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In this paper, we will argue that, while Big Data enthusiasts imply that the analysis of massive data sets can produce serendipitous (that is, unexpected and fortunate) discoveries, the way those models are currently designed not only does not create serendipity so easily but also frequently generates zemblanitous (that is, expected and unfortunate) findings. [ABSTRACT FROM AUTHOR]

In this paper we discuss the role played by abductive reasoning and admirableness in the creative processes in the Big Data era. The conceptions of creativity, in the mechanistic and antimechanistic scope, are introduced by means of examples in the domain of photographic production, as is the case of Maureen Bisilliat (1995) and Nan Goldin (2012) works. As our investigation is essentially theoretical and interdisciplinary, it is mainly based on the literature on the concepts of creativity, abductive reasoning, and admirableness; it is grounded in different areas of knowledge, including Pragmatism, Literature, Visual arts and the Philosophy of Information. We discuss the limitations of mechanistic conceptions of creativity, arguing in defense of the hypothesis that creative action is not strictly related to the created object, but to a long collaborative process between agents that will assist (directly or indirectly) in the production of artistic works. In the conclusion, the role of emotions is highlighted in the dynamics of the creative/abductive process, as proclaimed by Charles Sanders Peirce (1958). Inspired by this author, we emphasize the role of admirableness in the process of creation - understood here as a feeling that encourages us to investigate and get to know the objects that attract our attention, without the desire for controlling them. [ABSTRACT FROM AUTHOR]

In this paper we investigate if sentences presented as the result of the application of statistical models and artificial intelligence to large volumes of data - the so-called 'Big Data' - can be characterized as semantically true, or as quasi-true, or even if such sentences can only be characterized as probably quasifalse and, in a certain way, post-true; that is, if, in the context of Big Data, the representation of a data domain can be configured as a total structure, or as a partial structure provided with a set of sentences assumed to be true, or if such representation cannot be configured as a partial structure provided with a set of sentences assumed to be true. [ABSTRACT FROM AUTHOR]