@jamieeburgess, @mike_gessner,
You’re right - I may need to revise my factial recognition analogy a bit, because @jamieeburgess is right - Facial recognition really doesn’t need the full-on flexibility of machine learning for identification, nor does radar detection. But they both use the same two part process as Sense does - Identification followed by classification.
Facial identification is typically done using biometric algorithms that discover and measure facial features, or principle component analysis that translates facial reference points into simpler identifying metrics. @mike_gessner, I’m fairly certain that radar identification is done via DSP and convolutional algorithms, since most military systems were developed/designed well before machine learning really came into being. Both facial recognition and radar identification use fixed algorithms and a smaller set of well defined features to do identification than typicallly used for machine learning.
The real analogy should be the current state of the art in machine vision and image recognition, that uses complex neural networks like ResNet, to identify thousands of different everyday objects, animals, and other nouns.