Turing’s Connectionism: An Investigation of Neural Network ArchitecturesSpringer Science & Business Media, 2012. gada 6. dec. - 200 lappuses Alan Mathison Turing (1912-1954) was the first to carry out substantial re search in the field now known as Artificial Intelligence (AI). He was thinking about machine intelligence at least as early as 1941 and during the war cir culated a typewritten paper on machine intelligence among his colleagues at the Government Code and Cypher School (GC & CS), Bletchley Park. Now lost, this was undoubtedly the earliest paper in the field of AI. It probably concerned machine learning and heuristic problem-solving; both were topics that Turing discussed extensively during the war years at GC & CS, as was mechanical chess [121]. In 1945, the war in Europe over, Turing was recruited by the National Physical Laboratory (NPL)! in London, his brief to design and develop an electronic stored-program digital computer-a concrete form of the universal Turing machine of 1936 [185]. Turing's technical report "Proposed Electronic 2 Calculator" , dating from the end of 1945 and containing his design for the Automatic Computing Engine (ACE), was the first relatively complete spec ification of an electronic stored-program digital computer [193,197]. (The document "First Draft of a Report on the EDVAC", produced by John von Neumann and the Moore School group at the University of Pennsylvania in May 1945, contained little engineering detail, in particular concerning elec tronic hardware [202]. |
No grāmatas satura
1.–5. rezultāts no 60.
ix. lappuse
... the higher ones ; for example , could a machine be made that could learn by experience ? This will be theoretical work , and better done away from here " [ 44 ] . Turing left the NPL for Cambridge in the autumn of Foreword ix.
... the higher ones ; for example , could a machine be made that could learn by experience ? This will be theoretical work , and better done away from here " [ 44 ] . Turing left the NPL for Cambridge in the autumn of Foreword ix.
xi. lappuse
... examples networks of neuron - like boolean elements con- nected together in a largely random manner ( we call these " Turing Nets " ) . He described a certain form of Turing Net as " the simplest model of a nervous system " and ...
... examples networks of neuron - like boolean elements con- nected together in a largely random manner ( we call these " Turing Nets " ) . He described a certain form of Turing Net as " the simplest model of a nervous system " and ...
xv. lappuse
... example is the interconnectivity of his networks : Why does each node only have two incoming links ? Would networks with more inputs not perform better ? An answer shall be given thanks to recent investigations in the domain of random ...
... example is the interconnectivity of his networks : Why does each node only have two incoming links ? Would networks with more inputs not perform better ? An answer shall be given thanks to recent investigations in the domain of random ...
xx. lappuse
... Example : Evolve Networks that Regenerate Bitstreams 97 4.4 Signal Processing in Turing Networks 101 4.5 Pattern Classification 106 4.6 Examples : Pattern Classification with Genetic Algorithms 4.7 A Learning Algorithm for Turing ...
... Example : Evolve Networks that Regenerate Bitstreams 97 4.4 Signal Processing in Turing Networks 101 4.5 Pattern Classification 106 4.6 Examples : Pattern Classification with Genetic Algorithms 4.7 A Learning Algorithm for Turing ...
xxi. lappuse
... List of Tables List of Examples , Theorems , Definitions , Propositions , and Corollaries . Bibliography .. Index . 163 169 171 173 181 183 .. 187 197 1. Introduction Turing believes machines think Turing lies with men Contents xxiii.
... List of Tables List of Examples , Theorems , Definitions , Propositions , and Corollaries . Bibliography .. Index . 163 169 171 173 181 183 .. 187 197 1. Introduction Turing believes machines think Turing lies with men Contents xxiii.
Saturs
1 | |
Intelligent Machinery | 17 |
Synthesis of Logical Functions and Digital Systems with | 63 |
Organizing Unorganized Machines | 83 |
Network Properties and Characteristics | 120 |
Epilogue | 169 |
List of Tables 181 | 180 |
Index | 197 |
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Turing’s Connectionism: An Investigation of Neural Network Architectures Christof Teuscher Ierobežota priekšskatīšana - 2002 |
Bieži izmantoti vārdi un frāzes
100-node A-type network A-type machine A-type network A-type unorganized machine artificial neural networks attractor length attractors B-type link behaviour binary bitstream boolean networks brain Church-Turing thesis clock cycles complex configuration connectionism connectionist Copeland and Proudfoot CP-type Definition delay described disabled dynamical systems enabled connections encoding evolution evolutionary algorithm example finite flip-flop four-input function realized genetic algorithm hardware Hypercomputation implementation incoming links initial node values input nodes inputs and outputs interconnection switches inverter L-system links per node logical functions MATLAB McCulloch-Pitts monostable multiplexer NAND gate network architecture network genome network nodes network output neurons Output activity output nodes parameters pattern classification possible presented problem Proposition random boolean networks randomly Section self-organizing shift-register shown in Figure shows signal simulated stable std_logic supervised learning synchronous tape TBI-type network threshold elements Turing networks Turing neural networks Turing's neural networks Two-input Typical activities unit universal Turing machine vector VHDL weights