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. >> > --------------------------------------------------- > > I essentially agree with this although I would not do it in just the way > you guys are indicating. The mind is the solution to combinatorial > explosion problem. However, it is not all that simple. While simulation > and other kinds of imagination are important to producing good insight > about being able to understand what is going on and reacting to it in an > appropriate way, it does not show how you can't react to many different > situations quickly. The problem of responding to many different situations > at once throws the crackpot into the assumption of simplicity about this > because when you deal with different kinds of situations at once there > could be good reasons to see them as combined. This introduces a potential > for more complexity into the problem. Should the different situations be > treated as separate or should they be considered to be interrelated? If > you are going to rely on the imagination or previous correlation then you > are effectively introducing additional categories of possibilities (of > situations to be recognized). Is this methodology really a simplification > of complexity or is it just a rerouting of complexity? The potential for > combinatorial complexity occurs because of the introduction or definition > of separate components, not because someone has an intrinsic desire to make > thorough inspections of the possibilities. > > Jim Bromer > > > > > On Sat, Nov 3, 2012 at 7:53 PM, Todor Arnaudov <[email protected]> wrote: > >> Nice link, Aaron, >> >> That's what I was talking about with 3D-reconstruction - it involves also >> reconstruction of the light sources - in GI. 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. >> >> Different human activities are encoded in the possible motions of the >> human body and their possible interactions with other bodies (in classical >> physics sense), i.e. initially those bodies must be identified, and that >> must be evaluated in as lower resolution, as general the activity is. >> >> The symbols are essentially low resolution representations, they are >> always fewer than the raw data. >> >> The activities obviously encoded in the trajectories of those bodies >> (that's something physics is dealing with for centuries), and when >> generalized in low resolution, the trajectories would converge to the cases >> of carrying, throwing, kicking etc., depending on some rough/global >> specifics like is it accelerating, is it by a hand, an arm, head, are the >> arms moving in parallel etc. etc. >> >> -- >> ** Todor "Tosh" Arnaudov ** >> * >> -- Twenkid Research:* http://research.twenkid.com >> >> -- *Self-Improving General Intelligence Conference*: >> http://artificial-mind.blogspot.com/2012/07/news-sigi-2012-1-first-sigi-agi.html >> >> *-- Todor Arnaudov's Researches Blog**: * >> http://artificial-mind.blogspot.com >> >> >> >>> - *From:* Aaron Hosford <[email protected]> >>> >>> >>> - *To:* [email protected] >>> >>> >>> - *Subject:* Simulation for Perception, Symbols for Understanding >>> >>> >>> - *Date:* Tue, 30 Oct 2012 09:30:55 -0500 >>> >>> http://www.rec.ri.cmu.edu/about/news/11_01_minds.php >>> >>> Recognizing and predicting human activity in video footage is a >>> difficult problem. People do not all perform the same action in the same >>> way. Different actions may look very similar on video. And videos of the >>> same action can vary wildly in appearance due to lighting, perspective, >>> background, the individuals involved, and more. >>> To minimize the effects of these variations, Carnegie Mellon's Mind’s >>> Eye software will generate 3D models of the human activities and match >>> these models to the person’s motion in the video. It will compare the >>> video motion to actions it’s already been trained to recognize (such as >>> walk, jump, and stand) and identify patterns of actions (such as pick up >>> and carry). The software examines these patterns to infer what the person >>> in the video is doing. It also makes predictions about what is likely to >>> happen next and can guess at activities that might be obscured or occur >>> off-camera. >>> This project's approach is to use 3D simulation to detect and classify >>> behavior, and then generate symbolic information about the events that were >>> observed. I'm encouraged to see someone doing work on this stage of >>> cognition, as I see perception as the "missing link" that's stopping AGI >>> from developing. >>> >>> I wonder, will a certain naysayer feel vindicated that someone else sees >>> simulation as vital to intelligence (and is using it to solve precisely the >>> problems he says it's needed to solve), or will he be annoyed that the >>> ultimate form the information takes is symbolic, which is compatible with >>> semantic nets or any number of other existing AGI approaches? >>> >> >> >> >> *AGI* | Archives <https://www.listbox.com/member/archive/303/=now> >> <https://www.listbox.com/member/archive/rss/303/10561250-164650b2> | >> Modify <https://www.listbox.com/member/?&;> Your Subscription >> <http://www.listbox.com> >> > > *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
