Implicit schemes are popular methods for the integration of time dependent PDEs such as hyperbolic and parabolic PDEs. However the necessity to solve corresponding linear systems at each time step constitutes a complexity bottleneck in their application to PDEs with rough coefficients. We present a generalization of gamblets introduced in \cite{OwhadiMultigrid:2015} enabling the resolution of these implicit systems in near-linear complexity and provide rigorous a-priori error bounds on the resulting numerical approximations of hyperbolic and parabolic PDEs. These generalized gamblets induce a multiresolution decomposition of the solution space that is adapted to both the underlying (hyperbolic and parabolic) PDE (and the system of ODEs resulting from space discretization) and to the time-steps of the numerical scheme.

The human body is the most complex machine ever created. With a complex network of interconnected organs, millions of cells and the most advanced processor, the human body is the most automated system in this planet. This article will draw comparisons between the working of a human body to that of a data center. It will also draw parallels between human body automation and data center automation and explain the different levels of automation we need to drive in data centers. This article is divided into four parts covering each of body main functions and drawing parallels on automation.

When Jason Nichols joined GE Global Research in 2011, soon after completing postdoctoral work in organic chemistry at the University of California, Berkeley, he anticipated a long career in chemical research. But after four years creating materials and systems to treat industrial wastewater, Nichols moved to the company's machine-learning lab. This year he began working with augmented reality. Part chemist, part data scientist, Nichols is now exactly the type of hybrid employee crucial to the future of a company working to inject artificial intelligence into its machines and industrial processes. Fifteen years ago, GE's machine operators and technicians monitored its aircraft engines, locomotives, and gas turbines by listening to their clanks and whirs and checking their gauges.

Until few years ago, #ArtificialIntelligence (#AI) was similar to nuclear fusion in unfulfilled promise. It had been around a long time but had not reached the spectacular heights foreseen in its initial stages. However now, Artificial intelligence (AI) is no longer the future. It's realizing its potential in achieving man-like capabilities, so it's the right time to ask: How can business leaders adapt AI to take advantage of the specific strengths of man and machine? AI is swiftly becoming the foundational technology in areas as diverse as self-driving cars, financial trading, connected houses etc. Self-learning algorithms are now routinely embedded in mobile and online services.

Each of these is so small that it makes extremely close contact with the surface, forming a minute attraction on a molecular level. It consists of pads covered with not hairs, but microscale wedges made of silicone rubber--the same stuff that those fancy spatulas are made of. The handheld gripper consists of pairs of adhesive pads, whose microscale wedges point in opposite directions. The wedges lie flat, making super close contact with the object, and boom, adhesion.

Here's a glimpse of what the future will look like. This week, Scientific American published its annual report on emerging technologies. The list is a compilation of what the publication calls "disruptive solutions" that are "poised to change the world." To qualify, a particular technology must be attracting funding or showing signs of an imminent breakthrough, but must not have reached widespread adoption yet. Here are a few of the cutting-edge technologies that made the list--and the companies that are already making strides with them.

Welcome to the first post of my series Deep Learning for Rookies by me, a rookie. I'm writing as a reinforcement learning strategy to process and digest the knowledge better. But if you are a deep learning rookie, then this is for you as well because we can learn together as rookies! Deep learning is probably one of the hottest tech topics right now. Large corporations and young startups alike are all gold-rushing this fancy field. If you think big data is important, then you should care about deep learning. The Economist says that data is the new oil in the 21st Century. If data is the crude oil, databases and data warehouses are the drilling rigs that digs and pumps the data on the internet, then think of deep learning as the oil refinery that finally turns crude oil into all the useful and insightful final products.

You might think putting a helipad on Trump Tower would give the president's Manhattan residence an added veneer of affluence. After all, nothing conveys wealth and power quite like arriving at your own skyscraper aboard Marine One, right? Not according to Penny, an artificial intelligence that uses satellite imagery to predict income levels in the Big Apple and how they change as you tinker with the urban landscape. When I called up the president's Manhattan residence via Penny's clean, intuitive interface, it saw nothing but wealth. "PENNY is 100% confident that this is a HIGH median income area," it reported.

The max-product {belief propagation} (BP) is a popular message-passing heuristic for approximating a maximum-a-posteriori (MAP) assignment in a joint distribution represented by a graphical model (GM). In the past years, it has been shown that BP can solve a few classes of linear programming (LP) formulations to combinatorial optimization problems including maximum weight matching, shortest path and network flow, i.e., BP can be used as a message-passing solver for certain combinatorial optimizations. However, those LPs and corresponding BP analysis are very sensitive to underlying problem setups, and it has been not clear what extent these results can be generalized to. In this paper, we obtain a generic criteria that BP converges to the optimal solution of given LP, and show that it is satisfied in LP formulations associated to many classical combinatorial optimization problems including maximum weight perfect matching, shortest path, traveling salesman, cycle packing, vertex/edge cover and network flow.

You probably know that by 2022 an estimated 5 million jobs worldwide will be lost to AI-enabled automation technologies. You probably also know that Oxford University says that 47 percent of American jobs are at risk of being automated, and you probably know two or three more harrowing statistics along the same lines. But did you know that AI is able to spot genetic diseases that human doctors can't detect? Or greatly reduce power consumption using smart energy grids? Or educate children with hyperpersonal teaching techniques?