My philosophy of AI has never been logic-based or neural-based. I did explore neural nets during the neural-net mania of the nineties. I did a lot of reading, and experimented with some with feedforward nets I wrote using simulated annealing and backpropagation (which never did work very well). Neural nets seem to have potential as one tool among several types of incremental learning algorithms, including genetic algorithms and statistical methods, but in themselves, they are no more than that -- useful tools, but not the solution.
Language, which includes logic, is a way of representing ideas simply and crudely. Good for communication and internal reasoning -- "if I do this then this will happen, unless state X is the case, which means that this other thing will happen," etc. My project uses an artificial language (Jinnteera) for both these things, and the language is integral to the whole thing. But it does not function as the core knowledge-representation scheme. So this brings us to what I've been calling the missing piece. Artificial neural nets (as they currently exist) can function as general-learning algorithms, but they don't represent knowledge of the real spatiotemporal world well. They are too low-level for handling what in human intelligence is thought of as mental imagery. Yes, in the brain, it is all neural based, but in a non-massively-parallel von Neuman computer system (even a PDP system), building a 100-billion-node neural net is computationally intractable (is that the right word?). It has to be done differently. The missing piece lies between low-level learning algorithms and highest-level logical-linguistic knowledge representation. When a human translator, at the U.N., for example, translates between Chinese and English, he (or she) does it infinitely more effectively than any translation software could do it, because there is an intermediate knowledge representation that is neither Chinese nor English, but that can be readily translated to or from either language by a fluent speaker. The intermediate knowledge representation is non-linguistic -- it consists of mental models constructed of sensorimotor patterns representing a 3-D temporal world. This sounds very vague and abstract, but I'm working on making it concrete, in my system (Gnoljinn) -- developing the data structures in code for implementing this knowledge-representation scheme. There's been some talk here recently about 3-D vision systems, and this points roughly in the direction I'm going in. Gnoljinn uses a single sensory modality right now -- vision -- and will be restricted to it for a good while, because, while it might be useful to have other sensory modalities, none of them are absolutely necessary for higher intelligence, and it's best to keep things as simple as possible starting out. I seriously wonder if I can do this project myself, or whether I need to try to find some collaborators. Yan King Yin wrote: John Scanlon wrote: > [...] > Logical deduction or inference is not thought. It is mechanical symbol manipulation that can can be programmed into any scientific pocket calculator. > [...] Hi John, I admire your attitude for attacking the core AI issues =) One is either neural-based or logic-based, using a crude dichotomy. So your approach is closer to neural-based? Mine is closer to the logic-based end of the spectrum. You did not have a real argument against logical AI. What you said was just some sentiments about the ill-defined concept of "thought". You may want to take some time to express an argument why logic-based AI is doomed. In fact, both Ben's and my system have certain "neural" characteristics, eg being graphical, having numerical truth values, etc. In the end we may all end up somewhere between logic and neural... ----- 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/?list_id=303
