1. Just that memories fade over time, and it is the details we lose first. Or at least this is how my memory works. Maybe they are all stored, and it's just the recall that becomes more difficult. It's not fundamental to the design, just a mechanism for conserving potentially limited storage space.
2. I'm working on it on my own personal time, so there's no telling how long it will take. But I'd like to think the language system could be up and running by the end of this year, and the perceptual system in 5 years. That's probably wildly optimistic, but I have to aim for something. As for the reasoning system, a full general intelligence is probably decades off, but since improvement will be incremental it's likely that even an incomplete reasoning mechanism could prove useful in industry. On Tue, Oct 16, 2012 at 3:16 PM, Piaget Modeler <[email protected]>wrote: > > Aaron, just two comments: > > "Higher levels of abstraction can be generated by looking at patterns in > objects (just as objects are generated by looking at patterns of parts) and > adding additional nodes which serve to group together the lower level nodes > into patterns based on link types. Memory stores only these higher-level > nodes (parts, objects, & upward), not the lower levels which served in > their construction, and memory fades from the lowest levels upward, causing > us to lose detail but not gist." > > 1. What makes you think memory stores only the higher-level nodes? I > contend that the lower level nodes are also stored and can be reactivated > (or "recalled") as needed. > > 2. I concur with your theory. How long until the implementation? > > ~PM. > > > ------------------------------------------------------------------------------------------------------------------------------------------------ > *Confidential *- *This message is meant solely for the intended > recipient. Please do not copy or forward this message without * > *the consent of the sender. If you have received this message in error, > please delete the message and notify the sender.* > > ------------------------------ > Date: Tue, 16 Oct 2012 14:34:37 -0500 > Subject: Re: [agi] Re: Superficiality Produces Misunderstanding - Not Good > Enough > From: [email protected] > To: [email protected] > > > Well, I'm not really clear what you're getting at, mainly because when > talking about intelligence & thinking, all the terms we have to use are so > versatile & loosely defined that to narrow down what's being communicated > to a sufficiently narrow set of interpretations, we have to say so much > that the key point becomes a needle in a haystack of contextual > information. I'm sure what you're saying here makes perfect sense to you, > but the words you're using aren't sufficiently grounded (or are grounded > differently for you than for me) that I don't follow. > > I get the impression that you're saying (both here & in your previous > emails on Algorithmic Synthesis) that claiming two things are associated > isn't enough -- that the *kind* of association is important too. I agree > with you here. It's not enough to say, these are the parts and they go > together; how things connect must be considered to have productive thoughts > about them. This is directly analogous to the treatment of sentences as > bags of words: It's not enough to just look at the set of words to > determine the sentence's meaning; the way they connect to each other > matters. This is where I'm starting from in my system's design. > > #1: Figure out how the human mind represents meaning. > #2: Figure out how to work with meaning to produce intelligent thought. > > #2 cannot proceed until #1 is effectively implemented. Roger Schank has > provided quite a bit of inspiration to me, based on how he represents > meaning as semantic links connecting basic concepts together. From the > natural language perspective, it is relatively easy to see how this can be > implemented. I'm not alone in having successfully built a parser that > extracts a semantic network from a sentence which represents that > sentence's meaning with a fair degree of accuracy. > > From the perceptual perspective, it is also fairly easy to see how > semantic networks can be used to represent information. The visual field > can be broken into chunks or fields, each representing an object or a part > of an object. The objects are semantically connected to each other > according to the spatial or behavioral interactions they are participating > in, and the parts of objects are semantically linked to the objects and > other parts according to their arrangement. Nodes representing objects and > parts generated at a particular time can then be interconnected across > multiple time frames, resulting in a narrative description of the field of > vision as a sequence of events unfolds. Other senses can be integrated > directly with vision in the same manner. > > Higher levels of abstraction can be generated by looking at patterns in > objects (just as objects are generated by looking at patterns of parts) and > adding additional nodes which serve to group together the lower level nodes > into patterns based on link types. Memory stores only these higher-level > nodes (parts, objects, & upward), not the lower levels which served in > their construction, and memory fades from the lowest levels upward, causing > us to lose detail but not gist. > > Language (or rather the semantic nets which represent meaning) can then be > treated as predicates which match the upper levels of the perceptual > network, acquiring a non-Boolean or fuzzy truth value based on how well > they match perceptual information retrieved from memory. Thinking is > implemented at this level, as well. Thinking processes serve to generate > truthful predicates based both on direct observation of higher-level > perceptual subnets, and indirect reasoning based on observed patterns in > these perceptual subnets. Reasoning can reach as far down the hierarchy of > nodes as was stored in memory, but starts from the top-most level and does > not reach down to these lower levels except when higher-level abstractions > indicate that additional or finer-grained detail is needed. (This is how we > avoid the combinatorial bottleneck.) Predicates generated by observation or > reasoning can be directly read off and converted to natural language using > the same mechanisms as the semantic parser, but in reverse. (I've got much > of this mechanism working, too.) > > I have yet to start work on the perceptual systems, but the semantic > representation of meanings/predicates is rolling along nicely. Perception > is going to take a lot more work, because there's a lot more data to > process, but I'm watching the research as it unfolds, and I see a lot being > done in the direction of object detection. Even if we create a perceptual > system that isn't as detailed in representation as human perception (i.e. > it represents objects and their interactions, but not their parts or lower > level abstractions), it should be possible to start work on a reasoning > system that handles higher-level abstractions and is able to communicate > its thoughts verbally or in text. This is the key point at which artificial > general intelligence gains traction as a technology worthy of financial > investment. > > > *AGI* | Archives <https://www.listbox.com/member/archive/303/=now> > <https://www.listbox.com/member/archive/rss/303/23050605-bcb45fb4> | > Modify<https://www.listbox.com/member/?&;>Your Subscription > <http://www.listbox.com> > ------------------------------------------- AGI Archives: https://www.listbox.com/member/archive/303/=now RSS Feed: https://www.listbox.com/member/archive/rss/303/21088071-c97d2393 Modify Your Subscription: https://www.listbox.com/member/?member_id=21088071&id_secret=21088071-2484a968 Powered by Listbox: http://www.listbox.com
