My background is in automation and robotics; I studied system identification: a discipline where you would use mathematical means to identify a dynamic system model by observing input/output.
You treat the system as a black box and estimate a set of parameters that can describe it (e.g., Kalman filter).
I struggle to understand what's the fundamental difference between system identification and ML/AI. Anyone?
You ultimately have a bunch of data and try to estimate/fit a model that can describe a particular behavior.
It all comes down to a big optimization/interpolation problem. Isn't what they call "Learning" just really "estimating" ?
Then the more CPU/memory/storage you have, the more parameters/data you can estimate/process, the more accurate and sophisticated the model can be.
You treat the system as a black box and estimate a set of parameters that can describe it (e.g., Kalman filter).
I struggle to understand what's the fundamental difference between system identification and ML/AI. Anyone?
You ultimately have a bunch of data and try to estimate/fit a model that can describe a particular behavior.
It all comes down to a big optimization/interpolation problem. Isn't what they call "Learning" just really "estimating" ?
Then the more CPU/memory/storage you have, the more parameters/data you can estimate/process, the more accurate and sophisticated the model can be.