The second event in this series highlighted the localization industry’s quick adoption of ChatGPT, GPT-4 and other large language models (LLMs). Compared to the shock and surprise in February of this year, this time participants saw action across the board. Over 3,000 watched the event live and in recording later. The event featured panel discussions on […]
by: Konstantin Dranch Last Friday Custom.MT ran the industry’s first localization prompt engineering hackathon. We discovered an engaging new format for linguists, project managers, and executives to learn while networking and brainstorming for fresh ideas. Here are some pointers on how to host your own event like that. Why do you need a hackathon? Buy-side […]
Custom.MT integrated ChatGPT and made it available through a Trados Plugin. Try it, the first 50,000 characters are on us. Like all technology teams out there, Custom.MT is experiencing the AI revolution with awe and excitement. As soon as OpenAI made waves, we decided to make it work for localization people. Working on our first […]
This guide is useful to train your own Microsoft AutoML translation model. For example, you can train a domain model, such as medical, legal, video games, financial reporting with your translation memory accumulated over the years. Alternatively, you can make an organization-specific model that knows all the product and people names, and follows your individual […]
This guide is useful to train your own Google AutoML translation model. For example, you can train a domain model, such as medical, legal, video games, financial reporting with your translation memory accumulated over the years. Alternatively, you can make an organization-specific model that knows all the product and people names, and follows your individual […]
This guide is useful to train your own Amazon translation model. For example, you can train a domain model, such as medical, legal, video games, financial reporting with your translation memory accumulated over the years. Alternatively, you can make an organization-specific model that knows all the product and people names, and follows your individual styles. […]
This Excel template is designed to help translation managers calculate savings from moving parts of their translation budget to machine translation post-editing workflow. The table factors in the translation memory, repetitions in the text, and different levels of discount depending on the match rate. With some patience and persistence, you can adjust for differences in translation rates by language.
The year starts with two major trends: a boom in AI, and a dim economic outlook. To stay relevant in a world where globalization processes are slowing down, and machines are becoming more adept at language tasks, localization teams need to reinvent themselves once more. We present a collection of specific ideas that respond to market trends and advances in technology.
by Konstantin Dranch Through ChatGPT, millions of people learned that computers can already communicate and brainstorm better than most humans. Businesses are now aware of its capabilities, while AI labs roll out a much bigger GPT-4 model. I offer you an early outline the impact of this technology on localization. As of 2023, the language […]
Running professional human evaluations of machine translation requires detailed methodology and software tools to streamline the process. The most detailed approach to evaluation today is to label errors and assign weights to critical issues using a variation of DQF/MQM ontology. In this article, we outline Custom.MT’s journey to selecting and implementing a tool for human […]
We’re seeing a rise in the number of projects and requests that rely on raw machine translation. When Custom.MT started, clients wanted to train models to improve the productivity of human translators, and to reduce the cost. Today, more than half of the requests we get are about doing the translation fully automatically, without a […]
Today’s race for leadership in machine translation (MT) is between IT Giants Google and Microsoft, a few small specialized companies like DeepL and ModernMT, and open-source projects. What does it take, and how does it feel — to expand the boundaries of science and business at the very top of the machine translation field?