Leakproofing the Singularity Artificial Intelligence Confinement Problem
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Safety of Artificial Superintelligence
Environment. Technology. Resources. Rezekne, Latvia Proceedings of the 12th International Scientific and Practical Conference. Volume II, 180-183 Safety of Artificial Superintelligence Aleksejs Zorins Peter Grabusts Faculty of Engineering Faculty of Engineering Rezekne Academy of Technologies Rezekne Academy of Technologies Rezekne, Latvia Rezekne, Latvia [email protected] [email protected] Abstract—The paper analyses an important problem of to make a man happy it will do it with the fastest and cyber security from human safety perspective which is usu- cheapest (in terms of computational resources) without ally described as data and/or computer safety itself with- using a common sense (for example killing of all people out mentioning the human. There are numerous scientific will lead to the situation that no one is unhappy or the predictions of creation of artificial superintelligence, which decision to treat human with drugs will also make him could arise in the near future. That is why the strong neces- sity for protection of such a system from causing any farm happy etc.). Another issue is that we want computer to arises. This paper reviews approaches and methods already that we want, but due to bugs in the code computer will presented for solving this problem in a single article, analy- do what the code says, and in the case of superintelligent ses its results and provides future research directions. system, this may be a disaster. Keywords—Artificial Superintelligence, Cyber Security, The next sections of the paper will show the possible Singularity, Safety of Artificial Intelligence. solutions of making superintelligence safe to humanity. -
Liability Law for Present and Future Robotics Technology Trevor N
Liability Law for Present and Future Robotics Technology Trevor N. White and Seth D. Baum Global Catastrophic Risk Institute http://gcrinstitute.org * http://sethbaum.com Published in Robot Ethics 2.0, edited by Patrick Lin, George Bekey, Keith Abney, and Ryan Jenkins, Oxford University Press, 2017, pages 66-79. This version 10 October 2017 Abstract Advances in robotics technology are causing major changes in manufacturing, transportation, medicine, and a number of other sectors. While many of these changes are beneficial, there will inevitably be some harms. Who or what is liable when a robot causes harm? This paper addresses how liability law can and should account for robots, including robots that exist today and robots that potentially could be built at some point in the near or distant future. Already, robots have been implicated in a variety of harms. However, current and near-future robots pose no significant challenge for liability law: they can be readily handled with existing liability law or minor variations thereof. We show this through examples from medical technology, drones, and consumer robotics. A greater challenge will arise if it becomes possible to build robots that merit legal personhood and thus can be held liable. Liability law for robot persons could draw on certain precedents, such as animal liability. However, legal innovations will be needed, in particular for determining which robots merit legal personhood. Finally, a major challenge comes from the possibility of future robots that could cause major global catastrophe. As with other global catastrophic risks, liability law could not apply, because there would be no post- catastrophe legal system to impose liability. -
Classification Schemas for Artificial Intelligence Failures
Journal XX (XXXX) XXXXXX https://doi.org/XXXX/XXXX Classification Schemas for Artificial Intelligence Failures Peter J. Scott1 and Roman V. Yampolskiy2 1 Next Wave Institute, USA 2 University of Louisville, Kentucky, USA [email protected], [email protected] Abstract In this paper we examine historical failures of artificial intelligence (AI) and propose a classification scheme for categorizing future failures. By doing so we hope that (a) the responses to future failures can be improved through applying a systematic classification that can be used to simplify the choice of response and (b) future failures can be reduced through augmenting development lifecycles with targeted risk assessments. Keywords: artificial intelligence, failure, AI safety, classification 1. Introduction Artificial intelligence (AI) is estimated to have a $4-6 trillion market value [1] and employ 22,000 PhD researchers [2]. It is estimated to create 133 million new roles by 2022 but to displace 75 million jobs in the same period [6]. Projections for the eventual impact of AI on humanity range from utopia (Kurzweil, 2005) (p.487) to extinction (Bostrom, 2005). In many respects AI development outpaces the efforts of prognosticators to predict its progress and is inherently unpredictable (Yampolskiy, 2019). Yet all AI development is (so far) undertaken by humans, and the field of software development is noteworthy for unreliability of delivering on promises: over two-thirds of companies are more likely than not to fail in their IT projects [4]. As much effort as has been put into the discipline of software safety, it still has far to go. Against this background of rampant failures we must evaluate the future of a technology that could evolve to human-like capabilities, usually known as artificial general intelligence (AGI). -
On the Differences Between Human and Machine Intelligence
On the Differences between Human and Machine Intelligence Roman V. Yampolskiy Computer Science and Engineering, University of Louisville [email protected] Abstract [Legg and Hutter, 2007a]. However, widespread implicit as- Terms Artificial General Intelligence (AGI) and Hu- sumption of equivalence between capabilities of AGI and man-Level Artificial Intelligence (HLAI) have been HLAI appears to be unjustified, as humans are not general used interchangeably to refer to the Holy Grail of Ar- intelligences. In this paper, we will prove this distinction. tificial Intelligence (AI) research, creation of a ma- Others use slightly different nomenclature with respect to chine capable of achieving goals in a wide range of general intelligence, but arrive at similar conclusions. “Lo- environments. However, widespread implicit assump- cal generalization, or “robustness”: … “adaptation to tion of equivalence between capabilities of AGI and known unknowns within a single task or well-defined set of HLAI appears to be unjustified, as humans are not gen- tasks”. … Broad generalization, or “flexibility”: “adapta- eral intelligences. In this paper, we will prove this dis- tion to unknown unknowns across a broad category of re- tinction. lated tasks”. …Extreme generalization: human-centric ex- treme generalization, which is the specific case where the 1 Introduction1 scope considered is the space of tasks and domains that fit within the human experience. We … refer to “human-cen- Imagine that tomorrow a prominent technology company tric extreme generalization” as “generality”. Importantly, as announces that they have successfully created an Artificial we deliberately define generality here by using human cog- Intelligence (AI) and offers for you to test it out. -
AI and You Transcript Guest: Roman Yampolskiy Episode 16 First Aired: Monday, October 5, 2020
AI and You Transcript Guest: Roman Yampolskiy Episode 16 First Aired: Monday, October 5, 2020 Hi, welcome to episode 16. Today we have a feast, because I am talking with Dr. Roman Yampolskiy, a legend within the AI community. He is a tenured associate professor in the department of Computer Engineering and Computer Science at the University of Louisville in Kentucky where he is the director of Cyber Security. Look up his home page and Wikipedia entry because he has qualifications too numerous to detail here. Some of those include that he is also a Visiting Fellow at the Singularity Institute, he was also one of the scientists at the Asilomar conference on the future of AI, and the author of over 100 publications, including the book Artificial Superintelligence: A Futuristic Approach, which I would strongly recommend, and many peer-reviewed papers, including one coauthored with yours truly. Most of them are on the subject of AI Safety. In this episode we will talk mostly about a recent paper of his, a table-thumping 73 pages titled “On Controllability of Artificial Intelligence,” which you may recognize is talking about what we call the Control Problem. That’s the most fundamental problem in the whole study of the long-term impact of AI, and Roman explains it very well in this episode. If you’re looking at the possible future of artificial intelligence having serious negative as well as positive consequences for everyone, which is a concept that’s gotten a lot of popularization in the last few years, of course, and you’re finding computer professionals pooh-poohing that idea because AI right now requires a great deal of specialized effort on their part to do even narrow tasks, and the idea of it becoming an existential threat is just too sensationalist, the product of watching too many Terminator movies, then pay attention to Roman, because he has impeccable computer science credentials and yet does not shy away from exploring fundamental threats of AI. -
The Age of Artificial Intelligence an Exploration
THE AGE OF ARTIFICIAL INTELLIGENCE AN EXPLORATION Edited by Steven S. Gouveia University of Minho, Portugal Cognitive Science and Psychology Copyright © 2020 by the Authors. All rights reserved. No part of this publication may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, electronic, mechanical, photocopying, recording, or otherwise, without the prior permission of Vernon Art and Science Inc. www.vernonpress.com In the Americas: In the rest of the world: Vernon Press Vernon Press 1000 N West Street, C/Sancti Espiritu 17, Suite 1200, Wilmington, Malaga, 29006 Delaware 19801 Spain United States Cognitive Science and Psychology Library of Congress Control Number: 2020931461 ISBN: 978-1-62273-872-4 Product and company names mentioned in this work are the trademarks of their respective owners. While every care has been taken in preparing this work, neither the authors nor Vernon Art and Science Inc. may be held responsible for any loss or damage caused or alleged to be caused directly or indirectly by the information contained in it. Every effort has been made to trace all copyright holders, but if any have been inadvertently overlooked the publisher will be pleased to include any necessary credits in any subsequent reprint or edition. Cover design by Vernon Press using elements designed by FreePik. TABLE OF CONTENTS LIST OF ACRONYMS vii LIST OF FIGURES xi LIST OF TABLES xiii INTRODUCTION xv SECTION I: INTELLIGENCE IN ARTIFICIAL INTELLIGENCE 1 CHAPTER 1 TOWARDS THE MATHEMATICS OF INTELLIGENCE 3 Soenke Ziesche Maldives National University, Maldives Roman V. Yampolskiy University of Louisville, USA CHAPTER 2 MINDS , BRAINS AND TURING 15 Stevan Harnad Université du Québec à Montréal, Canada; University of Southampton, UK CHAPTER 3 THE AGE OF POST -INTELLIGENT DESIGN 27 Daniel C. -
Agent Foundations for Aligning Machine Intelligence with Human Interests: a Technical Research Agenda
Agent Foundations for Aligning Machine Intelligence with Human Interests: A Technical Research Agenda In The Technological Singularity: Managing the Journey. Springer. 2017 Nate Soares and Benya Fallenstein Machine Intelligence Research Institute fnate,[email protected] Contents 1 Introduction 1 1.1 Why These Problems? . .2 2 Highly Reliable Agent Designs 3 2.1 Realistic World-Models . .4 2.2 Decision Theory . .5 2.3 Logical Uncertainty . .6 2.4 Vingean Reflection . .7 3 Error-Tolerant Agent Designs 8 4 Value Specification 9 5 Discussion 11 5.1 Toward a Formal Understanding of the Problem . 11 5.2 Why Start Now? . 11 1 Introduction Since artificial agents would not share our evolution- ary history, there is no reason to expect them to be The property that has given humans a dominant ad- driven by human motivations such as lust for power. vantage over other species is not strength or speed, but However, nearly all goals can be better met with more intelligence. If progress in artificial intelligence continues resources (Omohundro 2008). This suggests that, by unabated, AI systems will eventually exceed humans in default, superintelligent agents would have incentives general reasoning ability. A system that is \superintelli- to acquire resources currently being used by humanity. gent" in the sense of being \smarter than the best human (Just as artificial agents would not automatically acquire brains in practically every field” could have an enormous a lust for power, they would not automatically acquire a impact upon humanity (Bostrom 2014). Just as human human sense of fairness, compassion, or conservatism.) intelligence has allowed us to develop tools and strate- Thus, most goals would put the agent at odds with gies for controlling our environment, a superintelligent human interests, giving it incentives to deceive or ma- system would likely be capable of developing its own nipulate its human operators and resist interventions tools and strategies for exerting control (Muehlhauser designed to change or debug its behavior (Bostrom 2014, and Salamon 2012). -
Intelligence Explosion FAQ
MIRI MACHINE INTELLIGENCE RESEARCH INSTITUTE Intelligence Explosion FAQ Luke Muehlhauser Machine Intelligence Research Institute Abstract The Machine Intelligence Research Institute is one of the leading research institutes on intelligence explosion. Below are short answers to common questions we receive. Muehlhauser, Luke. 2013. “Intelligence Explosion FAQ.” First published 2011 as “Singularity FAQ.” Machine Intelligence Research Institute, Berkeley, CA Contents 1 Basics 1 1.1 What is an intelligence explosion? . 1 2 How Likely Is an Intelligence Explosion? 2 2.1 How is “intelligence” defined? . 2 2.2 What is greater-than-human intelligence? . 2 2.3 What is whole-brain emulation? . 3 2.4 What is biological cognitive enhancement? . 3 2.5 What are brain-computer interfaces? . 4 2.6 How could general intelligence be programmed into a machine? . 4 2.7 What is superintelligence? . 4 2.8 When will the intelligence explosion happen? . 5 2.9 Might an intelligence explosion never occur? . 6 3 Consequences of an Intelligence Explosion 7 3.1 Why would great intelligence produce great power? . 7 3.2 How could an intelligence explosion be useful? . 7 3.3 How might an intelligence explosion be dangerous? . 8 4 Friendly AI 9 4.1 What is Friendly AI? . 9 4.2 What can we expect the motivations of a superintelligent machine to be? 10 4.3 Can’t we just keep the superintelligence in a box, with no access to the Internet? . 11 4.4 Can’t we just program the superintelligence not to harm us? . 11 4.5 Can we program the superintelligence to maximize human pleasure or desire satisfaction? . -
Hello, World: Artificial Intelligence and Its Use in the Public Sector
Hello, World: Artificial intelligence and its use in the public sector Jamie Berryhill Kévin Kok Heang Rob Clogher Keegan McBride November 2019 | http://oe.cd/helloworld OECD Working Papers on Public Governance No. 36 Cover images are the European Parliament and the dome of Germany’s Reichstag building processed through Deep Learning algorithms to match the style of Van Gogh paintings. tw Hello, World: Artificial Intelligence and its Use in the Public Sector Authors: Jamie Berryhill, Kévin Kok Heang, Rob Clogher, Keegan McBride PUBE 2 This document and any map included herein are without prejudice to the status of or sovereignty over any territory, to the delimitation of international frontiers and boundaries and to the name of any territory, city or area. 1. Note by Tukey: The information in this document with reference to ‘Cyprus’ relates to the southern part of the island. There is no single authority representing both Turkish and Greek Cypriot people on the island. Turkey recognises the Turkish Republic of Northern Cyprus (TRNC). Until a lasting and equitable solution is found within the context of the United Nations, Turkey shall preserve its position concerning the ‘Cyprus issue’. 2. Note by all the European Union Member States of the OECD and the European Commission: The Republic of Cyprus is recognised by all members of the United Nations with the exception of Turkey. The information in this document relates to the area under the effective control of the Government of the Republic of Cyprus. HELLO, WORLD: ARTIFICIAL INTELLIGENCE AND ITS USE IN THE PUBLIC SECTOR © OECD 2019 3 Foreword Artificial Intelligence (AI) is an area of research and technology application that can have a significant impact on public policies and services in many ways. -
Uncontrollability of Artificial Intelligence
Uncontrollability of Artificial Intelligence Roman V. Yampolskiy Computer Science and Engineering, University of Louisville [email protected] Abstract means. However, it is a standard practice in computer sci- ence to first show that a problem doesn’t belong to a class Invention of artificial general intelligence is pre- of unsolvable problems [Turing, 1936; Davis, 2004] before dicted to cause a shift in the trajectory of human civ- investing resources into trying to solve it or deciding what ilization. In order to reap the benefits and avoid pit- approaches to try. Unfortunately, to the best of our falls of such powerful technology it is important to be knowledge no mathematical proof or even rigorous argu- able to control it. However, possibility of controlling mentation has been published demonstrating that the AI artificial general intelligence and its more advanced control problem may be solvable, even in principle, much version, superintelligence, has not been formally es- less in practice. tablished. In this paper we argue that advanced AI Yudkowsky considers the possibility that the control can’t be fully controlled. Consequences of uncontrol- problem is not solvable, but correctly insists that we should lability of AI are discussed with respect to future of study the problem in great detail before accepting such grave humanity and research on AI, and AI safety and se- limitation, he writes: “One common reaction I encounter is curity. for people to immediately declare that Friendly AI is an im- 1 possibility, because any sufficiently powerful AI will be 1 Introduction able to modify its own source code to break any constraints The unprecedented progress in Artificial Intelligence (AI) placed upon it. -
Personal Universes: a Solution to the Multi-Agent Value Alignment Problem
Personal Universes: A Solution to the Multi-Agent Value Alignment Problem Roman V. Yampolskiy Computer Engineering and Computer Science Speed School of Engineering University of Louisville [email protected] Abstract AI Safety researchers attempting to align values of highly capable intelligent systems with those of humanity face a number of challenges including personal value extraction, multi-agent value merger and finally in-silico encoding. State-of-the-art research in value alignment shows difficulties in every stage in this process, but merger of incompatible preferences is a particularly difficult challenge to overcome. In this paper we assume that the value extraction problem will be solved and propose a possible way to implement an AI solution which optimally aligns with individual preferences of each user. We conclude by analyzing benefits and limitations of the proposed approach. Keywords: AI Safety, Alternate Reality, Simulation, Value Alignment Problem, VR 1. Introduction to the Multi-Agent Value Alignment Problem Since the birth of the field of Artificial Intelligence (AI) researchers worked on creating ever capable machines, but with recent success in multiple subdomains of AI [1-7] safety and security of such systems and predicted future superintelligences [8, 9] has become paramount [10, 11]. While many diverse safety mechanisms are being investigated [12, 13], the ultimate goal is to align AI with goals, values and preferences of its users which is likely to include all of humanity. Value alignment problem [14], can be decomposed into three sub-problems, namely: personal value extraction from individual persons, combination of such personal preferences in a way, which is acceptable to all, and finally production of an intelligent system, which implements combined values of humanity. -
A Survey of Research Questions for Robust and Beneficial AI
A survey of research questions for robust and beneficial AI 1 Introduction Artificial intelligence (AI) research has explored a variety of problems and approaches since its inception, but for the last 20 years or so has been focused on the problems surrounding the construction of intelligent agents|systems that perceive and act in some environment. In this context, the criterion for intelligence is related to statistical and economic notions of rationality|colloquially, the ability to make good decisions, plans, or inferences. The adoption of probabilistic representations and statistical learning methods has led to a large degree of integration and cross-fertilization between AI, machine learning, statistics, control theory, neuroscience, and other fields. The establishment of shared theoretical frameworks, combined with the availability of data and processing power, has yielded remarkable successes in various component tasks such as speech recognition, image classification, autonomous vehicles, machine translation, legged locomotion, and question-answering systems. As capabilities in these areas and others cross the threshold from laboratory research to economically valuable technologies, a virtuous cycle takes hold whereby even small improvements in performance are worth large sums of money, prompting greater investments in research. There is now a broad consensus that AI research is progressing steadily, and that its impact on society is likely to increase. The potential benefits are huge, since everything that civilization has to offer is a product of human intelligence; we cannot predict what we might achieve when this intelligence is magnified by the tools AI may provide, but the eradication of disease and poverty are not unfathomable. Because of the great potential of AI, it is valuable to investigate how to reap its benefits while avoiding potential pitfalls.