Speeder Reader couples the notion of dynamic typography with the notion of the car as interface. A speed-reading protocol called RSVP (for Rapid Serial Visual Presentation) allows people to learn to read up to 2000 words per minute. This is because it flashes words or short phrases onto the screen in front of you, affixed in one spot; you don't have to move your eyes around a page to read.

Speeder Reader gives you a gas pedal to control your rate of speed-reading and a steering wheel to navigate between streams of text. You can also jump forward or backwards in the text (by sentence, paragraph, or chapter).

In Western culture, the act of driving is very personally empowering (just like reading!). By combining the driving interface with dynamic text, we're offering a model of reading as a medium that gets you places. Here's a preprint of the Computers and Graphics journal paper that goes into more detail on the design and technology behind Speeder Reader.

SPEEDER READER credits
Maribeth Back, Jonathan Cohen, Rich Gold

Just for kicks. Themes: semantic web (e.g. linked data, conversational interfaces — though these are considered to be web 4.0 by some?), metaverse (a.k.a. living in the ether/augmented reality), brain-computer interfaces(!)

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.