Vor Ort
Merkzettel Warenkorb
Produktbild: From Deep Learning to Rational Machines

From Deep Learning to Rational Machines What the History of Philosophy Can Teach Us about the Future of Artificial Intelligence

31,99 €

inkl. gesetzl. MwSt., Versandkostenfrei

Beschreibung

Produktdetails

Einband

Gebundene Ausgabe

Erscheinungsdatum

15.02.2024

Verlag

Oxford University Press

Seitenzahl

415

Maße (L/B/H)

21,9/15,2/4 cm

Gewicht

580 g

Sprache

Englisch

ISBN

978-0-19-765330-2

Beschreibung

Produktdetails

Einband

Gebundene Ausgabe

Erscheinungsdatum

15.02.2024

Verlag

Oxford University Press

Seitenzahl

415

Maße (L/B/H)

21,9/15,2/4 cm

Gewicht

580 g

Sprache

Englisch

ISBN

978-0-19-765330-2

Herstelleradresse

Libri GmbH
Europaallee 1
36244 Bad Hersfeld
DE

Email: gpsr@libri.de

Noch keine Bewertungen vorhanden

Verfassen Sie die erste Bewertung zu diesem Artikel

Helfen Sie anderen Kundinnen und Kunden durch Ihre Meinung.

Kundinnen und Kunden meinen

Bewertungen (0)
Die Leseprobe wird geladen.
  • Produktbild: From Deep Learning to Rational Machines

    • Acknowledgments
    • Preface
    • Note on Abbreviated Citations to Historical Works
    • 1 Moderate Empiricism and Machine Learning
    • 1.1 Playing with fire? Nature vs. nurture for computer science
    • 1.2 How to simmer things down: From Forms and slates to styles of learning
    • 1.3 From dichotomy to continuum
    • 1.4 Of faculties and fairness: Introducing the new empiricist DoGMA
    • 1.5 Of models and minds
    • 1.6 Other dimensions of the rationalist-empiricist debate
    • 1.7 The DoGMA in relation to other recent revivals of empiricism
    • 1.8 Basic strategy of the book: Understanding deep learning through empiricist faculty psychology
    • 2 What is Deep Learning, and How Should We Evaluate Its Potential?
    • 2.1 Intuitive inference as deep learning's distinctive strength
    • 2.2 Deep learning: Other marquee achievements
    • 2.3 Deep learning: Questions and concerns
    • 2.4 Can we (fairly) measure success? Artificial intelligence vs. artificial rationality
    • 2.5 Avoiding comparative biases: Lessons from comparative psychology for the science of machine behavior
    • 2.6 Summary
    • 3 Perception
    • 3.1 The importance of perceptual abstraction in empiricist accounts of reasoning
    • 3.2 Four approaches to abstraction from the historical empiricists
    • 3.3 Transformational abstraction: Conceptual foundations
    • 3.4 Deep convolutional neural networks: Basic features
    • 3.5 Transformational abstraction in DCNNs
    • 3.6 Challenges for DCNNs as models of transformational abstraction
    • 3.7 Summary
    • 4 Memory
    • 4.1 The trouble with quantifying human perceptual experience
    • 4.2 Generalization and catastrophic interference
    • 4.3 Empiricists on the role of memory in abstraction
    • 4.4 Artificial neural network models of memory consolidation
    • 4.5 Deep reinforcement learning
    • 4.6 Deep-Q Learning and Episodic Control
    • 4.7 Remaining questions about modeling memory
    • 4.8 Summary
    • 5 Imagination
    • 5.1 Imagination: The mind's laboratory
    • 5.2 Fodor's challenges, and Hume's imaginative answers
    • 5.3 Imagination's role in synthesizing ideas: Autoencoders and Generative Adversarial Networks
    • 5.4 Imagination's role in synthesizing novel composite ideas: vector interpolation, variational autoencoders, and transformers
    • 5.5 Imagination's role in creativity: Creative Adversarial Networks
    • 5.6 Imagination's role in simulating experience: Imagination-Augmented Agents
    • 5.7 Biological plausibility and the road ahead
    • 5.8 Summary
    • 6 Attention
    • 6.1 Introduction: Bootstrapping control
    • 6.2 Contemporary theories of attention in philosophy and psychology
    • 6.3 James on attention as ideational preparation
    • 6.4 Attention-like mechanisms in DNN architectures
    • 6.5 Language models, self-attention, and transformers
    • 6.6 Interest and innateness
    • 6.7 Attention, inner speech, consciousness, and control
    • 6.8 Summary
    • 7 Social and Moral Cognition
    • 7.1 From individual to social cognition
    • 7.2 Social cognition as Machiavellian struggle
    • 7.3 Smith and De Grouchy's sentimentalist approach to social cognition
    • 7.4 A Grouchean developmentalist framework for modeling social cognition in artificial agents
    • 7.5 Summary
    • Epilogue
    • References
    • Index