In these notes we describe heuristics to predict computational-to-statistical gaps in certain statistical problems. These are regimes in which the underlying statistical problem is information-theoretically possible although no efficient algorithm exists, rendering the problem essentially unsolvable for large instances. The methods we describe here are based on mature, albeit non-rigorous, tools from statistical physics. These notes are based on a lecture series given by the authors at the Courant Institute of Mathematical Sciences in New York City, on May 16th, 2017.

The field of multi-agent system (MAS) is an active area of research within artificial intelligence, with an increasingly important impact in industrial and other real-world applications. In a MAS, autonomous agents interact to pursue personal interests and/or to achieve common objectives. Distributed Constraint Optimization Problems (DCOPs) have emerged as a prominent agent model to govern the agents' autonomous behavior, where both algorithms and communication models are driven by the structure of the specific problem. During the last decade, several extensions to the DCOP model have been proposed to enable support of MAS in complex, real-time, and uncertain environments. This survey provides an overview of the DCOP model, offering a classification of its multiple extensions and addressing both resolution methods and applications that find a natural mapping within each class of DCOPs. The proposed classification suggests several future perspectives for DCOP extensions and identifies challenges in the design of efficient resolution algorithms, possibly through the adaptation of strategies from different areas.

Google's mission is to organize the world's information and make it universally accessible and useful. From the start, they have also made significant efforts to do this in a way that doesn't deplete the world's natural resources. The company has been fully carbon neutral since 2007 and ten years later they are hoping they have achieved the next major goal – drawing every watt of energy they use for their business operations from renewable sources. Kate E Brandt, their lead for sustainability, spoke to me about some of the ways they have been tackling this ambitious challenge while she was visiting London to speak at the Economist Sustainability Summit 2018. She told me "We set a goal in 2012 that we wanted to reach a point where 100% of the energy used for our operations was coming from renewables – so it's a longstanding commitment.

PALO ALTO, Calif., March 27, 2018 (GLOBE NEWSWIRE) -- HP today unveiled a set of industry-leading machine learning (ML) solutions, including the HP Z8, the world's most powerful workstation for ML development1. HP Z Workstations, with new NVIDIA technology, are ideal for local processing at the edge of the network - giving developers more control, better performance and added security over cloud-based solutions. The use of workstations for machine learning development is the gateway to automating workflows in areas such as facial identification, sentiment analysis, fraud detection and predictive analytics. "We're on the cusp of a new era of innovation powered by machine learning technology that enables customers to analyze, predict and problem solve in ways never thought possible. Artificial intelligence will transform workflows and empower end users to make smarter decisions, more quickly, than ever before," said Xavier Garcia, vice president and general manager, HP Z Workstations, HP Inc. "HP's latest solutions provide ground-breaking performance and scalability, offering the most powerful and comprehensive ML edge computing solution.

Another weak spot was Nvidia, which fell 7.8 percent after the chipmaker temporarily suspended self-driving tests across the globe after an Uber Technologies Inc autonomous vehicle killed a woman. Investors rotated out of the tech sector, which had long outperformed the market on hopes of new technologies such as artificial intelligence (AI) and internet of things (IoT). "There is a sense that there will be more regulations on Facebook or FANG and that the cost of compliance will increase," said Nobuhiko Kuramochi, chief strategist at Mizuho Securities. The so-called FANG, a quartet of tech stocks that include Facebook, Amazon.com, Netflix and Alphabet, have been a darling of many investors.

Well-established methods for the solution of stochastic partial differential equations (SPDEs) typically struggle in problems with high-dimensional inputs/outputs. Such difficulties are only amplified in large-scale applications where even a few tens of full-order model runs are impracticable. While dimensionality reduction can alleviate some of these issues, it is not known which and how many features of the (high-dimensional) input are actually predictive of the (high-dimensional) output. In this paper, we advocate a Bayesian formulation that is capable of performing simultaneous dimension and model-order reduction. It consists of a component that encodes the high-dimensional input into a low-dimensional set of feature functions by employing sparsity-enforcing priors and a decoding component that makes use of the solution of a coarse-grained model in order to reconstruct that of the full-order model. Both components are represented with latent variables in a probabilistic graphical model and are simultaneously trained using Stochastic Variational Inference methods. The model is capable of quantifying the predictive uncertainty due to the information loss that unavoidably takes place in any model-order/dimension reduction as well as the uncertainty arising from finite-sized training datasets. We demonstrate its capabilities in the context of random media where fine-scale fluctuations can give rise to random inputs with tens of thousands of variables. With a few tens of full-order model simulations, the proposed model is capable of identifying salient physical features and produce sharp predictions under different boundary conditions of the full output which itself consists of thousands of components.

Uncertainty analysis in the form of probabilistic forecasting can provide significant improvements in decision-making processes in the smart power grid for better integrating renewable energies such as wind. Whereas point forecasting provides a single expected value, probabilistic forecasts provide more information in the form of quantiles, prediction intervals, or full predictive densities. This paper analyzes the effectiveness of an approach for nonparametric probabilistic forecasting of wind power that combines support vector machines and nonlinear quantile regression with non-crossing constraints. A numerical case study is conducted using publicly available wind data from the Global Energy Forecasting Competition 2014. Multiple quantiles are estimated to form 20%, 40%, 60% and 80% prediction intervals which are evaluated using the pinball loss function and reliability measures. Three benchmark models are used for comparison where results demonstrate the proposed approach leads to significantly better performance while preventing the problem of overlapping quantile estimates.

Determining the best method for training a machine learning algorithm is critical to maximizing its ability to classify data. In this paper, we compare the standard "fully supervised" approach (that relies on knowledge of event-by-event truth-level labels) with a recent proposal that instead utilizes class ratios as the only discriminating information provided during training. This so-called "weakly supervised" technique has access to less information than the fully supervised method and yet is still able to yield impressive discriminating power. In addition, weak supervision seems particularly well suited to particle physics since quantum mechanics is incompatible with the notion of mapping an individual event onto any single Feynman diagram. We examine the technique in detail -- both analytically and numerically -- with a focus on the robustness to issues of mischaracterizing the training samples. Weakly supervised networks turn out to be remarkably insensitive to systematic mismodeling. Furthermore, we demonstrate that the event level outputs for weakly versus fully supervised networks are probing different kinematics, even though the numerical quality metrics are essentially identical. This implies that it should be possible to improve the overall classification ability by combining the output from the two types of networks. For concreteness, we apply this technology to a signature of beyond the Standard Model physics to demonstrate that all these impressive features continue to hold in a scenario of relevance to the LHC.

A pair of autonomous robots developed by Carnegie Mellon University's Robotics Institute will soon be driving through miles of pipes at the U.S. Department of Energy's former uranium enrichment plant in Piketon, Ohio, to identify uranium deposits on pipe walls. The CMU robot has demonstrated it can measure radiation levels more accurately from inside the pipe than is possible with external techniques. In addition to savings in labor costs, its use significantly reduces hazards to workers who otherwise must perform external measurements by hand, garbed in protective gear and using lifts or scaffolding to reach elevated pipes. DOE officials estimate the robots could save tens of millions of dollars in completing the characterization of uranium deposits at the Portsmouth Gaseous Diffusion Plant in Piketon, and save perhaps $50 million at a similar uranium enrichment plant in Paducah, Kentucky. "This will transform the way measurements of uranium deposits are made from now on," predicted William "Red" Whittaker, robotics professor and director of the Field Robotics Center.

Living on the water could become a breeze if this concept yacht is ever launched. Plans for a stunning 50-foot-long (15-metre) luxury houseboat that includes an aquatic parking spot for planes have been unveiled by engineers. The £150,000 ($210,000) 'HydroHouse', which can serve as a pier for boats and yachts, has two electric engines powered by a huge bank of solar panels on its roof. Designed by Russian naval architect Maxim Zhivov, customers could use the craft as a permanent residence or a holiday home. Living on the water could become a breeze if this concept yacht is ever launched.