Share:


Creativity in computer science

    Piotr Giza   Affiliation

Abstract

The aim of this paper is to briefly explore creative thinking in computer science, and compare it to natural sciences, mathematics or engineering. It is also meant as polemics with some theses of the pioneer work under the same title by Daniel Saunders and Paul Thagard because I point to important motivations in computer science the authors do not mention, and give examples of the origins of problems they explicitly deny. Computer science is a very specific field for it relates the abstract, theoretical discipline – mathematics, on the one hand, and engineering, often concerned with very practical tasks of building computers, on the other. It is like engineering in that it is concerned with solving practical problems or implementing solutions, often with strongly financial reasons, e.g. increasing a company’s income. It is like mathematics in that is deals with abstract symbols, logical relations, algorithms, computability problems, etc. Saunders and Thagard analyse rich experimental material from historical and contemporary work in computer science and argue that, as opposed to natural sciences, computer science is not concerned with describing and explaining natural phenomena. Now, I argue that there is a field of research in artificial intelligence (which, in turn, is a branch of computer science), called machine discovery, where explanation of natural phenomena, finding experimental laws and explanatory models is the primary goal. This goal is achieved by constructing computer systems whose job is to simulate various processes involved in scientific discovery done by human researchers, and help them in making new discoveries. On the other hand, motivations that give rise to ingenious projects in computer science can be very strange and include curiosity, fun or attempts to be famous out of boring, stable life of a successful programmer in a big corporation. A good example is the phenomenon of open-source software, especially the development of the Linux operating system and its applications when, from economical point of view, Microsoft absolutely dominated the software market of personal computers.

Keyword : artificial intelligence, automated discovery systems, communication analogy, computer science, creative society, creativity, natural sciences, technology

How to Cite
Giza, P. (2021). Creativity in computer science. Creativity Studies, 14(2), 444-460. https://doi.org/10.3846/cs.2021.14699
Published in Issue
Nov 9, 2021
Abstract Views
88
PDF Downloads
51
Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

References

Allan, R. A. (2001). A history of the personal computer: The people and the technology. Allan Publishing.

Bridewell, W., & Langley, P. (2010). Two kinds of knowledge in scientific discovery. Topics in Cognitive Science, 2(1), 36–52. https://doi.org/10.1111/j.1756-8765.2009.01050.x

Cartwright, N. (2002). How the laws of physics lie. Clarendon Press/Oxford University Press.

Džeroski, S., Langley, P., & Todorovski, L. (2007). Computational discovery of scientific knowledge. In S. Džeroski & L. Todorovski (Eds.), Lectures notes in computer science: Vol. 4660: State-of-the-Art-Survey. Lecture notes in artificial intelligence. Computational discovery of scientific knowledge: Introduction, techniques, and applications in environmental and life sciences (pp. 1–14). J. G. Carbonell & J. Siekmann (Eds.). Springer-Verlag. https://doi.org/10.1007/978-3-540-73920-3

Fischer, P., & Żytkow, J. M. (1991, 25–27 October). Discovering quarks and hidden structure. In Z. W. Ras, M. Zemankova, & M. L. Emrich (Eds.), Methodologies for intelligent systems, Vol. 5: Proceedings of the 5th International Symposium on Methodologies for Intelligent Systems (pp. 362–370). Elsevier Science Publishing Co.

Gillies, D. (1996). Artificial intelligence and scientific method. Oxford University Press.

Giza, P. (2002). Automated discovery systems and scientific realism. Minds and Machines, 12, 105–117. https://doi.org/10.1023/A:1013726012949

Giza, P. (2006). Filozoficzne i metodologiczne aspekty komputerowych systemów odkryć naukowych. Wydawnictwo Uniwersytetu Marii Curie-Skłodowskiej w Lublinie.

Giza, P. (2018). Sign use and cognition in automated scientific discovery: Are computers only special kinds of signs? International Journal of General Systems, 47(3), 193–207. https://doi.org/10.1080/03081079.2017.1414209

Giza, P. (Forthcoming). Automated discovery systems, machine learning and data science: New developments, current issues and philosophical lessons. Philosophy Compass.

Hey, T., Tansley, S., & Tolle, K. (Eds.). (2009). The fourth paradigm: Data-intensive scientific discovery. Microsoft Corporation.

Holland, J. H., Holyoak, K. J., Nisbett, R. E., & Thagard, P. R. (1993). Computational models of cognition and perception. Induction: Processes of inference, learning, and discovery. J. A. Feldman, P. J. Hayes, & D. E. Rumelhart (Eds.). The Massachusetts Institute of Technology.

Karpatne, A., Atluri, G., Faghmous, J. H., Steinbach, M., Banerjee, A., Ganguly, A., Shekhar, Sh., Samatova, N., & Kumar, V. (2017). Theory-guided data science: A new paradigm for scientific discovery from data. Association for the Advancement of Artificial Intelligence Transactions on Knowledge and Data Engineering, 29(10), 2318–2331. https://doi.org/10.1109/TKDE.2017.2720168

Kocabas, S., & Langley P. (2001). An Integrated framework for extended discovery in particle physics. In K. P. Jantke & A. Shinohara (Eds.), Discovery Science. DS 2001. Lecture Notes in Computer Science, vol. 2226. Springer. https://doi.org/10.1007/3-540-45650-3_18

Langley, P., & Arvay, A. (2019). Scientific discovery, process models, and the social sciences. In M. Addis, P. C. R. Lane, P. D. Sozou, & F. Gobet (Eds.), Synthese library: Studies in epistemology, logic, methodology, and philosphy of science. Scientific discovery in the social sciences, Vol. 413 (pp. 173–190). O. Bueno (Ed.-in-Chief). Springer Nature Switzerland AG. https://doi.org/10.1007/978-3-030-23769-1_11

Langley, P., Sánchez, J. N. J., Todorovski, L., & Džeroski, S. (2002, 8–12 July). Inducing process models from continuous data. In C. Sammut & A. G. Hoffmann (Eds.), ICML ‘02: Proceedings of the Nineteenth International Conference on Machine Learning (pp. 347–354). Sydney, Australia. Morgan Kaufmann Publishers Inc.

Langley, P., Simon, H. A., Bradshaw, G. L., & Zytkow, J. M. (1987). Scientific discovery: Computational explorations of the creative processes. The Massachusetts Institute of Technology. https://doi.org/10.7551/mitpress/6090.001.0001

Park, Ch., Bridewell, W., & Langley, P. (2010, 11–15 July). Integrated systems for inducing spatiotemporal process models. In Proceedings of the Twenty-Fourth AAAI Conference on Artificial Intelligence, Vols. 1–3 (pp. 1555–1560). Atlanta, Georgia, United States. Association for the Advancement of Artificial Intelligence Press.

Raghu, M., & Schmidt, E. (2020). A survey of deep learning for scientific discovery. https://arxiv.org/pdf/2003.11755.pdf

Rapaport, W. J. (2020). Philosophy of computer science. University at Buffalo/The State University of New York.

Rose, D. (1989, 26–27 June). Using domain knowledge to aid scientific theory revision. In A. Maria Segre (Ed.), Proceedings of the 6th international Workshop on Machine Learning (pp. 272–277). Ithaca, New York, United States. Morgan Kaufmann Publishers, Inc. https://doi.org/10.1016/B978-1-55860-036-2.50076-X

Saunders, D., & Thagard, P. (2005). Creativity in computer science. In J. C. Kaufman & J. Baer (Eds.), Creativity across Domains: Faces of the muse (pp. 153–168). Lawrence Erlbaum Associates, Inc., Publishers.

Silberschatz, A., Galvin, P., & Gagne, G. (2008). Operating system concepts. Wiley.

Sterling, Th. (2001). Scientific and engineering computation series. Beowulf Cluster Computing with Linux. J. Kowalik (Ed.). The Massachusetts Institute of Technology Press. https://doi.org/10.7551/mitpress/1556.001.0001

Thagard, P. (1993). Computational philosophy of science. The Massachusetts Institute of Technology.

Thagard, P. (1992). Conceptual revolutions. Princeton University Press. https://doi.org/10.1515/9780691186672

Thagard, P., & Croft, D. (1999, 17–19 December). Scientific discovery and technological innovation: Ulcers, dinosaur extinction, and the programming language Java. In L. Magnani, N. J. Nersessian, & P. Thagard (Eds.), Model-based reasoning in scientific discovery. Proceedings of the International Conference on Model-Based Reasoning in Scientific Discovery (pp. 125–138). Springer Science+Business Media, LLC. https://doi.org/10.1007/978-1-4615-4813-3_8

Thagard, P., Findlay, S., Litt, A., Saunders, D., Stewart, T. C., & Zhu, J. (2014). The cognitive science of science: Explanation, discovery, and conceptual change. The Massachusetts Institute of Technology.

Torvalds, L., & Diamond, D. (2001). Just for fun: The story of an accidental revolutionary. HarperCollins Publishers.

Valdés-Pérez, R. E., Żytkow, J. M., & Simon, H. A. (1993, 11–15 July). Scientific model-building as search in matrix spaces. In R. Fikes & W. G. Lehnert (Eds.), Proceedings of the Eleventh National Conference on Artificial Intelligence (pp. 472–478). Washington, D.C., United States. Association for the Advancement of Artificial Intelligence Press.

Zytkow, J. M. (1987, June 22–25). Combining many searches in the FAHRENHEIT discovery system. In P. Langley (Ed.), Proceedings of the Fourth International Workshop on Machine Learning (pp. 281–287). University of California, Irvine. Irvine, United States. Morgan Kauffman Publishers, Inc. https://doi.org/10.1016/B978-0-934613-41-5.50032-5

Żytkow, J. M., Zhu, J., & Zembowicz, R. (1992, 12–16 July). Operational definition refinement: A discovery process. In Proceedings of the Tenth National Conference on Artificial Intelligence (pp. 76–81). San Jose, California, United States. Association for the Advancement of Artificial Intelligence Press.