Watch a soap bubble's colors shift and swirl. You're seeing thin-film interference - light waves creating rainbow patterns as the film drains.
Physicists understand the theory: drainage, evaporation, surface tension, thermal fluctuations. In controlled experiments, they can model these effects beautifully.
The Challenge
But predicting the exact lifetime of a specific bubble right in front of you? Different story. Real bubbles are messy. Microscopic dust. Temperature variations. Air currents. Soap irregularities. All interacting in ways that make individual predictions incredibly difficult.
Where Machine Learning Enters
Can AI analyze interference patterns in the first 10 seconds and predict when that specific bubble will pop? Not from theory - from learning patterns across hundreds of real bubbles. Train neural networks on color dynamics: band spacing, drainage velocity, chromatic shifts. Let the model discover which visual signatures predict collapse.
The Unknown
What's unexplored is whether those rainbow patterns contain enough information for accurate prediction. Film bubbles. Extract features. Train models. Test if AI learns what equations and human eyes both miss.
Real research. Unknown outcome.
Tools needed: Smartphone camera • Computer • Free ML tools
