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Evolution of the Web

Web 1.0 - Documents Web

Static Web Pages

Early web pages that were simple, static, and delivered via HTML, without any dynamic features or interactivity.

Basic HTML

The foundational markup language used for creating static web pages during the early days of the internet.

E-mail

An electronic mail system that became one of the first uses of the Internet. It allows users to send and receive messages and files electronically over a network.

Search Engines

Search engines are software systems designed to carry out web searches, allowing users to find information on the World Wide Web. They work by crawling, indexing, and ranking web pages based on relevant queries. Popular search engines include Google, Bing, and Yahoo.

Web 2.0 - Social Web

Dynamic and Interactive Web

Web 2.0 introduced user interaction and dynamic content, moving beyond static pages to more engaging websites.

Social Media Integration

The era when social media platforms became central to online interaction, integrating with various web services and sites.

Web 3.0 - Semantic Web

Web3 vs Web 3.0 — a note on terminology

These two terms are routinely confused, and they do not mean the same thing. In this mind map, and in academic usage, Web 3.0 means the Semantic Web: the W3C programme of making data machine-readable through RDF, ontologies and linked data, so that software can reason over meaning rather than parse layout. Web3 is a separate, later coinage from the cryptocurrency industry, describing blockchain-based decentralised applications and token ownership. The two agendas share the ambition of decentralisation but almost nothing else: different standards bodies, different technologies, different research communities. When you read "Web 3.0" in a tutorial, check which one is meant.

Blockchain and Decentralization

The concept of distributing data across multiple nodes or networks to enhance security and reduce centralization, often associated with blockchain technology.

Linked Data

A method of publishing structured data so that it can be interlinked and become more useful through semantic queries.

Web 4.0 - Intelligent Web

AI (Artificial Intelligence)

AI refers to the simulation of human intelligence by machines, allowing them to perform tasks such as learning, reasoning, problem-solving, and decision-making. AI is increasingly integrated into web technologies to power smarter applications and services.

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  • Symbolic AI: Symbolic AI uses explicit, human-readable rules and logic to perform reasoning tasks. It relies on symbolic representations of knowledge and logical inference to simulate intelligent behavior, commonly seen in expert systems.

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  • Statistical AI: Statistical AI focuses on data-driven approaches, using statistics and probability to model uncertainty and make predictions. Machine learning is a key component of statistical AI, relying on algorithms that improve with experience and data.

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  • Supervised/Unsupervised Learning: Supervised learning involves training a machine learning model on labeled data, where the algorithm learns to map inputs to outputs. Unsupervised learning, on the other hand, involves discovering patterns in data without explicit labels, often used for clustering or anomaly detection.

    • Wikipedia - Supervised Learning
    • Wikipedia - Unsupervised Learning

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    • Machine Learning (ML): Machine learning is a subset of AI that involves training models to recognize patterns in data and make predictions or decisions based on that data. It enables systems to learn from experience and improve over time without being explicitly programmed.

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    • Deep Learning: Deep learning is a subset of machine learning that uses neural networks with many layers (hence "deep") to model complex patterns in data. Deep learning is particularly effective for tasks such as image recognition, speech processing, and natural language understanding.

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  • NLP (Natural Language Processing), NLU (Natural Language Understanding), and NLG (Natural Language Generation)

    • NLP: NLP is the field of AI focused on the interaction between computers and humans through natural language. It involves processing and analyzing large amounts of natural language data.
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    • NLU: NLU is a subfield of NLP that focuses on the machine's ability to understand and interpret the meaning behind natural language inputs.

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    • NLG: NLG refers to the ability of AI systems to generate coherent and contextually appropriate natural language outputs based on data and inputs.

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  • Autonomous Systems: Autonomous systems are machines or software that can perform tasks independently without human intervention, using AI and machine learning to make real-time decisions. Examples include robots, self-driving cars and automated customer service systems.

IoT/IoE Integration (Internet of Things/Internet of Everything)

IoT refers to the network of physical objects embedded with sensors, software, and connectivity that allows them to collect and exchange data. IoE expands this concept to include people, processes, and data as well. These technologies are integrated into web applications to enhance automation, data-driven insights, and interconnectivity between smart devices.