The initiative will collaborate on assessment benchmarks and data audit methodologies to develop culturally tailored communications-deployable AI for low-resource languages around the world.
MeetKai, a sovereign AI company building an end-to-end national AI stack, and GSMA, a global industry association representing mobile network operators, announced a groundbreaking initiative to close the artificial intelligence gap in low-resource languages (languages with limited digital resources) globally. This initiative builds on the GSMA's mission to close the AI language gap by enabling practical, large-scale AI adoption through communications infrastructure and enabling communities everywhere to benefit from the digital revolution.
Of the approximately 7,000 languages spoken around the world, less than 20 are considered “high resource” for AI, leaving billions of people without the benefit of current technology. This “AI language gap” deepens the digital divide, perpetuates bias, and risks excluding entire communities from critical public and private services. Through this initiative, MeetKai and GSMA will work together to develop and deploy efficient and culturally aligned language models that can be operated and distributed at scale across GSMA members' telecommunications networks.
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“We believe every country and every community should have the ability to shape the future of AI,” said James Kaplan, CEO of MeetKai. “Working with GSMA will connect our model and evaluation capabilities to the world's most powerful delivery layer, the telecommunications network. Together, we aim to deliver practical, culturally tailored AI that can serve people at scale, starting with the use of precise language and expanding through strong governance, benchmarking, and high-impact deployment.”
“Mobile networks are particularly well placed to ensure that AI is inclusive, locally relevant and accessible,” said Lewis Powell, Director of AI Technology at the GSMA. “This collaboration with MeetKai strengthens the GSMA's ability to bridge the AI language gap by accelerating work on language models, assessment frameworks, and responsible data approaches, enabling more communities, regardless of geography, to benefit from AI in ways that reflect their local culture, language, and needs.”
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