Following the journey

Sometimes the hardest part of starting something isn't knowing what to do. It's deciding to begin before you know how.

For years I had wondered if technology could play a meaningful role in restoring native fisheries. The idea stayed in the back of my mind because it felt beyond my experience. I didn't know machine learning. I wasn't a software developer. I had never built a computer vision model.

What I did have was curiosity, a willingness to learn, and a desire to contribute.

So I started asking questions.

Artificial intelligence became my teacher. One question led to another. One small success led to the next. Before long, I was learning how computer vision worked, building image datasets, and training my first fish recognition model.

Looking back, Current Sight didn't begin with technology.

It began with curiosity.

One challenge quickly became obvious. The river I wanted to study was nearly a thousand miles from my home.

Like many grassroots projects, Current Sight didn't have a budget for field crews or expensive equipment.

It had family.

I shipped my dad an underwater action camera, we jumped on FaceTime, figured out how everything worked together, and he headed to the river.

A few hours later, the first videos arrived.

Some of the footage was exactly what I needed. Some of it wasn't. Both were valuable.

Watching my dad become genuinely excited about collecting data reminded me that conservation isn't always driven by large organizations or sophisticated equipment.

Sometimes it starts with people who simply want to help.

When I finally made it to the Eel River myself, I couldn't wait to collect underwater footage. The water was crystal clear. The fish were everywhere. Everything looked perfect.

Then I reviewed the videos.

Many of the fish were simply too far away from the camera for me to confidently identify. If I couldn't identify the fish, I couldn't confidently verify whether my computer vision model was correct.

At first, it felt disappointing. Then I realized something important.

I hadn't failed. I'd learned exactly what better data collection would require.

Camera angle. Distance. Lighting. Current. Background.

Every one of those variables influences whether an image becomes useful training data. That lesson changed how I collect footage today, and it will continue to shape how Current Sight develops in the future.

One lesson from my professional career carried into Current Sight from the very beginning: meaningful work is rarely accomplished alone.

I've spent years leading cross-functional teams and building programs where success depended on bringing people together around a common purpose. I knew conservation would be no different.

While I began by learning the technology behind computer vision, I also knew that technology alone wouldn't make a meaningful impact on the river. If Current Sight was going to become something useful, it would need the knowledge, experience, and perspectives of people who had already dedicated their careers to fisheries conservation.

So I started reaching out. One conversation led to another.

I connected with photographers, fisheries scientists, restoration practitioners, and people who simply cared deeply about the Eel River. Every conversation taught me something new.

Pat Higgins invited me into the river to experience fish monitoring firsthand and generously shared lessons from decades of restoration work. Keane shared a significant collection of underwater imagery that dramatically expanded my dataset and accelerated the development of my model.

Neither conversation was about asking someone else to solve the problem for me. It was about learning from people who had already walked parts of the path I was beginning.

The more people I meet, the more convinced I become that conservation moves forward through collaboration. Technology can support the work. People make it possible.