ML requires training, significant amounts of compute power, and large datasets.
The Apollo program used Kalman filters with limited compute resources.
Kalman filters are for predicting system states in the presence of uncertainty; ML is really searching for and matching patterns, under uncertainty not in its training set, it tends to to find the glitch in the matrix.
ML requires training, significant amounts of compute power, and large datasets.
The Apollo program used Kalman filters with limited compute resources.
Kalman filters are for predicting system states in the presence of uncertainty; ML is really searching for and matching patterns, under uncertainty not in its training set, it tends to to find the glitch in the matrix.