ONTARIO, CA and NEWPORT BEACH, CA April 4, 2017 The Safariland Group ("Safariland"), the parent company of VIEVU and a leading global provider of safety and survivability products designed for the public safety, military, professional and outdoor markets, and Veritone, a leading provider of artificial intelligence solutions, today announced their intent to enter into an agreement to integrate their product offerings to apply artificial intelligence to uniquely extract and process crucial data from police body-worn camera footage. The Veritone Platform will be available to Safariland's law enforcement agency customers as a complement to VIEVU's body-worn cameras, accessories and software, later this year. The integration will allow VIEVU's customers to upload large volumes of video and audio recordings into the Veritone Platform and process them in near real-time, enabling law enforcement personnel to rapidly extract actionable information for use in investigations, monitoring and training, as well as to respond more quickly and efficiently to public record requests. "The Veritone Platform will enable law enforcement agencies to save thousands of hours of manual searching by using intelligent audio and video analysis, allowing them to focus time and resources on more mission-critical tasks," said Chad Steelberg, chief executive officer of Veritone. "The Veritone Platform uses more than 40 best-of-breed cognitive engines, ranging from transcription and face recognition to sentiment analysis to object recognition, which will provide VIEVU's users the ability to derive comprehensive, actionable insights from their body camera footage in near real-time. Importantly, we have long admired VIEVU's products and its dedication to innovation, and we look forward to working together to advance public safety with artificial intelligence technology."

Most consumers don't really know what artificial intelligence (AI) does, and the basic misunderstanding has some fearful of the technology. In a survey of 6,000 customers in six countries, the findings from Pegasystems study released this week found that consumers are hesitant to embrace AI devices and services. Some 36% are comfortable to engage with businesses using AI even if it results in a better customer experience. About 72% said they have some sort of fear about AI, with 24% worried about robots taking over the world. Only 34% of survey respondents thought they had directly experienced AI, but when asked about the technologies in their lives, the survey found that 84% use at least one AI-powered service or device such as virtual home assistants, intelligent chatbots, or predictive product suggestions.

The global machine learning as a service (MLaaS) market is poised to grow from $1.07 billion in 2016 to $19.86 billion in 2025, at a CAGR of more than 38%, according to a new report from Transparency Market Research. Demand for MLaaS has been highest in the healthcare and life sciences industry, due primarily to the need to integrate structured and unstructured data in these areas, especially data generated by electronic health records. Other industries that will benefit from this technology moving forward include manufacturing, retail, telecom, finance, energy and utilities, education, and the government, as MLaaS can improve the decision-making capabilities of devices used in those areas, the report stated. Enterprises' move to the cloud is another important factor behind the expected growth of the MLaaS market, the report noted--as more companies shift toward cloud computing, it is easier for them to take advantage of machine learning. SEE: 5 steps to turn your company's data into profit MLaaS solutions are typically deployed in both the public and private cloud, though private cloud accounts for most of the revenue generated in the global MLaaS market, the report noted. "Enterprises are preferring private cloud-based MLaaS solutions over their public cloud-based counterparts due to data security reasons," according to the press release.

In particular, it is particularly amenable to machine learning and interactive data workloads, and can provide an order of magnitude greater performance than traditional Hadoop data processing tools. In this course, we will provide a deep-dive into Spark as a framework, understand it's design, how to optimally utilize it's design, and how to develop effective machine learning applications with Spark on HDInsight. The course covers the fundamentals of Spark, it's core APIs and design, relational data processing with Spark SQL, the fundamentals of Spark job execution, performance tuning, tracking and debugging. Users will get hands-on experience with processing streaming data with Spark streaming, training machine learning algorithms with Spark ML and R Server on Spark, as well as HDInsight configuration and platform specific considerations such as remote developing and access with Livy and IntelliJ, secure Spark, multi-user notebooks with Zeppelin, and virtual networking with other HDInsight clusters.

Medable announced today the launch of Cerebrum, a new cloud-based machine learning tool for healthcare apps including HealthKit, ResearchKit, and CareKit compatible apps. In recent years, we've seen a number of healthcare-focused developers emerge that provide HIPAA-compliant health app development as well as cloud-based data management and analytics. We've covered some of Medable's work with a ResearchKit app focused on patient's with LVADs as well as a virtual care clinic. They also recently launched Axon, a do-it-yourself platform for development of ResearchKit apps. As health apps collect ever increasing types and volumes of data on individuals, a core challenge is how to analyze that data and generate actionable insights that can improve patient care.

WASHINGTON – House Republican Majority Leader Rep. Kevin McCarthy says veterans need more educational opportunities that meet the demands of the fast-paced technology industry. The California lawmaker is introducing legislation Thursday giving the Department of Veterans Affairs $75 million to start a pilot program to provide accelerated computer courses in everything from robotics and basic programming to artificial intelligence and virtual reality. McCarthy, who is second-in-command to the House speaker, said the GI bill doesn't cover many such courses and the VA approval process for changing curriculums or course offerings creates bureaucratic delays that are not conducive to the quickly changing technology fields. Under his proposal, veterans, instead of going to a traditional college -- or in addition to a traditional degree -- could get a shorter-term nano degree or micro credential. "And they could be in the work force right away and be a major asset," McCarthy told USA TODAY.

LONDON • Machines or software may eventually replace a third of graduate-level jobs worldwide, with legal frameworks for regulating employment and safety becoming rapidly outdated, says a new report by the International Bar Association (IBA), a global forum for the legal profession set up in 1947. The innovation in artificial intelligence (AI) and robotics could force governments to order quotas of human workers, upend traditional working practices and pose new dilemmas for insuring driverless cars, says the report, released this week. The IBA's survey found that the previous manufacturing model of poorer, emerging economies having a competitive advantage due to cheaper workforces will soon be eroded by robot production lines and intelligent computer systems. To illustrate, a German car worker costs more than £40 (S$70) an hour, but a robot costs only between £5 and £8 an hour. "A production robot is thus cheaper than a worker in China," the report notes.

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In many sequential decision-making problems one is interested in minimizing an expected cumulative cost while taking into account \emph{risk}, i.e., increased awareness of events of small probability and high consequences. Accordingly, the objective of this paper is to present efficient reinforcement learning algorithms for risk-constrained Markov decision processes (MDPs), where risk is represented via a chance constraint or a constraint on the conditional value-at-risk (CVaR) of the cumulative cost. We collectively refer to such problems as percentile risk-constrained MDPs. Specifically, we first derive a formula for computing the gradient of the Lagrangian function for percentile risk-constrained MDPs. Then, we devise policy gradient and actor-critic algorithms that (1) estimate such gradient, (2) update the policy in the descent direction, and (3) update the Lagrange multiplier in the ascent direction. For these algorithms we prove convergence to locally optimal policies. Finally, we demonstrate the effectiveness of our algorithms in an optimal stopping problem and an online marketing application.

The Gibbs sampler is a particularly popular Markov chain used for learning and inference problems in Graphical Models (GMs). These tasks are computationally intractable in general, and the Gibbs sampler often suffers from slow mixing. In this paper, we study the Swendsen-Wang dynamics which is a more sophisticated Markov chain designed to overcome bottlenecks that impede the Gibbs sampler. We prove O(\log n) mixing time for attractive binary pairwise GMs (i.e., ferromagnetic Ising models) on stochastic partitioned graphs having n vertices, under some mild conditions, including low temperature regions where the Gibbs sampler provably mixes exponentially slow. Our experiments also confirm that the Swendsen-Wang sampler significantly outperforms the Gibbs sampler when they are used for learning parameters of attractive GMs.