Much of the tech stack in today’s translation activity will be irrigated by AI. This means using machine learning algorithms and data to augment human productivity. It also increasingly requires that everyone involved is aware of the ethical implications of this technology.

TAUS has just published a new report on the role of language data in the AI paradigm – LD4AI. This explores the origins and scale-up of the current role for language data moderation in translation pipelines driven by machine learning, supported by “humans in the loop.” One finding is that large-scale data management will expand the kind of jobs required. In this respect, it may be useful to understand how language also acts to produce data beyond the translation moment. This will likely foster new types of work for language professionals. Let's look a little closer at why “language data” is a richer concept than you might think.

In this data age, we can personalize the provision of information because we are rapidly learning more stuff about hundreds of millions of individual users of services. The underlying idea is that an AI-enabled information system (internet, website, media, etc) will be able to give end-users exactly the content they want. Why? Because these users offer increasingly rich data profiles to the machines running online services. How far, then, will a person’s experience of language(s) - one crucial variable - offer a challenge or a solution to tomorrow’s personalized data game?

We look back on the corona crisis of well over a year ago and happily conclude that it was not as severe as it could have been. Economies came back to life after a lockdown of a few months, and people started traveling again in June-July 2020. Most translation companies experienced a dip in revenue, but it wasn’t devastating. And now, a year later, everything is more or less back to what we call the new normal. Time to start revisiting all those aspects of the industry that are ready for change, revitalization, or improvement?

This is the worst-case scenario. Unemployment rates in the USA exceed 35%, and all western economies are slowly climbing out of the recession. Several airline companies have gone bust. Governments have taken control of economies and nationalized vital sectors. And global supply chains have been restructured. In 2021, the translation industry is likely to retract by 30 to 40%, if anyone can actually measure it. Many agencies have not survived and global suppliers have undergone a major restructuring. The only possibly good news is that big global-tech companies have further stepped up their investments in their machine translation and AI programs.

The language technology innovator and service provider Tilde has developed considerable expertise in providing language services to the Presidency of the Council of the European Union. We caught up with Mārcis Pinnis, Chief AI Officer, and Artūrs Vasiļevskis, Head of Machine Translation, to find out more about the challenges and promises of this ongoing project.

How should the translation industry engage with the current conversation about ethical concerns in technology use? Here are some preliminary notes for an answer.

Maybe it’s time for secondary school students to learn more about what machine translation is all about as part of their general education! No, not a whole course on the technical background and workings of MT; the focus should be on exploratory, even playful learning about a technology that will soon be as natural to them as game-playing or video-editing on smartphones. Here are three reasons why a short MT101 course could be useful for early teens learning foreign languages.

We’re entering a digital ‘translate’ era in which people and machines are learning to cooperate more effectively to deliver rich services and language experiences at scale. The resulting synergies will underpin all new industry strategies in the coming years. As current S-curve wisdom has it, things will evolve slowly at first and then accelerate rapidly. So it’s time to look up from our shiny new instrument panels and scan the road ahead for emerging opportunities.

Here’s a suggestion: we should adopt a new usage of the existing English lexeme “translate” as a non-count noun. This would enable the word to cover emerging phenomena in the language industry as it crystallizes more deeply into the digital ecology. This usage is obviously calqued on the current trend of using “compute” as a noun, meaning something like “computational capacity” (as in “How much compute do you need for that machine learning task?”).

In our quest for insight into the creation of unicorns in the language services sector, we spoke to Vasco Pedro, the CEO of Unbabel, a technology-centric translation company founded in 2013. We wanted to know which ingredients would be crucial to building out current companies in this space, given the standard inhibitors of fragmentation, product delivery complexity, and hesitations about the role of AI.