If one wants general AI that can deal with/understand the world, the system needs to learn based on (raw, unadulterated) data from the world. These data are highly dynamic and rarely fit neatly into human-labeled categories. This is part of why the currently-hyped supervised pattern recognition is not all that helpful for general AI.
By the way, the above requirement of real world data probably even applies to building chatbots. (A successful chatbot will need some understanding of the world it is talking about; this is what researchers mean when they say "grounding" is important for NLP).
By the way, the above requirement of real world data probably even applies to building chatbots. (A successful chatbot will need some understanding of the world it is talking about; this is what researchers mean when they say "grounding" is important for NLP).