Artificial Intelligence (AI) is a core subject in the AKTU B.Tech 7th Semester (CSE/IT) syllabus. This page is designed to help students browse papers, access previous year papers, quickly revise important AI concepts, and prepare for exams with high-scoring notes.
What is Artificial Intelligence?
Artificial Intelligence is the science of building machines that can think, analyze, learn, plan, communicate, and make decisions like humans. AI uses algorithms, data, and mathematical models to imitate human intelligence.
AI enables computers to:
Understand language
Recognize image, Learn from data
Reason logically, Make predictions
Play strategic games Drive car Assist humans
Why PYQs Are Important for AI
- Repeated Questions: Almost 40–55% of questions repeat every 2–3 years.
- Fixed Pattern: Long questions often follow the same structure.
- Easy Scoring: AI theory answers are predictable
- Exam-Oriented Topics: Helps identify high-weight units.
- Better Time Management: Students can focus only on important portions.
AI is the foundation behind various modern technologies including:
Google Assistant, ChatGPT
Tesla Autopilot, Netflix recommendations
YouTube suggestions, Amazon Alexa
Cybersecurity detection
Quick Revision Notes
Key AI Terminology
- Agent: Anything that perceives environment and acts.
- Environment Types: Deterministic, stochastic, static, dynamic, partially observable.
- Heuristic: A guiding function that estimates cost.
- State Space: All possible states a problem can have.
- Utility: A measure of performance or reward.
- Knowledge Base: A repository of facts & rules.
Extra Definitions
- Local maxima/minima → Problem in hill climbing.
- Gradient descent → Optimization algorithm in ML.
- Entropy → Uncertainty measure in decision-making.
- Belief state → Probability distribution over states.
- Fuzzy set → Allows partial truth (0 to 1).
Formula Zone (Expanded)
Real-World Applications
Artificial Intelligence is now deeply integrated into almost every modern technology we use. Whether it’s voice assistants, smart search, or chatbots, AI enhances user experiences by making systems more intelligent and personalized. In daily life, AI powers tools like Google Assistant, Siri, and Alexa, enabling natural voice queries and smart home automation. Similarly, features like text prediction, spell correction, and content recommendations on platforms such as YouTube, Netflix, and Amazon work through advanced machine learning algorithms. These applications help the page rank for search terms like ai applications, artificial intelligence uses, and ai examples in real life.
In the business world, AI plays a massive role in solving complex problems. Companies use fraud detection systems, customer behavior analysis, sales forecasting models, and automated chat support to improve accuracy and efficiency. Modern CRM tools rely heavily on machine learning to identify potential leads and predict customer actions. Industries also use predictive maintenance, process automation, and robotics for manufacturing, making operations faster and error-free.
Healthcare has been completely transformed by AI, with diagnostic systems, medical imaging analysis, virtual health assistants, and AI-based drug discovery becoming more accurate than ever. Machine learning models help identify diseases like cancer and diabetes at early stages, ranking the page for ai in healthcare, medical imaging ai, and similar keywords.
Government and public sectors also use AI for traffic prediction, automated surveillance, crime analysis, weather forecasting, and public service automation. These real-world applications show the huge impact of AI across all fields.
Career Opportunities After Studying AI
Learning Artificial Intelligence opens the door to some of the fastest-growing and highest-paying careers in the tech industry. Students who build strong knowledge in AI, ML, and deep learning can work as Machine Learning Engineers, responsible for designing and training predictive models. Data Scientists use machine learning techniques to analyze data and help companies make smarter decisions. For software development roles, companies hire AI Software Engineers who build intelligent applications integrated with ML algorithms.
There are also highly specialized roles such as NLP Engineers, who work with text and language models, and Computer Vision Engineers, who focus on facial recognition, image classification, and object detection. Students interested in robotics can work as Robotics Engineers, developing automated machines and industrial robots.
Fresh graduates can also join as AI Research Interns, Automation Experts, AI Product Analysts, or AI Consultants, helping businesses integrate artificial intelligence into their products.
Download AKTU AI 4th Year PYQs PDF – BCS701
Sort Questions (One-Line Q&A)
What is the difference between supervised, unsupervised, and reinforcement learning?
A: Supervised uses labeled data, unsupervised finds patterns, and reinforcement learns by rewards.
What is overfitting and underfitting in machine learning?
A: Overfitting fits training too closely, underfitting learns too little.
How does the A* algorithm work, and why are heuristics important?
A: A* uses cost + heuristic to find the best path efficiently.
What are activation functions, and why are ReLU and Sigmoid important?
A: They add non-linearity; ReLU speeds learning, Sigmoid gives probability output.
What are BFS and DFS, and where are they used?
A: BFS finds shortest paths level-wise, DFS explores deep paths first.
What is a neural network, and how does backpropagation help?
A: A neural network has layered neurons, and backpropagation adjusts weights to learn.