Cisco estimates that global cloud traffic will grow 45% annually until 2016, with translation services growing at around 15% to 20% per year. According to Ian Henderson, CTO of Rubic, a translation and location company, this means that many new machine translators must enter the industry each year to handle the content.
On the other hand, Raymond Kurzweil, one of the brightest minds in the world, director of technology at Google and a futurist known for his predictions about artificial intelligence, predicts that machines will match human intelligence and perform several feats that seem to us science fiction nowadays, including human-quality translation, by year 2029.
Current happenings also suggest a strong role for non-human translation, with machine translation (MT) advancing rapidly. Three simultaneous-translation devices have been announced since June 2012, including one by Microsoft that renders live audio translations from the spoken word, respecting the tones and inflexions of the speaker.
Perfect is hard
But perfecting translation machine engines remains one of the toughest challenges in artificial intelligence. For several decades, computer scientists with the help of armies of linguists, tried rule-based approaches, i.e. teaching machine translation systems the linguistic rules or similarities between two languages (sometimes not related languages, like English and Japanese) and including the necessary dictionaries. Progress was extremely slow and suffered several setbacks, like the ALPAC report in 1966.
Technology did not cease to advance until statistical systems, using vasts amounts of data, have made it possible to train translation engines fast and efficiently for several domains. See our presentation in Budapest including a short history of machine translation.
[slideshare id=8510213&style=border: 1px solid #CCC; border-width: 1px 1px 0; margin-bottom: 5px;&sc=no]
Click here for the longer version, a recommended review of a lucid article courtesy of Gadget Web Site.
Undoubtedly, ever growing content and the demand for translating online data into multiple languages is growing fast. Exponentially. Pangeanic launched its Pangea machine translation project in 2008, reporting real-life implementations in many events, and it is now a successful, customizable software capable of re-training itself and creating engines on the fly. The project has won international name and is part of EU-funded projects.
“Human translation and machine translation are kind of like ‘frenemies,’” translation expert Nataly Kelly said. “They live alongside each other, but not without a lot of tension.” Sometimes, machine translations are so atrocious, human translators prefer to start from scratch.
Machine Translation companies and their output are becoming more and more ubiquitious every day. And as experts, we know that the aim of the technology is not replacing multilingual humans. Machines (rather automatic translation software) cannot fully replace human translators…yet. In fact, human translators often clean up machine translation (post-editing). Thus, the technology becomes an enhancer rather than a replacement.
It is this need for accuracy that keeps the (human translation) business growing. In fact, it is one of the few industries to have grown during the worldwide recession. It is approximately a $34 billion market. Machine translation’s market is around $200 million with growth forecasts of around 18,65%.
“Demand for translation is booming because content creation is exploding,” says Kelly. “And since much of that content is created, and demanded, in multiple languages, human translators alone can’t keep up. They need machine translations to improve–and fast.”
Next time you think languages, think Pangeanic
Your Machine Translation Customization Solutions