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Rubén Castillo Sánchez

AI tools like ChatGPT, Claude Code, and other language-driven systems can sound remarkably human. They explain, summarize, argue, write code, and often appear far more confident than the underlying system deserves. That creates a question that matters well beyond research labs: what are these systems actually doing when they seem to “think”?

This book takes that question seriously.

Rubén Castillo Sánchez walks through how modern AI systems work, from the early ideas behind machine intelligence to neural networks, language models, attention, reasoning models, agents, and reinforcement learning. Along the way, he also tackles the harder part of the conversation: what we mean by intelligence, understanding, imitation, and trust when the machine in front of us can speak so fluently.

What I like about this book is that it doesn’t treat AI as either magic or fraud. It gives readers a way to interpret AI behavior with more precision. You get enough technical detail to understand the machinery, but the book never loses sight of the practical question: when should you trust these systems, and when should you push back?

A few of the topics you’ll find inside:

• how neural networks and LLMs are built

• Why language models sound so convincing

• What the Turing Test still tells us

• where hallucinations come from

• How to think about reasoning, agents, and alignment

• When AI makes sense in the workplace, and when it doesn’t

This is a strong fit for business managers, tech leaders, and curious professionals who want a grounded understanding of today’s AI tools without getting buried in academic jargon.

• Full details: Machines that Think - Rubén Castillo Sánchez

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