7.1Emerging technology

Emerging technology refers to new and advanced technologies that are developed to improve the way people live and work. These technologies were not available or widely used in the past but are now growing rapidly. With continuous advancement in technology, traditional methods are changing very fast. Technologies that were once considered impossible are now becoming a reality.

Emerging technologies are used in many fields such as education, business, media, healthcare, and science. They also include improvements in existing technologies. These technologies have a strong impact on society and future development.

7.2 Concept of Artificial Intelligence (AI)

Artificial Intelligence (AI) is a branch of computer science that deals with building machines capable of solving complex problems that require human intelligence. Computers are programmed in such a way that they can think and act like humans.

AI can be referred to as the simulation of human intelligence by machines. Machines are capable of mimicking human activities such as learning, reasoning, and perception. As a result, the devices are able to take their own decisions according to a particular situation.

The Father of AI, John McCarthy, a professor of computer science at Stanford, coined the term “Artificial Intelligence” in 1956. He defines AI as “the science and engineering of making intelligent machines.” Today, AI is applied in almost all areas.

Characteristics of Artificial Intelligence (AI)

For a system to be considered as Artificial Intelligence, it must have certain characteristics. These characteristics enable machines to behave like humans and make intelligent decisions.

1. Learning: AI systems have the ability to learn from experience and data. They can improve their performance over time without being explicitly programmed again and again.

2. Reasoning: AI can use logical thinking to solve problems. It can analyze available information and draw conclusions just like humans do.

3. Problem Solving: AI systems can identify problems and find suitable solutions. They can handle complex tasks that usually require human intelligence.

4. Perception: AI can interpret information from the environment using sensors such as cameras, microphones, and scanners. This allows machines to see, hear, and understand their surroundings.

5. Decision Making: Based on the given data and situation, AI systems can make their own decisions. This helps in performing tasks automatically and efficiently.

6. Natural Language Processing: AI can understand and respond to human language. Examples include voice assistants like Siri, Alexa, and Google Assistant.

Application of Artificial Intelligence 

Artificial Intelligence is widely used in many fields to make work easier, faster, and more accurate. Some important applications of AI are explained below:

1. Healthcare
AI is used in hospitals to diagnose diseases, analyze medical reports, and suggest treatment plans. It helps doctors detect diseases like cancer at an early stage and improves patient care. Example: AI-based medical imaging, virtual health assistants.

2. Autonomous Vehicles:
AI is used in self-driving cars to recognize roads, traffic signals, pedestrians, and obstacles. It helps vehicles make decisions in real time and improves road safety. Example: Self-driving cars, driver-assistance systems.

3. Customer Service
AI-powered chatbots and virtual assistants handle customer queries, provide instant replies, and offer 24/7 support. This reduces workload and improves customer satisfaction. Example: Website chatbots, automated customer support systems.

4. Finance
In the finance sector, AI is used for fraud detection, risk analysis, and online banking services. It helps detect suspicious transactions and manage financial data efficiently. Example: Credit card fraud detection, smart banking apps.

5. Smart Assistants
AI-based smart assistants can understand voice commands and perform tasks such as setting alarms, playing music, or searching for information on the internet. Example: Siri, Alexa, Google Assistant.

6. Robotics
AI is used in robots to perform tasks that are difficult or dangerous for humans. These robots are used in industries, homes, and hospitals. Example: Industrial robots, cleaning robots, and surgical robots.

7.3 Cloud Computing 

Cloud Computing is one of the most demanding technologies because of the ease it provides to organizations while setting up infrastructure. It refers to the delivery of on-demand computing services such as servers, storage, operating systems, application software, databases, and networking through the internet.All the services provided by cloud service providers are stored in the cloud or virtual space, and users can access them remotely.

In our daily life, we use cloud services such as Google, Gmail, Salesforce, Google Drive, Apple iCloud, and cloud file storage like Dropbox, which are examples of cloud computing.

Advantages of Cloud Computing

• Reduced cost: Cloud computing removes the expenses of buying expensive hardware and software, need of IT expert to manage the infrastructure. Instead, you can use the service provided by the cloud service providers, and the payment model is also pay-per-use.

• Storage service: Users can store and maintain large volumes of data in cloud without worrying about its security. The management and maintenance is handled by the service provider.

• Reliability: The data backup, disaster recovery policies make cloud computing very reliable and also they implement mechanisms to avoid any problems in delivering cloud services. You can access the services from any device at any time from any location.

• Security: Cloud data centers are secure and also the cloud service provider implements policies, technologies and controls that help secure your data, apps and infrastructure from potential threats.

Cloud Computing Services

Cloud computing services deliver on-demand IT resources—like servers, storage, databases, software, and analytics over the internet with pay-as-you-go pricing, allowing businesses to scale flexibly without owning physical hardware, primarily categorized as IaaS (Infrastructure), PaaS (Platform), and SaaS (Software)

Software as a Service (SaaS)
Software as a Service (SaaS) provides ready-to-use software applications through the internet. Users can access the software without installing or maintaining it. The service provider handles updates, security, and maintenance. Users only need an internet connection to use the software.

Examples of SaaS services:

• Google Apps
• Microsoft Office 365
• Gmail, Yahoo Mail
• Facebook

• Cisco WebEx, Salesforce, GoToMeeting

Infrastructure as a Service (IaaS)
Infrastructure as a Service (IaaS) provides basic computing resources through the internet. These resources include servers, storage, operating systems, and networking. Users do not need to buy or maintain hardware. The cloud service provider manages and upgrades everything. This makes IaaS cost-effective and easy to use.

Examples of IaaS services:

• Physical or virtual servers
• Storage (block and file storage)
• Networking services (firewalls, load balancers, IP addresses, virtual networks)

Platform as a Service (PaaS)
Platform as a Service (PaaS) provides a complete environment for developing and running software applications. It helps developers focus on coding instead of managing servers, storage, and databases. PaaS supports the full software development process such as building, testing, and deploying applications.

Examples of PaaS services:

• Operating systems
• Programming environments
• Databases (MySQL, Oracle)
• Web servers (Tomcat)
• Runtime environments (Java runtime)

Distributed Computing

Distributed computing is a method where many computers are connected together to work on the same task. When one computer is not powerful enough to solve a big or complex problem, distributed computing is used.
All connected computers share data and processing power and work like a single system to improve performance.

Advantages of Distributed Computing

1. Scalability
If the workload increases, the system can be easily expanded by adding more computers instead of replacing old hardware. This makes the system flexible.

2. Improved Performance
In distributed computing, a large task is divided into smaller parts. Each computer works on a part at the same time. This parallel processing improves overall performance.

3. Cost-Effective
The initial setup cost is high, but in the long run it is cost-effective because resources are shared and efficiently used.

4. Reliability
Distributed systems are more reliable than single computer systems. If one computer fails, the other computers continue to work. Failure of one node does not stop the whole system.

 IoT (Internet of Things)

The Internet of Things (IoT) is a network of connected devices that collect and share data through the internet. These devices use sensors to collect information about their use and environment.
Sensors are found in devices like mobile phones, electrical appliances, and even airplanes. IoT allows devices to communicate and share data in real time without human involvement.

A simple example of an IoT can be a smart home. Let’s suppose you forgot to switch off your fan while you were leaving for the office. Now, instead of going back to your place and switching it off, you can simply use your smartphone app to switch off your fan. With face recognition technology, you don’t need a key for entering or exiting your home.

Some real-world examples of IoT are wearable fitness trackers (like Fitbits), voice assistants (Siri and Alexa), smart cars (Tesla), and smart appliances (iRobot).

Components of Internet of Things (IoT)

A complete IoT system integrates four distinct components: sensors/devices, connectivity, data processing, and a user interface.

1. Sensors – collecting data
2. Connectivity – sending data to the cloud
3. Data processing – making data useful
4. User interface – delivering information to the user

Sensors/Devices

First, data collection is important, which is done by the sensors. Sensors are devices that collect very minute data from the surrounding environment. These sensors are embedded within the devices. The data collected can be using simple sensors such as temperature monitoring sensors to complex sensors that collect video feed.A device can have multiple sensors. For example, our phone is a device that has multiple sensors such as GPS, an accelerometer, and a camera.

Connectivity

Next, the collected data is sent to a cloud infrastructure. The sensors can be connected to the cloud through various mediums such as cellular networks, satellite networks, Wi-Fi, Bluetooth, wide-area networks (WAN), and many more. The selection of the connectivity medium depends on factors such as power consumption, range, and bandwidth. So, choosing the best connectivity option in the IoT system is important.

Data Processing

Once the data is pushed to the cloud, processing is done on the data. Processing can be very simple, such as checking whether a temperature reading on devices like ACs or heaters is within an acceptable range. It can sometimes also be very complex, such as identifying objects (for example, intruders in your house) using computer vision on video.

However, there might be situations where user interaction is required. For example, what if the temperature is too high or if there is an intruder in your house? That’s where the user comes into the picture.

User Interface

Next, the information is made available to the end user by notifying them through alarms on their phones or through text messages or emails. Users can also use an interface to check on their IoT system.

For example, a user who has a camera installed in his or her house might want to check the video recordings through a web server or an IoT system. This can be programmed in such a way that it does not require any human intervention. For instance, if any intruders are sensed, the system can generate an alert not only to the owner of the house but also to the concerned authorities.

Advantages of IoT

• IoT enables smart control over homes and cities, which enhances security.
• By automating activities, it saves a lot of time.
• Human intervention is not required, as IoT devices are connected and can communicate with one another to perform various tasks.
• IoT apps can be used to provide personal assistance, such as alerting you about your regular plans.
• Even if you are away from the location, the information is easily accessible in real time through IoT devices.

Big Data

Big data means very large and complex data. This data is difficult to store and process using traditional tools. Traditional databases cannot handle big data efficiently. Big data includes large amounts of data that are created very quickly. It also includes different types of data. Big data can be structured, semi-structured, or unstructured.

Sources of Big Data

Big data comes from many sources, such as:

• Social media
• Stock markets
• Weather reports
• Online shopping websites
• Bank and credit card transactions
• Scientific instruments
• Mobile phones and devices

Characteristics of Big Data

Volume:
Volume means the amount of data. Big data contains a very large quantity of data. The amount of data is increasing every year.

Variety:
Variety means different types of data. Data can be text, images, audio, or videos. Data can be structured or unstructured.

Velocity:
Velocity means the speed at which data is created. Big data is generated and processed very quickly.

Variability: This refers to the inconsistency in the data, thus hampering the process of being able to handle and manage the data effectively. It also refers to the inconsistent speed at which big data is loaded into databases.

Data Mining

Data mining is the process of finding useful and valuable information from large amounts of data. It uses statistics, artificial intelligence, and machine learning. It helps to find patterns, relationships, and unusual data. Data mining helps in analyzing data and discovering trends. It gives a better understanding of how data can be used. Data mining is used in science, research, business, and social media. It is also called Knowledge Discovery in Data (KDD).

Data mining includes data sets, pre-processing, classification, databases, statistics, analytics, and evaluation.

With data mining, businesses can understand customer behavior. Some behaviors are very hard to find using human analysis. Social media apps like Facebook, TikTok, Instagram, and Twitter use data mining. They use it to understand user behavior and show targeted advertisements.

Supermarkets also use data mining. They study customer buying behavior using loyalty or membership cards. Stores track what customers buy, when they buy, and at what price. By analyzing this data, stores decide when to give discounts. They also decide when to sell products at full price. Data mining helps stores know which products are bought the most.

Advantages of Data Mining

Data mining is very helpful for companies.

Better marketing:
Data mining helps companies understand customers’ likes and dislikes. It helps in choosing the right group of customers for marketing.

Improved customer relationship:
Understanding customer behavior improves customer relationships. Companies can solve customer problems early. This helps in providing better customer service.

Increased cost efficiency:
Data mining helps understand supplier pricing behavior. This helps reduce manufacturing costs.

Employee productivity:
Data mining studies employee behavior patterns. It helps employees make better decisions and policies. This increases overall employee productivity.

Compiled By Er. Basant Kumar yadav