"Loom is applying artificial intelligence and machine learning into the entire log analysis process, from start to finish and at the end you will get a human touch," explained Sabo Taylor Diab, Vice President, Marketing at Loom Systems, in this SYS-CON.tv DevOps at Cloud Expo taking place October 31 - November 2, 2017, at the Santa Clara Convention Center in Santa Clara, CA, will feature technical sessions from a rock star conference faculty and the leading industry players in the world. The widespread success of cloud computing is driving the DevOps revolution in enterprise IT. Now as never before, development teams must communicate and collaborate in a dynamic, 24/7/365 environment. There is no time to wait for long development cycles that produce software that is obsolete at launch.

Data science and machine learning for domain specific insights are at the core of Alibaba's strategy for global expansions. Apple's iPhone made its debut 10 years ago. Here's a look at how it has impacted business and the enterprise. The war in retail has long ago gone technological. Amazon is the poster child of this transition, paving the way first by taking its business online, then embracing the cloud and offering ever more advanced services for compute and storage to thirrd parties via Amazon Web Services (AWS).

Data science and machine learning for domain specific insights are at the core of Alibaba's strategy for global expansions. Apple's iPhone made its debut 10 years ago. Here's a look at how it has impacted business and the enterprise. The war in retail has long ago gone technological. Amazon is the poster child of this transition, paving the way first by taking its business online, then embracing the cloud and offering ever more advanced services for compute and storage to thirrd parties via Amazon Web Services (AWS).

Stephen Hawking had warned a few years back that Artificial Intelligence (AI) could spell the end of human race, a threat echoed by Elon Musk, the founder of Tesla, Bill Gates, and many others. This is the apocalyptic vision that the robots we create, may decide we are an obsolete model deserving "retirement". The truth, a more banal one, is that are we letting machine algorithms take over what were human decisions earlier, decision making of the governments, the businesses and even ours. Today, algorithms decide who should get a job, which part of a city needs to be developed, who should get into a college, and in the case of a crime, what should be the sentence. It is not super intelligence of robots that is the threat to life as we know it, but machines taking over thousands decisions that are critical to us and deciding social outcomes. What are these decisions, and how are machines making them?

IBM and Galvanize have launched the first online Cognitive Builder Course hosted on IBM Cloud and powered by IBM Watson. The course is aimed at enterprise developers and interested university students who seek to build on their fundamental Python programming skills, while gaining knowledge about machine learning and artificial intelligence. Currently, the technology industry is facing a shortage of experienced developers to address the growing demand for cognitive and AI development. Recently'Upskilling India' a study conducted by IBM Institute for Business Value indicates 60 percent of global executives expect that employees will need new and different skills to be successful. The study also highlights that 61 percent of the respondents believe that India's higher education system is slow in responding to the changing social demands and needs, followed by 59 percent of the respondents expressing the challenge in maintaining a relevant curriculum for students.

India is poised for a rapid climb when it comes to grids and factories incorporating self-healing concepts. The energy revolution is manifested in government programmes like access to reliable 24x7 power for all and the renewable energy target of 175GW by 2022. A lot of work is going on to include self-healing as part of the larger smart grid project, integration and balancing of renewable energy and the transmission and distribution (T&D) focus. The blackout and grid failure of 2012 underlined the need for self-healing grids which prevent damage from spreading across regions. From tractors to cement plants, biscuits to motorbikes, Indian industries are implementing sensor pilots or intelligent plant frameworks and driving the fourth industrial revolution.

Silicon Valley-based Drive.ai announced Tuesday that Andrew Ng has joined its board of directors. Ng was the chief scientist at Chinese tech giant Baidu until March, and previously founded and led the Google Brain project, an artificial intelligence effort. Drive.ai also revealed plans to launch a pilot test later this year so customers can ride in its self-driving vehicles. To fund this, it raised $50 million from venture capital firm New Enterprise Associates. "This is one horse worth betting on," Ng told CNN Tech.

Washington, DC - A US federal appeals court has thrown out a lawsuit by the families of two Yemeni men allegedly killed as innocent bystanders in a US drone strike in 2012 but one of the judges said US "democracy is broken" after announcing the ruling. The unanimous ruling by a three-judge panel in Washington on Friday upheld a lower court's finding that it had no say over the president's drone programme. The case began in 2015 when two family members of Faisal bin Ali Jaber, who brought the "wrongful death" case against then-President Barack Obama in 2015, were killed by a drone strike Yemen in 2012. Faisal's nephew Waleed, 26, and brother-in-law Salem, a father of seven and noted anti-extremist imam, were killed in the strike alongwith three others. Faisal's lawsuit requested an apology from the US government and declaration that the strike was unlawful.

Markov Decision Processes (MDPs) are an effective model to represent decision processes in the presence of transitional uncertainty and reward tradeoffs. However, due to the difficulty in exactly specifying the transition and reward functions in MDPs, researchers have proposed uncertain MDP models and robustness objectives in solving those models. Most approaches for computing robust policies have focused on the computation of maximin policies which maximize the value in the worst case amongst all realisations of uncertainty. Given the overly conservative nature of maximin policies, recent work has proposed minimax regret as an ideal alternative to the maximin objective for robust optimization. However, existing algorithms for handling minimax regret are restricted to models with uncertainty over rewards only and they are also limited in their scalability. Therefore, we provide a general model of uncertain MDPs that considers uncertainty over both transition and reward functions. Furthermore, we also consider dependence of the uncertainty across different states and decision epochs. We also provide a mixed integer linear program formulation for minimizing regret given a set of samples of the transition and reward functions in the uncertain MDP. In addition, we provide two myopic variants of regret, namely Cumulative Expected Myopic Regret (CEMR) and One Step Regret (OSR) that can be optimized in a scalable manner. Specifically, we provide dynamic programming and policy iteration based algorithms to optimize CEMR and OSR respectively. Finally, to demonstrate the effectiveness of our approaches, we provide comparisons on two benchmark problems from literature. We observe that optimizing the myopic variants of regret, OSR and CEMR are better than directly optimizing the regret.

In recent years, AI-based applications have increasingly been used in real-world domains. For example, game theory-based decision aids have been successfully deployed in various security settings to protect ports, airports, and wildlife. This article describes our unique problem-to-project educational approach that used games rooted in real-world issues to teach AI concepts to diverse audiences. Specifically, our educational program began by presenting real-world security issues, and progressively introduced complex AI concepts using lectures, interactive exercises, and ultimately hands-on games to promote learning. We describe our experience in applying this approach to several audiences, including students of an urban public high school, university undergraduates, and security domain experts who protect wildlife. We evaluated our approach based on results from the games and participant surveys.