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Artificial Intelligence and the Ethics of Self-Learning Robots Shannon Vallor Santa Clara University, [email protected]
Santa Clara University Scholar Commons Philosophy College of Arts & Sciences 10-3-2017 Artificial Intelligence and the Ethics of Self-learning Robots Shannon Vallor Santa Clara University, [email protected] George A. Bekey Follow this and additional works at: http://scholarcommons.scu.edu/phi Part of the Philosophy Commons Recommended Citation Vallor, S., & Bekey, G. A. (2017). Artificial Intelligence and the Ethics of Self-learning Robots. In P. Lin, K. Abney, & R. Jenkins (Eds.), Robot Ethics 2.0 (pp. 338–353). Oxford University Press. This material was originally published in Robot Ethics 2.0 edited by Patrick Lin, Keith Abney, and Ryan Jenkins, and has been reproduced by permission of Oxford University Press. For permission to reuse this material, please visit http://www.oup.co.uk/academic/rights/permissions. This Book Chapter is brought to you for free and open access by the College of Arts & Sciences at Scholar Commons. It has been accepted for inclusion in Philosophy by an authorized administrator of Scholar Commons. For more information, please contact [email protected]. ARTIFICIAL INTELLIGENCE AND 22 THE ETHICS OF SELF - LEARNING ROBOTS Shannon Va ll or and George A. Bekey The convergence of robotics technology with the science of artificial intelligence (or AI) is rapidly enabling the development of robots that emulate a wide range of intelligent human behaviors.1 Recent advances in machine learning techniques have produced significant gains in the ability of artificial agents to perform or even excel in activities for merly thought to be the exclusive province of human intelligence, including abstract problem-solving, perceptual recognition, social interaction, and natural language use. -
Lecture Note on Deep Learning and Quantum Many-Body Computation
Lecture Note on Deep Learning and Quantum Many-Body Computation Jin-Guo Liu, Shuo-Hui Li, and Lei Wang∗ Institute of Physics, Chinese Academy of Sciences Beijing 100190, China November 23, 2018 Abstract This note introduces deep learning from a computa- tional quantum physicist’s perspective. The focus is on deep learning’s impacts to quantum many-body compu- tation, and vice versa. The latest version of the note is at http://wangleiphy.github.io/. Please send comments, suggestions and corrections to the email address in below. ∗ [email protected] CONTENTS 1 introduction2 2 discriminative learning4 2.1 Data Representation 4 2.2 Model: Artificial Neural Networks 6 2.3 Cost Function 9 2.4 Optimization 11 2.4.1 Back Propagation 11 2.4.2 Gradient Descend 13 2.5 Understanding, Visualization and Applications Beyond Classification 15 3 generative modeling 17 3.1 Unsupervised Probabilistic Modeling 17 3.2 Generative Model Zoo 18 3.2.1 Boltzmann Machines 19 3.2.2 Autoregressive Models 22 3.2.3 Normalizing Flow 23 3.2.4 Variational Autoencoders 25 3.2.5 Tensor Networks 28 3.2.6 Generative Adversarial Networks 29 3.3 Summary 32 4 applications to quantum many-body physics and more 33 4.1 Material and Chemistry Discoveries 33 4.2 Density Functional Theory 34 4.3 “Phase” Recognition 34 4.4 Variational Ansatz 34 4.5 Renormalization Group 35 4.6 Monte Carlo Update Proposals 36 4.7 Tensor Networks 37 4.8 Quantum Machine Leanring 38 4.9 Miscellaneous 38 5 hands on session 39 5.1 Computation Graph and Back Propagation 39 5.2 Deep Learning Libraries 41 5.3 Generative Modeling using Normalizing Flows 42 5.4 Restricted Boltzmann Machine for Image Restoration 43 5.5 Neural Network as a Quantum Wave Function Ansatz 43 6 challenges ahead 45 7 resources 46 BIBLIOGRAPHY 47 1 1 INTRODUCTION Deep Learning (DL) ⊂ Machine Learning (ML) ⊂ Artificial Intelli- gence (AI). -
AI Computer Wraps up 4-1 Victory Against Human Champion Nature Reports from Alphago's Victory in Seoul
The Go Files: AI computer wraps up 4-1 victory against human champion Nature reports from AlphaGo's victory in Seoul. Tanguy Chouard 15 March 2016 SEOUL, SOUTH KOREA Google DeepMind Lee Sedol, who has lost 4-1 to AlphaGo. Tanguy Chouard, an editor with Nature, saw Google-DeepMind’s AI system AlphaGo defeat a human professional for the first time last year at the ancient board game Go. This week, he is watching top professional Lee Sedol take on AlphaGo, in Seoul, for a $1 million prize. It’s all over at the Four Seasons Hotel in Seoul, where this morning AlphaGo wrapped up a 4-1 victory over Lee Sedol — incidentally, earning itself and its creators an honorary '9-dan professional' degree from the Korean Baduk Association. After winning the first three games, Google-DeepMind's computer looked impregnable. But the last two games may have revealed some weaknesses in its makeup. Game four totally changed the Go world’s view on AlphaGo’s dominance because it made it clear that the computer can 'bug' — or at least play very poor moves when on the losing side. It was obvious that Lee felt under much less pressure than in game three. And he adopted a different style, one based on taking large amounts of territory early on rather than immediately going for ‘street fighting’ such as making threats to capture stones. This style – called ‘amashi’ – seems to have paid off, because on move 78, Lee produced a play that somehow slipped under AlphaGo’s radar. David Silver, a scientist at DeepMind who's been leading the development of AlphaGo, said the program estimated its probability as 1 in 10,000. -
Accelerators and Obstacles to Emerging ML-Enabled Research Fields
Life at the edge: accelerators and obstacles to emerging ML-enabled research fields Soukayna Mouatadid Steve Easterbrook Department of Computer Science Department of Computer Science University of Toronto University of Toronto [email protected] [email protected] Abstract Machine learning is transforming several scientific disciplines, and has resulted in the emergence of new interdisciplinary data-driven research fields. Surveys of such emerging fields often highlight how machine learning can accelerate scientific discovery. However, a less discussed question is how connections between the parent fields develop and how the emerging interdisciplinary research evolves. This study attempts to answer this question by exploring the interactions between ma- chine learning research and traditional scientific disciplines. We examine different examples of such emerging fields, to identify the obstacles and accelerators that influence interdisciplinary collaborations between the parent fields. 1 Introduction In recent decades, several scientific research fields have experienced a deluge of data, which is impacting the way science is conducted. Fields such as neuroscience, psychology or social sciences are being reshaped by the advances in machine learning (ML) and the data processing technologies it provides. In some cases, new research fields have emerged at the intersection of machine learning and more traditional research disciplines. This is the case for example, for bioinformatics, born from collaborations between biologists and computer scientists in the mid-80s [1], which is now considered a well-established field with an active community, specialized conferences, university departments and research groups. How do these transformations take place? Do they follow similar paths? If yes, how can the advances in such interdisciplinary fields be accelerated? Researchers in the philosophy of science have long been interested in these questions. -
The Deep Learning Revolution and Its Implications for Computer Architecture and Chip Design
The Deep Learning Revolution and Its Implications for Computer Architecture and Chip Design Jeffrey Dean Google Research [email protected] Abstract The past decade has seen a remarkable series of advances in machine learning, and in particular deep learning approaches based on artificial neural networks, to improve our abilities to build more accurate systems across a broad range of areas, including computer vision, speech recognition, language translation, and natural language understanding tasks. This paper is a companion paper to a keynote talk at the 2020 International Solid-State Circuits Conference (ISSCC) discussing some of the advances in machine learning, and their implications on the kinds of computational devices we need to build, especially in the post-Moore’s Law-era. It also discusses some of the ways that machine learning may also be able to help with some aspects of the circuit design process. Finally, it provides a sketch of at least one interesting direction towards much larger-scale multi-task models that are sparsely activated and employ much more dynamic, example- and task-based routing than the machine learning models of today. Introduction The past decade has seen a remarkable series of advances in machine learning (ML), and in particular deep learning approaches based on artificial neural networks, to improve our abilities to build more accurate systems across a broad range of areas [LeCun et al. 2015]. Major areas of significant advances include computer vision [Krizhevsky et al. 2012, Szegedy et al. 2015, He et al. 2016, Real et al. 2017, Tan and Le 2019], speech recognition [Hinton et al. -
Blame, What Is It Good For?
Blame, What is it Good For? Gordon Briggs1 Abstract— Blame is an vital social and cognitive mechanism [6] and reside in a much more ambiguous moral territory. that humans utilize in their interactions with other agents. In Additionally, it is not enough to be able to correctly reason this paper, we discuss how blame-reasoning mechanisms are about moral scenarios to ensure ethical or otherwise desirable needed to enable future social robots to: (1) appropriately adapt behavior in the context of repeated and/or long-term outcomes from human-robot interactions, the robot must interactions and relationships with other social agents; (2) avoid have other social competencies that support this reasoning behaviors that are perceived to be rude due to inappropriate mechanism. Deceptive interaction partners or incorrect per- and unintentional connotations of blame; and (3) avoid behav- ceptions/predictions about morally-charged scenarios may iors that could damage long-term, working relationships with lead even a perfect ethical-reasoner to make choices with other social agents. We also discuss how current computational models of blame and other relevant competencies (e.g. natural unethical outcomes [7]. What these challenges stress is the language generation) are currently insufficient to address these need to look beyond the ability to simply generate the proper concerns. Future work is necessary to increase the social answer to a moral dilemma in theory, and to consider the reasoning capabilities of artificially intelligent agents to achieve social mechanisms needed in practice. these goals. One such social mechanism that others have posited as I. INTRODUCTION being necessary to the construction of a artificial moral agent is blame [8]. -
The Power and Promise of Computers That Learn by Example
Machine learning: the power and promise of computers that learn by example MACHINE LEARNING: THE POWER AND PROMISE OF COMPUTERS THAT LEARN BY EXAMPLE 1 Machine learning: the power and promise of computers that learn by example Issued: April 2017 DES4702 ISBN: 978-1-78252-259-1 The text of this work is licensed under the terms of the Creative Commons Attribution License which permits unrestricted use, provided the original author and source are credited. The license is available at: creativecommons.org/licenses/by/4.0 Images are not covered by this license. This report can be viewed online at royalsociety.org/machine-learning Cover image © shulz. 2 MACHINE LEARNING: THE POWER AND PROMISE OF COMPUTERS THAT LEARN BY EXAMPLE Contents Executive summary 5 Recommendations 8 Chapter one – Machine learning 15 1.1 Systems that learn from data 16 1.2 The Royal Society’s machine learning project 18 1.3 What is machine learning? 19 1.4 Machine learning in daily life 21 1.5 Machine learning, statistics, data science, robotics, and AI 24 1.6 Origins and evolution of machine learning 25 1.7 Canonical problems in machine learning 29 Chapter two – Emerging applications of machine learning 33 2.1 Potential near-term applications in the public and private sectors 34 2.2 Machine learning in research 41 2.3 Increasing the UK’s absorptive capacity for machine learning 45 Chapter three – Extracting value from data 47 3.1 Machine learning helps extract value from ‘big data’ 48 3.2 Creating a data environment to support machine learning 49 3.3 Extending the lifecycle -
An Ethical Framework for Smart Robots Mika Westerlund
An Ethical Framework for Smart Robots Mika Westerlund Never underestimate a droid. Leia Organa Star Wars: The Rise of Skywalker This article focuses on “roboethics” in the age of growing adoption of smart robots, which can now be seen as a new robotic “species”. As autonomous AI systems, they can collaborate with humans and are capable of learning from their operating environment, experiences, and human behaviour feedback in human-machine interaction. This enables smart robots to improve their performance and capabilities. This conceptual article reviews key perspectives to roboethics, as well as establishes a framework to illustrate its main ideas and features. Building on previous literature, roboethics has four major types of implications for smart robots: 1) smart robots as amoral and passive tools, 2) smart robots as recipients of ethical behaviour in society, 3) smart robots as moral and active agents, and 4) smart robots as ethical impact-makers in society. The study contributes to current literature by suggesting that there are two underlying ethical and moral dimensions behind these perspectives, namely the “ethical agency of smart robots” and “object of moral judgment”, as well as what this could look like as smart robots become more widespread in society. The article concludes by suggesting how scientists and smart robot designers can benefit from a framework, discussing the limitations of the present study, and proposing avenues for future research. Introduction capabilities (Lichocki et al., 2011; Petersen, 2007). Hence, Lin et al. (2011) define a “robot” as an Robots are becoming increasingly prevalent in our engineered machine that senses, thinks, and acts, thus daily, social, and professional lives, performing various being able to process information from sensors and work and household tasks, as well as operating other sources, such as an internal set of rules, either driverless vehicles and public transportation systems programmed or learned, that enables the machine to (Leenes et al., 2017). -
Nudging for Good: Robots and the Ethical Appropriateness of Nurturing Empathy and Charitable Behavior
Nudging for Good: Robots and the Ethical Appropriateness of Nurturing Empathy and Charitable Behavior Jason Borenstein* and Ron Arkin** Predictions are being commonly voiced about how robots are going to become an increasingly prominent feature of our day-to-day lives. Beyond the military and industrial sectors, they are in the process of being created to function as butlers, nannies, housekeepers, and even as companions (Wallach and Allen 2009). The design of these robotic technologies and their use in these roles raises numerous ethical issues. Indeed, entire conferences and books are now devoted to the subject (Lin et al. 2014).1 One particular under-examined aspect of human-robot interaction that requires systematic analysis is whether to allow robots to influence a user’s behavior for that person’s own good. However, an even more controversial practice is on the horizon and warrants attention, which is the ethical acceptability of allowing a robot to “nudge” a user’s behavior for the good of society. For the purposes of this paper, we will examine the feasibility of creating robots that would seek to nurture a user’s empathy towards other human beings. We specifically draw attention to whether it would be ethically appropriate for roboticists to pursue this type of design pathway. In our prior work, we examined the ethical aspects of encoding Rawls’ Theory of Social Justice into robots in order to encourage users to act more socially just towards other humans (Borenstein and Arkin 2016). Here, we primarily limit the focus to the performance of charitable acts, which could shed light on a range of socially just actions that a robot could potentially elicit from a user and what the associated ethical concerns may be. -
On How to Build a Moral Machine
On How to Build a Moral Machine Paul Bello [email protected] Human & Bioengineered Systems Division - Code 341, Office of Naval Research, 875 N. Randolph St., Arlington, VA 22203 USA Selmer Bringsjord [email protected] Depts. of Cognitive Science, Computer Science & the Lally School of Management, Rensselaer Polytechnic Institute, Troy, NY 12180 USA Abstract Herein we make a plea to machine ethicists for the inclusion of constraints on their theories con- sistent with empirical data on human moral cognition. As philosophers, we clearly lack widely accepted solutions to issues regarding the existence of free will, the nature of persons and firm conditions on moral agency/patienthood; all of which are indispensable concepts to be deployed by any machine able to make moral judgments. No agreement seems forthcoming on these mat- ters, and we don’t hold out hope for machines that can both always do the right thing (on some general ethic) and produce explanations for its behavior that would be understandable to a human confederate. Our tentative solution involves understanding the folk concepts associated with our moral intuitions regarding these matters, and how they might be dependent upon the nature of hu- man cognitive architecture. It is in this spirit that we begin to explore the complexities inherent in human moral judgment via computational theories of the human cognitive architecture, rather than under the extreme constraints imposed by rational-actor models assumed throughout much of the literature on philosophical ethics. After discussing the various advantages and challenges of taking this particular perspective on the development of artificial moral agents, we computationally explore a case study of human intuitions about the self and causal responsibility. -
An Architecture for Ethical Robots
1 An architecture for ethical robots Dieter Vanderelst & Alan Winfield Bristol Robotics Laboratory, University of the West of England T Block, Frenchay Campus, Coldharbour Lane, Bristol, BS16 1QY, United Kingdom Abstract—Robots are becoming ever more autonomous. This Anderson [2] and our previous work [27] are the only instances expanding ability to take unsupervised decisions renders it im- of robots having been equipped with a set of moral principles. perative that mechanisms are in place to guarantee the safety of So far, most work has been either theoretical [e.g., 25] or behaviours executed by the robot. Moreover, smart autonomous robots should be more than safe; they should also be explicitly simulation based [e.g., 3]. ethical – able to both choose and justify actions that prevent The approach taken by Anderson and Anderson [1, 2] and harm. Indeed, as the cognitive, perceptual and motor capabilities others [25, 3] is complementary with our research goals. These of robots expand, they will be expected to have an improved authors focus on developing methods to extract ethical rules capacity for making moral judgements. We present a control for robots. Conversely, our work concerns the development architecture that supplements existing robot controllers. This so-called Ethical Layer ensures robots behave according to a of a control architecture that supplements the existing robot predetermined set of ethical rules by predicting the outcomes of controller, ensuring robots behave according to a predeter- possible actions and evaluating the predicted outcomes against mined set of ethical rules [27, 26]. In other words, we are those rules. To validate the proposed architecture, we implement concerned with methods to enforce the rules once these have it on a humanoid robot so that it behaves according to Asimov’s been established [10]. -
Which Consequentialism? Machine Ethics and Moral Divergence
MIRI MACHINE INTELLIGENCE RESEARCH INSTITUTE Which Consequentialism? Machine Ethics and Moral Divergence Carl Shulman, Henrik Jonsson MIRI Visiting Fellows Nick Tarleton Carnegie Mellon University, MIRI Visiting Fellow Abstract Some researchers in the field of machine ethics have suggested consequentialist or util- itarian theories as organizing principles for Artificial Moral Agents (AMAs) (Wallach, Allen, and Smit 2008) that are ‘full ethical agents’ (Moor 2006), while acknowledging extensive variation among these theories as a serious challenge (Wallach, Allen, and Smit 2008). This paper develops that challenge, beginning with a partial taxonomy ofconse- quentialisms proposed by philosophical ethics. We discuss numerous ‘free variables’ of consequentialism where intuitions conflict about optimal values, and then consider spe- cial problems of human-level AMAs designed to implement a particular ethical theory, by comparison to human proponents of the same explicit principles. In conclusion, we suggest that if machine ethics is to fully succeed, it must draw upon the developing field of moral psychology. Shulman, Carl, Nick Tarleton, and Henrik Jonsson. 2009. “Which Consequentialism? Machine Ethics and Moral Divergence.” In AP-CAP 2009: The Fifth Asia-Pacific Computing and Philosophy Conference, October 1st-2nd, University of Tokyo, Japan, Proceedings, edited by Carson Reynolds and Alvaro Cassinelli, 23–25. AP-CAP 2009. http://ia-cap.org/ap-cap09/proceedings.pdf. This version contains minor changes. Carl Shulman, Henrik Jonsson, Nick Tarleton 1. Free Variables of Consequentialism Suppose that the recommendations of a broadly utilitarian view depend on decisions about ten free binary variables, where we assign a probability of 80% to our favored option for each variable; in this case, if our probabilities are well-calibrated and our errors are not correlated across variables, then we will have only slightly more than a 10% chance of selecting the correct (in some meta-ethical framework) specification.