Why in the News?

The IndiaAI Mission has selected eight Responsible AI projects under the Safe and Trusted AI Pillar. These initiatives aim to develop indigenous tools and frameworks while establishing guidelines for ethical and trustworthy AI technologies. This selection underscores the importance of safeguards for responsible AI development and deployment.

About India AI Mission

• Vision and Budget: The mission aims to enhance AI capabilities in India and ensure its utility for the country’s growth, with a budget of ₹10,371.92 crore.
• Ecosystem Development: It will create a comprehensive ecosystem to foster AI innovation through strategic collaborations between the public and private sectors.
• Key Components:
  • Compute Capacity: Establishes a high-end AI computing ecosystem with over 10,000 GPUs through public-private partnerships.
  • Innovation Centre: Focuses on developing indigenous Large Multimodal Models (LMMs) and foundational models for critical sectors.
  • Datasets Platform: Provides streamlined access to quality non-personal datasets for startups and researchers.
  • Application Development Initiative: Promotes impactful AI solutions in critical sectors by addressing problem statements from various government bodies.
  • Future Skills: Expands AI education and training opportunities, especially in Tier 2 and Tier 3 cities.
  • Startup Financing: Supports deep-tech AI startups with easier access to funding for innovative projects.
  • Safe & Trusted AI: Implements frameworks and guidelines to ensure responsible AI development and deployment.
• Implementation: The mission will be executed by the ‘IndiaAI’ Independent Business Division under the Digital India Corporation (DIC).

About Artificial Intelligence (AI)

• It refers to the simulation of human intelligence in machines programmed to think and learn like humans.
• It encompasses various subfields, including
  • Machine learning: A subset of AI that involves the use of algorithms to enable computers to learn from data without explicit programming. Techniques include supervised, unsupervised, and reinforcement learning.
  • Natural Language Processing: This allows machines to understand, interpret, and respond to human language, facilitating applications like chatbots and language translation.
  • Computer Vision: This field enables machines to interpret and make decisions based on visual data from the world, used in applications like facial recognition and autonomous vehicles
• AI systems can analyze data, recognize patterns, and make decisions, making them invaluable across diverse applications such as healthcare, finance and transportation.