IBM Research is engaged in a research program in symbiotic cognitive computing to investigate how to embed cognitive computing in physical spaces. This article proposes 5 key principles of symbiotic cognitive computing.  We describe how these principles are applied in a particular symbiotic cognitive computing environment and in an illustrative application.  

There are many arguments surrounding whether or not marijuana should be grown and used for medical reasons. But the impact on the climate is one factor in the debate that may have been overlooked, - until now. A new report, by a clean energy policy research institute, has found growing marijuana makes up one per cent of energy use in states like Colorado and Washington. The impact on the climate is one factor in the debate that may have been overlooked, until now. A new report, by a clean energy policy research institute, has found growing marijuana makes up 1 per cent of energy use in states like Colorado and Washington.

Stocks extended their winning streak to a fourth session on the Tokyo Stock Exchange on Thursday, backed by the yen's further drop against the dollar and a brighter U.S. economic outlook. The Nikkei average rose 79.86 points, or 0.47 percent, to finish at 16,899.10. On Wednesday, it gained 83.59 points. The Topix index closed up 6.12 points, or 0.45 percent, at 1,353.93, after climbing 7.60 points the previous day. The Tokyo market opened sharply higher thanks to the weaker yen and gains on Wall Street.

In artificial neural networks, learning from data is a computationally demanding task in which a large number of connection weights are iteratively tuned through stochastic-gradient-based heuristic processes over a cost-function. It is not well understood how learning occurs in these systems, in particular how they avoid getting trapped in configurations with poor computational performance. Here we study the difficult case of networks with discrete weights, where the optimization landscape is very rough even for simple architectures, and provide theoretical and numerical evidence of the existence of rare - but extremely dense and accessible - regions of configurations in the network weight space. We define a novel measure, which we call the "robust ensemble" (RE), which suppresses trapping by isolated configurations and amplifies the role of these dense regions. We analytically compute the RE in some exactly solvable models, and also provide a general algorithmic scheme which is straightforward to implement: define a cost-function given by a sum of a finite number of replicas of the original cost-function, with a constraint centering the replicas around a driving assignment. To illustrate this, we derive several powerful new algorithms, ranging from Markov Chains to message passing to gradient descent processes, where the algorithms target the robust dense states, resulting in substantial improvements in performance. The weak dependence on the number of precision bits of the weights leads us to conjecture that very similar reasoning applies to more conventional neural networks. Analogous algorithmic schemes can also be applied to other optimization problems.

Machine Learning helps make complex systems more efficient. By applying advanced Machine Learning techniques such as Cognitive Fingerprinting, wind project operators can utilize these tools to learn from collected data, detect regular patterns, and optimize their own operations. In his session at 18th Cloud Expo, Stuart Gillen, Director of Business Development at SparkCognition, discussed how research has demonstrated the value of Machine Learning in delivering next generation analytics to improve safety, performance, and reliability in today's modern wind turbines.

In the last several years, interest in artificial intelligence (AI) has surged. Venture capital investments in companies developing and commercializing AI-related products and technology have exceeded 2 billion since 2011.1 Technology companies have invested billions more acquiring AI startups. Press coverage of the topic has been breathless, fueled by the huge investments and by pundits asserting that computers are starting to kill jobs, will soon be smarter than people, and could threaten the survival of humankind. Amid all the hype, there is significant commercial activity underway in the area of AI that is affecting or will likely soon affect organizations in every sector. Business leaders should understand what AI really is and where it is heading. The first steps in demystifying AI are defining the term, outlining its history, and describing some of the core technologies underlying it. The field of AI suffers from both too few and too many definitions. Nils Nilsson, one of the founding researchers in the field, has written that AI "may lack an agreed-upon definition. . .

Three scientists won the Nobel Prize in chemistry on Wednesday for developing the world's smallest machines, work that could revolutionize computer technology and lead to a new type of battery. Frenchman Jean-Pierre Sauvage, British-born Fraser Stoddart and Dutch scientist Bernard "Ben" Feringa share the 8 million kronor ( 930,000) prize for the "design and synthesis of molecular machines," the Royal Swedish Academy of Sciences said. Machines at the molecular level are 1,000th the width of a human hair and have taken chemistry to a new dimension, the academy said. Molecular machines "will most likely be used in the development of things such as new materials, sensors and energy storage systems." Stoddart has already developed a molecule-based computer chip with 20 kB memory.

Motivated by electricity consumption metering, we extend existing nonnegative matrix factorization (NMF) algorithms to use linear measurements as observations, instead of matrix entries. The objective is to estimate multiple time series at a fine temporal scale from temporal aggregates measured on each individual series. Furthermore, our algorithm is extended to take into account individual autocorrelation to provide better estimation, using a recent convex relaxation of quadratically constrained quadratic program. Extensive experiments on synthetic and real-world electricity consumption datasets illustrate the effectiveness of our matrix recovery algorithms.

If you want 5G, there's a good chance you'll need a small cell nearby to deliver it. Putting up that cell may be hard because of a host of problems, but Nokia Bell Labs thinks it can solve some of them with drones and tiny solar panels. Nokia's F-Cell is an experimental LTE small cell that doesn't need any wires. It gets power from solar panels on its surface and communicates with the carrier's core network over a high-speed wireless connection. No one even needs to climb up on a roof to install it: The company recently delivered an F-Cell to the roof of one of its buildings in Sunnyvale, California, using a drone.

IMAGE: Sandia National Laboratories researchers are drawing inspiration from neurons in the brain, such as these green fluorescent protein-labeled neurons in a mouse neocortex, with the aim of developing neuro-inspired computing... view more ALBUQUERQUE, N.M. - Computation is stuck in a rut. The integrated circuits that powered the past 50 years of technological revolution are reaching their physical limits. This predicament has computer scientists scrambling for new ideas: new devices built using novel physics, new ways of organizing units within computers and even algorithms that use new or existing systems more efficiently. To help coordinate new ideas, Sandia National Laboratories has assisted organizing the Institute of Electrical and Electronics Engineers (IEEE) International Conference on Rebooting Computing held Oct. 17-19. "We're taking a stab at the scope of what neural algorithms can do. We're not trying to be exhaustive, but rather we're trying to highlight the kind of application over which algorithms may be impactful," said Brad Aimone, a computational neuroscientist and co-author of one paper.