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.

In this paper, we elaborate on how AI can support diversity and inclusion and exemplify research projects conducted in that direction. We start by looking at the challenges and progress in making large language models (LLMs) more transparent, inclusive, and aware of social biases. Even though LLMs like ChatGPT have impressive abilities, they struggle to understand different cultural contexts and engage in meaningful, human like conversations. A key issue is that biases in language processing, especially in machine translation, can reinforce inequality. Tackling these biases requires a multidisciplinary approach to ensure AI promotes diversity, fairness, and inclusion. We also highlight AI's role in identifying biased content in media, which is important for improving representation. By detecting unequal portrayals of social groups, AI can help challenge stereotypes and create more inclusive technologies. Transparent AI algorithms, which clearly explain their decisions, are essential for building trust and reducing bias in AI systems. We also stress AI systems need diverse and inclusive training data. Projects like the Child Growth Monitor show how using a wide range of data can help address real world problems like malnutrition and poverty. We present a project that demonstrates how AI can be applied to monitor the role of search engines in spreading disinformation about the LGBTQ+ community. Moreover, we discuss the SignON project as an example of how technology can bridge communication gaps between hearing and deaf people, emphasizing the importance of collaboration and mutual trust in developing inclusive AI. Overall, with this paper, we advocate for AI systems that are not only effective but also socially responsible, promoting fair and inclusive interactions between humans and machines.

Large-scale automatic speech translation systems today lack key features that help machine-mediated communication feel seamless when compared to human-to-human dialogue. In this work, we introduce a family of models that enable end-to-end expressive and multilingual translations in a streaming fashion. First, we contribute an improved version of the massively multilingual and multimodal SeamlessM4T model-SeamlessM4T v2. This newer model, incorporating an updated UnitY2 framework, was trained on more low-resource language data. SeamlessM4T v2 provides the foundation on which our next two models are initiated. SeamlessExpressive enables translation that preserves vocal styles and prosody. Compared to previous efforts in expressive speech research, our work addresses certain underexplored aspects of prosody, such as speech rate and pauses, while also preserving the style of one's voice. As for SeamlessStreaming, our model leverages the Efficient Monotonic Multihead Attention mechanism to generate low-latency target translations without waiting for complete source utterances. As the first of its kind, SeamlessStreaming enables simultaneous speech-to-speech/text translation for multiple source and target languages. To ensure that our models can be used safely and responsibly, we implemented the first known red-teaming effort for multimodal machine translation, a system for the detection and mitigation of added toxicity, a systematic evaluation of gender bias, and an inaudible localized watermarking mechanism designed to dampen the impact of deepfakes. Consequently, we bring major components from SeamlessExpressive and SeamlessStreaming together to form Seamless, the first publicly available system that unlocks expressive cross-lingual communication in real-time. The contributions to this work are publicly released and accessible at https://github.com/facebookresearch/seamless_communication

That's what the doll in Squid Game says. But how would you know! You got subtitles on your plate. Shows like Squid Game and Money Heist topping Netflix charts opened up a whole new genre of drama and entertainment for the audience to explore with different language content. People locked inside the doors during the pandemic brought the world closer together in its unique ways.

Lilt, the modern language service and technology provider, today announced it was named a winner in the Business Intelligence Group's Artificial Intelligence Excellence Awards program. Lilt's localization solution combines a community of the world's best professional translators with its AI-powered translation platform, bringing human-powered, technology-assisted translations to global enterprises like Intel, ASICS, Canva, DigitalOcean, WalkMe, and others. "We're thrilled to be recognized as a winner of the Artificial Intelligence Excellence Awards," said Spence Green, CEO of Lilt. "As a language service and technology provider, our AI and machine learning platform enables our customers to provide their customers with a consistent global experience, regardless of what language they speak." Lilt provides businesses with the ability to offer the same global experience to all customers, partners, and employees irrespective of language.

Translating between human languages is something which artificial intelligence – specifically machine learning – has proven to be very competent at. So much so that the CEO of one of the world's largest employers of human translators has warned that many of them should be facing up to the stark reality of losing their job to a machine. One Hour Translation CEO Ofer Shoshan told me that within one to three years, neural machine technology (NMT) translators will carry out more than 50% of the work handled by the $40 billion market. His words stand in stark contrast to the often-repeated maxim that, in the near future at least, artificial intelligence will primarily augment, rather than replace, human professionals. Shoshan told me that the quality of machine translation has improved by leaps and bounds in recent years, to the point where half a million human translators and 21,000 agencies could soon find themselves out of work.

Evolution shows that social mimicry is a major component of our survival mechanism, pushing us to belong to groups. We're all subject to the chameleon effect, this tendency to unconsciously mirror what "others" do, and one of its most apparent manifestations is language and accents. Members of the same social group tend to mimic the speech patterns of others, leading to the rise of different regional accents within the same language. In the Southern United States, for example, English has developed in contact with Spanish, leading speakers on the two sides of the borders to pick up dialectal elements Like in Puerto Rico, the mix was so deep that "Spanglish" appeared. On both sides of the pond, in the United States and Britain, people speak English, yet with very distinctive accents that, in some cases, could be mutually unintelligible.

Tools and apps like Google Translate are getting better and better at translating one language into another. Alexander Waibel, professor of computer science at Carnegie Mellon University's Language Technologies Institute (@LTIatCMU), tells Here & Now's Jeremy Hobson how translation technology works, where there's still room to improve and what could be in store in the decades to come. "Over the years I think there's been a big trend on translation to go increasingly from rule-based, knowledge-based methods to learning methods. Systems have now really achieved a phenomenally good accuracy, and so I think, within our lifetime I'm fairly sure that we'll reach -- if we haven't already done so -- human-level performance, and/or exceeding it. "The current technology that really has taken the community by storm is of course neural machine translation.

English is spoken by hundreds of millions of people worldwide, but do the development of translation technology and "hybrid" languages threaten its status? Which country boasts the most English speakers, or people learning to speak English? According to a study published by Cambridge University Press, up to 350 million people there have at least some knowledge of English - and at least another 100 million in India. There are probably more people in China who speak English as a second language than there are Americans who speak it as their first. But for how much longer will English qualify as the "world's favourite language"?

Since launching in 2006, Google Translate has grown to over 500 million users worldwide, translating more than 100 billion words daily. In 2016, the tool supported 103 languages, with 92% of its users residing outside of the United States. While the tech giant sits comfortably atop the growing list of translator apps, there's one longstanding giant in the shadows, actively innovating and developing the blueprint for how companies like Google define the future of global communications. Founded in 1968, Systran stands as the leading provider of language translation software products, delivering real-time language solutions compatible for desktop, mobile, and web-based platforms. Credited as a pioneer in machine translation for over four decades, Systran remains committed to advancing multilingual communications around the world, removing language barriers between people and businesses to make forging meaningful connections seamless.