The AI Glossary (Richard R Khan)（Z-Library）

【One-Line Pitch】 A concept-first reference that turns AI's intimidating vocabulary into plain-language explanations, so non-specialists can follow the debates shaping technology and policy. Best for students, journalists, policymakers, and curious professionals who need clarity rather than math. 【Book Arc】 - **Opening (~0%–10%)**: Sets the mission — a shared lexicon for AI discourse — and explains how to use the book: alphabetical browsing, random reading, topic groupings, page references, and mind maps. - **Early (~10%–32%)**: Core vocabulary of how models work and learn: activation functions, classification, CNNs, data lakes, dimensionality reduction, error minimization, feature engineering, AutoML, and code generation. - **Middle (~32%–50%)**: Architecture and training mechanics deepen — hidden and input layers, output layers, loss functions, Markov decision processes, label propagation, and semi-supervised techniques. - **Late (~50%–75%)**: Application-facing and governance terms come into view: ethical AI principles, privacy and security, scientific discovery systems like AlphaFold and GNoME, and industry use cases. - **Ending (~75%–100%)**: The reference apparatus closes the loop — terms grouped by topic, cross-references ("See also"), and continuous-learning guidance for returning readers. 【Key Takeaways】 - **A shared vocabulary is the book's central argument** (Opening): ambiguous definitions cause experts, policymakers, and journalists to talk past each other; a common lexicon enables productive debate. - **Entries are conceptual, not just alphabetical** (Opening): each term gets key features, examples, and cross-links, so readers see how supervised, unsupervised, and reinforcement learning relate within machine learning. - **Analogy is the teaching method** (Early): activation functions become dance-floor decisions, CNNs become visual detectives, feature engineering becomes a chef preparing ingredients — memorable hooks before technical detail. - **The pipeline from data to decision is the recurring spine** (Early–Middle): input layers, hidden layers, output layers, loss functions, and error minimization form one continuous story of how models learn and improve. - **Automation lowers the expertise barrier** (Early): AutoML and code generation show AI increasingly handling algorithm selection, tuning, and even programming itself. - **Ethics and governance are treated as first-class topics** (Late): transparency, privacy, and security appear alongside technical entries, not as an afterthought. - **Scientific discovery is a flagship application** (Late): AlphaFold's protein-structure prediction and GNoME's materials discovery illustrate AI accelerating research beyond commercial use cases. - **The book is built for non-linear reading** (Ending): random reading, topic groupings, hyperlinks, and mind maps support curiosity-driven exploration rather than cover-to-cover study. 【Reading Tips】 - **Skim the how-to-use section first** (~0%–10%): the navigation tools (topic groupings, mind maps, cross-references) determine whether the book works for you as reference or as a course. - **Deep-read entries in your own domain, skim the rest**: the analogy-first format makes unfamiliar terms quick to absorb, but depth is limited — treat it as orientation, not mastery. - **Follow the "See also" chains**: the real value is in the connections between terms; reading three linked entries teaches more than one isolated definition. - **Use the topic-grouped section as a syllabus**: if you want structured learning rather than browsing, work through one topic cluster at a time. - **Keep it open beside other resources**: this glossary pairs well with a technical textbook or course when jargon blocks comprehension. 【Coverage Limits】 The excerpts sample only a fraction of the 32 indexed chunks, so this guide cannot verify the full term list, the completeness of the topic groupings, or the depth of later governance and application entries. Specific page counts, chapter titles, and any terms beyond those quoted are not covered by the available material.