Guide to deep learning basics : logical, historical and philosophical perspectives

✍ Scribed by Sandro Skansi (editor)

• Publisher: Springer
• Year: 2020
• Tongue: English
• Leaves: 144
• Edition: 1st ed. 2020
• Category: Library

✦ Synopsis

This stimulating text/reference presents a philosophical exploration of the conceptual foundations of deep learning, presenting enlightening perspectives that encompass such diverse disciplines as computer science, mathematics, logic, psychology, and cognitive science. The text also highlights select topics from the fascinating history of this exciting field, including the pioneering work of Rudolf Carnap, Warren McCulloch, Walter Pitts, Bulcsú László, and Geoffrey Hinton.

Topics and features:

• Provides a brief history of mathematical logic, and discusses the critical role of philosophy, psychology, and neuroscience in the history of AI
• Presents a philosophical case for the use of fuzzy logic approaches in AI
• Investigates the similarities and differences between the Word2vec word embedding algorithm, and the ideas of Wittgenstein and Firth on linguistics
• Examines how developments in machine learning provide insights into the philosophical challenge of justifying inductive inferences
• Debates, with reference to philosophical anthropology, whether an advanced general artificial intelligence might be considered as a living being
• Investigates the issue of computational complexity through deep-learning strategies for understanding AI-complete problems and developing strong AI
• Explores philosophical questions at the intersection of AI and transhumanism

This inspirational volume will rekindle a passion for deep learning in those already experienced in coding and studying this discipline, and provide a philosophical big-picture perspective for those new to the field.

✦ Table of Contents

Preface
Contents
1 Mathematical Logic: Mathematics of Logic or Logic of Mathematics
References
2 The McCulloch–Pitts Paper from the Perspective of Mathematical Logic
2.1 Introduction
2.2 Networks Without Cycles
2.3 Relative Inhibition
2.4 Networks with Cycles
References
3 From the Linguistic Turn to the Cognitive Turn and Back Again
3.1 Introduction
3.2 Turning Back for Missing Pieces of the Meaning, or: Why Philosophy Matters?
3.3 The Definition, or What Is Intelligence?
3.4 The Term Intelligence'' 3.5 The Essence of Intelligence and AI References 4 Why Not Fuzzy Logic? 4.1 A Problem and a Movement 4.2 Zadeh's Proposal 4.3 Philosophical Concerns 4.3.1 Fuzzy Logic and the Sorites Paradox 4.3.2 The Problem of Higher-Order Vagueness 4.3.3 The Problem with Contradictions 4.3.4 Vagueness Is Not Fuzziness 4.4 Conclusion References 5 Meaning as Use: From Wittgenstein to Google's Word2vec 5.1 Introduction 5.2 The Role of Context in Wittgenstein's Philosophy of Language 5.3 Firth'sContext of Situation'' and ``Collocation''
5.4 Word2vec
5.5 Conclusion: Differences Between Wittgenstein's Understanding of Word Meaning and that Facilitated by Word2vec
References
6 Rudolf Carnap–The Grandfather of Artificial Neural Networks: The Influence of Carnap's Philosophy on Walter Pitts
References
7 A Lost Croatian Cybernetic Machine Translation Program
7.1 Beginnings of Machine Translation and Artificial Intelligence in the USA and USSR
7.2 The Formation of the Croatian Group in Zagreb
7.3 Contributions of the Croatian Group
7.4 Conclusion
References
8 The Architectures of Geoffrey Hinton
8.1 Context
8.2 Building Blocks
8.3 Tinkering
8.4 Deep Learning
8.5 New Approaches
8.6 Five-Year Fog
References
9 Machine Learning and the Philosophical Problems of Induction
9.1 Introduction
9.2 What Are the Philosophical Problems of Induction?
9.3 Why Are the Philosophical Problems of Induction Relevant to Machine Learning?
9.4 Supervised Learning and the New Riddle of Induction
9.4.1 No-Free-Lunch Theorems
9.4.2 Is the No-Free-Lunch Theorem the New Riddle of Induction?
9.5 Unsupervised Learning and the Problem of Similarity
References
10 The Artificial Intelligence Singularity: What It Is and What It Is Not
10.1 Introduction
10.2 Singularity and AI Singularity
10.3 Various Questions and Philosophical Anthropology
10.4 Philosophical Anthropology, Life, and AIs
10.5 Coming into Existence and Degrees of Life
10.6 Possibility of Immanent Activity as a Sign of Being Alive and AIs
10.7 Questions Left Unanswered—Instead of a Conclusion
References
11 AI-Completeness: Using Deep Learning to Eliminate the Human Factor
11.1 Learning How to Multiply
11.2 AI-Complete
11.3 The Gap
11.4 The Walkaround
11.5 The Bridge
11.6 Multiplying the Multiplication
11.7 Eliminating the Human Factor
References
12 Transhumanism and Artificial Intelligence: Philosophical Aspects
12.1 Ontology of Transhumanism and Posthumanism
12.2 Transhumanism: Man-Cyborg
12.3 Posthumanism and Superintelligence
12.4 Conclusion
References
Index