Recent Trends in Technology

(c) IoT (Internet of Things)

The device senses its environment in real time and uses internet connectivity to act on that data, which is exactly the defining feature of IoT — smart devices that combine sensors, networking and automatic control.

Cloud Computing

Cloud computing is the delivery of computing services — servers, storage, databases, networking, software and analytics — over the Internet ("the cloud") on a pay-as-you-use basis. Instead of owning and maintaining their own hardware and software, users access these resources on demand from a cloud service provider such as Amazon Web Services (AWS), Microsoft Azure or Google Cloud. The provider owns and manages the physical infrastructure, while the user simply connects through the Internet and pays only for what is consumed. The service is usually offered in three models: IaaS (Infrastructure as a Service), PaaS (Platform as a Service) and SaaS (Software as a Service).

Three advantages:

1. Cost saving – There is no need to buy expensive servers, storage devices or software licences. The user pays only for the resources actually used, which reduces capital expenditure and maintenance cost.

2. Scalability and flexibility – Resources can be increased or decreased instantly according to demand. A website can add more servers during heavy traffic and release them when traffic falls, without buying new hardware.

3. Accessibility and automatic updates – Data and applications can be accessed from anywhere at any time using any Internet-connected device. The service provider also handles software updates, backups and security patches, so the user does not have to manage them.

(c) Google Docs

Google Docs is a SaaS (Software as a Service) application — it runs entirely on the cloud and is used directly through a browser without installing anything. Amazon EC2 and Microsoft Azure are IaaS/PaaS platforms (infrastructure and platform), while VMWare is virtualization software.

The three "V"s of big data are volume, velocity, and variety. They are the three characteristics used to define big data — data that is too large, too fast, and too varied to be handled by traditional data-processing tools such as ordinary relational database systems.

1. Volume Volume refers to the sheer amount or size of the data. Big data involves enormous quantities of data that keep growing — typically measured in terabytes (TB), petabytes (PB), and beyond. For example, a social media platform like Facebook generates several petabytes of data every day from posts, photos, videos, likes, and comments. Traditional storage and processing systems cannot store or analyse data of this scale, so distributed systems such as Hadoop's HDFS are used.

2. Velocity Velocity refers to the speed at which data is generated, collected, and processed. In big data, data arrives continuously and often needs to be analysed in real time or near real time. For example, stock market transactions, sensor readings from IoT devices, and GPS signals from vehicles are produced continuously and must be processed instantly to be useful. Stream-processing tools like Apache Kafka and Apache Spark Streaming handle such high-speed data streams.

3. Variety Variety refers to the different forms and sources of data. Big data is not limited to neat, structured rows and columns; it includes:

• Structured data — organised data such as tables in a relational database.
• Semi-structured data — partially organised data such as XML or JSON files.
• Unstructured data — raw data with no fixed format, such as text, images, audio, and video.

Because these different types come from many sources (social media, sensors, emails, transactions), special tools are needed to store and process them together.

Conclusion Together, the three Vs — volume (how much), velocity (how fast), and variety (how diverse) — explain why big data requires special technologies like Hadoop, NoSQL databases, and distributed computing rather than conventional database management systems.

(d) Hardware as a Service (HaaS)

The three standard cloud computing service models are IaaS, PaaS and SaaS. "Hardware as a Service" is not a recognised cloud service model — hardware is delivered as part of IaaS.

Artificial Intelligence (AI) is the branch of computer science that deals with designing machines and software that can perform tasks which normally require human intelligence — such as learning, reasoning, problem solving, understanding language, perceiving the environment, and taking decisions. An AI system acquires knowledge (data), processes it using algorithms, and improves its performance with experience, without being explicitly programmed for every situation. Its main goal is to build systems that think and act intelligently like humans.

Application areas of AI in education

1. Personalised learning (Adaptive learning) — AI analyses each student's strengths, weaknesses, speed and learning style, and then adjusts the difficulty and sequence of lessons accordingly. For example, platforms like Duolingo and Khan Academy recommend the next topic based on past performance.

2. Intelligent Tutoring Systems (ITS) — AI-based tutors give one-to-one guidance, hint at the next step when a student is stuck, and provide instant feedback. Example: Carnegie Learning's MATHia.

3. Automated assessment and grading — AI checks objective tests instantly and even evaluates essays, coding assignments and spoken answers, saving teachers' time and giving students quick results. Example: Gradescope, automated essay scoring tools.

4. Smart content and curriculum design — AI generates customised notes, quizzes, practice questions and digital content according to the syllabus and the learner's level.

5. Virtual assistants and chatbots — AI chatbots answer students' queries about admissions, courses, schedules and doubts 24 hours a day. Example: college helpdesk chatbots.

6. Learning analytics and early warning — AI studies attendance, marks and activity data to predict which students are at risk of failing or dropping out, so teachers can help them in time.

7. Speech and language support — AI speech recognition and text-to-speech help students with disabilities, translate lessons into different languages, and assist in learning pronunciation.

8. Automation of routine tasks — AI handles attendance (face recognition), timetable preparation, grading of MCQ papers and administrative paperwork, freeing teachers to focus on teaching.

Thus AI makes education more personalised, accessible, efficient and effective for both students and teachers.

Five examples of AI applications in education:

1. Intelligent Tutoring Systems (ITS) — AI tutors such as Carnegie Learning's MATHia give one-to-one hints, instant feedback and practice problems matched to each student's progress.

2. Automated essay scoring and feedback — Tools such as Grammarly or ETS's e-rater check grammar, structure and coherence of written work and suggest improvements, reducing the teacher's marking load.

3. Adaptive learning platforms — Platforms such as Khan Academy and Duolingo analyse a learner's strengths and weaknesses and recommend the next topic or activity suited to that learner.

4. Chatbots and virtual teaching assistants — AI chatbots answer routine queries (admission dates, course content, deadlines) at any time and can act as study partners for revision.

5. Learning analytics and early-warning systems — AI studies attendance, quiz scores and engagement data to predict students at risk of failing or dropping out, so teachers can give counselling or extra support early.

(c) Volume - Velocity - Variety - Veracity

The four V's that define Big Data are Volume (the huge amount of data), Velocity (the speed at which data is generated and processed), Variety (the different forms of data — structured, semi-structured and unstructured), and Veracity (the uncertainty or trustworthiness of the data).

Cloud Computing Services

Cloud computing is the delivery of computing resources — servers, storage, databases, networking, software, and analytics — over the Internet ("the cloud") on a pay-as-you-use basis, instead of owning and maintaining physical hardware or software locally. The user accesses these resources on demand from a cloud service provider such as Amazon Web Services (AWS), Microsoft Azure, or Google Cloud.

Cloud computing services are the ready-made, on-demand IT services offered by these providers over the Internet. They are broadly classified into three service models:

1. Infrastructure as a Service (IaaS) — Provides fundamental computing infrastructure such as virtual machines, storage, and networks on a pay-per-use basis. The user manages the operating system and applications. Example: AWS EC2, Google Compute Engine.

2. Platform as a Service (PaaS) — Provides a ready-made platform (operating system, runtime, development tools, and database) for developing, testing, and deploying applications without managing the underlying infrastructure. Example: Google App Engine, Microsoft Azure App Service.

3. Software as a Service (SaaS) — Provides complete, ready-to-use application software over the Internet, usually on a subscription basis. Example: Gmail, Google Docs, Microsoft 365.

Key characteristics: on-demand self-service, broad network access, resource pooling, rapid elasticity (scaling up/down as needed), and measured (pay-as-you-go) service.

Common deployment models: public cloud, private cloud, hybrid cloud, and community cloud.

Advantages: no heavy upfront investment in hardware, easy scalability, automatic updates and maintenance, access from anywhere, and cost savings. Disadvantages: dependence on Internet connectivity, data security and privacy concerns, and limited control over the underlying infrastructure.

Cloud Computing

Cloud computing is the delivery of computing services — servers, storage, databases, networking, software and analytics — over the Internet ("the cloud") on a pay-as-you-use basis. Instead of owning and maintaining physical servers or data centres, a user or organisation rents these resources from a cloud service provider such as Amazon Web Services (AWS), Microsoft Azure or Google Cloud. The provider owns and manages the hardware, while the user accesses the services through a web browser or API and pays only for what is consumed.

The main service models are IaaS (Infrastructure as a Service, e.g. virtual machines and storage), PaaS (Platform as a Service, e.g. application hosting and development environments) and SaaS (Software as a Service, e.g. Gmail, Google Docs, Dropbox). The common deployment models are public, private, hybrid and community clouds.

Advantages of Cloud Computing

1. Cost efficiency — No need to buy and maintain expensive servers; the user pays only for the resources actually used.
2. Scalability and flexibility — Resources can be increased or decreased instantly according to demand.
3. Accessibility — Data and applications can be accessed from anywhere at any time using any Internet-connected device.
4. Automatic backup and recovery — Data is stored on remote servers with backup, so loss due to hardware failure is minimised.
5. Low maintenance — The provider handles software updates, security patches and hardware upkeep.
6. Collaboration — Many users can work on the same document or project simultaneously from different locations.

Disadvantages of Cloud Computing

1. Requires Internet connection — The services cannot be used without a reliable and fast Internet link.
2. Security and privacy risk — Sensitive data is stored on a third-party server, so there is a risk of data leakage or unauthorised access.
3. Downtime — If the provider's server fails or is under attack, the service becomes unavailable to all users.
4. Limited control — The user has little control over the hardware, software version and infrastructure of the cloud.
5. Vendor lock-in — Moving data and applications from one cloud provider to another is difficult and costly.
6. Recurring cost — Although cheap at first, continuous subscription charges may become expensive over a long period.

(d) Sensors

Sensors are embedded in smartphones (accelerometer, gyroscope, proximity, light sensor, etc.) and are the key components of IoT that detect changes in the environment and respond to them.

The five popular application areas of AI are:

1. Expert Systems An expert system is a computer program that uses AI to simulate the decision-making ability of a human expert. It has a knowledge base (facts and rules) and an inference engine (which applies the rules to reach conclusions). It is used in medical diagnosis (e.g., MYCIN), financial planning, and fault diagnosis in machines. It gives expert-level advice without needing the human expert to be present.

2. Natural Language Processing (NLP) NLP is the area of AI that enables computers to understand, interpret, and generate human language. It is used in chatbots, voice assistants (Siri, Alexa), machine translation (Google Translate), and sentiment analysis. It bridges the gap between human communication and computer understanding.

3. Robotics Robotics combines AI with mechanical engineering to build machines that can sense their environment, plan actions, and perform physical tasks. Robots are used in manufacturing (assembly lines), surgery (robotic surgery), space exploration (Mars rovers), and hazardous work like bomb disposal. AI gives robots the ability to adapt to changing conditions rather than just repeat fixed motions.

4. Computer Vision Computer vision allows machines to "see" and interpret images and video. It is used in face recognition, self-driving cars (detecting lanes, pedestrians, and signals), medical imaging (detecting tumours in X-rays), and quality inspection in factories. It extracts meaning from visual data using techniques like image processing and deep learning.

5. Speech Recognition Speech recognition converts spoken words into text that a computer can process. It is used in voice assistants, voice-to-text dictation, automated call centres, and hands-free control of devices. It identifies phonemes and words from audio signals and is now accurate enough for everyday use.

These five areas together cover how AI perceives the world (vision, speech), understands it (NLP), reasons about it (expert systems), and acts on it (robotics).

Mobile computing is the use of portable computing devices such as smartphones, tablets and laptops, connected through wireless networks (Wi-Fi, 4G/5G, Bluetooth), to access data, applications and communication services anywhere and at any time while the user is moving.

Advantages

• Access to information, email and services from any location at any time.
• Increases productivity, since work can be done while travelling.
• Fast communication through calls, messages and video conferencing.
• Supports services such as mobile banking, online shopping, navigation and e-learning.
• Devices are small, portable and often cheaper than desktop systems.

Disadvantages

• Depends on network coverage and bandwidth; connections may be slow or unstable.
• Limited battery life, screen size and storage.
• Higher security risks, such as theft or loss of devices and attacks on wireless networks.
• Data usage can be costly and radiation or health concerns exist with heavy use.
• Can distract users and cause over-dependence on devices.