Should We Fear Artificial Intelligence?
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Artificial General Intelligence and Classical Neural Network
Artificial General Intelligence and Classical Neural Network Pei Wang Department of Computer and Information Sciences, Temple University Room 1000X, Wachman Hall, 1805 N. Broad Street, Philadelphia, PA 19122 Web: http://www.cis.temple.edu/∼pwang/ Email: [email protected] Abstract— The research goal of Artificial General Intelligence • Capability. Since we often judge the level of intelligence (AGI) and the notion of Classical Neural Network (CNN) are of other people by evaluating their problem-solving capa- specified. With respect to the requirements of AGI, the strength bility, some people believe that the best way to achieve AI and weakness of CNN are discussed, in the aspects of knowledge representation, learning process, and overall objective of the is to build systems that can solve hard practical problems. system. To resolve the issues in CNN in a general and efficient Examples: various expert systems. way remains a challenge to future neural network research. • Function. Since the human mind has various cognitive functions, such as perceiving, learning, reasoning, acting, I. ARTIFICIAL GENERAL INTELLIGENCE and so on, some people believe that the best way to It is widely recognized that the general research goal of achieve AI is to study each of these functions one by Artificial Intelligence (AI) is twofold: one, as certain input-output mapping. Example: various intelligent tools. • As a science, it attempts to provide an explanation of • Principle. Since the human mind seems to follow certain the mechanism in the human mind-brain complex that is principles of information processing, some people believe usually called “intelligence” (or “cognition”, “thinking”, that the best way to achieve AI is to let computer systems etc.). -
Artificial Intelligence: Distinguishing Between Types & Definitions
19 NEV. L.J. 1015, MARTINEZ 5/28/2019 10:48 AM ARTIFICIAL INTELLIGENCE: DISTINGUISHING BETWEEN TYPES & DEFINITIONS Rex Martinez* “We should make every effort to understand the new technology. We should take into account the possibility that developing technology may have im- portant societal implications that will become apparent only with time. We should not jump to the conclusion that new technology is fundamentally the same as some older thing with which we are familiar. And we should not hasti- ly dismiss the judgment of legislators, who may be in a better position than we are to assess the implications of new technology.”–Supreme Court Justice Samuel Alito1 TABLE OF CONTENTS INTRODUCTION ............................................................................................. 1016 I. WHY THIS MATTERS ......................................................................... 1018 II. WHAT IS ARTIFICIAL INTELLIGENCE? ............................................... 1023 A. The Development of Artificial Intelligence ............................... 1023 B. Computer Science Approaches to Artificial Intelligence .......... 1025 C. Autonomy .................................................................................. 1026 D. Strong AI & Weak AI ................................................................ 1027 III. CURRENT STATE OF AI DEFINITIONS ................................................ 1029 A. Black’s Law Dictionary ............................................................ 1029 B. Nevada ..................................................................................... -
An Evolutionary Heuristic for Human Enhancement
18 TheWisdomofNature: An Evolutionary Heuristic for Human Enhancement Nick Bostrom and Anders Sandberg∗ Abstract Human beings are a marvel of evolved complexity. Such systems can be difficult to enhance. When we manipulate complex evolved systems, which are poorly understood, our interventions often fail or backfire. It can appear as if there is a ‘‘wisdom of nature’’ which we ignore at our peril. Sometimes the belief in nature’s wisdom—and corresponding doubts about the prudence of tampering with nature, especially human nature—manifest as diffusely moral objections against enhancement. Such objections may be expressed as intuitions about the superiority of the natural or the troublesomeness of hubris, or as an evaluative bias in favor of the status quo. This chapter explores the extent to which such prudence-derived anti-enhancement sentiments are justified. We develop a heuristic, inspired by the field of evolutionary medicine, for identifying promising human enhancement interventions. The heuristic incorporates the grains of truth contained in ‘‘nature knows best’’ attitudes while providing criteria for the special cases where we have reason to believe that it is feasible for us to improve on nature. 1.Introduction 1.1. The wisdom of nature, and the special problem of enhancement We marvel at the complexity of the human organism, how its various parts have evolved to solve intricate problems: the eye to collect and pre-process ∗ Oxford Future of Humanity Institute, Faculty of Philosophy and James Martin 21st Century School, Oxford University. Forthcoming in Enhancing Humans, ed. Julian Savulescu and Nick Bostrom (Oxford: Oxford University Press) 376 visual information, the immune system to fight infection and cancer, the lungs to oxygenate the blood. -
Artificial Intelligence/Artificial Wisdom
Artificial Intelligence/Artificial Wisdom - A Drive for Improving Behavioral and Mental Health Care (06 September to 10 September 2021) Department of Computer Science & Information Technology Central University of Jammu, J&K-181143 Preamble An Artificial Intelligence is the capability of a machine to imitate intelligent human behaviour. Machine learning is based on the idea that machines should be able to learn and adapt through experience. Machine learning is a subset of AI. That is, all machine learning counts as AI, but not all AI counts as machine learning. Artificial intelligence (AI) technology holds both great promises to transform mental healthcare and potential pitfalls. Artificial intelligence (AI) is increasingly employed in healthcare fields such as oncology, radiology, and dermatology. However, the use of AI in mental healthcare and neurobiological research has been modest. Given the high morbidity and mortality in people with psychiatric disorders, coupled with a worsening shortage of mental healthcare providers, there is an urgent need for AI to help identify high-risk individuals and provide interventions to prevent and treat mental illnesses. According to the publication Spectrum News, a form of AI called "deep learning" is sometimes better able than human beings to spot relevant patterns. This five days course provides an overview of AI approaches and current applications in mental healthcare, a review of recent original research on AI specific to mental health, and a discussion of how AI can supplement clinical practice while considering its current limitations, areas needing additional research, and ethical implications regarding AI technology. The proposed workshop is envisaged to provide opportunity to our learners to seek and share knowledge and teaching skills in cutting edge areas from the experienced and reputed faculty. -
Machine Guessing – I
Machine Guessing { I David Miller Department of Philosophy University of Warwick COVENTRY CV4 7AL UK e-mail: [email protected] ⃝c copyright D. W. Miller 2011{2018 Abstract According to Karl Popper, the evolution of science, logically, methodologically, and even psy- chologically, is an involved interplay of acute conjectures and blunt refutations. Like biological evolution, it is an endless round of blind variation and selective retention. But unlike biological evolution, it incorporates, at the stage of selection, the use of reason. Part I of this two-part paper begins by repudiating the common beliefs that Hume's problem of induction, which com- pellingly confutes the thesis that science is rational in the way that most people think that it is rational, can be solved by assuming that science is rational, or by assuming that Hume was irrational (that is, by ignoring his argument). The problem of induction can be solved only by a non-authoritarian theory of rationality. It is shown also that because hypotheses cannot be distilled directly from experience, all knowledge is eventually dependent on blind conjecture, and therefore itself conjectural. In particular, the use of rules of inference, or of good or bad rules for generating conjectures, is conjectural. Part II of the paper expounds a form of Popper's critical rationalism that locates the rationality of science entirely in the deductive processes by which conjectures are criticized and improved. But extreme forms of deductivism are rejected. The paper concludes with a sharp dismissal of the view that work in artificial intelligence, including the JSM method cultivated extensively by Victor Finn, does anything to upset critical rationalism. -
The Transhumanist Reader Is an Important, Provocative Compendium Critically Exploring the History, Philosophy, and Ethics of Transhumanism
TH “We are in the process of upgrading the human species, so we might as well do it E Classical and Contemporary with deliberation and foresight. A good first step is this book, which collects the smartest thinking available concerning the inevitable conflicts, challenges and opportunities arising as we re-invent ourselves. It’s a core text for anyone making TRA Essays on the Science, the future.” —Kevin Kelly, Senior Maverick for Wired Technology, and Philosophy “Transhumanism has moved from a fringe concern to a mainstream academic movement with real intellectual credibility. This is a great taster of some of the best N of the Human Future emerging work. In the last 10 years, transhumanism has spread not as a religion but as a creative rational endeavor.” SHU —Julian Savulescu, Uehiro Chair in Practical Ethics, University of Oxford “The Transhumanist Reader is an important, provocative compendium critically exploring the history, philosophy, and ethics of transhumanism. The contributors anticipate crucial biopolitical, ecological and planetary implications of a radically technologically enhanced population.” M —Edward Keller, Director, Center for Transformative Media, Parsons The New School for Design A “This important book contains essays by many of the top thinkers in the field of transhumanism. It’s a must-read for anyone interested in the future of humankind.” N —Sonia Arrison, Best-selling author of 100 Plus: How The Coming Age of Longevity Will Change Everything IS The rapid pace of emerging technologies is playing an increasingly important role in T overcoming fundamental human limitations. The Transhumanist Reader presents the first authoritative and comprehensive survey of the origins and current state of transhumanist Re thinking regarding technology’s impact on the future of humanity. -
Transcript of the Highlights of Elon Musk's
Powered by What do electric cars have in common with life on Mars? One man — Elon Musk. This multitalented inventor founded SpaceX, a company that develops and manufactures space vehicles, and Tesla Motors, which produces 100% electricity-powered luxury roadsters. How does one person balance diverse interests and manage successful endeavors in space technology, sustainable energy and the Internet? From his early days teaching himself to program computers, learn how Elon Musk has worked to improve the quality of life for everyone through technology. Powered by Elon Musk - www.OnInnovation.com In Thomas Edison’s Menlo Park complex, the machine shop served as a bridge between two worlds. One was a world of practicality — advancing the creation of Edison’s most commercially successful inventions. The other, a world of experimentation — exploring what was possible. A similar integration of experiment and practicality exists in Hawthorne, California, at a company known as SpaceX. Its business is developing rockets that place commercial satellites in orbit. But SpaceX has broader ambitions: creating technologically astounding rockets that, at some point in the future, could carry manned missions to Mars. At the helm of SpaceX is, an individual who came to prominence in the creation of PayPal . who is Chairman and CEO of the electric sports car manufacturer Tesla Motors . and who has a passion for doing things others only dream about. Bottom:The second floor of Thomas Edison’s Menlo Park Laboratory (lower right) in Greenfield Village served as the center of the experimental process. New ideas and solutions were developed and tested here. Edison inventions and patent models (left) were displayed in the laboratory as a reminder of Edison’s ingenuity. -
Between Ape and Artilect Createspace V2
Between Ape and Artilect Conversations with Pioneers of Artificial General Intelligence and Other Transformative Technologies Interviews Conducted and Edited by Ben Goertzel This work is offered under the following license terms: Creative Commons: Attribution-NonCommercial-NoDerivs 3.0 Unported (CC-BY-NC-ND-3.0) See http://creativecommons.org/licenses/by-nc-nd/3.0/ for details Copyright © 2013 Ben Goertzel All rights reserved. ISBN: ISBN-13: “Man is a rope stretched between the animal and the Superman – a rope over an abyss.” -- Friedrich Nietzsche, Thus Spake Zarathustra Table&of&Contents& Introduction ........................................................................................................ 7! Itamar Arel: AGI via Deep Learning ................................................................. 11! Pei Wang: What Do You Mean by “AI”? .......................................................... 23! Joscha Bach: Understanding the Mind ........................................................... 39! Hugo DeGaris: Will There be Cyborgs? .......................................................... 51! DeGaris Interviews Goertzel: Seeking the Sputnik of AGI .............................. 61! Linas Vepstas: AGI, Open Source and Our Economic Future ........................ 89! Joel Pitt: The Benefits of Open Source for AGI ............................................ 101! Randal Koene: Substrate-Independent Minds .............................................. 107! João Pedro de Magalhães: Ending Aging .................................................... -
Challenges to the Omohundro—Bostrom Framework for AI Motivations
Challenges to the Omohundro—Bostrom framework for AI motivations Olle Häggström1 Abstract Purpose. This paper contributes to the futurology of a possible artificial intelligence (AI) breakthrough, by reexamining the Omohundro—Bostrom theory for instrumental vs final AI goals. Does that theory, along with its predictions for what a superintelligent AI would be motivated to do, hold water? Design/methodology/approach. The standard tools of systematic reasoning and analytic philosophy are used to probe possible weaknesses of Omohundro—Bostrom theory from four different directions: self-referential contradictions, Tegmark’s physics challenge, moral realism, and the messy case of human motivations. Findings. The two cornerstones of Omohundro—Bostrom theory – the orthogonality thesis and the instrumental convergence thesis – are both open to various criticisms that question their validity and scope. These criticisms are however far from conclusive: while they do suggest that a reasonable amount of caution and epistemic humility is attached to predictions derived from the theory, further work will be needed to clarify its scope and to put it on more rigorous foundations. Originality/value. The practical value of being able to predict AI goals and motivations under various circumstances cannot be overstated: the future of humanity may depend on it. Currently the only framework available for making such predictions is Omohundro—Bostrom theory, and the value of the present paper is to demonstrate its tentative nature and the need for further scrutiny. 1. Introduction Any serious discussion of the long-term future of humanity needs to take into account the possible influence of radical new technologies, or risk turning out irrelevant and missing all the action due to implicit and unwarranted assumptions about technological status quo. -
Mirrorshade Women: Feminism and Cyberpunk
Mirrorshade Women: Feminism and Cyberpunk at the Turn of the Twenty-first Century Carlen Lavigne McGill University, Montréal Department of Art History and Communication Studies February 2008 A thesis submitted to McGill University in partial fulfilment of the requirements of the degree of Doctor of Philosophy in Communication Studies © Carlen Lavigne 2008 2 Abstract This study analyzes works of cyberpunk literature written between 1981 and 2005, and positions women’s cyberpunk as part of a larger cultural discussion of feminist issues. It traces the origins of the genre, reviews critical reactions, and subsequently outlines the ways in which women’s cyberpunk altered genre conventions in order to advance specifically feminist points of view. Novels are examined within their historical contexts; their content is compared to broader trends and controversies within contemporary feminism, and their themes are revealed to be visible reflections of feminist discourse at the end of the twentieth century. The study will ultimately make a case for the treatment of feminist cyberpunk as a unique vehicle for the examination of contemporary women’s issues, and for the analysis of feminist science fiction as a complex source of political ideas. Cette étude fait l’analyse d’ouvrages de littérature cyberpunk écrits entre 1981 et 2005, et situe la littérature féminine cyberpunk dans le contexte d’une discussion culturelle plus vaste des questions féministes. Elle établit les origines du genre, analyse les réactions culturelles et, par la suite, donne un aperçu des différentes manières dont la littérature féminine cyberpunk a transformé les usages du genre afin de promouvoir en particulier le point de vue féministe. -
1 This Is a Pre-Production Postprint of the Manuscript Published in Final Form As Emily K. Crandall, Rachel H. Brown, and John M
Magicians of the Twenty-first Century: Enchantment, Domination, and the Politics of Work in Silicon Valley Item Type Article Authors Crandall, Emily K.; Brown, Rachel H.; McMahon, John Citation Crandall, Emily K., Rachel H. Brown, and John McMahon. 2021. “Magicians of the Twenty-First Century: Enchantment, Domination, and the Politics of Work in Silicon Valley.” Theory & Event 24(3): 841–73. https://muse.jhu.edu/article/797952 (July 28, 2021). DOI 10.1353/tae.2021.0045 Publisher Project Muse Download date 27/09/2021 11:51:24 Link to Item http://hdl.handle.net/20.500.12648/1921 This is a pre-production postprint of the manuscript published in final form as Emily K. Crandall, Rachel H. Brown, and John McMahon, “Magicians of the Twenty-first Century: Enchantment, Domination, and the Politics of Work in Silicon Valley,” Theory & Event 24 (3): 841-873. Magicians of the Twenty-first Century: Enchantment, Domination, and the Politics of Work in Silicon Valley Emily K. Crandall, Rachel H. Brown, John McMahon Abstract What is the political theorist to make of self-characterizations of Silicon Valley as the beacon of civilization-saving innovation? Through an analysis of “tech bro” masculinity and the closely related discourses of tech icons Elon Musk and Peter Thiel, we argue that undergirding Silicon Valley’s technological utopia is an exploitative work ethic revamped for the industry's innovative ethos. On the one hand, Silicon Valley hypothetically offers a creative response to what Max Weber describes as the disenchantment of the modern world. Simultaneously, it depoliticizes the actual work necessary for these dreams to be realized, mystifying its modes of domination. -
Global Catastrophic Risks Survey
GLOBAL CATASTROPHIC RISKS SURVEY (2008) Technical Report 2008/1 Published by Future of Humanity Institute, Oxford University Anders Sandberg and Nick Bostrom At the Global Catastrophic Risk Conference in Oxford (17‐20 July, 2008) an informal survey was circulated among participants, asking them to make their best guess at the chance that there will be disasters of different types before 2100. This report summarizes the main results. The median extinction risk estimates were: Risk At least 1 million At least 1 billion Human extinction dead dead Number killed by 25% 10% 5% molecular nanotech weapons. Total killed by 10% 5% 5% superintelligent AI. Total killed in all 98% 30% 4% wars (including civil wars). Number killed in 30% 10% 2% the single biggest engineered pandemic. Total killed in all 30% 10% 1% nuclear wars. Number killed in 5% 1% 0.5% the single biggest nanotech accident. Number killed in 60% 5% 0.05% the single biggest natural pandemic. Total killed in all 15% 1% 0.03% acts of nuclear terrorism. Overall risk of n/a n/a 19% extinction prior to 2100 These results should be taken with a grain of salt. Non‐responses have been omitted, although some might represent a statement of zero probability rather than no opinion. 1 There are likely to be many cognitive biases that affect the result, such as unpacking bias and the availability heuristic‒‐well as old‐fashioned optimism and pessimism. In appendix A the results are plotted with individual response distributions visible. Other Risks The list of risks was not intended to be inclusive of all the biggest risks.