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Zur Lösung der wirklich bedeutenden Probleme: Was von Maschinen erwartet wird

  • HAUPTBEITRAG
  • WAS VON MASCHINEN ERWARTET WIRD
  • Published:
Informatik-Spektrum Aims and scope

Zusammenfassung

Einige der bedeutendsten Probleme dieser Welt müssen jetzt gelöst werden. Als Non-Profit-Organisation und Technologieunternehmen nutzt Archai die Möglichkeiten kollektiver Intelligenz, um durchschlagskräftige Lösungen zur Bewältigung des Klimawandels zu entwickeln. In diesem Artikel wird dargelegt, was von den Maschinen – also von modernen Technologien – erwartet wird, wenn sie einen Beitrag zur Bewältigung großer und drängender Probleme leisten sollen. Der vorgeschlagene formelle Rahmen für die Herleitung dieser Erwartungen baut darauf auf, dass das individuelle wahrgenommene Risiko einer Wissenschaftlerin, eines Ingenieurs oder jeder anderen potenziellen Mitwirkenden eine kollektive Problemlösung verhindern und die Lösungsfindung verschleppen kann. Es wird aufgezeigt, wie dieses Risiko mit der strukturellen Güte von Problemen zusammenhängt und es wird vorgeschlagen, je ein System zur Bewältigung der beiden grundlegenden Problemstrukturtypen aufzubauen. Der Fokus liegt dabei auf der Bewältigung von ,,ill-structured problems“, weil ,,well-structured problems“ idealisierte Ausnahmen sind, die der unfassbaren Natur der bedeutendsten Probleme nicht gerecht werden.

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References

  1. Anderson JR (1983) A spreading activation theory of memory. J Verbal Lear Verbal Behav 22(3):261–295

    Article  MathSciNet  Google Scholar 

  2. Archer D (2005) Fate of fossil fuel CO2 in geologic time. J Geophys Res 110(C09S05):1–6, doi:10.1029/2004JC002625

    Google Scholar 

  3. Babai L (2017) Quasipolynomial Claim Restored. http://people.cs.uchicago.edu/∼laci/update.html, last access: 9.1.2017

  4. Balietti S, Mäs M, Helbing D (2015) On disciplinary fragmentation and scientific progress. PloS one 10(3):e0118747, doi:10.1371/journal.pone.0118747

    Article  Google Scholar 

  5. Benson DA, Karsch-Mizrachi I, Lipman DJ, Ostell J, Sayers EW (2010) GenBank. Nucleic Acids Res 38(Database issue):D46–D51, doi:10.1093/nar/gkp1024

    Article  Google Scholar 

  6. Berners-Lee T, Hendler J, Lassila O (2001) The semantic web. Sci Am 284(5):28–37

    Article  Google Scholar 

  7. Bingham A, Spradlin D (2011) NASA’s New Innovation Framework. FT Press, Upper Saddle River

    Google Scholar 

  8. Boden MA (2004) The Creative Mind: Myths and Mechanisms, 2nd ed. Routledge, New York

    Google Scholar 

  9. Bruton JT, Nelson TG, Zimmerman TK, Fernelius JD, Magleby SP, Howell LL (2016) Packing and deploying Soft Origami to and from cylindrical volumes with application to automotive airbags. R Soc Open Sci 3(9):1–15

    Article  MathSciNet  Google Scholar 

  10. Burt RS (1992) Structural Holes: The Social Structure of Competition. Harvard University Press, Cambridge London

    Google Scholar 

  11. Burt RS (2004) Structural holes and good ideas. Am J Soc 110(2):349–399

    Article  Google Scholar 

  12. Bywater RP, Middleton JN (2016) Melody discrimination and protein fold classification. Heliyon 2(10):e00175

    Article  Google Scholar 

  13. Chen C, Chen Y, Horowitz M, Hou H, Liu Z, Pellegrino D (2009) Towards an explanatory and computational theory of scientific discovery. J Informetr 3(3):191–209, doi:10.1016/j.joi.2009.03.004

    Article  Google Scholar 

  14. Collins AM, Loftus EF (1975) A spreading-activation theory of semantic processing. Psychol Rev 82(6):407–428

    Article  Google Scholar 

  15. Conti M (Producer) (2017) The Incredible Inventions of Intuitive AI. 5 June [TED Talk]. https://www.ted.com/talks/maurice_conti_the_incredible_inventions_of_intuitive_ai, last access: 5.6.2017

  16. Crestani F (1997) Application of spreading activation techniques in information retrieval. Artif Intell Rev 11(6):453–482

    Article  Google Scholar 

  17. De Groot AD (1978) Thought and Choice in Chess, 2nd ed., vol. 4. Mouton Publishers, The Hague

    Google Scholar 

  18. Dietrich A (2004) The cognitive neuroscience of creativity. Psychon Bull Rev 11(6):1011–1026, doi:10.3758/BF03196731

    Article  Google Scholar 

  19. Duncker K (1945) On problem-solving. Psychol Monogr 58(5):i-113, doi:10.1037/h0093599

  20. Foldit (2017) The Science Behind Foldit. http://fold.it/portal/info/about, last access: 6.6.2017

  21. Ganesalingam M, Gowers T (2016) A fully automatic theorem prover with human-style output. J Auto Reason 58:253–291, doi:10.1007/s10817-016-9377-1

    Article  MathSciNet  MATH  Google Scholar 

  22. Goldbloom A (Producer) (2016) What Has Kaggle Learned from 2 Million Machine Learning Models? 5 June 2016. https://youtu.be/8KzjARKIgTo, last access: 5.6.2017

  23. Gowers T (2009) Is Massively Collaborative Mathematics Possible? https://gowers.wordpress.com/2009/01/27/is-massively-collaborative-mathematics-possible/, last access: 27.1.2017

  24. Gowers T, Nielsen M (2009) Massively collaborative mathematics. Nature 461(7266):879–881

    Article  Google Scholar 

  25. Holyoak KJ (1990) Problem Solving. In: Smith EE, Osherson DN (eds) Thinking: An Invitation to Cognitive Science, 2nd ed., vol. 3. MIT Press, Cambridge, pp 267–296

    Google Scholar 

  26. Holyoak KJ, Thagard P (1989) Analogical mapping by constraint satisfaction. Cogn Sci 13(3):295–355, doi:10.1207/s15516709cog1303_1

    Article  Google Scholar 

  27. Jacquet J, Hagel K, Hauert C, Marotzke J, Röhl T, Milinski M (2013) Intra- and intergenerational discounting in the climate game. Nature Clim Change 3(12):1025–1028

    Article  Google Scholar 

  28. Jeppesen LB, Lakhani KR (2010) Marginality and Problem-Solving Effectiveness in Broadcast Search. Organ Sci 21(5):1016–1033, doi:10.1287/orsc.1090.0491

    Article  Google Scholar 

  29. Jung-Beeman M, Bowden EM, Haberman J, Frymiare JL, Arambel-Liu S, Greenblatt R, Kounios J (2004) Neural activity when people solve verbal problems with insight. PLoS Biol 2(4):0500–0510

    Article  Google Scholar 

  30. Kandel ER, Siegelbaum SA (2000) Overview of Synaptic Transmission. In: Kandel ER, Schwartz JH, Jessell TM (eds) Principles of Neural Science, 4th ed. McGraw-Hill, New York

    Google Scholar 

  31. Klarreich E (2017a) Complexity Theory Problem Strikes Back. https://www.quantamagazine.org/20170105-graph-isomorphism-retraction/, last access: 5.1.2017

  32. Klarreich E (2017b) Graph Isomorphism Vanquished – Again. https://www.quantamagazine.org/graph-isomorphism-vanquished-again-20170114/, last access: 14.1.2017

  33. Koestler A (1964) The Act of Creation. Hutchinson (UK) Macmillan (US), London

    Google Scholar 

  34. Lenat DB (1995) CYC: A large-scale investment in knowledge infrastructure. Commun ACM 38(11):33–38

    Article  Google Scholar 

  35. Lou T, Tang J (2013) Mining Structural Hole Spanners Through Information Diffusion in Social Networks. Paper presented at the Proceedings of the 22nd International Conference on World Wide Web, Rio de Janeiro, Brazil

  36. Lubart T (2005) How can computers be partners in the creative process: Classification and commentary on the special issue. Int J Hum Comput Stud 63(4):365–369, doi:10.1016/j.ijhcs.2005.04.002

    Article  Google Scholar 

  37. Mednick SA (1962) The associative basis of the creative process. Psychol Rev 69(3):220

    Article  Google Scholar 

  38. Miller GA (1995) WordNet: A lexical database for English. Commun ACM 38(11):39–41

    Article  Google Scholar 

  39. Newell A, Shaw JC, Simon HA (1959) Report on a General Problem Solving Program. Paper presented at the IFIP congress

  40. Nicholls A, Sharp KA, Honig B (1991) Protein folding and association: Insights from the interfacial and thermodynamic properties of hydrocarbons. Proteins Struct Funct Genet 11(4):281–296, doi:10.1002/prot.340110407

    Article  Google Scholar 

  41. Park IU, Peacey MW, Munafo MR (2014) Modelling the effects of subjective and objective decision making in scientific peer review. Nature 506(7486):93–96, doi:10.1038/nature12786

    Article  Google Scholar 

  42. Polymath DHJ (2012) A new proof of the density Hales-Jewett theorem. Ann Math 175(3):1283–1327, doi:10.4007/annals.2012.175.3.6

    Article  MathSciNet  MATH  Google Scholar 

  43. Shadbolt N, Berners-Lee T, Hall W (2006) The semantic web revisited. IEEE Intell Syst 21(3):96–101

    Article  Google Scholar 

  44. Shneiderman B (2007) Creativity support tools: Accelerating discovery and innovation. Commun ACM 50(12):20–32, doi:10.1145/1323688.1323689

    Article  Google Scholar 

  45. Simon HA (1973) The structure of ill structured problems. Artif Intell 4(3-4):181–201

    Article  Google Scholar 

  46. Sjöberg L (2000) Factors in risk perception. Risk Anal 20(1):1–11, doi:10.1111/0272-4332.00001

    Article  MathSciNet  Google Scholar 

  47. Song S, Miller KD, Abbott LF (2000) Competitive Hebbian learning through spike-timing-dependent synaptic plasticity. Nat Neurosci 3(9):919–926

    Article  Google Scholar 

  48. Sowa JF (1976) Conceptual graphs for a data base interface. IBM J Res Devel 20(4):336–357

    Article  MathSciNet  MATH  Google Scholar 

  49. Sowa JF (2006) The Challenge of Knowledge Soup. Paper presented at the Research Trends in Science, Technology and Mathematics Education, Goa, IN

    Google Scholar 

  50. Thagard P, Shelley C (1997) Abductive Reasoning: Logic, Visual Thinking, and Coherence. In: Dalla ML, Chiara Doets K, Mundici D, van Benthem J (eds) Logic and Scientific Methods: Volume One of the Tenth International Congress of Logic, Methodology and Philosophy of Science, Florence, August 1995. Springer, Dordrecht, pp 413–427

    Chapter  Google Scholar 

  51. Thagard P, Stewart TC (2011) The AHA! Experience: Creativity through emergent binding in neural networks. Cognit Sci 35(1):1–33, doi:10.1111/j.1551-6709.2010.01142.x

    Article  Google Scholar 

  52. UN (2015) Transforming our World: The 2030 Agenda for Sustainable Development. United Nations

  53. UN (2017) Goal 13: Take Urgent Action to Combat Climate Change and Its Impacts. http://www.un.org/sustainabledevelopment/climate-change-2/, last access: 5.6.2017

  54. Wolverton M, Hayes-Roth B (1994) Retrieving Semantically Distant Analogies with Knowledge-Directed Spreading Activation. Paper presented at the Proceedings of the 12th National Conference on Artificial Intelligence, Menlo Park, CA

  55. Yokoi T (1995) The EDR electronic dictionary. Commun ACM 38(11):42–44

    Article  Google Scholar 

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  1. Archai – Open Science for an Enjoyable Planet Earth, 6343, Risch ZG, Schweiz

    Philip Gross

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Gross, P. Zur Lösung der wirklich bedeutenden Probleme: Was von Maschinen erwartet wird. Informatik Spektrum 41, 38–51 (2018). https://doi.org/10.1007/s00287-018-1090-5

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