DOCSLIB.ORG

Deterministic Artificial Intelligence Intelligence, Espoused in the Book with Applications to Both Electromechanics (E.G

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Deterministic Artificial Intelligence Intelligence, Espoused in the Book with Applications to Both Electromechanics (E.G Edited by Timothy Sands Kirchhoff’s laws give a mathematical description of electromechanics. Similarly, translational motion mechanics obey Newton’s laws, while rotational motion mechanics comply with Euler’s moment equations, a set of three nonlinear, coupled differential equations. Nonlinearities complicate the mathematical treatment of Deterministic Artificial Intelligence the seemingly simple action of rotating, and these complications lead to a robust lineage of research culminating here with a text on the ability to make rigid bodies in Deterministic Artificial rotation become self-aware, and even learn. This book is meant for basic scientifically inclined readers commencing with a first chapter on the basics of stochastic artificial intelligence to bridge readers to very advanced topics of deterministic artificial Intelligence intelligence, espoused in the book with applications to both electromechanics (e.g. the forced van der Pol equation) and also motion mechanics (i.e. Euler’s moment equations). The reader will learn how to bestow self-awareness and express optimal Edited by Timothy Sands learning methods for the self-aware object (e.g. robot) that require no tuning and no interaction with humans for autonomous operation. The topics learned from reading this text will prepare students and faculty to investigate interesting problems of mechanics. It is the fondest hope of the editor and authors that readers enjoy the book. ISBN 978-1-78984-111-4 Published in London, UK © 2020 IntechOpen © Quardia / iStock Deterministic Artificial Intelligence Edited by Timothy Sands Published in London, United Kingdom Supporting open minds since 2005 Deterministic Artificial Intelligence http://dx.doi.org/10.5772/intechopen.81309 Edited by Timothy Sands Contributors Matthew Cooper, Kyle Baker, Emmanuel Oyekanlu, Azura Che Soh, Behzad Vaferi, Raheni T D, P Thirumoorthi, Tatiana Mikhaylovna Zubkova, Brendon Smeresky, Alexa Rizzo © The Editor(s) and the Author(s) 2020 The rights of the editor(s) and the author(s) have been asserted in accordance with the Copyright, Designs and Patents Act 1988. All rights to the book as a whole are reserved by INTECHOPEN LIMITED. The book as a whole (compilation) cannot be reproduced, distributed or used for commercial or non-commercial purposes without INTECHOPEN LIMITED’s written permission. Enquiries concerning the use of the book should be directed to INTECHOPEN LIMITED rights and permissions department ([email protected]). Violations are liable to prosecution under the governing Copyright Law. Individual chapters of this publication are distributed under the terms of the Creative Commons Attribution - NonCommercial 4.0 International which permits use, distribution and reproduction of the individual chapters for non-commercial purposes, provided the original author(s) and source publication are appropriately acknowledged. More details and guidelines concerning content reuse and adaptation can be found at http://www.intechopen.com/copyright-policy.html. Notice Statements and opinions expressed in the chapters are these of the individual contributors and not necessarily those of the editors or publisher. No responsibility is accepted for the accuracy of information contained in the published chapters. The publisher assumes no responsibility for any damage or injury to persons or property arising out of the use of any materials, instructions, methods or ideas contained in the book. First published in London, United Kingdom, 2020 by IntechOpen IntechOpen is the global imprint of INTECHOPEN LIMITED, registered in England and Wales, registration number: 11086078, 7th floor, 10 Lower Thames Street, London, EC3R 6AF, United Kingdom Printed in Croatia British Library Cataloguing-in-Publication Data A catalogue record for this book is available from the British Library Additional hard and PDF copies can be obtained from [email protected] Deterministic Artificial Intelligence Edited by Timothy Sands p. cm. Print ISBN 978-1-78984-111-4 Online ISBN 978-1-78984-112-1 eBook (PDF) ISBN 978-1-83880-728-3 An electronic version of this book is freely available, thanks to the support of libraries working with Knowledge Unlatched. KU is a collaborative initiative designed to make high quality books Open Access for the public good. More information about the initiative and links to the Open Access version can be found at www.knowledgeunlatched.org We are IntechOpen, the world’s leading publisher of Open Access books Built by scientists, for scientists 4,800+ 123,000+ 135M+ Open access books available International authors and editors Downloads Our authors are among the 151 Top 1% 12.2% Countries delivered to most cited scientists Contributors from top 500 universities TE A NA A LY IV T R I A C L S C BOOK CITATION INDEX I N D E X E D Selection of our books indexed in the Book Citation Index in Web of Science™ Core Collection (BKCI) Interested in publishing with us? Contact [email protected] Numbers displayed above are based on latest data collected. For more information visit www.intechopen.com Meet the editor Dr. Timothy Sands graduated from Columbia University, Stan- ford University, and the Naval Postgraduate School. He is an International Scholar Laureate of the Golden Key International Honor Society, a Fellow of the Defense Advanced Research Projects Agency, a panelist of the National Science Foundation Graduate Research Fellowship program, and an interviewer for undergraduate admissions at Stanford University. He has published prolifically in archival journals, conference proceedings, books, and book chapters, in addition to giving plenary, keynote, and invitational presentations; he holds one patent in spacecraft attitude control. He is currently the Associate Dean of the Naval Postgraduate School’s Graduate School of Engineering and Applied Science having previously served as a university chief academic officer, dean, and research center director. Contents Preface III Section 1 Stochastic Approaches 1 Chapter 1 3 Stochastic Artificial Intelligence: Review Article by T.D. Raheni and P. Thirumoorthi Chapter 2 23 Simulated Real-Time Controller for Tuning Algorithm Using Modified Hill Climbing Approach Based on Model Reference Adaptive Control System by Ahmed Abdulelah Ahmed, Azura Che Soh, Mohd Khair Hassan, Samsul Bahari Mohd Noor and Hafiz Rashidi Harun Chapter 3 49 Random Forest-Based Ensemble Machine Learning Data-Optimization Approach for Smart Grid Impedance Prediction in the Powerline Narrowband Frequency Band by Emmanuel Oyekanlu and Jia Uddin Chapter 4 69 Application of Artificial Neural Networks for Accurate Prediction of Thermal and Rheological Properties of Nanofluids by Behzad Vaferi Chapter 5 99 The Technique of Automated Design of Technological Objects with the Application of Artificial Intelligence Elements by Tatyana Zubkova and Marina Tokareva Section 2 Deterministic Approaches 117 Chapter 6 119 Deterministic Approaches to Transient Trajectory Generation by Matthew A. Cooper Contents Preface XIII Section 1 Stochastic Approaches 1 Chapter 1 3 Stochastic Artificial Intelligence: Review Article by T.D. Raheni and P. Thirumoorthi Chapter 2 23 Simulated Real-Time Controller for Tuning Algorithm Using Modified Hill Climbing Approach Based on Model Reference Adaptive Control System by Ahmed Abdulelah Ahmed, Azura Che Soh, Mohd Khair Hassan, Samsul Bahari Mohd Noor and Hafiz Rashidi Harun Chapter 3 49 Random Forest-Based Ensemble Machine Learning Data-Optimization Approach for Smart Grid Impedance Prediction in the Powerline Narrowband Frequency Band by Emmanuel Oyekanlu and Jia Uddin Chapter 4 69 Application of Artificial Neural Networks for Accurate Prediction of Thermal and Rheological Properties of Nanofluids by Behzad Vaferi Chapter 5 99 The Technique of Automated Design of Technological Objects with the Application of Artificial Intelligence Elements by Tatyana Zubkova and Marina Tokareva Section 2 Deterministic Approaches 117 Chapter 6 119 Deterministic Approaches to Transient Trajectory Generation by Matthew A. Cooper Chapter 7 141 Sinusoidal Trajectory Generation Methods for Spacecraft Preface Feedforward Control by Kyle A. Baker Chapter 8 153 Modern Control System Learning by Brendon Smeresky and Alex Rizzo This book is immersed in the ubiquitous understanding of artificial intelligence with an overview of stochastic artificial intelligence followed by four chapters on such algorithms. A particularly important contribution prepares readers for the deter- ministic (non-stochastic) treatment of the topic: namely, deterministic algorithms can be used in stochastic artificial intelligence, but the approach remains stochastic. Deterministic artificial intelligence is examined next in three chapters that apply the approach to disparate facets of mechanical motion control. Deterministic artificial intelligence applied to motion control typically necessitates analytic expressions of desired motion displacement, velocity, and acceleration. The first two chapters of the section of this text examine methods of autonomous generation of such trajectories. The final chapter utilizes the prerequisite material to enumerate and critically evaluate the deterministic approach compared to nominal methods, including optimal and classical feedback methods. The text is meant for basic scientifically inclined readers who possess the basics of mathematics (while calculus certainly aids the reader to get more out of the chapters). The topics learned from reading this text will prepare students
Load more

Recommended publications
  • 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.).
    [Show full text]
  • 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 .....................................................................................
    [Show full text]
  • 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.
    [Show full text]
  • 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.
    [Show full text]
  • 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.
    [Show full text]
  • 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.
    [Show full text]
  • 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.
    [Show full text]
  • 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.
    [Show full text]
  • 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.
    [Show full text]
  • The New Empiricism and the Semantic Web: Threat Or Opportunity?
    The New Empiricism and the Semantic Web: Threat or Opportunity? Henry S. Thompson ICCS, School of Informatics University of Edinburgh 1. Some (provocatively over-simplified) history Research effort, with its emphasis on evaluation and mea- The early AI success stories of the 1970s were based in small surable progress, things began to change. (D)ARPA fund- ’worlds’ with carefully bounded semantic domains: Wino- ing significantly expanded the amount of digitised and tran- grad’s SHRDLU (WIN72) is perhaps the canonical example. scribed speech data available to the research community. In- The rapid growth of efforts to found the next generation of stead of systems whose architecture and vocabulary were systems on general-purpose knowledge representation lan- based on linguistic theory (in this case acoustic phonetics), guages (I’m thinking of several varieties of semantic nets, new approaches based on statistical modelling and Bayesian from plain to partitioned, as well as KRL, KL-ONE and probability emerged and quickly spread. ”Every time I fire a their successors, ending (not yet, of course) with CYC (See linguist my system’s performance improves” (Fred Jellinek, (BRA08) for all these) stumbled to a halt once their failure head of speech recognition at IBM, c. 1980, latterly repudi- to provide any viable inference mechanism was recognised ated by Fred but widely attested). as incorrigible. Today progress in speech recognition and many other Progress continued on two fronts, however: branches of perceptual-interface aspects of AI depend to a considerable extent on only two things: larger datasets • Inference engines for less elaborate KR languages have for training, and better machine learning techniques.
    [Show full text]
  • Artificial Pain May Induce Empathy, Morality, and Ethics in The
    philosophies Article Artificial Pain May Induce Empathy, Morality, and Ethics in the Conscious Mind of Robots Minoru Asada Institute for Open and Transdisciplinary Research Initiatives, Osaka University, Suita 565-0871, Japan; [email protected] Received: 6 June 2019; Accepted: 8 July 2019; Published: 13 July 2019 Abstract: In this paper, a working hypothesis is proposed that a nervous system for pain sensation is a key component for shaping the conscious minds of robots (artificial systems). In this article, this hypothesis is argued from several viewpoints towards its verification. A developmental process of empathy, morality, and ethics based on the mirror neuron system (MNS) that promotes the emergence of the concept of self (and others) scaffolds the emergence of artificial minds. Firstly, an outline of the ideological background on issues of the mind in a broad sense is shown, followed by the limitation of the current progress of artificial intelligence (AI), focusing on deep learning. Next, artificial pain is introduced, along with its architectures in the early stage of self-inflicted experiences of pain, and later, in the sharing stage of the pain between self and others. Then, cognitive developmental robotics (CDR) is revisited for two important concepts—physical embodiment and social interaction, both of which help to shape conscious minds. Following the working hypothesis, existing studies of CDR are briefly introduced and missing issues are indicated. Finally, the issue of how robots (artificial systems) could be moral agents is addressed. Keywords: pain; empathy; morality; mirror neuron system (MNS) 1. Introduction The rapid progress of observation and measurement technologies in neuroscience and physiology have revealed various types of brain activities, and the recent progress of artificial intelligence (AI) technologies represented by deep learning (DL) methods [1] has been remarkable.
    [Show full text]
  • Juniper and Cloud Ingenuity Transform Wi-Fi to Enable Wayfinding and Asset Tracking for Enhanced User Experience
    SOLUTION BRIEF | JUNIPER AND CLOUD INGENUITY Juniper and Cloud Ingenuity transform Wi-Fi to Enable Wayfinding and Asset Tracking for Enhanced User Experience Transform your Wi-Fi installation into a strategic business asset. Enhance your user’s experience with wayfinding capabilities from their mobile device and provide IT with the ability for enterprise-wide asset search. THE CHALLENGE BENEFITS Traditional Wi-Fi is blind to the user’s experience and their Now a student on a campus can quickly find their way to class needs. This poor experience reflects negatively on your brand or the library. A doctor can quickly be located within a hospital when customers engage with your company and impacts the for an emergency surgery, or to save a life. The possibilities are productivity of your business end user’s ability to do their job. This limitless in terms of the value of global asset search and location. also impacts the productivity of your IT support team who cannot Juniper brings Mist AI to the table for rapid problem awareness, solve problems with devices that they cannot see. and Cloud Ingenuity’s application provides the insight needed to react leading to faster resolution and a proactive approach to end THE JUNIPER-CLOUD INGENUITY SOLUTION user experience. Juniper and Cloud Ingenuity have partnered to deliver an integrated solution that enables your end users with wayfinding capabilities and provides IT with global asset search capability. Juniper modernizes the wireless infrastructure; Cloud Ingenuity enables IT to positively impact user experience which transforms Wi-Fi into a strategic business tool. The Challenge Many Wi-Fi infrastructures leverage legacy hardware and provide a poor user experience.
    [Show full text]