MARLO, the 3D bipedal robot that belongs to electrical engineering professor Jessy Grizzle and his team of students, is starting to really figure out this walking thing. Here, robotics PhD student Ross Hartley watches as MARLO demonstrate's her ability to conquer tough terrain. Image credit: Evan Dougherty, Michigan EngineeringANN ARBOR--An unsupported bipedal robot at the University of Michigan can now walk down steep slopes, through a thin layer of snow, and over uneven and unstable ground. The robot's feedback control algorithms should be able to help other two-legged robots as well as powered prosthetic legs gain similar capabilities. "The robot has no feeling in her tiny feet, but she senses the angles of her joints--for instance, her knee angles, hip angles and the rotation angle of her torso," said Jessy Grizzle, professor of electrical engineering and computer science and of mechanical engineering.

The use of a range of Machine Learning algorithms to help people make sense of large complex unstructured data sources is increasing rapidly. As a provider of solutions addressing major challenges in the area of defence and national security, Montvieux is involved in a number of projects applying state-of-the-art techniques such as Deep Belief networks to model significant patterns in data and predict future events. Their clients' needs are by nature fast-moving, and they have identified a need for intelligent visualisation and support tools to assist in their work. UWE's Artificial Intelligence group has a long history of theoretical and applied work creating and applying Machine Learning systems, with an emphasis on the use of intelligent interactive systems to facilitate this process. The student's time will be equally split between UWE and Montvieux's offices in Tewkesbury, to provide a valuable range of experiences and environments.

Deep learning is a technique used to solve complex problems such as natural language processing and image recognition. We are now able to solve these computational problems quickly, thanks to a component called the Graphics Processing Unit (GPU). Originally used to generate high-resolution computer images at fast speeds, the GPU's computational efficiency makes it ideal for executing deep learning algorithms. Analysis which used to take weeks can now be completed in a few days. While all modern computers have a GPU, not all GPUs can be programmed for deep learning.

Well, maybe not that last one, but you see what I mean. One fundamental principle in Machine Learning and Data Science methods is that randomly applying methods and hoping for the best is never a good strategy. Knowing your data, and the underlying fundamentals of the algorithms applied to this dataset are positively correlated with how accurate, meaningful, and insightful you'd like your results to be. There is a world of wrong one can do by creating incorrect models. It is so important now more than ever because with modern machine learning libraries, and amazing toolsets like scikit learn, theano etc, it is easy to fool oneself into thinking we are using the right classifier which in reality might not be the case.

Last month at SAS Global Forum 2016, I presented the paper, Ensemble Modeling: Recent Advances and Applications, that I wrote along with my colleagues yeliu and M_Maldonado. In this paper, we shared a SAS Enterprise Miner subflow that can be incorporated into your predictive modeling flow to implement the following ensemble methods that take model performance into account: top-t, hill-climbing, clustering-based selection, and stacking methods. After importing this XML file into your project, you can copy the entire flow into the diagram that has your predictive modeling flow, connect the flows together, and run. See the README file for instructions on how to import these XML files and quickly get started with these more sophisticated ensemble methods. Note there are several nodes that directly create ensemble models in SAS Enterprise Miner, and they've been covered in previous SAS Global Forum papers: See Leveraging Ensemble Models in SAS Enterprise Miner and The Power of the Group Processing Facility in SAS Enterprise Miner for more information.

Over half a million students fail to graduate from high school every year. In higher education, similar issues of retention arise, especially for STEM students. Experienced educators can pinpoint students at risk of failure, but the solution doesn't scale well, cannot be used to rank students with the highest risk, and is open to personal biases. Dr. Everaldo Aguiar's PhD research looked out how to use machine learning, based on large amounts of historical data collected by schools, to see if at risk students could be identified. In the recent Computer Science Seminar held May 19 at Northeastern University–Seattle, Dr. Aguiar presented the development, deployment and evaluation of machine learning models that detect, ahead of time, students at risk of underachieving their academic goals.

And it did so on the set of "America's Greatest Makers," the Intel-funded reality TV show on TBS that wrapped up its first season Tuesday night with a million-dollar prize awarded to the inventors of a gamified toothbrush for kids. There in the middle of the panel, alongside fellow judges like NBA superstar Shaquille O'Neal, was Intel's chief geek and visionary, CEO Brian Krzanich [pictured above]. BK, as the 56-year-old Krzanich is known around the office, is the epitome of the "celebritization" trend in high-tech and other industries, a marketing strategy that strives to pump up the personality factor of a company. "The show took Intel's name and gave it a personality," said Dr. Anubha Sacheti, a Boston-area pediatric dentist whose toothbrush team, Grush, took the first season prize. Their invention, which is designed to get kids to brush better, features a kill-the-germs game on a mobile app tied by Bluetooth to a brush, which acts as a joystick.

These two fields of massive innovation will generate more jobs in the coming 2 decades than anything else as they will become our necessity. The counter-narrative is- how good are these new technologies for us? Will AI replace humans and bots will take over? Will we see bots dictating our 24 hours to keep us healthy and safe? Or will they overpower us, even in thinking?

Would it be an exaggeration to say we caress our smartphones? Our connection to them is emotional: We paw at them idly and endlessly. There are times when your phone can be your best friend. Google and Microsoft are betting big on artificially intelligent helpers--not like Apple's Siri, which is stuck only on its products, but a device-agnostic digital personality that follows you wherever you go. As Google's founders put it in their annual letter: "[O]ver time, the computer itself--whatever its form factor--will be an intelligent assistant helping you through your day. We will move from mobile first to an AI first world."

This week around 33,500 visitors flocked to the Jacob K. Javits Centre in New York City for the annual National Retail Federation (NRF) Convention and Expo. Held from January 17 – 20, the event is an opportunity to take stock following the busy holiday season and get a sense of how 2016 will shape up for retail. Much to this journalist's disappointment, nobody announced their plans for a hostile takeover of Amazon at the event, however several key themes emerged. "Tech has untethered customers dramatically and therefore we think that loyalty in itself has shifted dramatically over the past years, over this era of very powerful consumer empowerment," Bousquet-Chavanne said. In response, organisations will have to invest in their operations and systems to make them more agile.