Earthquake Alert! 6.7 temblor, epicenter 3.8 miles west of Ventura, California--impact will be in eleven minutes--evacuate, evacuate!" While you run to the hall closet to grab your earthquake kit, you shout out: "Alexa, where is my emergency evac location?" Walk north to Wilshire, then take a left on Warner," she responds. As you and your neighbors pour into the building stairwell, you hear audio from a phone: "Google Earth Q estimates substantial potential for structural damage in the West San Fernando Valley and Coastal West Los Angeles to pre-2006 code dwellings and buildings. Most of West LA will experience total loss of power for anywhere from six to twenty-four hours in duration."

With technological advancement, IT technology developers are making efforts to develop software that can ease the physical work life. One such advancement in technology is intelligent automation. The intelligent automation is a combination of automation and artificial intelligence. This new technology has revolutionized the way data is handled and processed. The intelligent automation system determines and synthesizes a massive amount of information, automates the business and operational workflows and adapts it.

The critical locus of the loss function of a neural network is determined by the geometry of the functional space and by the parameterization of this space by the network's weights. We introduce a natural distinction between pure critical points, which only depend on the functional space, and spurious critical points, which arise from the parameterization. We apply this perspective to revisit and extend the literature on the loss function of linear neural networks. For this type of network, the functional space is either the set of all linear maps from input to output space, or a determinantal variety, i.e., a set of linear maps with bounded rank. We use geometric properties of determinantal varieties to derive new results on the landscape of linear networks with different loss functions and different parameterizations.

Convolutional neural networks (CNNs) have achieved breakthrough performances in a wide range of applications including image classification, semantic segmentation, and object detection. Previous research on characterizing the generalization ability of neural networks mostly focuses on fully connected neural networks (FNNs), regarding CNNs as a special case of FNNs without taking into account the special structure of convolutional layers. In this work, we propose a tighter generalization bound for CNNs by exploiting the sparse and permutation structure of its weight matrices. As the generalization bound relies on the spectral norm of weight matrices, we further study spectral norms of three commonly used convolution operations including standard convolution, depthwise convolution, and pointwise convolution. Theoretical and experimental results both demonstrate that our bounds for CNNs are tighter than existing bounds.

Resilient Coverage: Exploring the Local-to-Global Tradeoff Ragesh K. Ramachandran 1, Lifeng Zhou 2 and Gaurav S. Sukhatme 1 Abstract -- We propose a centralized control framework to select suitable robots from a heterogeneous pool and place them at appropriate locations to monitor a region for events of interest. In the event of a robot failure, the framework repositions robots in a user-defined local neighborhood of the failed robot to compensate for the coverage loss. The central controller augments the team with additional robots from the robot pool when simply repositioning robots fails to attain a user-specified level of desired coverage. The size of the local neighborhood around the failed robot and the desired coverage over the region are two settings that can be manipulated to achieve a user-specified balance. We investigate the tradeoff between the coverage compensation achieved through local repositioning and the computation required to plan the new robot locations. We also study the relationship between the size of the local neighborhood and the number of additional robots added to the team for a given user-specified level of desired coverage. The computational complexity of our resilient strategy (tunable resilient coordination), is quadratic in both neighborhood size and number of robots in the team. At first glance, it seems that any desired level of coverage can be efficiently achieved by augmenting the robot team with more robots while keeping the neighborhood size fixed. However, we show that to reach a high level of coverage in a neighborhood with a large robot population, it is more efficient to enlarge the neighborhood size, instead of adding additional robots and repositioning them.

The CP 2002 paper entitled "Breaking Row and Column Symmetries in Matrix Models" by Flener et al. [6] describes some of the first work for identifying and analyzing row and column symmetry in mat rix models and for efficiently and effectively dealing with such symmetry u sing static symmetry-breaking ordering constraints. This commentary provides a retrospective on that work and highlights some of the subsequent work on the topic.

UPS has become the first drone delivery service to receive full approval from the Federal Aviation Administration. The company's program, called Flight Forward, is operated in partnership with Matternet, which provides drone logistics networking company in Mountain View, California. Previously, UPS's pilots were only allowed to fly the drones within line of sight, but the FAA approval means they'll be able to significantly expand their delivery range. 'This is history in the making, and we aren't done yet,' said David Abney, UPS chief executive officer in a statement. UPS's Flight Forward drone delivery program is the first to earn full approval by the FAA (pictured one of the drones they will use in the program) The program's currently deployed in Raleigh, North Carolina, where UPS's drones have made more than 1,000 flights carrying deliveries around the WakeMed Health & Hospitals campus.

Creating accurate maps today is a painstaking, time-consuming manual process, even with access to satellite imagery and mapping software. Many regions -- particularly in the developing world -- remain largely unmapped. To help close this gap, Facebook AI researchers and engineers have developed a new method that uses deep learning and weakly supervised training to predict road networks from commercially available high-resolution satellite imagery. The resulting model sets a new bar for the state of the art for accuracy, and because it is able to accommodate regional differences in road networks, it can effectively predict roads around the globe. We are now sharing the details of our model and making data available to the global mapping community through Map With AI, a new set of specialized map-editing services and tools. Map With AI includes an editor interface, RapiD, which allows mapping experts to easily review, verify, and adjust the map as needed.

At the core of today's state-of-the-art Artificial Intelligence (AI) algorithms is the ability to learn complex patterns from a sample of data. In the manufacturing context, an example of a pattern might be the ways in which a set of parameters contained in that data, which are related to a process in a factory, vary together. When considering AI, it's important to understand what the data requirements are at the outset. The algorithm learns the patterns by being shown many examples of the parameter values in question--typically between a few thousand and several million. This data sample is a representation of the history of the factory process.

Björn Brinne added: "The report is correct that there are a number of urgent challenges that need to be addressed. Many deep learning projects to date have been focused on small pockets of research, which presents issues in relation to repeatability, auditability and scalability which are needed to make a global impact. Also, lack of skills, cost and complexity remain as barriers. "For the NHS, this is a major challenge as budgets and talent are already limited. "There's also the data issue – deploying deep learning models in the health sector requires retraining them when new data comes in, a complex and often costly task. "Additionally, to begin with, the quantity of data available will be limited and the quality of it inconsistent, which could lead to inaccuracies. There are also obvious challenges in the sensitivity of the data that is needed and requirements for consent." "In order to overcome these challenges, deep learning needs to move away from being used as a research tool, and instead become operationalised to make outputs more robust and usable. This will make deep learning accessible for a wider group of users in the medical industry, so that data pools become greater and more varied over time, improving model performance and, by extension, the quality and effectiveness of patient care."