This work presents a study of still-life aesthetics through the lens of artificial intelligence computer vision. Positing the question, can machines be taught aesthetics, here the artist trains a machine to look at thousands of still-life paintings, some in their entirety, and some in their details, to try and guide a machine to learn both composition and the painterly nuances of aesthetic. This work tries to start to teach AI the conceptual distinction between the compositional and the painterly. In any painting what is the relation of the part (as fetish) to the whole (as icon)? How can one teach a computer compositional structure and painterly texture?

Through this training, the machine is able to abstract out a relational sense of form, color, composition and produce outputs that resemble an uncanny likeness, yet the obvious departure to (real) life, very similar to this moment of digital transition that we are living in. For the video work, the artist uses this learning of the machine, across the icon and fetish, to interpolate from the fetish to the icon to the fetish, in a seamless morphing of varying detail levels of still life paintings. The work highlights the increasing digitization of natural ecology too- and the need for rapidly changing media. Still Life (the artistic subject of centuries) has become (Un)Still in today’s times.

A grid artwork is created, where for the central work, the AI develops a sense of form from studying examples of whole paintings in its dataset of European still lives of floral arrangements. In the surrounding works, the AI develops its aesthetics by only studying random details in the still life paintings, where i randomly zoom into the paintings and limit the region the AI trains on. I further create a distinction for these, by altering the training process for both, using a slower learning rate for the central piece (allowing larger time for the AI to learn details) and faster learning rate for the outer pieces (not giving the AI enough scope to pick up on details).

An experiment in understanding computer vision (and an attempt towards advancing the field of AI art), this work tries to start to teach AI the conceptual distinction between the compositional and the painterly. In any painting what is the relation of the part (as fetish) to the whole (as icon)? How can one teach a computer compositional structure and painterly texture? This work tries to start poking into these set of questions.