Causality has been recently introduced in databases, to model, characterize and possibly compute causes for query results (answers). Connections between query-answer causality, consistency-based diagnosis, database repairs (wrt. integrity constraint violations), abductive diagnosis and the view-update problem have been established. In this work we further investigate connections between query-answer causality and abductive diagnosis and the view-update problem. In this context, we also define and investigate the notion of query-answer causality in the presence of integrity constraints.

In this paper, we explore the idea of building an adaptive user interest model. Our proposed system uses implicit data extracted from a user’s search queries to select categorical information from DBpedia. By combining the categorical information collected from multiple queries and exploiting the semantic relationships between these categories, it becomes possible for our system to build a model of the user's interests. This model is designed to be responsive to changes in the user's interests over time by including concepts of aging and expiration. Our system also includes mechanisms to pinpoint the correct categories when an ambiguous term is queried. We evaluated our system using a predefined set of test queries and shown to correctly model user short term and long term interests.

Questions form an integral part of our everyday communication, both offline and online. Getting responses to our questions from others is fundamental to satisfying our information need and in extending our knowledge boundaries. A question may be represented using various factors such as social, syntactic, semantic, etc. We hypothesize that these factors contribute with varying degrees towards getting responses from others for a given question. We perform a thorough empirical study to measure effects of these factors using a novel question and answer dataset from the website Reddit.com. We also use a sparse non-negative matrix factorization technique to automatically induce interpretable semantic factors from the question dataset. Such interpretable factor-based analysis overcomes limitations faced by prior related research. We also document various patterns on response prediction we observe during our analysis. For instance, we found that preference-probing questions are rarely answered by actors.

You may remember how, back in 2011, IBM's supercomputer Watson competed against the world's best "Jeopardy" champions and won. Fast-forward a few years and cognitive computing and machine learning have become the latest tech buzzwords that promise to revolutionize industries such as healthcare by providing real-time, actionable insights and much more. Entire markets, including banking and finance, law, and auditing and accounting, to name a few, are also facing disruption as well as opportunities with ongoing advancements in cognitive technology. The power of IBM Watson is finally being realized now that it can understand, reason and learn from the wealth of big data that most businesses are struggling to make sense of. Early childhood education appears to be next on the agenda.

Officially, fashion's biggest party is a fund-raising event for the Metropolitan Museum of Art's fashion department, but ever since Anna Wintour, Vogue's high-powered editor, took over as chairwoman in 1999, it has turned into the industry's equivalent of the Oscar's red carpet, bringing together an invite-only list of dressed-up celebrities and bigwigs. This year, as everyone was asked "Who are you wearing?" one attendee--model Karolina Kurkova--got to say IBM Watson, in collaboration with high-fashion label Marchesa. Covered in fabric flowers embedded with LEDs, the "cognitive dress" was light, elegant, and romantic, as is Marchesa's signature. It continually changed color with the help of Watson's powerful analytical technology, tying it perfectly into the theme of this year's fashion exhibit at the Met, called "Manus x Machina: Fashion in an Age of Technology." But while the dress was beautiful, the lights were sometimes overbearing.

At last night's Met Gala, the lavish annual fashion event hosted by Vogue, model Karolina Kurkova wore a dress that was half man-, half machine-made. The "Cognitive Dress"--perhaps one of the least fashion-forward names found on the red carpet--is the product of a partnership between British design studio Marchesa and Watson, IBM's friendly cognitive computer. The gown, a white tulle design embroidered with 150 LED-connected flowers, is an interesting glimpse of how humans and machines can work together to create something that otherwise wouldn't be possible. To design the dress, Marchesa's designers first chose five sentiments they wanted the dress to express: joy, patience, excitement, encouragement, and curiosity. Then they fed two datasets into IBM's Cognitive Color Tool, a program that uses color psychology to match emotion to hues.

IBM and its partners are building solutions that will allow individual patients and larger health populations to benefit as providers share and apply insights in real-time. In this video, learn how the IBM Watson Health Cloud can help an avid runner with a heart condition continue to live an active life. This scenario describes the future of health and where things are going, not necessarily what you'd get when you walk into a doctor's office today. For more information on Watson Health, please visit http://ibm.com/watsonhealth. IBMers -- learn more about Security Intelligence on Think Academy (internal site): https://ibm.biz/IBMThinkAcademy

These days, it's hard to get that excited by smart clothing that are developed to show off the marriage of fashion and technology. After all, it's not as if we don't see these garments popping up at every trade show and event worth mentioning. Between Nicole Sherzinger's Twitter dress through to Intel's various attempts to marry its chips to the catwalk, it feels as if much of this has already been done. Moreover, it's not as if this sort of product is ever going to be available for people to buy. It doesn't help, either, that Watson didn't even design the dress itself, but Marchesa's team that acted upon its suggestions.

This is a very thorough yet practical introduction. Nice explanation of how you constructed the attention custom layer.

Artificial intelligence (AI) is the convenient future. It is one of the most promising and transformative opportunities of our time. We are closer to the near future where virtual assistants, bots, and software agents will act more and more like people. Some the biggest advances in AI are being developed inside tech giants such as Google (Deep Mind) and IBM (Watson). But there are still a lot of great opportunities for young startups to explore.