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Why is the discovery of the magnetic field so essential analogy for explaining the challenges in feature discovery, feature selection and feature engineering and representation learning, which Artificial Intelligence (AI) faces during supervised machine learning?
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For example, in order to uncover the magnetic field from a hidden to an observable object, it takes lots of trial and error variation of the kind I have described above. One must have magnets and iron objects before one can observe the consequences of the initially still hidden magnetic field (object). Once we have the consequences, we can use the feature and measurement variation methods analog to those outlined above, to hunt for the still hidden causes, i.e. hidden factors/objects. There could be many more imperatively hidden objects (IHO) like the magnetic field, which we cannot sense and hence still know nothing about, even though they could profoundly affect our lives. The magnetic field is a good analogy to communication the possibility that many similar dimensions are still awaiting their discovery.
Dear Thomas,
Nothing! The magnetic field has nothing special or hidden. This field is well known for a long time ago. For instead, in average its value is very small, 0.5 Oe, but it was enough for guiding us using a compass. The name cames from the Greeks which gave the name of magnetite as a the stones of Magnesia ( region of the present Turkey). Nowadays it is employed in most of the electric motors or memories for computers, etc...but there are nothing strange that we cannot know about the magnetic field.
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