Intelligent Machines: from Turing to Deep Blue to Watson and Beyond
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2009 U.S. Tournament.Our.Beginnings
Chess Club and Scholastic Center of Saint Louis Presents the 2009 U.S. Championship Saint Louis, Missouri May 7-17, 2009 History of U.S. Championship “pride and soul of chess,” Paul It has also been a truly national Morphy, was only the fourth true championship. For many years No series of tournaments or chess tournament ever held in the the title tournament was identi- matches enjoys the same rich, world. fied with New York. But it has turbulent history as that of the also been held in towns as small United States Chess Championship. In its first century and a half plus, as South Fallsburg, New York, It is in many ways unique – and, up the United States Championship Mentor, Ohio, and Greenville, to recently, unappreciated. has provided all kinds of entertain- Pennsylvania. ment. It has introduced new In Europe and elsewhere, the idea heroes exactly one hundred years Fans have witnessed of choosing a national champion apart in Paul Morphy (1857) and championship play in Boston, and came slowly. The first Russian Bobby Fischer (1957) and honored Las Vegas, Baltimore and Los championship tournament, for remarkable veterans such as Angeles, Lexington, Kentucky, example, was held in 1889. The Sammy Reshevsky in his late 60s. and El Paso, Texas. The title has Germans did not get around to There have been stunning upsets been decided in sites as varied naming a champion until 1879. (Arnold Denker in 1944 and John as the Sazerac Coffee House in The first official Hungarian champi- Grefe in 1973) and marvelous 1845 to the Cincinnati Literary onship occurred in 1906, and the achievements (Fischer’s winning Club, the Automobile Club of first Dutch, three years later. -
A Survey of Top-Level Ontologies to Inform the Ontological Choices for a Foundation Data Model
A survey of Top-Level Ontologies To inform the ontological choices for a Foundation Data Model Version 1 Contents 1 Introduction and Purpose 3 F.13 FrameNet 92 2 Approach and contents 4 F.14 GFO – General Formal Ontology 94 2.1 Collect candidate top-level ontologies 4 F.15 gist 95 2.2 Develop assessment framework 4 F.16 HQDM – High Quality Data Models 97 2.3 Assessment of candidate top-level ontologies F.17 IDEAS – International Defence Enterprise against the framework 5 Architecture Specification 99 2.4 Terminological note 5 F.18 IEC 62541 100 3 Assessment framework – development basis 6 F.19 IEC 63088 100 3.1 General ontological requirements 6 F.20 ISO 12006-3 101 3.2 Overarching ontological architecture F.21 ISO 15926-2 102 framework 8 F.22 KKO: KBpedia Knowledge Ontology 103 4 Ontological commitment overview 11 F.23 KR Ontology – Knowledge Representation 4.1 General choices 11 Ontology 105 4.2 Formal structure – horizontal and vertical 14 F.24 MarineTLO: A Top-Level 4.3 Universal commitments 33 Ontology for the Marine Domain 106 5 Assessment Framework Results 37 F. 25 MIMOSA CCOM – (Common Conceptual 5.1 General choices 37 Object Model) 108 5.2 Formal structure: vertical aspects 38 F.26 OWL – Web Ontology Language 110 5.3 Formal structure: horizontal aspects 42 F.27 ProtOn – PROTo ONtology 111 5.4 Universal commitments 44 F.28 Schema.org 112 6 Summary 46 F.29 SENSUS 113 Appendix A F.30 SKOS 113 Pathway requirements for a Foundation Data F.31 SUMO 115 Model 48 F.32 TMRM/TMDM – Topic Map Reference/Data Appendix B Models 116 ISO IEC 21838-1:2019 -
Artificial Intelligence
BROAD AI now and later Michael Witbrock, PhD University of Auckland Broad AI Lab @witbrock Aristotle (384–322 BCE) Organon ROOTS OF AI ROOTS OF AI Santiago Ramón y Cajal (1852 -1934) Cerebral Cortex WHAT’S AI • OLD definition: AI is everything we don’t yet know how program • Now some things that people can’t do: • unique capabilities (e.g. Style transfer) • superhuman performance (some areas of speech, vision, games, some QA, etc) • Current AI Systems can be divided by their kind of capability: • Skilled (Image recognition, Game Playing (Chess, Atari, Go, DoTA), Driving) • Attentive (Trading: Aidyia; Senior Care: CareMedia, Driving) • Knowledgeable, (Google Now, Siri, Watson, Cortana) • High IQ (Cyc, Soar, Wolfram Alpha) GOFAI • Thought is symbol manipulation • Large numbers of precisely defined symbols (terms) • Based on mathematical logic (implies (and (isa ?INST1 LegalAgreement) (agreeingAgents ?INST1 ?INST2)) (isa ?INST2 LegalAgent)) • Problems solved by searching for transformations of symbolic representations that lead to a solution Slow Development Thinking Quickly Thinking Slowly (System I) (System II) Human Superpower c.f. other Done well by animals and people animals Massively parallel algorithms Serial and slow Done poorly until now by computers Done poorly by most people Not impressive to ordinary people Impressive (prizes, high pay) "Sir, an animal’s reasoning is like a dog's walking on his hind legs. It is not done well; but you are surprised to find it done at all.“ - apologies to Samuel Johnson Achieved on computers by high- Fundamental design principle of power, low density, slow computers simulation of vastly different Computer superpower c.f. neural hardware human Recurrent Deep Learning & Deep Reasoning MACHINE LEARNING • Meaning is implicit in the data • Thought is the transformation of learned representations http://karpathy.github.io/2015/05/21/rnn- effectiveness/ . -
Knowledge Graphs on the Web – an Overview Arxiv:2003.00719V3 [Cs
January 2020 Knowledge Graphs on the Web – an Overview Nicolas HEIST, Sven HERTLING, Daniel RINGLER, and Heiko PAULHEIM Data and Web Science Group, University of Mannheim, Germany Abstract. Knowledge Graphs are an emerging form of knowledge representation. While Google coined the term Knowledge Graph first and promoted it as a means to improve their search results, they are used in many applications today. In a knowl- edge graph, entities in the real world and/or a business domain (e.g., people, places, or events) are represented as nodes, which are connected by edges representing the relations between those entities. While companies such as Google, Microsoft, and Facebook have their own, non-public knowledge graphs, there is also a larger body of publicly available knowledge graphs, such as DBpedia or Wikidata. In this chap- ter, we provide an overview and comparison of those publicly available knowledge graphs, and give insights into their contents, size, coverage, and overlap. Keywords. Knowledge Graph, Linked Data, Semantic Web, Profiling 1. Introduction Knowledge Graphs are increasingly used as means to represent knowledge. Due to their versatile means of representation, they can be used to integrate different heterogeneous data sources, both within as well as across organizations. [8,9] Besides such domain-specific knowledge graphs which are typically developed for specific domains and/or use cases, there are also public, cross-domain knowledge graphs encoding common knowledge, such as DBpedia, Wikidata, or YAGO. [33] Such knowl- edge graphs may be used, e.g., for automatically enriching data with background knowl- arXiv:2003.00719v3 [cs.AI] 12 Mar 2020 edge to be used in knowledge-intensive downstream applications. -
Using Linked Data for Semi-Automatic Guesstimation
Using Linked Data for Semi-Automatic Guesstimation Jonathan A. Abourbih and Alan Bundy and Fiona McNeill∗ [email protected], [email protected], [email protected] University of Edinburgh, School of Informatics 10 Crichton Street, Edinburgh, EH8 9AB, United Kingdom Abstract and Semantic Web systems. Next, we outline the process of GORT is a system that combines Linked Data from across guesstimation. Then, we describe the organisation and im- several Semantic Web data sources to solve guesstimation plementation of GORT. Finally, we close with an evaluation problems, with user assistance. The system uses customised of the system’s performance and adaptability, and compare inference rules over the relationships in the OpenCyc ontol- it to several other related systems. We also conclude with a ogy, combined with data from DBPedia, to reason and per- brief section on future work. form its calculations. The system is extensible with new Linked Data, as it becomes available, and is capable of an- Literature Survey swering a small range of guesstimation questions. Combining facts to answer a user query is a mature field. The DEDUCOM system (Slagle 1965) was one of the ear- Introduction liest systems to perform deductive query answering. DE- The true power of the Semantic Web will come from com- DUCOM applies procedural knowledge to a set of facts in bining information from heterogeneous data sources to form a knowledge base to answer user queries, and a user can new knowledge. A system that is capable of deducing an an- also supplement the knowledge base with further facts. -
Why Has AI Failed? and How Can It Succeed?
Why Has AI Failed? And How Can It Succeed? John F. Sowa VivoMind Research, LLC 10 May 2015 Extended version of slides for MICAI'14 ProblemsProblems andand ChallengesChallenges Early hopes for artificial intelligence have not been realized. Language understanding is more difficult than anyone thought. A three-year-old child is better able to learn, understand, and generate language than any current computer system. Tasks that are easy for many animals are impossible for the latest and greatest robots. Questions: ● Have we been using the right theories, tools, and techniques? ● Why haven’t these tools worked as well as we had hoped? ● What other methods might be more promising? ● What can research in neuroscience and psycholinguistics tell us? ● Can it suggest better ways of designing intelligent systems? 2 Early Days of Artificial Intelligence 1960: Hao Wang’s theorem prover took 7 minutes to prove all 378 FOL theorems of Principia Mathematica on an IBM 704 – much faster than two brilliant logicians, Whitehead and Russell. 1960: Emile Delavenay, in a book on machine translation: “While a great deal remains to be done, it can be stated without hesitation that the essential has already been accomplished.” 1965: Irving John Good, in speculations on the future of AI: “It is more probable than not that, within the twentieth century, an ultraintelligent machine will be built and that it will be the last invention that man need make.” 1968: Marvin Minsky, technical adviser for the movie 2001: “The HAL 9000 is a conservative estimate of the level of artificial intelligence in 2001.” 3 The Ultimate Understanding Engine Sentences uttered by a child named Laura before the age of 3. -
Logic-Based Technologies for Intelligent Systems: State of the Art and Perspectives
information Article Logic-Based Technologies for Intelligent Systems: State of the Art and Perspectives Roberta Calegari 1,* , Giovanni Ciatto 2 , Enrico Denti 3 and Andrea Omicini 2 1 Alma AI—Alma Mater Research Institute for Human-Centered Artificial Intelligence, Alma Mater Studiorum–Università di Bologna, 40121 Bologna, Italy 2 Dipartimento di Informatica–Scienza e Ingegneria (DISI), Alma Mater Studiorum–Università di Bologna, 47522 Cesena, Italy; [email protected] (G.C.); [email protected] (A.O.) 3 Dipartimento di Informatica–Scienza e Ingegneria (DISI), Alma Mater Studiorum–Università di Bologna, 40136 Bologna, Italy; [email protected] * Correspondence: [email protected] Received: 25 February 2020; Accepted: 18 March 2020; Published: 22 March 2020 Abstract: Together with the disruptive development of modern sub-symbolic approaches to artificial intelligence (AI), symbolic approaches to classical AI are re-gaining momentum, as more and more researchers exploit their potential to make AI more comprehensible, explainable, and therefore trustworthy. Since logic-based approaches lay at the core of symbolic AI, summarizing their state of the art is of paramount importance now more than ever, in order to identify trends, benefits, key features, gaps, and limitations of the techniques proposed so far, as well as to identify promising research perspectives. Along this line, this paper provides an overview of logic-based approaches and technologies by sketching their evolution and pointing out their main application areas. Future perspectives for exploitation of logic-based technologies are discussed as well, in order to identify those research fields that deserve more attention, considering the areas that already exploit logic-based approaches as well as those that are more likely to adopt logic-based approaches in the future. -
Knowledge on the Web: Towards Robust and Scalable Harvesting of Entity-Relationship Facts
Knowledge on the Web: Towards Robust and Scalable Harvesting of Entity-Relationship Facts Gerhard Weikum Max Planck Institute for Informatics http://www.mpi-inf.mpg.de/~weikum/ Acknowledgements 2/38 Vision: Turn Web into Knowledge Base comprehensive DB knowledge fact of human knowledge assets extraction • everything that (Semantic (Statistical Web) Web) Wikipedia knows • machine-readable communities • capturing entities, (Social Web) classes, relationships Source: DB & IR methods for knowledge discovery. Communications of the ACM 52(4), 2009 3/38 Knowledge as Enabling Technology • entity recognition & disambiguation • understanding natural language & speech • knowledge services & reasoning for semantic apps • semantic search: precise answers to advanced queries (by scientists, students, journalists, analysts, etc.) German chancellor when Angela Merkel was born? Japanese computer science institutes? Politicians who are also scientists? Enzymes that inhibit HIV? Influenza drugs for pregnant women? ... 4/38 Knowledge Search on the Web (1) Query: sushi ingredients? Results: Nori seaweed Ginger Tuna Sashimi ... Unagi http://www.google.com/squared/5/38 Knowledge Search on the Web (1) Query:Query: JapaneseJapanese computerscomputeroOputer science science ? institutes ? http://www.google.com/squared/6/38 Knowledge Search on the Web (2) Query: politicians who are also scientists ? ?x isa politician . ?x isa scientist Results: Benjamin Franklin Zbigniew Brzezinski Alan Greenspan Angela Merkel … http://www.mpi-inf.mpg.de/yago-naga/7/38 Knowledge Search on the Web (2) Query: politicians who are married to scientists ? ?x isa politician . ?x isMarriedTo ?y . ?y isa scientist Results (3): [ Adrienne Clarkson, Stephen Clarkson ], [ Raúl Castro, Vilma Espín ], [ Jeannemarie Devolites Davis, Thomas M. Davis ] http://www.mpi-inf.mpg.de/yago-naga/8/38 Knowledge Search on the Web (3) http://www-tsujii.is.s.u-tokyo.ac.jp/medie/ 9/38 Take-Home Message If music was invented Information is not Knowledge. -
Scenarios for Using the ARPANET at the International Conference On
VAlirc NIC 11863 SCENARIOS for using the ARPANET at the INTERNATIONAL CONFERENCE ON COMPUTER COMMUNICATION Washington, D.C. October 24—26, 1972 ARPA Network Information Center Stanford Research Institute Menlo Park, California 94025 , 11?>o - 3 £: 3c? - 16 $<}0-l!:}o 3 - & i 3o iW |{: 3 cp - 3 NIC 11863 SCENARIOS for using the ARPANET at the INTERNATIONAL CONFERENCE ON COMPUTER COMMUNICATION Washington, D.C. October 24—26, 1972 ARPA Network Information Center Stanford Research Institute Menlo Park, California 94025 SCENARIOS FOR USING THE ARPANET AT THE ICCC We intend that the following scenarios be used by individuals to browse the ARPA Computer Network (ARPANET) in its current early stage of development and thereby to introduce themselves to some possibilities in computer communication. The scenarios include only a few of the existing ARPANET resources. They were chosen for this booklet (somewhat haphazardly) to exhibit variety and sophistication, while retaining simplicity. The scenarios are by no means complete or perfect. We have tried to make them accurate, but are certain that they contain errors. The scenarios are, therefore, only one kind of tool for experiencing computer communication. We assume that you will attend the various showings of film and videotape, pay close attention at the several scheduled demonstrations of specific resources, approach the ARPANET aggressively yourself using these scenarios, and unhesitatingly call upon the ICCC Special Project People for the advice and encouragement you are sure to need. The account numbers and passwords provided in these scenarios were generated spe cifically for the ICCC. It is hoped that some of them will remain available after the ICCC for continued browsing. -
THE PSYCHOLOGY of CHESS: an Interview with Author Fernand Gobet
FM MIKE KLEIN, the Chess Journalists Chess Life of America's (CJA) 2017 Chess Journalist of the Year, stepped OCTOBER to the other side of the camera for this month's cover shoot. COLUMNS For a list of all CJA CHESS TO ENJOY / ENTERTAINMENT 12 award winners, It Takes A Second please visit By GM Andy Soltis chessjournalism.org. 14 BACK TO BASICS / READER ANNOTATIONS The Spirits of Nimzo and Saemisch By GM Lev Alburt 16 IN THE ARENA / PLAYER OF THE MONTH Slugfest at the U.S. Juniors By GM Robert Hess 18 LOOKS AT BOOKS / SHOULD I BUY IT? Training Games By John Hartmann 46 SOLITAIRE CHESS / INSTRUCTION Go Bogo! By Bruce Pandolfini 48 THE PRACTICAL ENDGAME / INSTRUCTION How to Write an Endgame Thriller By GM Daniel Naroditsky DEPARTMENTS 5 OCTOBER PREVIEW / THIS MONTH IN CHESS LIFE AND US CHESS NEWS 20 GRAND PRIX EVENT / WORLD OPEN 6 COUNTERPLAY / READERS RESPOND Luck and Skill at the World Open BY JAMAAL ABDUL-ALIM 7 US CHESS AFFAIRS / GM Illia Nyzhnyk wins clear first at the 2018 World Open after a NEWS FOR OUR MEMBERS lucky break in the penultimate round. 8 FIRST MOVES / CHESS NEWS FROM AROUND THE U.S. 28 COVER STORY / ATTACKING CHESS 9 FACES ACROSS THE BOARD / How Practical Attacking Chess is Really Conducted BY AL LAWRENCE BY IM ERIK KISLIK 51 TOURNAMENT LIFE / OCTOBER The “secret sauce” to good attacking play isn’t what you think it is. 71 CLASSIFIEDS / OCTOBER CHESS PSYCHOLOGY / GOBET SOLUTIONS / OCTOBER 32 71 The Psychology of Chess: An Interview with Author 72 MY BEST MOVE / PERSONALITIES Fernand Gobet THIS MONTH: FM NATHAN RESIKA BY DR. -
YEARBOOK the Information in This Yearbook Is Substantially Correct and Current As of December 31, 2020
OUR HERITAGE 2020 US CHESS YEARBOOK The information in this yearbook is substantially correct and current as of December 31, 2020. For further information check the US Chess website www.uschess.org. To notify US Chess of corrections or updates, please e-mail [email protected]. U.S. CHAMPIONS 2002 Larry Christiansen • 2003 Alexander Shabalov • 2005 Hakaru WESTERN OPEN BECAME THE U.S. OPEN Nakamura • 2006 Alexander Onischuk • 2007 Alexander Shabalov • 1845-57 Charles Stanley • 1857-71 Paul Morphy • 1871-90 George H. 1939 Reuben Fine • 1940 Reuben Fine • 1941 Reuben Fine • 1942 2008 Yury Shulman • 2009 Hikaru Nakamura • 2010 Gata Kamsky • Mackenzie • 1890-91 Jackson Showalter • 1891-94 Samuel Lipchutz • Herman Steiner, Dan Yanofsky • 1943 I.A. Horowitz • 1944 Samuel 2011 Gata Kamsky • 2012 Hikaru Nakamura • 2013 Gata Kamsky • 2014 1894 Jackson Showalter • 1894-95 Albert Hodges • 1895-97 Jackson Reshevsky • 1945 Anthony Santasiere • 1946 Herman Steiner • 1947 Gata Kamsky • 2015 Hikaru Nakamura • 2016 Fabiano Caruana • 2017 Showalter • 1897-06 Harry Nelson Pillsbury • 1906-09 Jackson Isaac Kashdan • 1948 Weaver W. Adams • 1949 Albert Sandrin Jr. • 1950 Wesley So • 2018 Samuel Shankland • 2019 Hikaru Nakamura Showalter • 1909-36 Frank J. Marshall • 1936 Samuel Reshevsky • Arthur Bisguier • 1951 Larry Evans • 1952 Larry Evans • 1953 Donald 1938 Samuel Reshevsky • 1940 Samuel Reshevsky • 1942 Samuel 2020 Wesley So Byrne • 1954 Larry Evans, Arturo Pomar • 1955 Nicolas Rossolimo • Reshevsky • 1944 Arnold Denker • 1946 Samuel Reshevsky • 1948 ONLINE: COVID-19 • OCTOBER 2020 1956 Arthur Bisguier, James Sherwin • 1957 • Robert Fischer, Arthur Herman Steiner • 1951 Larry Evans • 1952 Larry Evans • 1954 Arthur Bisguier • 1958 E. -
Liquidation on the Chess Board Mastering the Transition Into the Pawn Endgame
Joel Benjamin Liquidation on the Chess Board Mastering the Transition into the Pawn Endgame Third, New & Extended Edition New In Chess 2019 Contents Explanation of symbols..............................................6 Acknowledgments ..................................................7 Prologue – The ABCs of chess ........................................9 Introduction ......................................................11 Chapter 1 Queen endings .......................................14 Chapter 2 Rook endings ....................................... 44 Chapter 3 Bishop endings .......................................99 Chapter 4 Knight endings ......................................123 Chapter 5 Bishop versus knight endings .........................152 Chapter 6 Rook & minor piece endings ..........................194 Chapter 7 Two minor piece endings .............................220 Chapter 8 Major piece endings..................................236 Chapter 9 Queen & minor piece endings.........................256 Chapter 10 Three or more piece endings ..........................270 Chapter 11 Unbalanced material endings .........................294 Chapter 12 Thematic positions ..................................319 Bibliography .....................................................329 Glossary .........................................................330 Index of players ..................................................331 5 Acknowledgments Thank you to the New In Chess editorial staff, particularly Peter Boel and René Olthof, who provided