Community-Based Language Initiatives

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

Our paper describing the first 2 years of our #IndigenousAI project is now available (https://lnkd.in/dE6ThQ_z), describing a proposal of an AI development cycle specific for the context, our engagements with Indigenous communities in Brazil, and related technical discussions. Since 2022 we have been exploring application areas and technologies in which Artificial Intelligence (AI) and modern Natural Language Processing (NLP), such as Large Language Models (LLMs), can be employed to foster the usage and facilitate the documentation of Indigenous languages which are in danger of disappearing. We start by discussing the decreasing diversity of languages in the world and how working with Indigenous languages poses unique ethical challenges for AI and NLP. To address those challenges, we propose an alternative development AI cycle based on community engagement and usage. Then, we report encouraging results in the development of high-quality machine learning translators for Indigenous languages by fine-tuning state-of-the-art (SOTA) translators with tiny amounts of data and discuss how to avoid some common pitfalls in the process. We also present prototypes we have built in projects done in 2023 and 2024 with Indigenous communities in Brazil, aimed at facilitating writing, and discuss the development of Indigenous Language Models (ILMs) as a replicable and scalable way to create spell-checkers, next-word predictors, and similar tools. Finally, we discuss how we envision a future for language documentation where dying languages are preserved as interactive language models. This is joint work of IBM Research and the USP - Universidade de São Paulo through the C4AI - Center for A.I. # USP+IBM+FAPESP Brazil, by Claudio Pinhanez, Paulo Cavalin, Luciana Storto, Thomas Finbow, Alexander Cobbinah, Julio Nogima, Marisa Affonso Vasconcelos, Ph.D., Pedro Henrique Domingues, Priscila Mizukami, Nicole Grell Macias Dalmiglio, Majoi Gongora, and Isabel Gonçalves. Some info about the project: https://lnkd.in/d7bg4jPB #IndigenousAI #IBMResearch #ResponsibleAI #AI4Good #C4AI #USP Paper link: https://lnkd.in/dE6ThQ_z

Taking Santhali Digital: What It Teaches Us About Language, Technology, and Learning A remarkable story is unfolding in the heart of eastern India, where coders, teachers, and grassroots educators are scripting a new future for one of India's oldest languages; Santhali. Having led a digital learning initiative in Jharkhand, I have seen firsthand how language sits at the heart of all meaningful education. It is not just a medium of instruction. It is a vessel of worldview, memory, and belonging. Yet it took over a hundred years since Ol Chiki’s creation for the script to find real traction in the digital world. Why? Because our digital infrastructures, Unicode standards, keyboards, operating systems, have been built without the multilingual, oral, and script-diverse realities of India in mind. What this movement around Santhali teaches us is clear: (i) Learning in one’s mother tongue builds not just comprehension, but confidence (ii) Community-led digital innovation is often more sustainable than top-down (iii) Edtech must support scripts like Ol Chiki to be truly inclusive interventions The future of education in India lies not in scaling uniformity, but in honoring multiplicity. When every child sees their language on a screen, they know they belong in the classroom and in the world. #Santhali #OlChiki #EdTech #LanguageJustice #Jharkhand #MultilingualEducation #DigitalInclusion #GrassrootsInnovation

In the epilogue to Empire of #AI: Dreams and Nightmares in Sam Altman's OpenAI, Karen Hao explains how large language models contribute to #language loss and highlights an alternative approach: the Māori community’s use of AI to support language revitalization. Peter-Lucas Jones and Keoni Mahelona have deceloped a speech recognition tool for te reo Māori using historical and contemporary recordings. Their work is guided by: - consent - reciprocity - community sovereignty throughout the process The purpose of this tool and related tools is to support te reo Māori language learning and language access. Instead of focusing on scale, they’re building a small, purpose-specific language model aligned with cultural values. About the book: https://lnkd.in/gTRtcGKj Peter Lucas Jones on Māori Data Sovereignty & Language Revitalization at Vancouver AI Community Meetup: https://lnkd.in/ggduJBWa Podcast Māori AI research is changing the world...: https://lnkd.in/gT5Eyyva #Indigenous