Undeniable, inescapable, exhilarating and breaking free from the exclusive domain of science, artificial intelligence has become our main preoccupation.
A major generator of new mathematical thinking, AI is the result of easy access to information and data, as facilitated by computer technology. Big Data has come to be seen as an unlimited source of knowledge, the use of which is still being fully explored, but its industrialization has swiftly followed in the footsteps of mathematicians; today's tools are increasingly designed to replace human beings, which comes with social and philosophical consequences.
Drawing on examples of scientific work and the insights of experts, this book offers food for thought on the consequences and future of AI technology in education, health, the workplace and aging.
Table of Contents
Author Presentation xi
Preface xv
Marianne SARAZIN
Part 1 Growing with Artificial Intelligence 1
Introduction to Part 1 3
Marianne SARAZIN
Chapter 1 From Human to Artificial Intelligence 5
Bruno SALGUES
1.1 The different forms of intelligence 5
1.1.1 Human intelligence typologies 5
1.1.2 Artificial intelligence (AI) and human intelligence 5
1.1.3 Object intelligence and human assistance 5
1.2 History of "artificial" intelligence 7
1.2.1 Mechanical forms 7
1.2.2 The desire to model neurons and cybernetics 8
1.2.3 The arrival of computers 9
1.2.4 Different uses of artificial intelligence in healthcare 12
1.2.5 Automated fields or scored answers 15
1.2.6 Expert systems 16
1.2.7 Vector differentiation or vector forest 17
1.2.8 Convolution matrix 18
1.2.9 Multi-cameral or democratic systems 19
1.2.10 System dynamics 19
1.2.11 Machine learning 20
1.2.12 Deep learning 22
1.2.13 The keys to adopting artificial intelligence in healthcare 23
1.2.14 Organ processing using artificial intelligence 24
1.2.15 Medical procedures aided by artificial intelligence: drug dosage 25
Chapter 2 The Philosopher’s Point of View: The Challenges of AI for Our Humanity 29
François-Xavier CLÉMENT
2.1 Introduction 29
2.2 The beginnings of AI 30
2.3 Man: master or slave of AI - examples of behavioral approaches 33
2.3.1 Teenagers and their phones 33
2.3.2 Consumer behavior and AI 34
2.3.3 Memory use and AI 34
2.3.4 Human intelligence versus AI 35
2.3.5 Game master: human or AI? 37
2.4 And what about humanity? 38
2.4.1 New language 38
2.4.2 New thinking 40
2.4.3 A new moral 41
2.5 AI in education: changing learning styles among students in 2020 43
2.5.1 Generations and technology 43
2.5.2 Student behavior 44
2.6 A few concluding words 45
Part 2 Working with Artificial Intelligence 47
Introduction to Part 2 49
Marianne SARAZIN
Chapter 3 For Strategic and Responsible "Piloting" of AI-related Open Innovation Projects 51
Aline COURIE-LEMEUR
3.1 Introduction 51
3.2 Innovation development and the "open innovation" model: challenges and risks 52
3.2.1 Definition of open innovation 52
3.2.2 Dangers of open innovation 54
3.3 Steering "open innovation" projects as part of the development of artificial intelligence techniques 56
3.3.1 Strategic and responsible management 57
3.3.2 The attributes of strategic, responsible management 60
3.4 In a nutshell 63
3.5 Conclusion 66
Chapter 4 Management and AI: Myths and Realities 67
Gilles ROUET
4.1 Introduction 67
4.1.1 Paradigm shift in the business world 67
4.1.2 New management model 68
4.1.3 Mixing genres: intrusion of everyday technologies into the business world 68
4.2 Management in our digital environment 69
4.2.1 The contribution of digital technology to companies 70
4.2.2 Artificial intelligence 72
4.2.3 Analysis concepts for large databases 73
4.3 Humans and machines 76
4.3.1 Optimizing AI-based tools within companies 77
4.3.2 Putting people before AI in companies 78
4.3.3 Evolution of essential skills 79
4.3.4 And then… 80
4.4 Conclusion 81
Part 3 Managing Healthcare with Artificial Intelligence 83
Introduction to Part 3 85
Marianne SARAZIN
Chapter 5 How to Bring the Medical World Out of the Pre-digital Age? 87
Marc SOLER
5.1 Introduction 87
5.2 Healthcare professionals’ relationship with digital technology 88
5.2.1 Level of training of healthcare professionals 88
5.2.2 Technology and healthcare professionals 90
5.3 Creating a universal medical record: a utopia? 93
5.4 "Artificial intelligence" at the service of healthcare: the French government’s position 95
5.5 The approach of technology suppliers? 96
5.6 IBM’s Watson system: its history and application to the medical field 98
5.6.1 Concept 98
5.6.2 Oncology applications 99
5.6.3 An admission of failure 99
5.6.4 The clinician’s point of view 100
5.6.5 Illustrative examples 102
5.6.6 The importance of source data 104
5.6.7 Conclusion 106
5.7 The role of start-ups 107
5.7.1 A few examples 108
5.7.2 Special case of BenevolentAI 109
5.8 Conclusion 111
Chapter 6 Data Quality: A Major Challenge for AI in Healthcare 113
Marysa GERMAIN
6.1 Introduction 113
6.2 From patient data to AI 114
6.2.1 The legal framework for processing medical data 114
6.2.2 The technological framework 117
6.2.3 The hospital’s database management department: the DIM 118
6.2.4 Caregivers’ views on the digitization of medical information 120
6.3 Data quality and consolidation 120
6.3.1 Medical data 120
6.3.2 Implementation of a continuous process of quality improvement 122
Part 4 Aging with Artificial Intelligence 133
Introduction to Part 4 135
Marianne SARAZIN
Chapter 7 Proposed Method for Developing an Aging Score 137
Marianne SARAZIN
7.1 Introduction 137
7.2 Focus on the determinants of age-related frailty 138
7.3 Choice of marker variables to determine age 139
7.3.1 Rational marker selection based on expertise: an operational approach based on a literature review 139
7.3.2 Selection of markers using variables with values within normality limits 141
7.3.3 Conclusion 144
7.4 Choice of normal aging control population 145
7.4.1 Initial hypotheses defining the choice of the control population and the construction of the score 145
7.4.2 First approach: rational selection of the control population based on the literature 146
7.4.3 Second approach: selection of the control population by classification using the dynamic clustering method 146
7.4.4 Results 148
7.4.5 Conclusion 150
7.5 Mathematical modeling of the aging score 151
7.5.1 Initial concept 151
7.5.2 Calculating biological age from a control population sample 152
7.5.3 Conclusions 159
7.6 Calculating biological age: modeling dependence between marker variables using a Gaussian copula 160
7.6.1 Method 160
7.6.2 Source population 162
7.6.3 Results 162
7.6.4 Conclusions 166
7.7 Calculation of biological age for any population (using a Gaussian copula) 166
7.7.1 Method 166
7.7.2 Source population 169
7.7.3 Results 169
7.7.4 Conclusions 175
7.8 Perspectives on this work 176
7.8.1 Advantages and limitations of this work 176
7.8.2 Perspectives 178
Chapter 8 Automatic Detection of Behavioral Changes in a Smart Home 179
Cyriak AZEFAC
8.1 Introduction 179
8.2 Definitions 181
8.3 Methodology 182
8.3.1 Attribute extraction 184
8.3.2 Unsupervised classification 186
8.3.3 Auto-encoder 187
8.3.4 Clustering 188
8.4 Case study: ARUBA 188
8.5 Conclusion 189
Conclusion 191
Marianne SARAZIN
References 193
List of Authors 203
Index 205
