Glauner / Plugmann Innovative Technologies for Market Leadership

Investing in the Future
1. Auflage 2020
ISBN: 978-3-030-41309-5
Verlag: Springer International Publishing
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, 301 Seiten

Reihe: Future of Business and Finance

This book introduces the reader to the latest innovations in fields such as artificial intelligence, systems biology or surgery, and gives advice on what new technologies to consider for becoming a market leader of tomorrow. Companies generally acquire information on these fields from various sources such as market reports, scientific literature or conference events, but find it difficult to distinguish between mere hype and truly valuable innovations. This book offers essential guidance in the form of structured and authoritative contributions by experts in innovative technologies spanning from biology and medicine to augmented reality and smart power grids. The authors identify high-potential fields and demonstrate the impact of their technologies to create economic value in real-world applications. They also offer business leaders advice on whether and how to implement these new technologies and innovations in their companies or businesses.


Prof. Dr. Patrick Glauner is the Founder & CEO of skyrocket.ai GmbH, an artificial intelligence consulting firm based in Bavaria, Germany. In parallel, he is Full Professor of Artificial Intelligence at Deggendorf Institute of Technology, a position he is honored to hold since the age of 30. His research on AI was featured in New Scientist and cited by McKinsey and others. He is also Area Editor of the International Journal of Computational Intelligence Systems (IJCIS). Previously, he held managerial positions at the European Organization for Nuclear Research (CERN), at Krones Group and at Alexander Thamm GmbH. He studied at Imperial College London and also holds an MBA. He is an alumnus of the German National Academic Foundation (Studienstiftung des deutschen Volkes). Prof. Dr. Dr. Philipp Plugmann has been doing multidisciplinary work for the last 20 years in parallel to practicing as a dentist in his own clinic in Leverkusen, Germany. He is also Full Professor for Interdisciplinary Periodontology and Prevention at SRH University of Applied Health Sciences. His first book on innovation in medical technology published in 2011 was reviewed by Cisco. His second book on innovation published with Springer in 2018 got more than 50,000 chapter downloads in its first fifteen months. Previously, he held multiple adjunct faculty appointments for more than twelve years and has won multiple teaching awards. He also holds an MBA, a MSc in Business Innovation, a MSc in Periodontology and Implant Therapy (DGParo) and is currently pursuing his third doctorate. Plugmann has given research talks in the field of innovation at Conferences at Harvard Business School, Berkeley Haas School of Business, Max Planck Institute for Innovation and Competition, and Nanyang Tech University, Singapore. Plugmann is a serial entrepreneur and advisor to several companies, including a global technology consultancy - DataArt.

Autoren/Hrsg.

Glauner, Patrick

Plugmann, Philipp

Inhaltsverzeichnis

1;Foreword;6
2;Preface;8
3;About the Book;9
4;Contents;10
5;Editors and Contributors;12
5.1;About the Editors;12
5.2;Contributors;13
6;Smart Grid, Future Innovation and Investment Opportunities;15
6.1;1 Introduction;15
6.2;2 Energy Transformation;16
6.3;3 Smart Grid and Renewable Energy;17
6.4;4 Harnessing Variability;18
6.4.1;4.1 Harnessing Variability at Distribution Grid Level (Small Energy Level);19
6.4.2;4.2 Hydrogen Production;21
6.4.3;4.3 Desalination Plants;22
6.4.4;4.4 CO2 Extraction from Nature;23
6.5;5 Conclusion;24
6.6;References;24
7;Quantum Technologies;25
7.1;1 Introduction;25
7.2;2 Concepts;26
7.2.1;2.1 Superposition: Life Is Uncertain;26
7.2.2;2.2 Measuring: To Measure or Not to Measure Is the Question;27
7.2.3;2.3 Entanglement: Spooky Action at a Distance;28
7.3;3 Applications;28
7.3.1;3.1 Quantum Computers;28
7.3.2;3.2 Shor's Algorithm: The End of Encryption?;30
7.3.3;3.3 Quantum Networks;30
7.3.4;3.4 Quantum “Blind” Clouds;31
7.3.5;3.5 Other Applications with Quantum Mechanics;31
7.4;4 Conclusions;32
7.5;References;33
8;Security in Intelligent Transportation Telematics;34
8.1;1 The ITS Ecosystem;34
8.2;2 Application Scenario Versus Vulnerability;36
8.3;3 Signature as the Primary Security Measure;37
8.4;4 Security, Safety, Integrity—and Privacy—Issues;38
8.5;5 Why May Someone Want to Attack Any ITS Network?;40
8.6;6 Conclusions;41
8.7;References;42
9;Innovation and Future Technology Scenarios in Health Care: Ideas and Studies;43
9.1;1 Self-Driving Hospital Beds;44
9.2;2 Reorganization of Medical Studies with Contests and Crowdsourcing;45
9.3;3 All-In-Data-Approach in Health Care for New Business Models;47
9.3.1;3.1 Introduction;47
9.3.2;3.2 Theoretical Background;48
9.3.3;3.3 Research Design;48
9.3.4;3.4 First Study;48
9.3.5;3.5 Follow-Up Study;49
9.3.6;3.6 Future Health Care IT Service Prototype Model;51
9.3.7;3.7 Findings;51
9.3.8;3.8 Conclusions of the Study;52
9.4;4 Drone-Supported Emergency Concepts in Combination with Automotive Health Systems;52
9.5;5 Conclusions;53
9.6;References;54
10;Unlocking the Power of Artificial Intelligence for Your Business;56
10.1;1 Introduction;56
10.2;2 Motivation: China Is Spearheading AI Innovation;57
10.3;3 Artificial Intelligence;58
10.3.1;3.1 History;58
10.3.2;3.2 Machine Learning;59
10.3.3;3.3 The Three Pillars of Machine Learning;60
10.3.4;3.4 Neural Networks;61
10.3.5;3.5 Recent Advances and Deep Learning;61
10.3.6;3.6 Frontiers;62
10.4;4 AI Transformation of a Company;63
10.5;5 The Fear of an Out-of-Control AI Is Exaggerated;68
10.6;6 Conclusions;69
10.7;References;69
11;Innovation Means: Asking the Right Questions;71
11.1;1 Introduction;71
11.2;2 So Let Us Innovate;72
11.3;3 What Is Innovation Anyway?;73
11.4;4 Do You Really Need to Constantly Innovate?;73
11.5;5 Where Is Your Game Plan?;73
11.6;6 Innovation Is Hard;74
11.7;7 Innovation Is Not Rocket Science Though;74
11.8;8 It All Starts with a Question;75
11.9;9 Innovate by Gut Feeling;75
11.9.1;9.1 Learn to Ask Questions Again;76
11.10;10 The Innovation Canvas;76
11.10.1;10.1 What Do We Want to Innovate?;77
11.10.2;10.2 Why Are We Doing This?;78
11.10.2.1;10.2.1 Trigger;78
11.10.2.2;10.2.2 Demand;78
11.10.2.3;10.2.3 Value Proposition;78
11.10.2.4;10.2.4 Competitive Advantages;78
11.10.3;10.3 Who Do We Do It For?;79
11.10.3.1;10.3.1 Target Groups;79
11.10.3.2;10.3.2 Marketing;79
11.10.3.3;10.3.3 Pilot Application;79
11.10.3.4;10.3.4 Expansion Stages;80
11.10.3.5;10.3.5 Activities;80
11.10.3.6;10.3.6 Resources;80
11.10.3.7;10.3.7 External Partners;81
11.10.3.8;10.3.8 Other Projects;81
11.10.4;10.4 How Does It Pay Off?;81
11.10.4.1;10.4.1 Cost Structure;81
11.10.4.2;10.4.2 Project Budget;82
11.10.4.3;10.4.3 Revenue Streams;82
11.10.4.4;10.4.4 Risks;83
11.11;11 Nothing Worth Without Culture;83
11.12;12 Drive Your Innovation Culture!;83
11.13;13 Conclusions;84
11.14;Reference;84
12;Innovative Technologies in the Ageing Population: Breaking the Boundaries;85
12.1;1 Introduction;85
12.2;2 Demographic Shift;86
12.3;3 Digital Sovereignty;87
12.4;4 Digital Education and Social Interaction;89
12.5;5 Data Security and Trust;90
12.6;6 Usability;91
12.7;7 Artificial Intelligence as an Innovation Driver of Digital Technologies Amongst the Elderly;91
12.8;8 The Future of Humans and Technology;93
12.9;9 The Digital Divide;94
12.10;10 Conclusions;95
12.11;References;96
13;Using Augmented Reality and Machine Learning in Radiology;98
13.1;1 Introduction;98
13.2;2 Related Work;100
13.3;3 Methodology;102
13.3.1;3.1 The Machine Learning Algorithm;104
13.3.1.1;3.1.1 Preprocessing;104
13.3.1.2;3.1.2 Loss Objective;105
13.3.1.3;3.1.3 Input Multiple 2D Slices to Take Advantage of 3D Data;105
13.3.1.4;3.1.4 ROI Cropping;105
13.3.1.5;3.1.5 Using the Liver Segmentation for the Lesion Segmentation;106
13.3.1.6;3.1.6 Lesion Detector Module;106
13.3.1.7;3.1.7 3D Conditional Random Fields;106
13.3.1.8;3.1.8 Loss Balancing;106
13.3.2;3.2 Using Unity and WebRTC to Deliver PC Rendering Power to HoloLens;107
13.3.2.1;3.2.1 Server;109
13.3.2.2;3.2.2 Client;110
13.4;4 Evaluation and Discussion;111
13.5;5 Conclusions and Outreach;113
13.6;References;114
14;Digitalization in Mechanical Engineering;116
14.1;1 Introduction;116
14.2;2 Comparison to Traditional Industrial Automation;118
14.3;3 Opportunities and Challenges;118
14.4;4 The Way of Thinking: People, Processes, and Technology;120
14.5;5 Selected Use Cases and Applications;121
14.5.1;5.1 Reducing the Number of Simulation Runs;121
14.5.2;5.2 Intelligent Mechatronic Modules: Cyber-Physical Systems;122
14.5.3;5.3 Self-X and Organic Computing;123
14.5.4;5.4 Automatically Layouting New Machine Variants;124
14.6;6 Conclusions;125
14.7;References;125
15;Lean Launch Data Engineering Projects with Super Type Power;127
15.1;1 Introduction;127
15.2;2 Towards Type Safe and Reusable Spark Applications;128
15.2.1;2.1 Loosely Typed Data;128
15.2.2;2.2 Type-Setting the Data;130
15.2.3;2.3 Sending Them for Classes;131
15.2.4;2.4 A Quick Summary;132
15.3;3 Sailing Safe Through the Storm;132
15.3.1;3.1 An Untyped Storm Topology;133
15.3.2;3.2 Storm Is Dangerous;135
15.3.3;3.3 Phantom Types to the Rescue;136
15.4;4 Conclusion;138
15.5;References;139
16;Ubiquitous Computing: From 5G to the Edge and Beyond;140
16.1;1 Ubiquitous Computing;140
16.2;2 The Journey: Or How We Got Here;141
16.2.1;2.1 The Becoming of the Inter-Networking Network;141
16.2.2;2.2 Hard- and Software Evolution;142
16.2.3;2.3 Mobile Telecommunications Everywhere;143
16.3;3 Mixing The Dough;144
16.4;4 Status Quo 2019;148
16.5;5 The Evolutionary Revolution to 5G;148
16.6;6 Edge Computing;150
16.7;7 Benefits of 5G and Edge Computing;151
16.8;8 Top Five Use Case Categories;152
16.8.1;8.1 Human Beings;152
16.8.2;8.2 Smart Mobility;153
16.8.3;8.3 Smart Logistics;154
16.8.4;8.4 Smart Environment;154
16.8.5;8.5 Smart Industry;155
16.9;9 Conclusions/What Is Left to Do;156
16.10; References;157
17;Autonomous Driving on the Thin Trail of Great Opportunities and Dangerous Trust;159
17.1;1 Introduction;159
17.2;2 Understanding the Environment;161
17.3;3 The Critical Role of Artificial Intelligence;161
17.4;4 Ambitious Goals and Their Consequences;162
17.4.1;4.1 Advances in Autonomous Driving and Artificial Intelligence;163
17.4.2;4.2 Contemporary Forecasts and Challenges;164
17.5;5 The Challenge of Easy Access to Complex Technologies;165
17.6;6 Interpreting Deep Learning Models in Self-Driving Cars;166
17.6.1;6.1 Convolutional Neural Networks for End-to-End Driving;166
17.6.2;6.2 Visualizing What Deep Learning Models Learn;167
17.7;7 Conclusions;169
17.8;References;170
18;Analytic Philosophy for Biomedical Research: The Imperative of Applying Yesterday's Timeless Messages to Today's Impasses;172
18.1;1 Successes and Lingering Challenges in Biomedicine Today;173
18.2;2 The Current State of Theory in Biomedical Research;176
18.3;3 Lessons from the History of Philosophy and Rational Thought;177
18.3.1;3.1 Ancient Philosophy;178
18.3.2;3.2 After the Galilean Revolution in Science;182
18.4;4 Precedents of “Philosophical Biology”;184
18.5;5 The Imperative for a Coherent and Unified Theoretical and Philosophical Biology;186
18.5.1;5.1 Contours of a Revived Philosophical Biology;187
18.5.2;5.2 Theoretical Methods and Tools (TMT);188
18.5.3;5.3 Theoretical Problems and Solutions (TPS);191
18.5.4;5.4 Inherent and Experimental Verifiability;196
18.6;6 Conclusions;197
18.7;References;197
19;Proposal-Based Innovation: A New Approach to Opening Up the Innovation Process;206
19.1;1 Introduction;207
19.2;2 A New Approach to Innovation: Task and Goal;207
19.3;3 Manufacturing Industries: A Definition for This Chapter;208
19.4;4 Challenges and Opportunities in Manufacturing Industries;209
19.4.1;4.1 An Example;209
19.4.2;4.2 The Options;210
19.4.3;4.3 Insight Is Essential;210
19.4.4;4.4 Early Warning Indicators;211
19.4.5;4.5 Need for Worldwide Intelligence?;211
19.4.6;4.6 Changes and Influences;211
19.4.6.1;4.6.1 Changes in the Nature of Globalization;212
19.4.6.2;4.6.2 Changing World Order;212
19.4.6.3;4.6.3 Innovation Centers Are Shifting;213
19.4.6.4;4.6.4 How Innovation Has Changed;214
19.4.6.5;4.6.5 Increasing R&D Expenditures;215
19.4.6.6;4.6.6 Time to Market;215
19.4.6.7;4.6.7 Digital Divide;216
19.4.6.8;4.6.8 Increasing Competition;216
19.4.6.9;4.6.9 Corporate Social Responsibility (CSR);216
19.4.6.10;4.6.10 Growing Middle Class;217
19.4.6.11;4.6.11 Aging Society;217
19.4.6.12;4.6.12 Megacities;217
19.5;5 About Startups and Manufacturing;218
19.5.1;5.1 Services Startup Environment;218
19.5.2;5.2 Manufacturing Services Startups;218
19.5.3;5.3 Mass Manufacturing Startups;219
19.5.4;5.4 Conclusion;220
19.6;6 Open Innovation (OI);221
19.6.1;6.1 General Limitations;222
19.6.2;6.2 Cultural Barriers;223
19.6.3;6.3 Process Barriers;223
19.6.4;6.4 Intermediaries;223
19.7;7 Barriers in Web Search;225
19.7.1;7.1 The Language Barrier Web;226
19.7.2;7.2 The Relevance Barrier Web;227
19.8;8 Proposal-Based Innovation (PBI);228
19.8.1;8.1 PBI in the Global Environment;228
19.8.2;8.2 The Concept of PBI;229
19.8.3;8.3 Artificial Intelligence (AI);232
19.8.4;8.4 The Vision;233
19.9;9 Conclusion and a Special Concern;233
19.10;References;235
20;Technologies and Innovations for the Plastics Industry: Polymer 2030;237
20.1;1 Technologies and Innovations for the Plastics Industry: Polymer 2030: Fit for the Future Thanks to Innovations and Technology;237
20.2;2 Structure of the Plastics Sector;239
20.3;3 Megatrends and New Business Models for the Plastics Industry;240
20.4;4 Trends and Technologies: Best Practice Examples for the Plastics Industry;244
20.4.1;4.1 Technologies for Individualisation in the Plastics Industry;244
20.4.2;4.2 Resource Efficiency;244
20.4.3;4.3 Digital Transformation;246
20.5;5 Recommended Approaches for the Plastic Industry;246
20.6;References;247
21;How Do Innovative Business Concepts Enable Investment Opportunities in the Complete Construction Value Chain?;248
21.1;1 Introduction to the Global Construction Market;249
21.2;2 What Is the Construction Value Chain?;249
21.3;3 How Is the World Population Developing?;250
21.4;4 Globally, What Are the Major Impacting Trends on Construction?;250
21.5;5 Is the Technology Breakthrough There?;252
21.5.1;5.1 Smart Building Material and Green Technology;252
21.5.1.1;5.1.1 Interpanel GmbH;252
21.5.1.2;5.1.2 Nuki Home Solutions GmbH;254
21.5.1.3;5.1.3 Airthings;254
21.5.1.4;5.1.4 Breeze Technologies;254
21.5.1.5;5.1.5 Field Factors;255
21.5.2;5.2 Artificial Intelligence, Data Analytics, and Internet of Things;255
21.5.2.1;5.2.1 Fieldwire;256
21.5.2.2;5.2.2 INDUS.AI;257
21.5.2.3;5.2.3 Building Radar;257
21.5.2.4;5.2.4 bGrid;258
21.5.2.5;5.2.5 reINVENT Innovation GmbH;258
21.5.3;5.3 Building Information Modeling (BIM), Virtual (VR) and Augmented Reality (AR);259
21.5.3.1;5.3.1 Finalcad;259
21.5.3.2;5.3.2 Matterport;260
21.5.3.3;5.3.3 IrisVR;261
21.5.3.4;5.3.4 XYZ Reality;261
21.5.4;5.4 Robotics, Drones, and 3D Printing;262
21.5.4.1;5.4.1 MX3D;262
21.5.4.2;5.4.2 KEWAZO;263
21.5.4.3;5.4.3 XtreeE;264
21.5.4.4;5.4.4 Apis Cor;264
21.5.4.5;5.4.5 Yingchuang Building Technique;264
21.5.4.6;5.4.6 ICON3D;265
21.5.4.7;5.4.7 RedWorks Construction Technologies Inc.;265
21.5.5;5.5 Smart and Mobile/Modular Homes;265
21.5.5.1;5.5.1 haus.me;265
21.5.5.2;5.5.2 Mighty Buildings Inc.;266
21.5.5.3;5.5.3 Containerwerk eins GmbH;266
21.6;6 Conclusion;266
21.7;References;267
22;Motivation, Employees, and Communication in the Start-Up Phase;268
22.1;1 Motivation: The Engine of Our Founder Scene;268
22.2;2 Creating a Basis for the Team;269
22.2.1;2.1 Create Room for Feedback;269
22.2.2;2.2 Agile Planning;270
22.2.3;2.3 Fast Communication;270
22.2.4;2.4 Limitation of Freedom;271
22.3;3 Limits of Capabilities;271
22.4;4 Motivation Comes from Success;271
22.5;References;273
23;AI to Solve the Data Deluge: AI-Based Data Compression;274
23.1;1 Introduction;274
23.2;2 Data Compression Preliminaries;276
23.3;3 AI for Data Compression;279
23.3.1;3.1 DeepZip: Compressing Numerical Data with Neural Networks;281
23.3.1.1;3.1.1 Probability Prediction Block;281
23.3.1.2;3.1.2 Arithmetic Encoder Block;281
23.3.1.3;3.1.3 DeepZip Compression;282
23.3.1.4;3.1.4 Thoughts on DeepZip;283
23.3.2;3.2 Classification and Anomaly Detection on Lossy Compressed Data;283
23.3.2.1;3.2.1 Tensor Decomposition for Natural Compression;284
23.4;4 Conclusion;287
23.5;References;287
24;Digital Transformation in Plastics Industry: From Digitization Toward Virtual Material;289
24.1;1 Introduction: What Is an Innovative Technology?;289
24.2;2 The Perspective of Material Science;291
24.3;3 The Perspective of Covestro;293
24.4;4 Virtual Customer Experience of Materials;295
24.5;5 Suggestions;297
24.6;6 Conclusion;299
24.7;References;299
25;Correction to: Analytic Philosophy for Biomedical Research: The Imperative of Applying Yesterday's Timeless Messages to Today's Impasses;301