Bertolaso / Sterpetti A Critical Reflection on Automated Science

Will Science Remain Human?
1. Auflage 2020
ISBN: 978-3-030-25001-0
Verlag: Springer International Publishing
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, 302 Seiten

Reihe: Religion and Philosophy

This book provides a critical reflection on automated science and addresses the question whether the computational tools we developed in last decades are changing the way we humans do science. More concretely: Can machines replace scientists in crucial aspects of scientific practice? The contributors to this book re-think and refine some of the main concepts by which science is understood, drawing a fascinating picture of the developments we expect over the next decades of human-machine co-evolution. The volume covers examples from various fields and areas, such as molecular biology, climate modeling, clinical medicine, and artificial intelligence. The explosion of technological tools and drivers for scientific research calls for a renewed understanding of the human character of science. This book aims precisely to contribute to such a renewed understanding of science.

Autoren/Hrsg.

Bertolaso, Marta

Sterpetti, Fabio

Inhaltsverzeichnis

1;Foreword: The Social Trends Institute;6
2;Acknowledgments;7
3;Contents;8
4;Introduction. Human Perspectives on the Quest for Knowledge;10
4.1;Introducing the New Series;10
4.2;The Theme of the Volume;11
4.3;Overview of the Volume;13
4.4;References;17
5;Part I: Can Discovery Be Automated?;18
5.1;Why Automated Science Should Be Cautiously Welcomed;19
5.1.1;Introduction;19
5.1.2;Some Advantages of Automated Science;20
5.1.3;Styles of Automated Representation;21
5.1.4;Two Views on Science;22
5.1.5;Epistemic Opacity;23
5.1.6;Representational Opacity;24
5.1.7;Problems with Automated Science;25
5.1.8;Types of Representation;27
5.1.9;Reliabilism;32
5.1.10;Conclusion;33
5.1.11;References;33
5.2;Instrumental Perspectivism: Is AI Machine Learning Technology Like NMR Spectroscopy?;35
5.2.1;Introduction;35
5.2.2;Routes to Scientific Knowledge;37
5.2.3;The New Technologies;39
5.2.3.1;The Instrumental Stance;40
5.2.3.2;Theoretical Support;42
5.2.3.3;Replicability and Convergence;45
5.2.3.4;AI Instrumental Perspectives;46
5.2.4;References;48
5.3;How Scientists Are Brought Back into Science—The Error of Empiricism;51
5.3.1;Introduction;51
5.3.2;Machine-Learning;55
5.3.2.1;Machine-Learning Technologies;55
5.3.2.2;What Machines Can Do;56
5.3.3;Empiricist Epistemologies;58
5.3.3.1;Basic Assumptions of Empiricism;58
5.3.3.2;Scientific Explanation;59
5.3.3.3;Data and Phenomena;61
5.3.3.4;The Semantic View of Theories;63
5.3.4;Knowledge in the Age of Machine-Learning Technologies;65
5.3.4.1;Empiricist Epistemologies: Theories Add Absolutely Nothing to Data-Models;65
5.3.4.2;Scientific Realism in Defense of Science;66
5.3.4.3;The Pragmatic Value of Scientific Knowledge in Epistemic Tasks;66
5.3.4.4;Preparing the Data;67
5.3.4.5;Epistemic Tasks in Engineering and Biomedical Sciences;69
5.3.4.6;The Error of Empiricism;70
5.3.5;References;70
5.4;Information at the Threshold of Interpretation: Science as Human Construction of Sense;74
5.4.1;Introduction: The Origin of Sense;74
5.4.2;The Modern Origin of Elaboration of Information as Formal Deduction: Productivity and Limits of ‘Nonsense’ in the Foundational Debate in Mathematics;76
5.4.2.1;Reconquering Meaning;78
5.4.3;The Role of ‘Interpretation’ in Programming, as Elaboration of Information;81
5.4.4;Which Information Is Handled by a Magic Demon?;82
5.4.5;The Biology of Molecules, Well Before the Threshold of Biological Meaning;86
5.4.6;From Geodetics to Formal Rules and Back Again;90
5.4.6.1;Computations as Norms;92
5.4.6.2;Back to Geodetics in Artificial Intelligence and to Sense Construction;95
5.4.7;Input-Output Machines and Brain Activity;97
5.4.8;A Societal Conclusion;98
5.4.9;References25;101
5.5;Mathematical Proofs and Scientific Discovery;107
5.5.1;The Method of Mathematics and the Automation of Science;107
5.5.1.1;The Analytic View of the Method of Mathematics;110
5.5.1.2;The Analytic Method as a Heuristic Method;112
5.5.1.3;The Analytic View and the Automation of Science;118
5.5.2;Proofs and Programs;119
5.5.3;Mathematical Knowledge;122
5.5.3.1;Mathematical Starting Points;123
5.5.3.2;Gödel’s Disjunction;124
5.5.3.3;Intrinsic and Extrinsic Justification;128
5.5.3.4;Lucas’ and Penrose’s Arguments;131
5.5.3.5;Lucas’s and Penrose’s Arguments and the Axiomatic View;133
5.5.3.6;Absolute Provability and the Axiomatic View;135
5.5.3.7;The Debate on Gödel’s Disjunction and the Axiomatic View;137
5.5.4;Conclusions;138
5.5.5;References;139
6;Part II: Automated Science and Computer Modelling;143
6.1;The Impact of Formal Reasoning in Computational Biology;144
6.1.1;Introduction;144
6.1.2;Formal and Informal Reasoning;145
6.1.3;Informal Reasoning in Molecular and Cell Biology;148
6.1.4;Examples of Computational Methods;151
6.1.4.1;Computational Models in Cell Biology;151
6.1.4.2;Image Analysis;154
6.1.4.3;Bioinformatics;156
6.1.5;Discussion;158
6.1.6;References;159
6.2;Phronesis and Automated Science: The Case of Machine Learning and Biology;161
6.2.1;Introduction;161
6.2.1.1;Machine Learning and Its Scope;162
6.2.2;Automated Science;163
6.2.2.1;Rules Are Not Enough in Machine Learning;164
6.2.2.2;Experimental Science and Rules;167
6.2.2.3;Techne, Phronesis and Automated Science;169
6.2.2.4;A Possible Objection and Reply;173
6.2.3;Conclusion;175
6.2.4;References;175
6.3;A Protocol for Model Validation and Causal Inference from Computer Simulation;177
6.3.1;Introduction;177
6.3.2;Modelling and Simulation in Systems Biology;179
6.3.3;Case Study: Cell Proliferation Modelling;181
6.3.3.1;First Model: Bottom-Up ABM Modelling of Epithelial Cell Growth;183
6.3.3.2;Second Model: Integration of the First Agent-Based Model and an ODE System into a Multiscale Model;185
6.3.3.3;Third Model: Simplified Educated-Phenomenological Model;189
6.3.4;Towards a Protocol for Causal Inference from Computer Simulation;189
6.3.4.1;From Formal Model to Stable Code;189
6.3.4.2;Measurement by Simulation;192
6.3.4.3;Verification, Validation, Revision;193
6.3.4.4;Accuracy and Robustness;195
6.3.4.5;Causal Inference;197
6.3.5;Computer Simulation, Causal Discovery Algorithms, and RCTs;202
6.3.6;Causal Inference from Modeling and Simulation;205
6.3.7;Appendices;207
6.3.7.1;Appendix A: Rules Dictating Cell Behaviour;207
6.3.7.2;Appendix B: The Causal Structure Underpinning the Set of Rules;211
6.3.7.3;Appendix C: Cell Growth Benchmark;212
6.3.7.4;Appendix D: Modifications of the ABM Component for the Second Model;212
6.3.7.5;Appendix E: Testing the Modelling Assumptions of the Second Model;213
6.3.8;References;217
6.4;Can Models Have Skill?;220
6.4.1;Introduction;220
6.4.2;Verification and Validation;221
6.4.3;An Alternative Picture;225
6.4.4;Tuning;228
6.4.5;Conclusion;236
6.4.6;References;236
6.5;Virtually Extending the Bodies with (Health) Technologies;238
6.5.1;Introduction;238
6.5.2;The Extension Thesis;239
6.5.3;The EMT Bodily Extension;240
6.5.4;Social and Second Bodies;241
6.5.5;Extending the Body and the Health-Extended Bodies;242
6.5.6;Conclusion and What’s Next;246
6.5.7;References;247
7;Part III: Automated Science and Human Values;249
7.1;Behold the Man: Figuring the Human in the Development of Biotechnology;250
7.1.1;Introduction;250
7.1.2;Perfecting What?;252
7.1.3;From Understanding to Know-How;253
7.1.4;From Purpose to Risk;255
7.1.5;From Risk to Telos;259
7.1.6;Figuring the Human;263
7.1.7;What Science, Which Human?;266
7.1.8;References;268
7.2;The Dehumanization of Technoscience;270
7.2.1;Diagnosis of a Problem: The Dehumanization of Technoscience;270
7.2.1.1;Technoscience and Its Semantic Field;270
7.2.1.2;The Symptoms of the Problem;271
7.2.1.3;Possible Causes;272
7.2.2;Searching for a Solution;273
7.2.2.1;A Pluralist Ontology and a Systemic Model;273
7.2.2.2;Technoscience as a Personal Action;274
7.2.2.3;Technoscience at the Service of a (Truly) Human Life;276
7.2.3;Concluding Summary;277
7.2.4;References;278
7.3;What Is ‘Good Science’?;279
7.3.1;Introduction;279
7.3.2;The External Ethics of Science;281
7.3.3;The Social Ethics of Science;284
7.3.4;The Internal Ethics of Science;288
7.3.5;Conclusion;291
7.3.6;References;291
7.4;Cultivating Humanity in Bio- and Artificial Sciences;293
7.4.1;The Humanity of Technoscience in Biotechnology;293
7.4.2;Technologies of Life and the Separation of Facts and Values;294
7.4.3;Reductionist Assumptions in Life Sciences and Artificial Sciences;297
7.4.4;For Science to Remain Human: Normatively Defining Human Nature or Cultivating Human Skills?;299
7.4.5;References;302