Neuromorphic computing – brain inspired computing – has long been a tantalizing goal. The human brain does with around 20 watts what supercomputers do with megawatts. While neuromorphic computing progress has been intriguing, it has still not proven very practical. This week neuromorphic computing takes another step forward with a workshop being offered to users from academia, industry and education interested in using two European neuromorphic systems that have been years in development and are coming online for broader use – the BrainScaleS system launching at the Kirchhoff Institute for Physics of Heidelberg University and SpiNNaker, a complementary approach and similarly sized system at the University of Manchester. Ramping up BrainScaleS and SpiNNaker is an important milestone, strengthening Europe's position in hardware development for alternative computing. Both projects are part of the European Human Brain Project, originally funded by the European Commission's Future Emerging Technologies program (2005-2015).

Editor's note: Register for the free webcast "How the machine learning wave is changing the way organizations look at analytics," hosted by Patrick Hall, senior machine learning scientist at SAS, and Andrew Pease, principal business solutions manager at SAS, to learn how different organizations are finding success with machine learning. The inherent trade-off between accuracy and interpretability in predictive modeling can be a catch-22 for analysts and data scientists working in regulated industries. Professionals in the regulated verticals of banking and insurance often feel locked into using traditional, linear modeling techniques to create their predictive models. This is mainly due to strenuous regulatory and documentation requirements. As machine learning becomes more mainstream, the forces of innovation and competition often drive these same analysts and data scientists to break out of the mold and try new algorithms with more predictive capacity.

In recent years, deep learning has taken the lead in predictive accuracy in many fields of machine learning, and companies are struggling to keep up with the speed of innovation. Arno Candel demonstrates how successful enterprises can augment simple statistical models with more accurate data-driven models to gain a competitive edge. Arno describes how to build smart applications that include data munging, model training and validation, and real-time production deployment--every step is based on open source code (R, Python, Java, Scala, JavaScript, REST) that runs on distributed platforms including Hadoop, Spark, and standard compute clusters. Arno also presents use cases from verticals including insurance, fraud, churn, fintech, and marketing and offers live demos of smart applications on large real-world datasets in distributed clusters.

I am really excited to announce that, after several months of development, the new version of Datumbox is out! The 0.7.0 version brings multi-threading support, fast disk-based training for datasets that don't fit in memory, several algorithmic enhancements and better architecture. The focus of version 0.7.0 is to finally bring multi-threading support to the framework and make the disk-based training ultra fast. Moreover it brings several algorithmic enhancements in all the Regression-based algorithms, the Collaborative Filtering model and the N-grams extractor which is used in NLP applications. The architecture of the framework has been redesigned to separate the project into multiple modules (note that the artifactId of the main library is now datumbox-framework-lib) and to simplify its structure.

Strong interest in Artificial Intelligence and Machine Learning is driving rapid advances into the basic elements of computers are architected. GPUs are one example -- a GPU consists of a large number of processor cores that can all work in parallel and are tuned to be very performant when operating on very specific kinds problems, like image processing. While originally developed primarily for graphic processing, GPU's are increasingly being used for other computationally intensive problems in machine learning. Our current concept for how a computer works was first conceived by Turing and von Neumann in the 1940's. In the von Neumann model for computing, there is a central processing unit or CPU that uses internal registers for processing data.

See machine learning in action during "A Glimpse into the Future of Email Marketing – Reaping the Benefits of Machine Learning," featuring Kath Pay, Dela Quist, Skip Fidura and Jeremy Swift, May 19 at the Email Innovations Summit in Las Vegas. "Machine learning" has moved out of science fiction and into real-life applications, like powering Tesla cars that run on autopilot and robots that can beat humans at the Japanese game of Go. For marketers, it gets them closer to their email nirvana: true 1:1 personalization on a mass scale. Machine learning, at its simplest, is a method of data analysis that allows computers to learn – to analyze, predict and act – without explicit instructions or programming. That last phrase – "without explicit instructions or programming" – highlights the difference between today's rule-based marketing automation and systems that use machine learning.

XGBoost is a library designed and optimized for tree boosting. Gradient boosting trees model is originally proposed by Friedman et al. By embracing multi-threads and introducing regularization, XGBoost delivers higher computational power and more accurate prediction. More than half of the winning solutions in machine learning challenges hosted at Kaggle adopt XGBoost (Incomplete list). XGBoost has provided native interfaces for C, R, python, Julia and Java users.

You'll always have more fun with Grand Theft Auto when you prioritise chaos over common sense. So perhaps it's not such a surprise that watching an artificial deer'play' the game, with no purpose, has become an instant hit online. In essence, 'San Andreas Deer Cam' by digital artist Brent Watanabe is simple; instead of a human avatar or player, the game focuses instead on a single deer which is controlled by an artificial intelligence. With no direction from the artist, the deer wanders and trots around the 100 square miles of San Andreas, and interacts with its other AI inhabitants. What it surprising is how complex and oddly resonant the interactions of the deer turn out to be; in what is a testament to the AI skills of Rockstar as much as Watanabe, its adventures are peculiarly complex.

The common wisdom about Artificial Intelligence is that we are building increasingly intelligent machines that will ultimately surpass human capabilities, steal our jobs, possibly even escape human control and take over the world. Our next speaker, Jerry Kaplan, Fellow at the Stanford Center for Legal Informatics, believes this narrative is both misguided and counterproductive. Join Jerry Kaplan on Tuesday, March 22, at 10AM PT for an unorthodox tour of the history of Artificial Intelligence, learn why it is so misunderstood, and what we can do to ensure that the engines of progress don't motor on without us.

First of all, though, there might still be things left to achieve with Go. Ke Jie, an 18-year-old Go virtuoso from China ranked #1 in the world, seemed cautiously optimistic about his own chances following Lee's first defeat last week, saying "it's 60 percent in favor of me." And many Go players have said they want to learn as much about AlphaGo as possible -- after all, it's only ever played a handful of games in public, demonstrating unorthodox, crushing tactics. It seems likely that AlphaGo will eventually be released to the public, and don't be surprised to see a match against Ke at some point; Lee Se-dol was chosen for his iconic stature and long career, but Ke is considered the stronger player today. DeepMind founder Demis Hassabis (above) has also said the company plans to test a version without any human training at all -- just the program teaching itself.