Ethical Artificial Intelligence
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Infinite Ethics
INFINITE ETHICS Nick Bostrom Faculty of Philosophy Oxford University [Published in Analysis and Metaphysics, Vol. 10 (2011): pp. 9-59] [This is the final version. Earlier versions: 2003, 2005, 2008, 2009] www.nickbostrom.com ABSTRACT Aggregative consequentialism and several other popular moral theories are threatened with paralysis: when coupled with some plausible assumptions, they seem to imply that it is always ethically indifferent what you do. Modern cosmology teaches that the world might well contain an infinite number of happy and sad people and other candidate value-bearing locations. Aggregative ethics implies that such a world contains an infinite amount of positive value and an infinite amount of negative value. You can affect only a finite amount of good or bad. In standard cardinal arithmetic, an infinite quantity is unchanged by the addition or subtraction of any finite quantity. So it appears you cannot change the value of the world. Modifications of aggregationism aimed at resolving the paralysis are only partially effective and cause severe side effects, including problems of “fanaticism”, “distortion”, and erosion of the intuitions that originally motivated the theory. Is the infinitarian challenge fatal? 1. The challenge 1.1. The threat of infinitarian paralysis When we gaze at the starry sky at night and try to think of humanity from a “cosmic point of view”, we feel small. Human history, with all its earnest strivings, triumphs, and tragedies can remind us of a colony of ants, laboring frantically to rearrange the needles of their little ephemeral stack. We brush such late-night rumination aside in our daily life and analytic 1 philosophy. -
Apocalypse Now? Initial Lessons from the Covid-19 Pandemic for the Governance of Existential and Global Catastrophic Risks
journal of international humanitarian legal studies 11 (2020) 295-310 brill.com/ihls Apocalypse Now? Initial Lessons from the Covid-19 Pandemic for the Governance of Existential and Global Catastrophic Risks Hin-Yan Liu, Kristian Lauta and Matthijs Maas Faculty of Law, University of Copenhagen, Copenhagen, Denmark [email protected]; [email protected]; [email protected] Abstract This paper explores the ongoing Covid-19 pandemic through the framework of exis- tential risks – a class of extreme risks that threaten the entire future of humanity. In doing so, we tease out three lessons: (1) possible reasons underlying the limits and shortfalls of international law, international institutions and other actors which Covid-19 has revealed, and what they reveal about the resilience or fragility of institu- tional frameworks in the face of existential risks; (2) using Covid-19 to test and refine our prior ‘Boring Apocalypses’ model for understanding the interplay of hazards, vul- nerabilities and exposures in facilitating a particular disaster, or magnifying its effects; and (3) to extrapolate some possible futures for existential risk scholarship and governance. Keywords Covid-19 – pandemics – existential risks – global catastrophic risks – boring apocalypses 1 Introduction: Our First ‘Brush’ with Existential Risk? All too suddenly, yesterday’s ‘impossibilities’ have turned into today’s ‘condi- tions’. The impossible has already happened, and quickly. The impact of the Covid-19 pandemic, both directly and as manifested through the far-reaching global societal responses to it, signal a jarring departure away from even the © koninklijke brill nv, leiden, 2020 | doi:10.1163/18781527-01102004Downloaded from Brill.com09/27/2021 12:13:00AM via free access <UN> 296 Liu, Lauta and Maas recent past, and suggest that our futures will be profoundly different in its af- termath. -
Artificial Consciousness and the Consciousness-Attention Dissociation
Consciousness and Cognition 45 (2016) 210–225 Contents lists available at ScienceDirect Consciousness and Cognition journal homepage: www.elsevier.com/locate/concog Review article Artificial consciousness and the consciousness-attention dissociation ⇑ Harry Haroutioun Haladjian a, , Carlos Montemayor b a Laboratoire Psychologie de la Perception, CNRS (UMR 8242), Université Paris Descartes, Centre Biomédical des Saints-Pères, 45 rue des Saints-Pères, 75006 Paris, France b San Francisco State University, Philosophy Department, 1600 Holloway Avenue, San Francisco, CA 94132 USA article info abstract Article history: Artificial Intelligence is at a turning point, with a substantial increase in projects aiming to Received 6 July 2016 implement sophisticated forms of human intelligence in machines. This research attempts Accepted 12 August 2016 to model specific forms of intelligence through brute-force search heuristics and also reproduce features of human perception and cognition, including emotions. Such goals have implications for artificial consciousness, with some arguing that it will be achievable Keywords: once we overcome short-term engineering challenges. We believe, however, that phenom- Artificial intelligence enal consciousness cannot be implemented in machines. This becomes clear when consid- Artificial consciousness ering emotions and examining the dissociation between consciousness and attention in Consciousness Visual attention humans. While we may be able to program ethical behavior based on rules and machine Phenomenology learning, we will never be able to reproduce emotions or empathy by programming such Emotions control systems—these will be merely simulations. Arguments in favor of this claim include Empathy considerations about evolution, the neuropsychological aspects of emotions, and the disso- ciation between attention and consciousness found in humans. -
Computability and Complexity
Computability and Complexity Lecture Notes Herbert Jaeger, Jacobs University Bremen Version history Jan 30, 2018: created as copy of CC lecture notes of Spring 2017 Feb 16, 2018: cleaned up font conversion hickups up to Section 5 Feb 23, 2018: cleaned up font conversion hickups up to Section 6 Mar 15, 2018: cleaned up font conversion hickups up to Section 7 Apr 5, 2018: cleaned up font conversion hickups up to the end of these lecture notes 1 1 Introduction 1.1 Motivation This lecture will introduce you to the theory of computation and the theory of computational complexity. The theory of computation offers full answers to the questions, • what problems can in principle be solved by computer programs? • what functions can in principle be computed by computer programs? • what formal languages can in principle be decided by computer programs? Full answers to these questions have been found in the last 70 years or so, and we will learn about them. (And it turns out that these questions are all the same question). The theory of computation is well-established, transparent, and basically simple (you might disagree at first). The theory of complexity offers many insights to questions like • for a given problem / function / language that has to be solved / computed / decided by a computer program, how long does the fastest program actually run? • how much memory space has to be used at least? • can you speed up computations by using different computer architectures or different programming approaches? The theory of complexity is historically younger than the theory of computation – the first surge of results came in the 60ties of last century. -
Inventing Computational Rhetoric
INVENTING COMPUTATIONAL RHETORIC By Michael W. Wojcik A THESIS Submitted to Michigan State University in partial fulfillment of the requirements for the degree of Digital Rhetoric and Professional Writing — Master of Arts 2013 ABSTRACT INVENTING COMPUTATIONAL RHETORIC by Michael W. Wojcik Many disciplines in the humanities are developing “computational” branches which make use of information technology to process large amounts of data algorithmically. The field of computational rhetoric is in its infancy, but we are already seeing interesting results from applying the ideas and goals of rhetoric to text processing and related areas. After considering what computational rhetoric might be, three approaches to inventing computational rhetorics are presented: a structural schema, a review of extant work, and a theoretical exploration. Copyright by MICHAEL W. WOJCIK 2013 For Malea iv ACKNOWLEDGEMENTS Above all else I must thank my beloved wife, Malea Powell, without whose prompting this thesis would have remained forever incomplete. I am also grateful for the presence in my life of my terrific stepdaughter, Audrey Swartz, and wonderful granddaughter Lucille. My thesis committee, Dean Rehberger, Bill Hart-Davidson, and John Monberg, pro- vided me with generous guidance and inspiration. Other faculty members at Michigan State who helped me explore relevant ideas include Rochelle Harris, Mike McLeod, Joyce Chai, Danielle Devoss, and Bump Halbritter. My previous academic program at Miami University did not result in a degree, but faculty there also contributed greatly to my the- oretical understanding, particularly Susan Morgan, Mary-Jean Corbett, Brit Harwood, J. Edgar Tidwell, Lori Merish, Vicki Smith, Alice Adams, Fran Dolan, and Keith Tuma. -
Letters to the Editor
Articles Letters to the Editor Research Priorities for is a product of human intelligence; we puter scientists, innovators, entrepre - cannot predict what we might achieve neurs, statisti cians, journalists, engi - Robust and Beneficial when this intelligence is magnified by neers, authors, professors, teachers, stu - Artificial Intelligence: the tools AI may provide, but the eradi - dents, CEOs, economists, developers, An Open Letter cation of disease and poverty are not philosophers, artists, futurists, physi - unfathomable. Because of the great cists, filmmakers, health-care profes - rtificial intelligence (AI) research potential of AI, it is important to sionals, research analysts, and members Ahas explored a variety of problems research how to reap its benefits while of many other fields. The earliest signa - and approaches since its inception, but avoiding potential pitfalls. tories follow, reproduced in order and as for the last 20 years or so has been The progress in AI research makes it they signed. For the complete list, see focused on the problems surrounding timely to focus research not only on tinyurl.com/ailetter. - ed. the construction of intelligent agents making AI more capable, but also on Stuart Russell, Berkeley, Professor of Com - — systems that perceive and act in maximizing the societal benefit of AI. puter Science, director of the Center for some environment. In this context, Such considerations motivated the “intelligence” is related to statistical Intelligent Systems, and coauthor of the AAAI 2008–09 Presidential Panel on standard textbook Artificial Intelligence: a and economic notions of rationality — Long-Term AI Futures and other proj - Modern Approach colloquially, the ability to make good ects on AI impacts, and constitute a sig - Tom Dietterich, Oregon State, President of decisions, plans, or inferences. -
Computability Theory
CSC 438F/2404F Notes (S. Cook and T. Pitassi) Fall, 2019 Computability Theory This section is partly inspired by the material in \A Course in Mathematical Logic" by Bell and Machover, Chap 6, sections 1-10. Other references: \Introduction to the theory of computation" by Michael Sipser, and \Com- putability, Complexity, and Languages" by M. Davis and E. Weyuker. Our first goal is to give a formal definition for what it means for a function on N to be com- putable by an algorithm. Historically the first convincing such definition was given by Alan Turing in 1936, in his paper which introduced what we now call Turing machines. Slightly before Turing, Alonzo Church gave a definition based on his lambda calculus. About the same time G¨odel,Herbrand, and Kleene developed definitions based on recursion schemes. Fortunately all of these definitions are equivalent, and each of many other definitions pro- posed later are also equivalent to Turing's definition. This has lead to the general belief that these definitions have got it right, and this assertion is roughly what we now call \Church's Thesis". A natural definition of computable function f on N allows for the possibility that f(x) may not be defined for all x 2 N, because algorithms do not always halt. Thus we will use the symbol 1 to mean “undefined". Definition: A partial function is a function n f :(N [ f1g) ! N [ f1g; n ≥ 0 such that f(c1; :::; cn) = 1 if some ci = 1. In the context of computability theory, whenever we refer to a function on N, we mean a partial function in the above sense. -
Comments to Michael Jackson's Keynote on Determining Energy
Comments to Michael Jackson’s Keynote on Determining Energy Futures using Artificial Intelligence Prof Sirkka Heinonen Finland Futures Research Centre (FFRC) University of Turku ENERGIZING FUTURES 13–14 June 2018 Tampere, Finland AI and Energy • Concrete tools: How Shaping Tomorrow answers the question How can AI help? • Goal of foresight crystal clear: Making better decisions today Huge Challenge – The Challenge The world will need to cut energy-related carbon dioxide emissions by 60 percent by 2050 -even as the population grows by more than two billion people Bold Solution on the Horizon The Renewable Energy Transition Companies as pioneers on energy themes, demand, supply, consumption • Google will reach 100% RE for its global operations this year • GE using AI to boost different forms of energy production and use tech-driven data reports to anticipate performance and maintenance needs around the world BUT …also the role of governments, cities and citizens …NGOs, media… new actors should be emphasised AI + Energy + Peer-to-Peer Society • The transformation of the energy system aligns with the principles of a Peer-to-Peer Society. • Peer-to-peer practices are based on the active participation and self-organisation of citizens. Citizens share knowledge, skills, co-create, and form new peer groups. • Citizens will use their capabilities also to develop energy-related products and services Rethinking Concepts Buildings as Power stations – global (economic) opportunity to construct buildings that generate, store and release solar energy -
Introduction to the Theory of Computation Computability, Complexity, and the Lambda Calculus Some Notes for CIS262
Introduction to the Theory of Computation Computability, Complexity, And the Lambda Calculus Some Notes for CIS262 Jean Gallier and Jocelyn Quaintance Department of Computer and Information Science University of Pennsylvania Philadelphia, PA 19104, USA e-mail: [email protected] c Jean Gallier Please, do not reproduce without permission of the author April 28, 2020 2 Contents Contents 3 1 RAM Programs, Turing Machines 7 1.1 Partial Functions and RAM Programs . 10 1.2 Definition of a Turing Machine . 15 1.3 Computations of Turing Machines . 17 1.4 Equivalence of RAM programs And Turing Machines . 20 1.5 Listable Languages and Computable Languages . 21 1.6 A Simple Function Not Known to be Computable . 22 1.7 The Primitive Recursive Functions . 25 1.8 Primitive Recursive Predicates . 33 1.9 The Partial Computable Functions . 35 2 Universal RAM Programs and the Halting Problem 41 2.1 Pairing Functions . 41 2.2 Equivalence of Alphabets . 48 2.3 Coding of RAM Programs; The Halting Problem . 50 2.4 Universal RAM Programs . 54 2.5 Indexing of RAM Programs . 59 2.6 Kleene's T -Predicate . 60 2.7 A Non-Computable Function; Busy Beavers . 62 3 Elementary Recursive Function Theory 67 3.1 Acceptable Indexings . 67 3.2 Undecidable Problems . 70 3.3 Reducibility and Rice's Theorem . 73 3.4 Listable (Recursively Enumerable) Sets . 76 3.5 Reducibility and Complete Sets . 82 4 The Lambda-Calculus 87 4.1 Syntax of the Lambda-Calculus . 89 4.2 β-Reduction and β-Conversion; the Church{Rosser Theorem . 94 4.3 Some Useful Combinators . -
Exploratory Engineering in AI
MIRI MACHINE INTELLIGENCE RESEARCH INSTITUTE Exploratory Engineering in AI Luke Muehlhauser Machine Intelligence Research Institute Bill Hibbard University of Wisconsin Madison Space Science and Engineering Center Muehlhauser, Luke and Bill Hibbard. 2013. “Exploratory Engineering in AI” Communications of the ACM, Vol. 57 No. 9, Pages 32–34. doi:10.1145/2644257 This version contains minor changes. Luke Muehlhauser, Bill Hibbard We regularly see examples of new artificial intelligence (AI) capabilities. Google’s self- driving car has safely traversed thousands of miles. Watson beat the Jeopardy! cham- pions, and Deep Blue beat the chess champion. Boston Dynamics’ Big Dog can walk over uneven terrain and right itself when it falls over. From many angles, software can recognize faces as well as people can. As their capabilities improve, AI systems will become increasingly independent of humans. We will be no more able to monitor their decisions than we are now able to check all the math done by today’s computers. No doubt such automation will produce tremendous economic value, but will we be able to trust these advanced autonomous systems with so much capability? For example, consider the autonomous trading programs which lost Knight Capital $440 million (pre-tax) on August 1st, 2012, requiring the firm to quickly raise $400 mil- lion to avoid bankruptcy (Valetkevitch and Mikolajczak 2012). This event undermines a common view that AI systems cannot cause much harm because they will only ever be tools of human masters. Autonomous trading programs make millions of trading decisions per day, and they were given sufficient capability to nearly bankrupt one of the largest traders in U.S. -
Artificial Intelligence As a Positive and Negative Factor in Global Risk
The Singularity Institute Artificial Intelligence as a Positive and Negative Factor in Global Risk Eliezer Yudkowsky The Singularity Institute Yudkowsky, Eliezer. 2008. Artificial intelligence as a positive and negative factor in global risk. In Global catastrophic risks, ed. Nick Bostrom and Milan M. Cirkovic, 308–345. New York: Oxford University Press. This version contains minor changes. Eliezer Yudkowsky 1. Introduction By far the greatest danger of Artificial Intelligence is that people conclude too early that they understand it. Of course this problem is not limited to the field of AI. Jacques Monod wrote: “A curious aspect of the theory of evolution is that everybody thinks he understands it” (Monod 1975). My father, a physicist, complained about people making up their own theories of physics; he wanted to know why people did not make up their own theories of chemistry. (Answer: They do.) Nonetheless the problem seems tobe unusually acute in Artificial Intelligence. The field of AI has a reputation formaking huge promises and then failing to deliver on them. Most observers conclude that AI is hard; as indeed it is. But the embarrassment does not stem from the difficulty. It is difficult to build a star from hydrogen, but the field of stellar astronomy does not have a terrible reputation for promising to build stars and then failing. The critical inference is not that AI is hard, but that, for some reason, it is very easy for people to think they know far more about Artificial Intelligence than they actually do. In my other chapter for Global Catastrophic Risks, “Cognitive Biases Potentially Affect- ing Judgment of Global Risks” (Yudkowsky 2008), I opened by remarking that few people would deliberately choose to destroy the world; a scenario in which the Earth is destroyed by mistake is therefore very worrisome. -
Programming with Recursion
Purdue University Purdue e-Pubs Department of Computer Science Technical Reports Department of Computer Science 1980 Programming With Recursion Dirk Siefkes Report Number: 80-331 Siefkes, Dirk, "Programming With Recursion" (1980). Department of Computer Science Technical Reports. Paper 260. https://docs.lib.purdue.edu/cstech/260 This document has been made available through Purdue e-Pubs, a service of the Purdue University Libraries. Please contact [email protected] for additional information. '. \ PI~Q(:[V\!'<lMTNr. 11'1'1'11 nr:CUI~SrON l1irk Siefke$* Tcchnische UniveTsit~t Berlin Inst. Soft\~are Theor. Informatik 1 Berlin 10, West Germany February, 1980 Purdue University Tech. Report CSD-TR 331 Abstract: Data structures defined as term algebras and programs built from recursive definitions complement each other. Computing in such surroundings guides us into Nriting simple programs Nith a clear semantics and performing a rigorous cost analysis on appropriate data structures. In this paper, we present a programming language so perceived, investigate some basic principles of cost analysis through it, and reflect on the meaning of programs and computing. -;;----- - -~~ Visiting at. the Computer Science Department of Purdue University with the help of a grant of the Stiftung Volksl.... agem.... erk. I am grateful for the support of both, ViV-Stiftung and Purdue. '1 1 CONTENTS O. Introduction I. 'llle formnlislIl REe Syntax and semantics of REC-programs; the formalisms REC(B) and RECS; recursive functions. REC l~ith VLI-r-t'iblcs. -2. Extensions and variations Abstract languages and compilers. REC over words as data structure; the formalisms REC\\'(q). REe for functions with variable I/O-dimension; the forma l:i sms VREC (B) .