Making AI intelligible : philosophical foundations

Making AI intelligible philosophical foundations

Can humans and artificial intelligences share concepts and communicate? This book shows that philosophical work on the metaphysics of meaning can help answer these questions. Herman Cappelen and Josh Dever use the externalist tradition in philosophy to create models of how AIs and humans can underst...

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Bibliographic Details
Other Authors: Cappelen, Herman, author (author), Dever, Josh, author
Format: eBook
Language:Inglés
Published: Oxford : Oxford University Press 2021.
Edition:First edition
Series:Oxford scholarship online.
Subjects:
Artificial intelligence > Philosophy.
See on Biblioteca Universitat Ramon Llull:https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009624653806719
Table of Contents:
  • Cover
  • Making AI Intelligible Philosophical Foundations: Philosophical Foundations
  • Copyright
  • Contents
  • Part I: Introduction and Overview
  • Chapter 2: Alfred (the Dismissive Sceptic): Philosophers, Go Away!
  • A Dialogue with Alfred (the Dismissive Sceptic)
  • Part II: A Proposal for how to Attribute Content to AI
  • Chapter 3: Terminology: Aboutness, Representation, and Metasemantics
  • Loose Talk, Hyperbole, or 'Derived Intentionality'?
  • Aboutness and Representation
  • AI, Metasemantics, and the Philosophy of Mind
  • Chapter 4: Our Theory: De-Anthropocentrized Externalism
  • First Claim: Content for AI Systems Should Be Explained Externalistically
  • Second Claim: Existing Externalist Accounts of Content Are Anthropocentric
  • Third Claim: We Need Meta-Metasemantic Guidance
  • A Meta-Metasemantic Suggestion: Interpreter-centric Knowledge-Maximization
  • Chapter 5: Application: The Predicate 'High Risk'
  • The Background Theory: Kripke-Style Externalism
  • Starting Thought: SmartCredit Expresses High Risk Contents Because of its Causal History
  • Anthropocentric Abstraction of 'Anchoring'
  • Schematic AI-Suitable Kripke-Style Metasemantics
  • Complications and Choice Points
  • Taking Stock
  • Appendix to Chapter 5: More on Reference Preservation in ML Systems
  • Chapter 6: Application: Names and the Mental Files Framework
  • Does SmartCredit Use Names?
  • The Mental Files Framework to the Rescue?
  • Epistemically Rewarding Relations for Neural Networks?
  • Case Studies, Complications, and Reference Shifts
  • Taking Stock
  • Chapter 7: Application: Predication and Commitment
  • Predication: Brief Introduction to the Act Theoretic View
  • Turning to AI and Disentangling Three Different Questions
  • The Metasemantics of Predication: A Teleofunctionalist Hypothesis
  • Some Background: Teleosemantics and Teleofunctional Role.
  • Predication in AI
  • AI Predication and Kinds of Teleology
  • Why Teleofunctionalism and Not Kripke or Evans?
  • Teleofunctional Role and Commitment (or Assertion)
  • Theories of Assertion and Commitment for Humans and AI
  • Part III: Conclusion
  • Chapter 8: Four Concluding Thoughts
  • Dynamic Goals
  • A Story of Neural Networks Taking Over in Ways We Cannot Understand
  • Why This Story is Disturbing and Relevant
  • Taking Stock and General Lessons
  • The Extended Mind and AI Concept Possession
  • Background: The Extended Mind and Active Externalism
  • The Extended Mind and Conceptual Competency
  • From Experts Determining Meaning to Artificial Intelligences Determining Meaning
  • Some New Distinctions: Extended Mind Internalist versus Extended Mind Externalists
  • Kripke, Putnam, and Burge as Extended Mind Internalists
  • Concept Possession, Functionalism, and Ways of Life
  • Implications for the View Defended in This Book
  • An Objection Revisited
  • Reply to the Objection
  • What Makes it a Stop Sign Detector?
  • Adversarial Perturbations
  • Explainable AI and Metasemantics
  • Bibliography
  • Index.

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