before, the temporary interestingness of the data corresponds to the number of saved bits. This compression progress is proportional to the observer's internal reward, also called curiosity reward. A reinforcement learning algorithm is used to maximize future expected reward by learning to execute action sequences that cause additional interesting input data with yet unknown but learnable predictability or regularity. The principles can be implemented on artificial agents which then exhibit a form of artificial curiosity. Truth in beauty and mathematics Mathematical considerations, such as symmetry and complexity, are used for analysis in theoretical aesthetics. This is different from the aesthetic considerations of applied aesthetics used in the study of mathematical beauty. Aesthetic considerations such as symmetry and simplicity are used in areas of philosophy, such as ethics and theoretical physics and cosmology to define truth, outside of empirical considerations. Beauty and Truth have been argued to be nearly synonymous, as reflected in the statement "Beauty is truth, truth beauty" in the poem "Ode on a Grecian Urn" by John Keats, or by the Hindu motto "Satyam Shivam Sundaram" (Satya (Truth) is Shiva (God), and Shiva is Sundaram (Beautiful)). The fact that judgments of beauty and judgments of truth both are influenced by processing fluency, which is the ease with which information can be processed, has been presented as an explanation for why beauty is sometimes equated with truth. Recent research found that people use beauty as an indication for truth in mathematical pattern tasks. However, scientists including the mathematician David Orrell and physicist Marcelo Gleiser have argued that the emphasis on aesthetic criteria such as symmetry is equally capable of leading scientists astray. Computational approaches Computational approaches to aesthetics emerged amid efforts to use computer science methods "to predict, convey, and evoke emotional response to a piece of art. It this field, aesthetics is not considered to be dependent on taste but is a matter of cognition, and, consequently, learning. In 1928, the mathematician George David Birkhoff created an aesthetic measure M = O/C as the ratio of order to complexity. Since about 2005, computer scientists have attempted to develop automated methods to infer aesthetic quality of images. Typically, these approaches follow a machine learning approach, where large numbers of manually rated photographs are used to "teach" a computer about what visual properties are of relevance to aesthetic quality. A study by Y. Li and C.J. Hu employed Birkhoff's measurement