And with the social media giants battling for more users, Twitter has unleashed a new option that could help them stay in the game. Members can now create videos up to 140 seconds long and a small test group on Vine has also been granted this ability, allowing them to turn'the six second Vine into a trailer for a bigger story'. Twitter has unleashed a new feature that could help them stay in the game. The firm announced members can now create videos up to 140 seconds long and a small group on Vine also has this option, allowing them to turn'the six second Vine into a trailer for a bigger story' To start making longer videos, users simply tap on a video Tweet or Vine on their own timeline and will be redirected to a'new, full-screen viewing experience'. This new screen also gives you video editing tools, which allow users to trim down the clips to customize the beginning and end of the video.

Artificial Intelligence is a technology that uses human-like learning to perform tasks. The idea of Artificial Intelligence or AI is nothing new. As a concept it has been in our literature and art for centuries. But these ideas had no foundation other than as philosophies of nature and science fiction. Connectionist paradigms of Artificial Intelligence have a somewhat more flexible approach than their rule-based cousins, but both have applications in modern technologies. Machine learning is based on artificial neural networks, which are a much-simplified version of how our brains work.

Dan Schutzer (photo left), a senior technology consultant at the Financial Services Roundtable's BITS technology division defined artificial intelligence as'the theory and development of computer systems able to perform tasks that normally require human intelligence, such as visual perception, speech recognition, decision-making, and translation between languages.' According to Schutzer, efforts in the 1990s to build artificial intelligence-like systems for use in financial services has resulted in'disillusionment as realization set in that these systems were harder and more costly to build and maintain than first anticipated.' Fast forward to 2015 when advances in high performance computing, algorithmic theory and cloud computing are bringing us closer to true AI capabilities for commercial use by the financial industry. Patrick Tucker, author of The Naked Future: What Happens In a World That Anticipates Your Every Move?, wrote that "When the cost of collecting information on virtually every interaction falls to zero, the insights that we gain from our activity, in the context of the activity of others, will fundamentally change the way we relate to one another, to institutions, and with the future itself." "One of the first things to note about AI is its ability to process enormous amounts of data very quickly and far more data than it's ever been processed in the past by humans or computer programs. That is going to enable banks to improve the services they provide to customers, including better, more targeted advice," said Astrid Raetze (photo right), a partner at Baker & McKenzie.

Structure Of Intelligent Agents 12. Types of agent program 13. Goal based Agents 14. Utility-based agents 15.

This paper studies two issues concerning relevance in structured argumentation in the context of the ASPIC+ framework, arising from the combined use of strict and defeasible inference rules. One issue arises if the strict inference rules correspond to classical logic. A longstanding problem is how the trivialising effect of the classical Ex Falso principle can be avoided while satisfying consistency and closure postulates. In this paper, this problem is solved by disallowing chaining of strict rules, resulting in a variant of the ASPIC+ framework called ASPIC*, and then disallowing the application of strict rules to inconsistent sets of formulas. Thus in effect Rescher & Manor's paraconsistent notion of weak consequence is embedded in ASPIC*. Another issue is minimality of arguments. If arguments can apply defeasible inference rules, then they cannot be required to have subset-minimal premises, since defeasible rules based on more information may well make an argument stronger. In this paper instead minimality is required of applications of strict rules throughout an argument. It is shown that under some plausible assumptions this does not affect the set of conclusions. In addition, circular arguments are in the new ASPIC* framework excluded in a way that satisfies closure and consistency postulates and that generates finitary argumentation frameworks if the knowledge base and set of defeasible rules are finite. For the latter result the exclusion of chaining of strict rules is essential. Finally, the combined results of this paper are shown to be a proper extension of classical-logic argumentation with preferences and defeasible rules.

The news that Christina Grimmie -- the 22-year-old singer who, as a New Jersey teen, made a name for herself on YouTube before broadening her fame in 2014 on Season 6 of "The Voice" – was shot and killed Friday while signing autographs for fans after a concert in Orlando, Fla., is tragic. But for fans of "The Voice" who watched Grimmie show off, during her time on the show, not only her impressive vocal chops and stage presence, but also her musical creativity, willingness to experiment and upbeat resilience, the loss must be heartbreaking. Those who watched Grimmie turn four chairs during her blind audition and then stick around to finish third on the show, behind only sweet, shy, country-singing runner-up Jake Worthington (of Team Blake Shelton) and silky-soulful winner Josh Kaufman (of Team Usher), knew she was an unusual talent. Grimmie's coach, Adam Levine, believed in her so fiercely that, at one point, he promised the audience she would end up winning the show. Then, when she didn't, he announced that he planned to sign her to his own label.

GE understands solving the world's toughest problems through advanced manufacturing techniques and processes requires collaboration. By crowdsourcing innovation--both internally and externally--GE is improving customer value and driving advancements across industries. By sourcing and supporting innovative ideas, wherever they might come from, and applying GE's scale and expertise, GE's approach to open innovation is helping to address customer needs more efficiently and effectively. GE invites data scientists, analysts, etc. to compete for an opportunity to co-develop a wastewater optimization methodology and algorithm set. This algorithm set will catalyze a site-selection decision support tool-a data science model that melds data elements and key business rules sets into a decisioning criteria.

Uncertainty hinders many interesting applications of planning - it may come in the form of sensor noise, unpredictable environments, or known limitations in problem models. In this paper we explore heuristic guidance for forward-chaining planning with continuous random variables, while ensuring a probability of plan success. We extend the Metric Relaxed Planning Graph heuristic to capture a model of uncertainty, providing better guidance in terms of heuristic estimates and dead-end detection. By tracking the accumulated error on numeric values, our heuristic is able to check if preconditions in the planning graph are achievable with a sufficient degree of confidence; it is also able to consider acting to reduce the accumulated error. Results indicate that our approach offers improvements in performance compared to prior work where a less-informed relaxation was used.

The analytics community has long been discussing whether analytics is about art or science. Analytics is more an art than a science in its ability to form conditions to drive business toward an action that is based on the confidence that the action will improve business performance. This ability to be actionable have recognized recently as the most important aspect in analytics [1]. The concept is known as Prescriptive Analytics [2] shares some similar statements with Actionable Analytics, but some meaningful differences are present as well. Classification rules plays a significant role in practical predictive analytics.

The race is on for artificial intelligence in cybersecurity, empowering computer solutions with the ability to understand threats and respond immediately to them without (or with reduced) human intervention. Will it will survive the hype? A lot depends on how well the cybersecurity industry draws on the lessons we learned in trying to implement artificial intelligence in legal applications. IBM recently announced its foray into the cybersecurity–artificial intelligence arena using its flagship technology Watson. Watson has famously demonstrated its remarkable versatility already; so far it is has won the game show Jeopardy against two former champions and released its own cookbook (admittedly with mixed success from those who have tried the recipes). While it is a remarkable piece of technology, that doesn't necessarily equate to success in cybersecurity.