y fused into abstraction and evolve it to take contemporary forms of dynamism. The artist explores this by matching the more organic forms in Eastern abstraction and the more geometric forms in Western with the animation language of their corresponding diptych parts.

Above all, machine intelligence or ‘artificial’ intelligence based art in itself is a craft of abstraction drawing from data (in other words- accessing inputs from machinic memory), combined with codified processes. In this work, the artist attempts to augment the aesthetics of abstractionism and our engagement with it through a machinic lens of enquiry to them.

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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. The artist incorporates this distinction directly in the craft of the machine training process too, varying learning rates corresponding to compositional and painterly learning. 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.

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