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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.

The low and shrinking numbers of women in higher computer science education is a well-known problem in most Western countries. The dominant Western perception of the relationship between gender and computer science codes the latter as "masculine," and the low number of women is seen at least partly as an effect of that coding. Malaysia represents a different case. There are large numbers of women in computer science, and computer science is not perceived as "masculine." Rather, it is deemed as providing suitable jobs and good careers for women. This reflects an understanding of gender where femininities are constructed by association to office work, commonly recognized as a woman-friendly space because it is seen as more safe and protected than, for example, construction sites and factories. The findings suggest that gender and computer science may be more diversely coproduced than commonly believed in Western research.