27-July-2024-Editorial

IndiaAI MISSION

The Indian government’s commitment to advancing artificial intelligence (AI) technology is reflected in its new budgetary allocation for the IndiaAI Mission.

The Union Budget for 2024-25 allocated Rs 551.75 crore to the Ministry of Electronics and Information Technology to enhance AI infrastructure, including the procurement of high-performance Graphic Processing Units (GPUs).

This initiative aims to support domestic AI development and reduce reliance on expensive foreign hardware.

Objectives of IndiaAI Mission

• Robust AI Infrastructure: Establish a strong AI computing infrastructure to support the development and testing of AI systems.
• Data Quality Enhancement: Improve the quality of data available for AI applications.
• Indigenous AI Technologies: Develop AI technologies within India.
• Attracting Talent: Draw top talent to work on AI projects.
• Industry Collaboration: Foster cooperation between industries.
• Support for AI Startups: Assist AI startups with resources and funding.
• Ethical AI Practices: Promote responsible and ethical use of AI.

Financial Support

• Cabinet Approval: The Union Cabinet approved the Rs 10,372 crore IndiaAI Mission in March.
• GPUs and AI Models: Aim to establish a computing capacity of over 10,000 GPUs and develop foundational models with more than 100 billion parameters trained on datasets in major Indian languages for sectors like healthcare, agriculture, and governance.
• Initial Efforts: Initial procurement of 300 to 500 GPUs to kickstart the project.

Importance of GPU Procurement

• Critical for AI Models: GPUs are essential for training and building large-scale AI models.
• Parallel Operations: GPUs are vital for AI, media analytics, and 3D rendering solutions.
• Support for Startups: Provides necessary computing power for Indian startups.

Key Components of the IndiaAI Mission

IndiaAI Compute Capacity

• High-End Ecosystem: Creation of a high-end AI computing ecosystem with over 10,000 GPUs.
• AI Marketplace: Establishment of an AI marketplace for resources.

IndiaAI Innovation Centre

• Development of AI Models: Focus on developing indigenous Large Multimodal Models (LMMs) and foundational models.
• Budget Allocation: Close to Rs 2,000 crore earmarked for this centre.

IndiaAI Datasets Platform

• Unified Platform: A single platform to provide seamless access to quality non-personal datasets for startups and researchers.

IndiaAI Application Development Initiative

• Promotion of AI Applications: Targeting problem statements from various governmental sectors for large-scale socio-economic transformation.

IndiaAI FutureSkills

• AI Education Expansion: Undergraduate, master’s, and Ph.D. programs along with Data and AI Labs in smaller cities.

IndiaAI Startup Financing

• Funding Access: Streamlined funding access for deep-tech AI startups.
• Government Financing: Approximately Rs 2,000 crore allocated for this purpose.

Safe & Trusted AI

• Guidelines and Frameworks: Ensuring responsible AI practices, including indigenous tools for project assessment.

Key Highlights of India’s AI Market

• Adoption Across Sectors: Growing AI adoption in healthcare, finance, retail, manufacturing, and agriculture.
• Focus on Data Analytics: Increasing importance of AI-driven data analytics.
• Government Initiatives: Digital India, Make in India, Smart Cities Mission, GI Cloud (MeghRaj), and Global INDIAai Summit.
• Research and Development: Active involvement of Indian research institutions and academic organizations.
• AI Clusters: Emerging AI clusters in cities like Bengaluru, Hyderabad, Mumbai, Chennai, Pune, and NCR.

Opportunities in India’s AI Market

• IoT and Precision Farming: Boost productivity through AI-powered precision farming and crop monitoring.
• Fraud Detection and Customer Service: AI solutions for banking sector needs.
• Healthcare Innovations: Predictive diagnostics, personalized treatment, and drug discovery.
• Retail Sector: AI-driven recommendation engines and chatbots.

Challenges for IndiaAI Mission

• GPU Capacity and Infrastructure: Ambitious goal of building 10,000 GPUs but facing procurement and deployment challenges.
• High GPU Costs: Expensive GPUs like Nvidia’s A100 chip pose barriers.
• Data Access and Quality: Insufficient datasets for training AI models in Indic languages.
• Limited AI Expertise and High Costs: Shortage of skilled professionals and high deployment costs.
• Infrastructure Deficiencies: Lack of comprehensive AI and cloud computing facilities.
• Ethical Concerns: Ensuring ethical AI use and avoiding biases in AI models.
• Geopolitical and Regulatory Issues: Impact of geopolitical tensions and export control regulations.
• Environmental Concerns: High energy consumption and environmental toll of AI and data centres.

Way Forward

• Incentivize Hardware Manufacturing: Expand the Production Linked Incentive (PLI) scheme for IT hardware and semiconductors.
• Startup Support: Provide financial incentives, mentorship, and incubation facilities for AI startups.
• Comprehensive Data Ecosystem: Develop a National Data Platform with standardized formats and quality checks.
• Ethical AI: Develop AI ethics guidelines, establish ethics boards, and conduct regular AI audits.
• AI for Societal Impact: Prioritize AI applications in healthcare, agriculture, and education.
• Sustainable AI: Invest in energy-efficient AI algorithms and hardware, promote renewable energy for data centres.
• Talent Gap: Foster partnerships for internships and research projects, attract overseas talent, and improve salaries and benefits.

This comprehensive approach aims to position India as a global leader in AI technology while addressing challenges and ensuring sustainable and ethical AI development.