IndiaAI Mission gets Rs 2,000 Crore: Budget 2025-26

Context: The Union Budget 2025-26 has sanctioned Rs 2,000 crore for the IndiaAI Mission for 2025-26, which is nearly a fifth of the scheme’s total outlay of Rs 10,370 crore. 

Major Highlights:

• The government has shortlisted 10 companies that will provide nearly 19,000 graphics processing units (GPUs)- high end chips needed to develop machine learning tools -  for setting up artificial intelligence (AI) data centres. 
  • The initial aim of the IndiaAI Mission was to procure 10,000 GPUs.
• The government also aims to build a domestic large language model (LLM) of its own, as part of the IndiaAI Mission.
• The government will set up a new centre of excellence for AI for education with an outlay of Rs 500 crore. 

About IndiaAI Mission

• IndiaAI Mission is an initiative of the Ministry of Electronics and Information Technology (MeitY). Total outlay: Rs 10,370 crores. 
• It aims to build a comprehensive AI ecosystem that fosters innovation by democratising computing access, enhancing data quality and developing indigenous AI capabilities.
• The mission aims to develop:
  • IndiaAI Compute Capacity: establish a computing capacity of more than 10,000 GPUs, via public-private partnerships, offering AI services and resources.
  • IndiaAI Innovation Centre: develop and deploy indigenous Large Multimodal Models and domain-specific foundational models, with a capacity of >100 billion parameters, for priority sectors like healthcare, agriculture, and governance.
  • IndiaAI Datasets Platform: streamline the access to high-quality non-personal datasets for AI innovation. 
  • Responsible AI development.  
• A major portion of the total scheme outlay has been earmarked for building computing infrastructure.
• The idea is that if such an infrastructure exists in the country, start-ups could plug into it for developing AI systems. 

Challenges in AI development in India

• Talent shortage: Indian professionals lack skills requisite for the AI development in India. As 20% of companies reported that 50 to 100 AI projects are stalled at the planning stage due to shortage of skilled talent pool.
• Data privacy and security concerns: AI development in India poses challenges to privacy, as India lacks the stringent and comprehensive implementation of data privacy rules.
• Intellectual property violation: AI models threaten copyrighted work and sanctity of intellectual property rights.
• Infrastructure deficit: Despite initiatives like AIRAWAT, India's AI-first compute infrastructure, the country still faces challenges in providing adequate computational resources necessary for advanced AI research and applications
• Data deficit: There is a deficit of digitised data in India leading to limited creating a barrier in the development of AI based decision making. Eg; 26% of AI decision makers cited insufficient access to trusted data.

Suggestive Measures for AI Development: 

• Government initiatives: 
  • National AI Mission: The Indian government has launched the IndiaAI Mission with a budget of Rs. 10,307 Cr. to foster AI development across various sectors, including healthcare, agriculture, and education
  • National Strategy for Artificial Intelligence: NITI aayog has released this strategy to focus on leveraging AI for inclusive growth and positions India as a global leader in AI.
• Digital Public Infrastructure (DPI): Collaborations between the government and private sector have led to the development of DPI, facilitating scalable AI solutions and fostering innovation
• Enhancing computing capacity: Government should provide subsidised GPU to AI based startups in challenging circumstances when the US has changed India’s position to ‘watchful’ in US AI rules.
• Strengthening data governance: Implementing comprehensive data protection rules and digitisation of data can balance privacy and efficacy of data required for AI development.
• Fostering collaboration: Efforts to encourage partnership between academia, industry and government. Eg; Apprenticeship of AI students with Industry and government. 

About Large Language Models (LLM)

• LLMs are a subset of AI models designed to understand and generate human-like text by learning patterns from vast datasets. Examples: Open AI, chatGPT, Gemini. 
• Other Notable AI Models:
  • Convolutional Neural Networks (CNNs): Primarily used in image recognition tasks, such as facial recognition and medical image analysis. Eg; DeepMInd’s AlphaFold
  • Recurrent Neural Networks (RNNs): Suited for sequential data processing, like time-series analysis and language modeling. Eg; Google Translate
  • Generative Adversarial Networks (GANs): Generate new data samples similar to the training data, used in image and video generation. Eg; DALL-E

IndiaAI Mission seeks to position India at the forefront of the global AI landscape, leveraging technology to address societal challenges and enhance the nation's economic development.