What if you could speak every language… without ever recording again? 🌍 In this short, I show how I made three versions of myself — English, Hindi, and Spanish — using Gan.AI. Just upload a short clip, and Gan.AI automatically recreates your voice, face, and lip-sync perfectly across 40+ languages. No studio. No reshoots. No translators. Just pure AI magic! 💡 Try it yourself: https://www.gan.ai 🎯 Go multilingual in 2025 — one face, every language. Here's the complete list of supported languages: https://lnkd.in/ddkKrjEM #GanAI #AIVideo #AIContentCreation #YouTubeAutomation #AIAvatars #TextToVideo #AITranslation #VideoLocalization #MultilingualCreator #AIVideoGenerator #AIForCreators #CreatorTools #AITools #AIToolsForYouTube

I'm Not a Linguist (And Why That's Better for You) Let me be clear: I'm not a linguist. I didn't study linguistics at Oxford. I don't have a PhD in etymology. I'm not teaching you "proper" grammar. I'm a professional who discovered that vocabulary precision is the difference between being heard and being overlooked. And that perspective — someone who needed this solution, not someone who studied it academically — is exactly why the Vocabulary Builder Kit works. Here's what linguists do... They analyze language patterns. They research historical word origins. They understand language at a theoretical level. Here's what I do... I show you how to use vocabulary to win opportunities, earn respect, and communicate with authority. The difference? A linguist teaches you about words. I teach you how to deploy words for impact. A linguist focuses on correctness. I focus on credibility, confidence, and results. Vocabulary Builder Kit isn't an academic course. It's a strategic toolkit. Every word comes with: • Business context (proposals, pitches, presentations, content) • Strategic commentary (when this word matters most) • Real-world application (not just definitions) You don't need a linguist for this. You need someone who understands the gap between having expertise and expressing it with authority. That's what VBK delivers. Words That Match Your Intelligence. First 5 spots: FREE lifetime access After that: ₦2,000/month after 14-Day FREE trial. Start now: https://lnkd.in/dpSby-dy Did you notice the double "I" in Linguist on the image? It was meant to drive home the point — Visually. To your excellence, —Eric Akuranya #VocabularyBuilderKit #CommunicationAuthority #ProfessionalDevelopment #CareerGrowth #Entrepreneurship #PersonalBrand #Leadership

The Golden Translation Strategy – Clear, Systematic, and Reliable This strategy moves through two deliberate stages. In SL1 → SL2, the translator engages in deep analysis of the source text—its structure, syntax, and meaning—then mentally reformulates the ideas in the target language to prevent literal transfer and structural interference. In TL1 → TL2, a first draft is produced, then refined into a polished version through revision that enhances coherence, naturalness, and stylistic quality. This method is “golden” because it mirrors the natural cognitive path of understand → rethink → draft → refine, ensuring translations that are accurate, readable, and methodologically sound. It also provides trainees with a disciplined, step-by-step framework that reduces both linguistic and meaning-related errors. Mohamed El-nemr

Ever read a translation that made zero sense? Especially with Signed Languages and other low incidental languages. You probably have. Because words without context aren’t communication, they’re friction. That’s where most machine translation tools miss the mark. They’re built to process text, not people and culture. At PIVOT, we do it differently. Yes, we can use machine learning, but only with context. Our models are trained to understand tone, intent, and cultural meaning and then reviewed by humans who actually live in those languages. Because “translation” isn’t the same as “understanding.” And if your message isn’t understood, it’s not accessible. PIVOT is a platform to bridges that gap, giving organizations a way to foster understanding clearly across 7,000+ spoken and 300+ signed languages, without losing what makes their message human. This also empowers the importance of including native language users in the development, building, and reviewing of translations. #LanguageAccess #Accessibility #InclusiveInnovation #MachineLearning #AIForGood #PIVOT #dozanu #DigitalInclusion

Quick RAG question: If you have a multilingual dataset in OpenSearch (English as the base articles + translations in other languages), and all articles are semantically chunked + embedded, what’s the best retrieval strategy for multilingual semantic search given that users query can be in any language and the translated articles in the open search chunks are limited except from the root articles “English”? Option A: • Always search only the English embeddings (base articles). • Even if the user queries in German/Spanish, run semantic search against English chunks. • Then map results back to the user’s language via metadata (root_article_id, translation fields). Option B: • Search all embeddings across all languages directly. • Let the multilingual model handle cross-language similarity. • Then deduplicate using the root_article_id to avoid returning duplicate translations. Ssssooooo my question.. stay with me here: Which do you think ranks better for relevance and language preference and why? Curious how others are handling this in real systems 👀… Especially with models like Snowflake Arctic / multilingual-MiniLM / LaBSE

🌍 RAG for Multilingual Retrieval RAG can handle documents and queries in multiple languages, broadening access and coverage. --- 🔍 Simple analogy: It’s like having a translator 🗣️ who helps you search in any language you need. --- 💡 How it works: - Use multilingual embeddings and models - Map queries to the right document language - Retrieve answers across languages, sometimes with translation This is vital for global companies or international user bases. --- 🚀 Key takeaway: Multilingual RAG makes powerful, cross-language search and answer possible. #MultilingualRAG #LLM #GlobalSearch #TechExplained

🗣️ Multilingual Prompting Strategies 1) Specify languages and roles “You are a translator and editor. Understand [source], answer in [target].” Example: “Understand English, answer in Italian, keep glossary terms.” 2) Split the job Ask separately for: translation, terminology, tone, and final formatting. Example: “First translate, then proofread, then give a final Markdown version.” 3) Glossary solves half the problems Provide untranslatable terms and target equivalents.  Example: “Keep: CTA, UTM. ‘landing page’ → ‘açılış səhifəsi’; ‘conversion rate’ → ‘konversiya dərəcəsi’.” 4) Few-shot on both languages Show 1–2 pairs of input → desired output. The model mirrors structure and tone. #chatgpt #promptengineering #prompt #multilingual

HOW TO LEARN A FOREIGN LANGUAGE BY YOURSELF IN 2 SENTENCES ------------------------------------------------------------------------------- Pick up any page in any language you want in Wikipedia and study each word by translating it with Google, Google Translate or Chat GPT. You'll be amazed by your progress. So 5 words a day. 150 a month! This is the old way of learning foreign languages: Translating texts. TRY THIS METHOD! Bonne chance!

🇵🇱 Polish outperforms 🇬🇧 English in long-context understanding An arXiv study titled: One Ruler to Measure Them All tested leading language models across 26 languages and context lengths up to 128k tokens.    🇵🇱 Polish topped the charts outperforming English, French, and German in long-context understanding.     🇬🇧 English, despite being the dominant training language, ranked only 6th overall.      🇭🇺 Hungarian landed solidly in the upper-middle tier, matching or even surpassing several high-resource languages up to 64k tokens. Top-tier models like Gemini 1.5 Flash and Qwen 2.5 72B delivered near-perfect results across all languages, but smaller models (e.g. 8B variants) showed steep accuracy drops beyond 64k tokens, especially for mid-resource languages. 🧠 Takeaway: Multilingual capability no longer means English-first. Hungarian and several other mid-resource languages are catching up fast, a promising sign for local innovation and research that rely on native-language LLM performance. Picture and source: Kim, Y., Russell, J., Karpinska, M., & Iyyer, M. (2025). _One ruler to measure them all: Benchmarking multilingual long-context language models_ (No. arXiv:2503.01996). arXiv. Credit: I found this study thanks to Rafal Rutyna's post.