It's no secret that much of the wisdom of the world lies in unstructured data, or the kind that's not necessarily quantifiable and tidy. So it is in cybersecurity, and now IBM is putting Watson to work to make that knowledge more accessible. Towards that end, IBM Security on Tuesday announced a new year-long research project through which it will collaborate with eight universities to help train its Watson artificial intelligence system to tackle cybercrime. Knowledge about threats is often hidden in unstructured sources such as blogs, research reports and documentation, said Kevin Skapinetz, director of strategy for IBM Security. "Let's say tomorrow there's an article about a new type of malware, then a bunch of follow-up blogs," Skapinetz explained.

It's no secret that much of the wisdom of the world lies in unstructured data, or the kind that's not necessarily quantifiable and tidy. So it is in cybersecurity, and now IBM is putting Watson to work to make that knowledge more accessible. Towards that end, IBM Security on Tuesday announced a new year-long research project through which it will collaborate with eight universities to help train its Watson artificial-intelligence system to tackle cybercrime. Knowledge about threats is often hidden in unstructured sources such as blogs, research reports and documentation, said Kevin Skapinetz, director of strategy for IBM Security. "Let's say tomorrow there's an article about a new type of malware, then a bunch of follow-up blogs," Skapinetz explained.

There are some human attributes robots could never replace - or at least that's what you might hope. But one university has brought that into question by replacing one of their teaching assistants with a machine. In February 2011, Watson appeared alongside two other contestants to compete for the cash prize. During the show, clues are given to contestants that'require analysis and understanding of subtle meaning, irony, riddles and other language complexities' that humans can perform naturally but computers, traditionally, do not. Watson had to be programmed to make decisions and conclusions in this way by a team of experts at IBM. Watson was given clues as electronic texts, as they were also asked to the human contestants.

Natural language question understanding has been one of the most important challenges in artificial intelligence. Indeed, eminent AI benchmarks such as the Turing test require an AI system to understand natural language questions, with various topics and complexity, and then respond appropriately. During the past few years, we have witnessed rapid progress in question answering technology, with virtual assistants like Siri, Google Now, and Cortana answering daily life questions, and IBM Watson winning over humans in Jeopardy!. Many questions the systems encounter are simple lookup questions (e.g., "Where is Chichen Itza?" or "Who's the manager of Man Utd?"). The answers can be found by searching the surface forms.

IBM will launch a Korean version of its AI platform Watson next year in cooperation with local IT service vendor SK C&C, the companies have announced. SK announced Monday that it signed a cooperation agreement with Big Blue on May 4 and will together build an integrated system to market Watson in South Korea. They will develop Korean data analysis solutions based on machine learning and natural language semantic analysis technology for Watson within this year, and will commercialise it sometime in the first half of 2017, SK said. IBM and SK will also build a "Watson Cloud Platform" at the Korean company's datacentre in Pangyo -- the local version of Silicon Valley -- that IT developers and managers can access to make their own applications. For example, an open market business can apply the Watson solution to its product search features to make a personalized contents recommendation solution.

The first Monday in May brings one of the marquee fashion events of the year -- the Met Gala. Held at the Metropolitan Museum of Art in New York City as a benefit for the museum's Costume Institute, this year's gala comes with an unexpected high-tech twist. The theme of the evening, and the accompanying museum exhibition, is "Manus x Machina: Fashion in an Age of Technology." In keeping with the theme is a rather unlikely collaboration -- IBM is joining forces with the fashion house Marchesa, known for its whimsical, romantic designs. For Monday's event, Marchesa designers and co-founders Georgina Chapman and Keren Craig teamed up with IBM's cognitive computing system Watson to design a "cognitive dress" that will be worn by a yet-to-be-named model.

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.