Emerging Trends

Written and reviewed by Rohit Singh, Computer Science & Commerce faculty at UniDrill (MCA, Osmania University · 6 years teaching). Based on NCERT Computer Science Class XI, Chapter 3.

Who is thinking inside your phone?

Your map app finds the fastest route by looking at traffic right now. A photo app tags your friends' faces before you type a single name. A voice assistant answers a question you spoke in Hindi. None of these was written as a fixed list of "if this, then that" rules covering every case. Something in them is learning, recognising and deciding.

This chapter is a tour of the technologies that do this and the ones that surround them. Each topic is easy to understand, and each has a neighbour that looks like it. NTA's favourite trick is to ask for the neighbour, so for every topic we also show the near-miss.

An emerging trend is a state of the art technology that is gaining popularity and setting a new trend among users. Some fade away; those that last change the digital economy and the way societies interact.

Artificial intelligence and machine learning

Artificial intelligence (AI) tries to simulate the natural intelligence of human beings in machines, so that they behave intelligently. An intelligent machine imitates cognitive functions like learning, decision making and problem solving. To work with minimum human help, an AI system is given a knowledge base, a store of facts, assumptions and rules it can use to decide. It can also learn from past experience or outcomes to make new decisions. Digital assistants such as Siri, Google Now, Cortana and Alexa are powered by AI.

Machine learning (ML) is a subsystem of AI. Instead of being explicitly programmed with rules, computers learn from data using statistical techniques. The algorithms are called models. A model is first trained on training data, then tested on testing data. After repeated training, once it reaches an acceptable level of accuracy, it is used to make predictions about new, unknown data.

Natural language processing (NLP) deals with the interaction between humans and computers in human spoken languages like Hindi and English. Predictive typing in a search engine and spell checking are examples. It makes voice control of devices possible through speech to text and text to speech conversion. Machine translation converts text from one language to another with fair accuracy, and automated customer service lets software answer queries and complaints.

Beyond the textbook. For the exam: ML is a subsystem of AI, and NLP is another application area. In reality: the family is nested. AI is the biggest circle, ML sits inside it, and a further technique called deep learning, which uses many layered networks, sits inside ML. Why it matters: when a question asks which term is the broadest, the answer is AI, and when it asks which learns from data without being explicitly programmed, the answer is ML.

Immersive experiences: VR and AR

Immersive experiences let you visualise, feel and react by stimulating your senses, which makes interaction more involved, realistic and engaging. They are used in 3D movies, video games, and training such as driving simulators and flight simulators.

VR is a comparatively new field with applications in gaming, military training, medical procedures, entertainment, social science and psychology, and engineering. Remember: VR creates something new, while AR only augments the perception of the real world with extra information.

Pause and predict: a traveller points a phone camera at a monument and sees its history and visitor reviews on the screen over the live camera view. Is this VR or AR?

Check your answer: AR. The real monument stays in view and information is placed over it. In VR the traveller would be inside an entirely computer made scene.

Robotics

A robot is a machine that can carry out one or more tasks automatically with accuracy and precision. Unlike other machines it is programmable by a computer, so it follows instructions given through programs. Robots were first thought of for repetitive, boring, stressful or labour intensive industrial work. Sensors are one of a robot's prime components. Robotics is an interdisciplinary branch combining mechanical engineering, electronics and computer science, concerned with the design, fabrication, operation and application of robots.

Drones are used in journalism, filming and aerial photography, short distance delivery, disaster management, search and rescue, healthcare, geographic mapping, structural inspection, agriculture, wildlife monitoring, law enforcement and border patrol. Robots are used in industry, medical science, bionics, scientific research and the military.

Big data and data analytics

Today data is created at a colossal rate: about 2.5 quintillion bytes a day, and the pace keeps rising as IoT grows. Data sets so large and complex that traditional data processing tools cannot handle them are called big data. They are not only huge but also unstructured: posts, chats, photos, tweets, blogs, news items, comments, audio and video. Big data brings challenges in integration, storage, analysis, searching, processing, transfer, querying and visualisation. It can hold rich knowledge of high business value, so there is a strong effort to build software to analyse it.

Big data is described by five Vs.

Worked example: which V? A company collects customer chats, voice calls and photos together. The stream arrives continuously. Some of it is spam. The company then finds which products customers prefer. Name the V behind each statement.

Data analytics is the process of examining data sets to draw conclusions about the information they contain, with the help of specialised systems and software. Businesses use it to take better decisions, and researchers use it to prove or disprove models, theories and hypotheses. Pandas is a Python library that makes data analysis much simpler.

Beyond the textbook. For the exam: big data has five Vs: volume, velocity, variety, veracity and value. In reality: the original description used only three, volume, velocity and variety. Veracity and value were added later, and some authors list even more. Why it matters: if a question gives a different count, check whether it follows the five V list given here.

Internet of Things, Web of Things and sensors

Imagine your bulb, fan and refrigerator joined the network of your laptop and phone. The Internet of Things (IoT) is a network of devices with embedded hardware and software that can connect and exchange data with other devices on the same network. Today many household devices have advanced hardware (microcontrollers) and software but work in isolation, with a human giving every input. IoT brings them together as an intelligent network. If a microwave, air conditioner, door lock or CCTV camera is connected, you can control it remotely through your smartphone.

Sensors. A phone changes between vertical and horizontal display with the help of two sensors. The accelerometer detects the orientation of the phone, and the gyroscope tracks rotation or twist of your hand and adds to the information from the accelerometer. A smart sensor takes input from the physical environment, uses built in computing resources to perform predefined functions when it detects specific input, and processes the data before passing it on. Smart sensors are one reason IoT is growing.

Smart cities. Fast urbanisation strains resources such as land, water, waste, air, health, traffic and safety. A smart city uses computer and communication technology along with IoT to manage and distribute resources efficiently. A smart building senses earthquake tremors and warns nearby buildings, a smart bridge uses wireless sensors to detect a loose bolt, cable or crack and alerts the authorities by SMS, and a smart tunnel detects leakage or congestion. Sensor readings travel as wireless signals across sensor nodes to a central computer for analysis.

Beyond the textbook. For the exam: IoT connects devices to share data and to be controlled remotely. In reality: every connected device is one more entry point for attackers, so changing default passwords and keeping device software updated matters as much as the convenience. Why it matters: it links this chapter to the security and societal impact topics, where connected devices are often the weakest link.

Cloud computing

Cloud computing delivers computer based services over the Internet (the cloud), accessible from anywhere with any device. The services are software, hardware (servers), databases, storage and so on, provided by cloud service providers who usually charge on a pay per use basis, like the electricity bill. You already use it when you back up pictures online or host a website. A user can run a bigger application or process a lot of data without having the storage or computing power on a personal computer, as long as there is an Internet connection. Cloud resources are on demand and cost effective: you avail only the resources you need.

The cloud is understood as "everything as a service". There are three standard models.

Worked example: choose the model. Read each need and name IaaS, PaaS or SaaS.

Common wrong step. Choosing SaaS for the first row because the company will "use" the cloud. SaaS is for finished applications; a company that wants raw servers and storage to build its own platform is buying infrastructure.

All three models charge by usage, which removes the need for a big up front investment for new or growing organisations. The Government of India has an initiative for its own cloud called GI Cloud, named MeghRaj.

Beyond the textbook. For the exam: the cloud is a set of services you pay for as you use. In reality: "the cloud" is not a vague sky. It is data centres full of servers that belong to a provider, located somewhere on the planet. Why it matters: this is why cloud services need an Internet connection, and why questions on privacy and data location arise in the societal impact chapters.

Grid computing

A grid is a network of geographically dispersed and heterogeneous computational resources that creates the sense of a virtual supercomputer with huge processing power and storage. The member resources are called nodes. They come together temporarily to solve a single large task and reach a common goal. Today countless nodes, from mobile phones to workstations, are connected to a LAN or the Internet, so using their spare memory and processing power is economical, and the grid lets scientists solve computation heavy problems without buying costly hardware.

A grid can be a data grid, which manages large, distributed data with multi-user access, or a CPU (processor) grid, which moves processing from one PC to another, or divides a large task into sub-tasks for parallel processing on different nodes. Setting up a grid needs middleware to implement the distributed processor architecture. The Globus toolkit is an open source toolkit for building grids, with software for security, resource management, data management, communication and fault detection.

Grid computing is different from the IaaS cloud service: in IaaS a provider rents the infrastructure to users, whereas in a grid multiple computing nodes join together to solve a common computational problem.

Pause and predict: thousands of volunteers' computers share their idle processing power to analyse a single scientific problem. Cloud or grid?

Check your answer: grid. The nodes join together temporarily and pool their own resources for one common task. No provider is renting out services.

Blockchain

Traditionally, digital transactions are recorded in a centralised database and updated one by one. Ticket booking sites and banks work this way. With all data in one place, there is a chance of it being hacked or lost. Blockchain works on a decentralised, shared database: every computer (node) holds a copy.

A block is a secured chunk of data or a valid transaction. Each block has a header that every node can see, while only the owner can access the private data inside. The blocks are linked into a chain. Blockchain is a system that lets a group of connected computers maintain a single updated and secure ledger. It is an append only open ledger, updated only after all the nodes in the network authenticate the transaction.

Its best known use is in digital currency. Because it is decentralised, open and secure, it is also seen as a way to bring transparency, accountability and efficiency to business and governance. Possible uses are healthcare (data sharing between providers for better diagnosis), land registration records (avoiding ownership disputes and encroachments), voting (preventing alteration of votes), and sectors such as banking, media, telecom, travel and hospitality.

Beyond the textbook. For the exam: a blockchain cannot be altered by a single member because every member holds a copy and all nodes must authenticate new blocks. In reality: each block also carries a cryptographic fingerprint (a hash) of the block before it, so changing an old block would change its fingerprint and break every later link, and the other copies would expose the change. Why it matters: it explains the word "chain", and why people describe a blockchain as tamper evident.

Exam traps

60-second revision

Where this chapter connects

• Previous: Encoding Schemes and Number System explains how all the data behind these technologies is stored as numbers.
• Earlier link: Computer System introduced microcontrollers and storage, which IoT and cloud computing build on.
• Data link: Understanding Data goes deeper on the kinds of data that big data analytics works with.
• Class XII link: Societal Impacts looks at the security and privacy issues these technologies raise.