AI-powered tools have become integral in various industries, and their influence is becoming more and more prominent in translation and localization. Two notable AI-driven technologies in this field are NMT and LLMs. While both are powerful tools, understanding their differences is essential as their applications, underlying architectures, and functionalities have distinct strengths and weaknesses.
Localization is an essential part of business growth and global reach. Regardless of the size of your company, proper localization ensures that your message gets across to potential customers around the world in a clear and effective manner. Keep reading for four tips on how you can streamline and optimize your translation workflow for maximum impact.
Computer Assisted Translation (CAT) tools are decisive within localization workflows. Not only do they assist linguists in their work with multiple features, such as translation memories (TMs), automation rules, built-in glossaries, and more.
If we wanted to describe the localization landscape, we would certainly talk about languages, communication needs, clients, and vendors.
In a world where linguistic diversity is the norm, and as our digital society continues to evolve and expand, it is paramount that tools like ChatGPT (which we talked about in a previous blog post) embrace diversity and inclusivity to create a more meaningful and impactful online experience
There are many exciting new and emerging technologies making a splash these days and it can be easy to confuse them with one another.
Technology touches virtually every area of our lives, including how we learn. Which is why it’s no surprise that Technology Enhanced Learning (TEL) is rising in popularity.
ChatGPT is a highly advanced chatbot that utilizes cutting-edge technology to engage in natural and varied conversations with users.
Moving translation management tools into the cloud seems like an inevitable call for IT development in localization.
There’s no time like the present for education to catch up with technological advancements to better serve and engage their users—aka learners. There is also a high percentage of teachers leaving traditional education institutions to work in the eLearning industry as of late, which is another factor the education system needs to take into account as they look to the future.
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