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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.
Provides a timely reconsideration of major concepts by which science is understood, such as discovery and justification, in order to understand whether science can be automated Paints a fascinating picture of contemporary developments of machine-supported science Contains broad range of examples from molecular biology, climate modeling, clinical medicine, and artificial intelligence
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PART I CAN DISCOVERY BE AUTOMATED?.- 1. Paul Humphreys (University of Virginia) -- Why automated science should be cautiously welcomed.- 2. Emanuele Ratti (University of Notre Dame) -- Predictions, phronesis and machine learning in biology.- 3. Fridolin Gross (Universität Kassel) -- The impact of formal reasoning in computational biology.- 4. Mieke Boon (University of Twente) -- How scientists are brought back into science The error of empiricism.- 5. Marta Bertolaso (Campus Bio-Medico University of Rome) Identifying observables in bio-medical sciences.- PART II KNOWLEDGE JUSTIFICATION AND TRUST BUILDING.- 6. Sandra D. Mitchell (University of Pittsburgh) -- Unsimple truths: Multiple perspectives and integrative strategies.- 7. Giuseppe Longo (CNRS et Ecole Normale Supérieure, Paris & Tufts University, Boston) -- Some bias on bio-medical knowledge induced by the digital networks and the political bias on their use.- 8. Fabio Sterpetti (Campus Bio-Medico University of Rome) -- Mathematical proofs and scientific models.- 9. Eric Winsberg (University of South Florida) -- Can models have skill?.- 10. Barbara Osimani (Ludwig-Maximilians-Universität München and University of Ancona) -- Social games and epistemic losses: reliability and higher order evidence in medicine and pharmacology.- PART III HUMAN VALUES IN SCIENCE.- 11. Christopher Tollefsen (University of South Carolina) -- What is 'good science'?.- 12. Melissa Moschella (Columbia University).- 13. Mariachiara Tallacchini (Catholic University of the Sacred Heart) Auto-reflexivity in contemporary science.- PART IV SCIENCE OF THE HUMAN?.- 14. Francesco Bianchini (University of Bologna) -- Virtually extending the bodies with (health) technologies.- 15. Benjamin Hurlbut (Arizona State University) Behold the Man: figuring the human in the development of biotechnology.- 16. Alfredo Marcos (University of Valladolid) -- Dehumanizing technoscience.
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