Artificial Intelligence is rapidly embedding itself within militaries, economies, and societies, reshaping their very foundations. Given the depth and breadth of its consequences, it has never been more pressing to understand how to ensure that AI systems are safe, ethical, and have a positive societal impact. This book aims to provide a comprehensive approach to understanding AI risk. Our primary goals include consolidating fragmented knowledge on AI risk, increasing the precision of core ideas, and reducing barriers to entry by making content simpler and more comprehensible. The book has been designed to be accessible to readers from diverse backgrounds. You do not need to have studied AI, philosophy, or other such topics. The content is skimmable and somewhat modular, so that you can choose which chapters to read. We introduce mathematical formulas in a few places to specify claims more precisely, but readers should be able to understand the main points without these.

In this paper we discuss a hybrid approach combining Case-Based Reasoning (CBR) .

We discuss several aspects of legal arguments, primarily arguments about the meaning of statutes. First, we discuss how the requirements of argument guide the specification and selection of supporting cases and how an existing case base influences argument formation.

We focus on realistic, complex domains where the concepts, terms and predicates used by domain rules or by rule-based models are not well-defined. Often, in such inherently ill-defined domains the rules do not encompass all the situations they are asked or assumed to cover, admit tacit exceptions, or can be contradicted and annulled by other rules. Interpretation is therefore required of the terms and predicates used. The law is a prototypical example of such an area, where terms used in legal statutes are not completely defined by legal regulations. The use of case-based reasoning (CBR) to complement and supplement other types of reasoning involves many computational questions of system architecture and control. The key focus of this work is how and when to interleave CBR with other modes of reasoning in the context of applying a rule or model to a new set of facts in light of a corpus of cases of past application. The goal is to generate an explanation or argument as to how the new fact situation might be interpreted. In particular, we report on a system called CABARET (CAse-BAsed REasoning Tool), a hybrid architecture we have built to study and experiment with these issues.

This paper describes some work on automatically generating finite counterexamples in topology, and the use of counterexamples to speed up proof discovery in intermediate analysis, and gives some examples theorems where human provers are aided in proof discovery by the use of examples.

A programming language based on sets; motivation and examples.

The overall design aim of ABSET was to devise an interactive programming language in which it is possible, at will, to take or defer decisions about a program: we therefore require that decisions which are logically separable can indeed be taken separately and in aiiy order. Further, we think it important that the language should allow the nature of each decision to be clear, particularly choice of representations.

The equivalence problem for program schemes, or for programs, is reduced to the proving of a theorem in second-order logic. This work extends Manna's first-order logic reductions. Some examples of the technique are given together with a suggested method for obtaining proofs in special cases by firstorder methods. INTRODUCTION Several workers in recent years have considered using techniques and ideas of various mathematical theories of computation for proving interesting results about computer programs. This paper is concerned with two of these approaches.

The program contains a rough theory of mass spectrometry which is still in its formative stages.

We have de,Jeloped an automated system for the design of laboratory experiments in molecular biology. The system uses a planning method known as skeletal plan refinement that attempts to emulate the human cognitive task of experiment design. This paper describes the theory, history, and implementation of the design system and illustrates its function in the domain of DNA cloning experiments.