2018-2019

Machine learning systems construct landscape understanding through statistical pattern analysis rather than direct observation, creating synthetic natural environments that reveal algorithmic interpretation of photographic data. This work documents a specific geographic location—County Highway 5 in Bovina, NY—as training material for neural networks that generate new visualizations based on computational analysis of rural imagery. The resulting images function as evidence of artificial intelligence developing its own systematic approach to representing natural spaces.

The work establishes accuracy as irrelevant to machine vision, prioritizing computational coherence over observational fidelity. Neural networks create internally consistent landscape representations that follow algorithmic logic rather than photographic documentation. These synthetic environments demonstrate how artificial intelligence constructs meaning from visual data according to its own organizational principles, producing landscapes that exist as computational artifacts rather than documentary records.

For more on the concepts behind this work, see the essay Little AI.

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Related Work

Learning Nature • Winter Woods • Cloud Canyon • Dandelions - AI / machine learning generated images and videos

Little AI - AI / Machine learning sculpture