In 2001, after decades of fits and starts, Google entered the fray and quickly outpaced its competitors. Starting with six languages (English, Portuguese, German, Italian, Spanish, and French), Google Translate quickly grew its repertoire, quality, and speed. By 2005, the company’s program, which then supported eight languages, won a machine translation contest by using 1,000 computers to tackle 1,000 sentences in 40 hours. Today, in 2016, entire websites in 103 languages are instantly translatable in under a second. Every month, the service boasts over 500 million users, 92 percent of whom come from outside the United States. And each day, Google generates over a billion translations, more than the contents of a million books and more than all professional translators accomplish in a year. Translation of text, though, is but a warm-up for what programmers hope to accomplish—or what they claim they already have.

Last week, New York City-based Waverly Labs announced its recent invention, Pilot, a set of two ear buds that costs $299. Scheduled to be released by spring of 2017, the device purports to offer near-simultaneous translation for four languages. Inspired “when he met a French girl,” Andrew Ochoa, the company's founder, says that Pilot promises "a life untethered, free of language barriers." After the announcement, Forbes questioned Waverly Labs' credibility but ignored the larger assumption at the core of Waverly Labs' project: Issues of funding aside, if someone wanted to fall in love using machine translation, would it work?

Despite how quickly machine translation has progressed in the last few decades, language is a data set that's far more complex than it seems, so no matter how quickly translation technology evolves, the stochastic messiness of speech will always outpace it. However, as Josh's encounter in the cafe will show, what may be considered machine translation’s failure is ultimately a human triumph.

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Josh wasn't planning on meeting anyone but the coincidence is too attractive to pass up. Just as he's about to explain that his mom gave him this book after he broke his leg in a car accident, that he's had the same paperback copy for over a decade, the waitress is refilling his mug with hot water. When the waitress speaks, he notices something a little off about her cadence, how the accents fall a little less rhythmically than those around her. Now that his translators are in, Josh figures he may as well ask where she's from—the first true test of the device in his ear.

Computers, like the IBM-Georgetown machine, used to learn languages the same way that humans do: by internalizing the messy spattering of rules, exceptions, and exceptions to exceptions found in all languages. Because grammar is so complex, the programs used to have to master millions of commands, and beyond basic phrases, the resulting translations often sounded clunky. In 1949, the scientist Warren Weaver proposed an alternative to rule-based translation called statistical machine translation (SMT). Instead of attacking language one minutia at a time, Weaver suggested a two-pronged approach: First, the computer would mine millions of documents looking for statistically significant linguistic patterns, thereby discovering the grammar, syntax, and morphology rules for itself. At the same time, the program would create a model to predict how certain phrases are translated and where in the sentence they should appear. For example, after billions of iterations the computer would realize that, in German, the verb typically comes at the end of the sentence.