Can We Think Without Categories?
Author(s): Manovich, Lev
In this article methods developed for the purpose of what I call “Media Analytics” are contextualized, put into a historical framework and discussed in regard to their relevance for “Cultural Analytics”. Largescale analysis of media and interactions enable NGOs, small and big businesses, scientific research and civic media to create insight and information on various cultural phenomena. They provide quantitative analytical data about aspects of digital culture and are instrumental in designing procedural components for digital applications such as search, recommendations, and contextual advertising. A survey on key texts and propositions from 1830 on until the present sketches the development of “Data Society’s Mind”. I propose that even though Cultural Analytics research uses dozens of algorithms, behind them there is a small number of fundamental paradigms. We can think them as types of data society’s and AI society’s cognition. The three most general paradigmatic approaches are data visualization, unsupervised machine learning, and supervised machine learning. I will discuss important challenges for Cultural Analytics research. Now that we have very large cultural data available, and our computers can do complex analysis quite quickly, how shall we look at culture? Do we only use computational methods to provide better answers to questions already established in the 19th and 20th century humanities paradigms, or do these methods allow fundamentally different new concepts?
Manovich, Lev: Can We Think Without Categories?. In: Digital Culture & Society. Rethinking AI, Jg. 4 (2018), Nr. 1, S. 17–27. DOI: https://doi.org/10.25969/mediarep/13523.
Except where otherwise noted, this item's license is described as Creative Commons - Attribution - Non Commercial - No Derivatives