India Unveils BharatGen: Pioneering Government-Funded AI in Indian Languages

India launched BharatGen, a ground-breaking initiative to develop generative AI in Indian languages. Spearheaded by IIT Bombay, the project aims to create high-quality text and multimodal content. The launch also featured the introduction of four thematic hubs under the National Quantum Mission (NQM) to advance quantum computing and related fields.


Devdiscourse News Desk | New Delhi | Updated: 30-09-2024 22:58 IST | Created: 30-09-2024 22:58 IST
India Unveils BharatGen: Pioneering Government-Funded AI in Indian Languages
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On Monday, India unveiled BharatGen, a revolutionary initiative aimed at making generative AI accessible in multiple Indian languages. The project is the world's first State-funded effort of its kind, according to Science and Technology Minister Jitendra Singh.

Led by IIT Bombay under the National Mission on Interdisciplinary Cyber-Physical Systems (NM-ICPS), BharatGen seeks to produce AI systems that can generate high-quality text and multimodal content in various Indian languages.

The launch event was graced by Principal Scientific Advisor A K Sood, Department of Science and Technology Secretary Abhay Karandikar, and Department of Telecommunications Secretary Neeraj Mittal. The Science and Technology Minister sent a video message for the occasion.

In addition, Sood inaugurated four thematic hubs focused on Quantum Computing, Quantum Communication, Quantum Sensing and Metrology, and Quantum Materials and Devices under the National Quantum Mission (NQM). These hubs, located in premier institutions like IISc Bengaluru and IIT Madras, aim to drive significant research and innovation in their respective fields.

The BharatGen initiative, characterized by its open-source foundational models, is expected to democratize AI in India, enabling researchers and developers to work collaboratively.

The project is set to conclude in two years, benefiting government, private, educational, and research sectors. A core aspect is data-efficient learning, targeting Indian languages with limited digital presence, and aiming to develop effective models through collaboration and fundamental research.

(With inputs from agencies.)

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