“DreamWorks executives didn’t say how much they spent developing the software. They released it in hopes it would be adopted as an industry standard and integrated into commonly used software platforms. This would increase its utility for DreamWorks even if it gave competitors access to an element of the company’s tool kit, according to studio executives.” (Erica Orden, Wall Street Journal)
“University of Manitoba computer scientists in the Human-Computer Interaction laboratory are the first to develop a lightweight and elegant software solution that leaps over this hurdle: They created See You, See Me. This software is a boon to computer makers like Microsoft who want to develop table top computers and wall displays that many people – like school children in a classroom or architects at a drafting table — can simultaneously interact with.
See You, See Me enables computers to distinguish between user touches with near-perfect accuracy; and if a rare mistake occurs the software provides a quick remedy. It uses the finger orientation extracted from the user’s hand’s shadow to determine where people are and to keep track of who is doing what to the screen” (University of Manitoba)
“The system needs around an hour of training to develop a model able to read out any text in a person’s own voice. That model is converted into one able to read out text in another language by comparing it with a stock text-to-speech model for the target language. Individual sounds used by the first model to build up words using a person’s voice in his or her own language are carefully tweaked to give the new text-to-speech model a full ability to sound out phrases in the second language.” (Tom Simonite, Technology Review)
“Behind the scenes, Lifebrowser uses several machine-learning techniques to sift through personal data and determine what is important to its owner. When judging photos, Lifebrowser looks at properties of an image file for clues, including whether the file name was modified or the flash had fired. It even examines the contents of a photo using machine-vision algorithms to learn how many people were captured in the image and whether it was taken inside or outdoors. The “session” of photos taken at one time is also considered as a group, for cues such as how long an event was and how frequently photos were taken.” (Tom Simonite, Technology Review)
Computer scientists have developed a simple algorithm that accurately guesses your hometown using the location information of photos uploaded to Flickr
“These guys have studied the geographical clusters of photos that users upload to Flickr, the popular picture sharing website. The task they set themselves is to determine an individual’s home town looking only at the geotags of photographs they have uploaded.
It’s no surprise that people take most of their photographs near their home. But they also take photographs in clusters at other locations such as holiday destinations and such like. That makes the problem of estiamting the home location a little more difficult. The trick that Jahanbakhsh and pals solve is to find an algorithm that can separate the home location from the other clusters.” (KFC, Technology Review)
“The key to this technology, Ferrucci said, is that it queries both itself and its users for feedback on the answers it generates. ‘As you use the system, it will follow up with you and ask you questions that will help improve its confidence of its answer. In its work with you it will capture new information it can use,’ he said.
One field IBM is investigating is medicine. The company is working with medical researchers and doctors from Columbia University to adapt Watson so it can offer medical diagnosis and treatment.” (Joab Jackson, Computerworld)