Neuroevolution is a process of training neural networks (NN) through an evolutionary algorithm, usually to serve as a state-to-action mapping model in control or reinforcement learning-type problems. This paper builds on the Neuro Evolution of Augmented Topologies (NEAT) formalism that allows designing topology and weight evolving NNs. Fundamental advancements are made to the neuroevolution process to address premature stagnation and convergence issues, central among which is the incorporation of automated mechanisms to control the population diversity and average fitness improvement within the neuroevolution process. Insights into the performance and efficiency of the new algorithm is obtained by evaluating it on three benchmark problems from the Open AI platform and an Unmanned Aerial Vehicle (UAV) collision avoidance problem.

In his role as principal data scientist at consulting firm Booz Allen Hamilton Inc., Kirk Borne sees the world in terms of data connections. "Life is about who is connected to whom and what is connected to what," Borne said, and he pointed to graph databases and graph analytics applications as new ways to capitalize on such connections. That's because graph databases, a form of NoSQL software, document the connections between data points quite different compared to mainstream relational databases. Graph systems represent data not as elements in tables, but as nodes linked to one another by edges with a set of properties that delineate the relationship between nodes. Therefore, one of the advantages of graph databases is they allow data analysts to navigate through data sets without the need to create and run complex queries to join combinations of tables together, as in the relational model.

In model-based reinforcement learning, the agent interleaves between model learning and planning. These two components are inextricably intertwined. If the model is not able to provide sensible long-term prediction, the executed planner would exploit model flaws, which can yield catastrophic failures. This paper focuses on building a model that reasons about the long-term future and demonstrates how to use this for efficient planning and exploration. To this end, we build a latent-variable autoregressive model by leveraging recent ideas in variational inference. We argue that forcing latent variables to carry future information through an auxiliary task substantially improves long-term predictions. Moreover, by planning in the latent space, the planner's solution is ensured to be within regions where the model is valid. An exploration strategy can be devised by searching for unlikely trajectories under the model. Our method achieves higher reward faster compared to baselines on a variety of tasks and environments in both the imitation learning and model-based reinforcement learning settings.

After getting a preview of what is on tap for HANNOVER MESSE 2019, it appears that attendees are in for another exciting event. During a January preview for the event, over 120 journalists, including yours truly, gathered for presentations and discussions from the Hannover Messe team, and over 40 exhibitors, expanding on the topics and technologies that so many will experience. Onuora Ogbukagu - the Corporate Spokesman and Director Marketing & Communications Industry, Energy, Logistics, for Deutsche Messe AG in Hannover - opened that Preview Event sharing topics this year which include digitalization, industrial automation, machine learning, artificial intelligence, and energy solutions. Dr. Jochen Köckler, CEO, Deutsche Messe AG, followed up in his overview of HANNOVER MESSE 2019 trends and topics by noting, "The 72nd year of Hannover Messe and it is booming with more than 6, 500 exhibitors from more than 75 countries is truly an international trade fair." Köckler also highlighted 2019 keynote themes for these topics including integrated industry, Industry 4.0, artificial intelligence/machine learning, and 5G for Industry.

With billions of people and things connected by sensors and devices, and unprecedented processing power, storage capacity, and access to knowledge, the possibilities for innovation are endless. Advances in artificial intelligence are visible everywhere - from the Roombas that clean our homes, to the algorithms that suggest the movies we watch, to self-driving cars and drones delivering packages. Artificial intelligence is just one aspect of the explosion in technological breakthroughs including robotics, the Internet of Things, 3-D printing, nanotechnology, biotechnology, materials science, energy storage, and quantum computing.

NIMS, the University of Tokyo, Niigata University and RIKEN have jointly designed a multilayered metamaterial that realizes ultra-narrowband wavelength-selective thermal emission by combining the machine learning (Bayesian optimization) and thermal emission properties calculations (electromagnetic calculation). The joint team then experimentally fabricated the designed metamaterial and verified the performance. These results may facilitate the development of highly efficient energy devices. Thermal radiation, a phenomenon that an object emits heat as electromagnetic waves, is potentially applicable to a variety of energy devices, such as wavelength-selective heaters, infrared sensors and thermophotovoltaic generators. Highly efficient thermal emitters need to exhibit emission spectrum with narrow bands in practically usable wavelength range..

The following essay is reprinted with permission from The Conversation, an online publication covering the latest research. As an artificial intelligence researcher, I often come across the idea that many people are afraid of what AI might bring. It's perhaps unsurprising, given both history and the entertainment industry, that we might be afraid of a cybernetic takeover that forces us to live locked away, "Matrix"-like, as some sort of human battery. And yet it is hard for me to look up from the evolutionary computer models I use to develop AI, to think about how the innocent virtual creatures on my screen might become the monsters of the future. Might I become "the destroyer of worlds," as Oppenheimer lamented after spearheading the construction of the first nuclear bomb?

Overcoming the visual barrier and developing "see-through vision" has been one of mankind's long-standing dreams. However, visible light cannot travel through opaque obstructions (e.g. walls). Unlike visible light, though, Radio Frequency (RF) signals penetrate many common building objects and reflect highly off humans. This project creates a breakthrough artificial intelligence methodology by which the skeletal structure of a human can be reconstructed with RF even through visual occlusion. In a novel procedural flow, video and RF data are first collected simultaneously using a co-located setup containing an RGB camera and RF antenna array transceiver. Next, the RGB video is processed with a Part Affinity Field computer-vision model to generate ground truth label locations for each keypoint in the human skeleton. Then, a collective deep-learning model consisting of a Residual Convolutional Neural Network, Region Proposal Network, and Recurrent Neural Network 1) extracts spatial features from RF images, 2) detects and crops out all people present in the scene, and 3) aggregates information over dozens of time-steps to piece together the various limbs that reflect signals back to the receiver at different times. A simulator is created to demonstrate the system. This project has impactful applications in medicine, military, search & rescue, and robotics. Especially during a fire emergency, neither visible light nor infrared thermal imaging can penetrate smoke or fire, but RF can. With over 1 million fires reported in the US per year, this technology could save thousands of lives and tens-of-thousands of injuries.

In the monitoring of a complex electric grid, it is of paramount importance to provide operators with early warnings of anomalies detected on the network, along with a precise classification and diagnosis of the specific fault type. In this paper, we propose a novel multi-stage early warning system prototype for electric grid fault detection, classification, subgroup discovery, and visualization. In the first stage, a computationally efficient anomaly detection method based on quartiles detects the presence of a fault in real time. In the second stage, the fault is classified into one of nine pre-defined disaster scenarios. The time series data are first mapped to highly discriminative features by applying dimensionality reduction based on temporal autocorrelation. The features are then mapped through one of three classification techniques: support vector machine, random forest, and artificial neural network. Finally in the third stage, intra-class clustering based on dynamic time warping is used to characterize the fault with further granularity. Results on the Bonneville Power Administration electric grid data show that i) the proposed anomaly detector is both fast and accurate; ii) dimensionality reduction leads to dramatic improvement in classification accuracy and speed; iii) the random forest method offers the most accurate, consistent, and robust fault classification; and iv) time series within a given class naturally separate into five distinct clusters which correspond closely to the geographical distribution of electric grid buses.

The area of building energy management has received a significant amount of interest in recent years. This area is concerned with combining advancements in sensor technologies, communications and advanced control algorithms to optimize energy utilization. Reinforcement learning is one of the most prominent machine learning algorithms used for control problems and has had many successful applications in the area of building energy management. This research gives a comprehensive review of the literature relating to the application of reinforcement learning to developing autonomous building energy management systems. The main direction for future research and challenges in reinforcement learning are also outlined.