Artificial Intelligence

Machine translation (MT) costs vary based on different options. Customized MT, tailored to business slang, provides better translations but comes with higher upfront costs. When choosing an MT method, consider both fixed (e.g., domain-specific content translation) and variable costs (tied to annual translation volume). Compare the Customized NMT to other modern-day solutions. Its complexity is justified only when newer solutions can’t replace it.

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.

In the realm of machine translation (MT), the evolution from traditional neural machine translation (NMT) – pioneered by Google Translate in 2016 – to the advent of large language models (LLM) marks a significant paradigm shift.

The Nimdzi Language Technology Atlas maps over 800 different technology solutions across a number of key product categories. The report highlights trends and things to watch out for. This is the only map you will ever need to navigate your way across the language technology landscape.

Thirty years ago, one of the most prominent newspapers in Brazil published a story in which I described how "everything that I achieved in my profession was because of the computer." I explained how I enhanced my computers to increase performance and that my clients wanted their jobs printed in color.

Summer is upon us. Your brand planning is already a distant memory and as your team gets ready for the upcoming summer vacation season, your eyes might already be set on September, with updated goals, adjustments to your brand strategy, and some exciting new ideas to try out.

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.

Developing your own approach to using generative AI models such as ChatGPT — one that is both practical AND ethically sound — is perhaps the best way of proving naysayers wrong and ensuring that you get the most out of this promising piece of technology. Perhaps surprisingly, the first key to success with generative AI models is to learn how to talk to them.

In November 2022, OpenAI released ChatGPT, gaining 100 million users within two months and setting a record for fastest-growing user base. Arguably,ever before have we seen such an event get  so much hype in the history of technology. Language technology suddenly got propelled into the center of the wider public’s attention in a way unheard of since Google Translate entered the scene.

It won’t come as any surprise that, like so many other organizations, Nimdzi Insights has been experimenting with ChatGPT since its public release in November 2022. While large language models (LLMs) have been around for a number of years, they have mostly gone unnoticed until ChatGPT broke the dam.