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.

R remains the leading tool, with 49% share, but Python grows faster and almost catches up to R. RapidMiner remains the most popular general Data Science platform. Big Data tools used by almost 40%, and Deep Learning usage doubles.

Machine learning is quickly becoming an indispensable tool for many large companies. Everyone has, for sure, heard about Google's AI algorithm beating the World Champion in Go, as well as technologies like RankBrain, but machine learning does not have to be a mystical subject relegated to the domain of math researchers. There are many approachable libraries and technologies that show promise of being very useful to any industry that has data to play with. Machine learning also has the ability to turn traditional website marketing and SEO on its head. Late last year, my colleagues and I (rather naively) began an experiment in which we threw several popular machine learning algorithms at the task of predicting ranking in Google. We ended up with an assembly that achieved 41 percent true positive and 41 percent true negative on our data set. In the following paragraphs, I will take you through our experiment, and I will also discuss a few important libraries and technologies that are important for SEOs to begin understanding.

Chinese technology company Baidu today announced the launch of a few new services within its Baidu Open Cloud public cloud infrastructure portfolio. Baidu TianSuan (Smart Big Data) lets customers "collect, store, process and analyze big data," Baidu said in a statement. Baidu TianXiang (Smart Multimedia Cloud) includes face recognition and live video streaming, while Baidu TianGong (Intelligent IoT Service) is a full-stack platform for integrating cloud applications with internet-connected devices. The additions bring Baidu more in line with the world's leading cloud infrastructure providers, including Amazon Web Services, Microsoft Azure, and Google Cloud Platform. Amazon and Microsoft have both introduced Internet of Things (IoT) services.

Cory Edwards is the director of Adobe's social business center of excellence, responsible for the company's social business operations and integration of social media. Since the early 2000s, brands have experimented with social media platforms and networks to communicate with customers and prospects -- first through weblogs, then eventually through social networks such as Facebook and Twitter. Although the capabilities and sophistication have continued to evolve, at its core, social media has remained a platform to facilitate human-to-human communication. Robots, though more specifically virtual robots or chatbots powered by artificial intelligence (AI), are transforming the way brands do business with their customers. Domino's was one of the first companies to dabble in AI, allowing customers to order pizza by tweeting a pizza emoji to @Dominos.

Google has finally revealed a commercial use for DeepMind -- a British artificial intelligence company it acquired for over 600 million in 2014. DeepMind made headlines for beating the best human in the world at the notoriously complex board game Go and it's recently started working with hospitals in the UK on a number of healthcare projects but the startup is yet to make any money for Google, until now. Google announced on Wednesday that it has been using a DeepMind-built AI system to control certain parts of its power-hungry data centers over the last few months as it looks to make its vast server farms more environmentally friendly. Last year, a Greenpeace report predicted that the electricity consumption of data centers is set to account for 12% of global electricity consumption by 2017 and companies like Google, Amazon, Facebook and Apple have some of the biggest data centers in the world. Google said it has been able to reduce the energy consumption of its data center cooling units -- used to stop Google's self-built servers from overheating -- by as much as 40% with the help of a DeepMind AI system.