Nokia has acquired SpaceTime Insight to expand its Internet of Things (IoT) portfolio and IoT analytics capabilities, and accelerate the development of new IoT applications for key vertical markets. Based in San Mateo, California, with offices in the U.S., Canada, U.K., India and Japan, SpaceTime Insight provides machine learning-powered analytics and IoT applications for some of the world's largest transportation, energy and utilities organizations, including Entergy, FedEx, NextEra Energy, Singapore Power and Union Pacific Railroad. Its machine learning models and other advanced analytics, designed specifically for asset-intensive industries, predict asset health with a high degree of accuracy and optimize related operations. As a result, SpaceTime Insight's applications help customers reduce cost and risk, increase operational efficiencies, reduce service outages and more. The acquisition supports Nokia's software strategy by bringing SpaceTime Insight's sales expertise and proven track record in IoT application development, machine learning and data science to the Nokia Software IoT product unit. It will strengthen Nokia's IoT software portfolio and IoT analytics capabilities, and accelerate the development of Nokia's IoT offerings to deliver high-value IoT applications and services to new and existing customers.

Artificial intelligence is quickly growing in importance in the'smart building' sector. Paul Skelton looks at the road ahead for a complex technology. When Mark Chung received an unexpectedly high $500 monthly electricity bill, he turned to his utility for help and answers. However, despite'smart' meters being installed in his home, they were no help. So Mark – an electrical engineer trained at Stanford University – took matters into his own hands.

We consider big spatial data, which is typically produced in scientific areas such as geological or seismic interpretation. The spatial data can be produced by observation (e.g. using sensors or soil instrument) or numerical simulation programs and correspond to points that represent a 3D soil cube area. However, errors in signal processing and modeling create some uncertainty, and thus a lack of accuracy in identifying geological or seismic phenomenons. Such uncertainty must be carefully analyzed. To analyze uncertainty, the main solution is to compute a Probability Density Function (PDF) of each point in the spatial cube area. However, computing PDFs on big spatial data can be very time consuming (from several hours to even months on a parallel computer). In this paper, we propose a new solution to efficiently compute such PDFs in parallel using Spark, with three methods: data grouping, machine learning prediction and sampling. We evaluate our solution by extensive experiments on different computer clusters using big data ranging from hundreds of GB to several TB. The experimental results show that our solution scales up very well and can reduce the execution time by a factor of 33 (in the order of seconds or minutes) compared with a baseline method.

Multinomial logit bandit is a sequential subset selection problem which arises in many applications. In each round, the player selects a $K$-cardinality subset from $N$ candidate items, and receives a reward which is governed by a {\it multinomial logit} (MNL) choice model considering both item utility and substitution property among items. The player's objective is to dynamically learn the parameters of MNL model and maximize cumulative reward over a finite horizon $T$. This problem faces the exploration-exploitation dilemma, and the involved combinatorial nature makes it non-trivial. In recent years, there have developed some algorithms by exploiting specific characteristics of the MNL model, but all of them estimate the parameters of MNL model separately and incur a regret no better than $\tilde{O}\big(\sqrt{NT}\big)$ which is not preferred for large candidate set size $N$. In this paper, we consider the {\it linear utility} MNL choice model whose item utilities are represented as linear functions of $d$-dimension item features, and propose an algorithm, titled {\bf LUMB}, to exploit the underlying structure. It is proven that the proposed algorithm achieves $\tilde{O}\big(dK\sqrt{T}\big)$ regret which is free of candidate set size. Experiments show the superiority of the proposed algorithm.

Remote sensing lacks methods like the word vector representations and pre-trained networks that significantly boost performance across a wide range of natural language and computer vision tasks. To fill this gap, we introduce Tile2Vec, an unsupervised representation learning algorithm that extends the distributional hypothesis from natural language -- words appearing in similar contexts tend to have similar meanings -- to geospatial data. We demonstrate empirically that Tile2Vec learns semantically meaningful representations on three datasets. Our learned representations significantly improve performance in downstream classification tasks and similarly to word vectors, visual analogies can be obtained by simple arithmetic in the latent space.

At its Build developer conference this week, Microsoft is putting a lot of emphasis on artificial intelligence and edge computing. To a large degree, that means bringing many of the existing Azure services to machines that sit at the edge, no matter whether that's a large industrial machine in a warehouse or a remote oil-drilling platform. The service that brings all of this together is Azure IoT Edge, which is getting quite a few updates today. IoT Edge is a collection of tools that brings AI, Azure services and custom apps to IoT devices. As Microsoft announced today, Azure IoT Edge, which sits on top of Microsoft's IoT Hub service, is now getting support for Microsoft's Cognitive Services APIs, for example, as well as support for Event Grid and Kubernetes containers.

Microsoft's Build conference kicks off today, then, with no time for a break, it's Google I/O. What to expect from Microsoft? What does Google have planned? To celebrate the MMO's anniversary, a tale from its past. 'Eve Online' turned 15, and its history is epic Yesterday was the 15th anniversary of the legendarily fascinating virtual world EVE Online, a massively multiplayer spaceship game that has become famous for the incredible stories that sometimes emerge from the community about heists and wars between thousands of players. EVE is so interesting that it even has its own historian, Andrew Groen, a video game writer formerly of Wired who studies the politics and sociology at work in EVE's virtual community over its 15-year run.

Year by year control of normal and emergency conditions of up-to-date power systems becomes an increasingly complicated problem. With the increasing complexity the existing control system of power system conditions which includes operative actions of the dispatcher and work of special automatic devices proves to be insufficiently effective more and more frequently, which raises risks of dangerous and emergency conditions in power systems. The paper is aimed at compensating for the shortcomings of man (a cognitive barrier, exposure to stresses and so on) and automatic devices by combining their strong points, i.e. the dispatcher's intelligence and the speed of automatic devices by virtue of development of the intelligent system "Artificial dispatcher" on the basis of deep machine learning technology. For realization of the system "Artificial dispatcher" in addition to deep learning it is planned to attract the game theory approaches to formalize work of the up-to-date power system as a game problem. The "gain" for "Artificial dispatcher" will consist in bringing in a power system in the normal steady-state or post-emergency conditions by means of the required control actions.

The Internet of Things empowers a high level of interconnectivity between smart phones, sensors and wearable devices. These technological developments provide unprecedented opportunities to rethink about the future of machine learning and artificial intelligence: Centralized computational intelligence can be often used for privacy-intrusive and discriminatory services that create'filter bubbles' and undermine citizens' autonomy by nudging [11, 27, 15]. In contrast, this paper envisions a more socially responsible design for digital society based on decentralized learning and collective intelligence formed by bottomup planetary-scale networks run by citizens [17, 16]. In this context, the structural elements of decentralized deep learning processes play a key role. The effectiveness of several classification and prediction operations often relies heavily on hyperparameter optimization [24, 46] and on the learning structure, for instance, the number of layers in a neural network, the interconnectivity of the neurons, the activation or deactivation of certain pathways i.e. dropout regularization [44], can enhance learning performance.

Many people discussed on the innovative ideas to extend time in the air (TITA), ranging from traditional tethered solutions to really innovative, small rotary engines. Since this topic arouse, the performance of the Lithium-ion battery have been improved and on the other hand, TITAs are also supposed to get longer. With the innovations in the UAVs, the battery suppliers are also accelerating their growth. UL created a new standard to help ensure the safety of Li-ion batteries used in UAVs. Alternatives such as hydrogen fuel cells would also work well, but these new and innovative solutions had a bright future.