In our ongoing series showcasing the awesome community contributed content in the Cortana Intelligence GaIlery, I have with me Val Fontama. Val Fontama is a Principal Data Scientist Manager on the Azure team. Today he is chatting with us about the Predictive Maintenance model in the Gallery that predicts yield failure in a semiconductor manufacturing process. Predictive maintenance helps you deal with a problem even before it occurs saving you time and money. You can access the model used in this conversation and follow along.

The technique can be implemented via Barnes-Hut approximations, allowing it to be applied on large real-world datasets. We applied it on data sets with up to 30 million examples. An accessible introduction to t-SNE and its variants is given in this Google Techtalk. Below, implementations of t-SNE in various languages are available for download. Some of these implementations were developed by me, and some by other contributors. For the standard t-SNE method, implementations in Matlab, C, CUDA, Python, Torch, R, Julia, and JavaScript are available.

Google Photos, the imaging app has got a set of new features to make its users fall in love with it even more. Google Photos is known for its smart cloud searching services which helps you look for images easily by typing in what the photo is about, or what it contains. For example, if you type'baby' it'll show results of all photos with babies, making it really easy to find your memories. Now the app has got a set of new features which includes the machine learning used in the search to make the app a lot more intuitive. Google Photos now allows its users to rediscover old memories of the people in your most recent photos.

This updated and revised second edition of the leading reference volume on distance metrics includes a wealth of new material that reflects advances in a developing field now regarded as an essential tool in many areas of pure and applied mathematics. Its publication coincides with intensifying research efforts into metric spaces and especially distance design for applications. Accurate metrics have become a crucial goal in computational biology, image analysis, speech recognition and information retrieval. The content focuses on providing academics with an invaluable comprehensive listing of the main available distances. As well as standalone introductions and definitions, the encyclopedia facilitates swift cross-referencing with easily navigable bold-faced textual links to core entries, and includes a wealth of fascinating curiosities that enable non-specialists to deploy research tools previously viewed as arcane. Its value-added context is certain to open novel avenues of research.

I have tried to exhibit credit scoring case studies with German Credit Data. This article includes detail programming of predictive modeling 1. Univariate And Bi-Variate Analysis 2. Information Value and Weight Evidence to access prediction power of variables 3. Multivariate Analysis and Dimension Reduction using Variable Clustering 4. Different Machine Learning Techniques and their performance evaluation using ROC, AUC and KS The basic difference of traditional modeling and machine learning is that "in traditional modeling we intend to setup a modelimg framework and try to establish relationships while in machine learning we allow the model to learn from the data by understanding the hidden patterns". Hence the first one requires analyst to have solid understanding of statistical techniques and business knowledge while the later one is more complex in nature and computational intensive, hence requires higher computation power of the systems and analyst needs to be tech savvy. Kindly note that while traditional techniques perform well on small to large amount of data, machine learning will certainly learn better on high-dimensional and complex data such as BigData setup. If you want to do more experiments and not sure where to get a problem definition or data to machine learning, you may explore the online machine learning repository here http://archive.ics.uci.edu/ml/.

Unlike other components to an enterprises' technology mix, determining the ROI of machine learning is a less-than-obvious process, particularly when solutions are new and little by way of case studies or benchmarks exist. While we're far from a world where SMBs (small- and mid-sized businesses) outside of Silicon Valley integrate AI into their regular operations, we will undoubtedly see an explosion of novel uses in industry and enterprise over the next 5 to 10 years, and executives are rightly concerned with how to make the most of those technology, time, and staffing decisions. If you're a business who's new to the machine learning scene (and that's a vast majority), there are more burning questions than answers at present. "What are the criterion needed for a company to derive maximal value from the application of machine learning in a business problem?" Tapping into our hundreds of interviews (on our podcast and otherwise), as well as reaching out to other experts in the field, allowed us to glean valuable insight from researchers and executives across the globe.

Alibaba will be among 13 businesses working with the Hangzhou government on a'brain' for the city and will work with the National Astronomical Observatory of China (NAOC) on deep space exploration projects, it announced at its annual Computing Conference this week. According to the retail and cloud computing giant, it will be supplying a range of its tech services such as AI, deep learning and data storage. The B2B technology supply side to the Alibaba business is growing fast and puts it very much in battle with Amazon on a global playing field. The Hangzhou City Brain project is a new government initiative to address its urban city living issues, such as traffic congestion. It will use Alibaba Cloud's AI program "ET" and big data analytics capabilities to perform real-time traffic prediction by using its video and image recognition technologies.

Just about every service industry--from retailers to restaurants to hotels--has developed some kind of robot to attend to your needs on the cheap. The latest effort in the banking world (there are already robotic bank receptionists in China and Japan) is to take the rote responses of bots to the next level, by adding a touch of human empathy. The Royal Bank of Scotland (paywall) plans to unveil its new artificial intelligence system, known as "Luvo," by the end of the year. The AI service, designed by IBM, will attend to customer banking needs through its mobile or online as a chatbot. It will function similarly to Siri, the iPhone virtual assistant that answers questions with a distinct voice and "personality."

Escaping the trough of disillusionment for virtual and augmented reality [TechCrunch]: S. Somasegar writes about AR/VR's long road to mass adoption, stating, "Gartner has placed VR within its tech hype cycle as precariously struggling out of the trough of disillusionment, described as a period of waning interest as'experiments and implementations fail to deliver.'" However, while Somasegar says mainstream adoption is still likely three to five years away, "We still believe that in twenty years, VR will be a ubiquitous force and as pervasive and transformative as the internet was in the 90s or the smartphone was in the 2000s. Every 2D interface will be re-imagined and re-architected for 3D." He goes on to outline some of the big opportunities in AR/VR just waiting to be tapped by "those brave enough to weather the trough of disillusionment." Google artificial intelligence guru says A.I. won't kill jobs [Fortune]: Mustafa Suleyman, co-founder of artificial intelligence startup DeepMind, recently addressed some common concerns around AI at an O'Reilly event, and Jonathan Vanian recapped the highlights in Fortune this week.

The DeepMind artificial intelligence (AI) being developed by Google's parent company, Alphabet, can now intelligently build on what's already inside its memory, the system's programmers have announced. Their new hybrid system – called a Differential Neural Computer (DNC) – pairs a neural network with the vast data storage of conventional computers, and the AI is smart enough to navigate and learn from this external data bank. What the DNC is doing is effectively combining external memory (like the external hard drive where all your photos get stored) with the neural network approach of AI, where a massive number of interconnected nodes work dynamically to simulate a brain. "These models... can learn from examples like neural networks, but they can also store complex data like computers," write DeepMind researchers Alexander Graves and Greg Wayne in a blog post. At the heart of the DNC is a controller that constantly optimises its responses, comparing its results with the desired and correct ones.