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In 2011, Michael Sinanian claimed that “translation technology will evolve within the next five to ten years to make the process instantaneous and transparent to the end-user, rendering foreign language competency effectively useless.” It’s not the only claim of its kind, though it is one of the most audacious.
Yes, machine translation has improved markedly in recent years, learning to treat languages as more than just “words in a bag.” But it’s still far from perfect—it’s not even passable. Run any paragraph through Google Translate and you’ll see why. For professional purposes, when a business deal or matters of national security are at hand—rough is not good enough.
Yes, speech recognition is becoming more sophisticated—we use it ourselves in Transparent Language Online. But anyone who has had a conversation with Siri or Alexa knows it’s not nuanced enough to replace real human communication. Not to mention major players like Siri, Cortana, and Alexa understand less than 2 dozen languages total. These and other speech recognition technologies have a lot of language left to learn, in terms of both breadth and depth.
Yes, artificial intelligence is doing better with context and meaning. Computers are getting better at dealing with natural language, recognizing analogies, homophones, and so on. The onset of AI-powered translation earpieces also looks very promising. But those devices are limited by the same problem that plagues all devices: tech failure. Headphones break. Batteries die. Machines freeze. Human language competency will always have their place, if not as the central form of communication, as least as a backup or control.
These advances in automation and machine learning also mean companies need more people capable of analyzing and understanding all the big data involved. Business mogul Mark Cuban believes that demand for liberal arts major—language majors in particular—will increase as a result:
“I personally think there’s going to be a greater demand in 10 years for liberal arts majors than for programming majors and maybe even engineering,” Cuban said. He cited degrees such as English, philosophy, and foreign languages as being the most valuable.”
AI companies, for example, are already turning towards linguists to help with product development and customer service. While developers create the actual code, people from language backgrounds are brought on to ensure natural language processing.
Even if advances in speech recognition, machine translation, and artificial intelligence do reach the point that Sinanian predicts, there are other valuable reasons to learn a language. Speaking someone’s language is a verbal sign of respect—it can bridge cultural gaps and create instantaneous bonds among diverse people. Language learning also bestows cognitive benefits, encourages critical thinking, and boosts cultural competency. Even if or when a machine can do the talking, it is critical to know how a culture views time, politeness, and other values. Even non-verbal miscommunication can spoil a deal, risk a negotiation, or derail a potential friendship.
We couldn’t agree more with Sinanian that technology will “eliminate cultural barriers and thus enhance global human cooperation”—it’s part of our mission. But we can’t foresee any technology, no matter how revolutionary, that obviates the need for human language learning. After all, what’s human cooperation or connection without real human communication?
Instead of focusing on how tech could replace language learning, we look at how tech can enhance language learning. To that end, we’re not 5 or 10 years out from seeing revolutionary results. We’re seeing it every day in our language training programs. Learn more about how we’re transforming the economics and logistics of language learning here.