What's the difference between value groups/values and classes/objects? A rose by any other name would smell as sweet. It seems to me that value groups and classes are both just sets or categories of things defined by shared characteristics. I see the concept "value group" and the concept "class" as being part of the same value group/class, I suppose. :) Maybe the prototype-based approach, a la Javascript, fits a little better than the class-based approach, or maybe there's another sibling paradigm that's even more appropriate than either, but however you look at it, there are things and categories of things, and the human mind clearly works with both. (I can give examples, and I can point out patterns in natural language that provide insights into how we think about things, if you like.) The key concept here to me is not that they are called classes/objects, but that the clumping of related attributes into things and the clumping of things into similar groups allows us to perform encapsulation and abstraction, and the human mind takes advantage of these to limit complexity.
On Mon, Nov 5, 2012 at 1:15 AM, Piaget Modeler <[email protected]>wrote: > Aaron wrote: "So why does the brain clump things into objects and > classes?" > > This is an assumption, based on a particular paradigm. Other paradigms > would view this differently. > One paradigm says that we organize the world into value groups rather than > classes. So we would > group "red, yellow, blue" into "colors", or "1, 2, 3" into numbers. There > may not necessarily be an > object associated with "red" or "3". > > Just another opinion. > > ~PM. > > ------------------------------ > Date: Sun, 4 Nov 2012 22:58:20 -0600 > Subject: Re: [agi] Re: Simulation for Perception, Symbols for Understanding > From: [email protected] > To: [email protected] > > > I'm of the opinion that if we want to deal with complexity effectively, we > should look at existing technologies used to handle it. The Object Oriented > paradigm is, I think, an excellent example. It is specifically designed to > limit complexity through encapsulation, clumping related information > together and putting it behind a firewall, of sorts. The bonus is, we > already think in terms of objects and classes, so not only does maintenance > of an Object Oriented program become easier due to limits in the > interconnectedness of classes introduced by encapsulation, but reasoning > about it becomes easier due to our natural way of understanding things in > Object Oriented terms. > > So why does the brain clump things into objects and classes? I think the > reason the Object Oriented approach works for software development carries > over perfectly to thought and reasoning. It is simpler to categorize things > and ignore their detailed internal workings in favor of high level > summaries of expectations. Saying dogs can bite is saying there is a "bite" > method for class "dog". Who cares about how a dog does its biting when > we're trying to decide whether to go near one or not? > > Once you've shifted to an Object Oriented perspective, it's also fairly > easy to describe a situation in those terms, and it comes out looking > remarkably like natural language. (In many Object Oriented languages, > method calls directly parallel English grammar: if dog.bite(me, time = > past) then me.avoid(dog).) This is more evidence, to me, that Object > Oriented is a useful metaphor for how our minds are organized. > > The simulation techniques these guys are using is a way to recognize the > current behaviors of people and objects in the visual field, which can then > be used to generate Object Oriented descriptions of the scene. (I don't > have a reference on hand, but has been shown, I believe, that typically > once a person looks away from a scene, they only remember a general > description, not all the details. It's true of me personally, at the > least.) Once an effective description has been put together in this > high-level representational scheme, it is much easier to identify a small > set of *relevant *possibilities and reason about them to put together a > plan of action. Combinatorics are still present, but they are on the scale > of thousands of cases instead of billions. After a plan of action has been > generated at the abstract level, the process of generalization can then be > reversed to move back down the generalization/specialization hierarchy > towards a detailed simulation, at which point flaws in the plan can be > identified and it can be iteratively revised through repeated > generalization/specialization cycles until an effective one is produced. > > > > On Sun, Nov 4, 2012 at 6:27 AM, Jim Bromer <[email protected]> wrote: > > On Tue, Oct 30, 2012 at 2:11 PM, [email protected] > <[email protected]>wrote: > > They need certainty or confidence values, and a list of possibilities, not > just a single outcome. Then reasoning can choose which interpretation(s) > make the most sense in context. But for their purposes -- automated video > logging & alerts -- this works fine. Once the work is done, attaching > confidence vaues and multiple possibilities should be relatively minor. > > On Sat, Nov 3, 2012 at 7:53 PM, Todor Arnaudov <[email protected]> wrote: > > You don't need millions of dumb samples of "all possible cases of ..." > like the brute force (dumb) machine learning, the problem must be > approached right with finding the appropriate correlations, then there is > not a combinatorial explosion. > > --------------------------------------------------- > > > *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
