Intelligence, Artificial Intelligence and Wisdom in the Global Sustainable Information Society †
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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 ..................................................................................... -
World-Systems Evolution and Global Futures
World-Systems Evolution and Global Futures Series Editors Christopher Chase-Dunn, University of California, Riverside, CA, USA Barry K. Gills, Political and Economic Studies, University of Helsinki, Helsinki, Finland Leonid E. Grinin, National Research University Higher School of Economics, Moscow, Russia Andrey V. Korotayev, National Research University Higher School of Economics, Moscow, Russia This series seeks to promote understanding of large-scale and long-term processes of social change, in particular the many facets and implications of globalization. It critically explores the factors that affect the historical formation and current evolu- tion of social systems, on both the regional and global level. Processes and factors that are examined include economies, technologies, geopolitics, institutions, conflicts, demographic trends, climate change, global culture, social movements, global inequalities, etc. Building on world-systems analysis, the series addresses topics such as globali- zation from historical and comparative perspectives, trends in global inequalities, core-periphery relations and the rise and fall of hegemonic core states, transnational institutions, and the long-term energy transition. This ambitious interdisciplinary and international series presents cutting-edge research by social scientists who study whole human systems and is relevant for all readers interested in systems approaches to the emerging world society, especially historians, political scientists, economists, sociologists, geographers and anthropologists. More information about this series at http://www.springer.com/series/15714 Cadell Last Global Brain Singularity Universal History, Future Evolution and Humanity’s Dialectical Horizon Cadell Last Vrije Universiteit Brussel Brussels, Belgium ISSN 2522-0985 ISSN 2522-0993 (electronic) World-Systems Evolution and Global Futures ISBN 978-3-030-46965-8 ISBN 978-3-030-46966-5 (eBook) https://doi.org/10.1007/978-3-030-46966-5 # Springer Nature Switzerland AG 2020 This work is subject to copyright. -
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. -
Evolutionary Transitions: Foundation for an Evolutionary-Systemic Worldview
Research proposal submitted to the FWO by F. Heylighen, W. Callebaut and J. Bernheim: Evolutionary transitions: foundation for an evolutionary-systemic worldview BACKGROUND AND MOTIVATION Fast technological developments and the process of globalisation bring about an entangled dynamic of contradictory forces: integration and differentiation, competition and co- operation, individualism and solidarity. Our society seems to be in a very unstable transitional phase, so that it is difficult to conceive how the desired result—a sustainable, integrated world system respecting individual and collective diversity (Heylighen, 2003; Stewart, 2000)—will come about. The ever accelerating changes and growing complexity in society produce a lot of uncertainty, anxiety, and alienation (Geyer, 1992). Sociological research seems to indicate that the feelings of insecurity and distrust are strongest among the people who least profess belief in a religious or philosophical world view (Elchardus, 1998). Psychologists researching life satisfaction, on the other hand, have found that having such beliefs increases well-being, by providing a sense of life's meaning, feelings of hope and trust, a long-term perspective on life's woes, and a sense of belonging to a larger whole (Myers, 1993). World views offer a coherent set of answers to these universal issues (Aerts, Apostel et al., 1994; Heylighen, 2000b). However, the traditional systems of belief (religions, such as Catholicism, or ideologies, such as communism) seem to have been eroded by deep-going social changes. Although scientific knowledge has perhaps played the most important part in this loss of authority, science itself offers a meagre alternative, as it does not provide unequivocal answers to the ultimate questions, and is fragmented into ever more specialist subdisciplines, thus losing view of the whole. -
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. -
A Framework for Designing Compassionate and Ethical Artificial Intelligence and Artificial Consciousness
Interdisciplinary Description of Complex Systems 18(2-A), 85-95, 2020 A FRAMEWORK FOR DESIGNING COMPASSIONATE AND ETHICAL ARTIFICIAL INTELLIGENCE AND ARTIFICIAL CONSCIOUSNESS Soumya Banerjee* University of Oxford Oxford, United Kingdom DOI: 10.7906/indecs.18.2.2 Received: 23 March 2020. Regular article Accepted: 7 May 2020. ABSTRACT Intelligence and consciousness have fascinated humanity for a long time and we have long sought to replicate this in machines. In this work, we show some design principles for a compassionate and conscious artificial intelligence. We present a computational framework for engineering intelligence, empathy, and consciousness in machines. We hope that this framework will allow us to better understand consciousness and design machines that are conscious and empathetic. Our hope is that this will also shift the discussion from fear of artificial intelligence towards designing machines that embed our cherished values. Consciousness, intelligence, and empathy would be worthy design goals that can be engineered in machines. KEY WORDS artificial intelligence, artificial consciousness, engineering intelligence, empathy CLASSIFICATION JEL: C88, L86 *Corresponding author, : [email protected]; -; - S. Banerjee INTRODUCTION Consciousness has intrigued humanity for centuries. It is only now with the emergence of complex systems science and systems biology that we are beginning to get a deeper understanding of consciousness. Here we introduce a computational framework for designing artificial consciousness and artificial intelligence with empathy. We hope this framework will allow us to better understand consciousness and design machines that are conscious and empathetic. We also hope our work will help shift the discussion from a fear of artificial intelligence towards designing machines that embed our values in them. -
Cosmic Evolutionary Philosophy and a Dialectical Approach to Technological Singularity
Cosmic Evolutionary Philosophy and a Dialectical Approach to Technological Singularity Cadell Last Evolution, Cognition, and Complexity (ECCO) group cadelllast.com The philosophy of cosmic evolution provides a worldview for thinking about the human position and relationship to universal processual dynamics. This evolutionary process is conceptualized as a network of relations with a specific astrophysical singularity origin as the first cause in a progressive complexification of organization from sub-atomic particles to human civilization. Throughout this complexification of organization key events are identified as representing qualitative phase transitions where new relations form emergent integrated ordered wholes. The hypothesized/anticipated next stage of cosmic evolutionary imminence is often described by futurists as a technological singularity where complex organization generated by scientific knowledge in the form of artificial general intelligence and distributed digital networks create an integrated ordered whole beyond any previous known level of complexity. This paper does not challenge this hypothesis/anticipation but rather seeks to reframe the human position and relationship to such processes by approaching technological singularity from the internal subjective view of the conscious ideational landscape of human mind in-itself via the triadic logic of negative dialectics ((1) abstract, (2) transform, (3) concrete). Consequently, such a view focuses on the motion of embodied cognitive processes teleodynamically oriented towards ideal real attractor states. From this works the fundamental chaos, uncertainty, and unpredictability of the future is situated next to a structural analysis of the ordered intention, goal-directedness, and value-laden cognitive motion that we can observe on the abstract horizon of human becoming. !1 Sections: 1. Philosophy of Cosmic Evolution……………………………………………………………..3 2. -
Artificial Intelligence Measurement and Evaluation at The
Artificial Intelligence Measurement and Evaluation at the National Institute of Standards and Technology AIME Planning Team June 14, 2021 Preamble: This document is being distributed as read-ahead material for the 2021 NIST AI Measurement and Evaluation Workshop, to be held on June 15–17, 2021. It is in an abbreviated and draft form and will be expanded and updated based on feedback received during and after the workshop. Executive Summary As part of its mission, NIST seeks to support the advancement & deployment of measured Artificial Intelligence (AI) technologies. Toward that end, NIST’s AI Measurement and Evaluation (AIME) focus area conducts research and development of metrics and measurement methods in emerging and existing areas of AI, contributes to the development of standards, and promotes the adoption of standards, guides, and best practices for the measurement and evaluation of AI technologies as they mature and find new applications. This document provides a brief, high-level summary of NIST’s AIME research and standards activities. Note, this document is living—periodic updates will be published as progress is made and plans evolve. 1 Introduction AI technologies have been and currently are being deployed in applications of consequence to individuals and people groups, often without effectively measuring crucial system properties. In several cases, AI systems have been deployed at some effort/expense, only to be abandoned (or “put on hold” for an extended period) when indicators suggest that the system is problematic with respect to one or more of these properties. It is clear that evaluation of and standards for the measurement and evaluation of such properties are critically needed as AI technologies move from the lab into society.