AI Solutions For Language Translation

AI doesn’t speak just one language. It never should. It should speak to, and for, all of us! From the steppes of Mongolia to the villages of India and the ministries of Chile, local AI experts are proving that sovereign, locally useful AI models can flourish even with limited resources. These efforts show that the barriers to multilingual AI can be overcome with creativity, determination, and modest funding. The question now is: how can we support and scale these efforts globally? #Mongolia – Egune AI Very happy to see Bloomberg News highlight Egune AI today, a small startup that built the first Mongolian-language foundation model from scratch. This team made the country 1 of just 8 to develop its own national model. With only $3.5M in local seed funding, they now power over 70% of the nation’s AI market. Their work protects Mongolian language and culture through homegrown AI - a powerful example of what’s possible when communities build for themselves. #India – Bhashini India’s BHASHINI - (Digital India BHASHINI Division) is a government-backed, public–private mission to make AI inclusive for all Indian languages. Launched under the National Language Translation Mission, Bhashini supports over 35 languages through an open-source model which provides real-time translation tools in text -to-text, speech-to-text, and video translation services. Through the “Bhasha Daan” crowdsourcing initiative, thousands of people are contributing text, voice and video data and translations to help the AI learn. Bhashini bridges digital gaps across the country and creates datasets for underrepresented languages. It has  already hit 1 billion+ inferences.     #Chile (Latin America) – #LatamGPT Chile is leading a regional push for AI sovereignty through a Spanish-language foundation model called Latam GPT. Under the leadership of my dear friend Minister Aisen Etcheverry, the Ministry of Science, Technology, Knowledge and Innovation is building a model that reflects Latin America’s own histories, dialects, and values. With support from CENIA and a university-backed supercomputer, the project is advancing on just a few million dollars in funding. The model is designed to be open, adaptable, and shared across countries — “AI by Latin America, for Latin America.”    The call to action: Multilingual AI capacity is often described as a roadblock to universal access. But these efforts prove it doesn’t have to be. 🔹 How do we support and scale grassroots AI infrastructure? 🔹 Can we pool funding, talent, and knowledge to help more countries build their own models? 🔹 What does a global ecosystem look like when every language has a voice in shaping it? #AIforAll #LocalAI #MultilingualAI #Innovation #aipolicy Nick Martin Hugging Face Satwik Mishra Bloomberg News Nick Cain Mary Rodriguez, MBA Mathilde Barge Nagi Otgonshar  Ashwini Vaishnaw S Krishnan Abhishek Singh Tara Chklovski Room to Read Vivian Schiller Aspen Digital

There are very few use cases where I think AI content is ready for primetime. I believe language translation is ready. Both YouTube and Meta have announced language translation across text (yawn) and speech (!!!) Not to mention amazing companies like Deeptune, Papercup, and an army of others who have exceptionally impressive demos and strong traction. And here's why it matters: 70% of all internet users are active on at least one Meta platform. 15% of the world speaks English. But 58.8% of all websites are in English, and 65% of the top 250 YouTube channels are in English. In other words: we have an English-centric content universe in a non-English-centric world. The impact of that? The US, UK, Canada, Australia, etc. become great exporters of culture but poor importers of culture. And incredible brands in non-English speaking territories are limited in their reach through influencer marketing and media spend. But with AI-based English translations that are viewed as seamlessly as English-native content, there is no reason why HolaSoyGerman can't have US fandom and reach that competes with Markiplier. And for those of you creating content for the US market, think of how your reach can expand when you're able to activate more than double your current market through AI dubbing. The market for great content will become more competitive, but great creators will clock a huge win without spending hundreds of hours on Duolingo (not that there's anything wrong with that...I'm on a 700 day streak...) What do you think? Is your brand or content going to use this tech in the next year? If you want to discuss the strategy behind using these companies or platforms, slide into my DMs! #creatoreconomy #ai #localization https://lnkd.in/eTDj-hg8

From K-beauty to K-pop Demon Hunters (a favorite in my household), Koreans understand how to scale systems that resonate globally. When I visited Korea earlier this year with Raveendrnathan Loganathan, I experienced something unforgettable: Relentless iteration, deep execution, and a long-view investment in culture and innovation. We’re bringing that same mindset to our Agentic Enterprise.  With Salesforce Help now fluent in Spanish, French, Portuguese, German, Italian, and Japanese, our multilingual agents handle 85% of global case volume, quickly and securely. Built on Agentforce, Data Cloud, UMA, Einstein, and Service Cloud, our stack ensures that AI agents can: - Ingest and act on real-time customer context via UMA orchestration - Surface grounded, accurate responses across any language - Escalate with empathy when human intervention is needed This is live in Paris, Seoul, and Dubai, with full provisioning and traffic, anchored by customer demand. This is what it looks like when:  1. Multilingual LLMs meet real-time data orchestration  2. Governance, privacy, and policy are embedded from the start  3. Agents scale with your people, not in place of them To our product, engineering and customer success teams led by Srini Tallapragada, Steve F., Joseph Inzerillo, and Jim Roth: You’re architecting the future of enterprise service with AI Agents, AI that understands, systems that scale, and teams that lead. Let’s go! 🙌 #Leadership #International #ArtificialIntelligence #Agentforce #MultilingualAI #AgenticEnterprise

When your deadline is an FDA filing—not a blog post—“good enough” translation simply isn’t good enough. That’s why I’ve been hands-on with X-doc.ai’s brand-new Deep and Master models. They’re built for the moments when a single mistranslated term could stall a clinical trial, void a patent, or derail a billion-dollar merger. Deep blazes through high-volume projects—think hundreds of SOPs or investor disclosures—in minutes, all while locking in consistent terminology across every file. When speed is king, Deep keeps the crown. Master takes its time—about ten minutes per file—and rewards you with human-level nuance and sentence-by-sentence consistency. I ran a 280-page Chinese clinical protocol through it last night; the output read like a seasoned pharma translator spent a week polishing it. Regulatory-submission ready, straight out of the box. Better still, both models play nicely with your existing workflow: pre-format a PDF to Word, upload, and watch the magic. No more juggling freelancers, no more six-figure translation bills—just audit-ready accuracy at startup speed and at roughly 2 % of traditional costs. Ready to see the difference a purpose-built translator makes when the stakes are sky-high? 🔗 Check it out: https://x-doc.ai/ & follow X-doc.ai for updates. #Xdoc #Xdoctranslation #AItranslator #translation #AItools #localization #legaltech #pharmatech

With LLMs, you can bring the quality to the next level without bringing new training data - basically, you can pay to improve the translation quality. Before, the only way to achieve this was through human services. Key points: NMT limitations: - Quality ceiling due to diminishing returns on data and tech investments - Struggles with low-frequency requirements in training data - Can't account for context (speaker, product category, audience) - Primarily works at sentence level, limiting consistency How LLMs differ: - No inherent quality ceiling - Adaptable via prompt engineering, not just training - Can improve through multi-agent solutions without more training data - Can handle context and work across multiple sentences While LLMs aren't always cheaper, they offer unprecedented potential for automating large-scale high-quality translation. This could be game-changing for large, recurring content needs in enterprise localization. It also means the translation automation market expands x10 to the whole translation market, which means more venture money and better products and solutions in the upcoming 2-3 years. #MachineTranslation #LLM #Localization

An LLM might know what “Apple” means. But how can it know whether you’re talking about a fruit or a tech company? If we want to move deeper into AI-enabled workflows that’s the kind of nuance they’re going to need to handle in Localization. Our field is drowning in unstructured data, support tickets, product specs, marketing copy and while large language models can process these, they still need structure, context, and precision to make more sense of it all. That’s where vector embeddings and knowledge graphs can make a difference. Vector embeddings allow machines to understand meaning numerically. They help detect semantic similarities, flag hallucinations, and improve machine translation quality. Knowledge graphs, on the other hand, model relationships. They organize information about entities, people, brands, terms, and how they relate to each other. They don’t just help LLMs understand language better. They help them reason through it. Benjamin Loy, Principal engineer at Smartling laid this out beautifully in our recent LocDiscussion interview. He’s not just thinking about these technologies in theory, he’s already using them. Vector embeddings, as he explains, have been instrumental for hallucination detection. They’re fast, cheap, and effective when you’re comparing a source and target sentence. But he also sees the promise of knowledge graphs as the future of terminology 2.0, especially when you’re dealing with problems like multilingual named entities, pronoun disambiguation, and complex domain-specific knowledge. It’s rare to find someone who can articulate the trade-offs so clearly. Ben doesn’t oversell the shiny new thing, he talks candidly about ROI, token cost, storage, and where the real gains might be. It’s the kind of pragmatic, forward-looking perspective that localization needs more of right now. So here’s the question: If you were redesigning your multilingual technology infrastructure from scratch, would you prioritize vectors or graphs? What’s your bet on the future?

🚨 Critical Release: AI Interpreting Solutions Evaluation Toolkit - Part A 🚨 As a founding member of the SAFE AI in Interpreting Task Force, I'm proud to announce the launch of our comprehensive AI Interpreting Solutions Evaluation Toolkit, developed in collaboration with CoSET (Coalition for Sign Language Equity in Technology). The continued collaboration between SAFE AI and CoSET represents a unique and invaluable alliance between spoken and signed language interpreting communities to address how AI is disrupting our profession. This resource comes at a pivotal moment for our profession, which is facing unprecedented headwinds that threaten both quality and equity in language access: 🔸 Market Saturation: AI interpreting solutions are flooding the market daily, with vendors making bold claims about being "equivalent to human interpreting" without rigorous testing or evidence 🔸 Federal Policy Shifts: Current federal directives are rolling back language access compliance funding while simultaneously pushing agencies to adopt AI interpreting solutions across the board—despite legal requirements remaining intact 🔸 Budget Pressures: Healthcare institutions, educational systems, and courts are under intense financial pressure from federal cuts, making them vulnerable to choosing cost over quality This toolkit provides what decision-makers desperately need: practical, risk-informed checklists covering organizational readiness, setting-specific guidance, risk assessment frameworks, vendor evaluation criteria, and RFP templates. To language access directors, compliance teams, CTOs, and procurement professionals: This free resource can help you navigate vendor claims, assess true readiness, and maintain both innovation AND compliance with civil rights requirements. Read the press release here: https://lnkd.in/gXcNZiAX Download Part A now: https://lnkd.in/ggDTsMkP Parts B (Technical Specifications) and C (Legal & Practical Considerations) coming soon. #LanguageAccess #Interpreting #CivilRights #HealthcareEquity #deafai #LegalCompliance #SAFEAITaskForce Nimdzi Insights CSA Research American Translators Association Morgan Teller Lierley Nate Klause Giovanna Carriero-Contreras Lorena Ortiz Schneider Cynthia E. Roat Bill Rivers

🎓 Excited to share my new video series: Building Production-Ready Multilingual AI Support Systems on AWS! Learn how to build a scalable customer support system that handles Spanish, French, and Russian queries using LORA adapters and AWS SageMaker. Perfect for ML engineers and architects looking to implement multilingual AI solutions. 🔹 Part 1: Architecture & Foundations [https://lnkd.in/eKvpHDGN] 🔹 Part 2: AWS Setup & Configuration [https://lnkd.in/e3qAxHXd] 🔹 Part 3: LORA Adapter Implementation [https://lnkd.in/e4YnP5vU] 🔹 Part 4: Query Processing Pipeline [https://lnkd.in/eQ8ni3Wv] 🔹 Part 5: Testing & Live Monitoring [https://lnkd.in/eKYrWNHA] ✨ Features: Cost-effective multilingual support Dynamic language switching Real-time monitoring Production-ready testing Watch the complete series here: https://lnkd.in/esaztJ-9 #ArtificialIntelligence #AWS #MachineLearning #CloudComputing #SageMaker #MLOps

Leveraging Agent-Based Approaches for Complex Tasks There's growing buzz around how agent-based approaches can help AI tackle complex tasks that seem beyond the reach of today's LLMs. Here are two promising methods (all references in the comments): Role-Playing Agents: Traditional machine translators often fall short in complex translation tasks, such as translating books—something even highlighted in a recent NYT article. A recent paper introduces a multi-agent approach to mimic organizational task execution. They created specialized AI agents acting as a CEO, senior editors, junior editors, translators, localization specialists, and proofreaders. The senior editor agent sets editorial standards, junior editor agents plan the workflow, localization specialists handle cultural references, and proofreader agents focus on proofreading. This collaborative effort, with editor agents critiquing AI translations against editorial guidelines, resulted in AI translations preferred over human ones by evaluators. I've also found critique agents invaluable in my work. Increasing Agent Count: For challenging tasks, employing multiple agents to perform the same task and then combining their work (e.g., through averaging or voting) can enhance performance. A recent study applied an ensemble of LLM models to reasoning and code generation tasks, finding that performance improved with more models. Remarkably, an ensemble of 15 Llama2-70B models outperformed a single GPT-3.5 model. In my own work on evaluating natural language generation, I found that an ensemble of 10 LLM evaluators surpassed a single evaluator. Note: While these approaches significantly boost performance, they also increase costs and slow down the system.

What if AI could speak our language—not just with words, but most importantly, with culture, traditions, and identity? What we are talking about here is Cultural Intelligence at Scale. This is so exciting because we really do know how AI really can respect languages, traditions and identities. And we deliver it with Microsoft. Imagine being in a world where AI tools truly see you—beyond language and into the depth of your culture. That’s not sci-fi; it’s real. And it matters. Here’s why: ● In a recent meta-analysis of over 128,000 people, we saw cultural intelligence (meaning, how we adapt across cultures), is twice as strong a predictor of performance and adjustment as language skill is alone (CQ ≈ .36 vs. language ≈ .16); as proven in ScienceDirect, NeuralSlate, The Culture Factor News, and ResearchGate. ● New AI frameworks have the ability to translate languages while preserving cultural context, idioms, and meaning; even in low-resource or endangered languages, and this aligns with the identity behind the words (arXiv). ● Indigenous youth are using AI to save native languages by building coding apps that protect their traditions and give them a voice in tech (as seen in Wired, Teen Vogue, and Wikipedia). Together, these advancements point to a future where AI doesn't just speak. It understands, it honors, and it preserves our many heritages. When AI respects culture, it builds trust, supports belonging, and strengthens communities. And that’s the kind of modern work we’re proud to support. 👉 Follow me for more content that blends culture, creativity, and connection. 👉 Be the first to know what’s new: https://lnkd.in/ekjY27_V ♻️Let’s widen the circle—share this with a fellow changemaker. #CulturalCohesion #InnovationAtScale #GlobalImpact