BlueData, provider of the leading Big-Data-as-a-Service (BDaaS) software platform, today announced the new summer release for BlueData EPIC . This release builds upon BlueData's innovations in running large-scale distributed analytics and machine learning (ML) workloads on Docker containers, with new functionality to deliver even greater agility and cost savings for enterprise Big Data and AI initiatives. Last spring, BlueData introduced support for hybrid cloud environments – leveraging the inherent infrastructure portability and flexibility of Docker containers. This past fall, BlueData delivered a major new release that added deep learning (DL), GPU acceleration, and multi-cloud support to the container-based BlueData EPIC platform. And last month, BlueData announced a new turnkey solution to accelerate AI and ML / DL deployments in the enterprise.

The age of the smart TV is over, says Hisense - the future of television is artificial intelligence. These next-generation sets don't just display images and play sound, but know what's actually happening on-screen and bring you into the action, starting with the 2018 FIFA World Cup. At CES Asia, China's biggest technology show, the company demonstrated its vision with an AI-powered TV that works as a virtual football pundit to help even non-fans wrap their heads around all the tournament's finer details. It includes facial recognition that identifies all of the tournament's 736 players - in both agony and ecstasy - and present full biographies, statistics and headlines plucked from recent articles. If you decide to change sides at half time, the app can even direct you to a store where you can buy a replica of a specific player's kit (though you'll need to scan a quick QR code with your smartphone to actually make the purchase).

Mumbai: If you think that your artificial intelligence (AI)-powered virtual assistant like Apple's Siri, Google Assistant, Samsung's Bixby or Microsoft's Cortana are smart just because they can respond to your "wake" messages and answer your questions, you may want to think again. On 18 June, an artificial intelligence (AI) system engaged in the first ever live, public debates with humans. At an event held at International Business Machines Corp.'s (IBM) Watson West site in San Francisco, a champion debater and IBM's AI system, Project Debater, began by preparing arguments for and against the statement: "We should subsidize space exploration." Both sides then delivered a four-minute opening statement, a four-minute rebuttal, and a two-minute summary. "Just think about that for a moment. An AI system engaged with an expert human debater, listened to her argument, and responded convincingly with its own, unscripted reasoning to persuade an audience to consider its position on a controversial topic," said Arvind Krishna, director at IBM Research, in an 18 June blog.

The search giant is building alliances as it races Amazon to develop new ways for people to shop, from voice-enabled smart speakers to faster and more convenient home grocery delivery. Google last year teamed up with Walmart Inc. WMT 0.73% to let users order a selection of the retailer's products on Google's virtual assistant and speakers, a challenge to Amazon's Alexa service. Earlier this month, Google joined with Carrefour SA, Europe's largest retailer, to offer same-day delivery of perishable groceries to people's homes in France. The deal announced Monday also could help Google boost advertising revenue, which has been threatened by Amazon recently as businesses increasingly shift ads to the internet shopping site. The JD investment comes as Google seeks to strengthen its connections in China.

In the previous blog posts in this series, we discussed the motivation for clustering attacks and the data used and how to calculate the distance between two attacks using different methods on each feature we extracted. In this final blog post, we'll discuss the clustering algorithm itself – how to use the distance we calculated to create clusters from the data. We will discuss clustering in real time when only a small amount of data can be stored in memory. Finally, we'll show some results of the algorithm based on real data from Imperva customers. Now we have all the basic ingredients to input into the algorithm.

There is no doubt that the artificial intelligence (AI) phenomenon will have a profound impact on businesses large and small this year; that part is easy to predict. What impact it will have, and whether this is a good or a bad thing, is harder to tell. Let's start with the basics of AI. "In our broad definition, AI is a collective term for computer systems that can sense their environment, think, learn, and take action in response to what they're sensing and their objectives. Forms of AI in use today include, among others, digital assistants, chatbots and machine learning. AI is already at work in industry (from sport and manufacturing to investing and healthcare). Here, we take a closer look at the AI trends and predictions for the coming year. There's always a tendency to think of AI as represented by emotionless robots. But humans are, well, humans, and we will continue to interact with AI in our very human way. Facial recognition software might have been developed with ...

Microsoft is the latest tech giant to find itself in the crosshairs over a controversial government contract. In January, it was announced that Microsoft's Azure Government arm was working with U.S. Immigration and Customs Enforcement (ICE) to assist with'facial recognition and identification', providing access to its deep learning AI technology. Along with that, Microsoft said it was'proud to support' ICE - a section that was deleted and, ultimately, restored after it was made public, according to BuzzFeed. In January, it was announced that Microsoft's Azure Government arm was working with ICE to assist with'facial recognition and identification,' providing access to its deep learning AI tech That hasn't stopped many from calling on Microsoft employees to resign from the company or urge CEO Satya Nadella to speak about the firm's dealings with ICE. Microsoft has since responded that deleting the content was a'mistake' and issued a follow up blog post further detailing the matter. 'In response to questions we want to be clear: Microsoft is not working with U.S. Immigration and Customs Enforcement or U.S. Customs and Border Protection on any projects related to separating children from their families at the border, and contrary to some speculation, we are not aware of Azure or Azure services being used for this purpose,' the firm wrote in a statement.

We study finite-sum nonconvex optimization problems, where the objective function is an average of $n$ nonconvex functions. We propose a new stochastic gradient descent algorithm based on nested variance reduction. Compared with conventional stochastic variance reduced gradient (SVRG) algorithm that uses two reference points to construct a semi-stochastic gradient with diminishing variance in each iteration, our algorithm uses $K+1$ nested reference points to build a semi-stochastic gradient to further reduce its variance in each iteration. For smooth nonconvex functions, the proposed algorithm converges to an $\epsilon$-approximate first-order stationary point (i.e., $\|\nabla F(\mathbf{x})\|_2\leq \epsilon$) within $\tilde{O}(n\land \epsilon^{-2}+\epsilon^{-3}\land n^{1/2}\epsilon^{-2})$ number of stochastic gradient evaluations. This improves the best known gradient complexity of SVRG $O(n+n^{2/3}\epsilon^{-2})$ and that of SCSG $O(n\land \epsilon^{-2}+\epsilon^{-10/3}\land n^{2/3}\epsilon^{-2})$. For gradient dominated functions, our algorithm also achieves a better gradient complexity than the state-of-the-art algorithms.

Genetic Algorithms (GAs) are used to solve search and optimization problems in which an optimal solution can be found using an iterative process with probabilistic and non-deterministic transitions. However, depending on the problem's nature, the time required to find a solution can be high in sequential machines due to the computational complexity of genetic algorithms. This work proposes a parallel implementation of a genetic algorithm on field-programmable gate array (FPGA). Optimization of the system's processing time is the main goal of this project. Results associated with the processing time and area occupancy (on FPGA) for various population sizes are analyzed. Studies concerning the accuracy of the GA response for the optimization of two variables functions were also evaluated for the hardware implementation. However, the high-performance implementation proposes in this paper is able to work with more variable from some adjustments on hardware architecture.

We study the problem of learning one-hidden-layer neural networks with Rectified Linear Unit (ReLU) activation function, where the inputs are sampled from standard Gaussian distribution and the outputs are generated from a noisy teacher network. We analyze the performance of gradient descent for training such kind of neural networks based on empirical risk minimization, and provide algorithm-dependent guarantees. In particular, we prove that tensor initialization followed by gradient descent can converge to the ground-truth parameters at a linear rate up to some statistical error. To the best of our knowledge, this is the first work characterizing the recovery guarantee for practical learning of one-hidden-layer ReLU networks with multiple neurons. Numerical experiments verify our theoretical findings.