In the next chapter event, we will be exploring How IoT, video data and machine learning can improve predictive models AKA'The Rise of Engineering Driven Data Analytics'. Audio, image, video, and other sensor-generated data is being combined with traditional business and transactional data to create opportunities for sophisticated analytics on more complex phenomena. Our guest speaker, Boris Savkovic, Lead Data Scientist, BuildingIQ will present on'The rise of IoT data analytics and machine learning in the smart building of tomorrow' Large-scale buildings (skyscrapers, hospitals, shopping centres etc.) account for approximately 40% of global energy consumption, with a large proportion of this energy consumed by heating, ventilation and cooling (HVAC) systems that regulate comfort and internal conditions/temperatures in a building. The business challenges in terms of the resulting energy costs and associated greenhouse gas emissions are substantial. However, these challenges also provide opportunities for innovative companies who can provide services and solutions that address these challenges, driving value for building owners while at the same time helping reduce global greenhouse gas emissions in a post-Paris agreement world.

In the West, we rely on the grid, which provides most people with ample power. In off-grid communities such as those in large parts of rural Africa, energy is a highly valued resource and consumers are much more aware of energy efficiency. The limited energy that is available is disproportionately expensive and innovative solutions, such as small-scale renewable power, that cannot yet compete in the West, are gaining ground where, compared to the cost of the fossil fuels they replace, they are seen as a bargain. Developments such as small solar home systems are bringing power for the first time to millions of off-grid consumers in sub-Saharan Africa, thanks to technological advances in LED lights, batteries and mobile payment, creating what many are referring to as the "clean energy revolution" in rural electrification. While LED lighting and phone charging were the first volume application in this rapidly evolving market, consumers of the clean energy revolution in Africa want, and increasingly demand, more. Customers now expect to be able to access the latest technology, media and communications through the growth of the largely familiar pay-as-you-go technology, even in areas where there is no little or no grid access.

In the matrix completion problem, we seek to recover or estimate a matrix, when only a fraction of its entries are observed. While it is impossible to complete an arbitrary matrix using only partial observations of its entries, it may be possible to fully recover matrix entries when the matrix has an appropriate underlying structure. For example, most low-rank matrices can be completed accurately with high probability, by solving a convex optimization problem (Candés and Recht, 2009). Consequently, algorithms for lowrank matrix completion have enjoyed widespread use across many disciplines, including collaborative filtering and recommender systems (Koren et al., 2009), multi-task learning and classification (Amit et al., 2007; Argyriou et al., 2007; Wu and Lange, 2015), computer vision (Chen and Suter, 2004), statistical genetics (Chi et al., 2013), as well as remote sensing (Malek-Mohammadi et al., 2014). In this paper, we consider matrix completion under a structural assumption that is closely related to the low-rank assumption; i.e., we assume that the matrix entries vary "smoothly" with respect to a graphical organization of the rows and columns. For example, in the context of a movie recommendation system, we seek to complete a user-by-movies ratings matrix. We may have additional information about users, such as if pairs of users are friends on a social media application, as well as additional information from a movie database, such as the co-occurrence of certain film principles. We expect the entries of a movie ratings matrix to vary "smoothly" over a neighborhood of users, defined by a friendship graph, and over a neighborhood of movies, defined by a shared movie principles graph. When such local similarity structure exists, and is available, it behooves us to leverage this information to predict missing entries in a matrix.

Updated 10-18-16: We've updated our recommendation for best Phablet (5.5 inches or greater) to the excellent Pixel XL. We haven't yet reviewed the standard-size Pixel, but one of the two is a likely candidate for best overall phone. The Android universe is teeming with options, from super-expensive flagship phones, to affordable models that make a few calculated compromises, to models expressly designed for, say, great photography. Chances are that whichever phone you buy, you'll keep it for at least two years. So choosing the best Android phone for you isn't a decision you should take lightly.

Google has been selling phones since the Nexus One landed almost seven years ago. In fact, there have been eight Nexus phones, one each year through 2014, and two last year. They have generally been good phones, especially in the last few years. But the Pixel is not a Nexus. With the Pixel, Google did more than partner with a phone maker to slap Android on an already-designed handset. It created its own hardware and software innovations on top of stock Android. The result is a phone that may displease Android purists, but should delight everyone else. This is Google's first real attempt to push a phone to the mass market, and the Pixel competes directly with the iPhone as well as pricey flagships from Samsung and LG. The Pixel is smaller, with a 5-inch 1080p display instead of the Pixel XL's 5.5-inch 1440p display. The smaller display--along with the Pixel's smaller battery--is the only difference between the two models. Nexus phones were built in partnerships with hardware partners like LG, Motorola, and Samsung.

A rover headed for the red planet will perform an unprecedented search for rocky remnants of dead Martians--so where should we send it? Fossil stromatolites, like this one from Bolivia, offer clues to the kinds of preserved life we may find on Mars. Nearly four billion years ago, when Earth was coming alive, Mars was gradually choking to death. The thick atmosphere that had warmed the red planet was leaking into space, and plummeting temperatures caused Martian lakes and rivers to freeze, turning the wet surface into a dry wasteland. But it's possible life took root in those early years.

Elon Musk tweeted last week that Tesla would be unveiling a product "unexpected by most" on Oct. 17. On Sunday, however, the Tesla chief announced on Twitter that the launch had been moved to Wednesday as the product needed more refinement. The product is being kept under wraps by Tesla and it is not clear what product the company plans to unveil or what refinements the it is working on. There has been speculation that the unveiling could be that of the Model 3 electric car, along with Autopilot 2.0 semi-autonomous driving system. With the launch of Autopilot 2.0, Tesla is expected to add more sensors to its cars and provide the vehicle more self-driving features.

Tech columnist Jennifer Jolly shows the best robotic sidekicks. If you grew up watching The Jetsons, Star Wars, or even WALL-E, at some point, you likely dreamed of having a robot sidekick. Seriously -- how great would it be to have a Rosie of your own, making dinner, folding laundry and basically making your every household chore her cheerful command? No such luck for us grown-ups (yet), but the latest robotic toys are more advanced than ever, and the cool factor extends far beyond mere child's play. Here are the best I've reviewed to date.

As we exercise, our bodies can produce a lot of heat as our muscles convert chemical energy into mechanical energy. To keep moving effectively during such conditions, we produce sweat to cool ourselves down. As the water in our sweat evaporates, it takes excess heat with it, allowing us to keep moving without overheating. A team from the University of Tokyo has created a robot that can exercise and cool off in a similar way, inspired by human sweat. The new robot is named Kengoro.

First there was analytics; then there was cognitive computing. Put them in a blender and the result is something completely new: cognitive analytics. "We see cognitive analytics as the way in which the human brain approaches a problem," says Stuart Gillen, director of business development at SparkCognition Inc., an Austin, Texas, company that creates products powered by artificial intelligence to enhance cybersecurity and leverages machine learning technology to predict equipment failures before they happen. "Rather than being focused on one particular technique, where we see a lot of artificial intelligence organizations going, we use a variety of different patterns and learn from them." Developers within corporate IT will play a key role in integrating cognitive analytics services with existing data stores and incoming data streams.