The original report Rise of the Machines - The State of Machine Translation was published on January 22, 2018. This is a 2022 update to the report brought to you by Nimdzi's MT Specialist Özge Ünlü.
It’s already been six years now since Google revealed that Google Translate processes 146 billion words a day — three times more than what all the professional translators in the world combined can do in a month. That was 2016 and things haven’t really slowed down in the machine translation (MT) universe since.
The doomsday scenario silently crept up upon us without anyone really noticing, with robots having already surpassed humans in terms of translation speed. Contrary to popular expectations, however, this has not killed jobs in our industry. Revenues of companies in the language services continue to increase every year, and the demand for quality linguists is at a record high. Machine translation in its raw form is still no match for human translation, and human professional translations are still the answer for projects that require a certain level of quality.
However, that doesn’t mean translation is unaffected by MT or that professional service providers can afford to ignore MT (spoiler: they really can’t if they want to remain relevant). Indeed, with MT post-editing being commonly performed for most types of content, and raw MT being used for things like user-generated content, it’s becoming more and more urgent for translation companies and global brands to understand and harness the power of machine translation.
The language services industry is undergoing a profound transformation with the emergence of cutting-edge technologies such as ChatGPT and large language models (LLMs). These powerful language generation models have captivated the attention of businesses and language professionals alike, offering exciting possibilities for translation, localization, and content creation.
We recently introduced you to the two- (or five-) second rule, which is essentially the reaction or decision-making time a linguist should spend judging whether to post-edit a segment of machine translation (MT) output or to retranslate it. This rule of thumb aims to help increase the linguist’s productivity when working with MT.
If you’re a driver, you’ve probably heard of the two-second rule. Staying at least two seconds behind any vehicle is considered a rule of thumb for drivers wanting to maintain a safe following distance at any speed. The two seconds don’t represent safe stopping distance but rather safe reaction time.
Do you remember the last time when people were NOT talking about machine translation (MT)? We don't. Wherever you go, there’s someone talking about MT. With few exceptions, it seems like the only major disruptors in our industry over the past few decades have been breakthroughs in language technology.