“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)
” ‘Your computer could be able to discover causal relationships, ranging from simple cases such as recognizing that you work more slowly when you haven’t had coffee, to complex ones such as identifying which genes cause greater susceptibility to diseases,’ said Griffiths. He is applying a statistical method known as Bayesian probability theory to translate the calculations that children make during learning tasks into computational models.” (Yasmin Anwar, UC Berkeley News)
“ACM Executive Director John White told me that ‘Pearl’s research was instrumental in moving machine-based reasoning from the rules-bound expert systems of the 1980s to a calculus that incorporates uncertainty and probabilistic models.’ In other words, he has figured out methods for trying to draw the best conclusion, even when there is a degree of uncertainty. It can be applied when trying to answer questions from a large amount of unstructured information, or trying to figure out what someone has said in languages that have lots of similar-sounding words—all things we do a lot today. (Michael J. Miller, PCMag.com)
“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)