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.

WASHINGTON – Neuroscientists acting as cartographers of the human mind have devised the most comprehensive map ever made of the cerebral cortex, the part of the brain responsible for higher cognitive functions such as abstract thought, language and memory. Using MRI images from the brains of 210 people, the researchers said on Wednesday they were able to pinpoint 180 distinct areas in the cerebral cortex, the brain's thin, wrinkly outermost layer made of so-called gray matter. These areas were present in both the left and right hemispheres of the cerebral cortex. More than half, 97 of them, were previously unknown. The researchers nailed down the specific function of some of the areas, but said they were only scratching the surface on understanding what all of the areas did.

On a previous post we learned how to train a machine learning classifier that is able to detect the different aspects mentioned on hotel reviews. With this aspect classifier, we were able to automatically know if a particular review was talking about cleanliness, comfort & facilities, food, Internet, location, staff and/or value for money. We also learned how to combine this classifier with the sentiment analysis classifier to get interesting insights and answer questions like are guests loving the location of a particular hotel but complaining about its cleanliness? These are the kind of questions we aim to answer with this tutorial and that will lead us to some interesting insights. The source code used for this process is available in this repository.

Over the course of the past year or so, there have been a number of incredible tech projects aimed at stopping the spread of Ebola. One approach that we've never come across before, however, involves plotting the possible spread of Ebola and other "filoviruses" of the same family by predicting which bat species they're most likely to be carried by. That's exactly the goal of a team of scientists, who recently used machine learning techniques to build just such a model. Their work may help prevent future spillover events in which it is important to predict which species of wildlife help spread contagion. "This work entailed collecting intrinsic features describing the world's bat species -- 1,116 species altogether -- and training a machine learning algorithm on these data to learn which features best predict the bat species that carry filoviruses," lead author of the study Barbara Han, a disease ecologist at the Cary Institute of Ecosystem Studies, tells Digital Trends.

Google tapped into the superior intelligence of its DeepMind neural network to find ways to vastly reduce the energy it uses in its data centers, which make up 40% of the worldwide Internet. "This will also help other companies who run on Google's cloud to improve their own energy efficiency," Google said in a blog about the achievement. "While Google is only one of many data center operators in the world, many are not powered by renewable energy as we are." Google has set a goal to eventually power its data centers using 100% renewable energy. Today, the company claims, renewable energy is used for 35% of its power needs.

The post highlights points raised by Yoav in his official response to the ASA statement (available as on page 4 in the ASA supplemental tab), as well as offers a list of relevant R resources. It is just as well relevant to the use of most other statistical methods: context matters, no single statistical measure suffices, specific thresholds should be avoided and reporting should not be done selectively. The latter problem is discussed mainly in relation to omitted inferences. We argue that the selective reporting of inferences problem is serious enough a problem in our current industrialized science even when no omission takes place. Many R tools are available to address it, but they are mainly used in very large problems and are grossly underused in areas where lack of replicability hits hard.