The book
Introduction to Artificial Intelligence (Korean title 『인공지능 개론』) was published by Silver Tree Books on 3 August 2026. It is Part 3 of the Silver Tree AI Trilogy, and with it the trilogy is complete.
The book is written for the gap between tool manuals that expire and graduate texts that never open. It is a first course in how artificial intelligence actually works, for readers who have used AI and now want to know what is happening underneath.
One line of argument
Fifteen chapters run as a single line of argument: concepts and history, search, logic, probability, machine learning, neural networks, deep learning, transformers, large language models, reinforcement learning, and then ethics, governance, and what comes next. Each chapter builds on the one before it, so that by the time the reader reaches transformers and LLMs the machinery is already familiar.
Arithmetic by hand
The book’s method is arithmetic by hand. Five core computations are worked through on paper rather than described.
- Pathfinding, to show how a machine searches.
- Bayesian spam filtering, to show how it reasons under uncertainty.
- Backpropagation, to show how a neural network learns.
- Attention, to show what a transformer actually computes.
- Q-value updates, to show how reinforcement learning improves a policy.
A reader who has done these five calculations once has touched the mechanism of most of modern AI. Everything else in the field is a variation on them at scale.
Built to age well
Fast-moving material is quarantined in “Further reading” boxes stamped with the date it was written, so the principles in the main text do not rot with the news. Every chapter closes with a summary, a glossary, and exercises. Appendices cover the mathematics, Python labs, a glossary, and worked solutions.
The principles that stay put while the products keep changing.
Contents
- Prologue. Going to touch the elephant.
- Part 1, Understanding AI. 1 Concepts and classification. 2 History and paradigms.
- Search and reasoning. 3 Problem solving by search. 4 Knowledge representation and logical inference. 5 Uncertainty and probabilistic reasoning.
- Machine learning. 6 Foundations. 7 Supervised learning. 8 Unsupervised learning and representation.
- Deep learning. 9 How neural networks work. 10 Deep learning and computer vision. 11 Sequence models and transformers. 12 NLP and LLMs. 13 Reinforcement learning.
- Ethics and the future. 14 AI ethics and governance. 15 Where AI stands and where it goes.
- Appendices. A Mathematics. B Python labs. C Glossary and solutions. References.
Bibliographic details
- Publisher: Silver Tree Books
- Published: 3 August 2026
- ISBN: 979-11-993543-8-8
- Extent: 465 pages, 194 × 260 mm
- Language: Korean
- Series: Silver Tree AI Trilogy, Part 3 (Understanding AI)
The book page on this site carries the full description. The book is sold through YES24.
The trilogy, complete
The three books were designed as one course, read in any order but built in this one.
- Understanding AI Through Prompts, Part 1, Using AI (July 2025). How to ask a language model so that it answers well.
- The Local LLM Bible, Part 2, Building AI (January 2026). How to run a model that an organization owns, from infrastructure to production.
- Introduction to Artificial Intelligence, Part 3, Understanding AI (August 2026). How the machinery underneath actually works.
Using, building, understanding. The order is deliberate. Most people meet AI first as a tool, then as a system they are responsible for, and only then ask how it works. The trilogy follows that path and ends at the principles, which is where it can stop, because the principles are what stay put while the products keep changing.