UPS has partnered with the German tech company Wingcopter to build a fleet of rugged, high speed delivery drones. The drones will be based on a model designed by Wingcopter, which can travel at speeds of up to 150mph and has a range of 75 miles. The drones can also endure a variety of difficult weather conditions, including wind speeds of up to 45mph. The agreements marks the first external partnership for UPS's Flight Forward program, which is focused on developing a range of drone delivery options, according to a report in TechCrunch. 'Drone delivery is not a one-size-fits-all operation,' UPS's Bala Ganesh said.

UPS Flight Forward (UPSFF) is collaborating with German drone-maker Wingcopter to develop the next generation of package delivery drones for a variety of use cases in the United States and internationally. UPSFF is a subsidiary of UPS dedicated to drone delivery. UPS chose Wingcopter for its unmanned aircraft technology and its track record in delivering a variety of goods over long distances in multiple international settings. "Drone delivery is not a one-size-fits-all operation," said Bala Ganesh, vice president of the UPS Advanced Technology Group. "Our collaboration with Wingcopter helps pave the way for us to start drone delivery service in new use-cases. UPS Flight Forward is building a network of technology partners to broaden our unique capability to serve customers and extend our leadership in drone delivery."

A cross-disciplinary group of researchers used AI as part of an analysis of photos posted online that recognizes an association between happiness, life satisfaction, and nature. Researchers from universities in Australia and Singapore say the analysis demonstrates the biophilia hypothesis that humans are naturally attracted to nature and people around the world have a preference for nature in their fun activities, vacations, and honeymoons. The analysis of more than 31,000 photos also found that people in nations with high life satisfaction scores like Costa Rica and Finland tend to take a higher proportion of photographs during fun activities like weddings or recreation. Nature also appears prominently in vacation and honeymoon photos. The frequency of nature in different activities varied widely across countries.

Recent advances in research have demonstrated the effectiveness of knowledge graphs (KG) in providing valuable external knowledge to improve recommendation systems (RS). A knowledge graph is capable of encoding high-order relations that connect two objects with one or multiple related attributes. With the help of the emerging Graph Neural Networks (GNN), it is possible to extract both object characteristics and relations from KG, which is an essential factor for successful recommendations. In this paper, we provide a comprehensive survey of the GNN-based knowledge-aware deep recommender systems. Specifically, we discuss the state-of-the-art frameworks with a focus on their core component, i.e., the graph embedding module, and how they address practical recommendation issues such as scalability, cold-start and so on. We further summarize the commonly-used benchmark datasets, evaluation metrics as well as open-source codes. Finally, we conclude the survey and propose potential research directions in this rapidly growing field.

The global degradation of water resources is a matter of great concern, especially for the survival of humanity. The effective monitoring and management of existing water resources is necessary to achieve and maintain optimal water quality. The prediction of the quality of water resources will aid in the timely identification of possible problem areas and thus increase the efficiency of water management. The purpose of this research is to develop a water quality prediction model based on water quality parameters through the application of a specialised recurrent neural network (RNN), Long Short-Term Memory (LSTM) and the use of historical water quality data over several years. Both multivariate single and multiple step LSTM models were developed, using a Rectified Linear Unit (ReLU) activation function and a Root Mean Square Propagation (RMSprop) optimiser was developed. The single step model attained an error of 0.01 mg/L, whilst the multiple step model achieved a Root Mean Squared Error (RMSE) of 0.227 mg/L.

This paper proposes a method to accelerate the training process of general fuzzy min-max neural network. The purpose is to reduce the unsuitable hyperboxes selected as the potential candidates of the expansion step of existing hyperboxes to cover a new input pattern in the online learning algorithms or candidates of the hyperbox aggregation process in the agglomerative learning algorithms. Our proposed approach is based on the mathematical formulas to form a branch-and-bound solution aiming to remove the hyperboxes which are certain not to satisfy expansion or aggregation conditions, and in turn decreasing the training time of learning algorithms. The efficiency of the proposed method is assessed over a number of widely used data sets. The experimental results indicated the significant decrease in training time of proposed approach for both online and agglomerative learning algorithms. Notably, the training time of the online learning algorithms is reduced from 1.2 to 12 times when using the proposed method, while the agglomerative learning algorithms are accelerated from 7 to 37 times on average.

The scheduling of multi-user remote laboratories is modeled as a multimodal function for the proposed optimization algorithm. The hybrid optimization algorithm, hybridization of the Nelder-Mead Simplex algorithm and Non-dominated Sorting Genetic Algorithm (NSGA), is proposed to optimize the timetable problem for the remote laboratories to coordinate shared access. The proposed algorithm utilizes the Simplex algorithm in terms of exploration, and NSGA for sorting local optimum points with consideration of potential areas. The proposed algorithm is applied to difficult nonlinear continuous multimodal functions, and its performance is compared with hybrid Simplex Particle Swarm Optimization, Simplex Genetic Algorithm, and other heuristic algorithms.

This correspondence comments on the findings reported in a recent Science Robotics article by Mitrokhin et al. [1]. The main goal of this commentary is to expand on some of the issues touched on in that article. Our experience is that hyperdimensional computing is very different from other approaches to computation and that it can take considerable exposure to its concepts before attaining practically useful understanding. Therefore, in order to provide an overview of the area to the first time reader of [1], the commentary includes a brief historic overview as well as connects the findings of the article to a larger body of literature existing in the area. I. INTRODUCTION The recent article by A. Mitrokhin, P. Sutor, C. Fermüller, and Y. Aloimonos, "Learning Sensorimotor Control with Neuromorphic Sensors: Toward Hyperdimensional Active Perception", which appeared in Science Robotics vol. 4 issue 30 (2019), presents a case for using a computation framework called hyperdimensional computing also known as Vector Symbolic Architectures (VSAs) for fusing motoric abilities of a robot with its perception system. The idea of computing with random vectors as basic objects is also known as Holographic Reduced Representation [2], Multiply-Add-Permute [3], Binary Spatter Codes [4], Binary Sparse Distributed Codes [5], Matrix Binding of Additive Terms [6], and Semantic Pointer Architecture [7]. All these frameworks are essentially equivalent. In the light of the present very high level of attention to the area of autonomous AIempowered systems from the industry and the society, we hope and believe that the application of VSAs in robotics will get an appropriately increasing attention from the community of AI/robotics researchers and practitioners. Our own experience with VSAs has shown that due to its considerable difference from the conventional computing paradigms the development of intuition and understanding required for practical applications needs to be supported by extended exposure to the details and interpretation of VSAs.

Researchers have taught a computer how to smell. Teams from Intel and Cornell University designed a neural algorithm based on the brain's olfactory system (which recognises scents) and used it to teach a neuromorphic computer chip how to smell 10 different hazardous chemicals. The process supposedly mimics the effect a scent has on the human. Nabil Imam, one of the co-authors of the study published last week in Nature, explained how this research intersects neuroscience and computing. "My friends at Cornell study the biological olfactory system in animals and measure the electrical activity in their brains as they smell odours," he said.

Machine learning can be intimidating for folks coming from a non-technical background. All machine learning jobs seem to require a healthy understanding of Python (or R). So how do non-programmers gain coding experience? Here's the good news – there are plenty of tools out there that let us perform machine learning tasks without having to code. You can easily build algorithms like decision trees from scratch in a beautiful graphical interface.