Uncertainty Quantification and Uncertainty Propagation Under Traditional and Ai-Based Data Processing (and Related Topics): Legacy of Grigory Tseytin
暫譯: 傳統與基於人工智慧的數據處理下的不確定性量化與不確定性傳播（及相關主題）：格里戈里·蔡廷的遺產

Why revisit uncertainty? Data processing is now often performed by Large Language Models (LLMs) and other AI tools that use natural-language texts. Many LLMs' results are spectacular, but often, there is no good indication of their accuracy. We need to revisit traditional methods for quantifying and propagating uncertainty, to see how they can help with these new challenges.

The book covers uncertainty of measurement results and uncertainty inherent in natural-languages text -- by using both linguistic and traditional AI techniques (e.g., fuzzy). It contains both general results -- e.g., what can be computed -- and applications to engineering, physics, chemistry, and education. It also analyzes the effect of emerging computing paradigms -- such as quantum computing -- on uncertainty-related computations.

This book can be recommended to everyone -- from students to researchers -- who is eager to learn, apply, and improve the uncertainty-related techniques.