Caltech Studies represents a critical juncture in the evolution of machine vision. It marks the first systematic attempt to teach computers to "see" through a process where three scientists meticulously traced the contours of objects in over 9,000 images. This manual annotation – the literal act of pointing and declaring "here it is" – constitutes a fundamental moment of pedagogy between human and machine, establishing the foundation for contemporary object recognition and artificial intelligence systems.

The particular significance of this historical dataset for my artistic practice lies in its methodological approach of manual tracing using a computer mouse. For the past decade, my work has centered on an intensive exploration of mouse movements as an artistic medium, examining them through the lens of information aesthetics. The decision to now work with others' lines – specifically those drawn within a scientific context – rather than my own, marks a significant evolution in my creative process: a movement from interior to exterior, from personal to collective gesture.

A critical dimension of my investigation emerges in the inherent imprecision of these hand-drawn contours. These "errors" transcend mere technical inadequacies to manifest as bias – an imprint of the dataset creators' identity and perspective. This original bias propagates throughout the development of machine learning, multiplying across derivative datasets and becoming an inextricable component of the system.

This observation leads to a fundamental critique of digitization and machine learning: in their statistical essence, these systems aspire to a kind of "data democracy," a averaging and summarization that inevitably eliminates peculiarities and anomalies. As a practitioner who has long operated at the intersection of digital technology and artistic expression, I perceive this as a crucial loss: the reduction of vibrant, unpredictable reality to digitally approximated models.

My project thus positions itself as a multidimensional reflection: simultaneously an homage to pioneering work in computer vision, an examination of human gesture's role in artificial intelligence development, and a critical interrogation of digital approximation's limitations. It seeks to render tangible the tension between technological progress and the irreplaceable value of "authentic experience" through artistic means.

The work situates itself within the discourse of post-digital aesthetics while engaging with questions of algorithmic bias and the materiality of digital processes. Through this investigation, I aim to problematize the relationship between human annotation and machine learning, revealing the subjective traces within seemingly objective systems.