Emerging Trends Chapter Notes | Informatics Practices for Class 11 - Humanities/Arts PDF Download

Introduction to Emerging Trends

• Computers have been integral to technological advancements for a long time, with new technologies emerging regularly.
• Staying updated with emerging trends is essential to understand current technologies and anticipate future developments.
• Many new technologies are introduced daily; some fade away, while others gain popularity and set new trends among users.
• Emerging trends represent state-of-the-art technologies that gain widespread attention and adoption.
• These trends are poised to significantly impact the digital economy and interactions within digital societies.

Artificial Intelligence (AI)

• AI enables machines to simulate human intelligence, allowing them to perform tasks intelligently with minimal human intervention.
• Examples include smartphone maps analyzing real-time traffic data to suggest the fastest routes and social media platforms automatically tagging friends in photos.
• Intelligent digital assistants like Siri, Google Now, Cortana, and Alexa are powered by AI.
• AI systems mimic human cognitive functions such as learning, decision-making, and problem-solving.
• Machines are programmed to create a knowledge base, consisting of facts, assumptions, and rules, to make decisions.
• AI systems can learn from past experiences or outcomes to improve future decision-making.

Machine Learning

• Machine Learning (ML) is a subset of AI that enables computers to learn from data using statistical techniques without explicit programming.
• ML involves algorithms, known as models, that learn from data to make predictions or decisions.
• Models are trained and tested using training and testing datasets, respectively, to achieve acceptable accuracy levels.
• Once trained, these models can predict outcomes for new, unseen data.
• ML is widely used in applications like recommendation systems, fraud detection, and predictive analytics.

Natural Language Processing (NLP)

• NLP enables interaction between humans and computers using natural languages like Hindi or English.
• Examples include predictive text in search engines, spell-checking features, and voice-based web searches or device control.
• NLP systems support text-to-speech and speech-to-text conversions, enhancing accessibility and usability.
• Machine translation, an emerging NLP application, allows machines to translate text between languages with reasonable accuracy.
• Automated customer service systems use NLP to interact with customers, addressing queries or complaints efficiently.

Immersive Experiences

• Immersive experiences, enabled by 3D videography, enhance engagement in movies, video games, and simulations.
• These experiences stimulate human senses, making interactions more realistic and engaging.
• Applications include training tools like driving and flight simulators, which provide practical learning environments.
• Immersive experiences are achieved through technologies like Virtual Reality (VR) and Augmented Reality (AR).

Virtual Reality

• Virtual Reality (VR) creates a three-dimensional, computer-generated environment that simulates the real world.
• Users can interact with and explore VR environments using VR headsets, which provide immersive sensory experiences.
• VR incorporates sensory inputs like sound, smell, motion, and temperature to enhance realism.
• Applications include gaming, military training, medical procedures, entertainment, social sciences, psychology, and engineering simulations.
• VR is a relatively new field, continuously evolving to offer more realistic and interactive experiences.

Augmented Reality

• Augmented Reality (AR) overlays computer-generated information onto the physical world, enhancing perception without creating a new environment.
• AR makes environments interactive and digitally manipulable, integrating tactile and sensory elements.
• Location-based AR apps provide real-time information about nearby places, such as historical sites, based on user location and camera input.
• Users can access place details and reviews, aiding decision-making for travelers and explorers.
• AR is used in navigation, education, gaming, and retail to enhance user experiences.

Robotics

• Robotics is an interdisciplinary field involving mechanical engineering, electronics, and computer science to design, build, and operate robots.
• A robot is a programmable machine that performs tasks automatically with accuracy and precision.
• Unlike traditional machines, robots can follow complex instructions via computer programs.
• Robots were initially developed for repetitive, labor-intensive, or stressful industrial tasks.
• Sensors are critical components, enabling robots to interact with their environment.
• Types of robots include wheeled, legged, manipulators, and humanoids, with humanoids resembling humans in appearance and behavior.
• Applications include industrial automation, medical science, bionics, scientific research, and military operations.
• Examples include NASA’s Mars Exploration Rover for planetary studies, Sophia the humanoid with AI and facial recognition, and drones for aerial tasks like filming, delivery, and disaster management.

Big Data

• Big Data refers to massive, complex datasets generated at a high rate, driven by widespread technology adoption and Internet usage.
• Over a billion Internet users contribute to approximately 2.5 quintillion bytes of data created daily, with growth fueled by the Internet of Things (IoT).
• Big Data is characterized by its volume and unstructured nature, including posts, messages, photos, videos, and tweets.
• Traditional data processing tools are inadequate for handling Big Data due to its size and complexity.
• Challenges include integration, storage, analysis, searching, processing, transfer, querying, and visualization.
• Big Data holds valuable insights and knowledge, driving efforts to develop specialized software and methods for processing and analysis.

Characteristics of Big Data

• Volume: Big Data is defined by its enormous size, which exceeds the processing capabilities of traditional database management systems.
• Velocity: Represents the rapid rate at which data is generated and stored, significantly higher than traditional datasets.
• Variety: Encompasses diverse data types, including structured, semi-structured, and unstructured data like text, images, and videos.
• Veracity: Refers to the trustworthiness of data, as inconsistencies, biases, or errors can lead to misleading results.
• Value: Big Data contains hidden patterns and knowledge of high business value, but processing requires cost-benefit analysis to ensure worthwhile investment.

Data Analytics

• Data analytics involves examining datasets to extract meaningful insights using specialized systems and software.
• It is widely used in commercial industries to support informed business decisions.
• In science and technology, data analytics helps verify or disprove models, theories, and hypotheses.
• Tools like the Pandas library in Python simplify data analysis tasks.
• Data analytics is increasingly popular due to its ability to uncover trends, patterns, and actionable insights.

Internet of Things (IoT)

• IoT is a network of devices with embedded hardware and software, enabling communication and data exchange with other devices on the same network.
• Devices include household items like bulbs, fans, and refrigerators, as well as computers, smartphones, and smartwatches.
• IoT enables devices to collaborate, reducing human intervention and creating intelligent networks.
• Examples include remotely controlling Internet-enabled devices like microwaves, air conditioners, door locks, or CCTV cameras using smartphones.
• IoT enhances automation, efficiency, and connectivity in various applications.

Web of Things (WoT)

• WoT extends IoT by using web services to connect physical devices, creating a unified interface for interaction.
• Unlike IoT, which may require multiple apps for different devices, WoT aims to integrate devices through a single web-based platform.
• WoT facilitates efficient communication among devices, paving the way for smart homes, offices, and cities.
• It leverages the web’s existing communication infrastructure to enhance device interoperability.

Sensors

• Sensors are devices that detect physical environment inputs and process data using built-in computing resources.
• Examples include accelerometers and gyroscopes in smartphones, which adjust screen orientation based on device positioning.
• Smart sensors perform predefined functions upon detecting specific inputs, processing data before transmission.
• Sensors are critical for IoT, enabling real-world monitoring and contributing to intelligent, sensor-based systems.
• Applications include environmental monitoring, health tracking, and automation systems.

Smart Cities

• Smart cities use IoT, WoT, and communication technologies to manage resources efficiently and enhance urban sustainability.
• Challenges like traffic congestion, pollution, and infrastructure management drive the adoption of smart city solutions.
• Examples include smart buildings with earthquake detection sensors, smart bridges with wireless sensors for structural monitoring, and smart tunnels detecting leaks or congestion.
• Sensor networks transmit data to centralized systems for analysis, optimizing city operations.
• Smart cities integrate transportation, power, water, waste management, and other services to improve efficiency and livability.

Cloud Computing

• Cloud computing delivers computer-based services like software, hardware, databases, and storage over the Internet, accessible from anywhere using smart devices.
• Cloud service providers charge based on usage, similar to electricity billing.
• Common uses include online backups, file storage, and website hosting.
• Cloud computing enables users to run large applications or process data without local storage or processing power, as long as they are connected to the Internet.
• It offers cost-effective, on-demand resources, reducing the need for significant upfront investments.
• India’s ‘MeghRaj’ initiative (GI Cloud) promotes cloud computing for government services.

Cloud Services

• Cloud services are categorized into three models: Infrastructure as a Service (IaaS), Platform as a Service (PaaS), and Software as a Service (SaaS).
• IaaS: Provides computing infrastructure like servers, virtual machines, storage, and network components. Users can configure and deploy software on remote hardware, outsourcing infrastructure needs and reducing costs.
• PaaS: Offers a platform for developing, testing, and deploying applications without managing underlying infrastructure. For example, a pre-configured server with MySQL and Python simplifies web application deployment.
• SaaS: Delivers on-demand access to software applications (e.g., Google Docs, Microsoft Office 365) via licensing or subscription, eliminating installation and configuration concerns.
• All models provide on-demand resources, charged per usage, enabling cost savings and scalability for organizations.

Grid Computing

• Grid computing involves a network of geographically dispersed, heterogeneous computational resources forming a virtual supercomputer.
• Unlike cloud computing, which focuses on service delivery, grid computing is application-specific, solving large computational tasks.
• Resources, called nodes, collaborate temporarily to achieve a common goal, such as solving scientific or research problems.
• Grid computing leverages existing devices like mobile phones and PCs connected via LAN or the Internet, utilizing their memory and processing power.
• Types include data grids for managing distributed data and CPU grids for parallel processing of subtasks.
• Unlike IaaS, grid computing involves nodes voluntarily sharing resources without a central provider.
• Middleware, like the open-source Globus Toolkit, is used to manage security, resources, data, communication, and fault detection in grids.

Blockchains

• Blockchain technology uses a decentralized, shared database where each participating computer (node) holds a copy of the database.
• Unlike centralized systems (e.g., banks or ticket booking sites), blockchains reduce risks of data hacking or loss.
• A block is a secure chunk of data or transaction, with a public header and private data accessible only to the owner.
• Blocks form a chain, creating a blockchain—a secure, updated ledger maintained by all nodes.
• Transactions are authenticated by all nodes, ensuring security and preventing unauthorized changes.
• Blockchains maintain an append-only ledger, enhancing transparency and security.
• Popular applications include digital currencies, but blockchains also ensure transparency, accountability, and efficiency in business and governance.
• Potential uses include healthcare for secure data sharing, land registration to prevent disputes, and voting systems for transparency.