|Author||: Pekka Ala-Pietilä ,Yann Bonnet,Urs Bergmann,Maria Bielikova ,Cecilia Bonefeld-Dahl,Nozha Boujemaa ,Wilhelm Bauer,Loubna Bouarfa ,Raja Chatila,Mark Coeckelbergh ,Virginia Dignum ,Jean-Francois Gagné ,Joanna Goodey,Sami Haddadin ,Gry Hasselbalch,Fredrik Heintz,Fanny Hidvegi ,Klaus Höckner,Mari-Noëlle Jégo-Laveissière,Leo Kärkkäinen,Sabine Theresia Köszegi ,Robert Kroplewski ,Ieva Martinkenaite,Raoul Mallart ,Catelijne Muller,Cécile Wendling ,Barry O’Sullivan ,Ursula Pachl,Nicolas Petit ,Andrea Renda,Francesca Rossi ,Karen Yeung,Françoise Soulié Fogelman ,Jaan Tallinn ,Jakob Uszkoreit ,Aimee Van Wynsberghe|
|Publisher||: HLEG AI|
|Release Date||: 2019-04-08|
|Pages||: 39 pages|
The Ethics Guidelines for Trustworthy Artificial Intelligence (AI) is a document prepared by the High-Level Expert Group on Artificial Intelligence (AI HLEG). This independent expert group was set up by the European Commission in June 2018, as part of the AI strategy announced earlier that year. The AI HLEG presented a first draft of the Guidelines in December 2018. Following further deliberations by the group in light of discussions on the European AI Alliance, a stakeholder consultation and meetings with representatives from Member States, the Guidelines were revised and published in April 2019.
|Author||: Pekka Ala-Pietilä ,Yann Bonnet,Urs Bergmann,Maria Bielikova ,Cecilia Bonefeld-Dahl,Wilhelm Bauer,Loubna Bouarfa ,Raja Chatila,Mark Coeckelbergh ,Virginia Dignum ,Jean-Francois Gagné ,Joanna Goodey,Sami Haddadin ,Gry Hasselbalch,Fredrik Heintz,Fanny Hidvegi ,Klaus Höckner,Mari-Noëlle Jégo-Laveissière,Leo Kärkkäinen,Sabine Theresia Köszegi ,Robert Kroplewski ,Ieva Martinkenaite,Raoul Mallart ,Catelijne Muller,Cécile Wendling ,Barry O’Sullivan ,Ursula Pachl,Nicolas Petit ,Andrea Renda,Francesca Rossi ,Karen Yeung,Françoise Soulié Fogelman ,Jaan Tallinn ,Jakob Uszkoreit ,Aimee Van Wynsberghe|
|Publisher||: European Commission|
|Release Date||: 2020-07-17|
|Pages||: 34 pages|
On the 17 of July 2020, the High-Level Expert Group on Artificial Intelligence (AI HLEG) presented their final Assessment List for Trustworthy Artificial Intelligence. Following a piloting process where over 350 stakeholders participated, an earlier prototype of the list was revised and translated into a tool to support AI developers and deployers in developing Trustworthy AI. The tool supports the actionability the key requirements outlined by the Ethics Guidelines for Trustworthy Artificial Intelligence (AI), presented by the High-Level Expert Group on AI (AI HLEG) presented to the European Commission, in April 2019. The Ethics Guidelines introduced the concept of Trustworthy AI, based on seven key requirements: human agency and oversight technical robustness and safety privacy and data governance transparency diversity, non-discrimination and fairness environmental and societal well-being and accountability Through the Assessment List for Trustworthy AI (ALTAI), AI principles are translated into an accessible and dynamic checklist that guides developers and deployers of AI in implementing such principles in practice. ALTAI will help to ensure that users benefit from AI without being exposed to unnecessary risks by indicating a set of concrete steps for self-assessment. Download the Assessment List for Trustworthy Artificial Intelligence (ALTAI) (.pdf) The ALTAI is also available in a web-based tool version. More on the ALTAI web-based tool: https://futurium.ec.europa.eu/en/european-ai-alliance/pages/altai-assessment-list-trustworthy-artificial-intelligence
The use of mathematical logic as a formalism for artificial intelligence was recognized by John McCarthy in 1959 in his paper on Programs with Common Sense. In a series of papers in the 1960's he expanded upon these ideas and continues to do so to this date. It is now 41 years since the idea of using a formal mechanism for AI arose. It is therefore appropriate to consider some of the research, applications and implementations that have resulted from this idea. In early 1995 John McCarthy suggested to me that we have a workshop on Logic-Based Artificial Intelligence (LBAI). In June 1999, the Workshop on Logic-Based Artificial Intelligence was held as a consequence of McCarthy's suggestion. The workshop came about with the support of Ephraim Glinert of the National Science Foundation (IIS-9S2013S), the American Association for Artificial Intelligence who provided support for graduate students to attend, and Joseph JaJa, Director of the University of Maryland Institute for Advanced Computer Studies who provided both manpower and financial support, and the Department of Computer Science. We are grateful for their support. This book consists of refereed papers based on presentations made at the Workshop. Not all of the Workshop participants were able to contribute papers for the book. The common theme of papers at the workshop and in this book is the use of logic as a formalism to solve problems in AI.
Good,No Highlights,No Markup,all pages are intact, Slight Shelfwear,may have the corners slightly dented, may have slight color changes/slightly damaged spine.
The definitive presentation of Soar, one AI's most enduring architectures, offering comprehensive descriptions of fundamental aspects and new components. In development for thirty years, Soar is a general cognitive architecture that integrates knowledge-intensive reasoning, reactive execution, hierarchical reasoning, planning, and learning from experience, with the goal of creating a general computational system that has the same cognitive abilities as humans. In contrast, most AI systems are designed to solve only one type of problem, such as playing chess, searching the Internet, or scheduling aircraft departures. Soar is both a software system for agent development and a theory of what computational structures are necessary to support human-level agents. Over the years, both software system and theory have evolved. This book offers the definitive presentation of Soar from theoretical and practical perspectives, providing comprehensive descriptions of fundamental aspects and new components. The current version of Soar features major extensions, adding reinforcement learning, semantic memory, episodic memory, mental imagery, and an appraisal-based model of emotion. This book describes details of Soar's component memories and processes and offers demonstrations of individual components, components working in combination, and real-world applications. Beyond these functional considerations, the book also proposes requirements for general cognitive architectures and explicitly evaluates how well Soar meets those requirements.
Handbook of Knowledge Representation describes the essential foundations of Knowledge Representation, which lies at the core of Artificial Intelligence (AI). The book provides an up-to-date review of twenty-five key topics in knowledge representation, written by the leaders of each field. It includes a tutorial background and cutting-edge developments, as well as applications of Knowledge Representation in a variety of AI systems. This handbook is organized into three parts. Part I deals with general methods in Knowledge Representation and reasoning and covers such topics as classical logic in Knowledge Representation; satisfiability solvers; description logics; constraint programming; conceptual graphs; nonmonotonic reasoning; model-based problem solving; and Bayesian networks. Part II focuses on classes of knowledge and specialized representations, with chapters on temporal representation and reasoning; spatial and physical reasoning; reasoning about knowledge and belief; temporal action logics; and nonmonotonic causal logic. Part III discusses Knowledge Representation in applications such as question answering; the semantic web; automated planning; cognitive robotics; multi-agent systems; and knowledge engineering. This book is an essential resource for graduate students, researchers, and practitioners in knowledge representation and AI. * Make your computer smarter * Handle qualitative and uncertain information * Improve computational tractability to solve your problems easily
Artificial Intelligence: A Modern Approach offers the most comprehensive, up-to-date introduction to the theory and practice of artificial intelligence. Number one in its field, this textbook is ideal for one or two-semester, undergraduate or graduate-level courses in Artificial Intelligence.