This article was originally published by Ozy. In 2016, Houston's petrochemical industry had countless job positions that were unfilled. And at the same time, a number of the city's residents were looking for work. So, how was Houston going to fix this? In an effort to help match eligible candidates with open positions, private companies began to step in.

He goes by the name "Marty." Tall, slow-moving and gray, he has big cartoonish eyes that disguise something unique about the newest employee at Giant Food Stores: Marty is deliberate and relentless, and -- unlike his fellow employees -- he has the ability to work a seemingly endless number of hours without pay. Though he doesn't say much, a small message is always plastered to his slender trunk: "This store is monitored by Marty for your safety," it reads. "Marty is an autonomous robot that uses image capturing technology to report spills, debris and other potential hazards to store employees to improve your shopping experience." After a pilot program that kicked off in several Pennsylvania stores this past fall, Giant Food Stores announced Monday that it will place Martys in each of the supermarket chain's 172 stores across Pennsylvania, Maryland, Virginia and West Virginia.

He goes by the name "Marty." Tall, slow-moving and gray, he has big cartoonish eyes that disguise something unique about the newest employee at Giant Food Stores: Marty is deliberate and relentless, and -- unlike his fellow employees -- he has the ability to work a seemingly endless number of hours without pay. Though he doesn't say much, a small message is always plastered to his slender trunk: "This store is monitored by Marty for your safety," it reads. "Marty is an autonomous robot that uses image capturing technology to report spills, debris and other potential hazards to store employees to improve your shopping experience." After a pilot program that kicked off in several Pennsylvania stores this past fall, Giant Food Stores announced Monday that it will place Martys in each of the supermarket chain's 172 stores across Pennsylvania, Maryland, Virginia and West Virginia.

End-to-end task-oriented dialogue is challenging since knowledge bases are usually large, dynamic and hard to incorporate into a learning framework. We propose the global-to-local memory pointer (GLMP) networks to address this issue. In our model, a global memory encoder and a local memory decoder are proposed to share external knowledge. The encoder encodes dialogue history, modifies global contextual representation, and generates a global memory pointer. The decoder first generates a sketch response with unfilled slots. Next, it passes the global memory pointer to filter the external knowledge for relevant information, then instantiates the slots via the local memory pointers. We empirically show that our model can improve copy accuracy and mitigate the common out-of-vocabulary problem. As a result, GLMP is able to improve over the previous state-of-the-art models in both simulated bAbI Dialogue dataset and human-human Stanford Multi-domain Dialogue dataset on automatic and human evaluation.

Many constraints restricting the result of some computations over an integer sequence can be compactly represented by register automata. We improve the propagation of the conjunction of such constraints on the same sequence by synthesising a database of linear and non-linear invariants using their register-automaton representation. The obtained invariants are formulae parameterised by a function of the sequence length and proven to be true for any long enough sequence. To assess the quality of such linear invariants, we developed a method to verify whether a generated linear invariant is a facet of the convex hull of the feasible points. This method, as well as the proof of non-linear invariants, are based on the systematic generation of constant-size deterministic finite automata that accept all integer sequences whose result verifies some simple condition. We apply such methodology to a set of 44 time-series constraints and obtain 1400 linear invariants from which 70% are facet defining, and 600 non-linear invariants, which were tested on short-term electricity production problems.

Improving predictive understanding of Earth system variability and change requires data-model integration. Efficient data-model integration for complex models requires surrogate modeling to reduce model evaluation time. However, building a surrogate of a large-scale Earth system model (ESM) with many output variables is computationally intensive because it involves a large number of expensive ESM simulations. In this effort, we propose an efficient surrogate method capable of using a few ESM runs to build an accurate and fast-to-evaluate surrogate system of model outputs over large spatial and temporal domains. We first use singular value decomposition to reduce the output dimensions, and then use Bayesian optimization techniques to generate an accurate neural network surrogate model based on limited ESM simulation samples. Our machine learning based surrogate methods can build and evaluate a large surrogate system of many variables quickly. Thus, whenever the quantities of interest change such as a different objective function, a new site, and a longer simulation time, we can simply extract the information of interest from the surrogate system without rebuilding new surrogates, which significantly saves computational efforts. We apply the proposed method to a regional ecosystem model to approximate the relationship between 8 model parameters and 42660 carbon flux outputs. Results indicate that using only 20 model simulations, we can build an accurate surrogate system of the 42660 variables, where the consistency between the surrogate prediction and actual model simulation is 0.93 and the mean squared error is 0.02. This highly-accurate and fast-to-evaluate surrogate system will greatly enhance the computational efficiency in data-model integration to improve predictions and advance our understanding of the Earth system.

The prevalence of networked sensors and actuators in many real-world systems such as smart buildings, factories, power plants, and data centers generate substantial amounts of multivariate time series data for these systems. The rich sensor data can be continuously monitored for intrusion events through anomaly detection. However, conventional threshold-based anomaly detection methods are inadequate due to the dynamic complexities of these systems, while supervised machine learning methods are unable to exploit the large amounts of data due to the lack of labeled data. On the other hand, current unsupervised machine learning approaches have not fully exploited the spatial-temporal correlation and other dependencies amongst the multiple variables (sensors/actuators) in the system for detecting anomalies. In this work, we propose an unsupervised multivariate anomaly detection method based on Generative Adversarial Networks (GANs). Instead of treating each data stream independently, our proposed MAD-GAN framework considers the entire variable set concurrently to capture the latent interactions amongst the variables. We also fully exploit both the generator and discriminator produced by the GAN, using a novel anomaly score called DR-score to detect anomalies by discrimination and reconstruction. We have tested our proposed MAD-GAN using two recent datasets collected from real-world CPS: the Secure Water Treatment (SWaT) and the Water Distribution (WADI) datasets. Our experimental results showed that the proposed MAD-GAN is effective in reporting anomalies caused by various cyber-intrusions compared in these complex real-world systems.

Innovation is unfolding behind the scenes too, with AI powering transformation in some of the world's oldest industries--from construction to cargo shipping to oil and energy. Here's how state-of-the-art tech across three giant industries is unlocking potential to reshape the world and improve our lives. The construction industry has long struggled with a safety problem. Building sites are high-risk environments for workers and communities. A fire at a residential development could easily cost millions.

Do Not Reproduce More Than Two Slides or Charts Without Permission Background and context Earlierthis year, a Boston Consulting Group studyfound thatcompanies in the US and Germanyhad implemented the new digitalindustrialtechnologies thatare collectivelyknown as Industry 4.0 at approximatelythe same pace.1 • German companieswere off to a somewhatfasterstartof implementation despite the commonperceptionthat US companieswere the front-runnersin embracing digitaltransformation • German companiesalso appearedto be better prepared foradoptthe new digital technologiesand to have higherambitions To gain further insights aboutthe status of Industry 4.0 adoption byUS manufacturers and the challenges theyface, BCG surveyed 380 US-based manufacturing executives and managers atcompaniesrepresenting a wide range of sizes in various industries (for methodology,see p.13). Do Not Reproduce More Than Two Slides or Charts Without Permission Executive summary Key findings from the research US ...

Topology design optimization offers a tremendous opportunity in design and manufacturing freedoms by designing and producing a part from the ground-up without a meaningful initial design as required by conventional shape design optimization approaches. Ideally, with adequate problem statements, to formulate and solve the topology design problem using a standard topology optimization process, such as SIMP (Simplified Isotropic Material with Penalization) is possible. However, in reality, an estimated over thousands of design iterations is often required for just a few design variables, the conventional optimization approach is, in general, impractical or computationally unachievable for real-world applications significantly diluting the development of the topology optimization technology. There is, therefore, a need for a different approach that will be able to optimize the initial design topology effectively and rapidly. In this study, a novel topology optimization approach based on conditional Wasserstein generative adversarial networks (CWGAN) is developed to replicate the conventional topology optimization algorithms in an extremely computationally inexpensive way. CWGAN consists of a generator and a discriminator, both of which are deep convolutional neural networks (CNN). The limited samples of data, quasi-optimal planar structures, needed for training purposes are generated using the conventional topology optimization algorithms. With CWGANs, the topology optimization conditions can be set to a required value before generating samples.