> if anything it finds out just how ill defined the world actually is.
Yeah I hear this but it seems only half-true to me. While for most intents and purposes the world is ill-defined, in another sense the world itself is "100% signal" and no noise. If we "zoom out" and take a grand view, imagining that we have a supercomputer and a huge database, and the algorithms are solved, I think every 'thing' in the universe has some unique features, and if you start to have them all in a database you may be able to uniquely identify any thing, at least those important to us. Everything one has excludes something else, but it also includes that specific thing. Every thing adds context to one thing and removes context from another. If you can draw a map of it, it seems to me like deep learning can, hypothetically, automatically differentiate it. Deep learning isn't just about one vector or one hierarchy of features, it's about how the world is ALL vectors like this, even if right now, the CS around it is pretty limited. It seems to me intuitively true at least. At the bare minimum, seeing as us humans are absurd about categorizing everything into objects, and it actually works very well functionally (we can manipulate, create and predict in the world)
If I understand your point, I'd suggest that it may apply best to the use of DL for low level AI -- seeing, hearing/generating speech, and recognizing/ navigating/ interpreting complex signals of other kinds. There classification is secondary to modeling the many subsymbolic facets endemic to raw analog signals.
I suspect DL will eventually settle into a less vaunted role in the historical saga of AI than it portends now. And that role may well be the 'grounding' of sensory experience -- the modeling of the world into something perceptually and cognitively manageable, like Plato's shadows on a cave wall.
Yeah I hear this but it seems only half-true to me. While for most intents and purposes the world is ill-defined, in another sense the world itself is "100% signal" and no noise. If we "zoom out" and take a grand view, imagining that we have a supercomputer and a huge database, and the algorithms are solved, I think every 'thing' in the universe has some unique features, and if you start to have them all in a database you may be able to uniquely identify any thing, at least those important to us. Everything one has excludes something else, but it also includes that specific thing. Every thing adds context to one thing and removes context from another. If you can draw a map of it, it seems to me like deep learning can, hypothetically, automatically differentiate it. Deep learning isn't just about one vector or one hierarchy of features, it's about how the world is ALL vectors like this, even if right now, the CS around it is pretty limited. It seems to me intuitively true at least. At the bare minimum, seeing as us humans are absurd about categorizing everything into objects, and it actually works very well functionally (we can manipulate, create and predict in the world)