From Synapses to Rules: Discovering Symbolic Rules from Neural Processed Data
One high-level ability of the human brain is to understand what it has learned. This seems to be the crucial advantage in comparison to the brain activity of other primates. At present we are technologically almost ready to artificially reproduce human brain tissue, but we still do not fully underst...
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| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
New York, NY
Springer US
2002
|
| Ausgabe: | 1st ed. 2002 |
| Schlagworte: | |
| Online-Zugang: | UBY01 Volltext |
| Zusammenfassung: | One high-level ability of the human brain is to understand what it has learned. This seems to be the crucial advantage in comparison to the brain activity of other primates. At present we are technologically almost ready to artificially reproduce human brain tissue, but we still do not fully understand the information processing and the related biological mechanisms underlying this ability. Thus an electronic clone of the human brain is still far from being realizable. At the same time, around twenty years after the revival of the connectionist paradigm, we are not yet satisfied with the typical subsymbolic attitude of devices like neural networks: we can make them learn to solve even difficult problems, but without a clear explanation of why a solution works. Indeed, to widely use these devices in a reliable and non elementary way we need formal and understandable expressions of the learnt functions. of being tested, manipulated and composed with These must be susceptible other similar expressions to build more structured functions as a solution of complex problems via the usual deductive methods of the Artificial Intelligence. Many effort have been steered in this directions in the last years, constructing artificial hybrid systems where a cooperation between the sub symbolic processing of the neural networks merges in various modes with symbolic algorithms. In parallel, neurobiology research keeps on supplying more and more detailed explanations of the low-level phenomena responsible for mental processes |
| Beschreibung: | 1 Online-Ressource (XXIII, 388 p) |
| ISBN: | 9781461507055 |
| DOI: | 10.1007/978-1-4615-0705-5 |
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| 520 | |a One high-level ability of the human brain is to understand what it has learned. This seems to be the crucial advantage in comparison to the brain activity of other primates. At present we are technologically almost ready to artificially reproduce human brain tissue, but we still do not fully understand the information processing and the related biological mechanisms underlying this ability. Thus an electronic clone of the human brain is still far from being realizable. At the same time, around twenty years after the revival of the connectionist paradigm, we are not yet satisfied with the typical subsymbolic attitude of devices like neural networks: we can make them learn to solve even difficult problems, but without a clear explanation of why a solution works. Indeed, to widely use these devices in a reliable and non elementary way we need formal and understandable expressions of the learnt functions. of being tested, manipulated and composed with These must be susceptible other similar expressions to build more structured functions as a solution of complex problems via the usual deductive methods of the Artificial Intelligence. Many effort have been steered in this directions in the last years, constructing artificial hybrid systems where a cooperation between the sub symbolic processing of the neural networks merges in various modes with symbolic algorithms. In parallel, neurobiology research keeps on supplying more and more detailed explanations of the low-level phenomena responsible for mental processes | ||
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| isbn | 9781461507055 |
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| spelling | From Synapses to Rules Discovering Symbolic Rules from Neural Processed Data edited by Bruno Apolloni, Franz Kurfess 1st ed. 2002 New York, NY Springer US 2002 1 Online-Ressource (XXIII, 388 p) txt rdacontent c rdamedia cr rdacarrier One high-level ability of the human brain is to understand what it has learned. This seems to be the crucial advantage in comparison to the brain activity of other primates. At present we are technologically almost ready to artificially reproduce human brain tissue, but we still do not fully understand the information processing and the related biological mechanisms underlying this ability. Thus an electronic clone of the human brain is still far from being realizable. At the same time, around twenty years after the revival of the connectionist paradigm, we are not yet satisfied with the typical subsymbolic attitude of devices like neural networks: we can make them learn to solve even difficult problems, but without a clear explanation of why a solution works. Indeed, to widely use these devices in a reliable and non elementary way we need formal and understandable expressions of the learnt functions. of being tested, manipulated and composed with These must be susceptible other similar expressions to build more structured functions as a solution of complex problems via the usual deductive methods of the Artificial Intelligence. Many effort have been steered in this directions in the last years, constructing artificial hybrid systems where a cooperation between the sub symbolic processing of the neural networks merges in various modes with symbolic algorithms. In parallel, neurobiology research keeps on supplying more and more detailed explanations of the low-level phenomena responsible for mental processes Artificial Intelligence Complex Systems Mathematical Logic and Foundations Neurosciences Statistical Physics and Dynamical Systems Artificial intelligence Statistical physics Dynamical systems Mathematical logic Apolloni, Bruno edt Kurfess, Franz edt Erscheint auch als Druck-Ausgabe 9780306474026 Erscheint auch als Druck-Ausgabe 9781461352044 Erscheint auch als Druck-Ausgabe 9781461507062 https://doi.org/10.1007/978-1-4615-0705-5 Verlag URL des Eerstveröffentlichers Volltext |
| spellingShingle | From Synapses to Rules Discovering Symbolic Rules from Neural Processed Data Artificial Intelligence Complex Systems Mathematical Logic and Foundations Neurosciences Statistical Physics and Dynamical Systems Artificial intelligence Statistical physics Dynamical systems Mathematical logic |
| title | From Synapses to Rules Discovering Symbolic Rules from Neural Processed Data |
| title_auth | From Synapses to Rules Discovering Symbolic Rules from Neural Processed Data |
| title_exact_search | From Synapses to Rules Discovering Symbolic Rules from Neural Processed Data |
| title_exact_search_txtP | From Synapses to Rules Discovering Symbolic Rules from Neural Processed Data |
| title_full | From Synapses to Rules Discovering Symbolic Rules from Neural Processed Data edited by Bruno Apolloni, Franz Kurfess |
| title_fullStr | From Synapses to Rules Discovering Symbolic Rules from Neural Processed Data edited by Bruno Apolloni, Franz Kurfess |
| title_full_unstemmed | From Synapses to Rules Discovering Symbolic Rules from Neural Processed Data edited by Bruno Apolloni, Franz Kurfess |
| title_short | From Synapses to Rules |
| title_sort | from synapses to rules discovering symbolic rules from neural processed data |
| title_sub | Discovering Symbolic Rules from Neural Processed Data |
| topic | Artificial Intelligence Complex Systems Mathematical Logic and Foundations Neurosciences Statistical Physics and Dynamical Systems Artificial intelligence Statistical physics Dynamical systems Mathematical logic |
| topic_facet | Artificial Intelligence Complex Systems Mathematical Logic and Foundations Neurosciences Statistical Physics and Dynamical Systems Artificial intelligence Statistical physics Dynamical systems Mathematical logic |
| url | https://doi.org/10.1007/978-1-4615-0705-5 |
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