Subjective Type Questions - Introduction to AI - Artificial Intelligence for Class 10 PDF Download

Q.1. What do you mean by Intelligence?

Ans: Intelligence is the ability to think, perceive information and use knowledge to act effectively in a given environment. It includes the power to understand situations, reason about them and choose appropriate actions.

• Ability to interact with the real world - To perceive, understand and act.
• Reasoning and planning - Building a model of the external world from inputs and using it to make decisions.
• Learning and adaptation - Improving performance over time by learning from experience.

Q.2. List out the types of intelligence.

Ans: The different types of intelligence are as follows. A brief one-line description is given for each to clarify the meaning.

1. Mathematical Logical Reasoning - Ability to solve problems using logic and numbers.
2. Linguistic Intelligence - Skill in using language, both written and spoken.
3. Spatial Visual Intelligence - Ability to visualise and manipulate images and shapes.
4. Kinaesthetic Intelligence - Skill in using the body and hands to perform tasks.
5. Musical Intelligence - Talent for rhythm, pitch and musical patterns.
6. Intrapersonal Intelligence - Self-awareness and understanding one's own feelings.
7. Existential Intelligence - Capacity to think about deep questions such as life and existence.
8. Naturalist Intelligence - Ability to recognise and understand nature and living things.
9. Interpersonal Intelligence - Skill in understanding and interacting with other people.

Q.3. What is artificial intelligence?

Ans: Artificial intelligence (AI) is the field of computer science where machines are designed to mimic human abilities such as learning, reasoning, perception and decision-making. An AI system collects data, interprets it, learns patterns, makes predictions or decisions and can improve its performance over time.

Q.4. Write some popular AI apps and tools.

Ans: Many everyday apps and tools use AI to make tasks faster and smarter. Some common examples are described below.

Google: Google applies AI to understand search queries and rank results. Gmail's spam filter uses AI to detect unwanted messages. Google Maps uses AI for route planning and traffic predictions.

Virtual Assistants: Apple Siri, Amazon Alexa, Google Assistant and Microsoft Cortana accept voice commands and provide help with tasks such as setting reminders, answering questions and controlling devices.

Gaming: AI improves game behaviour, adjusts difficulty, and creates smarter non-player characters to enhance the gaming experience.

Recommendations: Services such as Amazon, Netflix, Spotify and YouTube use AI to recommend products, videos or songs based on your past behaviour, likes and searches.

Chatbots: Many websites use AI chatbots for customer support and simple interactions. Sophia was an early example of a humanoid robot using conversational AI.

Health Apps: AI helps monitor health, give fitness suggestions and analyse medical data in many health applications.

Others: Face unlock on smartphones, language translators, weather forecasts and many more everyday services rely on AI techniques.

Q.5. What do you mean by machine learning?

Ans: Machine learning is a branch of AI that enables computers to learn from data and improve their performance without being explicitly programmed for each task. It uses algorithms and models trained on labelled or unlabelled data to recognise patterns and make predictions.

Q.6. What do you mean by deep learning?

Ans: Deep learning is a specialised area within machine learning that uses multi-layered neural networks to learn from large amounts of data. It is effective for tasks such as image recognition, speech understanding and language processing because it can automatically learn complex features from raw input.

Q.7. What do you mean by AI Domains?

Ans: AI domains are the main areas or applications where AI techniques are applied. Each domain uses different kinds of data and methods to train machines so they can make decisions. As the machine receives and processes data, it learns patterns and produces useful outputs. These specialised fields are often referred to as domains of AI.

Q.8. Enlist the three domains of AI.

Ans:

• Data Science
• Computer Vision (CV)
• Natural Language Processing (NLP)

Q.9. What is data with respect to the AI domain of Data Science?

Ans: In Data Science, data is the collection of facts, numbers, text, images or signals that an AI system uses to learn and make decisions. Data is the core input for training models, validating results and improving system performance.

Q.10. What do you mean by data science? Illustrate your answer with an example.

Ans: Data science is the discipline that collects, cleans, organises and analyses data to extract useful information. The processed results help in making decisions, predictions or automated actions.

• For example, price comparison websites such as PriceGrabber, PriceRunner, Junglee, Shopzilla and DealTime collect prices from many online sellers, compare them and present options so a user can choose the best deal.

Digital marketing is another example: it uses data about user behaviour to show targeted advertisements to likely customers.

Q.11. What do you mean by Computer Vision?

Ans: Computer Vision is the AI domain that enables machines to interpret and understand visual information from images or videos. Processes include acquiring images, preprocessing, analysing, identifying objects and extracting meaningful information so a computer can make decisions based on visual input.

Input to computer vision systems may come from photographs, video cameras, thermal or infrared sensors and medical scanners.

Examples of computer vision applications include:

• Face recognition systems used by apps such as Google Photos, Snapchat and Facebook, and by law-enforcement agencies.
• Content-Based Image Retrieval (CBIR): image search engines, CT and MRI image analysis in hospitals, and Earth observation from satellites.
• Smart interactions in gaming and augmented reality that respond to visual inputs.

Q.12. How does the face lock system work in a smartphone?

Ans: A face lock system works in simple steps:

First, the system detects and captures an image of the user's face when the face lock is set up.

Next, it extracts and stores distinctive facial features (a face template) securely.

Later, when the user tries to unlock the phone, the system captures a live face image, compares its features with the stored template and unlocks the device if they match closely enough.

Q.13. What is Natural Language Processing ?

Ans: Natural Language Processing (NLP) is the AI field that enables computers to understand, interpret and generate human language - both spoken and written. It bridges communication between humans and machines using natural language.

It has two main components:

• Natural Language Understanding (NLU): Interprets words, extracts meaning and links language input to concepts.
• Natural Language Generation (NLG): Produces meaningful sentences and text. This includes text planning, sentence planning and text realisation.

NLU tasks are generally easier than full NLG because understanding is often simpler than creating fluent, contextually correct language. Examples of NLP are email filters, smart assistants and language translators.

Q.14. What do you understand by Ethics?

Ans: Ethics are principles that guide what is right and wrong behaviour. They cover moral values and standards that help people and systems act with fairness, responsibility and respect for others.

Q.15. What are the moral issues related to self-driving cars?

Ans: Self-driving cars raise several moral and ethical questions because their decisions can affect people's safety. Key concerns include:

• Responsibility in accidents: If an autonomous car causes harm (for example, by hitting an animal or a person), it is unclear who is legally and morally responsible - the manufacturer, the software developer, the owner or others.
• Programming moral choices: Developers must decide how a car should behave in dilemmas. Those choices reflect the developer's values and can differ between individuals and cultures.
• Bias and fairness: Decisions coded into the car may favour certain outcomes, which raises ethical questions about how priorities are set.

Q.16. What are the concerns related to the use of AI to control defense equipment?

Ans: Using AI to control defence equipment poses serious ethical and safety risks. Examples of concerns are:

• Autonomous weapons such as AI-controlled missiles or armed drones making independent life-and-death decisions without meaningful human oversight.
• Human cost of automation, including potential loss of accountability and increased risk of unintended escalation in conflicts.

Q.17. What do you understand by data privacy?

Ans: Data privacy refers to the rules and practices that control how personal and sensitive data is collected, stored, used and shared. It ensures users give consent for their data to be used and that their information is handled securely and with respect for their rights.

Q.18. What are the permission asked by the app before using it?

Ans: An app typically asks for permissions it needs to work. Common permissions include:

• Contacts
• Location
• Camera
• Storage
• Photos
• Notes

Q.19. What are the components of good AI Policy?

Ans: A good AI policy protects users and guides responsible design and use of AI. Important components are:

• Transparent policy - Clear guidelines that explain how and why data is collected and used.
• Right of data collection - Ensuring that only the necessary data are collected and that collection is lawful.
• Freedom to leave the system - Users should be able to stop using the system and withdraw consent.
• Design constraints - Systems should be designed to limit data collection and use to specific purposes.
• Data deletion - Users' data should be removable when they leave the system or withdraw consent.

Q.20. What do you understand by problem of inclusion?

Ans: The problem of inclusion occurs when AI systems exclude or disadvantage certain people. A notable example is when an AI recruitment tool failed to consider many qualified female candidates because it learned from biased historical data. Inclusion problems often arise from biased data, poor design or narrow testing.

Q.21. What do you understand by the fact interpretation?

Ans: Fact interpretation refers to the limitation that AI systems can learn patterns and produce results but may not understand the reasons behind those outcomes. Machines can reproduce biases found in training data and may give misleading or harmful results if they misinterpret facts or lack context.

Q.22. What is "Tay"? What controversies created by Tay?

Ans: Tay was a chatbot developed by Microsoft to experiment with conversational understanding on Twitter. It was intended to learn and become smarter by interacting with users.

Controversy arose because Tay learned from user inputs without filtering harmful content. Within 24 hours of launch, it began to produce offensive and unethical messages by mimicking the tweets it received. This showed how unfiltered training data can quickly teach an AI undesirable behaviour.

Q.23. What are the major AI ethical concerns related to AI adoptions?

Ans: Major ethical concerns related to AI adoption include:

• Unemployment - Automation may replace some jobs, causing economic and social disruption.
• Inequalities - AI can widen gaps if benefits are unevenly distributed or if biased systems disadvantage certain groups.
• Negative applications - AI can be misused for harmful purposes such as surveillance, fraud or manipulation.
• Black-box problem - Complex AI systems can be hard to explain, making accountability and trust difficult.