TongTest: Criteria, Benchmarks, Evaluations, and Architecture for Artificial General Intelligence (en Inglés) - Song-Chun Zhu

Artificial General Intelligence (AGI) is advancing faster than our ability to define, evaluate, and govern it—and this book offers one of the first systematic frameworks capable of meeting that challenge. Built around a dual‑system model of capabilities and values, it provides a rigorous cognitive architecture and testing paradigm designed to clarify what AGI is, how it should be measured, and what it will take to develop it responsibly.

Across six tightly structured chapters, the authors map the intellectual foundations of AGI, introduce a unified framework of “one definition, two forms of completeness, three core features, and eight key questions,” and examine why current large models fall short of true general intelligence. The book then moves from theory to practice: it analyzes AGI evaluation methods, proposes a comprehensive testing ecosystem, and presents the TongAI framework as a concrete pathway toward building and aligning general artificial intelligence. The final chapters confront the governance and safety challenges that AGI inevitably raises, offering only part of the answer—and inviting readers to explore the rest within the text.

This book is written for researchers, engineers, and graduate‑level readers seeking a clear conceptual structure for AGI research; for technology leaders and policymakers who need a forward‑looking roadmap; and for anyone who wants a coherent, academically grounded view of AGI development, testing, and safety. Readers will gain a practical reference system for AGI standards, evaluation, cognitive architecture design, and long‑term governance—without requiring specialized prior knowledge beyond basic AI literacy.