AI and advancements in technology will revolutionise translation but not to the danger of human translators, they may just augment the process of translation. Arguably, future advances in technology will become more sophisticated and integrated, but understanding the wider context of speech may remain outside of the scope of machine learning altogether. Therefore it is more likely that quick machine translation could serve the human translator by carrying out the monotonous bulk of translation, while a human translator would still be needed to correct for nuances, deeper context or more accurate phrasing.
At present Google is developing neural machine translation (NMT) technology, utilising AI machine learning to pick up on a larger range of nuances and subtleties that have eluded machine translator programming so far. However in February 2017 at a translation competition at Sejong University in Seoul, Google’s NMT had the opportunity to compete against human translators from Korea’s International Interpreters & Translators Association and ultimately lost, with 90% of the NMT’s output being grammatically awkward. It seems that contrary to purely mathematical tasks, language has too many subjective selections that require a human touch, leaving applications of even NMT needing human input to be useful tools.