Comprehending procedural text, e.g., a paragraph describing photosynthesis, requires modeling actions and the state changes they produce, so that questions about entities at different timepoints can be answered. Although several recent systems have shown impressive progress in this task, their predictions can be globally inconsistent or highly improbable. In this paper, we show how the predicted effects of actions in the context of a paragraph can be improved in two ways: (1) by incorporating global, commonsense constraints (e.g., a non-existent entity cannot be destroyed), and (2) by biasing reading with preferences from large-scale corpora (e.g., trees rarely move). Unlike earlier methods, we treat the problem as a neural structured prediction task, allowing hard and soft constraints to steer the model away from unlikely predictions. We show that the new model significantly outperforms earlier systems on a benchmark dataset for procedural text comprehension (+8% relative gain), and that it also avoids some of the nonsensical predictions that earlier systems make.

Vespa's first electric scooter will go on sale in Europe in October and in the US at the start of 2019. The silent, battery-powered scooter is an electric version of the much-loved retro Vespa which was first released back in 1946. The re-imagined all-electric scooter, known as the Vespa Elettrica, has a maximum range of 62 miles (100km) and takes around four hours to charge the battery. Manufacturer Piaggio Group has remained tight-lipped about the price of its new scooter, as well as its top speed. It has previously revealed the Vespa Elettrica will be limited to 19pmh (30km/h) when set in Eco driving mode.

Drum roll… Starting from well over 100 nominations, we are excited to announce the finalists for this year's Data Impact Awards! Each year, nominees have raised the bar, and this year is no exception. The level of impact that organizations have shown and the variety of use cases are inspiring. From AI models that power retail customer decision engines to utility meter analysis that disables underperforming gas turbines, these finalists demonstrate how machine learning and analytics have become mission-critical to organizations around the world. Two weeks from today we will announce the winners at the Data Impact Awards Celebration on Tuesday, 11th September the week of Strata Data 2018, New York.

An AI is set to try and work out how a potentially limitless supply of energy can be used on Earth. It could finally solve the mysteries of fusion power, letting researchers capture and control the process that powers the sun and stars. Researchers at the U.S. Department of Energy's (DOE) Princeton Plasma Physics Laboratory (PPPL) and Princeton University hope to harness a massive new supercomputer to work out how the doughnut-shaped devices, known as tokamaks, can be used. In the middle of the rising Tokamak Building a well is preserved for the ITER machine. While ITER won't generate electricity, scientists hope it will demonstrate that such a fusion reactor can produce more energy than it consumes.

A Minecraft modder has added some fresh gameplay issues for players to deal with in the form of climate change. Nick Porillo's GlobalWarming mod alters the atmosphere based on certain actions like smelting ores. Temperatures will rise as carbon emissions increase, leading to violent storms, forest fires and a drop in snowfall levels as climate change takes hold. You can combat the changes in the atmosphere by planting trees to absorb carbon dioxide. To combat the issue on a larger scale, you can purchase carbon offsets, which gives other players a tree-planting bounty to complete -- that reflects one of the mod's themes of working together to fight climate change.

FREMONT, Calif., Aug 23, 2018 – Event organizers announced the lineup of more than 60 presenters, session tracks, tutorials and panel sessions for Fog World Congress 2018, produced by the OpenFog Consortium in collaboration with IEEE Communications Society. The event takes place in San Francisco, Oct. 1-3. Fog World Congress is the world's largest gathering of fog leaders and edge influencers focused on these ground-breaking technologies. Attendees will include technologists, data scientists, application developers, educators, researchers, analysts, VCs and investors, service providers, government agencies and enterprises representing a multitude of industries. The event is uniquely focused on fog computing use cases, architecture, standards, developments and research.

Clobotics, a leader in intelligent computer vision solutions for the wind power and retail industries, announced that it has closed an additional $11 million (USD) in funding in a continuation of its Series A round of financing. Venture capital raised in this round now totals $21 million (USD). New investors include Nantian Infotech VC and Wangsu, joining previous investments from KTB Network, GGV Capital and Capital Development Investment Fund Management Co., Ltd. With the new capital, Clobotics will continue to expand its business in North America to further penetrate the wind power and retail industries. The company will also invest in ongoing product development and continue to build its growing team of experts in computer vision, artificial intelligence (AI) and machine learning.

This research presents a deep learning based approach to predict stress fields in the solid material elastic deformation using convolutional neural networks (CNN). Two different architectures are proposed to solve the problem. One is Feature Representation embedded Convolutional Neural Network (FR-CNN) with a single input channel, and the other is Squeeze-and-Excitation Residual network modules embedded Fully Convolutional Neural network (SE-Res-FCN) with multiple input channels. Both the tow architectures are stable and converged reliably in training and testing on GPUs. Accuracy analysis shows that SE-Res-FCN has a significantly smaller mean squared error (MSE) and mean absolute error (MAE) than FR-CNN. Mean relative error (MRE) of the SE-Res-FCN model is about 0.25% with respect to the average ground truth. The validation results indicate that the SE-Res-FCN model can accurately predict the stress field. For stress field prediction, the hierarchical architecture becomes deeper within certain limits, and then its prediction becomes more accurate. Fully trained deep learning models have higher computational efficiency over conventional FEM models, so they have great foreground and potential in structural design and topology optimization.

With deployments of online monitoring systems in distribution networks, massive amounts of data collected through them contain rich information on the operating status of distribution networks. By leveraging the data, based on bidirectional generative adversarial networks (BiGANs), we propose an unsupervised approach for online distribution reliability assessment. It is capable of discovering the latent structure and automatically learning the most representative features of the spatio-temporal data in distribution networks in an adversarial way and it does not rely on any assumptions of the input data. Based on the extracted features, a statistical magnitude for them is calculated to indicate the data behavior. Furthermore, distribution reliability states are divided into different levels and we combine them with the calculated confidence level $1-\alpha$, during which clear criteria is defined empirically. Case studies on both synthetic data and real-world online monitoring data show that our proposed approach is feasible for the assessment of distribution operation reliability and outperforms other existed techniques.

AI's influence over our world will continue to grow, but it remains a technology in its infancy – however, missteps along the way shouldn't detract from the greater good it promises, says Lenore Kerrigan, country sales director for enterprise information management group, OpenText. In Davos 2018, the great and powerful debated the ethics of Artificial Intelligence (AI), with UK Prime Minister Theresa May launching the UK's Centre for Data Ethics and Innovation. The aim of this advisory body is to work closely with international partners to build a common understanding of how to ensure the safe, ethical and innovative deployment of AI. The move echoes a 2000-year-old debate by Roman poet Juvenal who asked, "Who guards the guardsmen?" The question probed at the very heart of power and its abuse, because if powerful people dictate how the world works, who keeps them in check?