nearly a Babel Fish,” the engineering site IEEE Spectrum declares.99xCharles Q. Choi, “Meta’s New Translation AI Is Nearly a Babel Fish,” IEEE Spectrum, January 15, 2025; https://spectrum.ieee.org/machine-translation Others promise that Star Trek’s universal translators will soon materialize in our palms.
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