Examining the modern origins of artificial intelligence, this book explores issues of what it means to be man or machine and looks at advances in robotics which have blurred the boundaries.
This introduction to computational geometry focuses on algorithms. Motivation is provided from the application areas as all techniques are related to particular applications in robotics, graphics, CAD/CAM, and geographic information systems. Modern insights in computational geometry are used to provide solutions that are both efficient and easy to understand and implement.
Geometric Algebra for Computer Science (Revised Edition) presents a compelling alternative to the limitations of linear algebra. Geometric algebra (GA) is a compact, time-effective, and performance-enhancing way to represent the geometry of 3D objects in computer programs. This book explains GA as a natural extension of linear algebra and conveys its significance for 3D programming of geometry in graphics, vision, and robotics. It systematically explores the concepts and techniques that are key to representing elementary objects and geometric operators using GA. It covers in detail the conformal model, a convenient way to implement 3D geometry using a 5D representation space. Numerous drills and programming exercises are helpful for both students and practitioners. A companion web site includes links to GAViewer, a program that will allow you to interact with many of the 3D figures in the book; and Gaigen 2, the platform for the instructive programming exercises that conclude each chapter. The book will be of interest to professionals working in fields requiring complex geometric computation such as robotics, computer graphics, and computer games. It is also be ideal for students in graduate or advanced undergraduate programs in computer science. Explains GA as a natural extension of linear algebra and conveys its significance for 3D programming of geometry in graphics, vision, and robotics. Systematically explores the concepts and techniques that are key to representing elementary objects and geometric operators using GA. Covers in detail the conformal model, a convenient way to implement 3D geometry using a 5D representation space. Presents effective approaches to making GA an integral part of your programming. Includes numerous drills and programming exercises helpful for both students and practitioners. Companion web site includes links to GAViewer, a program that will allow you to interact with many of the 3D figures in the book, and Gaigen 2, the platform for the instructive programming exercises that conclude each chapter.
Create your own innovative applications in computer vision, game design, music, robotics, and other areas by taking full advantage of Kinect?s extensive interactive, multi-media platform. With this book, you get a step-by-step walkthrough of the best techniques and tools to come out of the OpenKinect project, the largest and most active Kinect hacking community. Learn dozens of hacks for building interfaces that respond to body movements, gestures, and voice, using open source toolkits such as openFrameworks, the Processing IDE, and OpenKinect driver library. Whether you?re an artist, designer, researcher, or hobbyist, this book will give you a running start with Kinect. * Set up a development environment in Windows 7, Mac OSX, or Ubuntu * Build special effects apps with tools such as Synapse and Cinder * Create gestural interfaces to integrate and control digital music components * Capture the realistic motions of a 3D model with NI mate, Blender, and Animata * Design gesture-based games with the ZigFu SDK * Recreate the dimensions of any room in realtime, using RGBDemo * Use gestures to navigate robots and control PC interfaces
Science world luminary John Brockman assembles twenty-five of the most important scientific minds, people who have been thinking about the field artificial intelligence for most of their careers, for an unparalleled round-table examination about mind, thinking, intelligence and what it means to be human. ´´Artificial intelligence is today´s story--the story behind all other stories. It is the Second Coming and the Apocalypse at the same time: Good AI versus evil AI.´´ --John Brockman More than sixty years ago, mathematician-philosopher Norbert Wiener published a book on the place of machines in society that ended with a warning: ´´we shall never receive the right answers to our questions unless we ask the right questions.... The hour is very late, and the choice of good and evil knocks at our door.´´ In the wake of advances in unsupervised, self-improving machine learning, a small but influential community of thinkers is considering Wiener´s words again. In Possible Minds, John Brockman gathers their disparate visions of where AI might be taking us. The fruit of the long history of Brockman´s profound engagement with the most important scientific minds who have been thinking about AI--from Alison Gopnik and David Deutsch to Frank Wilczek and Stephen Wolfram--Possible Minds is an ideal introduction to the landscape of crucial issues AI presents. The collision between opposing perspectives is salutary and exhilarating; some of these figures, such as computer scientist Stuart Russell, Skype co-founder Jaan Tallinn, and physicist Max Tegmark, are deeply concerned with the threat of AI, including the existential one, while others, notably robotics entrepreneur Rodney Brooks, philosopher Daniel Dennett, and bestselling author Steven Pinker, have a very different view. Serious, searching and authoritative, Possible Minds lays out the intellectual landscape of one of the most important topics of our time.
Solve your AI and machine learning problems using complete and real-world code examples. Using a problem-solution approach, this book makes deep learning and machine learning accessible to everyday developers, by providing a combination of tools such as cognitive services APIs, machine learning platforms, and libraries. Along with an overview of the contemporary technology landscape, Machine Learning and Deep Learning with Cognitive Computing Recipes covers the business case for machine learning and deep learning. Covering topics such as digital assistants, computer vision, text analytics, speech, and robotics process automation this book offers a comprehensive toolkit that you can apply quickly and easily in your own projects. With its focus on Microsoft Cognitive Services offerings, you´ll see recipes using multiple different environments including TensowFlow and CNTK to give you a broader perspective of the deep learning ecosystem. What You Will Learn Build production-ready solutions using Microsoft Cognitive Services APIs Apply deep learning using TensorFlow and Microsoft Cognitive Toolkit (CNTK) Solve enterprise problems in natural language processing and computer vision Discover the machine learning development life cycle - from formal problem definition to deployment at scale Who This Book Is For Software engineers and enterprise architects who wish to understand machine learning and deep learning by building applications and solving real-world business problems.
This book discusses the problems and challenges in the interdisciplinary research field of self-adaptive software systems. Modern society is increasingly filled with software-intensive systems, which are required to operate in more and more dynamic and uncertain environments. These systems must monitor and control their environment while adapting to meet the requirements at runtime. This book provides promising approaches and research methods in software engineering, system engineering, and related fields to address the challenges in engineering the next-generation adaptive software systems. The contents of the book range from design and engineering principles (Chap. 1) to control-theoretic solutions (Chap. 2) and bidirectional transformations (Chap. 3), which can be seen as promising ways to implement the functional requirements of self-adaptive systems. Important quality requirements are also dealt with by these approaches: parallel adaptation for performance (Chap. 4), self-adaptive authorization infrastructure for security (Chap. 5), and self-adaptive risk assessment for self-protection (Chap. 6). Finally, Chap. 7 provides a concrete self-adaptive robotics operating system as a testbed for self-adaptive systems. The book grew out of a series of the Shonan Meetings on this ambitious topic held in 2012, 2013, and 2015. The authors were active participants in the meetings and have brought in interesting points of view. After several years of reflection, they now have been able to crystalize the ideas contained herein and collaboratively pave the way for solving some aspects of the research problems. As a result, the book stands as a milestone to initiate further progress in this promising interdisciplinary research field.
This volume comprises eight well-versed contributed chapters devoted to report the latest findings on the intelligent approaches to multimedia data analysis. Multimedia data is a combination of different discrete and continuous content forms like text, audio, images, videos, animations and interactional data. At least a single continuous media in the transmitted information generates multimedia information. Due to these different types of varieties, multimedia data present varied degrees of uncertainties and imprecision, which cannot be easy to deal by the conventional computing paradigm. Soft computing technologies are quite efficient to handle the imprecision and uncertainty of the multimedia data and they are flexible enough to process the real-world information. Proper analysis of multimedia data finds wide applications in medical diagnosis, video surveillance, text annotation etc. This volume is intended to be used as a reference by undergraduate and post graduate students of the disciplines of computer science, electronics and telecommunication, information science and electrical engineering. THE SERIES: FRONTIERS IN COMPUTATIONAL INTELLIGENCE The series Frontiers In Computational Intelligence is envisioned to provide comprehensive coverage and understanding of cutting edge research in computational intelligence. It intends to augment the scholarly discourse on all topics relating to the advances in artifi cial life and machine learning in the form of metaheuristics, approximate reasoning, and robotics. Latest research fi ndings are coupled with applications to varied domains of engineering and computer sciences. This field is steadily growing especially with the advent of novel machine learning algorithms being applied to different domains of engineering and technology. The series brings together leading researchers that intend to continue to advance the fi eld and create a broad knowledge about the most recent state of the art.
This book constitutes the refereed post-conference proceedings of two conferences: The 7 th EAI International Conference on ArtsIT, Interactivity and Game Creation (ArtsIT 2018), and the 3 rd EAI International Conference on Design, Learning, and Innovation (DLI 2018). Both conferences were hosed in Braga, Portugal, and took place October 24-26, 2018. The 51 revised full papers presented were carefully selected from 106 submissions. ArtsIT , Interactivity and Game Creation is meant to be a place where people in arts, with a keen interest in modern IT technologies, meet with people in IT, having strong ties to art in their works. The event also reflects the advances seen in the open related topics Interactivity (Interaction Design, Virtual Reality, Augmented Reality, Robotics) and Game Creation (Gamification, Leisure Gaming, GamePlay). ArtsIT has been successfully co-located with DLI as the design, learning and innovation frame the world of IT, opening doors into an increasingly playful worlds. So the DLI conference is driven by the belief that tools, techniques and environments can spark and nature a passion for learning, transformation domains such as education, rehabilitation/therapy, work places and cultural institutions.
Polynomial systems are fundamental tools in the solution of hard problems in science and engineering such as robotics, automated reasoning, artificial intelligence and signal processing. Similarly, from the early days of the digital era, Boolean variables have been the foundations of the computer operations. Hence, the application of common algebraic techniques to Boolean algebra is used now as a method to solve complex Boolean equation systems that before were only intended to solve using Boolean logic techniques. The aim of this project is to demonstrate that Zhegalkin polynomials (also known as Algebraic Normal Form - ANF) are an alternative way to represent Boolean functions. In order to test the hypothesis, a Zhegalkin SAT Solver (ZPSAT) was developed. The results conducted after the testing concluded that ZPSAT can solve a conjunction of XOR equations efficiently in terms of reliability and computing time. The heuristic used to build ZPSAT was based mainly on the concepts used by the Horn Formulae and a Fast-Multiplication method of two ANF polynomials known as Mobius transform.