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A quiet revolution has been going on in the medical profession and the field of personal health in the last two years, spearheaded by smartphones and tablets, that will change forever the way we obtain and process medical information. Called mHealth (Mobile Health), this industry phenomenon encompasses a wide range of applications - from self-treatment apps on sub-$100 Android phones in Kenya, through texting reminders for the immunization schedule of newborn babies in India, up to testing yourself for STDs with a smartphone kit, or your doctor panning and zooming radiology scans on the go.

We have a multi-year process to re-license based on advice from multiple sources that Creative Commons is not applicable to data. We wish it were, and it probably will be in the future but it wasn't clear when we began. Until that happens we have a process to move to the Open Database License, which explicitly covers data and not just creative works like photographs or text. The ODbL was in fact started as a result of investigations around the needs of Science Commons and we just helped it to its conclusion. At some point down the line I personally expect the ODbL and CC to be compatible and we will be able to cross-pollinate once more.

Before May 1st, not even the smartest of machine learning algorithms could have predicted Keith Urbahn's online relevancy score, or his potential to spark an incredibly viral information flow. While politicos "in the know" certainly knew him or of him, his previous interactions and size and nature of his social graph did little to reflect his potential to generate thousands of people's willingness to trust within a matter of minutes. While connections, authority, trust and persuasiveness play a key role in influencing others, they are only part of a complex set of dynamics that affect people's perception of a person, a piece of information or a product. Timing, initiating a network effect at the right time, and frankly, a dash of pure luck matter equally.

Methods and systems for automated identification of celebrity face images are provided that generate a name list of prominent celebrities, obtain a set of images and corresponding feature vectors for each name, detect faces within the set of images, and remove non-face images. An analysis of the images is performed using an intra-model analysis, an inter-model analysis, and a spectral analysis to return highly accurate biometric models for each of the individuals present in the name list. Recognition is then performed based on precision and recall to identify the face images as belonging to a celebrity or indicate that the face is unknown.

A new social network launched today around the sharing and engagement of news. XYDO takes the social graph and turns it into a network of news that is automatically curated through users Twitter and Facebook streams. Think Digg and Reddit, add social news feeds automatically and you have XYDO.