Today, social networking tools and graph technology can accurately map and extract valuable insights from the relationships between various entities in a network. Networks can also be analysed by machine learning, a technique in which a computer can adapt its own algorithms. Modern manufacturing equipment has been advancing rapidly; plants are filled with sensors to monitor equipment performance. The number of sensors that allow devices to connect to the internet is growing and so too is the volume and complexity of data available to plant managers. The collection, storage and analysis of this data is vital in unlocking the benefits big data can provide.

The genetic cause of autism spectrum disorder is notoriously hard to research. Genetic markers for the disorder are tough to match from patient to patient because they're so rare--one of the most common genetic signifiers is only found in less than one percent of those diagnosed with autism. Even when genetic anomalies are found, they must be checked against family members genomes to ensure it's not attributable to a more commonly inherited mutation that doesn't cause disease. Researchers at Princeton and the Simons Foundation turned the traditional approach on its head, teaching a machine learning algorithm to look for the genetic relationships that could cause autism. The algorithm scoured a digital network of the human genome's interactions, looking for relationships and connections that are similar to those in previously-known markers for autism.

This post is authored by Roope Astala, Senior Program Manager at Microsoft. We are thrilled to announce the support for multiple versions of R and Python in Azure ML.

Data scientists and deep and machine learning researchers rely on frameworks and libraries such as Torch, Caffe, TensorFlow, and Theano. Studies by Colfax Research and Kyoto University have found that existing open source packages such as Torch and Theano deliver significantly faster performance through the use of Intel Scalable System Framework (Intel SSF) technologies like the Intel compiler and performance libraries for Intel Math Kernel Library (Intel MKL), Intel MPI (Message Passing Interface), and Intel Threading Building Blocks (Intel TBB), and Intel Distribution for Python (Intel Python). Andrey Vladimirov (Head of HPC Research, Colfax Research) noted that "new Intel SSF hardware and software in combination with code modernization delivered an observed 50x machine learning performance improvement in our case study". In the Colfax Research and Kyoto case studies as well as general Python scientific computing benchmarks, results run up to two orders of magnitude (100x) faster as a result of using Intel SSF technologies. Python is a powerful and popular scripting language that provides fast and fundamental tools for machine learning and scientific computing through popular packages such as scikit-learn, NumPy and SciPy.

This guest post was written by Daniel Emaasit, a Ph.D Student of Transportation Engineering at the University of Nevada, Las Vegas. Daniel's research interests include the development of probabilistic machine learning methods for high-dimensional data, with applications to urban mobility, transport planning, highway safety, & traffic operations. Don't miss Daniel's webinar on Model-Based Machine Learning and Probabilistic Programming using RStan, scheduled for July 20, 2016 at 11:00 AM PST. This blog post follows my journey from traditional statistical modeling to Machine Learning (ML) and introduces a new paradigm of ML called Model-Based Machine Learning (Bishop, 2013). Model-Based Machine Learning may be of particular interest to statisticians, engineers, or related professionals looking to implement machine learning in their research or practice.

Co-authored by Rob Thomas (@robdthomas) Machine Learning represents the new frontier in analytics, and is the answer of how many companies can capitalize on the data opportunity. Machine Learning was first defined by Arthur Samuel in 1959 as a "Field of study that gives computers the ability to learn without being explicitly programmed." Said another way, this is the automation of analytics, so that it can be applied at scale. What is highly manual today (think about an analyst combing thousand line spreadsheets), becomes automatic tomorrow (an easy button) through technology. If Machine Learning was first defined in 1959, why is this now the time to seize the opportunity?

This summer schools is aimed at graduate students and industrial engineers and researchers who already have some basic knowledge of machine learning (and possibly but not necessarily of deep learning) and wish to learn more about this rapidly growing field of research.

The marriage of computing power and data is finally bearing fruit in the field of cognitive computing, sometimes called machine learning or, more controversially, artificial intelligence. In its most everyday form, we see it in tools such as Google Translate or Microsoft's Bing Translate, which can translate phrases and documents effortlessly across multiple languages. More futuristically, the promise of self-driving vehicles, which can complete entire road journeys without driver intervention, is already being realised. Yet the biggest revolution in work is happening at some of the most basic levels, such as reading and dissecting legal documents to extract meaning and useful information. The tedious slog of work can be transformed by computers which are able to read and parse legal phrases, and summarise them or enter relevant details into a database or spreadsheet.

I'm seated in a giant ballroom where vast rows of chairs face seven glowing supercomputers. Each liquid-cooled rack of servers is lit with a different color. Though they stand on a dais at the Paris Las Vegas resort as still as statues, the computers are locked in heated battle with each other. "The race for third is very tight," says Hakeem Oluseyi, an astrophysicist, in a rousing voice. ForAllSecure's team Mayhem stands as a silent sentinel in the DARPA Cyber Grand Challenge.

In collaboration with the Norwegian University of Science and Technology (NTNU) and the leading research institute SINTEF, Telenor will establish a lab focused on artificial intelligence and big data at NTNU in Trondheim, Norway. As the second initiative, Telenor will develop and launch a dedicated, next-generation Internet of Things (IoT) network in several Norwegian cities. Norwegian startups and students will get cost-free access to the IoT network in order to develop and test their products and services. The first pilot will be located in Oslo, in collaboration with StartupLab. "We need to build critical competencies within artificial intelligence and we want to give Norwegian startups the resources they need to succeed. This is imperative for our ability to seize digital opportunities and contribute to creating new jobs. Startups play a key role in net job creation. We aim to stimulate productivity in Norway by developing new competencies and supporting the startup community," says Sigve Brekke, President and CEO, Telenor Group.