eQOURSE’s Post

🌐 Embracing AI in Translation: A Future of Opportunities 🌐 The panel discussion on “AI and the Future of Translation and Interpretation” at the Middlebury Institute, moderated back in May by Dean Laura Burian, brought to light some fascinating insights about the evolving landscape of the language services industry. Here are eight key takeaways from the conversation: 1️⃣ AI as an Intelligent Assistant (IA): Tools like AI can be invaluable partners, aiding tasks such as terminology research, text summarization, and more. Embracing them can enhance productivity without replacing human translators. 2️⃣ Expanding Job Roles: The industry is evolving, and with it, new roles are emerging. Translators and interpreters should be open to applying their skills in different contexts and not be confined by traditional job titles. 3️⃣ Collaborate with Tech: Instead of fearing AI, work alongside tech developers. Shape the future by being part of the solution, not just passive observers. 4️⃣ The Rise of Standards: The demand for top-tier translation and interpretation is growing. Mediocre skills won’t suffice; excellence and adaptability are key to staying relevant. 5️⃣ Traditional Skills Matter: Fluency, cultural nuance, critical thinking, and analytical skills remain crucial. These foundational skills are what set human professionals apart from machines. 6️⃣ Client Education: It's vital to educate clients on how AI and human elements can blend to produce the best results, emphasizing ethical considerations and quality. 7️⃣ Improving Technology: AI translation tools are becoming increasingly accurate and efficient, handling massive volumes of work. This advancement should be seen as an opportunity to enhance human capabilities. 8️⃣ Continuous Learning: The field is still in flux, with many questions yet to be answered. Ongoing research and adaptation are essential to stay ahead. In this era of rapid technological advancement, the key is to stay curious, adaptable, and proactive. Let’s embrace AI, leverage its strengths, and continue to lead with the unique human touch in translation and interpretation. Read the full article here: https://lnkd.in/dhwnVBqm. Feel free to connect and share your thoughts on how AI is shaping your professional journey as a linguist! #AI #Translation #Interpretation #FutureOfWork #LanguageServices #CareerDevelopment #TechIntegration #ContinuousLearning

👏I love this title! The answer to that question is that it is not about AI OR translators. It is all about AI AND translators. Can AI replace everything or anyone? No - or not yet for the most passionate AI supporters, even if predicting the scope of technology impact remains risky. 💡Highlighting and showcasing the necessary combination of human and machine intelligence is the best way to bring some people (back) down to earth and to put decision makers on the right track. The case of language management in general and translation in particuliar stands out to explain why humans are and remain in the center. For some types of content vertical experience and domain expertise are key to ensure the right level of accuracy, consistency, sensitivity or compliance. 🔥AI has been leveraged in multilingual content workflows for nearly 20 years. Linguists have been early AI adopters and tamers. And they are still there to prove that value comes from humans too.

If AI has gotten so good at language translation, why does the BLS predict that jobs for interpreters and translators will continue to grow over the next decade? Daron Acemoglu argues, "I think how good AI has become is often exaggerated ... There is pretty much nothing that humans do as meaningful occupation that generative AI can now do. So in almost everything it can at best help humans, and at worst, not even do that." Read more in NPR's Planet Money newsletter. https://lnkd.in/gRsighcm

🔍A recent Slator article explores how self-reflection is enhancing large language models' translation capabilities. By enabling models to review and refine their translations, AI systems are achieving higher accuracy and more nuanced linguistic performance. This approach marks a significant leap forward in AI-driven translation technology. But can this self-reflection truly outperform the expertise of human translators? #AI #Translation #LanguageModels #Innovation #TechTransformation

One of the best quotes I’ve seen about AI is that, “AI won’t replace you; a person using AI will.” This article about translators is a perfect example. You’d think AI might replace translators, given that we already have apps on our phones that can translate real time. But it isn’t. “Companies continue to hire human translators and interpreters because they understand that AI cannot replace the expertise and judgment that these professionals bring to the table.” It’s another real world example of workers using AI to enhance their productivity, rather than being replaced by it. #artificialintelligence #translators #productivity https://n.pr/3xzSSo2

If one sector has been truly shaken up by AI, it’s the translation industry. What roles are still left for human skills and experience, asks Nataliya Horbachevska, CEO of Task Force LSP in the latest issue of the BPCC's Contact Magazine Online. 👉🏻 https://lnkd.in/dkJZgcbC #bpccmagazine #bpcccontactonline #HR #Language #AI #Linguisticservices #translating #interpretation

🤖Generative AI in the form of large language models is influencing the world of translation. As it continues to develop we’re seeing LLM technology applied in lots of multilingual contexts. ❓But that doesn’t mean AI is the answer to all our translation challenges. 🔎Our latest blog investigates the role of GenAI in today’s translation environment and takes a step away from the hype to ask how it can add value to commercial translation workflows. 👉Read it here https://hubs.ly/Q02PxRcR0 #LanguageServices #tworks #WeMakeGlobalEasy

🌐 Is Artificial Intelligence Really Ready to Replace Human Translators? 🧠 Dive into the debate sparked by AI advancements in the language industry. Here are some of the intriguing insights from a recent article: 🔍 Ever-shifting languages: From 'deepfake' to 'groomzilla', how dictionaries struggle to keep pace with evolving language trends driven by global events and cultural shifts. 💬 Dazzled by ChatGPT: Explore how impressive AI outputs are challenging conventional translation methods and the impact of large language models. 🤖 Embracing tech in translation: A historical look at how translators have integrated technology, from the Rosetta Stone to neural MT and current CAT tools. 🔮 GenAI and translation: Unpacking the capabilities and limitations of ChatGPT in translation tasks, and the debate surrounding its reliability compared to traditional MT. 💡 Customer benefits: Understanding the role of AI like ChatGPT in the language industry, its current drawbacks, and the importance of human expertise in critical translation tasks. In a world where the language is always in motion, can machines truly grasp the intricacies of human communication? Join the conversation on the future of translation with AI revolutionizing the landscape. #LanguageTech #AIvsHumans https://lnkd.in/eHrD3QQi

#Artificialintelligence (#AI) is undeniably powerful, but human #translators remain irreplaceable! While AI can aid in translation tasks, it can’t match the creativity, #culturalsensitivity, and nuanced understanding that human translators bring to every project. Explore why experts say human translation jobs are here to stay: https://ow.ly/tMhL50SFkaL

Want to Build to Multilingual Translation machine learning model? In my recent work, I experimented with the NLLB-200 model and developed a translation tool using Streamlit as the user interface. The tool allows users to input text in various languages, with FastText integrated for language prediction. FastText identifies the language of the input text, as NLLB-200 alone does not include this capability. This prediction is then used to direct the input to NLLB-200 for translation. A quick Intro on NLLB-200: 1. No Language Left Behind (NLLB-200) is a transformative addition to Meta AI’s series of extensive machine translation models, representing a major advancement in multilingual translation. NLLB-200 can perform translations across 200 languages with notable accuracy improvements, achieving a 44% accuracy rate over previous models. This model is particularly optimized for low-resource languages, with a strong emphasis on African languages, including Kamba and Lao. 2. The NLLB-200 is trained using LASER (Language-Agnostic Sentence Representations), a multilingual model that embeds sentences into a shared vector space, bridging the gap for underrepresented languages. By incorporating LASER's embeddings, NLLB-200 enhances performance in low-resource languages, allowing for more contextually accurate translations in languages with limited resources. Limitations of Using this Model For practical application, it is important to note that the model processes 516 tokens at a time, optimizing resource management while still enabling high-quality translations. Through this work, the integration of NLLB-200 and FastText offers a robust and scalable solution for multilingual translation, with a focus on inclusivity and accessibility across languages that often receive minimal representation in translation technology. #Machinelearning #NLP #AIengineer #AI #NLLB #UKAIJobs #UKAI #Surrey #UniversityAI