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If AI today isn't capable of autonomous abstraction, it isn't because it's a particularly hard technical problem. Rather, it's a subtle conceptual problem. It doesn't seem like many people are looking at it. You can't produce the right answers if you're asking the wrong questions
AI today is to human intelligence what the phonograph was to human musicians. Superficially, it may seem like it has a comparable output & is ready to replace musicians (which was a real fear back then). But it has radically different capabilities: it's merely a recording device.
Don't confuse music creation with sound recording. Musicians are the origin of music. Unlike a phonograph, they can play a never-played before score, or write never-heard before music. Or take up a new instrument. Or invent a new musical genre.
Historically, we went technology to record sound to technology capable of helping with music production and creation. New, more powerful tools. Eventually, we will see open-ended autonomous music creation. This will be the trajectory of AI as well.
From recorded abstraction, to cognitive augmentation tools, to autonomous abstraction.
Great metaphor. Struggled to find one.
I agree with the thrust of the point here, that derivate learning will only get you so far. I guess a good question is what learning is not derivative. How much is human intuition or thought truly unique?
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