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.

The 3rd edition of the Montreal AI Ethics Institute's The State of AI Ethics captures the most relevant developments in AI Ethics since October 2020. It aims to help anyone, from machine learning experts to human rights activists and policymakers, quickly digest and understand the field's ever-changing developments. Through research and article summaries, as well as expert commentary, this report distills the research and reporting surrounding various domains related to the ethics of AI, including: algorithmic injustice, discrimination, ethical AI, labor impacts, misinformation, privacy, risk and security, social media, and more. In addition, The State of AI Ethics includes exclusive content written by world-class AI Ethics experts from universities, research institutes, consulting firms, and governments. Unique to this report is "The Abuse and Misogynoir Playbook," written by Dr. Katlyn Tuner (Research Scientist, Space Enabled Research Group, MIT), Dr. Danielle Wood (Assistant Professor, Program in Media Arts and Sciences; Assistant Professor, Aeronautics and Astronautics; Lead, Space Enabled Research Group, MIT) and Dr. Catherine D'Ignazio (Assistant Professor, Urban Science and Planning; Director, Data + Feminism Lab, MIT). The piece (and accompanying infographic), is a deep-dive into the historical and systematic silencing, erasure, and revision of Black women's contributions to knowledge and scholarship in the United Stations, and globally. Exposing and countering this Playbook has become increasingly important following the firing of AI Ethics expert Dr. Timnit Gebru (and several of her supporters) at Google. This report should be used not only as a point of reference and insight on the latest thinking in the field of AI Ethics, but should also be used as a tool for introspection as we aim to foster a more nuanced conversation regarding the impacts of AI on the world.

Computer simulations are invaluable tools for scientific discovery. However, accurate simulations are often slow to execute, which limits their applicability to extensive parameter exploration, large-scale data analysis, and uncertainty quantification. A promising route to accelerate simulations by building fast emulators with machine learning requires large training datasets, which can be prohibitively expensive to obtain with slow simulations. Here we present a method based on neural architecture search to build accurate emulators even with a limited number of training data. The method successfully accelerates simulations by up to 2 billion times in 10 scientific cases including astrophysics, climate science, biogeochemistry, high energy density physics, fusion energy, and seismology, using the same super-architecture, algorithm, and hyperparameters. Our approach also inherently provides emulator uncertainty estimation, adding further confidence in their use. We anticipate this work will accelerate research involving expensive simulations, allow more extensive parameters exploration, and enable new, previously unfeasible computational discovery.

The first section discusses the importance of having systems that own M.S. thesis (Moore, 19)5), suggests that predicate calculus can understand the concept of knowledge, and how knowledge is be treated in a more natural manner than resolution and related to action. Section 2 points out some of the special problems combined with domain-dependent control information for greater that are involved in reasoning about knowledge, and section $ efficiency. Furthermore, the problems of reasoning about knowledge seem to require the full ability to handle quantifiers presents a logic of knowledge based on the idea of possible worlds. Section 4 integrates this with a logic of actions and gives an and logical connectives which only predicate calculus posseses.

A plan for a question required the composition of REQUEST and INFORM and led to the development of two new kinds of informing speech acts, INFORMREF To plan a yes/no question about some proposition P. one should think that the and INFORMIF, and their mediating acts. The INFORMREF acts lead to hearer knows whether P is true or false (or, at least "might know"). An approximate "what," "when," and "where" questions while INFORMIF results in a yes/no representation of AGT2's knowing whether P is true or false is OR (AGT2 question.2' The reason for these new acts is that, in planning a REQUEST that BELIEVE P, AGT2 BELIEVE -- P)).'9 Such goals are often created, as modelled someone else perform an INFORM act, one only has incomplete knowledge of by our type 4 inference, when a planner does not know the truth-value of P. their beliefs and goals; but an INFORM, as originally defined can only be Typical circumstances in which an agent may acquire such disjunctive beliefs planned when one knows what is to be said.

Dr. Uttley took an Honours degree in Mathematics at King's College, London where he also took a degree in Psychology and did post-graduate research in Visual Perception. At the Royal Radar establishment he designed and built analogue and digital computers. For the last five years Dr. Uttley has been working on theories of computing in the nervous system. ABSTRACT IN two previous papers it has been suggested that two particular mathematical principles may underlie the organization of nervous systems; the first is that of classification (Uttley, 1954, ref.. 13) and the second is that of. The suggestion is based on the similarity of behaviour of these formal systems and or animals. The design of classification computers is discussed in the first paper; the design of conditional probability computers Is discussed in a third paper (Uttley, 1958, ref. 15); in both papers working models are described. FUrther reference to these papers will be by date only. It is the aim of the present paper to consider whether the two principles might operate in nervous systems. Mere are four requirements for the principle of classification to operate in an area of a nervous system. Firstly, In that area, signalling must be binary; this would be the case if, for example, the impulse frequency were at either a very low rate or at a maximal rate, or if signalling were In terms of standard volleys; in general, if the fibre activity were in one of only two states. The second requirement Is that the fibres which form the input to the area be connected to neurons In as many different ways as possible; there are many areas in which this condition is met. The third requirement Is that more than one synapse of a neuron must become active for it to fire; this appears to be met. The fourth requirement is that there shall be some way of delaying signals for periods of the order of seconds. A block of isolated cortex does remain active for such periods when stimulated briefly so in this way the requirement might be met. If these conditions are all met each neuron will indicate, by firing, the occurrerze of a particular spatio-temporal pattern of activity in the input to the system.

Known in behaviour as "habituation" and in perception as "adaptation", it has been recognised from time immemorial yet still lacks explanation. Only recently Sharpless and Jasper (1956, ref. 10) could say "Habituaticn... has yet to be explained by any known neurophysiological principles". A review of the subject need not be given here as it has been well reviewed by Humphrey (1933, ref.6), Harris (1943, ref. 5), and Thorpe (1956, ref. 11). On one important matter they are agreed: habituation of typical form occurs in almost every form of life; in particular it appears as readily in forms having no neural apparatus as in the forms having a well developed brain. Amoeba shows it as freely as does the cat. The phenomenon evidently does not depend on specifically neurophysiological details. Its origin must lie in some property of much wider occurrence. The possibility of "fatigue" as an explanation must be rejected.

Biology seems to be a science in its own right, or set of sciences having common aims, and so it should have its own language and explanatory concepts; yet when any specifically biological concept is suggested and used as an explanatory concept it seems to be unsatisfactory and even mystical. There are many biological concepts of this kind: Purpose, Drive, elan vital, Entelechy, Gestalten.* Physicists and engineers seem, on the other hand, to have clearly defined concepts having great power within biology.

If ability to perform complex calculations were a sufficient criterion, then even a conventional digital computor could lay claim to more intelligence than any of usand perhaps we had better let it make away with the word and be done with it.

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