Machines were initially designed to formulate outputs based on the inputs that were represented by symbols. Good Old-Fashioned AI is the term for the collection of research methods that are based on symbolic (human-readable) representations of problems, logic and search. In machine learning, pattern recognition algorithms generally aim to provide a reasonable answer for all possible inputs and to perform "most likely" matching of the inputs. An example of pattern recognition is classification.
In such a growing flow of images and massive technological changes, this question then arise of how we percept an image; how we classify our observations and what is the logic behind that. These questions were the starting point of this project. Here, I sought to investigate why I’m interested for specific objects or situations. How my brain pattern recognition algorithm works, and what will be the results if I tend to apply it consicoucely by selecting and bringing these symbols together.
Rhythm express a “movement”, a regular recurrence or pattern in time. This photography series tries to release itself from time but simultaneously is based on time and rhythm. It is marked by the regulated substitution of opposite elements, the dynamics of the “played beat”, what we see, and the “rest beat”, what we just saw and is not there anymore. They are not connected over the time but they melt into each-other and make a new landscape.
Modern image recognition systems often operate on grayscale images, with the mechanism used to convert from color to grayscale. The main reason is that grayscale simplifies the algorithm and reduces computational requirements. Here by using the same method, I tend to bring attention more on the forms and the recognition procedure.
