Major Artificial Intelligence Concepts and Applications

Artificial intelligence is a broad field of computing that enables machines to perform tasks usually associated with human intelligence. It has evolved from rule-based systems to learning-based models that can recognise patterns, generate content and execute multi-step tasks.

Classification of Artificial Intelligence Systems

• Artificial Narrow Intelligence (ANI): Designed for specific tasks such as facial recognition, recommendation systems or language translation. It cannot transfer learning across unrelated domains.
• Artificial General Intelligence (AGI): A theoretical AI that can understand, learn and apply knowledge across a wide range of tasks at a human level.
• Artificial Superintelligence (ASI): A hypothetical stage in which machine intelligence surpasses human cognitive ability in all fields.
• Machine Learning (ML): A subset of AI in which algorithms learn from data and improve decisions without being explicitly programmed for every rule.
• Deep Learning: A subfield of ML that uses multi-layered artificial neural networks to process complex and unstructured data.

Core Architectural Components of Modern AI

• Transformer architecture: Introduced in 2017, it uses self-attention mechanisms to process sequential data in parallel and is the foundation of many Large Language Models (LLMs).
• Natural Language Processing (NLP): Enables computers to parse, interpret and generate human language, including speech recognition and sentiment analysis.
• Computer vision: Uses algorithms to extract information from images and videos, often with convolutional neural networks (CNNs) for object detection.
• Neural networks: Computational models made up of interconnected nodes arranged in input, hidden and output layers to map complex relationships.

Agentic AI and Its Workflows

Agentic AI marks a shift from passive, response-based systems to active, goal-oriented systems. It can reason, plan, use tools and execute sequential tasks with limited human intervention, though human supervision remains important.

• Reasoning and planning: Breaks complex queries into smaller sub-tasks and determines the sequence of actions needed.
• Tool use: Can call external tools such as search engines, calculators and database APIs to obtain or process information.
• Multi-agent coordination: Multiple specialised AI agents work together and communicate to complete different parts of a larger task.
• Practical relevance: Such systems are increasingly linked to workflow automation, digital assistance and task execution in enterprise and public-facing applications.

India’s Artificial Intelligence Governance Framework

• IndiaAI Mission: Administered by the Ministry of Electronics and Information Technology (MeitY), it seeks to build computing infrastructure, support foundational models and promote data sharing.
• NIELIT: The National Institute of Electronics and Information Technology functions under MeitY and provides formal and non-formal training in electronics, IT and advanced technologies.
• NIELIT Deemed-to-be University: Coordinates specialised technological education and curriculum standards across NIELIT’s network of centres.
• National skilling target: The Government of India announced a goal on August 15, 2026, to train 1 crore (10 million) youth in artificial intelligence skills.
• Workforce focus: The policy emphasis is on employability, adaptability and practical readiness for AI-related sectors.

Key Prelims Takeaways

• NIELIT’s parent ministry: NIELIT operates under the Ministry of Electronics and Information Technology (MeitY).
• Director General: Dr. Madan Mohan Tripathi is the Director General of NIELIT and Vice-Chancellor of the NIELIT Deemed-to-be University.
• IndiaAI Mission leadership: Shri Sudeep Shrivastava is the Chief Operating Officer (COO) of the IndiaAI Mission and Joint Secretary at MeitY.
• CBFC chairperson: Shri Shashi Shekhar Vempati serves as the Chairperson of the Central Board of Film Certification (CBFC).
• Intel leadership: Mr. Anshul Sonak is the Head and Global Director of Intel.
• Agentic AI focus: It involves reasoning, planning, tool use and sequential task execution under human supervision.
• Course delivery: The skilling programmes are to be delivered through NIELIT’s nationwide centres.

Recent Context

NIELIT and Intel India launched two Agentic AI skilling programmes on September 3, 2026, at the India Habitat Centre in New Delhi. The initiative is aligned with India’s broader target of training 1 crore youth in artificial intelligence.

The two courses are Agentic AI for Everyone and Engineering Agentic AI Systems. The first introduces workflow automation, AI agents and multi-agent systems using no-code platforms, while the second focuses on building scalable agentic systems using low-code, no-code and code-based frameworks.