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This same technology that enabled neural machine translation to improve machine translation is now finding its way into seemingly unrelated products (think ChatGPT or Dall·E). The translation “problem” has not been solved with the latest advancements in NMT, but there are lessons to be learned in other professions from how the professional translation community has been affected by and adapted to the widespread use of NMT both by translators and by end users.
This month in The Lab, Mark Shriner looks at trends in the delivery of interpretation services in healthcare. Specifically, drivers of the growth in...
Supertext is making a bold move by putting AI translation at the center of its strategy—but with a twist. CEO Sam Laübli explains how...
In this captivating interview from LocWorld, join Spence Green, the founder of Lilt, as he delves into the company’s trailblazing journey in the world...
