AI / machine learning generated images. 2018-2019.

Working at the intimate scale of a rural farm in upstate NY, this project explores what happens when artificial intelligence is applied to nature through the lens of beauty rather than efficiency. In contrast to corporate AI systems built on massive datasets and optimization for scale, this work proposes a more intimate approach. Using Generative Adversarial Networks (GANs), I trained a machine learning system with photographs of flowers I captured during the summer of 2018, creating an intentionally small, personal dataset that prioritizes aesthetic experience over computational power.

The resulting images emerge from a symbiotic creative process – neither purely machine-generated nor traditionally human-authored. As the AI attempts to understand and recreate nature, it reveals its own digital limitations and simulated intelligence. Pixelated beginnings give way to impressionistic interpretations that echo the Hudson River School painters who worked in this same landscape nearly two centuries ago. The system struggles with organic forms it cannot fully grasp, creating ghostly flowers and ethereal landscapes that exist nowhere in the physical world yet speak to our deep human visual connections with nature.

This is not photography, nor is it copying – it is interpretation and misinterpretation made visible. The machine builds its own unique vision of the natural world, one marked by gentle ambiguity and unexpected beauty. Each image captures a moment in an endless process of digital becoming, stopped only by human intervention. The work asks us to reconsider what we mean by intelligence, creativity, and our relationship to both technology and the natural world.

Nothing that emerges is accurate, but the work isn’t asking for accuracy—it’s asking for the machine to develop its own way of seeing, and for us to find beauty in that strange new vision.

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

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