OpsVeda today announced that it will participate at SAPPHIRE NOW and ASUG Annual Conference being held May 16–18 in Orlando, Florida. The company will be showcasing its platform that leverages machine learning techniques for proactive detection and remediation of potential operational disruptions. OpsVeda presents the full stack real-time operational intelligence platform, to power automated operational decision making in the enterprise. It helps the operations team to re-capture an estimated 10-20% of the revenue and margin leakage due to out of stocks, chargebacks, changes in customer buying behavior, expedites, missed deliveries and inventory obsolescence. Many of these issues go undetected until it is too late for any corrective action.

Over the years, I've been asked what I like best about my job. Since I spent the majority of my career in the public sector, one top answer is that I love the challenge of helping organizations with security solutions and enabling new technologies to help the business of government. I also enjoy learning and sharing what works and doesn't work in different contexts. This sharing works out in press interviews or speeches on cyberthreats, I really enjoy moderating panels and leading executive roundtables with public- and private-sector leaders at security and technology events. I often get asked to be a moderator for a few sessions at SecureWorld Expo events, InfraGard Conferences and regional technology forums, such as the upcoming MidWest Technology Leaders event. During these panel sessions, the participants typically talk about a range of (hopefully intriguing) topics that include top cybercrime trends, cyberthreat intelligence, attracting and retaining cybertalent, big industry security breaches, internal security incidents or the always interesting (but overused question) "what's keeping you up at night?" Inevitably, security and technology topics include well known themes that I have written about such as ransomware, IoT botnets, cloud computing, smart cities, smartphone security, government CISO plans, securing the smart grid, end-user training, etc. Hopefully, we get beyond the problems and spend a few minutes on solutions.

So, I'm struggling a bit with the idea of applying machine learning and artificial intelligence technologies to everything around me. Google recently gave me a Google Home device; I'm not quite sure why. Maybe they wanted to give it more real world voice recognition training opportunities. Maybe they wanted me to write about it, as good social marketing. Maybe they wanted to hear how I'm advising their competitors, naughty, naughty. It's something that I feel a natural affinity for anyway, having spent some time at a "smart home" startup a decade ago, which we'd now label "IoT" technology.

LAS VEGAS--(BUSINESS WIRE)--Baron, the worldwide provider of Critical Weather Intelligence, revealed new revolutionary features for its one-of-a kind Baron Lynx weather platform, delivering powerful weather storytelling tools in an easy-to-use platform. The key to claiming market-differentiating weather is wrapped into stunning mapping with custom looks, exclusive Baron data and the new Baron Hand Tracker. Recognized as the market-leading provider of storm tracking and location-specific severe weather, Baron has stormed the market with an extensive list of Lynx capabilities over the last year. "Baron has incorporated innovative new capabilities into Baron Lynx that delivers market differentiating weathercasts with stunning graphical power," said Mike Mougey, Vice President of Broadcast Sales. "Combining great storytelling, a unique weather look and accurate information is how weather is won," he added.

When an Icelander arrives at an office building and sees'Solarfri' posted, they need no further explanation for the empty premises: The word means'when staff get an unexpected afternoon off to enjoy good weather.' The people of this rugged North Atlantic island settled by Norsemen some 1,100 years ago have a unique dialect of Old Norse that has adapted to life at the edge of the Artic. Hundslappadrifa, for example, means'heavy snowfall with large flakes occurring in calm wind.' Linguistics experts wonder if this is the beginning of the end for the Icelandic tongue. Salome Sigurjonsdottir, 10, tests a voice-controlled television in an electronics store in Reykjavik. Linguistics experts, studying the future of a language spoken by fewer than 400,000 people in an increasingly globalized world, wonder if this is the beginning of the end for the Icelandic tongue.

Alibaba founder and chairman Jack Ma, the man Fortune Magazine just named one of the world's great leaders, predicts that technology will make many CEOs irrelevant in the not-too-distant future. "In 30 years, a robot will likely be on the cover of Time Magazine as the best CEO," Ma said in a speech over the weekend at an entrepreneurship conference in central China. And he warned of dark times ahead for people who are unprepared for the upheaval technology is set to bring. Related: Chinese giant outbids U.S. rival for firm that handles Mexico money transfers "In the next three decades, the world will experience far more pain than happiness," the billionaire said, adding that education systems must raise children to be more creative and curious or they will be ill-prepared for the future. Robots are quicker and more rational than humans, Ma said, and they don't get bogged down in emotions -- like getting angry at competitors.

When a robot almost looks human--almost, but not quite--it often comes across as jarringly fake instead of familiar. Robots that are clearly artificial, like WALL-E or R2-D2, don't have this problem. But androids like this one that imperfectly mimic human mannerisms and facial expressions are weird enough to be haunting. This phenomenon is known as the uncanny valley. It's a major obstacle for designers who try to make their robots look like people--and it may be just as much of a hurdle for developers who are creating bots that talk like people, but that don't have a body at all.

There is a wide gap between symbolic reasoning and deep learning. In this research, we explore the possibility of using deep learning to improve symbolic reasoning. Briefly, in a reasoning system, a deep feedforward neural network is used to guide rewriting processes after learning from algebraic reasoning examples produced by humans. To enable the neural network to recognise patterns of algebraic expressions with non-deterministic sizes, reduced partial trees are used to represent the expressions. Also, to represent both top-down and bottom-up information of the expressions, a centralisation technique is used to improve the reduced partial trees. Besides, symbolic association vectors and rule application records are used to improve the rewriting processes. Experimental results reveal that the algebraic reasoning examples can be accurately learnt only if the feedforward neural network has enough hidden layers. Also, the centralisation technique, the symbolic association vectors and the rule application records can reduce error rates of reasoning. In particular, the above approaches have led to 4.6% error rate of reasoning on a dataset of linear equations, differentials and integrals.

Teaching machines to accomplish tasks by conversing naturally with humans is challenging. Currently, developing task-oriented dialogue systems requires creating multiple components and typically this involves either a large amount of handcrafting, or acquiring costly labelled datasets to solve a statistical learning problem for each component. In this work we introduce a neural network-based text-in, text-out end-to-end trainable goal-oriented dialogue system along with a new way of collecting dialogue data based on a novel pipe-lined Wizard-of-Oz framework. This approach allows us to develop dialogue systems easily and without making too many assumptions about the task at hand. The results show that the model can converse with human subjects naturally whilst helping them to accomplish tasks in a restaurant search domain.

The scalable calculation of matrix determinants has been a bottleneck to the widespread application of many machine learning methods such as determinantal point processes, Gaussian processes, generalised Markov random fields, graph models and many others. In this work, we estimate log determinants under the framework of maximum entropy, given information in the form of moment constraints from stochastic trace estimation. The estimates demonstrate a significant improvement on state-of-the-art alternative methods, as shown on a wide variety of UFL sparse matrices. By taking the example of a general Markov random field, we also demonstrate how this approach can significantly accelerate inference in large-scale learning methods involving the log determinant.