Artificial Intelligence Beyond the Philosophy of Mind
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Non-Duality in Ken Wilber's Integral Philosophy
NON-DUALITY IN KEN WILBER’S INTEGRAL PHILOSOPHY: A CRITICAL APPRAISAL AND ALTERNATIVE PHYSICALIST PERSPECTIVE OF MYSTICAL CONSCIOUSNESS JEREMY JOHN JACOBS 2009 Ken Wilber Cover Photo: Denver Integral and Ken Wilber Meetup Group (http://www.meetup.com/denver- integral/photos/?photold=716584&photoAlbumld=102985. Accessed 20 February 2009). NON-DUALITY IN KEN WILBER’S INTEGRAL PHILOSOPHY: A CRITICAL APPRAISAL AND ALTERNATIVE PHYSICALIST PERSPECTIVE OF MYSTICAL CONSCIOUSNESS by JEREMY JOHN JACOBS Submitted in accordance with the requirements for the degree of DOCTOR OF THEOLOGY in the subject CHRISTIAN SPIRITUALITY at the UNIVERSITY OF SOUTH AFRICA Promoter PROFESSOR C E T KOURIE February 2009 Kenneth Earl Wilber (1949 - ) (theosophist.wordpress.com) Student Number: 3279-583-1 I declare that NON-DUALITY IN KEN WILBER’S INTEGRAL PHILOSOPHY: A CRITICAL APPRAISAL AND ALTERNATIVE PHYSICALIST PERSPECTIVE OF MYSTICAL CONSCIOUSNESS is my own work and that all sources that I have used or quoted have been indicated and acknowledged by means of complete references. _________________________ ______________ Signature Date Jeremy John Jacobs i DEDICATION To Kim and St John ii ACKNOWLEDGEMENTS My deepest gratitude goes to my wife Kim for her endless patience, encouragement, and support. I gratefully acknowledge Professor Celia Kourie’s professionalism. Her courteous manner of guidance and correction, and also the hospitality she extended to my alternative perspectives are an indication of her academic integrity. Special thanks to my sister Tania Jacobs who typed up reams of notes that I had gathered over the years, and finally my friend Andre Croucamp whose brilliant mind inspired me to think beyond the strictures of my creeds. -
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 ..................................................................................... -
KNOWLEDGE ACCORDING to IDEALISM Idealism As a Philosophy
KNOWLEDGE ACCORDING TO IDEALISM Idealism as a philosophy had its greatest impact during the nineteenth century. It is a philosophical approach that has as its central tenet that ideas are the only true reality, the only thing worth knowing. In a search for truth, beauty, and justice that is enduring and everlasting; the focus is on conscious reasoning in the mind. The main tenant of idealism is that ideas and knowledge are the truest reality. Many things in the world change, but ideas and knowledge are enduring. Idealism was often referred to as “idea-ism”. Idealists believe that ideas can change lives. The most important part of a person is the mind. It is to be nourished and developed. Etymologically Its origin is: from Greek idea “form, shape” from weid- also the origin of the “his” in his-tor “wise, learned” underlying English “history.” In Latin this root became videre “to see” and related words. It is the same root in Sanskrit veda “knowledge as in the Rig-Veda. The stem entered Germanic as witan “know,” seen in Modern German wissen “to know” and in English “wisdom” and “twit,” a shortened form of Middle English atwite derived from æt “at” +witen “reproach.” In short Idealism is a philosophical position which adheres to the view that nothing exists except as it is an idea in the mind of man or the mind of God. The idealist believes that the universe has intelligence and a will; that all material things are explainable in terms of a mind standing behind them. PHILOSOPHICAL RATIONALE OF IDEALISM a) The Universe (Ontology or Metaphysics) To the idealist, the nature of the universe is mind; it is an idea. -
The Extended Mind Thesis and Mechanistic Explanations
The Extended Mind Thesis and Mechanistic Explanations Peter Fazekas Institute of Philosophy, Research Centre for the Humanities Hungarian Academy of Sciences email: [email protected] Abstract The Extended Mind Thesis (EMT) is traditionally formulated against the bedrock of functionalism, and ongoing debates are typically bogged down with questions concerning the exact relationship between EMT and different versions of functionalism. In this paper, I offer a novel ally for EMT: the new mechanistic approach to explanation. I argue that the mechanistic framework provides useful resources not just to disambiguate EMT, and to show which objections fail to pose a serious challenge, but also to answer some of the deeper problems that stem from the functionalist roots of EMT. ! 1. Introduction The Extended Mind Thesis (EMT), first proposed by Clark and Chalmers (1998), claims that the vehicles of our cognitive processes and mental states can and sometimes do extend beyond the boundaries of our brain and even body (cf. Clark, 2008, p. 76). If a physical object plays a part in the machinery realising a cognitive process, then that physical object is part of the cognitive process, no matter whether it is inside or outside the head. This is the so-called parity principle (Clark & Chalmers, 1998, p. 8), the heart and soul of EMT. The most well-known illustration is the case of Otto and Inga. When Inga hears about a new exhibition at the Museum of Modern Art (MoMA) she retrieves from her biological memory that it is on 53rd Street and goes to the museum. Her counterpart, Otto, who suffers from a mild form of Alzheimer’s disease, uses a notebook to jot down and retrieve information. -
Is AI Intelligent, Really? Bruce D
Seattle aP cific nivU ersity Digital Commons @ SPU SPU Works Summer August 23rd, 2019 Is AI intelligent, really? Bruce D. Baker Seattle Pacific nU iversity Follow this and additional works at: https://digitalcommons.spu.edu/works Part of the Artificial Intelligence and Robotics Commons, Comparative Methodologies and Theories Commons, Epistemology Commons, Philosophy of Science Commons, and the Practical Theology Commons Recommended Citation Baker, Bruce D., "Is AI intelligent, really?" (2019). SPU Works. 140. https://digitalcommons.spu.edu/works/140 This Article is brought to you for free and open access by Digital Commons @ SPU. It has been accepted for inclusion in SPU Works by an authorized administrator of Digital Commons @ SPU. Bruce Baker August 23, 2019 Is AI intelligent, really? Good question. On the surface, it seems simple enough. Assign any standard you like as a demonstration of intelligence, and then ask whether you could (theoretically) set up an AI to perform it. Sure, it seems common sense that given sufficiently advanced technology you could set up a computer or a robot to do just about anything that you could define as being doable. But what does this prove? Have you proven the AI is really intelligent? Or have you merely shown that there exists a solution to your pre- determined puzzle? Hmmm. This is why AI futurist Max Tegmark emphasizes the difference between narrow (machine-like) and broad (human-like) intelligence.1 And so the question remains: Can the AI be intelligent, really, in the same broad way its creator is? Why is this question so intractable? Because intelligence is not a monolithic property. -
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. -
Hell on Earth
Welcome to Hell on Earth Artificial Intelligence, Babies, Bitcoin, Cartels, China, Democracy, Diversity, Dysgenics, Equality, Hackers, Human Rights, Islam, Liberalism, Prosperity, The Web Michael Starks The saddest day in US history. President Johnson, with two Kennedy’s and ex-President Hoover, gives America to Mexico - Oct 3rd 1965 Reality Press Las Vegas Copyright © 2020 by Michael Starks All rights reserved. No part of this publication may be reproduced, distributed, or transmitted without the express consent of the author. Printed and bound in the United States of America. ISBN 978-1-951440-81-7 “At what point is the approach of danger to be expected? I answer, if it ever reach us it must spring up amongst us; it cannot come from abroad. If destruction be our lot, we must ourselves be its author and finisher. As a nation of freemen we must live through all time or die by suicide.” Abraham Lincoln Heaven and Earth are inhumane--they view the myriad creatures as straw dogs TaoTe Ching This very body the Buddha, this very earth the lotus paradise Osho I can well imagine a religion in which there are no doctrines, so that nothing is spoken. Clearly, then, the essence of religion can have nothing to do with what is sayable. Wittgenstein What we are supplying are really remarks on the natural history of man, not curiosities; however, but rather observations on facts which no one has doubted and which have only gone unremarked because they are always before our eyes. Wittgenstein RFM I p142 Philosophers constantly see the method of science before their eyes and are irresistibly tempted to ask and answer questions in the way science does. -
How Empiricism Distorts Ai and Robotics
HOW EMPIRICISM DISTORTS AI AND ROBOTICS Dr Antoni Diller School of Computer Science University of Birmingham Birmingham, B15 2TT, UK email: [email protected] Abstract rently, Cog has no legs, but it does have robotic arms and a head with video cameras for eyes. It is one of the most im- The main goal of AI and Robotics is that of creating a hu- pressive robots around as it can recognise various physical manoid robot that can interact with human beings. Such an objects, distinguish living from non-living things and im- android would have to have the ability to acquire knowl- itate what it sees people doing. Cynthia Breazeal worked edge about the world it inhabits. Currently, the gaining of with Cog when she was one of Brooks’s graduate students. beliefs through testimony is hardly investigated in AI be- She said that after she became accustomed to Cog’s strange cause researchers have uncritically accepted empiricism. appearance interacting with it felt just like playing with a Great emphasis is placed on perception as a source of baby and it was easy to imagine that Cog was alive. knowledge. It is important to understand perception, but There are several major research projects in Japan. A even more important is an understanding of testimony. A common rationale given by scientists engaged in these is sketch of a theory of testimony is presented and an appeal the need to provide for Japan’s ageing population. They say for more research is made. their robots will eventually act as carers for those unable to look after themselves. -
Artificial Intelligence for Robust Engineering & Science January 22
Artificial Intelligence for Robust Engineering & Science January 22 – 24, 2020 Organizing Committee General Chair: Logistics Chair: David Womble Christy Hembree Program Director, Artificial Intelligence Project Support, Artificial Intelligence Oak Ridge National Laboratory Oak Ridge National Laboratory Committee Members: Jacob Hinkle Justin Newcomer Research Scientist, Computational Sciences Manager, Machine Intelligence and Engineering Sandia National Laboratories Oak Ridge National Laboratory Frank Liu Clayton Webster Distinguished R&D Staff, Computational Distinguished Professor, Department of Sciences and Mathematics Mathematics Oak Ridge National Laboratory University of Tennessee–Knoxville Robust engineering is the process of designing, building, and controlling systems to avoid or mitigate failures and everything fails eventually. This workshop will examine the use of artificial intelligence and machine learning to predict failures and to use this capability in the maintenance and operation of robust systems. The workshop comprises four sessions examining the technical foundations of artificial intelligence and machine learning to 1) Examine operational data for failure indicators 2) Understand the causes of the potential failure 3) Deploy these systems “at the edge” with real-time inference and continuous learning, and 4) Incorporate these capabilities into robust system design and operation. The workshop will also include an additional session open to attendees for “flash” presentations that address the conference theme. AGENDA Wednesday, January 22, 2020 7:30–8:00 a.m. │ Badging, Registration, and Breakfast 8:00–8:45 a.m. │ Welcome and Introduction Jeff Nichols, Oak Ridge National Laboratory David Womble, Oak Ridge National Laboratory 8:45–9:30 a.m. │ Keynote Presentation: Dave Brooks, General Motors Company AI for Automotive Engineering 9:30–10:00 a.m. -
Artificial Intelligence and National Security
Artificial Intelligence and National Security Artificial intelligence will have immense implications for national and international security, and AI’s potential applications for defense and intelligence have been identified by the federal government as a major priority. There are, however, significant bureaucratic and technical challenges to the adoption and scaling of AI across U.S. defense and intelligence organizations. Moreover, other nations—particularly China and Russia—are also investing in military AI applications. As the strategic competition intensifies, the pressure to deploy untested and poorly understood systems to gain competitive advantage could lead to accidents, failures, and unintended escalation. The Bipartisan Policy Center and Georgetown University’s Center for Security and Emerging Technology (CSET), in consultation with Reps. Robin Kelly (D-IL) and Will Hurd (R-TX), have worked with government officials, industry representatives, civil society advocates, and academics to better understand the major AI-related national and economic security issues the country faces. This paper hopes to shed more clarity on these challenges and provide actionable policy recommendations, to help guide a U.S. national strategy for AI. BPC’s effort is primarily designed to complement the work done by the Obama and Trump administrations, including President Barack Obama’s 2016 The National Artificial Intelligence Research and Development Strategic Plan,i President Donald Trump’s Executive Order 13859, announcing the American AI Initiative,ii and the Office of Management and Budget’s subsequentGuidance for Regulation of Artificial Intelligence Applications.iii The effort is also designed to further June 2020 advance work done by Kelly and Hurd in their 2018 Committee on Oversight 1 and Government Reform (Information Technology Subcommittee) white paper Rise of the Machines: Artificial Intelligence and its Growing Impact on U.S.