IndiaAI Mission's Pact with the Indian Parliament

 

Introduction
 

• During the Raisina Dialogue 2025, Union Minister Ashwini Vaishnaw announced a landmark agreement between the IndiaAI Mission and the Indian Parliament, enabling access to Parliament's extensive data for developing indigenous artificial intelligence (AI) technology.
• Emphasizing the necessity of creating India's own Large Language Models (LLMs), akin to ChatGPT, Minister Vaishnaw highlighted this strategic step towards strengthening India's AI capabilities and reducing dependency on foreign technologies.
   

Key Highlights of the IndiaAI Mission's Pact with the Indian Parliament
 

1. Importance of Indigenous AI:
   • Development of Local LLMs: Minister Ashwini Vaishnaw stressed the urgency of creating India's own Large Language Models (LLMs), akin to ChatGPT, to ensure technological independence, as open-source technologies may become proprietary in the future.
   • Data Sovereignty: Indigenous AI development safeguards data security and ensures sovereignty, reducing dependency on foreign models and protecting sensitive national data.
2. Landmark Pact with Parliament:
   • Extensive Data Utilization: The agreement grants access to Parliament's rich repository of multilingual datasets, accumulated over decades, for training advanced AI models tailored to India's needs.
   • AI Kosh Infrastructure: A unified compute infrastructure, AI Kosh, has been established to facilitate efficient training and deployment of AI models using these datasets.
3. Additional Data Sources for AI Development:
   • Diverse Contributions: Organizations such as Doordarshan and All India Radio (AIR) are providing additional datasets, enhancing the diversity and robustness of AI model training.
4. Indigenous GPU Development:
   • Timeline for GPUs: The government aims to develop indigenous Graphics Processing Units (GPUs) within the next 3 to 5 years, a critical step toward achieving self-reliance in AI computing infrastructure.
   • Evaluation of Instruction Sets: Two distinct instruction sets are being tested to identify the most effective approach for GPU development.
5. Funding and Infrastructure Expansion:
   • Significant Investments: The government is considering substantial funding, similar to the India Semiconductor Mission, to accelerate AI research and development.
   • Nationwide GPU Deployment: Plans include deploying high-end GPU-based computing facilities across India to support startups, researchers, and academia in advancing AI innovation.
6. Talent Development and Collaborative Partnerships:
   • Leveraging India's Talent Pool: India’s vast pool of skilled professionals is a major asset in driving AI innovation and development.
   • Public-Private Partnerships (PPPs): Collaborative efforts between government agencies, private enterprises, and academia are essential for staying competitive in the global AI race.
7. India's Strategic Position in the Global AI Landscape:
   • Focus on Domestic Needs: India is uniquely positioned to lead in AI by addressing its domestic challenges while remaining globally competitive.
   • Collaborative Opportunities with Global Leaders: Minister Vaishnaw highlighted potential partnerships with countries like the U.S., fostering international collaboration in AI development.
      

Importance of Indigenous AI Models
 

1. Technological Autonomy:
   Indigenous AI models reduce dependence on foreign technologies, ensuring control over AI infrastructure and better data privacy.
   • Example: India's efforts to develop its own Large Language Models (LLMs) like ChatGPT enhance sovereignty.
2. Data Sovereignty:
   Local AI development ensures sensitive data remains within the country, aligning with data protection laws and reducing external risks.
   • Example: China's indigenous AI(China's Baidu AI) systems process and store data locally, ensuring compliance with regulations.
3. Customization and Adaptability:
   Indigenous models offer tailored solutions for cultural, linguistic, and economic needs, making them more relevant to local contexts.
   • Example: India's AI Kosha provides datasets specific to Indian languages and diversity.
4. Economic Benefits:
   Fosters job creation, supports startups, and encourages collaboration between academia, industry, and government for economic growth.
   • Example: South Korea's investments in indigenous AI have driven innovation and created new industries.
5. Performance and Efficiency:
   Local AI solutions operate with lower latency, improving real-time decision-making and application performance.
   • Example: Japan's indigenous AI infrastructure enhances efficiency in manufacturing and healthcare.
      

Challenges Hindering India's AI Development
 

Financial Challenges

• High Investment Requirements: Sovereign AI development requires billions of dollars, making it difficult for Indian firms to compete with giants like Google and Microsoft. India's Rs 10,371 crore AI budget lags behind China's $20 billion annual AI investment.
• Funding Constraints: Limited long-term funding restricts large-scale AI projects, especially for startups and SMEs. The IndiaAI Startup Scheme provides Rs 945 crore over five years, benefiting only 650 startups, leaving many unfunded.

Regulatory and Ethical Challenges

• Lack of Clear Regulations: India lacks a comprehensive AI regulatory framework, leading to risks of misuse and ethical violations. The Digital Personal Data Protection Act (DPDP) 2023 addresses privacy but not algorithmic accountability or bias mitigation.
• Bias and Fairness Issues: AI models trained on unbalanced datasets may perpetuate biases in hiring and credit scoring. Tamil Nadu’s DEEP-MAX Scorecard assesses fairness before AI deployment in public services.
• Integration Challenges: Many Indian financial institutions face siloed data issues and outdated infrastructure, limiting high-ROI AI adoption (PwC report).

Strategic Challenges

• Global Competition: India competes with AI leaders like the U.S., China, and the EU, who heavily invest in foundational models and infrastructure. China's DeepSeek R1 challenges proprietary models like GPT-4.
• Collaboration Gaps: Unlike the U.S., where Stanford’s AI Lab fosters strong academia-industry partnerships, India lacks robust collaborations to bridge AI talent shortages.
   

Way Forward for India's Indigenous AI Development
 

1. Strengthening AI Infrastructure:
   • Deploy 18,000 GPU servers to create a robust AI computing infrastructure for startups, academia, and government.
   • Offer subsidized GPU access at under ₹100/hour to make AI research affordable.
2. Building Indigenous AI Models:
   • Develop foundational AI models, including LLMs, tailored to Indian languages and contexts to reduce foreign dependency.
   • Launch the first indigenous foundational model within 10 months to boost ethical and localized AI solutions.
3. Improving the Data Ecosystem:
   • Launch the IndiaAI Datasets Platform to provide high-quality, non-personal datasets for training AI models.
   • Implement the Bharat Data Sagar Initiative to curate diverse datasets reflecting India’s linguistic and cultural diversity.
4. Promoting Ethical AI Governance:
   • Establish an AI Safety Institute for bias mitigation, transparency, and ethical governance in AI development.
   • Foster public-private partnerships to ensure impactful and responsible AI solutions.
5. Fostering Innovation and Talent Development:
   • Expand the FutureSkills Program by setting up Data & AI Labs in Tier 2 and Tier 3 cities to build a skilled workforce.
   • Support startups through the IndiaAI Startups Global Acceleration Program, offering mentorship and global market access via partnerships like Station F in Paris.
      

Conclusion
 

The pact between the IndiaAI Mission and the Indian Parliament signifies a pivotal step in India's journey toward technological autonomy. By leveraging parliamentary data to develop indigenous AI models, including Large Language Models (LLMs), this collaboration strengthens India's AI ecosystem, ensures data sovereignty, and reduces reliance on foreign technologies. It positions India as a global leader in ethical and localized AI innovation, fostering inclusive growth and sustainable development.
 

Large Language Models (LLMs):  Overview Definition: LLMs are deep learning algorithms trained on vast datasets, often containing trillions of words, to process and generate human-like text. They use the transformer architecture, which efficiently handles long text sequences by focusing on the most relevant parts. How LLMs Work: Training Process: LLMs undergo unsupervised learning on unlabeled data to understand language patterns, followed by fine-tuning with labeled data for specific tasks like text generation or sentiment analysis. Transformer Model: The model uses an encoder-decoder structure and a self-attention mechanism, enabling it to understand complex sentences and generate coherent outputs. Examples of LLMs: GPT Series (OpenAI): Known for advanced text generation, widely used in chatbots and content creation. LLaMA (Meta): Versatile in tasks like question answering. Gemini (Google DeepMind): A multimodal model integrating text and visual data processing. Applications of LLMs: Text Generation: Creating articles, stories, and books. Chatbots and Virtual Assistants: Enabling conversational AI for customer support. Sentiment Analysis: Identifying emotional tone in customer feedback. Translation and Summarization: Translating languages and summarizing lengthy documents concisely.

 

Raisina Dialogue 2025 Theme: "Kālachakra – People, Peace and Planet", focusing on global interconnectedness, sustainable solutions, and strategic policymaking. Dates and Venue: Scheduled from March 17 to 19, 2025, in New Delhi, India. Inauguration: Inaugurated by Prime Minister Narendra Modi; Christopher Luxon, Prime Minister of New Zealand, will be the Chief Guest Organizers: Hosted by the Observer Research Foundation (ORF) in collaboration with the Ministry of External Affairs, Government of India. Thematic Pillars: Politics Interrupted: Shifting Sands and Rising Tides Resolving the Green Trilemma: Who, Where & How Digital Planet: Agents, Agencies and Absences Militant Mercantilism: Trade, Supply Chains & Exchange Rate Addiction The Tiger's Tale: Rewriting Development with a New Plan Investing in Peace: Drivers, Institutions & Leadership Significance: Serves as a global platform for discussions on international affairs, enhancing India's strategic role in sectors like trade, defense, and climate action while fostering partnerships worldwide.

 

Sansad Bhashini: What is it? An AI-powered initiative to modernize parliamentary processes with multilingual support and optimized documentation. Objective: Enhance accessibility and efficiency in legislative proceedings using AI. Key Features: Multilingual AI Support: Real-time speech-to-text and speech translation. AI Chatbot: Quick access to legislative documents and procedural rules. Automatic Summarization: Concise debate summaries for faster decision-making. Technology Used: Noise reduction, customizable vocabulary, and machine learning for continuous improvement. Significance: Enhances inclusivity, operational efficiency, and AI-driven governance. Partnership: MoU signed on March 19, 2025, between the Lok Sabha Secretariat and MeitY under the Digital India BHASHINI Division, with contributions from AI Kosh datasets.

 

IndiaAI Mission: Overview Launch Date: March 7, 2024 Objective: Establish a robust AI ecosystem by democratizing computing access, improving data quality, developing indigenous AI, fostering industry collaboration, funding startups, and promoting ethical AI. Budget and Implementation Budget: Rs 10,371.92 crore allocated over several years. Implementation: Managed by the IndiaAI Independent Business Division (IBD) under the Digital India Corporation (DIC). Key Pillars of IndiaAI Mission IndiaAI Compute Capacity: 10,000+ GPUs for startups and research. IndiaAI Innovation Centre: Rs 2,000 crore for Large Multimodal Models (LMMs) and foundational AI solutions. IndiaAI Datasets Platform: High-quality non-personal datasets for AI training. IndiaAI Application Development Initiative: AI applications in governance, healthcare, climate action, and education. IndiaAI FutureSkills: AI education and Data & AI Labs in Tier 2 and Tier 3 cities. IndiaAI Startup Financing: Financial support for deep-tech AI startups. Safe & Trusted AI: Ethical AI development with tools for bias mitigation and explainability. Key Achievements AIKosha and Compute Portal 300+ datasets and 80 AI models for innovation. 18,000+ GPUs subsidized for startups, academia, and government. Skill Development AI Competency Framework for public officials. FutureSkills Program expanding AI education in smaller cities. Startup Support Collaboration with Station F (Paris) under the IndiaAI Startups Global Acceleration Program to help Indian startups scale globally.

 

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