Delving into the deeply enigmatic nature of Artificial Intelligence (AI), AI: Unexplainable, Unpredictable, Uncontrollable explores the various reasons why the field is so challenging. Written by one of the founders of the field of AI safety, this book addresses some of the most fascinating questions facing humanity, including the nature of intelligence, consciousness, values and knowledge. Moving from a broad introduction to the core problems, such as the unpredictability of AI outcomes or the difficulty in explaining AI decisions, this book arrives at more complex questions of ownership and control, conducting an in-depth analysis of potential hazards and unintentional consequences. The book then concludes with philosophical and existential considerations, probing into questions of AI personhood, consciousness, and the distinction between human intelligence and artificial general intelligence (AGI). Bridging the gap between technical intricacies and philosophical musings, AI: Unexplainable, Unpredictable, Uncontrollable appeals to both AI experts and enthusiasts looking for a comprehensive understanding of the field, whilst also being written for a general audience with minimal technical jargon.

Over the past decade, a number of hardware and software advances have conspired to thrust deep learning and neural networks to the forefront of computing. Deep learning has created a qualitative shift in our conception of what software is and what it can do: Every day we're seeing new applications of deep learning, from healthcare to art, and it feels like we're only scratching the surface of a universe of new possibilities. This book offers the first introduction of foundational ideas from automated verification as applied to deep neural networks and deep learning. It is divided into three parts: Part 1 defines neural networks as data-flow graphs of operators over real-valued inputs. Part 2 discusses constraint-based techniques for verification. Part 3 discusses abstraction-based techniques for verification. The book is a self-contained treatment of a topic that sits at the intersection of machine learning and formal verification. It can serve as an introduction to the field for first-year graduate students or senior undergraduates, even if they have not been exposed to deep learning or verification.

Neural networks are members of a class of software that have the potential to enable intelligent computational systems capable of simulating characteristics of biological thinking and learning. Currently no standards exist to verify and validate neural network-based systems. NASA Independent Verification and Validation Facility has contracted the Institute for Scientific Research, Inc. to perform research on this topic and develop a comprehensive guide to performing V&V on adaptive systems, with emphasis on neural networks used in safety-critical or mission-critical applications. Methods and Procedures for the Verification and Validation of Artificial Neural Networks is the culmination of the first steps in that research. This volume introduces some of the more promising methods and techniques used for the verification and validation (V&V) of neural networks and adaptive systems. A comprehensive guide to performing V&V on neural network systems, aligned with the IEEE Standard for Software Verification and Validation, will follow this book.

This LNCS volume constitutes the proceedings of the First International Symposium on AI Verification, SAIV 2024, in Montreal, QC, Canada, during July 2024. The scope of the topics was broadly categorized into two groups. The first group, formal methods for artificial intelligence, comprised: formal specifications for systems with AI components; formal methods for analyzing systems with AI components; formal synthesis methods of AI components; testing approaches for systems with AI components; statistical approaches for analyzing systems with AI components; and approaches for enhancing the explainability of systems with AI components. The second group, artificial intelligence for formal methods, comprised: AI methods for formal verification; AI methods for formal synthesis; AI methods for safe control; and AI methods for falsification.

Knowledge-based (KB) technology is being applied to complex problem-solving and critical tasks in many application domains. Concerns have naturally arisen as to the dependability of knowledge-based systems (KBS). As with any software, attention to quality and safety must be paid throughout development of a KBS and rigorous verification and validation (V&V) techniques must be employed. Research in V&V of KBS has emerged as a distinct field only in the last decade and is intended to address issues associated with quality and safety aspects of KBS and to credit such applications with the same degree of dependability as conventional applications. In recent years, V&V of KBS has been the topic of annual workshops associated with the main AI conferences, such as AAAI, IJACI and ECAI. Validation and Verification of Knowledge Based Systems contains a collection of papers, dealing with all aspects of KBS V&V, presented at the Fifth European Symposium on Verification and Validation of Knowledge Based Systems and Components (EUROVAV'99 - which was held in Oslo in the summer of 1999, and was sponsored by Det Norske Veritas and the British Computer Society's Specialist Group on Expert Systems (SGES).

This book constitutes the refereed proceedings of the 9th International Symposium on Automated Technology for Verification and Analysis, ATVA 2011, held in Taipei, Taiwan, in October 2011. The 23 revised regular papers presented together with 5 invited papers, 11 short papers, and 2 tool papers, were carefully reviewed and selected from 75 submissions. The papers address all theoretical and practical aspects of automated analysis, verification and synthesis; thus providing a forum for interaction between the regional and the international research communities and industry in the field.