Hi, > In a private email, Moshe Looks added a common complaint that I'd > forgotten: > > ---- > > Complaint: Learning to be intelligent isn�t possible without building up > abstract cognitions hierarchically from a foundation of content-rich > sensory > and action streams. While Novamente can deal with content-rich sensory > and > action streams in principle, it�s not really centrally designed for this. > Work on AGI should begin with study of perception and action, and then one > should ask what sort of cognition naturally goes along with one�s working > perception and action modules � and the answer may or may not look like > Novamente. > Well, this is a bit of a straw-man. I guess its fairly close to the view of Patrick Winston and Project Genesis (http://genesis.csail.mit.edu/), but *I* was applying the words "content-rich" to modalities rather that sensory and action streams. For example, would you condsider a 30x30 binary pixel grid with a logo turtle "content-rich sensory and action streams"? Maybe, but I'm sure its not the first image that appears in the minds of the intrepid AGIers reading this ;-). Yet you can come up with all sorts of "rich" modality-specific problems, like resolving ambiguity, object completion, invariance under transformations, temporal patterns, etc, etc, etc... Clearly a buch of the brain's perception/action complexity is completely irrelevant in dealing with such a world, but not all of it! The Anwser still bascially works, I just wanted to get the question clear..
Moshe **************************** > Answer: Our intuition is that content-rich media aren�t critical, rather > that what�s important for learning to think is interaction with other > minds > in a shared perceptual environment in which you�re embodied. However, if > content-rich media are critical, Novamente can be used for rich > sensorimotor > processing perfectly well. While it�s always possible to code specialized > processing code for each type of sensor and actuator, we believe it�s > better > to begin with a common framework (such as BOA+PTL) and then specialize it > to > deal with the different modalities. This is conceptually analogous to the > way the brain uses the same basic neural mechanisms to deal with the > different human modalities, and also with cognition. > > ---- > >> Complaint: The design is too complicated, there are too many >> parts to coordinate, too many things that could go wrong >> >> Answer: Yes it IS complicated, and we wish it were simpler, but >> we haven�t found a simpler design that doesn�t seem patently >> unworkable. Note that the human brain is also mighty complicated >> � this may just be the nature of making general intelligence work >> with limited resources. >> >> Complaint: BOA and PTL are not enough, you need some kind of more >> fundamentally innovative, efficient, or (whatever) learning >> algorithm. This complaint never comes along with any suggestion >> regarding what this �mystery algorithm� might be, though � most >> often it is hypothesized that detailed understanding of the human >> brain will reveal it. >> >> Answer: This is possible, but it seems to us that a hybrid of BOA >> and PTL will be enough. The question is whether deeper >> integration of BOA and PTL than we�ve done now will allow BOA >> learning of reasonably large (500-1000 node) combinator trees. >> If so, then we almost surely don�t need any other learning >> algorithm, though other algorithms may be helpful. >> >> Complaint: You�re programming in too much stuff: you should be >> making more of a pure self-organizing learning system without so >> many in-built rules and heuristics >> >> Answer: Well, the human brain seems to have a lot of stuff >> programmed in, as well as a robust capability for self-organizing >> learning. Conceptually, we love the idea of a pure >> self-organizing learning system as much as anyone, but it doesn�t >> seem to be feasible given realistic time and processing power and >> memory constraints. >> >> Complaint: Programming explicit logical rules is just wrong; >> logic should occur as an emergent phenomenon from more >> fundamental subsymbolic dynamics >> >> Answer: Probabilistic logic is not necessarily symbolic; in the >> Novamente design we use PTL for both subsymbolic and symbolic >> learning, which we believe is a highly elegant approach. The >> differences between subsymbolic probabilistic logic and e.g. >> Hebbian learning are not really very great when you look at them >> mathematically rather than in terms of verbiage. The Novamente >> design is not tied to programming-in logical knowledge a la Cyc. >> It�s true that the PTL rules are programmed in (though in >> Novamente 2.0 they will be made adaptable), but this isn�t so >> different from the brain having particular kinds of long-term >> potentiation wired in, is it? > > > ------- > To unsubscribe, change your address, or temporarily deactivate your > subscription, > please go to http://v2.listbox.com/member/[EMAIL PROTECTED] > ------- To unsubscribe, change your address, or temporarily deactivate your subscription, please go to http://v2.listbox.com/member/[EMAIL PROTECTED]
