The new Surface Complete for Education warranty program allows schools to pool their accidental damage claims. Microsoft R Server Developer Edition is now available on the Linux version of the company s Data Science Virtual Machine (DSVM), enabling users to build models using Microsoft s ScaleR libraries. In January, Microsoft launched R Server Developer Edition, a free version of the analytics platform for developers, students and nonproduction deployments. The offering arrived nearly a year after the software maker announced it was acquiring Revolution Analytics, the leading commercial supporter of R, the popular open-source statistical computing language. Making Microsoft R Server Developer available on the Linux flavor of the DSVM offers a major bump in big data processing capabilities.

Data School's 8-week Master Course begins September 28. More than two-thirds of the available spots are gone! Learn more about the course and enroll: http://www.dataschool.io/learn/ This info session was recorded on September 13. View the chat history and complete Q&A: http://ccst.io/e/text-course

I recently came across this question on Cross Validated, and I thought it offered a great opportunity to use R and ggplot2 to explore, in depth, the assumptions underlying the k-means algorithm. The question, and my response, follow. K-means is a widely used method in cluster analysis. In my understanding, this method does NOT require ANY assumptions, i.e., give me a data set and a pre-specified number of clusters, k, then I just apply this algorithm which minimize the SSE, the within cluster square error. So k-means, it is essentially an optimization problem.

An interesting cheat sheet (a nice infographic!) was published by Microsoft sometime back to help beginning data scientists on how to choose a Machine Learning algorithm for different predictive analytics needs: Classification (to predict categories), Clustering (to discover structure), Regression (to predict values) and Anomaly Detection (to find unusual data points). Here's what Brandon, the author of the article "How to choose algorithms for Microsoft Azure Machine Learning", says about it: "It depends on the size, quality, and nature of the data. It depends what you want to do with the answer. It depends on how the math of the algorithm was translated into instructions for the computer you are using. And it depends on how much time you have. Even the most experienced data scientists can't tell which algorithm will perform best before trying them."

Manuel Le Gallo's research will inspire a new generation of extremely dense neuromorphic computing systems. Inspired by the way the human brain functions, a team of scientists at IBM Research in Zurich, have imitated the way neurons spike, for example when we touch a hot plate. These so-called artificial neurons can be used to detect patterns and discover correlations in Big Data with power budgets and at densities comparable to those seen in biology, something which scientists strived to accomplish for decades. They can also learn, unsupervised at high speeds using very little energy. The paper entitled "Stochastic phase-change neurons," which appeared today on the cover of Nature Nanotechnology, outlines the research and its findings.

Artificial Intelligence (AI), long the subject of science fiction, is now becoming more and more widespread and is seen as an increasingly important computer science across multiple industries.In Financial Services in particular, Machine Learning and Natural Language Processing is increasingly used today to make sense of big, complex data in a wide range of areas. One such area is regulatory compliance.The use of AI -- particularly Natural Language Understanding (NLU), a subset of Natural Language Processing –can help firms to realise a number of benefits, including improving the speed and efficiency with which they achieve compliance, and making that compliance much more robust. As we've seen over just the last couple of years with the introduction of MiFIDI & II, UCITS, AIFMD and the like, there is a constant stream of documents being issued by regulators, which can each run to hundreds, or even thousands, of pages. Wading through those documents and trying to pick out the pieces that are important so that appropriate rules can be built, code can be written and reporting systems can be automated (for example), is an onerous task for human beings. In order to achieve compliance, many regulated firms take the approach of partnering up with third party consulting firms, paying large sums to them to help interpret the regulations employing people to write up what everything means from a rules perspective, then attempting to code those rules into their systems.

Earlier this month Microsoft shared news of new bots in the Skype Bot Shop, including new ones from StubHub, Hipmunk, and IFTTT. Also released that day but not publicized was Your Face, a bot created by Microsoft that combines computer vision, emotion recognition, and facial recognition APIs from Microsoft Cognitive Services. Your Face doesn't have a name like Siri or Cortana but he has the face of an old man and is a pretty salty curmudgeon. Upload any photo or GIF and the bot will guess the age, analyze expression, and share a few opinions about the face sprinkled with salty curmudgeonness. Upload a photo or GIF of your own face and it will probably insult you.

The uses of artificial intelligence (AI) that get the most press are usually the big, splashy ones. Whether it's IBM's Watson beating Ken Jennings at Jeopardy, DeepMind besting Lee Sedol at Go, the massive influx of news about self-driving cars, the growing personal marketplace, or Elon Musk's increasingly public trepidation, these kinds of AI stories have a way of capturing public attention. But quietly, AI powers search and recommendation engines at places like Google and Netflix, filters out obscene images on your favorite social networks, and proves complex mathematical theorems. You probably hear far less about AI applications in retail. However, AI in retail is something that will affect everyone who shops online in the coming years.

Lounge apparel retailer FlyPolar uses a new service from vendor Sentient Technologies to test many elements quickly across multiple pages and is on track to double sales this month. Artificial intelligence can lead to real sales increases--and fast ones, too. Just ask loungewear retailer ShopFlyPolar.com. The web only merchant had hit a sales downturn, generating about 277,000 in web sales in February 2015 to about 50,000 in July 2016 says, says Shawn Broadus founder and CEO of FlyPolar. "We hit a slump," Broadus say.

Secret to GoogleBrain and RankBrain algorithm revealed. One is going to give a historical overview about GoogleBrain and analyse the pattern, then we will conclude our finding about the current situation and future changes in search engine algorithm. Back in 2006 there were some interests in implementing artificial intelligence in Google search engine algorithm. A few years later in 2014, GoogleBrain was established after acquisition of DeepMind, a British artificial intelligence company which was founded in 2010. They worked on how to play video games based on machine learning and artificial neural networks (ANNs).