“Next time you’re looking up at a billboard, there’s a chance it may be looking back down at you. Immersive Labs has developed software for digital billboards that can measure the age range, gender, and attention-level of a passerby and quantify the effectiveness of an outdoor marketing campaign. Beyond just bringing metrics to outdoor advertisements, facial detection technology can tailor ads to people based on their features.
Plan UK, a children’s charity group ran a bus stop advertisement as part of their “Because I Am A Girl” campaign, where women passing by would see a full 40-second clip, while if man saw the ad, it would only display a message directing him to their website. The next generation of systems could take this data collection much further – an algorithm could judge whether you look happy, sad, sick, healthy, comfortable, or nervous and direct personalized ads to you.” (Tarun Wadhwa, Forbes)
” ‘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)
Photo: Microsoft Research
“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)
Photo: Chris Pietsch for The New York Times
“In the weekend tournament, Dr. Fill finished 141st, or would have (only human solvers got official rankings). ‘It was within the range, but I wish it had done better,’ Dr. Ginsberg said on Sunday. ‘I’ll be back next year.’
Dr. Fill typically thrives on conventional crosswords, even ones with arcane clues and answers. Indeed, the seventh puzzle, a difficult one, it got perfectly.
But the computer program is literal minded, and tends to struggle on puzzles with humor, and puzzles with unusual themes or letter arrangements.” (Steve Lohr, NYTimes.com)
“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)
“The program utilizes the expertise of movie producers in assigning appropriate background music for pictures, enabling users to select music that will harmonize with their impressions of their last summer vacation without putting in an extraordinary amount of effort.” (AlphaGalileo Foundation)