Preface

Artificial intelligence is not a single invention but a long scientific and engineering project to build systems that perceive, reason, learn, and act in ways that resemble—or exceed—human cognitive performance on defined tasks. From early symbolic programs that manipulated logical formulas to today's large neural networks trained on vast corpora, the field has repeatedly redefined what 'intelligence' means in machines and what society should expect from automated decision-making.

This ForgEd textbook presents ten chapters that move from foundational ideas about intelligence and computation through the history of AI research, core machine learning and neural network theory, contemporary large language models and tool use, multimodal systems, ethical and safety considerations, workplace applications, and plausible futures for human-AI partnership. The tone is academic and explanatory: definitions, historical context, mechanisms, and implications—not hype or product marketing.

Readers should treat this material as general education, not as legal, medical, or professional licensing advice. AI capabilities change quickly; frameworks such as the NIST AI Risk Management Framework and OECD AI Principles are cited where they help structure responsible practice. Use the table of contents to study sequentially or to revisit specific topics before projects, coursework, or workplace conversations about AI adoption.