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Origins
How the idea of thinking machines appeared — from the first theories of computation to the return of neural networks.
3 articlesHow machines learn to think
Thinkorithm
How machines learn to think — explained without the hype.
A plain-language guide to artificial intelligence: where it came from, how it works, how to use it, and where it might go next.
Sections
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02Start here
How It Thinks
How modern AI models learn, store what they learn and produce answers — in plain language, without formulas.
4 articles
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In Practice
Guides, a prompt library and documented examples of AI in real use.
3 articles
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What’s Next
Forecasts, automation, opportunities and risks — with established facts kept apart from assumptions.
3 articlesFeatured articles
How It Thinks
How a Neural Network Learns
Weights, errors and many small corrections: the basic way almost every modern AI system learns, explained without a single formula.
How It Thinks
What a Large Language Model Actually Does
A plain explanation of how today's text-generating systems are built and trained, and why the way they work explains both their strengths and their mistakes.
In Practice
A Practical Guide to Working with AI Assistants
Simple habits that make AI assistants more useful and less risky, drawn from published public-sector guidance.
What’s Next
Automation and the Changing Shape of Work
What international organisations' studies say about AI and jobs — and why "exposure" to AI is not the same as job loss.
About this project
Thinkorithm is a small, independent project that explains how machines learn to think — in plain language and without formulas. Every article is based on official and primary sources, listed at its end, and keeps established facts apart from forecasts and open questions. Terms are explained where they first appear.