Artificial intelligence (AI) has infiltrated numerous aspects of our lives in recent years, thanks to improvements in the field of machine learning, where computers ostensibly program themselves. This drive towards digital self-learning has led to major breakthroughs in our day-to-day interactions with machines, most notably the rise of digital home assistants such as Amazon Echo, and the recently launched Google Lens, which identifies objects based on visual cues from your phone’s camera.

In the past it was more of a topic of discussion on theoretical applications, we now see machine learning being applied in smart cars, video games, digital marketing, virtual personal assistants, chatbots, and other areas of daily life. As AI moves to disrupt and improve more sectors, there are still barriers to overcome before we need to fear for our jobs. However, it has already found its way into a number of our most commonly used websites and platforms, with even grander plans in the pipeline.

But just how reliable is the technology?

GAME OVER

It’s worth recapping how machine learning and AI have already surpassed human abilities. In 1996, IBM’s Deep Blue computer first challenged world-leading chess player Garry Kasparov. While Kasparov won the first time, Deep Blue won the rematch in 1997. Following that competition, computers developed further and are now consistently better than us at chess.

Next on the list was Go, an ancient Chinese board game that seemed too complex for even the most advanced computer to win, owing to the fact that it’s said to have more possible moves than atoms in the visible universe. So when Google’s DeepMind AlphaGo AI computer program beat Lee Sedol 4-1 in March 2016, it came as a shock.

In May 2017 at the Future of Go summit, AlphaGo went on to beat world number one, Ke Jie, who initially claimed that he would never lose to a “cold machine.” Afterward, Jie admitted that “the advancement of AI has far exceeded our imagination.” At the event, robots not only challenged players but also worked alongside them, proving that they can help us as well as beat us.

NOT THERE YET

Now the industry’s focus is turning to translation. Language production and translation have, for a long time, constituted one of the toughest challenges for any machine to tackle. IBM already explored machine translations way back in the 1950s, but it was not until the ’90s, with the development of Altavista’s Babel Fish, that such tools became accessible to the public. However, machine translation had its limitations. It translated word by word using dictionaries, offering literal translations without regard for the complexities of semantics, syntax, and morphology.

One Google researcher noted that “People naively believe that if you take deep learning (Artificial Intelligence) and…1,000 times more data, a neural net will be able to do anything a human being can do, but that’s just not true.” Despite its rising popularity, AI translation is not quite there yet compared to experienced human translators.

A recent contest in South Korea pitted machine translation tools against a team of professionals in translating two texts from Korean to English and vice versa. According to VentureBeat, the results of the 50-minute test revealed that “90% of the NMT [neural machine translated] text was ‘grammatically awkward,’ or definitely never the kind of translation produced by any educated native speaker.” Many linguists and translators will be relieved by the resounding success of the humans in this latest battle against the machines.

THE END CALL

As technology evolves, more of the translation process is being delegated to software and computers. I agree that technology plays an important role in translation services. However, technology should be used as support to the human translation craft. While free google translation may help you understand the main ideas contained in a piece of content written in another language, Human translation is still a must when you need your content translated for your business or any other purpose requiring accuracy.

Only humans can precisely decide what is the right context for a text. Words change meaning depending on the context, and the choice of a translation will rely heavily on the context. Keeping humans in charge of the overall management of the human translations will assure the translated document mirrors the original document.

Slang, idiomatic expressions, localism, and technical domains are just a few other challenges to language translation automation. Language has some nuances that computers and artificial intelligence won't be able to handle any time soon.