Construction technology continues to evolve, including the creation of a variety of infrastructure, including three-dimensional (3D) prints. Now, the world's first 3D-printed steel structure, a'living laboratory' bridge, has been unveiled by a robot in Amsterdam. This pedestrian bridge with smart sensors will replace the old bridge under external restoration for the next two years. The 3D-printed footbridge, which is over four years in the making, is the result of a unique collaboration between MX3D, software company Autodesk, chief engineer Arup, steel giant ArcelorMittal, the City of Amsterdam, and the University of Twente, among others. MX3D made this design possible by turning welding robots with intelligent software into industrial 3D printers.

Labeled data is essential in modern systems that rely on Machine Learning (ML) for predictive modelling. Such systems may suffer from the cold-start problem: supervised models work well but, initially, there are no labels, which are costly or slow to obtain. This problem is even worse in imbalanced data scenarios. Online financial fraud detection is an example where labeling is: i) expensive, or ii) it suffers from long delays, if relying on victims filing complaints. The latter may not be viable if a model has to be in place immediately, so an option is to ask analysts to label events while minimizing the number of annotations to control costs. We propose an Active Learning (AL) annotation system for datasets with orders of magnitude of class imbalance, in a cold start streaming scenario. We present a computationally efficient Outlier-based Discriminative AL approach (ODAL) and design a novel 3-stage sequence of AL labeling policies where it is used as warm-up. Then, we perform empirical studies in four real world datasets, with various magnitudes of class imbalance. The results show that our method can more quickly reach a high performance model than standard AL policies. Its observed gains over random sampling can reach 80% and be competitive with policies with an unlimited annotation budget or additional historical data (with 1/10 to 1/50 of the labels).

The first ever 3D-printed steel bridge has opened in Amsterdam, the Netherlands. It was created by robotic arms using welding torches to deposit the structure of the bridge layer by layer, and is made of 4500 kilograms of stainless steel. The 12-metre-long MX3D Bridge was built by four commercially available industrial robots and took six months to print. The structure was transported to its location over the Oudezijds Achterburgwal canal in central Amsterdam last week and is now open to pedestrians and cyclists. More than a dozen sensors attached to the bridge after the printing was completed will monitor strain, movement, vibration and temperature across the structure as people pass over it and the weather changes.

Intelligent diagnosis method based on data-driven and deep learning is an attractive and meaningful field in recent years. However, in practical application scenarios, the imbalance of time-series fault is an urgent problem to be solved. This paper proposes a novel deep metric learning model, where imbalanced fault data and a quadruplet data pair design manner are considered. Based on such data pair, a quadruplet loss function which takes into account the inter-class distance and the intra-class data distribution are proposed. This quadruplet loss pays special attention to imbalanced sample pair. The reasonable combination of quadruplet loss and softmax loss function can reduce the impact of imbalance. Experiment results on two open-source datasets show that the proposed method can effectively and robustly improve the performance of imbalanced fault diagnosis.

A new technique of moon scanning could help automatically categorize the essential lunar features from telescopic images and can also remarkably enhance the efficiency of choosing sites for exploration. There is much more to choosing a landing or exploration site on the Moon than what is just observed. The lunar surface's visible area is bigger compared to Russia and is pockmarked by thousands of craters and crisscrossed by canyon-like rilles. However, scanning the larger area by eyes, searching for features that measure a few hundred meters across is strenuous and not typically perfect. This makes it hard to select an ideal area for exploration.

Organic farming, which does not involve any agricultural chemicals or synthesized fertilizers, is attracting attention in Japan, especially because it puts less strain on the environment. While the government has launched a strategy to promote organic agriculture, there are still many challenges to overcome, including ways to reduce physical burdens on farmers and costs, and expand sales channels. "We've recently been seeing an increase in the number of environmentally aware young customers," said Naoya Okada, president of Bio c' Bon Japon Co., which operates an organic food store in Tokyo's Ebisu district. Organic farming uses compost for soil cultivation, with farmers digging up weeds and not relying on agricultural chemicals. Momentum for compiling international organic agriculture standards is building, especially in Europe, as the farming method is believe to contribute to the conservation of biodiversity and the fight against global warming.

Mining firms may use artificial intelligence and machine learning to discover minerals to mine.

It was only a few years earlier that Artificial Intelligence (AI) was a brand-new concept that was much too technical for anyone to anticipate how it will affect the world. Things have surely altered by the year 2019. It has changed the game for a variety of sectors, including significant participants in the mining industry. Small increases in yields, pace, and efficiency may have a huge influence on the mining sector, therefore increasing efficiency and productivity is critical for profitability. Now, let's take a look at how the companies which are using AI in the mining sector.

Discovering a drug, material, or anything new requires finding and understanding molecules. It's a time- and labor-intensive process, which can be helped along by a chemist's expertise, but it can only go so quickly, be so efficient, and there's no guarantee for success. Connor Coley is looking to change that dynamic. The Henri Slezynger (1957) Career Development Assistant Professor in the MIT Department of Chemical Engineering is developing computational tools that would be able to predict molecular behavior and learn from the successes and mistakes. It's an intuitive approach and one that still has obstacles, but Coley says that this autonomous platform holds enormous potential for remaking the discovery process.

Butt rot (BR) damages associated with Norway spruce (Picea abies [L.] Karst.) account for considerable economic losses in timber production across the northern hemisphere. While information on BR damages is critical for optimal decision-making in forest management, the maps of BR damages are typically lacking in forest information systems. We predicted timber volume damaged by BR at the stand-level in Norway using harvester information of 186,026 stems (clear-cuts), remotely sensed, and environmental data (e.g. climate and terrain characteristics). We utilized random forest (RF) models with two sets of predictor variables: (1) predictor variables available after harvest (theoretical case) and (2) predictor variables available prior to harvest (mapping case). We found that forest attributes characterizing the maturity of forest, such as remote sensing-based height, harvested timber volume and quadratic mean diameter at breast height, were among the most important predictor variables. Remotely sensed predictor variables obtained from airborne laser scanning data and Sentinel-2 imagery were more important than the environmental variables. The theoretical case with a leave-stand-out cross-validation achieved an RMSE of 11.4 $m^3ha^{-1}$ (pseudo $R^2$: 0.66) whereas the mapping case resulted in a pseudo $R^2$ of 0.60. When the spatially distinct k-means clusters of harvested forest stands were used as units in the cross-validation, the RMSE value and pseudo $R^2$ associated with the mapping case were 15.6 $m^3ha^{-1}$ and 0.37, respectively. This indicates that the knowledge about the BR status of spatially close stands is of high importance for obtaining satisfactory error rates in the mapping of BR damages.