This book constitutes the refereed proceedings of the 30th Australasian Joint Conference on Artificial Intelligence, AI 2017, held in Melbourne, VIC, Australia, in August 2017. The 29 full papers were carefully reviewed and selected from 58 submissions. This volume covers a wide spectrum of research streams in artificial intelligence ranging from machine learning, optimization to big data science and their practical applications.
XVIth International Conference of the Italian Association for Artificial Intelligence, Bari, Italy, November 14-17, 2017, Proceedings
Author: Floriana Esposito
This book constitutes the refereed proceedings of the 16th International Conference of the Italian Association for Artificial Intelligence, AI*IA 2017, held in Bari, Italy, in November 2017. The 37 full papers presented were carefully reviewed and selected from 91 submissions. The papers are organized in topical sections on applications of AI; natural language processing; knowledge representation and reasoning; knowledge engineering, ontologies and the semantic web; machinelearning; philosophical foundations, metacognitive modeling and ethics; and planning and scheduling.
40th Annual German Conference on AI, Dortmund, Germany, September 25–29, 2017, Proceedings
Author: Gabriele Kern-Isberner
This book constitutes the refereed proceedings of the 40th Annual German Conference on Artificial Intelligence, KI 2017 held in Dortmund, Germany in September 2017. The 20 revised full technical papers presented together with 16 short technical communications were carefully reviewed and selected from 73 submissions. The conference cover a range of topics from, e. g., agents, robotics, cognitive sciences, machine learning, planning, knowledge representation, reasoning, and ontologies, with numerous applications in areas like social media, psychology, transportation systems and reflecting the richness and diversity of their field.
30th Canadian Conference on Artificial Intelligence, Canadian AI 2017, Edmonton, AB, Canada, May 16-19, 2017, Proceedings
Author: Malek Mouhoub
This book constitutes the refereed proceedings of the 30th Canadian Conference on Artificial Intelligence, Canadian AI 2017, held in Edmonton, AB, Canada, in May 2017. The 19 regular papers and 24 short papers presented together with 6 Graduate Student Symposium papers were carefully reviewed and selected from 62 submissions. The focus of the conference was on the following subjects: Data Mining and Machine Learning; Planning and Combinatorial Optimization; AI Applications; Natural Language Processing; Uncertainty and Preference Reasoning; and Agent Systems.
"The Mother of All AI Projects" Practical Advances in Artificial Intelligence (AI)
Author: Lance Eliot
An exclusive insider look at the making of self-driving cars and how advances in Artificial Intelligence (AI) are helping to achieve this moonshot goal. Apple CEO Tim Cook famously proclaimed that the development of a self-driving car is "the Mother of all AI projects." Read about the good, bad, and the ugly of self-driving cars. Author Dr. Lance B. Eliot is a popular AI expert, entrepreneur and seasoned AI developer known for his expertise and leadership in the field of self-driving cars.
This book presents an overview of the latest artificial intelligence systems and methods, which have a broad spectrum of effective and sometimes unexpected applications in medical, educational and other fields of sciences and technology. In digital artificial intelligence systems, scientists endeavor to reproduce the innate intellectual abilities of human and other organisms, and the in-depth study of genetic systems and inherited biological processes can provide new approaches to create more and more effective artificial intelligence methods. The book focuses on the intensive development of bio-mathematical studies on living organism patents, which ensure the noise immunity of genetic information, its quasi-holographic features, and its connection with the Boolean algebra of logic used in technical artificial intelligence systems. In other words, the study of genetic systems and creation of methods of artificial intelligence go hand in hand, mutually enriching enrich each other. These proceedings comprise refereed papers presented at the 1st International Conference of Artificial Intelligence, Medical Engineering, and Education (AIMEE2017), held at the Mechanical Engineering Institute of the Russian Academy of Sciences, Moscow, Russia on 21–23 August 2017. The topics discussed include advances in thematic mathematics and bio-mathematics; advances in thematica medical approaches; and advances in thematic technological and educational approaches. The book is a compilation of state-of-the-art papers in the field, covering a comprehensive range of subjects that are relevant to business managers and engineering professionals alike. The breadth and depth of these proceedings make them an excellent resource for asset management practitioners, researchers and academics, as well as undergraduate and postgraduate students interested in artificial intelligence and bioinformatics systems as well as their growing applications
Proceedings of the 20th International Conference of the Catalan Association for Artificial Intelligence, Deltebre, Terres de L'Ebre, Spain, October 25-27, 2017
Author: I. Aguiló
Publisher: IOS Press
Artificial intelligence in all its forms is increasingly interwoven into all our lives, and remains one of the most lively areas of discussion and interest in technology today. This book presents the proceedings of the 20th International Conference of the Catalan Association for Artificial Intelligence (CCIA’2017): ‘Recent Advances in Artificial Intelligence Research and Development’, held in Deltebre, Terres de l'Ebre, Spain, in October 2017. Despite its title, this annual conference is not only for researchers from the Catalan Countries, but is an international event which attracts participants from countries all around the world. In total, 41 original contributions were submitted to CCIA’2017. Of these, 21 were accepted as long papers for oral presentation and 13 were accepted as short papers to be presented as posters. These 34 submissions appear in this book organized around a number of different topics including: agents and multi-agent systems; artificial vision and image processing; machine learning; artificial neural networks; cognitive modeling; fuzzy logic and reasoning; robotics; and AI applications. The book also includes abstracts of the 3 presentations by invited speakers. The book offers a representative sample of the current state of the art in the artificial intelligence community, and will be of interest to all those working with AI worldwide.
Artificial intelligence and related technologies are changing both the law and the legal profession. In particular, technological advances in fields ranging from machine learning to more advanced robots, including sensors, virtual realities, algorithms, bots, drones, self-driving cars, and more sophisticated “human-like” robots are creating new and previously unimagined challenges for regulators. These advances also give rise to new opportunities for legal professionals to make efficiency gains in the delivery of legal services. With the exponential growth of such technologies, radical disruption seems likely to accelerate in the near future. This collection brings together a series of contributions by leading scholars in the newly emerging field of artificial intelligence, robotics, and the law. The aim of the book is to enrich legal debates on the social meaning and impact of this type of technology. The distinctive feature of the contributions presented in this edition is that they address the impact of these technological developments in a number of different fields of law and from the perspective of diverse jurisdictions. Moreover, the authors utilize insights from multiple related disciplines, in particular social theory and philosophy, in order to better understand and address the legal challenges created by AI. Therefore, the book will contribute to interdisciplinary debates on disruptive new AI technologies and the law.
After decades of basic research and more promises than impressive applications, artificial intelligence (AI) is starting to deliver benefits. A convergence of advances is motivating this new surge of AI development and applications. Computer capability as it has evolved from high throughput and high performance computing systems is increasing. AI models and operations research adaptations are becoming more mature, and the world is breeding big data not only from the web and social media but also from the Internet of Things. Organizations around the world have been realizing that there are substantial performance gains and increases in productivity for the use of AI and predictive analytics techniques. Their use is bringing a new era of breakthrough innovation and opportunities. This book, compiles research insights and applications in diverse areas such as manufacturing, supply chain management, pricing, autonomous vehicles, healthcare, ecommerce, and aeronautics. Using classical and advanced tools in AI such as deep learning, particle swarm optimization, support vector machines and genetic programming among others. This is a very distinctive book which discusses important applications using a variety of paradigms from AI and outlines some of the research to be performed. The work supersedes similar books that do not cover as diversified a set of sophisticated applications. The authors present a comprehensive and articulated view of recent developments, identifies the applications gap by quoting from the experience of experts, and details suggested research areas. Artificial Intelligence: Advances in Research and Applications guides the reader through an intuitive understanding of the methodologies and tools for building and modeling intelligent systems. The book's coverage is broad, starting with clustering techniques with unsupervised ensemble learning, where the optimal combination strategy of individual partitions is robust in comparison to the selection of an algorithmic clustering pool. This is followed by a case in a parallel-distributed simulator using deep learning for its configuration. Chapter Three presents a case for autonomous vehicles. Chapter Four discusses the novel use of genetic algorithms with support vector machines. Chapters Five through Thirteen focus on the applications. The book discusses how the use of AI can allow for productivity development and other benefits not just for businesses, but also for economies. Finally, you can find an interesting investigation of the transhuman dimension of AI.
Selected Papers from the XIX International Conference on Neuroinformatics, October 2-6, 2017, Moscow, Russia
Author: Boris Kryzhanovsky
This book describes new theories and applications of artificial neural networks, with a special focus on neural computation, cognitive science and machine learning. It discusses cutting-edge research at the intersection between different fields, from topics such as cognition and behavior, motivation and emotions, to neurocomputing, deep learning, classification and clustering. Further topics include signal processing methods, robotics and neurobionics, and computer vision alike. The book includes selected papers from the XIX International Conference on Neuroinformatics, held on October 2-6, 2017, in Moscow, Russia.
Recently, a new field of computer science was derived, including methods and techniques of problem solving that cannot be easily described by traditional algorithms. This field, called "cognitive computing" or "real-world computing," has a varied set of working methodologies, such as: fuzzy logic, approximate reasoning, genetic algorithms, chaos theory, and the Artificial Neural Networks (ANN). The objective of the present work is to introduce the problematic of the latter: definitions, principles and typology, as well as concrete applications in the field of information retrieval. During the past decade in the field of information retrieval has been experimented with artificial Intelligence (AI) techniques based on rules and knowledge. These techniques seem to have many limitations and difficulties of application, so that already in the present decade work has begun with the more recent. AI techniques, based on inductive learning: symbolic learning, genetic algorithms and neural networks (Chen, 1995). The earliest work in neural computing dates back to the early 1940s, which neuro-physicist Warren McCulloch and mathematician Walter Pitts proposed, based on their system studies. A formal neuron model implemented by electrical circuits (McMulloch, 1943), whose enthusiasm aroused the neuronal model drove research in this line during the 1950s and 1960s. In 1957 Frank Rosenblatt developed the Perceptron, a network model that possesses the generalization capability, so it has been used to this day in various applications, generally in recognition of patterns. In 1959 Bernard Widrow and Marcial Hoff of Stanford University developed the model ADALINE (ADAptative LINear Elements), first ANN applied to a real problem (noise filters in lines phone calls). In 1969 Marvin Minsky and Seymour Papert, of MIT, published a work in which they attack the neural model and consider that any research along these lines was sterile (Minsky, 1969). Due to this criticism the works on ANN stop to a new impetus during the 80's. Despite this pause, several researchers continued to work in that direction during the 1970s. Such is the case of the American James Anderson which develops the BSB (Brain-State-in-a-Box) model, or Finnish Teuvo Kohonen who does the same with one based on self-organizing maps. As of 1982 the interest for the neuronal computation began to take force again. The progress made in hardware and software, methodological advances around learning algorithms for ANN, and the new techniques of artificial intelligence, favored this rebirth. The same year, the first conference between neuronal computing researchers from the US and Japan. In 1985 the American Institute of physics establishes annual meeting Neural Networks for Computing. In 1987 the IEEE held the first conference on ANN. That same year the International Society of Neural Networks was created (INNS). An automatic learning system that identifies the expressions of denial and speculation in biomedical texts is presented, specifically in the collection of BioScope documents. The objective of the work is to compare the efficiency of this approach centered in automatic learning with which it is based on regular expressions. Between the systems that follow this latter approach, we have used NegEx because of its availability and popularity. The evaluation has been carried out on the three subcollections that form BioScope: clinical documents, scientific articles and abstracts of scientific articles. The results show the superiority of the approach based on automatic learning regarding the use of regular expressions. In the identification of negation expressions, the system improves the F1 measure of NegEx between 20 and 30%, depending on the collection of documents. In the identification of speculation, the proposed system exceeds the measure F1 of the best baseline algorithm between 10 and 20%.
Top expert Dr. Lance B. Eliot provides the latest new insights about AI Autonomous Vehicles (AV) that are emerging as driverless self-driving cars and are progressively appearing on our roadways and byways. Vital issues he addresses include present and future technological advances, societal readiness, business aspects, economic considerations, and other ramifications about how this disruptive innovation will transform the world. Referred to as the "AI Insider" and currently serving as the Executive Director of the Cybernetic Self-Driving Car Institute for Techbrium Inc., he provides a no-holds-barred analysis of how Artificial Intelligence and Machine Learning are both a strength and a potential weakness in the effort toward developing true SAE Level 5 self-driving cars.
14th International Work-Conference on Artificial Neural Networks, IWANN 2017, Cadiz, Spain, June 14-16, 2017, Proceedings
Author: Ignacio Rojas
This two-volume set LNCS 10305 and LNCS 10306 constitutes the refereed proceedings of the 14th International Work-Conference on Artificial Neural Networks, IWANN 2017, held in Cadiz, Spain, in June 2017. The 126 revised full papers presented in this double volume were carefully reviewed and selected from 199 submissions. The papers are organized in topical sections on Bio-inspired Computing; E-Health and Computational Biology; Human Computer Interaction; Image and Signal Processing; Mathematics for Neural Networks; Self-organizing Networks; Spiking Neurons; Artificial Neural Networks in Industry ANNI'17; Computational Intelligence Tools and Techniques for Biomedical Applications; Assistive Rehabilitation Technology; Computational Intelligence Methods for Time Series; Machine Learning Applied to Vision and Robotics; Human Activity Recognition for Health and Well-Being Applications; Software Testing and Intelligent Systems; Real World Applications of BCI Systems; Machine Learning in Imbalanced Domains; Surveillance and Rescue Systems and Algorithms for Unmanned Aerial Vehicles; End-User Development for Social Robotics; Artificial Intelligence and Games; and Supervised, Non-Supervised, Reinforcement and Statistical Algorithms.
The information deluge currently assaulting us in the 21st century is having a profound impact on our lifestyles and how we work. We must constantly separate trustworthy and required information from the massive amount of data we encounter each day. Through mathematical theories, models, and experimental computations, Artificial Intelligence with Uncertainty explores the uncertainties of knowledge and intelligence that occur during the cognitive processes of human beings. The authors focus on the importance of natural language-the carrier of knowledge and intelligence-for artificial intelligence (AI) study. This book develops a framework that shows how uncertainty in AI expands and generalizes traditional AI. It describes the cloud model, its uncertainties of randomness and fuzziness, and the correlation between them. The book also centers on other physical methods for data mining, such as the data field and knowledge discovery state space. In addition, it presents an inverted pendulum example to discuss reasoning and control with uncertain knowledge as well as provides a cognitive physics model to visualize human thinking with hierarchy. With in-depth discussions on the fundamentals, methodologies, and uncertainties in AI, this book explains and simulates human thinking, leading to a better understanding of cognitive processes.
Advances in Artificial Intelligence (AI) technology have opened up new markets and new opportunities for progress in critical areas such as health, education, energy, and the environment. In recent years, machines have surpassed humans in the performance of certain specific tasks, such as some aspects of image recognition. Experts forecast that rapid progress in the field of specialized artificial intelligence will continue. Although it is very unlikely that machines will exhibit broadly-applicable intelligence comparable to or exceeding that of humans in the next 20 years, it is to be expected that machines will reach and exceed human performance on more and more tasks. As a contribution toward preparing the United States for a future in which AI plays a growing role, this report surveys the current state of AI, its existing and potential applications, and the questions that are raised for society and public policy by progress in AI. The report also makes recommendations for specific further actions by Federal agencies and other actors. A companion document lays out a strategic plan for Federally-funded research and development in AI. Additionally, in the coming months, the Administration will release a follow-on report exploring in greater depth the effect of AI-driven automation on jobs and the economy. The report was developed by the NSTC's Subcommittee on Machine Learning and Artificial Intelligence, which was chartered in May 2016 to foster interagency coordination, to provide technical and policy advice on topics related to AI, and to monitor the development of AI technologies across industry, the research community, and the Federal Government. The report was reviewed by the NSTC Committee on Technology, which concurred with its contents. The report follows a series of public-outreach activities spearheaded by the White House Office of Science and Technology Policy (OSTP) in 2016, which included five public workshops co-hosted with universities and other associations that are referenced in this report.
Advances in Artificial Intelligence (AI) technology and related fields have opened up new markets and new opportunities for progress in critical areas such as health, education, energy, economic inclusion, social welfare, and the environment. In recent years, machines have surpassed humans in the performance of certain tasks related to intelligence, such as aspects of image recognition. Experts forecast that rapid progress in the field of specialized artificial intelligence will continue. Although it is unlikely that machines will exhibit broadly-applicable intelligence comparable to or exceeding that of humans in the next 20 years, it is to be expected that machines will continue to reach and exceed human performance on more and more tasks. AI-driven automation will continue to create wealth and expand the American economy in the coming years, but, while many will benefit, that growth will not be costless and will be accompanied by changes in the skills that workers need to succeed in the economy, and structural changes in the economy. Aggressive policy action will be needed to help Americans who are disadvantaged by these changes and to ensure that the enormous benefits of AI and automation are developed by and available to all. Following up on the Administration's previous report, Preparing for the Future of Artificial Intelligence, which was published in October 2016, this report further investigates the effects of AI-driven automation on the U.S. job market and economy, and outlines recommended policy responses. This report was produced by a team from the Executive Office of the President including staff from the Council of Economic Advisers, Domestic Policy Council, National Economic Council, Office of Management and Budget, and Office of Science and Technology Policy. The analysis and recommendations included herein draw on insights learned over the course of the Future of AI Initiative, which was announced in May of 2016, and included Federal Government coordination efforts and crosssector and public outreach on AI and related policy matters. Beyond this report, more work remains, to further explore the policy implications of AI. Most notably, AI creates important opportunities in cyberdefense, and can improve systems to detect fraudulent transactions and messages.
"Partially autonomous and intelligent systems have been used in military technology since at least the Second World War, but advances in machine learning and Artificial Intelligence (AI) represent a turning point in the use of automation in warfare. Though the United States military and intelligence communities are planning for expanded use of AI across their portfolios, many of the most transformative applications of AI have not yet been addressed. In this piece, we propose three goals for developing future policy on AI and national security: preserving U.S. technological leadership, supporting peaceful and commercial use, and mitigating catastrophic risk. By looking at four prior cases of transformative military technology -- nuclear, aerospace, cyber, and biotech -- we develop lessons learned and recommendations for national security policy toward AI"--Publisher's web site.
Advances in Artificial Intelligence (AI) technology and related fields have opened up new markets and new opportunities for progress in critical areas such as health, education, energy, economic inclusion, social welfare, and the environment. In recent years, machines have surpassed humans in the performance of certain tasks related to intelligence, such as aspects of image recognition. Experts forecast that rapid progress in the field of specialized artificial intelligence will continue. Although it is unlikely that machines will exhibit broadly-applicable intelligence comparable to or exceeding that of humans in the next 20 years, it is to be expected that machines will continue to reach and exceed human performance on more and more tasks. AI-driven automation will continue to create wealth and expand the American economy in the coming years, but, while many will benefit, that growth will not be costless and will be accompanied by changes in the skills that workers need to succeed in the economy, and structural changes in the economy. Aggressive policy action will be needed to help Americans who are disadvantaged by these changes and to ensure that the enormous benefits of AI and automation are developed by and available to all. Following up on the Administration's previous report, Preparing for the Future of Artificial Intelligence, which was published in October 2016, this report further investigates the effects of AI-driven automation on the U.S. job market and economy, and outlines recommended policy responses. This report was produced by a team from the Executive Office of the President including staff from the Council of Economic Advisers, Domestic Policy Council, National Economic Council, Office of Management and Budget, and Office of Science and Technology Policy. The analysis and recommendations included herein draw on insights learned over the course of the Future of AI Initiative, which was announced in May of 2016, and included Federal Government coordination efforts and cross-sector and public outreach on AI and related policy matters.