Machine learning has transformed major aspects of the modern world with great success. Self-driving cars, intelligent virtual assistants on smartphones, and cybersecurity automation are all examples of how far the technology has come.\nBut of all the applications of machine learning, few have the potential to so radically shape our economy as language translation. The content of language translation is the perfect model for machine learning to tackle. Language operates on a set of predictable rules, but with a degree of variation that makes it difficult for humans to interpret. Machine learning, on the other hand, can leverage repetition, pattern recognition, and vast databases to translate faster than humans can.\nThere are other compelling reasons that indicate language will be one of the most important applications of machine learning. To begin with, there are over 6,500 spoken languages in the world, and many of the more obscure ones are spoken by poorer demographics who are frequently isolated from the global economy. Removing language barriers through technology connects more communities to global marketplaces. More people speak Mandarin Chinese than any other language in the world, making China\u2019s growing middle class is a prime market for U.S. companies if they can overcome the language barrier.\nLet\u2019s take a look at how machine learning is currently being applied to the language barrier problem, and how it might develop in the future.\nNeural machine translation\nRecently, language translation took an enormous leap forward with the emergence of a new machine translation technology called Neural Machine Translation (NMT). The emphasis should be on the \u201cneural\u201d component because the inner workings of the technology really do mimic the human mind. The architects behind NMT will tell you that they frequently struggle to understand how it comes to certain translations because of how quickly and accurately it delivers them.\n\u00a0\u201cNMT can do what other machine translation methods have not done before \u2013 it achieves translation of entire sentences without losing meaning,\u201d says Denis A. Gachot, CEO of SYSTRAN, a language translation technologies company. \u201cThis technology is of a caliber that deserves the attention of everyone in the field. It can translate at near-human levels of accuracy and can translate massive volumes of information exponentially faster than we can operate.\u201d\nThe comparison to human translators is not a stretch anymore. Unlike the days of garbled Google Translate results, which continue to feed late night comedy sketches, NMT is producing results that rival those of humans. In fact, Systran\u2019s Pure Neural Machine Translation product was preferred over human translators 41% of the time in one test.\nMartin Volk, a professor at the Institute of Computational Linguistics at the University of Zurich, had this to say about neural machine translation in a 2017 Slator article:\n\u00a0\u201cI think that as computing power inevitably increases, and neural learning mechanisms improve, machine translation quality will gradually approach the quality of a professional human translator over the coming two decades. There will be a point where in commercial translation there will no longer be a need for a professional human translator.\u201d\nGisting to fluency\nOne telling metric to watch is gisting vs. fluency. Are the translations being produced communicating the gist of an idea, or fluently communicating details?\nPrevious iterations of language translation technology only achieved the level of gisting. These translations required extensive human support to be usable. NMT successfully pushes beyond gisting and communicates fluently. Now, with little to no human support, usable translations can be processed at the same level of quality as those produced by humans. Sometimes, the NMT translations are even superior.\u00a0\nQuality and accuracy are the main priorities of any translation effort. Any basic translation software can quickly spit out its best rendition of a body of text. To parse information correctly and deliver a fluent translation requires a whole different set of competencies. Volk also said, \u201cSpeed is not the key. We want to drill down on how information from sentences preceding and following the one being translated can be used to improve the translation.\u201d\nThis opens up enormous possibilities for global commerce. Massive volumes of information traverse the globe every second, and quite a bit of that data needs to be translated into two or more languages. That is why successfully automating translation is so critical. Tasks like e-discovery, compliance, or any other business processes that rely on document accuracy can be accelerated exponentially with NMT.\nEducation, e-commerce, travel, diplomacy, and even international security work can be radically changed by the ability to communicate in your native language with people from around the globe.\nPost language economy\nEverywhere you look, language barriers are a speed check on global commerce. Whether that commerce involves government agencies approving business applications, customs checkpoints, massive document sharing, or e-commerce, fast and effective translation are essential.\nIf we look at language strictly as a means of sharing ideas and coordinating, it is somewhat inefficient. It is linear and has a lot of rules that make it difficult to use. Meaning can be obfuscated easily, and not everyone is equally proficient at using it. But the biggest drawback to language is simply that not everyone speaks the same one.\nNMT has the potential to reduce and eventually eradicate that problem.\n\u201cYou can think of NMT as part of your international go-to-market strategy,\u201d writes Gachot. \u201cIn theory, the Internet erased geographical barriers and allowed players of all sizes from all places to compete in what we often call a \u2018global economy,\u2019 But we\u2019re not all global competitors because not all of us can communicate in the 26 languages that have 50 million or more speakers. NMT removes language barriers, enabling new and existing players to be global communicators, and thus real global competitors. We\u2019re living in the post-internet economy, and we\u2019re stepping into the post-language economy.\u201d\nMachine learning has made substantial progress but has not yet cracked the code on language. It does have its shortcomings, namely when it faces slang, idioms, obscure dialects of prominent languages and creative or colorful writing. It shines, however, in the world of business, where jargon is defined and intentional. That in itself is a significant leap forward.