4 ms·
While I applaud the use of novel techniques to approach problems that have a traditional solution, I am reluctant to believe that they can outperform the tradit
by donquichotte 4y ago
While I applaud the use of novel techniques to approach problems that have a traditional solution, I am reluctant to believe that they can outperform the traditional approaches for a certain type of problem (e.g. state estimation for systems where the dynamics are well understood and can be derived from first principles, like cars).
- zellyn 4y agoI'm pretty sure that deriving position from acceleration data from an inertial sensor is not that cleanly done from first principles: it tends to drift a bunch. There's no reason the trained model can't emulate the dynamics accurately and do just as well as the traditional approach, but then also learn from additional clues.
- dreamcompiler 4y agoYou can compensate for drift with GPS updates, just like they're doing with the ML net. I don't think "add moar ML!" adds value to every domain.
- gugagore 4y agoThe traditional solutions still involve careful running. People are mentioning Kalman filters. How are the covariances of the noise processes chosen? The best choice of parameters is chosen by experimentation and measurement, not first principles. Additionally, "first principles" can mean modeling the contact patch been the tire and road, and estimating the friction, based on weather and road conditions. Or it can mean a mixture of two models, one with and without slippage. How do you detect which model is "active"? That's more parameters that require tuning. Ideally, models are both data-driven and human-interpretable.