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Education

AI explainers, primers, and practical guides. Concepts made clear for curious readers who want to understand the technology, not just keep up with the news cycle.

What is RAG? A plain-English explainer for people who use AI but never built one

RAG, or retrieval-augmented generation, is the technique behind 'AI that can read your documents.' Here's what it actually does, what it does not fix, and when you actually need it.

What is symbolic AI? The original recipe — and where it still wins

What is symbolic AI? The original recipe — and where it still wins

Symbolic AI was the original AI — rules, search, logic, expert systems. It lost the photogenic half of the field after 2012 but still wins where transparency, hard constraints, and audit trails matter. With a paired expert-system lab.

AI vs Machine Learning vs Deep Learning vs LLMs — the actual differences

AI vs Machine Learning vs Deep Learning vs LLMs — the actual differences

Four layers everyone conflates — AI, Machine Learning, Deep Learning, and Large Language Models. What defines each, what each can and cannot do, where production systems use them in 2026, and how to tell which layer a system actually is. With a paired lab.

Human vs machine intelligence: where they actually differ

Human vs machine intelligence: where they actually differ

How humans and machines actually differ in 2026 across five axes — data efficiency, embodiment, transfer, common sense, energy — where they're surprisingly similar, and why the productive frame is complement, not replicate. With a 4-challenge lab.

What is intelligence? The definition AI inherits

What is intelligence? The definition AI inherits

Four serious, mutually inconsistent definitions of intelligence — psychometric, biological, generalisation-efficiency, and multiple-intelligences — and why which one you accept silently decides which AI you build. With an interactive yardstick lab.

What is artificial intelligence? A complete intuitive guide

What is artificial intelligence? A complete intuitive guide

A complete intuitive guide to what artificial intelligence actually is — the nested AI/ML/DL/LLM hierarchy, the one idea that drives modern AI, narrow vs general vs super, five myths the news still gets wrong, and a 60-second tour from 1956 to 2026. With an interactive lab.

Three cards labeled QUERY, KEY, VALUE — the three projections every transformer attention layer computes.

What is attention? The mechanism behind every modern LLM

The mechanism every modern LLM is built on, explained from scratch. Q/K/V, the scaled dot-product formula walked through term by term, multi-head attention, positional encoding, and the engineering variants that show up in 2026. With a live interactive lab.