Mood Diary investigates the

artist's agency in the age of artificial intelligence. Instead of using pre-trained AI as a passive tool, the work focuses on how the selection of training data serves as a primary creative intervention.

A grid of 100 photographs of the Toronto sky—capturing personal, emotional moments—serves as the foundational dataset. This archive is channeled through a custom-trained AI model to generate ambiguous topographies that oscillate between memory and abstraction. By blurring these sky-scapes, the work moves from individual nostalgia to collective memory, allowing viewers to project their own experiences onto the visuals.

​

To link this digital stream with the physical body, the output’s brightness and color are modulated by pre-recorded heartbeat data (integrated via Max/MSP).The core of the work is this negotiated uncertainty: exploring how AI can generate unexpected visuals while remaining anchored to the artist’s intent. It probes the liminal space between human authorshipand machine autonomy.