Hi, > Actually, in attractor neural nets it's well-known that using random > > asynchronous updating instead of deterministic synchronous updating does > NOT > > change the dynamics of a neural network significantly. The > attractors are > > the same and the path of approach to an attractor is about the > same. The > > order of updating turns out not to be a big deal in ANN's. It may be a > > bigger deal in backprop neural nets and the like, but those sorts of > "neural > > nets" are a lot further from anything I'm interested in... > > I'd rather get ride of the notion of "attractor" altogether. Though it may > be useful for perception, in high-level cognition I don't see > anything like > it. Of course, some beliefs are more stable than others, but are > they states > to which all processes converge?
You have a point, which si why I didn't use the term "attractor" in the Hebbian Logic paper. The results I cited about attractor neural nets have to do with attractors. But in the brain, or in a Hebbian Logic network, you don't have attractors -- what you have are "probabilistically invariant subsets of state space", i.e. subsets of the system's state space with the property that, once a system gets in there, it's likely to stay there a while. Attractors are a limiting case of this kind of state-space-subset, and they're a limiting case that doesn't occur in the cognitive domain. > > Hmmm.... Pei, I don't see how to get NARS' truth value functions out of > an > > underlying neural network model. I'd love to see the details.... If > truth > > value is not related to frequency nor to synaptic conductance, > then how is > > it reflected in the NN? > > What I mean is not that NARS, as a reasoning system, can be (partially or > completely) implemented by a network, but that NARS can be seen as a > network --- though different from conventional NN. > > I think NN is much better than traditional AI in its philosophy --- I like > parallel processing, distributed representation (to a certain extent), > incremental learning, competing results, and so on. However, > ironically, the > techniques of NN is less flexible than symbolic AI. I don't like > NN when it > uses fixed network topology, has no semantics (and even claims it to be an > advantage), takes the goal of learning as converging to a > function (mapping > input to output), does global updating, uses "activation" for both logical > and control purposes, and so on. > > My way to combine the two paradigms is not to build a hybrid > system that is > part symbolic and part connectionist, but to build a unified > system which is > similar to symbolic AI in certain aspects, and similar to NN in some other > aspects. Firstly, Novamente is not a hybrid system that's part symbolic and part connectionist, either. Webmind was, but Novamente isn't anymore. There's no more association spreading or activation spreading; these NN-like processes have been replaced by specialized deployments of PTL (probabilistic reasoning Novamente-style). Novamente does hybridize a bunch of things: BOA learning, combinatory logic, PTL inference, etc. ... but not any NN stuff.... Second, I do not advocate neural nets as an approach to AI, either. I think the approach has its merits, but overall I think that NN's are a really inefficient way to use von Neumann hardware. If we knew enough to *really* emulate the brain's NN in software, then the guidance offered by the brain would be so valuable as to offset the inefficiency of implementing massively-parallel-wetware-oriented structures and algorithms on von Neumann hardware. But we don't know nearly enough about the brain to make brain-emulating NN's; and the currently popular NN architectures seem to satisfy neither the goal of brain emulation, nor the goal of efficient/effective AI. My point in articulating Hebbian Logic is NOT to propose it as an optimally effective approach to AI, but rather to propose it as a conceptual solution to the conceptual problem of: **How the hell do logical inference and related stuff emerge from neural networks and other brainlike stuff?** No one in cognitive science seems to have a good explanation of this, beyond the really vague handwaving level. I think that the Hebbian Logic approach provides a significantly better explanation than anyone else has given so far. Even given that it also involves a bunch of handwaving (since I didn't work out all the technical details, and probably won't do so soon due to my own time limitations). Hebbian Logic *might* be a decent approach to practical AI --- I don't think it would be a terribly stupid approach --- but I like the Novamente approach better... -- Ben ------- To unsubscribe, change your address, or temporarily deactivate your subscription, please go to http://v2.listbox.com/member/[EMAIL PROTECTED]
