Facebook has cancelled its Project Aquila, a programme to develop drones to deliver high-speed internet to remote areas currently not connected to the internet. According to Facebook, which started its development on the high-altitude platform station (HAPS) technology in 2014, many other companies have started to develop similar technologies, which has led to Facebook deciding not to continue the project. Since the start of the programme, Facebook has been working on technology and policy to help the four billion people currently not connected to the internet gain access. Facebook wanted to do this by flying drones over remote areas currently lacking in internet infrastructure. Those drones were to use beam down high-speed wireless internet connections while using solar power to stay airborne for extended amounts of time.

Water company United Utilities is using artificial intelligence (AI) technology that is expected to help reduce electricity costs by 10% a year. Around 8MW of pumps, motors and biogas engines across eight of the water firm's sites will be connected to the technology provided by Open Energi over the next 12 months. It will enable a flexible approach to energy management with continuous monitoring of power demand and generation across the sites to reduce costs and increase renewable self-generation. The fully automated technology is expected to shift United Utilities' demand so it consumes more when it is generating high levels of electricity and much less during expensive peak periods as well as responds to fluctuations on the grid. Andy Pennick, Energy Manager at United Utilities said: "We are committed to providing safe, cost efficient and sustainable water and wastewater services to our customers… By bringing all our energy disciplines together, we can focus on future proofing our energy strategy and providing low carbon, secure energy at least cost.

A photo of the first Aquila high altitude aircraft being built by Facebook in England. Facebook said Tuesday it was shutting down the project, four years after its start. SAN FRANCISCO -- Facebook has grounded its Aquila internet drone project after four years. The project, aimed at building a drone that could fly over an isolated area and provide internet coverage, will shut down, the social media company announced Tuesday in a blog post. Facebook is abandoning efforts to build its own aircraft and will close the British facility involved in the project.

Long-lead forecasting for spatio-temporal problems can often entail complex nonlinear dynamics that are difficult to specify it a priori. Current statistical methodologies for modeling these processes are often overparameterized and thus, struggle from a computational perspective. One potential parsimonious solution to this problem is a method from the dynamical systems and engineering literature referred to as an echo state network (ESN). ESN models use so-called reservoir computing to efficiently estimate a dynamical neural network forecast, model referred to as a recurrent neural network (RNN). Moreover, so-called deep models have recently been shown to be successful at predicting high-dimensional complex nonlinear processes. These same traits can be used to characterize many spatio-temporal processes. Here we introduce a deep ensemble ESN (D-EESN) model. Through the use of an ensemble framework, this model is able to generate forecasts that are accompanied by uncertainty estimates. After introducing the D-EESN, we then develop a hierarchical Bayesian implementation. We use a general hierarchical Bayesian framework that accommodates non-Gaussian data types and multiple levels of uncertainties. The proposed methodology is first applied to a data set simulated from a novel non-Gaussian multiscale Lorenz-96 dynamical system simulation model and then to a long-lead United States (U.S.) soil moisture forecasting application.

Recently, the University of Washington published an article illustrating the use of laser energy by researchers for the propulsion of small drones. The nano drones represent a real asset for many missions but the autonomy of flight is a real challenge. To overcome this, researchers at University of Washington developed a wireless drone, powered by a small photovoltaic panel. "Before now the concept of wireless insect-sized flying robots was science fiction. Would we ever able to make them work without needing a wire?" said co-author Sawyer Fuller, an assistant professor in the UW Department of Mechanical Engineering.

This paper presents a theoretical study of gradient boosted trees (GBT: Friedman, 2001). Machine learning methods for prediction have generally been thought of as trading off both intelligibility and statistical uncertainty quantification in favor of accuracy. Recent results have started to provide a statistical understanding of methods based on ensembles of decision trees (Breiman et al., 1984). In particular, the consistency of methods related to Random Forests (RFs: Breiman, 2001) has been demonstrated in Biau (2012); Scornet et al. (2015) while Wager et al. (2014); Mentch and Hooker (2016); Wager and Athey (2017) and Athey et al. (2016) prove central limit theorems for RF predictions. These have then been used for tests of variable importance and nonparametric interactions in Mentch and Hooker (2017). In this paper, we extend this analysis to GBT. Analyses of RFs have relied on a subsampling structure to express the estimator in the form of a U-statistic from which central limit theorems can be derived. By contrast, GBT produces trees sequentially with the current tree depending on the values in those built previously, requiring a different analytical approach. While the algorithm proposed in Friedman (2001) is intended to be generally applicable to any loss function, in this paper we focus specifically on nonparametric regression (Stone, 1977, 1982).

Few user companies and organisations are putting artificial intelligence (AI) to work at significant scale, according to a McKinsey Global Institute (MGI) discussion paper. It shows AI adoption outside the technology sector to be exiguous and experimental, deployed commercially in only 12% of 160 use cases. Check out the latest findings on how the hype around artificial intelligence could be sowing damaging confusion. Also, read a number of case studies on how enterprises are using AI to help reach business goals around the world. You forgot to provide an Email Address.

Virtual high-throughput screening provides a strategy for prioritizing compounds for physical screens. Machine learning methods offer an ancillary benefit to make molecule predictions, yet the choice of representation has been challenging when selecting algorithms. We emphasize the effects of different levels of molecule representation. Then, we introduce N-gram graph, a novel representation for a molecular graph. We demonstrate that N-gram graph is able to attain most accurate prediction with several non-deep machine learning methods on multiple tasks.

The current trend of pushing CNNs deeper with convolutions has created a pressing demand to achieve higher compression gains on CNNs where convolutions dominate the computation and parameter amount (e.g., GoogLeNet, ResNet and Wide ResNet). Further, the high energy consumption of convolutions limits its deployment on mobile devices. To this end, we proposed a simple yet effective scheme for compressing convolutions though applying k-means clustering on the weights, compression is achieved through weightsharing, by only recording K cluster centers and weight assignment indexes. We then introduced a novel spectrally relaxed k-means regularization, which tends to make hard assignments of convolutional layer weights to K learned cluster centers during retraining. We additionally propose an improved set of metrics to estimate energy consumption of CNN hardware implementations, whose estimation results are verified to be consistent with previously proposed energy estimation tool extrapolated from actual hardware measurements. We finally evaluated Deep k-Means across several CNN models in terms of both compression ratio and energy consumption reduction, observing promising results without incurring accuracy loss. The code is available at https://github.

Rachel Thomas was selected by Forbes as one of "20 Incredible Women Advancing AI Research." She is co-founder of fast.ai and a researcher-in-residence at the University of San Francisco Data Institute, where she teaches in the Masters in Data Science program. Her background includes energy trading, a data scientist backend engineer at Uber, and a full-stack software instructor at Hackbright. QCon.ai is a AI and Machine Learning conference held in San Francisco for developers, architects & technical managers focused on applied AI/ML.