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 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
