Guide To Deep Learning Basics: Logical, Historical And Philosophical Perspectives

✍ Scribed by Sandro Skansi

• Publisher: Springer
• Year: 2020
• Tongue: English
• Leaves: 144
• 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......Page 5
Contents......Page 7
1 Mathematical Logic: Mathematics of Logic or Logic of Mathematics......Page 9
References......Page 13
2.1 Introduction......Page 15
2.2 Networks Without Cycles......Page 16
2.3 Relative Inhibition......Page 18
2.4 Networks with Cycles......Page 19
References......Page 20
3.1 Introduction......Page 21
3.2 Turning Back for Missing Pieces of the Meaning, or: Why Philosophy Matters?......Page 22
3.3 The Definition, or What Is Intelligence?......Page 25
3.4 The Term Intelligence''......Page 28<br>3.5 The Essence of Intelligence and AI......Page 32<br>References......Page 34<br>4.1 A Problem and a Movement......Page 36<br>4.2 Zadeh's Proposal......Page 37<br>4.3.1 Fuzzy Logic and the Sorites Paradox......Page 40<br>4.3.2 The Problem of Higher-Order Vagueness......Page 41<br>4.3.3 The Problem with Contradictions......Page 43<br>4.3.4 Vagueness Is Not Fuzziness......Page 44<br>4.4 Conclusion......Page 45<br>References......Page 46<br>5.1 Introduction......Page 48<br>5.2 The Role of Context in Wittgenstein's Philosophy of Language......Page 50<br>5.3 Firth'sContext of Situation'' and ``Collocation''......Page 53
5.4 Word2vec......Page 55
5.5 Conclusion: Differences Between Wittgenstein's Understanding of Word Meaning and that Facilitated by Word2vec......Page 57
References......Page 59
6 Rudolf Carnap–The Grandfather of Artificial Neural Networks: The Influence of Carnap's Philosophy on Walter Pitts......Page 61
References......Page 71
7 A Lost Croatian Cybernetic Machine Translation Program......Page 73
7.1 Beginnings of Machine Translation and Artificial Intelligence in the USA and USSR......Page 74
7.2 The Formation of the Croatian Group in Zagreb......Page 76
7.3 Contributions of the Croatian Group......Page 78
7.4 Conclusion......Page 82
References......Page 83
8.1 Context......Page 85
8.2 Building Blocks......Page 88
8.3 Tinkering......Page 90
8.4 Deep Learning......Page 92
8.5 New Approaches......Page 94
8.6 Five-Year Fog......Page 96
References......Page 97
9.1 Introduction......Page 99
9.2 What Are the Philosophical Problems of Induction?......Page 100
9.3 Why Are the Philosophical Problems of Induction Relevant to Machine Learning?......Page 103
9.4 Supervised Learning and the New Riddle of Induction......Page 105
9.4.1 No-Free-Lunch Theorems......Page 106
9.4.2 Is the No-Free-Lunch Theorem the New Riddle of Induction?......Page 108
9.5 Unsupervised Learning and the Problem of Similarity......Page 109
References......Page 112
10.1 Introduction......Page 113
10.2 Singularity and AI Singularity......Page 114
10.3 Various Questions and Philosophical Anthropology......Page 115
10.4 Philosophical Anthropology, Life, and AIs......Page 116
10.6 Possibility of Immanent Activity as a Sign of Being Alive and AIs......Page 117
10.7 Questions Left Unanswered—Instead of a Conclusion......Page 120
References......Page 121
11.1 Learning How to Multiply......Page 122
11.2 AI-Complete......Page 125
11.3 The Gap......Page 126
11.4 The Walkaround......Page 128
11.5 The Bridge......Page 129
11.6 Multiplying the Multiplication......Page 131
11.7 Eliminating the Human Factor......Page 133
References......Page 134
12.1 Ontology of Transhumanism and Posthumanism......Page 136
12.2 Transhumanism: Man-Cyborg......Page 138
12.3 Posthumanism and Superintelligence......Page 140
References......Page 142
Index......Page 143

✦ Subjects

Machine Learning