This document is designed to be a first-year graduate-level introduction to probabilistic programming. It not only provides a thorough background for anyone wishing to use a probabilistic programming system, but also introduces the techniques needed to design and build these systems. It is aimed at people who have an undergraduate-level understanding of either or, ideally, both probabilistic machine learning and programming languages. We start with a discussion of model-based reasoning and explain why conditioning as a foundational computation is central to the fields of probabilistic machine learning and artificial intelligence. We then introduce a simple first-order probabilistic programming language (PPL) whose programs define static-computation-graph, finite-variable-cardinality models. In the context of this restricted PPL we introduce fundamental inference algorithms and describe how they can be implemented in the context of models denoted by probabilistic programs. In the second part of this document, we introduce a higher-order probabilistic programming language, with a functionality analogous to that of established programming languages. This affords the opportunity to define models with dynamic computation graphs, at the cost of requiring inference methods that generate samples by repeatedly executing the program. Foundational inference algorithms for this kind of probabilistic programming language are explained in the context of an interface between program executions and an inference controller. This document closes with a chapter on advanced topics which we believe to be, at the time of writing, interesting directions for probabilistic programming research; directions that point towards a tight integration with deep neural network research and the development of systems for next-generation artificial intelligence applications.

Developing safe nuclear reactor materials depends on a critical, though tedious and time-consuming, task: sifting through electron microscopy images of materials exposed to radiation to identify radioactive damage. This monotonous task has traditionally fallen to image-processing algorithms programmed to identify patterns in images that look like Jackson Pollock paintings. Researchers at the University of Wisconsin-Madison and Oak Ridge National Laboratory may have found a faster and more accurate alternative: letting computers learn how to identify the damage by themselves. "Human detection and identification is error-prone, inconsistent and inefficient," said Dane Morgan, materials science and engineering professor. "Newer imaging technologies are outstripping human capabilities to analyze the data we can produce."

Today, the U.S. the Department of Energy released a US$5.8 million funding opportunity announcement (FOA) to support the research and development (R&D) of advanced tools and controls that will improve the resilience and reliability of the United States' power grid. Under this FOA, DOE's Office of Electricity (OE) Transmission Reliability Program will seek applications that explore the use of big data, artificial intelligence (AI), and machine learning technology and tools to derive more value from the vast amounts of sensor data already being gathered and used to monitor the health of the grid and support system operations. The projects funded by this FOA will shape future development and application of faster grid analytics and modeling; better grid asset management; and sub-second automatic control actions that will help system operators avoid grid outages, improve operations, and reduce costs. "A strong and resilient power grid is vital to America's security, economy, and modern way of life," said U.S. Secretary of Energy Rick Perry. "This investment in rapid, technology-driven innovation pushes the limits farther than we can imagine, and marks another important step in ensuring the reliable and secure flow of energy that Americans rely on every day."

The biggest feature in the SQL Server 2019 preview launched at Ignite is SQL Server Big Data clusters. Travis Wright, Microsoft's principal program manager for SQL Server, explains exactly what this means for administrators. Microsoft introduced a new community technology preview (CTP) of SQL Server 2019 at Microsoft Ignite on Monday (you can read about the full list of announced features here). As part of that announcement came SQL Server Big Data clusters, a scale-out, data virtualization platform built on top of the Kubernetes (K8s) container platform. SQL Server Big Data clusters is a big investment from Microsoft into a number of technologies -- and it is clear that taking one of its best-selling enterprise products and building on top of the K8s infrastructure is a moonshot at modernizing the data estate in most enterprises.

The last thing you want to see when you pull into a gas station is some doofus lighting up a smoke. Whether they missed the warning notices or, perhaps, the science class back at high school about open flames and flammable vapor is, in that moment at least, largely immaterial. As for your own course of action upon seeing such reckless behavior, you can either put your foot down and hightail it out of there before the whole place goes up, or yell at the smoker to put it the hell out. Tackling the very same issue, Shell has been working with Microsoft on a solution that aims to make all future visits to gas stations stress-free, at least in terms of potential explosive activity. The system uses Microsoft's Azure IoT Edge cloud intelligence system to quickly identify and deal with smokers at a gas station, and it's already being tested at two Shell stations in Thailand and Singapore.

While data-driven design has considerable potential in contemporary control systems where precise modeling of the dynamics is intractable (e.g., systems with complex contact forces), one of the biggest hurdles to overcome for practical deployment is maintaining safe execution during the learning process. Motivated by this issue, we study the data-driven design of a controller for the constrained Linear Quadratic Regulator (LQR) problem. In constrained LQR, we design a controller for a (potentially unknown) linear dynamical system that minimizes a given quadratic cost, subject to the additional requirement that both the state and input stay within a specified safe region. This is a problem that has received much attention within the model predictive control (MPC) community. For the LQR problem with no constraints, a natural method of exploration for learning the dynamics is to excite the system by injecting white noise. When safety is not an issue, this method is effective and recently Dean et al. [1] provide an end-to-end sample complexity S. Dean, S. Tu, N. Matni, and B. Recht are with the Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, CA, 94709 USA (email: dean sarah@berkeley.edu,

You've probably heard of fingerprint scans, iris scans, and perhaps even eye gaze scans, but what about foostep-based biometrics? New research published on the preprint server Arxiv.org Researchers at the Indian Institute of Technology in Delhi describe the system in a paper titled "Person Identification using Seismic Signals generated from Footfalls." It's based on a fog computing architecture, which employs edge devices to carry out much of the computing, storage, and communication involved in data collection. "[With our approach], individuals are only required to walk through the active region of the sensor," they wrote.

Solar-powered recording devices are eavesdropping on rainforests in Borneo to monitor biodiversity. The plan is to use artificial intelligence to automatically identify and record different animals and track changes over time. Many current methods for measuring biodiversity are impractical, relying on humans to regularly change batteries and put out recorders, or expensive, requiring huge reams of data to be sent via satellites.

Learning-based methods have been successful in solving complex control tasks without significant prior knowledge about the system. However, these methods typically do not provide any safety guarantees, which prevents their use in safety-critical, real-world applications. In this paper, we present a learning-based model predictive control scheme that can provide provable high-probability safety guarantees. To this end, we exploit regularity assumptions on the dynamics in terms of a Gaussian process prior to construct provably accurate confidence intervals on predicted trajectories. Unlike previous approaches, we do not assume that model uncertainties are independent. Based on these predictions, we guarantee that trajectories satisfy safety constraints. Moreover, we use a terminal set constraint to recursively guarantee the existence of safe control actions at every iteration. In our experiments, we show that the resulting algorithm can be used to safely and efficiently explore and learn about dynamic systems.

Fragility curves which express the failure probability of a structure, or critical components, as function of a loading intensity measure are nowadays widely used (i) in Seismic Probabilistic Risk Assessment studies, (ii) to evaluate impact of construction details on the structural performance of installations under seismic excitations or under other loading sources such as wind. To avoid the use of parametric models such as lognormal model to estimate fragility curves from a reduced number of numerical calculations, a methodology based on Support Vector Machines coupled with an active learning algorithm is proposed in this paper. In practice, input excitation is reduced to some relevant parameters and, given these parameters, SVMs are used for a binary classification of the structural responses relative to a limit threshold of exceedance. Since the output is not only binary, this is a score, a probabilistic interpretation of the output is exploited to estimate very efficiently fragility curves as score functions or as functions of classical seismic intensity measures.