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Since those days, “translation engines” have gradually improved, and recently the use of so-called deep neural nets has even suggested to some observers that human translators may be an endangered species. In this scenario, human translators would become, within a few years, mere quality controllers and glitch fixers rather than producers of fresh new text. I learned that although the older version of Google Translate can handle a very large repertoire of languages, its new deep-learning incarnation at the time worked for just nine languages. (It’s now expanded to 96.)* Accordingly, I limited my explorations to English, French, German, and Chinese. Google Translate is accessible for free to anyone on Earth, and will convert text in any of roughly 100 languages into text in any of the others. The practical utility of Google Translate and similar technologies is undeniable, and probably a good thing overall, but there is still something deeply lacking in the approach, which is conveyed by a single word: understanding. Machine translation has never focused on understanding language. Instead, the field has always tried to “decode”—to get away with not worrying about what understanding and meaning are. We humans know all sorts of things about couples, houses, personal possessions, pride, rivalry, jealousy, privacy, and many other intangibles that lead to such quirks as a married couple having towels embroidered HIS and HERS . Google Translate isn’t familiar with such situations. Google Translate isn’t familiar with situations, period. It’s all about ultra-rapid processing of pieces of text, not about thinking or imagining or remembering or understanding.

The translation engine was not imagining large or small amounts or numbers of things. It was just throwing symbols around, without any notion that they might symbolize something. Only when the halo has been evoked sufficiently in my mind do I start to try to express it—to “press it out”—in the second language. I try to say in Language B what strikes me as a natural B-ish way to talk about the kinds of situations that constitute the halo of meaning in question. I am not, in short, moving straight from words and phrases in Language A to words and phrases in Language B. Instead, I am unconsciously conjuring up images, scenes, and ideas, dredging up experiences I myself have had (or have read about, or seen in movies, or heard from friends), and only when this nonverbal, imagistic, experiential, mental “halo” has been realized—only when the elusive bubble of meaning is floating in my brain—do I start the process of formulating words and phrases in the target language, and then revising, revising, and revising. This process, mediated via meaning, may sound sluggish, and indeed, in comparison with Google Translate’s two or three seconds a page, it certainly is —but it is what any serious human translator does. This is the kind of thing I imagine when I hear an evocative phrase like deep mind. But then again, Google Translate can’t understand webpages, although it can translate them in the twinkling of an eye. Of course I grant that Google Translate sometimes comes up with a series of output sentences that sound fine (although they may be misleading or utterly wrong). A whole paragraph or two may come out superbly, giving the illusion that Google

Translate knows what it is doing, understands what it is “reading.” In such cases, Google Translate seems truly impressive—almost human! Praise is certainly due to its creators and their collective hard work. But at the same time, don’t forget what Google Translate did with these two Chinese passages, and with the earlier French and German passages. To understand such failures, one has to keep the ELIZA effect in mind. The bai-lingual engine isn’t reading anything—not in the normal human sense of the verb “to read.” It’s processing text. The symbols it’s processing are disconnected from experiences in the world. It has no memories on which to draw, no imagery, no understanding, no meaning residing behind the words it so rapidly flings around. He figured that if you multiplied the database by a factor of, say, a million or a billion, eventually it would be able to translate anything thrown at it, and essentially perfectly. I don’t think so. Having ever more “big data” won’t bring you any closer to understanding, because understanding involves having ideas, and lack of ideas is the root of all the problems for machine translation today. So I would venture that bigger databases—even much bigger ones—won’t turn the trick. All sorts of statistical facts about the huge databases are embodied in the neural nets, but these statistics merely relate words to other words, not to ideas. There’s no attempt to create internal structures that could be thought of as ideas, images, memories, or experiences. Despite my negativism, Google Translate offers a service many people value highly: It effects quick-anddirty conversions of meaningful passages written in Language A into not necessarily meaningful strings of words in Language B. As long as the text in Language

B is somewhat comprehensible, many people feel perfectly satisfied with the end product. From my point of view, there is no fundamental reason that machines could not, in principle, someday think; be creative, funny, nostalgic, excited, frightened, ecstatic, resigned, hopeful, and, as a corollary, able to translate admirably between languages. There’s no fundamental reason that machines might not someday succeed smashingly in translating jokes, puns, screenplays, novels, poems, and, of course, essays like this one. But all that will come about only when machines are as filled with ideas, emotions, and experiences as human beings are. And that’s not around the corner. Indeed, I believe it is still extremely far away. At least that is what this lifelong admirer of the human mind’s profundity fervently hopes.

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