Articles on AI fundamentals
The core of AI: innovation, automation and intelligent systems. Discover more in our articles.
AI Readiness
Data Literacy
A core skill in the digital age. With information everywhere, analysing and interpreting data is essential. The article highlights its role in smarter decisions at work and in daily life. Read more »
Information Literacy: Why is Important?
Beyond reading and writing. It now includes digital, media, and data skills. The article shows why mastering these literacies is vital for communication, decisions, and lifelong learning. Read more »
AI Literacy Maturity Ladder
A ladder for skills and growth. Strong AI literacy boosts innovation and competitiveness. The maturity ladder shows stages of AI skills, with strategies to progress effectively. Read more »
AI Maturity: Metrics and KPIs
Clear KPIs steer progress. Many organisations struggle to unlock AI's full potential. Defining metrics and KPIs helps overcome obstacles, align strategy, and track progress toward maturity. Read more »
Data Literacy
Involves understanding data, deriving insights, storytelling with visuals, and applying findings. The ability to read, interpret, analyse, and communicate data effectively. Read more »
Digital Literacy
Encompasses platform navigation, credibility assessment, privacy, and collaboration. The ability to navigate, assess, and create digital content responsibly across platforms. Read more »
Information Literacy
Supports critical thinking, problem-solving, and lifelong learning across contexts. The ability to identify, find, evaluate, and use information effectively. Read more »
Maker Literacy
Hands-on competency in designing, prototyping, and iterating with tools and materials. Practical capability to conceive, build, and refine artifacts as a way to learn and solve problems. Read more »
Metaliteracy
Integrates multiple literacies with emphasis on critical thinking, ethical participation, and producing content across formats. An overarching framework that encompasses information literacy and related literacies needed in the digital age. Read more »
AI Readiness Map
Interactive map about AI readiness of countries. Read more »
AI Literacy
Today's must-have skill to understand and exploit AI. As AI shapes daily life and work, understanding it is as vital as past computer literacy. The article explains why it matters and how to build your skills. Read more »
Culture
AI Literacy: Developing in Organisations
A strategic must for organisations. Beyond technical know-how, AI literacy covers ethics, limits, and impact. Building it strengthens organisations, guiding responsible and effective AI use. Read more »
AI Democratisation
AI-power to everyone. Once limited to labs, AI is now widely accessible through tools like ChatGPT and Copilot. This shift empowers people globally, lowering barriers to innovation and everyday use. Read more »
Data-Driven Culture
Organisational mindset prioritising evidence-based decisions with accessible, high-quality data. Alignment of incentives, processes, and tools so teams routinely use data to act. Read more »
Data-Driven Organisation
Company-wide use of data and analytics to guide strategy, operations, and innovation. Investments in governance, platforms, and skills to scale analytic impact. Read more »
Background
Generative AI vs. Traditional AI
Compare two approaches. AI spans diverse methods, but generative and traditional AI dominate. This article compares their applications, strengths, and limitations to show where each excels. Read more »
Multimodal AI vs. Traditional AI
Within AI, two distinct paths. AI now drives daily life and industries. This article explains how traditional AI differs from multimodal AI, exploring their strengths, uses, and future potential. Read more »
Large Language Models vs. Traditional Models
Shaping language AI's future. Both model types power machines to process human-like text. This article examines their strengths, costs, and ethical issues, highlighting trade-offs in NLP's evolution. Read more »
Transformer Models in AI
The backbone of modern AI. Introduced in 2017, transformers power translation, vision, and more. This article explains how they work and explores the challenges they still face. Read more »
Abductive Reasoning in AI
Best guess from incomplete data. It forms the most plausible explanation when information is uncertain. Unlike deduction, it doesn't guarantee truth but guides AI in fields like diagnosis, fault detection, and decision-making under uncertainty. Read more »
Transfer Learning
The engine behind AI, reuse knowledge across tasks. It allows AI models to apply insights from one task to another. This cuts training time, reduces data needs, and speeds up innovation in machine learning. Read more »
Chatbots Beyond Customer Service
Chatbots are reshaping education, healthcare, business, and government beyond customer service. This article explores how chatbots evolved into indispensable tools across industries, highlighting their expanding roles, key trends, and the challenges that must be addressed to realise their full potential. Read more »
AI-Powered Knowledge Systems: Evolution
Smarter information use. These systems tackle challenges like data volume, security, and personalisation. The article explores their stages and how they interconnect to transform knowledge management. Read more »
Large Language Model (LLM): History
Interactive history of Large Language Models. Read more »
Stock vs. AI Analysts
How AI transforming financial analysis and outperforming human analysts. Read more »
Core AI Concepts
Tasks AI
Common capability categories: classification, extraction,... A taxonomy framing what models are asked to do, guiding selection, data, and evaluation. Read more »
AI Tasks: How a Clear These Drives Project Success
Clear AI tasks: key to project success. Well-defined tasks shape the outcome of AI projects. This article shows why clarity matters and how precise task design drives successful implementation. Read more »
Foundation Models
The backbone of AI, versatile platform adaptable to a wide range of applications. Unlike task-specific models, foundation models use vast data and advanced algorithms. They act as flexible bases, enabling broad applications and specialised AI solutions. Read more »
Diffusion Models
Transforming noise into creativity. These models drive the creation of high-quality images, video, and audio. The article explains how they work, their uses, and the advantages they bring to AI. Read more »
Text-to-Text: The Role in NLP
A unifying Natural Language Processing (NLP) framework. This approach reframes diverse NLP tasks as text input-output. The article explains its role in advancing AI and shows examples of its wide-ranging impact. Read more »
X-To-Text AI
Transforming diverse input into textual output. This AI translates speech, images, or video into text. The article explores its modalities, applications, benefits, and the challenges it faces. Read more »
Evolution of Business Automation
From scripts to smart AI. Automation has evolved from simple tools to AI that mimics human intelligence. This shift reshapes operations, driving efficiency and enabling innovation across industries. Read more »
AI Tools: Components
A smoothie of capabilities. Like ingredients in a smoothie, tools such as ChatGPT, Copilot, and Gemini blend strengths to handle tasks from translation to coding. Knowing their parts helps unlock full potential. Read more »
Speech-to-Text
Transforming words into action. This technology makes devices understand and act on speech. From hands-free messaging to medical transcription, it boosts speed, accessibility, and ease of use. Read more »
Chatbots: Why Your Organisation Needs These
Chatbots boost efficiency, improve communication, and enhance learning across organisations. These AI-powered assistants strengthen customer interactions, streamline internal processes, and create smarter learning environments. Organisations adopting them gain measurable improvements in both efficiency and satisfaction. Read more »
Generative AI
Creates novel content based on learned patterns. Accelerates ideation and creative workflows. Models that produce new data samples conditioned on inputs or prompts, across modalities. Read more »
Large Language Model (LLM)
Power chat, content, and code assistants. A neural model optimised for language tasks such as summarisation and generation. Read more »
AI Literacy
Understand core AI concepts, capabilities, and limits. The knowledge and skills to use and ethically interact with AI systems and their outputs across tasks and contexts. Read more »
Foundation Model
Large, pre-trained models adaptable to many tasks via prompting or fine-tuning. Broadly trained base models that can be specialised for diverse downstream applications. Read more »
Multimodal AI
Processes and generates across text, images, audio, video. Enables richer reasoning and context-grounded outputs. AI systems that handle multiple data types jointly for perception and generation. Read more »
Algorithm
Step-by-step method for solving problems or computations. A finite, ordered set of rules or instructions executed to achieve a result in software, analytics, or AI. Read more »
Artificial Intelligence (AI)
Machines performing tasks requiring human-like intelligence. AI enables machines to mimic human intelligence, automating tasks and enhancing decisions and efficiency. Read more »
AI Hallucinations: understanding
A challenge for trust. While AI boosts automation and decisions, it can also generate false outputs. The article shows why tackling hallucinations is vital for ethical, reliable AI use. Read more »
AI Hallucination Detector Game
Learn to spot AI hallucinations in text, images, and audio. Read more »