MARC details
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02676nam a22002297a 4500 |
| 003 - CONTROL NUMBER IDENTIFIER |
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| control field |
IIITD |
| 005 - DATE AND TIME OF LATEST TRANSACTION |
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| control field |
20240906095511.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
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240813b |||||||| |||| 00| 0 eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER |
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| International Standard Book Number |
9780197653302 |
| 040 ## - CATALOGING SOURCE |
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| Original cataloging agency |
IIITD |
| 082 00 - DEWEY DECIMAL CLASSIFICATION NUMBER |
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| Classification number |
006.3 |
| Item number |
BUC-F |
| 100 1# - MAIN ENTRY--PERSONAL NAME |
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| Personal name |
Buckner, Cameron J. |
| 245 10 - TITLE STATEMENT |
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| Title |
From deep learning to rational machines : |
| Remainder of title |
what the history of philosophy can teach us about the future of artificial intelligence |
| Statement of responsibility, etc |
by Cameron J. Buckner |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT) |
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| Place of publication, distribution, etc |
New York : |
| Name of publisher, distributor, etc |
Oxford University Press, |
| Date of publication, distribution, etc |
©2024 |
| 300 ## - PHYSICAL DESCRIPTION |
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| Extent |
xxi, 415 p. : |
| Other physical details |
ill. ; |
| Dimensions |
22 cm. |
| 504 ## - BIBLIOGRAPHY, ETC. NOTE |
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| Bibliography, etc |
Includes bibliographical references (pages 349-401) and index. |
| 505 ## - FORMATTED CONTENTS NOTE |
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| Title |
1. Moderate empiricism and machine learning |
| -- |
2. What is deep learning, and how should we evaluate its potential? |
| -- |
3. Perception |
| -- |
4. Memory |
| -- |
5. Imagination |
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6. Attention |
| -- |
7. Social and moral cognition. |
| 520 ## - SUMMARY, ETC. |
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| Summary, etc |
"This book provides a framework for thinking about foundational philosophical questions surrounding machine learning as an approach to artificial intelligence. Specifically, it links recent breakthroughs in deep learning to classical empiricist philosophy of mind. In recent assessments of deep learning's current capabilities and future potential, prominent scientists have cited historical figures from the perennial philosophical debate between nativism and empiricism, which primarily concerns the origins of abstract knowledge. These empiricists were generally faculty psychologists; that is, they argued that the active engagement of general psychological faculties-such as perception, memory, imagination, attention, and empathy-enables rational agents to extract abstract knowledge from sensory experience. This book explains a number of recent attempts to model roles attributed to these faculties in deep neural network based artificial agents by appeal to the faculty psychology of philosophers such as Aristotle, Ibn Sina (Avicenna), John Locke David Hume, William James, and Sophie de Grouchy. It illustrates the utility of this interdisciplinary connection by showing how it can provide benefits to both philosophy and computer science: computer scientists can continue to mine the history of philosophy for ideas and aspirational targets to hit on the way to more robustly rational artificial agents, and philosophers can see how some of the historical empiricists' most ambitious speculations can be realized in specific computational systems"-- |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
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| Topical term or geographic name as entry element |
Machine learning |
| General subdivision |
Philosophy. |
| 776 08 - ADDITIONAL PHYSICAL FORM ENTRY |
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| Display text |
Online version: |
| Main entry heading |
Buckner, Cameron J. |
| Title |
From deep learning to rational machines |
| Place, publisher, and date of publication |
New York, NY : Oxford University Press, [2024] |
| International Standard Book Number |
9780197653326 |
| Record control number |
(DLC) 2023022567 |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) |
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| Source of classification or shelving scheme |
Dewey Decimal Classification |
| Koha item type |
Books |
