QuEST Weekly Meeting, September 25th

QUEST Topics for 25 Sept, 2009

1.)    Article from Science News on emotion and vision – By Jenny Lauren Lee <http://www.sciencenews.org/view/authored/id/123/name/Jenny_Lauren_Lee> August 29th, 2009; Vol.176 #5 <http://www.sciencenews.org/view/issue/id/46403/title/August_29th%2C_2009%3B_Vol.176_%235>  (p. 22).  ‘brain guesses the identity of objects before it has finished processing all the sensory information collected by the eyes… brain uses “affect” (pronounced AFF-ect) — a concept researchers use to talk about emotion in a cleaner, more clearly defined way — not only to tell whether an object is important enough to merit further attention, but also to see that object in the first place.  *** I would like to discuss ‘affect’ – here suggest affect determines what merits attention – I think it is more than that – as important as attention is I might suggest ‘affect’ is a type of qualia ***… The idea here is not that if we both see someone smile we would interpret it differently,” says Lisa Feldman Barrett of Boston College. “It’s that you might see the smile and I might completely miss it.”  **** this is a critical point – not even see – this is what we have been pushing as a subjective representation **** … traditional view, perception, judgment and emotions are considered separate processes, with emotions coming last in the procession. One perceives. One judges, using reason, what best to do with the information collected. And one keeps one’s emotions, as much as possible, out of the picture. **** this is the point we tried to overthrow in the ooda article ****… Researchers were struck by experiments in which people seemed to feel an emotion without being able to identify the object that had elicited it. In 1980 the late psychologist Robert Zajonc, then at the University of Michigan in Ann Arbor, wrote that it is possible that people can “like something or be afraid of it before [they] know precisely what it is.”… showed that people appear to prefer smooth, curved objects over sharp-edged ones, though the people tested could not always articulate a reason for their preference.  *** Affect is a Libet process ***

2.) What Turing meant to say – latest ‘think piece’ – focus on alignment of representations as the key to winning at the ‘Imitation Game’ – and thus the key idea in the Turing Test.  The implication of this idea in next generation CAPTCHAs, ad-hoc networks security/quality of service/Trust and possibly for Autism detection.

3.) Types of Qualia – and how are they stored and represented.

4.) News:

a.     Augmented Reality – discussion on the SOA and the killer apps … admit that several obstacles still remain and that the “killer app” for augmented reality has yet to emerge. **** I contend it is the ‘healthy feel’ app *** fundamentally new way to organize and interact with information.**** should be of interest to Qbase then ***… new language for AR called Augmented Reality Mark-up Language (ARML).

b.    Scientist discovers cure for color blindness – restored normal vision to two color-blind monkeys. The technique could prove to be a safe and effective cure for color blindness and other visual disorders related to the cones in the retina… Those suffering from red-green color blindness cannot distinguish between colors in the green-red-yellow part of the spectrum. This can make reading maps, using the internet and selecting a matching shirt and tie impossible. The disorder affects about 8 percent of Caucasian males, but less than 0.5 percent of females..

c.     Dog’s mind – Understanding a pointed finger may seem easy, but consider this: while humans and canines can do it naturally, no other known species in the animal kingdom can… learned ability to act submissive when an owner gets angry. “It’s a white-flag response…\

d.    Project Indect – “agents” to monitor and process information from web sites, discussion forums, file servers, peer-to-peer networks and even individual computers… It talks of the “construction of agents assigned to continuous and automatic monitoring of public resources such as: web sites, discussion forums, usenet groups, file servers, p2p [peer-to-peer] networks as well as individual computer systems, building an internet-based intelligence gathering system, both active and passive”…. research project, called Adabts – the Automatic Detection of Abnormal Behaviour and Threats in crowded Spaces…”The EU’s Joint Situation Centre (SitCen) was originally established in order to monitor and assess worldwide events and situations on a 24-hour basis with a focus on potential crisis regions, terrorism and WMD-proliferation…

e.     Netflix winner: widely followed because its lessons could extend well beyond improving movie picks. The researchers from around the world were grappling with a huge data set — 100 million movie ratings — and the challenges of large-scale predictive modeling, which can be applied across the fields of science, commerce and politics…. Major lesson = principal lesson for the winners. The blending of different statistical and machine-learning techniques “only works well if you combine models that approach the problem differently,… “Out of thousands, you have only two that succeeded. The big lesson for me was that most of those collaborations don’t work…New contest : new contest is going to present the contestants with demographic and behavioral data, and they will be asked to model individuals’ “taste profiles,… information about renters’ ages, gender, ZIP codes, genre ratings and previously chosen movies. Unlike the first challenge, the contest will have no specific accuracy target. Instead, $500,000 will be awarded to the team in the lead after six months, and $500,000 to the leader after 18 months.

f.       Surveillance software – New software will automatically integrate data from thousands of security cameras in a video surveillance network into a single sensor, eliminating existing problems with huge information overloads….”Network security monitoring is currently limited by the inability of operators to recall the relationships between more than about 40 cameras in a network…

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