Could somebody PLEASE, PLEASE explain how I should read those networks, such that the rest of the post makes snese?
They don’t really have rigorous meanings—they’re not actually neural nets. But loosely:
The first network is designed so that it registers certain properties of objects, and tries to see if there are any associations those properties. It makes no assumptions about what sorts of relationships should exist, or what the final results will be. The second network also starts off with two categories, and builds up associations between each category and the set of properties.
The categories that the second network uses are convenient, but as far as we can tell they have no existence outside of the network (or our minds). The category is a label—each object has the property of luminousness to some degree, density to some degree, but it doesn’t inherently possess the label or not. The category is just what things are assigned to. But the network treats that label as a property, too. So you can know all of the real properties of the objects, and the second network will still have one variable for it undefined: which category does it belong to?
The second network uses a concept that isn’t something that can be observed. The first network doesn’t carry any baggage with it like the second does.
The first network is designed so that it registers certain properties of objects, and tries to see if there are any associations those properties. It makes no assumptions about what sorts of relationships should exist, or what the final results will be. The second network also starts off with two categories, and builds up associations between each category and the set of properties.
The categories that the second network uses are convenient, but as far as we can tell they have no existence outside of the network (or our minds). The category is a label—each object has the property of luminousness to some degree, density to some degree, but it doesn’t inherently possess the label or not. The category is just what things are assigned to. But the network treats that label as a property, too. So you can know all of the real properties of the objects, and the second network will still have one variable for it undefined: which category does it belong to?
The second network uses a concept that isn’t something that can be observed. The first network doesn’t carry any baggage with it like the second does.