Carey J. Flack

Leaves in hand, I came into this project with a simple question: what does it take to teach a machine to see what we overlook? And what can teaching a machine, in turn, teach me about life around us?

I'm a hobby ecologist, so I'm naturally drawn to the small. For this project, I chose to investigate the vein networks in a leaf — they're vital to life on earth and almost entirely unseen, similar to an ML model's own inputs. I chose the creeping fig (Ficus pumila) as my subject; a little vine that eats fences. It's invasive here in California, so harvesting hundreds of leaves to experiment and troubleshoot felt less harmful to the ecosystem. I flattened them, laid them on the scanner glass a handful at a time, and scanned every single one before pressing them in a book. This took 10+ hours across many days. I came to appreciate what it takes to gather ML data.

Can a machine learn a vein, then dream a new one?

That was the real question. Veins are unseen, we don't think about them, but they hold up the whole leaf, and the leaf holds up the ecosystem, and the air we breathe. It felt like a fitting thing to ask a model to learn: the intimate, structural, easy-to-overlook parts. It reminds me of the overlooked dataset driving the models we love to use.

Watching it train, checkpoint by checkpoint, a hundred images at a time, I understood ML differently. The output isn't the point. The inputs are — the data, the pipeline, the thousands of small decisions. The labor is the real star here; whether you're building these datasets, the models that use them, or maintaining them, they're made by many, many hands.

What artists have been saying for so long is true: the purpose is the process, and the labor is the art.

So maybe it hasn't quite imagined new veins yet — or answered the biggest questions of the plant universe — but I do have ideas for the next pass. What's another epoch anyway?