Josh, This is an interesting idea that deserves detailed discussion.
Since the 90s there has been a strand in AI research that claims that robotics is necessary to the enterprise, based on the notion that having a body is necessary to intelligence. Symbols, it is said, must be grounded in physical experience to have meaning. Without such grounding AI practitioners are deceiving themselves by calling their Lisp atoms things like MOTHER-IN-LAW when they really mean no more than G2250.
I think these people correctly recognized a problem in traditional AI, though they attributed it to a wrong cause. My opinion on this issue can be summarized as the following: *. Meaning come from experience, and is grounded in experience. *. However, for AGI, this "experience" doesn't have to be "human experience". *. Every implemented system already has a "body" --- the hardware, and as long as the system has input and output, it has experience that comes from its body. Of course, since the body is not human body, the experience is not human experience. However, as far as this discussion is concerned, it doesn't matter, since this kind of experience is genuine experience that can be used to ground meaning of concepts. *. The failure of traditional AI is not to use standard computer hardware rather than special hardware (i.e., robot), but to ignore the experience of the system when handling meaning of concepts. A more detained discussion and a proposed solution can be found in http://nars.wang.googlepages.com/wang.semantics.pdf
This has given rise to a plethora of silly little robots (in Minsky's view, anyway) that scurry around the floor picking up coffeecups and like activities.
I also think it is not a fruitful direction for AI to move.
My view lies somewhere between the extremes on this issue: a) Meaning does not lie in a physical connection. I find meaning in the concept of price-theoretical market equilibria; I've never seen, felt, or smelled one. Meaning lies in working computational models, and true meaning lies in ones that ones that can make correct predictions.
As you can see from my comment and paper, I agree with your idea in its basic spirit. However, I think your above presentation is too vague, and far from enough for semantic analysis.
b) On the other hand, the following are true: 1. Without some connection to external constraints, there is a strong temptation on the part of researchers to define away the hard parts of the AI problem. Even with the best will in the world, this happens subconsciously.
Agree.
2. The hard part is learning: the AI has to build its own world model. My instinct and experience to date tell me that this is computationally expensive, involving search and the solution of tough optimization problems.
Agree, though I've been avoiding the phrase "world model", because the intuitive picture it provides: there is a "objective world" out there, and an AI is building an "internal model" of it, where the concepts represent objects, and beliefs represent factual relations among objects --- this is a picture you don't subscribe, I guess.
"That deaf, dumb, and blind kid sure plays a mean pinball." Thus Tommy. My robotics project discards a major component of robotics that is apparently dear to the embodiment crowd: Tommy is stationary and not autonomous. This not only saves a lot of construction but allows me to run the AI on the biggest system I can afford (currently ten processors) rather than having to shoehorn code and data into something run off a battery.
A good idea. As I said above: input/output is necessary for AGI, but any concrete form of them is not, in principle. An AGI doesn't have to be able to move itself around in the physical world (though it must somehow change its environment), and doesn't have to have a certain human sensor (though it must somehow sense its environment).
Tommy, the pinball wizard kid, was chosen as a name for the system because of a compelling, to me anyway, parallel between a pinball game and William James' famous description of a baby's world as a "blooming, buzzing confusion." The pinball player is in the same position as a baby in that he has a firehose input stream of sensation from the lights and bells of the game, but can do little but wave his arms and legs (flip the flippers), which very rarely has any effect at all.
Makes sense.
Tommy, the robot, consists at the moment of a pair of Firewire cameras and the ability to display messages on the screen and receive keyboard input -- ironically almost the exact opposite of the rock opera Tommy. Planned for the relatively near future is exactly one "muscle:" a single flipper. Tommy's world will not be a full-fledged pinball game, but simply a tilted table with the flipper at the bottom.
I'd suggest to add the "muscle" in as soon as possible to get a complete sensor-motor cycle.
Tommy, the scientific experiment and engineering project, is almost all about concept formation. He gets a voluminous input stream but is required to parse it into coherent concepts (e.g. objects, positions, velocities, etc). None of these concepts is he given originally. Tommy 1.0 will simply watch the world and try to imagine what happens next.
I fully agree with your focus. I guess your "concepts" are patterns or structures formed from certain "semantic primitives" by a fixed set of operators or connectors. I'm very interested in your choice.
The scientific rationale for this is that visual and motor skills arrive before verbal ones both in ontogeny and phylogeny. Thus I assume they are more basic and the substrate on which the higher cognitive abilities are based.
Though this kind of idea is shared by many people, I think it is weak evidence for AGI design --- we don't have to follow the evolution order in AGI design, even though it can be taken as a heuristic.
Furthermore I have a good idea what concepts need to be formed for competence in this area, and so I'll have a decent chance of being able to tell if the system is going in the right direction.
To me, this is a more justifiable reason for the project than the previous one. ;-)
I claim that most current AI experiments that try to mine meaning out of experience are making an odd mistake: looking at sources that are too rich, such as natural language text found on the Internet. The reason is that text is already a highly compressed form of data; it takes a very sophisticated system to produce or interpret. Watching a ball roll around a blank tabletop and realizing that it always moves in parabolas is the opposite: the input channel is very low-entropy (in actual information compared to nominal bits), and thus there is lots of elbow room for even poor, early, suboptimal interpretations to get some traction.
I don't think you have convinced me that this kind of experiment is better than the others (such as those in NLP) , but you get a good idea and it is worth a try. Pei ----- This list is sponsored by AGIRI: http://www.agiri.org/email To unsubscribe or change your options, please go to: http://v2.listbox.com/member/?member_id=231415&user_secret=fabd7936