Multidimensional data have become ubiquitous and are frequently involved in situations where the information is aggregated over multiple data atoms. The aggregation can be over time or other features, such as geographical location or group affiliation. We often have access to multiple aggregated views of the same data, each aggregated in one or more dimensions, especially when data are collected or measured by different agencies. However, data mining and machine learning models require detailed data for personalized analysis and prediction. Thus, data disaggregation algorithms are becoming increasingly important in various domains. The goal of this paper is to reconstruct finer-scale data from multiple coarse views, aggregated over different (subsets of) dimensions. The proposed method, called PREMA, leverages low-rank tensor factorization tools to provide recovery guarantees under certain conditions. PREMA is flexible in the sense that it can perform disaggregation on data that have missing entries, i.e., partially observed. The proposed method considers challenging scenarios: i) the available views of the data are aggregated in two dimensions, i.e., double aggregation, and ii) the aggregation patterns are unknown. Experiments on real data from different domains, i.e., sales data from retail companies, crime counts, and weather observations, are presented to showcase the effectiveness of PREMA.

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The US military is not discounting the future development of killer robots, or lethal autonomous weapon systems (LAWS), as agents in the US war machine. Artificial intelligence (AI) has shown much promise since its original inception by Alan Turing and his contemplation of machines that can learn to think and act like humans. Machine learning and its subset deep learning have inspired hope that machines can one day develop or even supersede human cognition. This is a potential technology that the Department of Defense (DoD) cannot and will not ignore. Whilst the DoD has established the Directive 3000.09,

Municipalities, just like the industrial and commercial sectors, are coming under increased pressure to reduce their energy consumption and outputs, not to mention the need to reduce costs overall. Municipal buildings and services have a huge energy savings potential, which can reduce their overall energy consumption and energy costs. At Maximpact our expert teams have assisted municipalities all over the world to identify their energy saving capacity in various sectors. As cities around the world become more urbanised and populations grow, the pressure of cities to find sustainable solutions to serve their communities is only going to increase. Changes to municipalities in becoming more energy efficient and using artificial intelligence to manage energy resources are part of a global trend of developing smart cities. Smart cities are looking to the future to redefine their energy outputs in cleaner, more sustainable and more cost-efficient ways.

Research by the UK's Bank of England (BoE) and Financial Conduct Authority (FCA) has found that the country's financial services businesses are fast deploying machine learning (ML) technology to tackle money laundering and fraud. The survey found that ML – defined as "the development of models for prediction and pattern recognition, with limited human intervention" – is increasingly being deployed, with use expected to more than double in the next three years. As well as addressing crime, businesses are developing ML tech for customer-facing applications such as customer services and marketing. The central bank and regulator combined forces to run the survey, having pinpointed ML as a'principal driver' of how innovative technology is transforming global finance. The survey was sent to organisations such as e-money institutions, banks, financial market infrastructure firms and investment managers.

Abstract: The phenomenal growth of social media, mobile applications, sensor based technologies and the Internet of Things is generating a flood of "Big Data" and disrupting our world in many ways. Simultaneously, we are seeing many interesting developments in machine learning and Artificial Intelligence (AI) technologies and methods. In this talk I will examine the paradigm shift caused by recent developments in AI and Big Data and ways to harness their power to create a smarter enterprise computing environment. Using examples from health care, smart cities, education, and businesses in general, I will highlight challenges and research opportunities for developing an enterprise of the future. Bio: Sudha Ram is Anheuser-Busch Endowed Professor of MIS, Entrepreneurship & Innovation in the Eller College of Management at the University of Arizona.

Google is now ready to release its Coral developer board globally after completing improvements throughout its six-month beta period. Google unveiled its Coral edge kit in March, offering developers a Raspberry Pi-like board with an attachable Google Edge TPU machine-learning accelerator. The kit is aimed at engineers and researchers who want to run TensorFlow models at the edge of a network, outside the data center. The Coral Dev Board itself costs $149, which includes a detachable Coral system-on-module (SoM) that can now be bought as a standalone product for $114. The SoM includes Google's Edge TPU with the NXP IMX8M SoC, Wi-Fi and Bluetooth, memory, and storage.

In the workplace of the not-too-distant future, employees will need to go beyond being tech-savvy to being able to comfortably work alongside digital colleagues. Artificial intelligence (AI), machine learning and intelligent bots will be automatically making decisions to streamline business processes and empower efficient automation. The widespread adoption of machines to do much of the "heavy lifting" will change some jobs from the inside out, making individual workers far more productive and less bogged down with repetitive tasks. Smart chatbots can already handle first- and even second-level customer service calls, and AI is powering everything from manufacturing lines to automated vehicles. For example, BHP is rolling out automated trucks at its iron ore and coal mines across Australia over the next 5 years, following the success of its Jimblebar mine trial program, which saw a 90 per cent reduction in the number of dangerous incidents.

In this paper, we consider same-day delivery with a heterogeneous fleet of vehicles and drones. Customers make delivery requests over the course of the day and the dispatcher dynamically dispatches vehicles and drones to deliver the goods to customers before their delivery deadline. Vehicles can deliver multiple packages in one route but travel relatively slowly due to the urban traffic. Drones travel faster, but they have limited capacity and require charging or battery swaps. To exploit the different strengths of the fleets, we propose a deep Q-learning approach. Our method learns the value of assigning a new customer to either drones or vehicles as well as the option to not offer service at all. To aid feature selection, we present an analytical analysis that demonstrates the role that different types of information have on the value function and decision making. In a systematic computational analysis, we show the superiority of our policy compared to benchmark policies and the effectiveness of our deep Q-learning approach.

What enables Stochastic Gradient Descent (SGD) to achieve better generalization than Gradient Descent (GD) in Neural Network training? This question has attracted much attention. In this paper, we study the distribution of the Stochastic Gradient Noise (SGN) vectors during the training. We observe that for batch sizes 256 and above, the distribution is best described as Gaussian at-least in the early phases of training. This holds across data-sets, architectures, and other choices.