Emerging Trends

REVISION SUMMARY: Emerging Trends (NCERT Class 11)

1. Chapter at a glance

• Emerging trends are state-of-the-art technologies that gain popularity and set new trends among users, impacting the digital economy and digital societies.
• Artificial Intelligence (AI) endeavours to simulate the natural intelligence of human beings into machines, enabling them to behave intelligently by imitating cognitive functions such as learning, decision-making and problem solving.
• Machine Learning is a subsystem of AI in which computers learn from data using statistical techniques without being explicitly programmed; models are trained and tested on data before making predictions on new data.
• Natural Language Processing (NLP) deals with interaction between humans and computers using human spoken languages and supports text-to-speech, speech-to-text and machine translation.
• Immersive experiences, achieved through Virtual Reality (VR) and Augmented Reality (AR), allow users to visualise, feel and react by stimulating senses in simulated or enhanced environments.
• The Internet of Things (IoT) is a network of devices with embedded hardware and software that communicate and exchange data with other devices on the same network, enabling remote access and collaboration.
• Big Data refers to data sets of enormous volume and complexity that cannot be processed by traditional tools; it is characterised by Volume, Velocity, Variety, Veracity and Value.
• Cloud Computing delivers computer-based services (IaaS, PaaS, SaaS) over the Internet on a pay-per-use basis; Grid Computing connects geographically dispersed heterogeneous nodes to act as a virtual supercomputer; Blockchain maintains a decentralised, shared, append-only ledger updated only after network authentication.

2. Key terms and definitions

• Emerging trends: State-of-the-art technologies that gain popularity and set a new trend among users.
• Artificial Intelligence (AI): Endeavours to simulate the natural intelligence of human beings into machines, making them behave intelligently by imitating cognitive functions like learning, decision-making and problem solving.
• Knowledge base: A store of information consisting of facts, assumptions and rules which an AI system can use for decision making.
• Machine Learning: A subsystem of AI wherein computers have the ability to learn from data using statistical techniques without being explicitly programmed; models are trained and tested on data to make predictions.
• Natural Language Processing (NLP): Deals with the interaction between humans and computers using human spoken languages such as Hindi or English; supports text-to-speech and speech-to-text conversion.
• Immersive experiences: Allow us to visualise, feel and react by stimulating our senses, making interaction more realistic and engaging; achieved using VR and AR.
• Virtual Reality (VR): A three-dimensional, computer-generated situation that simulates the real world; the user gets immersed and interacts with the environment.
• Augmented Reality (AR): Superimposition of computer-generated perceptual information over the existing physical surroundings, adding digital components to the physical world.
• Robotics: Interdisciplinary branch primarily concerned with the design, fabrication, operation and application of robots.
• Robot: A machine capable of carrying out one or more tasks automatically with accuracy and precision; programmable by a computer.
• Humanoids: Robots that resemble humans.
• Drone: An unmanned aircraft that can be remotely controlled or fly autonomously through software-controlled flight plans working with onboard sensors and GPS.
• Big Data: Data sets of enormous volume and complexity that cannot be processed and analysed using traditional data-processing tools.
• Volume: Enormous size of data that makes it difficult to process with traditional DBMS tools.
• Velocity: Rate at which data are being generated and stored; exponentially higher than traditional data sets.
• Variety: Dataset contains varied data (structured, semi-structured and unstructured) such as text, images, videos and web pages.
• Veracity: Trustworthiness of data; refers to inconsistency, bias, noise or issues with collection methods.
• Value: Hidden patterns and useful knowledge in big data that can be of high business value.
• Data Analytics: Process of examining data sets to draw conclusions about the information they contain with the aid of specialised systems and software.
• Internet of Things (IoT): Network of devices that have embedded hardware and software to communicate (connect and exchange data) with other devices on the same network.
• Web of Things (WoT): Allows use of web services to connect anything in the physical world, integrating devices so they communicate efficiently.
• Sensor: Device that takes input from the physical environment and uses built-in computing resources to perform predefined functions upon detection of specific input and then processes data before passing it on.
• Smart sensor: A sensor with built-in computing resources.
• Smart city: Uses computer and communication technology along with IoT to manage and distribute resources efficiently.
• Cloud Computing: Delivery of computer-based services (software, hardware, databases, storage) over the Internet, accessible from anywhere; charged on pay-per-use basis.
• Cloud service providers: Companies that provide cloud resources.
• Infrastructure as a Service (IaaS): Cloud service offering computing infrastructure such as servers, virtual machines, storage, network components and operating systems on demand.
• Platform as a Service (PaaS): Cloud service providing a platform or environment to develop, test and deliver software applications without managing underlying infrastructure.
• Software as a Service (SaaS): Cloud service providing on-demand access to application software, usually requiring licensing or subscription.
• Grid Computing: Computer network of geographically dispersed and heterogeneous computational resources that temporarily join to solve a single large task.
• Data grid: Grid used to manage large and distributed data with multi-user access.
• CPU/Processor grid: Grid where processing is moved from one PC to another or a large task is divided into subtasks for parallel processing.
• Blockchain: System that allows a group of connected computers to maintain a single updated and secure ledger; updated only after all nodes authenticate the transaction.
• Block: Secured chunk of data or valid transaction; has a header visible to every node.
• Ledger: Append-only open record maintained across all nodes in the blockchain network.

3. Syntax and constructs

None taught in this chapter (purely conceptual; no programming statements, functions or code constructs are present in the text).

4. Algorithms and worked logic

None taught in this chapter (no step-by-step algorithms, procedures or code logic to write or dry-run are described).

5. Common errors and exam pitfalls

• Confusing AI with Machine Learning or NLP (AI is the broad endeavour; ML and NLP are specific subsystems/applications).
• Mixing VR and AR definitions (VR creates a simulated world; AR only superimposes information on the existing physical world).
• Omitting any of the five V’s of Big Data or misstating their NCERT framing (especially Veracity vs. Value).
• Stating Cloud service models without their exact NCERT descriptions (IaaS, PaaS, SaaS) or confusing them with Grid Computing.
• Claiming Blockchain is centralised or that a single node can alter the ledger (text stresses decentralised, append-only nature and network-wide authentication).
• Adding examples or features not mentioned in the chapter text (e.g., specific programming libraries beyond the single reference to Pandas, or non-NCERT applications).