Hundreds of schoolchildren have been drafted in to make Amazon's Alexa devices in China as part of a controversial and often illegal attempt to meet production targets, documents seen by the Guardian reveal. Interviews with workers and leaked documents from Amazon's supplier Foxconn show that many of the children have been required to work nights and overtime to produce the smart-speaker devices, in breach of Chinese labour laws. According to the documents, the teenagers – drafted in from schools and technical colleges in and around the central southern city of Hengyang – are classified as "interns", and their teachers are paid by the factory to accompany them. Teachers are asked to encourage uncooperative pupils to accept overtime work on top of regular shifts. Some of the pupils making Amazon's Alexa-enabled Echo and Echo Dot devices along with Kindles have been required to work for more than two months to supplement staffing levels at the factory during peak production periods, researchers found.

This May, the global group of Google AI Impact Challenge grantees gathered in San Francisco to kick off the six-month Launchpad Accelerator program. With $25 million in funding from Google.org, credits from Google Cloud and mentorship by Google's AI experts, the teams sought to apply AI to address a wide range of problems problems, from protecting rainforests to coaching students on writing skills. Now in the second phase of the program, Tech Sprint Week, the grantees tackled their projects' greatest technical challenges with support from a team of mentors from Google. At Google for Startups' campus in London, teams continued work on their ideas and learned user experience design principles along the way. Grace Mitchell, a data scientist at grantee WattTime, opened up about her team's experience at Tech Sprint Week--and how they're using AI to build a globally accessible, open-source fossil fuel emissions monitoring platform for power plants.

Neural networks trained with backpropagation, the standard algorithm of deep learning which uses weight transport, are easily fooled by existing gradient-based adversarial attacks. This class of attacks are based on certain small perturbations of the inputs to make networks misclassify them. We show that less biologically implausible deep neural networks trained with feedback alignment, which do not use weight transport, can be harder to fool, providing actual robustness. Tested on MNIST, deep neural networks trained without weight transport (1) have an adversarial accuracy of 98% compared to 0.03% for neural networks trained with backpropagation and (2) generate non-transferable adversarial examples. However, this gap decreases on CIFAR-10 but still significant particularly for small perturbation magnitude less than 1/2.

--In this paper, a method of prediction on continuous time series variables from the production or flow - an LSTM algorithm based on multivariate tuning - is proposed. The algorithm improves the traditional LSTM algorithm and converts the time series data into supervised learning sequences regarding industrial data's features. The main innovation of this paper consists in introducing the concepts of periodic measurement and time window in the industrial prediction problem, especially considering industrial data with time series characteristics. Experiments using real-world datasets show that the prediction accuracy is improved, 54.05% higher than that of traditional LSTM algorithm. In industry, with the high-speed functioning of the enterprise product line, data are generated continuously. Malfunctions and abnormality often take place, which incur a great deal of money and resources, and even advanced equipment cannot avoid these problems[1]. Industrial companies have to pay a lot to maintain and ensure the normal operation of the manufacturing process. According to the statistics, the maintenance costs of all kinds of industrial enterprises account for about 15%-70% of total production costs[2]. Flow prediction is motivated by such industrial conundrums faced by many factories. Implementing flow prediction to forecast the output of the machine and to detect the problems in time via prediction, not only can production increase, but also a large number of workforce and resources for troubleshooting can be saved.

High entropy alloys (HEAs) have been increasingly attractive as promising next-generation materials due to their various excellent properties. It's necessary to essentially characterize the degree of chemical ordering and identify order-disorder transitions through efficient simulation and modeling of thermodynamics. In this study, a robust data-driven framework based on Bayesian approaches is proposed and demonstrated on the accurate and efficient prediction of configurational energy of high entropy alloys. The proposed effective pair interaction (EPI) model with ensemble sampling is used to map the configuration and its corresponding energy. The US Government retains and the publisher, by accepting the article for publication, acknowledges that the US government retains a nonexclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this manuscript, or allow others to do so, for US government purposes. Compared with the arbitrary determination of model complexity, we further conduct a physical feature selection to identify the truncation of coordination shells in EPI model using Bayesian information criterion. The results achieve efficient and robust performance in predicting the configurational energy, particularly given small data. The developed methodology is applied to study a series of refractory HEAs, i.e. NbMoTaW, NbMoTaWV and NbMoTaWTi where it is demonstrated how dataset size affects the confidence we can place in statistical estimates of configurational energy when data are sparse. Introduction As one of the typical multicomponent alloys, high entropy alloys (HEAs) consisting of four or more principal elements have been widely studied due to their exceptional mechanical properties [1, 2, 3, 4].

Recent advances in statistical inference have significantly expanded the toolbox of probabilistic modeling. Historically, probabilistic modeling has been constrained to (i) very restricted model classes where exact or approximate probabilistic inference were feasible, and (ii) small or medium-sized data sets which fit within the main memory of the computer. However, developments in variational inference, a general form of approximate probabilistic inference originated in statistical physics, are allowing probabilistic modeling to overcome these restrictions: (i) Approximate probabilistic inference is now possible over a broad class of probabilistic models containing a large number of parameters, and (ii) scalable inference methods based on stochastic gradient descent and distributed computation engines allow to apply probabilistic modeling over massive data sets. One important practical consequence of these advances is the possibility to include deep neural networks within a probabilistic model to capture complex non-linear stochastic relationships between random variables. These advances in conjunction with the release of novel probabilistic modeling toolboxes have greatly expanded the scope of application of probabilistic models, and allow these models to take advantage of the recent strides made by the deep learning community. In this paper we review the main concepts, methods and tools needed to use deep neural networks within a probabilistic modeling framework.

Thanks to a new partnership with the Alan Turing Institute, National Grid Electricity System Operator (ESO) announced it has developed new AI prediction models that have improved solar forecasting by one-third. Knowing how much power will be flowing into the grid on any given day is becoming increasingly crucial as the proportion of intermittent renewable power serving the grid goes up. Rob Rome, commercial operations manager at the ESO, said the new forecast models means the power system can become much more efficient at managing supply and demand. Improved solar forecasts will help us run the system more efficiently, ultimately meaning lower bills for consumers. National Grid worked with researchers and doctoral students at the Institute to develop the improved forecasting models.

This post originally appeared on PhilipLay.com. To read the post from the original source click here. Earlier this month an article in the Financial Times by John Thornhill, the paper's innovation editor, caught my attention. Thornhill was relaying an intriguing set of ideas expressed by the authors of a new book, What To Do When Machines Do Everything? Before discussing the future impact of today's unfolding industrial innovations such as driverless cars, robotic surgery, precision agriculture, or automated beer service (as in the photo above), the three authors – Malcolm Frank, Paul Roehrig, and Ben Pring – make their first key point, citing the example of an early 19th century innovation that enabled an entire industry that generates $620bn. in annual revenues today.

Floods are among the most destructive natural disasters, which are highly complex to model. The research on the advancement of flood prediction models contributed to risk reduction, policy suggestion, minimization of the loss of human life, and reduction the property damage associated with floods. To mimic the complex mathematical expressions of physical processes of floods, during the past two decades, machine learning (ML) methods contributed highly in the advancement of prediction systems providing better performance and cost-effective solutions. Due to the vast benefits and potential of ML, its popularity dramatically increased among hydrologists. Researchers through introducing novel ML methods and hybridizing of the existing ones aim at discovering more accurate and efficient prediction models. The main contribution of this paper is to demonstrate the state of the art of ML models in flood prediction and to give insight into the most suitable models. In this paper, the literature where ML models were benchmarked through a qualitative analysis of robustness, accuracy, effectiveness, and speed are particularly investigated to provide an extensive overview on the various ML algorithms used in the field. The performance comparison of ML models presents an in-depth understanding of the different techniques within the framework of a comprehensive evaluation and discussion. As a result, this paper introduces the most promising prediction methods for both long-term and short-term floods. Furthermore, the major trends in improving the quality of the flood prediction models are investigated. Among them, hybridization, data decomposition, algorithm ensemble, and model optimization are reported as the most effective strategies for the improvement of ML methods.

Decades of research in artificial intelligence (AI) have produced formidable technologies that are providing immense benefit to industry, government, and society. AI systems can now translate across multiple languages, identify objects in images and video, streamline manufacturing processes, and control cars. The deployment of AI systems has not only created a trillion-dollar industry that is projected to quadruple in three years, but has also exposed the need to make AI systems fair, explainable, trustworthy, and secure. Future AI systems will rightfully be expected to reason effectively about the world in which they (and people) operate, handling complex tasks and responsibilities effectively and ethically, engaging in meaningful communication, and improving their awareness through experience. Achieving the full potential of AI technologies poses research challenges that require a radical transformation of the AI research enterprise, facilitated by significant and sustained investment. These are the major recommendations of a recent community effort coordinated by the Computing Community Consortium and the Association for the Advancement of Artificial Intelligence to formulate a Roadmap for AI research and development over the next two decades.