Last Updated: November 6, 2019 3:45PM
When a barrier separates our customers from their customers, we’re the ones clients call on to build a bridge to the best possible solution.
A company comes to us because it needs to share its message with the world, but a difference in languages, cultures, and/or locations precludes that message from resonating. We tap into our global community of translators and experts, establish efficient workflows and frameworks, and solve that problem. And we always translate and localize our customer’s message with fast, scalable quality content.
For decades we’ve lent our project management expertise and global community to myriad customer needs that extend past linguistic services. As more and more of our loyal customers have started to develop AI systems, we saw a niche our highly-orchestrated global community could uniquely fill: helping customers train their nascent AI systems.
The History of Language in Artificial Intelligence
The link between language and AI is hardly a novel one. In fact, one of the most famous concepts of modern artificial intelligence was a type of natural language processing: the Turing test. Chomsky’s work on universal grammar and the Georgetown-IBM experiment are also major moments in the evolution of artificial intelligence. They too both centered around language.
AI has of course evolved from the days of hand-written rules and punch-card programming to today’s deep learning systems that can grow smarter with more data. And that’s where we come in.
Translation Tools that Transfer to AI Training
Over the past two decades, we’ve developed AI-driven tools that have allowed us to solve that time/cost/quality paradox for our clients. We’ve harnessed tools like Translation Memories, taxonomies, and ontologies that enable us to translate better, faster. We’ve been experts in training AI for language processing long before training AI became a need for multiple use cases. As we do with translation and localization projects, we leverage our community and our project management expertise to train AI with speed, quality, and scale. Artificial intelligence and translation may seem unrelated, but the practices to improve both draw on similar resources.
Because computers have enabled the existence of data on just about every topic in the world, companies are using machine learning to improve computer performance in everything from weather forecasting to automatic stock selection to machine translation to chatbot development to precision medicine and more.
But the value of a data set directly correlates to the performance of an algorithm. If those vast amounts of data aren’t processed correctly, developers will still suffer the consequences of “garbage in, garbage out.” (The concept holds true for translation as well.) Lionbridge’s talented community can create, collect, and annotate data as well as test algorithm results in 350+ languages, dozens of countries, and myriad file formats.
The opportunities are limitless when companies have the right algorithm—and access to huge volumes of high-quality training data. Check out all the ways Lionbridge can accelerate your AI development and if you found this post helpful, download the complete whitepaper.