Artificial intelligence has moved from research labs into everyday tools. This hub covers how modern AI actually works — the machine learning foundations, neural networks, and the large language models (LLMs) that power today's assistants: how they are trained, what they can reliably do, and where they fall short.
AI & TechnologyGuide
A complete, beginner-friendly map of modern artificial intelligence — how machine learning, deep learning, large language models, RAG, and AI agents all fit together, and where each one is used.
AI & TechnologyGuide
AI hallucinations are confident, false answers that chatbots invent when they don't know something. This guide explains why they happen, shows a real courtroom example, and covers proven ways to reduce them.
AI & TechnologyGuide
Machine learning is how computers learn from examples instead of being told every rule. This beginner's guide explains how it works, the three main types, and where you already use it every day.
AI & TechnologyGuide
How multi-agent AI systems work: the orchestrator-worker pattern, why they outperform a single agent, real token-cost tradeoffs, and when to actually use one.
AI & TechnologyGuide
RAG lets an AI model look things up before it answers — grounding its response in real, current, trusted data. Here's how retrieval-augmented generation works, in plain English.
AI & TechnologyTutorial
Fable 5 is the most capable Claude model — and the most expensive. The trick is to use it as the head chef that plans and delegates, while cheaper models do the cooking.
AI & TechnologyAnalysis
Three weeks after the US forced it offline, Claude Fable 5 is back. Inside the deal Anthropic struck with Washington, the new safety classifier, and what the reversal signals.
AI & TechnologyGuide
AI 'learns', has 'neurons', and gets 'trained' — sounding just like a brain. But how are LLMs actually trained, and is it anything like how you learn? A clear, myth-busting guide.
AI & TechnologyAnalysis
Days after the biggest IPO in history, SpaceX is acquiring AI-coding startup Cursor in a $60 billion all-stock deal — the boldest move yet in Elon Musk's plan to build an AI empire.
AI & TechnologyAnalysis
The US government ordered Anthropic to block its newest AI models, Fable 5 and Mythos 5, from all foreign nationals over national security. Anthropic disabled them worldwide and is disputing the finding. What happened, why it matters, and what comes next.
AI & TechnologyGuide
OpenAI's models now iterate every few weeks — but the real story is the shift to reasoning. A plain-English guide to the GPT-5 family, 'thinking' models, and why it matters.
AI & TechnologyGuide
A clear, jargon-free explanation of how ChatGPT works — how it predicts words, why it sounds so human, why it sometimes makes things up, and what it can't do.
Artificial intelligence (AI) is the field of building computer systems that perform tasks normally requiring human intelligence — recognising images, understanding language, making predictions, and generating text. Most modern AI is built on machine learning, where systems learn patterns from data rather than following hand-written rules. Large language models such as GPT and Claude are a recent, powerful branch of AI focused on understanding and generating human language.
A large language model (LLM) is an AI system trained on massive amounts of text to understand and generate human language. It works by repeatedly predicting the most likely next piece of text, which lets it answer questions, write, summarise, translate, and generate code. Examples include GPT, Claude, and Gemini.
Training happens in stages. Pretraining exposes the model to enormous text datasets so it learns the patterns of language. Fine-tuning then adapts it to specific tasks, and reinforcement learning from human feedback (RLHF) aligns its responses with human preferences for helpfulness, accuracy, and safety.
Because an LLM generates text by predicting likely words rather than looking up verified facts, it can produce fluent statements that are wrong — often called hallucination. The risk is reduced, though not eliminated, by better training data, retrieval tools that ground answers in real sources, and careful prompting.