
BharatGen, a consortium backed by the government, is set to introduce a revolutionary 17-billion-parameter multilingual AI model during the India AI Impact Summit 2026. This foundational model, known as Param2 17B, will support 22 Indian languages and has been meticulously developed using Indian data, as stated by the consortium. The launch of Param2 17B represents a significant milestone in India's ambition to enhance its domestic AI capabilities, aligning with the 'Viksit Bharat 2047' vision aimed at achieving technological self-sufficiency. BharatGen emphasized that this launch is not merely a technical achievement; it is pivotal in their mission to create an AI ecosystem that prioritizes India’s linguistic diversity and is trained on local data. The applications for this model are extensive, targeting sectors such as enterprise, governance, education, healthcare, and agriculture. Demonstrations are planned in collaboration with both government and industry partners to showcase its potential. Rishi Bal, CEO of BharatGen, highlighted the importance of language inclusivity and practical deployment in this initiative. "Progress in AI for India must focus on language accessibility and contextual relevance. BharatGen is committed to developing AI solutions that function effortlessly across various Indian languages and sectors," Bal remarked. He further noted that these models are tailored for real-world applications in citizen services, finance, healthcare, and education, all while bolstering India's sovereign AI framework. The summit will not only showcase these advancements but also facilitate rapid market entry for developers, startups, researchers, and enterprises looking to create AI solutions tailored for India and beyond. Supported by the IndiaAI Mission, BharatGen has also received government-backed high-performance computing resources to further its initiatives. The consortium plans to present multilingual AI systems capable of processing text, speech, and vision, leveraging the "Bharat Data Sagar," a comprehensive data repository developed through a combination of public and private datasets.
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