LEARNING ON THE JOB BY JERRY SALTZ SERIES

For example, the algorithm scored the creativity of Edvard Munch’s “The Scream” (1893) much higher than its late 19th-century counterparts. When we ran an analysis of 1,700 paintings, there were several notable findings. The algorithm made its conclusions without any encoded knowledge about art or art history, and made its assessments of paintings strictly by using visual analysis and considering their dates. Our parameter setting was the definition of creativity: originality and lasting influence. In our case, the input was what the algorithm saw in the paintings: color, texture, use of perspective and subject matter. For example, when you search the web using Google, Google uses an algorithm of this type to navigate the vast network of pages to identify the individual pages that are most relevant to your search.Īny algorithm’s output depends on its input and parameter settings.

LEARNING ON THE JOB BY JERRY SALTZ SERIES

Through a series of mathematical transformations, we showed that the problem quantifying creativity could be reduced to a variant of a class of algorithms called network centrality problems, which are widely used in the analysis of social interaction, epidemic analysis, and web searches. Using computer vision, we built a network of paintings from the 15th to 20th centuries. We used the most common definition for creativity, which emphasizes the originality of the product, along with its lasting influence. In prior work, my colleagues and I developed an algorithm that assessed the creativity of any given painting, while taking into account the painting’s context within within the scope of art history.

Learning on the job by jerry saltz