OLD hands at some water companies still on occasion whip out a pair of dousing rods or find a Y-shaped twig to search for a leak in an underground pipe. Dousing, or water witching as it is known in America, has no basis in scientific fact. A somewhat more reliable method involves using acoustic equipment called geophones to listen for escaping water. The trouble is it takes an experienced ear to distinguish the sound of a leak from the normal gurgle of water passing though pipes, let alone to predict from that sound where any trouble might be found. As this is a problem of pattern recognition, which is something that artificial intelligence can be good at, a Brazilian startup has used AI to develop an acoustic device that makes tracing leaks a lot easier.

Around a decade ago at the Indian Institute of Technology Bombay (IIT-B), classmates Sneh Rajkumar Vaswani, Chintan Subhash Raikar, and Prashant Iyengar were involved in a project to build intelligent underwater vehicles for the India Navy and the oil and gas industry. In the process, they developed a knack for artificial intelligence (AI) and robotics. That culminated in their setting up of emotix in 2015, and this startup has now developed Miko, India's first companion robot. Miko, weighing around 750 grams and standing a little over a foot, engages, educates, and entertains children above the age of five. Besides talking to and playing games with the kids, Miko is also equipped to answer basic questions related to general knowledge and academics.

Journal of Parallel and Distributed Computing 104, 19-35.

The data collected by smart meters contain a lot of useful information. One potential use of the data is to track the energy consumptions and operating statuses of major home appliances.The results will enable homeowners to make sound decisions on how to save energy and how to participate in demand response programs. This paper presents a new method to breakdown the total power demand measured by a smart meter to those used by individual appliances. A unique feature of the proposed method is that it utilizes diverse signatures associated with the entire operating window of an appliance for identification. As a result, appliances with complicated middle process can be tracked. A novel appliance registration device and scheme is also proposed to automate the creation of appliance signature database and to eliminate the need of massive training before identification. The software and system have been developed and deployed to real houses in order to verify the proposed method.

Advances in sensor technology, cloud computing, and machine learning (ML) continue to converge to accelerate innovation in the field of remote sensing. However, fundamental tools and technologies still need to be developed to drive further breakthroughs and to ensure that the Global Development Community (GDC) reaps the same benefits that the commercial marketplace is experiencing. This process requires us to take a collaborative approach. Data collaborative innovation -- that is, a group of actors from different data domains working together toward common goals -- might hold the key to finding solutions for some of the global challenges that the world faces. That is why Radiant.Earth is investing in new technologies such as Cloud Optimized GeoTiffs, Spatial Temporal Asset Catalogues (STAC), and ML. Our approach to advance ML for global development begins with creating open libraries of labeled images and algorithms.

It wasn't until this past year that I was introduced to the concept of machine learning. As with most advanced technologies, it took a personal experience to help me best understand the concepts and benefits. Case in point -- a couple of months had passed before I fully connected a Nest thermostat to our Wi-Fi network and the Internet. Before this connectivity, the well-designed thermostat managed our home temperature much like our previous thermostat. Once connected, however, it better understood our preferences and patterns for managing home comfort and reducing energy requirements.

Autonomously training interpretable control strategies, called policies, using pre-existing plant trajectory data is of great interest in industrial applications. Fuzzy controllers have been used in industry for decades as interpretable and efficient system controllers. In this study, we introduce a fuzzy genetic programming (GP) approach called fuzzy GP reinforcement learning (FGPRL) that can select the relevant state features, determine the size of the required fuzzy rule set, and automatically adjust all the controller parameters simultaneously. Each GP individual's fitness is computed using model-based batch reinforcement learning (RL), which first trains a model using available system samples and subsequently performs Monte Carlo rollouts to predict each policy candidate's performance. We compare FGPRL to an extended version of a related method called fuzzy particle swarm reinforcement learning (FPSRL), which uses swarm intelligence to tune the fuzzy policy parameters. Experiments using an industrial benchmark show that FGPRL is able to autonomously learn interpretable fuzzy policies with high control performance.

Attacks in cyber-physical systems (CPS) which manipulate sensor readings can cause enormous physical damage if undetected. Detection of attacks on sensors is crucial to mitigate this issue. We study supervised regression as a means to detect anomalous sensor readings, where each sensor's measurement is predicted as a function of other sensors. We show that several common learning approaches in this context are still vulnerable to \emph{stealthy attacks}, which carefully modify readings of compromised sensors to cause desired damage while remaining undetected. Next, we model the interaction between the CPS defender and attacker as a Stackelberg game in which the defender chooses detection thresholds, while the attacker deploys a stealthy attack in response. We present a heuristic algorithm for finding an approximately optimal threshold for the defender in this game, and show that it increases system resilience to attacks without significantly increasing the false alarm rate.

Most real world phenomena such as sunlight distribution under a forest canopy, minerals concentration, stock valuation, exhibit nonstationary dynamics i.e. phenomenon variation changes depending on the locality. Nonstationary dynamics pose both theoretical and practical challenges to statistical machine learning algorithms that aim to accurately capture the complexities governing the evolution of such processes. Typically the nonstationary dynamics are modeled using nonstationary Gaussian Process models (NGPS) that employ local latent dynamics parameterization to correspondingly model the nonstationary real observable dynamics. Recently, an approach based on most likely induced latent dynamics representation attracted research community's attention for a while. The approach could not be employed for large scale real world applications because learning a most likely latent dynamics representation involves maximization of marginal likelihood of the observed real dynamics that becomes intractable as the number of induced latent points grows with problem size. We have established a direct relationship between informativeness of the induced latent dynamics and the marginal likelihood of the observed real dynamics. This opens up the possibility of maximizing marginal likelihood of observed real dynamics indirectly by near optimally maximizing entropy or mutual information gain on the induced latent dynamics using greedy algorithms. Therefore, for an efficient yet accurate inference, we propose to build an induced latent dynamics representation using a novel algorithm LISAL that adaptively maximizes entropy or mutual information on the induced latent dynamics and marginal likelihood of observed real dynamics in an iterative manner. The relevance of LISAL is validated using real world datasets.

The demands of UQ in computer prediction, a problem we believe to be NP-Hard, cannot be met on our current HPC technology path. We see that cognitive computing, defined through the technology convergence of AI, Big Data and HPC is an essential next step. With vendor technology decisions being made now and in the next few years in AI and HPC, it is urgent that broad classes of HW and SW are explored to best leverage commercial technology roadmaps. To that end, we are using precision medicine data as a force multiplier and accelerator. This rich, complex, unstructured, heterogeneous, curated, massive data is likely the richest class of data to work on today and brings with it unique partnerships that buys down risk in exploring the many splintered paths forward each with their own tough challenges and also shares costs.