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From deep learning to rational machines: intelligence what the history of philosophy can teach us about the future of artificial

By: Publication details: Oxford University Press New York 2024Description: xxi, 415 pISBN:
  • 9780197653302
Subject(s): DDC classification:
  • 006.3 BUC
Summary: Front Matter Copyright PageGet accessArrow DedicationGet accessArrow PrefaceGet accessArrow AcknowledgmentsGet accessArrow ExpandNote on Abbreviated Citations to Historical WorksGet accessArrow Expand1 Moderate Empiricism and Machine LearningGet accessArrow View chapter Expand2 What Is Deep Learning, and How Should We Evaluate Its Potential?Get accessArrow View chapter Expand3 PerceptionGet accessArrow View chapter Expand4 MemoryGet accessArrow View chapter Expand5 ImaginationGet accessArrow View chapter Expand6 AttentionGet accessArrow View chapter Expand7 Social CognitionGet accessArrow View chapter EpilogueGet accessArrow View chapter [https://academic.oup.com/book/55239]
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Item type Current library Collection Call number Status Date due Barcode
Book Book Indian Institute of Management LRC General Stacks IT & Decisions Sciences 006.3 BUC (Browse shelf(Opens below)) Available 008271

Table of contents:
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 find the way to create more robust rational artificial agents, and philosophers can see how some of the historical empiricists’ most ambitious speculations can be realized in specific computational systems.

[https://academic.oup.com/book/55239]

Front Matter
Copyright PageGet accessArrow
DedicationGet accessArrow
PrefaceGet accessArrow
AcknowledgmentsGet accessArrow
ExpandNote on Abbreviated Citations to Historical WorksGet accessArrow
Expand1 Moderate Empiricism and Machine LearningGet accessArrow
View chapter
Expand2 What Is Deep Learning, and How Should We Evaluate Its Potential?Get accessArrow
View chapter
Expand3 PerceptionGet accessArrow
View chapter
Expand4 MemoryGet accessArrow
View chapter
Expand5 ImaginationGet accessArrow
View chapter
Expand6 AttentionGet accessArrow
View chapter
Expand7 Social CognitionGet accessArrow
View chapter
EpilogueGet accessArrow
View chapter

[https://academic.oup.com/book/55239]

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