From Matter to Life

Information and Causality
 Buch
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ISBN-13:
9781107150539
Einband:
Buch
Erscheinungsdatum:
28.02.2017
Seiten:
494
Autor:
Sara Imari Walker
Gewicht:
881 g
Format:
236x159x38 mm
Sprache:
Englisch
Beschreibung:

This book tackles the most difficult and profound open questions about life and its origins from an information-based perspective.
1. Introduction Sara Imari Walker, Paul C. W. Davies and F. R. Ellis; Part I. Physics and Life: 2. The 'hard problem' of life Sara Imari Walker and Paul C. W. Davies; 3. Beyond initial conditions and laws of motion: constructor theory of information and life Chiara Marletto; Part II. Bio from Bit: 4. (How) did information emerge? Anne-Marie Grisogono; 5. On the emerging codes for chemical evolution Jillian E. Smith-Carpenter, Sha Li, Jay T. Goodwin, Anil K. Mehta and David G. Lynn; 6. Digital and analogue information in organisms Denis Noble; 7. From entropy to information: biased typewriters and the origin of life Christoph Adami and Thomas Labar; Part III. Life's Hidden Information: 8. Cryptographic nature David Krakauer; 9. Noise and function Steven Weinstein and Theodore Pavlic; 10. The many faces of state space compression David Wolpert, Eric Libby, Joshua Grochow and Simon DeDeo; 11. Causality, information and biological computation: an algorithmic software approach to life, disease and the immune system Hector Zenil, Angelika Schmidt and Jesper Tegnér; Part IV. Complexity and Causality: 12. Life's information hierarchy Jessica Flack; 13. Living through downward causation: from molecules to ecosystems Keith D. Farnsworth, George F. R. Ellis and Luc Jaeger; 14. Automata and animats: from dynamics to cause-effect structures Larissa Albantakis and Giulio Tononi; 15. Biological information, causality and specificity - an intimate relationship Karola Stotz and Paul Griffiths; Part V. From Matter to Mind: 16. Major transitions in political order Simon DeDeo; 17. Bits from biology for computational intelligence Michael Wibral, Joseph Lizier and Viola Priesemann; 18. Machine learning and the questions it raises G. Andrew D. Briggs and Dawid Potgieter.

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