Six parts. 36 chapters.
From understanding AI to researching your own questions.

From the arrival of computers, software and algorithms to models and computing resources: how AI enters real life.

1. Why humans want to create AI: extending our capabilitiesStart not with technology but with people: why do we want to build something that can act for us? From tools to machines to programs that make their own judgments, this chapter makes clear that AI is an extension of human ability.
2. How computers emerged: the need to calculate and great inventionsWhere does the need to compute come from? Abacuses, mechanical calculators, vacuum tubes — every step was forced by a real problem. This chapter traces the shortest line through the origin of computers.
3. How software carries out human intentions: programs and algorithmsHardware only follows instructions; software is what translates human intent into something it understands. This chapter separates programs from algorithms, so you know what actually happens when you make the computer do something.
4. How AI programs actually run: models and computing resourcesA model is not magic floating in the cloud. It takes memory, burns compute and runs on a specific machine. This chapter explains what hardware and software each do behind a single AI answer.
5. From computation to AI products: technology enters everyday lifeHow does something that can compute become a product you actually use on your phone? This chapter covers the gates technology must pass: speed, cost, reliability and human habits.
6. What “AI is computing” means: capabilities and boundariesSaying AI is computing both locates AI and draws its boundary. This chapter helps you judge which things it can really do, and which only appear possible.

Start with decimal and binary numbers to understand the computational foundations of digital information, vectors, neural networks and language models.

7. Why computers use 0 and 1: from decimal numbers to bitsPeople count on ten fingers; computers know only two states. This chapter moves from decimal to binary and explains why only 0 and 1 is actually more reliable.
8. How the world becomes digital: text, sound, images and encodingA sentence, a sound, a photo — how do they become numbers a computer can store? This chapter is about encoding: translating the real world into 0s and 1s.
9. How data becomes vectors: from numbers to semantic representationNumbers alone are not enough — a machine has to grasp meaning. This chapter introduces vectors: turning words and sentences into number lists so that similar meanings sit closer together.
10. How neural networks compute: weights, training and parametersNeural networks sound mysterious, but they are mostly multiplication and addition. This chapter unpacks weights, training and parameters so you can see what learning actually is.
11. How tokens become sentences: probability and the next choiceA large model does not think character by character; it guesses token by token. This chapter covers tokens and probability, and explains why it sometimes says things that are wrong.
12. How intelligent behavior emerges from 0 and 1: computation and emergenceThe rules are simple; the results are not. This chapter is about emergence: why stacking many simple computations produces behavior that looks intelligent.

Make language, culture, professional experience and social understanding a starting point for AI application design.

13. Why many people once felt distant from computersLearning computing used to start with mathematics and programming, and that threshold kept people out. This chapter explains how the wall was built and why it is coming down.
14. What large language models actually changedIts biggest change is not speed but that it understands plain language directly. This chapter explains what that means for ordinary people: anyone can start using it, without learning how to use it first.
15. Why defining the problem matters more than simply writing instructionsAsking the right question matters more than writing the right command. This chapter shows how to turn a vague frustration into a concrete task AI can take on.
16. How different disciplines can use agents to solve problemsLaw, medicine, education and media each use it differently. This chapter uses examples from different fields to show how agents fit into real work, not just demonstrations.
17. How people without coding skills can design AI applicationsYou can design a working AI application without writing code. This chapter sets out the order: define the goal first, then the inputs and outputs, and only then choose the tools.
18. Why this is a new spring for humanities learnersWhen expression, understanding and judgment matter more than syntax, people trained in the humanities gain an advantage. This chapter gives the reasoning, and is clear about what it does not guarantee.

Understand changing work, the value of judgment, coordination, verification and responsibility, and consider your own direction.

19. Why work keeps changingWork has never been fixed. This chapter looks at several waves of technological change to show how jobs get recombined: what disappears is often a step in the work, not the whole industry.
20. Which kinds of work AI is enteringNot every job is affected the same way. This chapter breaks work down by task type to see which steps get taken over first and which stay with people.
21. Which human capabilities still matterAI can write and calculate, but some things it cannot replace. This chapter is about judgment, responsibility and trust between people, and why those remain human ground.
22. How people and AI complete tasks togetherThe best use is not handing work over but doing it together. This chapter is about division of labor: people set direction, AI produces drafts, people judge.
23. How new needs create new occupationsNew jobs do not appear from nothing; old needs change shape. This chapter looks at how roles like prompt engineer and AI trainer grew, and how they may change again.
24. Preparing for work and enterprise in the AI ageRather than chasing what is hot, build transferable ability. This chapter offers a path: start using the tools, then find the part of the work only you can do.

From questions to revised judgments, experiments and failure, explore open questions in sustained AI innovation.

25. Where innovation beginsInnovation often starts not with a new idea but with noticing that something is off. This chapter is about observation and questioning, and turning a nagging discomfort into a problem.
26. Why new information is not the same as innovationKnowing more is not the same as thinking new. This chapter separates information, knowledge and innovation, and explains why piling up material produces nothing new.
27. Why innovation requires revising judgmentsReal innovation usually overturns a premise everyone takes for granted. This chapter shows how to find that premise and judge whether it is worth overturning.
28. How ideas reach experiments and realityIdeas are cheap; testable ideas are not. This chapter shows how to turn a thought into something you can test at the lowest possible cost.
29. Why failure is part of innovationFailure is not the opposite of innovation; it is part of it. This chapter is about extracting useful information from failure instead of only remembering the lesson.
30. How far today's AI is from sustained innovationAI is good at recombining existing knowledge, but it still struggles to keep asking new questions. This chapter covers what it can do now and where it gets stuck.

Learn a six-position process method, explore personal agents and their cooperation, and consider unknowns in life, nature and the universe.

31. AI's highlights: rediscovering our own valueThe stronger AI gets, the more we have to answer who we are. This chapter asks where human value belongs once tools become powerful, and why the question becomes clearer rather than harder.
32. Why AI needs to understand real-world changeThe world changes; an answer that was right yesterday may be wrong today. This chapter explains why AI has to handle change, not only facts.
33. Change and processMost real tasks are not finished in an instant; they are a process. This chapter treats the process itself as the thing to be handled.
34. Helping AI actually get something doneAnswering a question is not the same as getting something done. This chapter sets out what completing a task requires: a goal, evidence, and the ability to resume after an interruption.
35. A visible future: everyone has an agent of their ownAgents are moving from demos into daily life. This chapter looks at which habits they change once they enter personal life, and which old problems remain.
36. The unknown and its boundariesThe course stops where certainty ends and leaves the open questions for discussion. This chapter covers the current limits of AI and which questions remain open for you to think further.