LEADER 07061nam 2200481 450 001 996464511703316 005 20220327133619.0 010 $a3-030-71873-5 035 $a(CKB)4100000011979292 035 $a(MiAaPQ)EBC6676116 035 $a(Au-PeEL)EBL6676116 035 $a(OCoLC)1260345423 035 $a(PPN)258876905 035 $a(EXLCZ)994100000011979292 100 $a20220327d2021 uy 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aKnowing our world $ean artificial intelligence perspective /$fGeorge F. Luger 210 1$aCham, Switzerland :$cSpringer,$d[2021] 210 4$d©2021 215 $a1 online resource (267 pages) 311 $a3-030-71872-7 320 $aIncludes bibliographical references and index. 327 $aIntro -- Preface -- Why Write This Book? -- The Story -- Acknowledgments -- Contents -- Part I: In the Beginning... -- Chapter 1: Creating Computer Programs: An Epistemic Commitment -- 1.1 Introduction and Focus of Our Story -- 1.2 The Foundation for Computation -- 1.2.1 The Turing Machine -- 1.2.2 The Post Production System and Unary Subtraction -- 1.3 Computer Languages, Representations, and Search -- 1.4 In Summary -- Chapter 2: Historical Foundations -- 2.1 Mary Shelley, Frankenstein, and Prometheus -- 2.2 Early Greek Thought -- 2.3 The Later Greeks: Plato, Euclid, and Aristotle -- 2.4 Post-medieval or Modern Philosophy -- 2.5 The British Empiricists: Hobbes, Locke, and Hume -- 2.6 Bridging the Empiricist/Rationalist Chasm: Baruch Spinoza -- 2.7 Bridging the Empiricist/Rationalist Chasm: Immanuel Kant -- 2.8 American Pragmatism: Peirce, James, and Dewey -- 2.9 The Mathematical Foundations for Computation -- 2.10 The Turing Test and the Birth of AI -- 2.11 In Summary -- Chapter 3: Modern AI and How We Got Here -- 3.1 Three Success Stories in Recent AI -- 3.1.1 Deep Blue at IBM (Hsu 2002 -- Levy and Newborn 1991 -- url 3.2) -- 3.1.2 IBM's Watson (Baker 2011, Ferrucci et al. 2010, 2013, url 3.3) -- 3.1.3 Google and AlphaGo (Silver et al. 2017, url 3.4) -- 3.2 Very Early AI and the 1956 Dartmouth Summer Research Project -- 3.2.1 The Logic Theorist (Newell and Simon 1956 -- Newell et al. 1958) -- 3.2.2 Geometry Theorem Proving (Gelernter 1959 -- Gelernter and Rochester 1958) -- 3.2.3 A Program that Plays Checkers (Samuel 1959) -- 3.2.4 The Dartmouth Summer Workshop in 1956 -- 3.3 Artificial Intelligence: Attempted Definitions -- 3.4 AI: Early Years -- 3.4.1 The Neats and Scruffies -- 3.4.2 AI: Based on "Emulating Humans" or "Just Good Engineering?" -- 3.5 The Birth of Cognitive Science. 327 $a3.6 General Themes in AI Practice: The Symbolic, Connectionist, Genetic/Emergent, and Stochastic -- 3.7 In Summary -- Part II: Modern AI: Structures and Strategies for Complex Problem-Solving -- Chapter 4: Symbol-Based AI and Its Rationalist Presuppositions -- 4.1 The Rationalist Worldview: State-Space Search -- 4.1.1 Graph Theory: The Origins of the State Space -- 4.1.2 Searching the State Space -- 4.1.3 An Example of State-Space Search: The Expert System -- 4.2 Symbol-Based AI: Continuing Important Contributions -- 4.2.1 Machine Learning: Data Mining -- 4.2.2 Modeling the Physical Environment -- 4.2.3 Expertise: Wherever It Is Needed -- 4.3 Strengths and Limitations of the Symbol System Perspective -- 4.3.1 Symbol-Based Models and Abstraction -- 4.3.2 The Generalization Problem and Overlearning -- 4.3.3 Why Are There No Truly Intelligent Symbol-Based Systems? -- 4.4 In Summary -- Chapter 5: Association and Connectionist Approaches to AI -- 5.1 The Behaviorist Tradition and Implementation of Semantic Graphs -- 5.1.1 Foundations for Graphical Representations of Meaning -- 5.1.2 Semantic Networks -- 5.1.3 More Modern Uses of Association-Based Semantic Networks -- 5.2 Neural or Connectionist Networks -- 5.2.1 Early Research: McCulloch, Pitts, and Hebb -- 5.2.2 Backpropagation Networks -- 5.3 Neural Networks and Deep Learning -- 5.3.1 AlphaGo Zero and Alpha Zero -- 5.3.2 Robot Navigation: PRM-RL -- 5.3.3 Deep Learning and Video Games -- 5.3.4 Deep Learning and Natural Language Processing -- 5.4 Epistemic Issues and Association-Based Representations -- 5.4.1 Inductive Bias, Transparency, and Generalization -- 5.4.2 Neural Networks and Symbol Systems -- 5.4.3 Why Have We Not Built a Brain? -- 5.5 In Summary -- Chapter 6: Evolutionary Computation and Intelligence -- 6.1 Introduction to Evolutionary Computation -- 6.2 The Genetic Algorithm and Examples. 327 $a6.2.1 The Traveling Salesperson Problem -- 6.2.2 Genetic Programming -- 6.2.3 An Example: Kepler's Third Law of Planetary Motion -- 6.3 Artificial Life: The Emergence of Complexity -- 6.3.1 Artificial Life -- 6.3.2 Contemporary Approaches to A-Life -- Synthetic Biological Models of Evolution -- Artificial Chemistry -- Other Abstract Machines and Evolutionary Computation -- Psychological and Sociological Foundations for Life and Intelligence -- 6.4 Evolutionary Computation and Intelligence: Epistemic Issues -- 6.5 Some Summary Thoughts on Part II: Chaps. 4, 5, and 6 -- 6.5.1 Inductive Bias: The Rationalist's a priori -- 6.5.2 The Empiricist's Dilemma -- 6.6 In Summary -- Part III: Toward an Active, Pragmatic, Model-Revising Realism -- Chapter 7: A Constructivist Rapprochement and an Epistemic Stance -- 7.1 A Response to Empiricist, Rationalist, and Pragmatist AI -- 7.2 The Constructivist Rapprochement -- 7.3 Five Assumptions: A Foundation for an Epistemic Stance -- 7.4 A Foundation for a Modern Epistemology -- 7.5 In Summary -- Chapter 8: Bayesian-Based Constructivist Computational Models -- 8.1 The Derivation of a Bayesian Stance -- 8.2 Bayesian Belief Networks, Perception, and Diagnosis -- 8.3 Bayesian-Based Speech and Conversation Modeling -- 8.4 Diagnostic Reasoning in Complex Environments -- 8.5 In Summary -- Chapter 9: Toward an Active, Pragmatic, Model-Revising Realism -- 9.1 A Summary of the Project -- 9.2 Model Building Through Exploration -- 9.3 Model Revision and Adaptation -- 9.4 What Is the Project of the AI Practitioner? -- 9.5 Meaning, Truth, and a Foundation for a Modern Epistemology -- 9.5.1 Neopragmatism, Kuhn, Rorty, and the Scientific Method -- 9.5.2 A Category Error -- 9.5.3 The Cognitive Neurosciences: Insights on Human Processing -- 9.5.4 On Being Human: A Modern Epistemic Stance -- Bibliography. 327 $aURL References (All url references checked 16 June 2021) -- Index. 606 $aArtificial intelligence 606 $aArtificial intelligence$xPhilosophy 615 0$aArtificial intelligence. 615 0$aArtificial intelligence$xPhilosophy. 676 $a006.3 700 $aLuger$b George F.$0722102 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a996464511703316 996 $aKnowing Our World$92106112 997 $aUNISA LEADER 01455nam a2200313 i 4500 001 991001615749707536 008 120228s2010 it 000 0 ita d 020 $a9788861376618 (v. 1) 020 $a9788861379411 (v. 2) 035 $ab14042447-39ule_inst 040 $aDip.to Scienze pedagogiche$bita 082 0 $a372.72 100 1 $aPerona, Mario$0476619 245 10$aGeometria con la carta /$cMario Perona, Eugenia Pellizzari e Daniela Lucangeli 260 $aTrento :$bErickson,$c2010- 300 $av. :$bill. ;$c30 cm 440 0$aProgrammi di potenziamento della cognizione numerica e logico-scientifica 505 0 $gv. 1. :$tRiconoscere le forme.$gPiegare per spiegare.$g-134 p. : ill. 505 0 $gv. 2. :$tEnti fondamentali della geometria.$g-213 p. : ill. 650 4$aGeometria$xInsegnamento$xScuola elementare 700 1 $aPellizzari, Eugenia$eauthor$4http://id.loc.gov/vocabulary/relators/aut$0731928 700 1 $aLucangeli, Daniela 907 $a.b14042447$b10-05-13$c28-02-12 912 $a991001615749707536 945 $aLE022 372 PER02.01 V. 01$g1$i2022000135412$lle022$nLE022/PINNELLI/2012$op$pE18.50$q-$rl$s- $t0$u5$v15$w5$x0$y.i15390135$z12-03-12 945 $aLE022 372 PER02.01 V. 02$g1$i2022000135399$lle022$nLE022/PINNELLI/2012$op$pE18.50$q-$rl$s- $t0$u2$v14$w2$x0$y.i15390147$z12-03-12 996 $aGeometria con la carta$91442083 997 $aUNISALENTO 998 $ale022$b28-02-12$cm$da $e-$fita$git $h0$i0 LEADER 04181nam 22008295 450 001 9910799217703321 005 20251008131255.0 010 $a9783031343629 010 $a303134362X 024 7 $a10.1007/978-3-031-34362-9 035 $a(PPN)27541969X 035 $a(MiAaPQ)EBC31051193 035 $a(Au-PeEL)EBL31051193 035 $a(CKB)29510373200041 035 $a(OCoLC)1416747565 035 $a(DE-He213)978-3-031-34362-9 035 $a(EXLCZ)9929510373200041 100 $a20231229d2023 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aCross-Border Life and Work $eSocial, Economic, Technological and Jurisdictional Issues /$fedited by Peter Droege, Stefan Güldenberg, Marco J. Menichetti, Stefan Seidel 205 $a1st ed. 2023. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2023. 215 $a1 online resource (147 pages) 225 1 $aContributions to Management Science,$x2197-716X 311 08$aPrint version: Droege, Peter Cross-Border Life and Work Cham : Springer International Publishing AG,c2024 9783031343612 327 $aRegional Affordances and Resilient Communities -- Site Selection According to Life Cycles in Agglomeration Areas: A Dynamic and Interdisciplinary Location Analysis of the Four-country-region Lake Constance-Alpine Rhine Valley -- Cross-Border Philanthropy?Current Challenges in Corporate Governance and Financial Risk Management -- Cross-border Wealth Management -- Crowdfunding in German-speaking Countries?A Literature Review from an Economics and Legal Perspective -- Family Business across National Borders: Strategies and Processes of Internationalization. 330 $aThis book discusses the risks, challenges, and opportunities of cross-border work and life from a multidisciplinary and multilevel perspective, including (a) the individual, (b) the social and organizational, and (c) the regional levels, taking into consideration the diverse and multilayered social, economic, technological, and jurisdictional issues involved. Emerging public policies and advanced information technologies (IT) have created new opportunities for work and life that thrive in global value chains and markets. Life, in general, and work, in particular, are increasingly organized across borders of various kinds and are subject to rapid change. At the same time, life and work have been determined by 19th and 20th century infrastructures and technologies. 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