The firm used various network names, some essentially masquerading as other service providers, including "Google Starbucks", "Xfinitywifi", "Attwifi", "I vote Trump! free Internet," and "I vote Hillary! free Internet." The company didn't retain individual data, emphasizes Gagan Singh, Avast's president of mobile. But, he says, it was able to observe device names, such as "Gagan's iPhone," and names of visited domains on the network--enough to detect users playing Pokémon Go and accessing Tinder. To avoid leaking data to other network operators who might be more hostile, Singh recommends that traveling Wi-Fi users connect through a virtual private network, whether Avast's or another provider's. "Obviously we prefer ours, but a number of different reputable companies have VPN products out there," he says.

Microservers are a system-on-chip device featuring multiple single-socket servers sharing cooling fans, power supply, chassis, and other such hardware. This common platform allows microprocessors to achieve much higher power efficiency than conventional servers, since more tasks can be carried out on the same volume of power. Microservers were developed in order to integrate server motherboard tasks on to an easily portable and power-efficient unit. This eliminates the need for support chips complementing the server function, which has resulted in the power efficient and space-saving design of microservers. According to Transparency Market Research, the global microservers market was valued at more than US 1 bn in 2012. Due to the rapidly increasing adoption of microservers in diverse industries, the market is expected to exhibit a stellar 43.4% CAGR from 2013 to 2019, with the market's valuation expected to rise to US 30.2 bn over the period.

When creating bots you have to converse with the users and maintain context of the conversation so you can understand the intent of the users, do the necessary actions. In this tutorial we will see how to use Wit.ai to create conversational models and use them in Elixir.

Is there anything that Google DeepMind can't do? It can defeat Go champions, potentially help those fighting blindness, and now it's helping Google itself to become more environmentally friendly. Bloomberg reported that the technology giant was able to use the artificial intelligence to reduce power consumption in its data centers. According to a blog post, DeepMind was able to help reduce the amount of energy used for cooling by up to 40 percent, which equates to around an overall 15 percent reduction. This is impressive considering Google said it used around 4,402,836 MWh (megawatt-hours) of electricity in 2014. For perspective, that's around what 366,903 US homes use yearly.

Tesla CEO Elon Musk unveiled the second phase of the electric carmaker's "master plan" on Wednesday, outlining a future in which individuals use solar power to provide their own energy, automated vehicles replace heavy-duty trucks and buses, and self-driving technology that could be "10 times safer than the U.S. vehicle average" becomes pervasive. Musk's plan, drawing on projects he's backed for years, represents an ambitious, public mission statement for a man and company known to reach for the improbable. "The main reason was to explain how our actions fit into a larger picture, so that they would seem less random," Musk wrote on Tesla's blog, noting that the company's first "master plan," was posted 10 years ago. "The point of all this was, and remains, accelerating the advent of sustainable energy, so that we can imagine far into the future and life is still good," Musk wrote. It's not some silly, hippy thing? it matters for everyone."

This post describes implementation of K-Means Clustering algorithm using TensorFlow. I have tested the code with GPU (Nvidia GTX 1080 Founders Edition) accelerated TensorFlow and for large dataset it seems to be 2-3 times faster than the CPU based sklearn Kmeans implementation based on number of samples.

One of the cybersecurity promises of machine learning (particularly "deep learning") is that it can accurately identify malware nobody has ever seen before because of what it's learned about malware it's seen in the past. Konstantin Berlin, senior research engineer at Invincea Labs, is trying to take the techology further, so that organizations can get more information about unfamiliar code than simply "it's benign" or "it's malicious." Berlin, who will be presenting his work next month at Black Hat, says security pros also want to know more about the malware family so they can plan their mitigation strategy accordingly. His technique, he says will do that, as well as improve malware triage and attribution by using new methods of recognizing similarities between malware samples. This can all be done in a customized way that enables each organization to choose what features and factors interest them most.

While Mahout has only been around for a few years, it has established itself as a frontrunner in the field of machine learning technologies. Mahout has currently been adopted by: Foursquare, which uses Mahout with Apache Hadoop and Apache Hiveto power its recommendation engine; Twitter, which creates user interest models using Mahout; and Yahoo!, which uses Mahout in their anti-spam analytic platform.

The landscape of the future is knee-deep in new innovations that were once only mere fantasy. Reality and intelligence are changing and we are already starting to experience this terrain blossom into view. Let's talk about the Pokémon in the room. In the last 2 weeks since it debuted in the US, Pokémon GO has taken the streets by storm. For a "free" app it is estimated to make about 1.6 million a day thanks to the option for in-app purchases.

Raw data in its unprocessed state does not offer much value, but with the right analytics techniques can offer rich insights that can aid various aspects of life such as making business decisions, political campaigns, and advancing medical science. As shown in Figure 1, the analytics cycle can be broadly classified into four categories or phases: descriptive, diagnostic, predictive and prescriptive. Machine Learning is an approach to data analysis that automates analytical model building and is used in all four types of analytics. The relevance and the growing use of analytics using machine learning can be demonstrated by its widespread use in the 2016 US presidential election campaign. Unprecedented growth in the availability of useful information coupled with advancements in technology are making it attractive to use analytics to build and run a better campaign.