Apple's Siri AI will be powered by Gemini OpenAI claims Translate can rewrite the output to take context and tone into account. OpenAI has debuted a dedicated ChatGPT-powered translation tool. While folks have been using the main chatbot for translation for some time, you can now find ChatGPT Translate on its own webpage, as spotted. The tool can translate text, voice inputs and images into more than 50 languages in seconds, OpenAI says. Most interestingly, ChatGPT Translate can rewrite the output to take various contexts and tones into account, much in the same way that more general text-generating AI tools can do .

This article reports on the third iteration of a survey of computerized tools and technologies taught as part of postgraduate translation training programmes. While the survey was carried out under the aegis of the EMT Network, more than half of responses are from outside that network. The results show the responsiveness of programmes to innovations in translation technology, with increased compulsory inclusion of machine translation, post-editing, and quality evaluation, and a rapid response to the release of generative tools. The flexibility required during the Covid-19 pandemic has also led to some lasting changes to programmes. While the range of tools being taught has continued to expand, programmes seem to be consolidating their core offering around cloud-based software with cost-free academic access. There has also been an increase in the embedding of professional contexts and workflows associated with translation technology. Generic file management and data security skills have increased in perceived importance, and legal and ethical issues related to translation data have also become more prominent. In terms of course delivery the shift away from conventional labs identified in EMT2017 has accelerated markedly, no doubt partly driven by the pandemic, accompanied by a dramatic expansion in the use of students' personal devices.

The evaluation of Large Language Models (LLMs) is a key element in their continuous improvement process and many benchmarks have been developed to assess the performance of LLMs in different tasks and topics. As LLMs become adopted worldwide, evaluating them in languages other than English is increasingly important. However, most LLM benchmarks are simply translated using an automated tool and then run in the target language. This means that the results depend not only on the LLM performance in that language but also on the quality of the translation. In this paper, we consider the case of the well-known Massive Multitask Language Understanding (MMLU) benchmark. Selected categories of the benchmark are translated into Spanish using Azure Translator and ChatGPT4 and run on ChatGPT4. Next, the results are processed to identify the test items that produce different answers in Spanish and English. Those are then analyzed manually to understand if the automatic translation caused the change. The results show that a significant fraction of the failing items can be attributed to mistakes in the translation of the benchmark. These results make a strong case for improving benchmarks in languages other than English by at least revising the translations of the items and preferably by adapting the tests to the target language by experts.

This study explores the use of Google Translate (GT) for translating mental healthcare (MHealth) information and evaluates its accuracy, comprehensibility, and implications for multilingual healthcare communication through analysing GT output in the MHealth domain from English to Persian, Arabic, Turkish, Romanian, and Spanish. Two datasets comprising MHealth information from the UK National Health Service website and information leaflets from The Royal College of Psychiatrists were used. Native speakers of the target languages manually assessed the GT translations, focusing on medical terminology accuracy, comprehensibility, and critical syntactic/semantic errors. GT output analysis revealed challenges in accurately translating medical terminology, particularly in Arabic, Romanian, and Persian. Fluency issues were prevalent across various languages, affecting comprehension, mainly in Arabic and Spanish. Critical errors arose in specific contexts, such as bullet-point formatting, specifically in Persian, Turkish, and Romanian. Although improvements are seen in longer-text translations, there remains a need to enhance accuracy in medical and mental health terminology and fluency, whilst also addressing formatting issues for a more seamless user experience. The findings highlight the need to use customised translation engines for Mhealth translation and the challenges when relying solely on machine-translated medical content, emphasising the crucial role of human reviewers in multilingual healthcare communication.

Sanjib Chaudhary chanced upon StoryWeaver, a multilingual children's storytelling platform, while searching for books he could read to his 7-year-old daughter. Chaudhary's mother tongue is Kochila Tharu, a language with about 250,000 speakers in eastern Nepal. Languages with a relatively small number of speakers, like Kochila Tharu, do not have enough digitized material for linguistic communities to thrive--no Google Translate, no film or television subtitles, no online newspapers. In industry parlance, these languages are "underserved" and "underresourced." This is where StoryWeaver comes in.

In an effort to break down language barriers, Meta has created a new AI translator that can convert spoken languages such as Hokkien into spoken English. Hokkien, a dialect of southern Min Chinese, is primarily spoken and lacks a standard writing system, making it difficult to develop translation tools for it. The open-source translation system, which is part of Meta's Universal Speech Translator (UST) project, has made significant progress in this challenge. The company, formerly known as Facebook, hopes that this, along with other AI methods in development, will eventually allow for real-time speech-to-speech translation across hundreds of languages, including spoken languages. Languages such as Hokkien are difficult to translate because machine translation tools need a large amount of written text to train on, and such languages lack a widely used writing system.

Imagine you are in a foreign country where you don't speak the language and your small child unexpectedly starts to have a fever seizure. You take them to the hospital, and the doctors use an online translator to let you know that your kid is going to be OK. But "your child is having a seizure" accidentally comes up in your mother tongue is "your child is dead." This specific example is a very real possibility, according to a 2014 study published in the British Medical Journal about the limited usefulness of AI-powered machine translation in communications between patients and doctors. Sometimes we need American-British translation, too.)

Artificial intelligence (AI) must be inclusive to reach its potential. AI applications that solve problems for a small segment of the population will fail to achieve widespread adoption. So, it's important that AI applications be designed and prepared with data that reflects as many segments of the global population as possible. Many moving parts need to be managed well to do that, and one of them is language. The more languages an AI application can handle, the more inclusive it is.

Meta's quest to translate underserved languages is marking its first victory with the open source release of a language model able to decipher 202 languages. Named after Meta's No Language Left Behind initiative and dubbed NLLB-200, the model is the first able to translate so many languages, according to its makers, all with the goal to improve translation for languages overlooked by similar projects. "The vast majority of improvements made in machine translation in the last decades have been for high-resource languages," Meta researchers wrote in a paper [PDF]. "While machine translation continues to grow, the fruits it bears are unevenly distributed," they said. According to the announcement of NLLB-200, the model can translate 55 African languages "with high-quality results."

Mozilla has added an official translation tool to Firefox that doesn't rely on cloud processing to do its work, instead performing the machine learning-based process right on your own computer. It's a huge step forward for a popular service tied strongly to giants like Google and Microsoft. The translation tool, called Firefox Translations, can be added to your browser here. It will need to download some resources the first time it translates a language, and presumably it may download improved models if needed, but the actual translation work is done by your computer, not in a datacenter a couple hundred miles away. This is important not because a lot of people need to translate in their browsers while offline -- like screen door for a submarine, it's not really a use case that makes sense.