I think it's more than a matter of 'pragmatics': In order to do unsupervised learning (clustering) of grounded entities and concepts, they *must* be derived from vector-encodable input data. Obviously, not all inputs need to represent continuous attributes/ features, but foundational ones do.
Peter http://adaptiveai.com/ -----Original Message----- Behalf Of Ben Goertzel Kevin, I'm sure you're right in a theoretical sense, but in practice, I have a strong feeling it will be a lot easier to teach an AGI stuff if one has a nonlinguistic world to communicate to it about. Rather than just communicating in math and English, I think teaching will be much easier if the system can at least perceive 2D pixel patterns. It'll be a lot nicer to be able to tell it "There's a circle" when there's a circle on the screen [that you and it both see] -- to tell it "the circle is moving fast", "You stopped the circle", etc. etc. Then to have it see a whole lot of circles so that, in an unsupervised way, it gets used to perceiving them.... This is not a matter of principle, it's a matter of pragmatics.... I think that a perceptual-motor domain in which a variety of cognitively simple patterns are simply expressed, will make world-grounded early language learning much easier... -- Ben ------- To unsubscribe, change your address, or temporarily deactivate your subscription, please go to http://v2.listbox.com/member/?[EMAIL PROTECTED]
