AI & Bike Fitting:

An interview with Pedro A. Favuzzi

Can you tell us about yourself and how you came into AI research?

I like to say that I am a scientist at heart. I did my PhD in physics what now seems like a life ago, after which I founded a few small startups and worked in IT consulting on the side, always working with small projects which I find more challenging and interesting. I have been fascinated with computer vision and deep learning pretty much since the moment I learned about it. A couple of years ago I was thinking about what to do next and I just decided to shift from software development to AI.

Can you tell us about the project you have been working on and how it relates to cycling analysis?

The mission of Kineticlab lab is to remove all the attrition in the bikefitting job and just help bikefitters be in the zone with the clients. You won’t need to waste your time behind a screen, putting markers, etc, and doing all of this will be done without the need for special equipment, cameras or expensive computers. That is KineticLab’s long term vision, for now, I have built a markerless joint tracking system, integrated in a video analysis tool.

How do you see AI transforming the way bike fitting is done?

I don’t think AI will change the way bikefitting is done, rather AI will smooth out the process, imagine if the only thing you had to do to measure the bike was to bring it into your studio, your clients will start pedaling and AI will detect and measure all the information you need. Do you change the seat height? Great, the AI will detect what change has been done, by how much, and keep track and organize the measurements. Finally I think AI will make communication with clients much more impactful and clear, enabling new visualizations that can translate what bikefitters have learned to see after years of practice to riders.

What are the current limitations of AI technology?

Resource requirements and data dependency. Most AI development has been towards increasing accuracy, by using huge amounts of data, and a lot more computation. ChatGPT was trained on 570 Gigabites of data, that is about 25 wikipedia's worth of textual data GPT4 requires probably (there no official number) a few terabytes of vram (memory in the graphic card). This means that a person would have to buy several hundreds of the latest graphic cards to run chatGPT4 on their device. This problem is even worse for computer vision. But I am very positive about future developments, I believe these challenges will be significantly smaller in the next 3 to 5 years.

Should people be fearful or excited about AI coming into their work?

That is a complex question. I don’t think we should be afraid of AI, and I really don’t think the terminator vision of AI is realistic. However we should definitely be afraid of how we (humans) will use AI. People should be afraid or at the very least concerned about how their employers will use AI. AI can either lead us to a Star Trek like future or an Elysium future. It's up to us to choose how we want to use AI, whether it will be for the well being of all or to make very very few feel superior to all the rest.