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AI translation and human translation
  Translation was one of the most in-demand services of 2017.
  Annual enterprise spending on translation services is expected to grow to US$45 billion by 2020, primarily driven by increasing globalization and an increasing amount of text being generated worldwide.
  The global machine translation market size is expected to reach USD 983.3 million by 2022, according to a new study by Grand View Research, Inc., exhibiting a 14.6% CAGR during the forecast period.
  The Bureau of Labor Statistics projects  a 17% employment growth for interpreters and translators by 2026 due to this need for businesses to go global. This is much faster than the average growth rate for all occupations, and the industry is on track to add another 11,400 positions during this time.
  Some of the bigger job prospects available for translators are in Chinese, German, Russian, Portuguese, and Spanish, which are among the more important languages for businesses in the global market.
  AI-enabled automated translation platforms like Google Translate, Microsoft Translator, and the recently released Amazon Translate have in the last 24 months taken a great leap forward in accuracy. This is for two reasons: one, they build on recent break-through improvements in neural machine translation (NMT) algorithms, and, two, they have access to a much larger amount of language data from search engines, social networks, and e-commerce sites.
  For less-demanding consumer (B2C) use cases, such as translating a web site for a casual browser , the accuracy of these fully-automated AI-based systems has recently become “good enough” for a large number of use cases. Typically these translations are offered for free and supported by ads, so the users are happy with whatever quality they can get, and the consequences of errors are low.
  In contrast, the accuracy of these existing systems is not adequate for many business use cases, such as creating a user interface in a new language, translating a tax document, or creating a user manual for a product in a new language. Yet AI is also having a big impact here, where human-in-the-loop uses cases allow the AI system to do an initial translation that is then refined by a human expert. Although this isn’t driving translation pricing all the way to zero, this technology is, nonetheless, having a profound impact on the translation marketplace, which is transforming in shape as a result of these forces.
  The recent acceleration in machine translation sophistication and reliability leads some observers to speculate that machines will essentially remove the need for expensive human translation even in the enterprise market, eliminating tens of thousands of jobs in product and service localization, publishing, marketing, and myriad other fields, even as the demand for translation explodes.
  However, this is a false extrapolation of current success. Although the hype around recent improvements is largely justified, the idea that machines will destroy language services as an industry and drastically reduce the need for translation and globalization teams is not.
  There are a number of reasons:
  As described above, the bar for successful language translation in the enterprise is substantially higher than for consumer applications.
  Even within the enterprise, the bar is also rising.
  Current language translation technologies will not  improve at the current pace unabated. The biggest recent advances have come from leveraging massive corpora of already-translated materials to learn translation models that can translate similar content in the future. Many enterprise cases are much more specific in terms of context and discipline, and also have lower volumes of already-translated data for these narrower contexts. These are technical challenges that AI algorithms are only today beginning to address, and new technology transfer ―if not also new R&D―are required to reach the next level in driving business value.
  The number of languages that can be profitably translated is increasing with the new lower-cost, AI-supported approach,  as we describe in more detail below. Hence, even as the costs for translating higher-priority languages might come down, the volume of emerging-priority languages continues to rise. These less-translated languages have less training data, making the automation problem harder, as noted above.
  Despite recent advancements in artificial intelligence, machine translation software still needs to be overseen by professional human translators. This can ensure that the correct dialect, grammar, and translations have been used when interpreting voice notes and texts.
  Although algorithms are becoming more accurate, machines are still unable to beat human translations when set against each other head to head. Last year, Sejong Cyber University in Korea put three machine translation programs in competition with a group of human translators, with the machines failing to live up to expectations. While the machines were much faster, they made more mistakes in the finished documents, with 90% of the machine-translated texts being “grammatically awkward”.
  Machines are still unable to form coherent, grammatically-correct sentences when translating a piece of text or information, making them much less reliable than a human translator. For industry-specific translations,such as law or medicine, businesses will need to provide highly accurate translations, which human interpreters will be far more able to do.
  AI tools will augment rather than replace humans at the high end of the enterprise market, increasing providers’ ability to handle vastly increased volume while simultaneously meeting the stringent requirements of highly specialized translation in healthcare, law, engineering, and other technical verticals. Growth will remain healthy in that segment of the market and prices will drop more slowly than for less-specialized translation.
  As we discussed in a previous article, the role of the modern linguist involves a skillset that goes beyond language translation. Specialist knowledge in marketing, medical and legal sectors is already prevalent and with the increase in digital jobs, additional computer skills are required.
  History confirms that displaced workers always find other jobs and the effects of the growing digital economy won't change this trend.
  While one might think that translation apps threaten the jobs of translators, they are in fact creating new opportunities.