Power, authority and influence increasingly rely on information networks. Although networks seem to have abolished the old hierarchical structures, such structures are now recast through networking effects that reproduce the power divide between central actors and peripheral content. The race for visibility, both for ranking high in search engines and for accumulating influence in social networking platforms, produces an implicit behaviour of accumulation of links or 'friends'. The resulting ‘self-referentiality’ is aimed at confirming one's own position in the network and linking to actors who are always already central. The power gained by connections to and from these centres overrules most of peripheral connectivity and suppresses the potential for dissent within a sphere of influence. This social phenomenon directly accounts for the creation of new public spheres of a global order, which include the production of borders between these spheres.
Exodus is the compound name for a 'research engine' into algorithms and visual strategies for searching the internet, revealing the structural properties of web content and its inherent distribution of influence. Exodus promotes bridging behaviour across the web's new borders of power.

MIT researcher Deb Roy wanted to understand how his infant son learned language -- so he wired up his house with videocameras to catch every moment (with exceptions) of his son's life, then parsed 90,000 hours of home video to watch "gaaaa" slowly turn into "water." Astonishing, data-rich research with deep implications for how we learn.

"The Laboratory for Intelligent Imaging and Neural Computing (LIINC) was founded in September 2000 by Paul Sajda. The mission of LIINC is to study fundamental processing strategies and representations used by biological vision systems and apply these to develop artificial vision systems capable of sophisticated and adaptive image and scene analysis. Our laboratory pursues both basic and applied neuroscience research projects, with emphasis in the following:

Network models for visual processing
Neuroimaging
Statistical representation of natural signals
Machine Learning"

More on Neuromatters
"The human brain reacts to images of interest at a pace that is far faster than a person can consciously register. Researchers at Columbia University have developed a technology, “Cortically Coupled Computer Vision (C3Vision)”, that takes advantage of this near-subconscious ability and pairs it with the processing power and efficiency of computers for rapid identification of images that the brain finds relevant.

C3Vision relies on a suite of patented machine learning algorithms which are trained to recognize what is of interest to a human viewer in a given context. Wearing an electroencephalography (EEG) cap with electrodes that capture electrical activity of the brain, a person is shown a sequence of images at a rapid pace. Each time an image of interest is displayed, the brain emits a distinctive electrical signal which is captured and decoded by the technology. Based on the strength of these neural responses, images are ranked. Over time, the technology learns what types of images are of interest to the viewer, and can eventually identify such images on its own.

“Computer vision systems are good at crunching through lots of data, but rather poor at characterizing images and scenes based on abstract and subjective concepts such as ‘that’s interesting’ or ‘I like that’ ,” said Paul Sajda, PhD, Director of the Laboratory for Intelligent Imaging and Neural Computing and a Professor of Biomedical Engineering and of Radiology at Columbia University’s School of Engineering and Applied Science and Columbia University Medical Center, respectively. “In contrast, the human visual system is exceedingly good at abstract and subjective scene analysis and image understanding, though would obviously be overwhelmed by having to analyze information from millions of images.

With a combined $4.6 million of support from DARPA Neuromatters and Columbia are collaborating on the development of an integrated image triage system based on the C3Vision technology. The system will be used and evaluated in operational environments by government image analysts to examine vast areas of satellite imagery for specific physical characteristics. The system may also extend to video surveillance and security, where the aim is to identify suspicious activity."

Neuromatters is a neurotechnology research & development company designing and building neural signal processing and brain-computer interface systems for capturing and decoding brain activity. Founded by recognized neuroengineering and machine learning experts, Neuromatters' goal is to evolve brain-computer interface systems into application areas where information overload is prevalent.

an exhibition of artists’ books and related material exploring the encyclopedic form at Printed Matter thru February.