Foundations of Knowledge Acquisition: Cognitive Models of Complex Learning:
One of the most intriguing questions about the new computer technology that has appeared over the past few decades is whether we humans will ever be able to make computers learn. As is painfully obvious to even the most casual computer user, most current computers do not. Yet if we could devise lear...
Gespeichert in:
| Weitere Verfasser: | , |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Boston, MA
Springer US
1993
|
| Schriftenreihe: | The Springer International Series in Engineering and Computer Science
194 |
| Schlagworte: | |
| Online-Zugang: | BTU01 URL des Erstveröffentlichers |
| Zusammenfassung: | One of the most intriguing questions about the new computer technology that has appeared over the past few decades is whether we humans will ever be able to make computers learn. As is painfully obvious to even the most casual computer user, most current computers do not. Yet if we could devise learning techniques that enable computers to routinely improve their performance through experience, the impact would be enormous. The result would be an explosion of new computer applications that would suddenly become economically feasible (e. g. , personalized computer assistants that automatically tune themselves to the needs of individual users), and a dramatic improvement in the quality of current computer applications (e. g. , imagine an airline scheduling program that improves its scheduling method based on analyzing past delays). And while the potential economic impact ofsuccessful learning methods is sufficient reason to invest in research into machine learning, there is a second significant reason: studying machine learning helps us understand our own human learning abilities and disabilities, leading to the possibility of improved methods in education. While many open questions remain aboutthe methods by which machines and humans might learn, significant progress has been made |
| Beschreibung: | 1 Online-Ressource (XI, 339 p) |
| ISBN: | 9781461531722 |
| DOI: | 10.1007/978-1-4615-3172-2 |
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| illustrated | Not Illustrated |
| indexdate | 2024-07-10T08:10:58Z |
| institution | BVB |
| isbn | 9781461531722 |
| language | English |
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| series2 | The Springer International Series in Engineering and Computer Science |
| spelling | Foundations of Knowledge Acquisition: Cognitive Models of Complex Learning edited by Susan Chipman, Alan L. Meyrowitz Boston, MA Springer US 1993 1 Online-Ressource (XI, 339 p) txt rdacontent c rdamedia cr rdacarrier The Springer International Series in Engineering and Computer Science 194 One of the most intriguing questions about the new computer technology that has appeared over the past few decades is whether we humans will ever be able to make computers learn. As is painfully obvious to even the most casual computer user, most current computers do not. Yet if we could devise learning techniques that enable computers to routinely improve their performance through experience, the impact would be enormous. The result would be an explosion of new computer applications that would suddenly become economically feasible (e. g. , personalized computer assistants that automatically tune themselves to the needs of individual users), and a dramatic improvement in the quality of current computer applications (e. g. , imagine an airline scheduling program that improves its scheduling method based on analyzing past delays). And while the potential economic impact ofsuccessful learning methods is sufficient reason to invest in research into machine learning, there is a second significant reason: studying machine learning helps us understand our own human learning abilities and disabilities, leading to the possibility of improved methods in education. While many open questions remain aboutthe methods by which machines and humans might learn, significant progress has been made Computer Science Artificial Intelligence (incl. Robotics) Computer Science, general Computer science Artificial intelligence Chipman, Susan edt Meyrowitz, Alan L. edt Erscheint auch als Druck-Ausgabe 9781461363903 https://doi.org/10.1007/978-1-4615-3172-2 Verlag URL des Erstveröffentlichers Volltext |
| spellingShingle | Foundations of Knowledge Acquisition: Cognitive Models of Complex Learning Computer Science Artificial Intelligence (incl. Robotics) Computer Science, general Computer science Artificial intelligence |
| title | Foundations of Knowledge Acquisition: Cognitive Models of Complex Learning |
| title_auth | Foundations of Knowledge Acquisition: Cognitive Models of Complex Learning |
| title_exact_search | Foundations of Knowledge Acquisition: Cognitive Models of Complex Learning |
| title_full | Foundations of Knowledge Acquisition: Cognitive Models of Complex Learning edited by Susan Chipman, Alan L. Meyrowitz |
| title_fullStr | Foundations of Knowledge Acquisition: Cognitive Models of Complex Learning edited by Susan Chipman, Alan L. Meyrowitz |
| title_full_unstemmed | Foundations of Knowledge Acquisition: Cognitive Models of Complex Learning edited by Susan Chipman, Alan L. Meyrowitz |
| title_short | Foundations of Knowledge Acquisition: Cognitive Models of Complex Learning |
| title_sort | foundations of knowledge acquisition cognitive models of complex learning |
| topic | Computer Science Artificial Intelligence (incl. Robotics) Computer Science, general Computer science Artificial intelligence |
| topic_facet | Computer Science Artificial Intelligence (incl. Robotics) Computer Science, general Computer science Artificial intelligence |
| url | https://doi.org/10.1007/978-1-4615-3172-2 |
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