Class 10 Computer Science AI and Contemporary Technologies Notes and Important Questions

Class 10 AI and Contemporary Technologies Notes (Computer Science)

Chapter 5 : AI and Contemporary Technologies Chapter 5 : AI and Contemporary Technologies (14 Teaching Hours) Theory Marks : 11 Unit Weightage : 22% Subject : Computer Science, Class 10 5.1 Artificial Intelligence and Machine Learning 5.1 Artificial Intelligence and Machine Learning Concept of Artificial Intelligence Concept of Artificial Intelligence Artificial Intelligence (AI) is the branch of computer science concerned with building machines/systems that can perform tasks which normally require human intelligence - such as understanding language, recognising images, making decisions and solving problems. AI and Its Applications AI and Its Applications Concept of Machine Learning Concept of Machine Learning Machine Learning (ML) is a subfield of AI in which a system learns patterns automatically from data and experience, rather than being explicitly programmed with fixed rules for every situation. The more relevant data it is given, the better it tends to perform. Types of AI (by capability) Types of AI (by capability) Relationship between AI and Machine Learning Relationship between AI and Machine Learning AI is the broader goal - making machines act intelligently. Machine Learning is one important method used to achieve Voice assistants (e.g. Siri, Google Assistant) that understand and respond to speech - Face recognition used to unlock phones or tag photos - Recommendation systems on YouTube, Netflix and online shopping sites - Self-driving cars that sense and react to their surroundings - Chatbots that answer customer questions automatically - Narrow AI (Weak AI) Narrow AI (Weak AI) - designed to perform one specific task well, such as a chess-playing program or a voice assistant. Almost all AI in use today is narrow AI. - General AI (Strong AI) General AI (Strong AI) - a theoretical AI that could perform any intellectual task a human can, across many different domains. This does not yet exist in practice. -

that goal, by allowing the machine to improve its own performance from data instead of being manually programmed for every possible case. Artificial Intelligence Machine Learning Fig 5.1 - Machine Learning is a subset of the broader field of AI 5.2 Learning Techniques 5.2 Learning Techniques Supervised Learning Supervised Learning In supervised learning, the system is trained on data that is already labelled labelled with the correct answer, and it learns to map inputs to the correct output. Once trained, it can predict the output for new, unseen data. Unsupervised Learning Unsupervised Learning In unsupervised learning, the system is given data without without labels and must find hidden patterns, similarities or groupings in the data by itself. Basic Concepts and Examples of Learning Techniques Basic Concepts and Examples of Learning Techniques Basis Basis Supervised Learning Supervised Learning Unsupervised Learning Unsupervised Learning Data used Labelled (answers already known) Unlabelled (no known answers) Goal Predict a known type of output Discover hidden patterns/groups Example Spam email detection Customer grouping/segmentation Supervised (labelled data) filled = spam, empty = not spam Model learns the rule Unsupervised (no labels) model finds its own groups Fig 5.1b - Supervised learning uses known labels; unsupervised learning finds its own groups Example: training a system with thousands of emails already marked "spam" or "not spam", so it learns to classify new emails on its own. - Example: grouping customers of a shop into clusters based on their shopping habits, without being told in advance what the groups should be. -

5.3 AI in Robotics 5.3 AI in Robotics AI and Robotics AI and Robotics A robot is a physical machine that can sense its environment and perform actions in the real world. When combined with AI, a robot can make intelligent decisions on its own instead of only following a fixed, pre-programmed sequence of movements. Applications of AI in Robots Applications of AI in Robots Sensors and Decision-Making Sensors and Decision-Making Robots use sensors sensors (such as cameras, proximity sensors and touch sensors) to gather information about their surroundings. AI then processes this sensor data to make a decision - for example, stopping when an obstacle is detected - and the robot's motors/actuators carry out that decision as physical movement. Simulation of Simple Robotic Tasks Simulation of Simple Robotic Tasks Before being deployed in the real world, robotic behaviour is often first tested using computer simulations, which model the robot's sensors, environment and actions virtually - allowing errors to be found and fixed safely and cheaply before real hardware is used. 5.4 Generative AI 5.4 Generative AI Concept of Generative AI Concept of Generative AI Generative AI refers to AI systems that can create new content create new content - such as text, images, audio, video or code - rather than simply analysing or classifying existing data. It learns patterns from huge amounts of existing content and uses them to generate original output. AI-Generated Text AI-Generated Text Generative AI models can write essays, stories, summaries, emails and even computer code based on a prompt/instruction given by the user. Example: ChatGPT, Claude. AI-Generated Images/Content AI-Generated Images/Content Industrial robots that assemble products and adjust automatically for small variations - Robotic vacuum cleaners that map a room and avoid obstacles - Medical robots that assist doctors during surgery with high precision - Delivery and warehouse robots that navigate and pick items automatically -

Some generative AI tools can create original images, artwork or designs from a text description. Example: tools that turn the prompt "a mountain village at sunrise" into a brand-new picture. Uses of Generative AI Uses of Generative AI Basic Limitations of Generative AI Basic Limitations of Generative AI 5.5 Application of Integrated AI Tools 5.5 Application of Integrated AI Tools AI Tools and Applications AI Tools and Applications Many everyday applications now have AI built directly into them ("integrated"), rather than requiring a separate AI program - helping users be more productive without needing any special technical skill. Using AI for Learning and Productivity Using AI for Learning and Productivity Examples of Integrated AI Tools Examples of Integrated AI Tools Tool Type Tool Type Example Use Example Use Writing assistant Suggests grammar corrections and better phrasing Smart assistant Sets reminders, answers questions by voice Translation app Converts spoken/written text between languages instantly Study/AI tutor apps Explains concepts and generates practice questions Drafting emails, reports, articles and study notes - Generating images, logos and design ideas - Writing and debugging computer programs - Creating practice questions and personalised study material - Can produce incorrect or made-up information that sounds convincing (often called "hallucination") - May reflect biases present in the data it was trained on - Does not truly "understand" content the way a human does - it predicts patterns - Raises questions about originality, copyright and misuse (e.g. fake images/videos) - AI-powered grammar and writing checkers - Smart search engines that understand natural-language questions - AI features inside word processors and spreadsheets (e.g. auto-summarise, auto-formula suggestions) - Translation tools that instantly convert text between languages -

AI-Assisted Content Creation and Problem Solving AI-Assisted Content Creation and Problem Solving Students and professionals increasingly use AI tools to brainstorm ideas, get explanations of difficult concepts, draft content, and check their own work - AI acting as a helpful assistant rather than doing the entire task without any human involvement. 5.6 Ethics in AI 5.6 Ethics in AI AI Bias AI Bias AI bias occurs when an AI system produces unfair results for certain groups of people, usually because the data it was trained on was itself unbalanced or unrepresentative. This can unintentionally lead to discrimination. Privacy Privacy Because AI systems often need large amounts of personal data to work well, there is a risk of that private information being collected, stored or used without a person's proper knowledge or consent. Security Security AI systems and the data they use must be protected from hacking, misuse or manipulation - for example, preventing someone from tricking a face-recognition system or stealing sensitive training data. Responsible Use of AI Responsible Use of AI Responsible use means applying AI in ways that respect human rights, honesty and safety - for example, clearly disclosing when content is AI-generated, and not using AI to spread misinformation or cause harm. Fairness and Ethical Considerations Fairness and Ethical Considerations Remember : Remember : The key ethical concerns in AI can be grouped as - Bias, Privacy, Security and Responsibility. Together they decide whether an AI system is used fairly and safely. 5.7 Internet of Things (IoT) 5.7 Internet of Things (IoT) AI decisions should be explainable and not blindly trusted without human review - AI should be tested to reduce bias across gender, ethnicity, and other groups - Developers and users share responsibility for how AI is applied - Laws and guidelines are still evolving to keep up with fast-changing AI capabilities -

Concept of IoT Concept of IoT The Internet of Things refers to a network of everyday physical devices - embedded with sensors, software and internet connectivity - that can collect and exchange data with each other and with users, often without direct human involvement. IoT Devices IoT Devices How IoT Works How IoT Works An IoT device uses a sensor sensor to collect data (e.g. temperature, motion), sends that data over the internet internet to a server or app, where it may be processed (sometimes using AI), and then an action or alert is triggered - either automatically or by a user. Sensor Internet/Cloud Processing Action/Alert Fig 5.2 - Basic flow of how an IoT device works Applications of IoT Applications of IoT 5.8 Virtual and Extended Reality (XR) 5.8 Virtual and Extended Reality (XR) Virtual Reality (VR) Virtual Reality (VR) Virtual Reality creates a completely computer-generated, immersive environment that replaces the user's real-world view, usually experienced through a VR headset that tracks head movement. Extended Reality (XR) Extended Reality (XR) Smart thermostats and lights that can be controlled remotely - Fitness bands/smartwatches that track heart rate and steps - Smart security cameras that send alerts to a phone - Sensors in vehicles, factories and farms - Smart home Smart home - automatically controlling lights, fans and appliances - Smart city Smart city - smart traffic lights, waste management, street lighting - Healthcare Healthcare - remote patient monitoring devices - Transportation Transportation - vehicle tracking, smart parking systems - Energy management Energy management - smart meters that monitor and optimise electricity usage -

Extended Reality is an umbrella term covering Virtual Reality (fully virtual), Augmented Reality (digital elements overlaid on the real world) and Mixed Reality (digital and real objects interacting together). Difference between VR and AR Difference between VR and AR Basis Basis Virtual Reality (VR) Virtual Reality (VR) Augmented Reality (AR) Augmented Reality (AR) Environment Completely artificial/computer-generated Real world with digital elements added on top Device needed VR headset (blocks out real view) Phone/AR glasses (keeps real view visible) Example Exploring a fully virtual museum A game overlaying characters onto your camera view Applications of VR/XR Applications of VR/XR 5.9 Cloud Computing 5.9 Cloud Computing Concept of Cloud Computing Concept of Cloud Computing Cloud computing means using computing resources - storage, processing power, and software - over the internet, hosted on remote servers, instead of relying only on a local computer's own hardware. Cloud Services Cloud Services Common Cloud Service Models Common Cloud Service Models Model Model Full Form Full Form What It Provides What It Provides IaaS Infrastructure as a Service Basic computing infrastructure (servers, storage) rented over the internet PaaS Platform as a Service A ready platform for developers to build and run their own applications SaaS Software as a Service Complete, ready-to-use software accessed directly via a browser (e.g. web email) Cloud Storage Cloud Storage Cloud storage allows files to be uploaded to and saved on remote servers over the internet, so they can be accessed, Education Education - virtual field trips, exploring 3D models of the human body or historical sites - Gaming Gaming - fully immersive game worlds experienced through a headset - Training and simulation Training and simulation - pilot training, surgical practice and hazardous-job training in a safe virtual environment - Storage Storage - saving files online for access from any device - Software Software - using applications directly through a web browser - Computing power Computing power - running heavy processing tasks on remote servers -

shared or backed up from any device with an internet connection. Examples: Google Drive, OneDrive, Dropbox. Applications of Cloud Computing Applications of Cloud Computing Advantages of Cloud Computing Advantages of Cloud Computing 5.10 E-Commerce, E-Governance and E-Education 5.10 E-Commerce, E-Governance and E-Education Concept of E-Commerce Concept of E-Commerce E-Commerce (Electronic Commerce) means buying and selling goods or services over the internet, rather than through a traditional physical shop. Online Services (E-Commerce) Online Services (E-Commerce) Concept of E-Governance Concept of E-Governance E-Governance means using information technology to deliver government services, information and processes to citizens electronically, making them faster, more transparent and more accessible. Digital Government Services Digital Government Services Online file storage and backup - Streaming services (music, video) delivered from remote servers - Web-based email and office applications - Businesses running their entire IT systems on cloud servers instead of physical ones - Access data/applications from anywhere with internet, on any device - Reduces the need to buy and maintain expensive physical hardware - Easy to scale storage/computing power up or down as needed - Automatic backups reduce the risk of losing data - Online shopping websites and apps - Online payment and digital wallet systems - Online ticket booking (bus, flight, movie) - Food and grocery delivery apps - Online applications for citizenship, passports and licenses - Online tax filing and payment systems -

Concept of E-Education Concept of E-Education E-Education (e-learning) means using digital technology and the internet to deliver teaching and learning, allowing students to learn beyond the limits of a physical classroom. Online Learning / Digital Education Platforms Online Learning / Digital Education Platforms Advantages and Applications Advantages and Applications Area Area Key Advantage Key Advantage E-Commerce Shop anytime, anywhere, with a wider choice of products E-Governance Faster, more transparent access to government services, less paperwork E-Education Flexible, accessible learning regardless of location Quick recall : Quick recall : E-Commerce = buying/selling online. E-Governance = government services online. E-Education = learning online. All three rely on the internet, cloud computing and often AI to work efficiently. Chapter Summary Chapter Summary Government information portals and public service announcements - Online voter registration and public record systems - Online classes and video lectures - Digital note banks, e-libraries and question banks - Learning management systems for assignments and results - Interactive quizzes and self-assessment tools - AI enables machines to perform tasks needing human-like intelligence; Machine Learning is a key method that lets AI learn from data. - Learning techniques are broadly supervised (labelled data) or unsupervised (unlabelled data, finds patterns). - AI combined with robotics allows machines to sense their environment and make decisions, often tested first through simulation. - Generative AI creates new text/images/content but has real limitations (hallucination, bias, misuse risk). - Integrated AI tools are now built directly into everyday apps to boost learning and productivity. - AI ethics centres on bias, privacy, security and responsible use. - IoT connects everyday physical devices to the internet to collect and act on data. -

Glossary - Key Terms at a Glance Glossary - Key Terms at a Glance Term Term One-line meaning One-line meaning Artificial Intelligence Machines performing tasks that normally need human intelligence Machine Learning System that learns patterns automatically from data Supervised learning Learning from labelled data with known answers Unsupervised learning Finding hidden patterns in unlabelled data Generative AI AI that creates new text, images or other content AI bias Unfair AI results caused by unbalanced training data IoT Network of physical devices connected to exchange data VR Fully computer-generated immersive environment Cloud computing Using storage/processing power over the internet E-Governance Delivering government services electronically Important Questions (Exam Practice) Important Questions (Exam Practice) Based on the SEE Computer Science question pattern (MCQ 1 mark, Short 2 marks, Long 4 marks). True or False True or False 1. Machine Learning is a subset of Artificial Intelligence. (True) 2. Unsupervised learning uses data that is already labelled. (False) 3. Generative AI can sometimes produce incorrect but convincing information. (True) 4. IoT devices work without ever needing an internet connection. (False) 5. Cloud computing allows access to data from any device with internet. (True) Fill in the Blanks Fill in the Blanks 1. ____________ learning uses labelled data to train an AI system. VR/XR create immersive or enhanced digital experiences, used in education, gaming and training. - Cloud computing delivers storage, software and processing power over the internet. - E-Commerce, E-Governance and E-Education apply these technologies to shopping, government services and learning. -

2. The full form of IoT is ____________. 3. ____________ reality creates a fully computer-generated immersive environment. 4. ____________ means buying and selling goods and services over the internet. 5. AI systems producing unfair results due to unbalanced data is called AI ____________. Very Short / 1-Mark Type Very Short / 1-Mark Type 1. Define Artificial Intelligence. 2. What is Generative AI? 3. Write the full form of VR and XR. 4. What is cloud storage? 5. Define E-Governance. Short Answer / 2-Mark Type Short Answer / 2-Mark Type 1. Differentiate between supervised and unsupervised learning. 2. What is AI bias? Why is it a concern? 3. Differentiate between E-Commerce and E-Governance. 4. What is IoT? Give two examples of IoT devices. 5. Write any two advantages of cloud computing. Long Answer / 4-Mark Type Long Answer / 4-Mark Type 1. What is Artificial Intelligence? Explain its relationship with Machine Learning with examples. 2. Explain the applications of AI in robotics with examples. 3. What is Generative AI? Explain its uses and limitations. 4. Explain the ethical considerations in AI - bias, privacy, security and responsible use. 5. Explain IoT and its applications in smart homes, smart cities and healthcare. 6. What is cloud computing? Explain its services and advantages. Study tip : Study tip : This unit is very example-driven - for every concept (AI, IoT, VR, Cloud), keep at least one real-life example ready, since most 2- and 4-mark questions ask you to explain "with examples".