✅ Answer: A) A subset of AI where systems learn from data to improve performance without explicit programmingMachine Learning (ML) is a subset of AI where systems learn from data to improve their performance without being explicitly programmed for each task. ML algorithms identify patterns in data and make predictions or decisions. Types: Supervised Learning (labelled data), Unsupervised Learning (unlabelled data), Reinforcement Learning (reward-based). Applications: spam filters, recommendation systems, fraud detection, medical diagnosis.

✅ Answer: A) AI’s ability to understand, interpret, and generate human languageNLP (Natural Language Processing) is the branch of AI that enables computers to understand, interpret, and generate human language. Applications: ChatGPT (text generation), Google Translate (translation), Siri/Alexa (voice assistants), sentiment analysis, spam detection. India’s government uses NLP for multilingual services in 22 official languages. Bhashini is India’s AI-powered language translation platform for Indian languages.

✅ Answer: B) AI-powered translation for Indian languagesBhashini is India’s AI-powered language translation platform for Indian languages, launched in 2022 under the Digital India initiative. It aims to break language barriers by providing translation, transcription, and voice services in all 22 scheduled Indian languages. It is developed by MeitY (Ministry of Electronics and IT). Bhashini enables government services to be accessible in local languages. It uses NLP and speech recognition technologies.

✅ Answer: A) A generator (creates fake data) and a discriminator (distinguishes real from fake)A GAN (Generative Adversarial Network) consists of two neural networks: a Generator (creates synthetic data — images, text, audio) and a Discriminator (tries to distinguish real data from generated data). They compete against each other — the generator improves to fool the discriminator, and the discriminator improves to detect fakes. GANs are used to create deepfakes, generate realistic images, and augment training data. They were invented by Ian Goodfellow in 2014.

✅ Answer: A) Whether a machine can exhibit intelligent behaviour indistinguishable from a humanThe Turing Test, proposed by Alan Turing in 1950, tests whether a machine can exhibit intelligent behaviour indistinguishable from a human. In the test, a human evaluator converses with both a human and a machine (without knowing which is which). If the evaluator cannot reliably distinguish the machine from the human, the machine passes the test. ChatGPT and other LLMs have come close to passing the Turing Test. Alan Turing is called the “Father of Computer Science.”

✅ Answer: A) Computer Vision, Deep Learning, and Sensor FusionAutonomous vehicles use Computer Vision (to see the road, pedestrians, signs), Deep Learning (to make driving decisions), and Sensor Fusion (combining data from cameras, LiDAR, radar, GPS). They also use HD maps and real-time data processing. Companies like Tesla, Waymo, and Cruise are developing autonomous vehicles. India is exploring autonomous vehicles for public transport. 5G connectivity is crucial for vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication.

✅ Answer: A) AI-based agricultural advisory for farmersKisan AI is an initiative to provide AI-based agricultural advisory to farmers in India. It uses AI to analyse soil data, weather forecasts, crop prices, and pest information to provide personalised advice to farmers. It is part of India’s Digital Agriculture Mission. Similar initiatives: Microsoft’s AI Sowing App (Andhra Pradesh), IBM’s Watson Decision Platform for Agriculture. AI can help India’s 140 million farming households improve productivity and income.

✅ Answer: A) AI systems that can explain their decisions in human-understandable termsExplainable AI (XAI) refers to AI systems that can explain their decisions and reasoning in human-understandable terms. Traditional deep learning models are “black boxes” — they make decisions without explaining why. XAI is crucial for high-stakes applications like medical diagnosis, loan approval, and criminal justice, where humans need to understand and trust AI decisions. Regulatory frameworks (EU AI Act) require explainability for high-risk AI systems.

✅ Answer: A) The world’s first comprehensive legal framework for regulating AIThe EU AI Act (2024) is the world’s first comprehensive legal framework for regulating AI. It classifies AI systems by risk level: Unacceptable Risk (banned — social scoring, real-time biometric surveillance), High Risk (regulated — medical devices, critical infrastructure), Limited Risk (transparency requirements), Minimal Risk (no regulation). It requires high-risk AI to be transparent, accurate, and human-overseen. India is developing its own AI governance framework.

✅ Answer: B) GoAlphaGo, developed by DeepMind (Google), defeated world Go champion Lee Sedol in 2016. Go is an ancient Chinese board game considered far more complex than chess (10^170 possible positions vs 10^43 for chess). AlphaGo used deep reinforcement learning. AlphaGo Zero (2017) learned Go from scratch without human data. AlphaZero (2017) mastered chess, Go, and shogi. These achievements demonstrated AI’s ability to master complex strategic games, a milestone in AI development.