South American bats speak dialects different from those of their North American counterparts. In response, a group of scientists has developed the first artificial intelligence (AI) algorithm for acoustic identification of bat species in Uruguay. It is available online, under a free license and in open-source code. Moreover, says team leader biologist Germán Botto of the Universidad de la República de Uruguay, new recordings collected by scientists using the algorithm in wind farm studies will enhance the system's proficiency in identifying species. "Windmill farms are a menace for birds…and bats," Botto told Mongabay.

There is an increasing need for monitoring and controlling uncertainties brought by distributed energy resources in distribution grids. For such goal, accurate three-phase topology is the basis for correlating and exterminating measurements in unbalanced distribution networks. Unfortunately, such topology knowledge is often unavailable due to limited investment, especially for secondary distribution grids. Also, the bus phase connectivity information is inaccurate due to human errors or outdated records. For this challenge, we utilize smart meter data at different phases for an information-theoretic approach to learn the structures. Specifically, we convert the system of three unbalanced phasors into symmetrical components, namely the positive, negative, and zero sequences. Then, we prove that Chow-Liu algorithm can find the optimal topology by utilizing power flow equation and the conditional independence relationships implied by the radial three-phase structure of distribution grids with the presence of incorrect bus phase labels. At last, by utilizing Carson's equation, we prove that the bus phase connection can be correctly identified using voltage measurements. For validation, we extensively simulate on IEEE $37$- and $123$-bus systems using real data from PG\&E, ADRES Project, and Pecan Street. We observe that the algorithm is highly accurate for finding three-phase topology in distribution grids even with strong load unbalancing condition and DERs. This ensures close monitoring and controlling DERs in distribution grids.

Bayesian optimization and Lipschitz optimization have developed alternative techniques for optimizing black-box functions. They each exploit a different form of prior about the function. In this work, we explore strategies to combine these techniques for better global optimization. In particular, we propose ways to use the Lip-schitz continuity assumption within traditional BO algorithms, which we call Lips-chitz Bayesian optimization (LBO). This approach does not increase the asymptotic run-time and in some cases drastically improves the performance (while in the worst case the performance is similar). Indeed, in a particular setting, we prove that using the Lips-chitz information yields the same or a better bound on the regret compared to using Bayesian optimization on its own. Moreover, we propose a simple heuristics to estimate the Lipschitz constant, and prove that a growing estimate of the Lipschitz constant is in some sense "harmless". Our experiments on 15 datasets with 4 acquisition functions show that in the worst case LBO performs similar to the underlying BO method while in some cases it performs substantially better. Thompson sampling in particular typically saw drastic improvements (as the Lipschitz information corrected for its well-known "over-exploration" phenomenon) and its LBO variant often outperformed other acquisition functions.

The development of smart cities and their fast-paced deployment is resulting in the generation of large quantities of data at unprecedented rates. Unfortunately, most of the generated data is wasted without extracting potentially useful information and knowledge because of the lack of established mechanisms and standards that benefit from the availability of such data. Moreover, the high dynamical nature of smart cities calls for new generation of machine learning approaches that are flexible and adaptable to cope with the dynamicity of data to perform analytics and learn from real-time data. In this article, we shed the light on the challenge of under utilizing the big data generated by smart cities from a machine learning perspective. Especially, we present the phenomenon of wasting unlabeled data. We argue that semi-supervision is a must for smart city to address this challenge. We also propose a three-level learning framework for smart cities that matches the hierarchical nature of big data generated by smart cities with a goal of providing different levels of knowledge abstractions. The proposed framework is scalable to meet the needs of smart city services. Fundamentally, the framework benefits from semi-supervised deep reinforcement learning where a small amount of data that has users' feedback serves as labeled data while a larger amount is without such users' feedback serves as unlabeled data. This paper also explores how deep reinforcement learning and its shift toward semi-supervision can handle the cognitive side of smart city services and improve their performance by providing several use cases spanning the different domains of smart cities. We also highlight several challenges as well as promising future research directions for incorporating machine learning and high-level intelligence into smart city services.

Toyota Motor is set to launch a pilot project testing a transportation system focusing on autonomous vehicles, in one of Japan's first such initiatives in a real-life setting over a wide area. The company will team up with the University of Tsukuba and the government of the city, just north of Tokyo, to run the project. Under the system envisioned, self-driving, single-seat electric vehicles will take passengers from their homes to the nearest bus stop, where they will be able to transfer to autonomous, fuel-cell powered buses. The experiment, set for launch in fiscal 2019 and running until fiscal 2022, will test the feasibility of the relevant technologies in situations involving regular traffic. One of the main aims of the project is to help resolve the issue of elderly citizens being isolated from their communities.

The enactive approach to cognition is typically proposed as a viable alternative to traditional cognitive science. Enactive cognition displaces the explanatory focus from the internal representations of the agent to the direct sensorimotor interaction with its environment. In this paper, we investigate enactive learning through means of artificial agent simulations. We compare the performances of the enactive agent to an agent operating on classical reinforcement learning in foraging tasks within maze environments. The characteristics of the agents are analysed in terms of the accessibility of the environmental states, goals, and exploration/exploitation tradeoffs. We confirm that the enactive agent can successfully interact with its environment and learn to avoid unfavourable interactions using intrinsically defined goals. The performance of the enactive agent is shown to be limited by the number of affordable actions.

Advances in renewable energy generation and introduction of the government targets to improve energy efficiency gave rise to a concept of a Zero Energy Building (ZEB). A ZEB is a building whose net energy usage over a year is zero, i.e., its energy use is not larger than its overall renewables generation. A collection of ZEBs forms a Zero Energy Community (ZEC). This paper addresses the problem of energy sharing in such a community. This is different from previously addressed energy sharing between buildings as our focus is on the improvement of community energy status, while traditionally research focused on reducing losses due to transmission and storage, or achieving economic gains. We model this problem in a multi-agent environment and propose a Deep Reinforcement Learning (DRL) based solution. Each building is represented by an intelligent agent that learns over time the appropriate behaviour to share energy. We have evaluated the proposed solution in a multi-agent simulation built using osBrain. Results indicate that with time agents learn to collaborate and learn a policy comparable to the optimal policy, which in turn improves the ZEC's energy status. Buildings with no renewables preferred to request energy from their neighbours rather than from the supply grid.

Analytics, Machine Learning, and artificial intelligence (AI) are used to interpret the past, optimize the present and predict the future. The energy sector heavily depends on optimization and predictions for energy production, energy grid balancing, and consumption habits. Additionally, the energy industry produces massive amounts of data. To turn this data into insights to improve productivity and cut costs, major energy players are turning to AI. Here we will look at the scopes of advanced analytics, machine learning and AI in the renewable energy sector.

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One lucrative new market that can be accessed through more innovative use of software is Industrial IoT (IIoT). For example, Statista recently found discrete manufacturing, transportation/ logistics, and utilities industries are projected to spend $40 billion each on IoT platforms, systems, and services by 2020. There are many software technologies that promise to revolutionize networks, including network slicing, ONAP and software-based architecture. However, it is that AI offers a transformative opportunity through its ability to industrialise large scale pattern recognition and automate complex processes. AI-augmented core and Radio Access Networks (RANs), for example, are set to offer carriers an unprecedented level of control and flexibility to develop higher performance services, providing a clear route to access mission critical applications such as IIoT, automotive and healthcare.