In the near futures, the U.S. Army plans to deploy packs of semi-autonomous robot tanks armed to the brim with chainguns, missiles, and other fearsome weaponry. Two classes of these Robotic Combat Vehicles (RCVs) are already under development, Breaking Defense reports, with a third on the way. As they make their way to future battlefields, Major Corey Wallace explained at a conference last week, they'll be used to lead the charge in both conventional and electronic warfare in the years to come. The U.S. Army is building RCV lights, mediums, and heavies. Respectively, the three are lightweight scouting vehicles, heavily armed mini tanks, and powerful artillery vehicles.

It seems the USA is determined to attest its dominance in the field of artificial intelligence. In August 2020, the Trump administration announced that The National Science Foundation and the Department of Energy have allocated $1 billion for advanced research in AI and quantum information. The investment will lead to the foundation of 12 new AI institutes and quantum information science (QIS) research institutes. The funds will be directed toward AI Research Institutes under the supervision of NSF and QIS Research Centers led by DOE. The $1 billion will be allocated for a period of five years in order to achieve advancements in fields like machine learning, computer vision, and quantum computing. The USA is aware of AI's importance for the "21st-century American workforce" and national economic growth.

It seems almost preordained at this point: Thou Shalt Run Thy Data Science Environment On a Cloud-Native Kubernetes Platform. This is 2020, after all. How else could it possibly run? But Tyler Whitehouse, a data scientist who worked at DARPA and IARPA, and his associates from Johns Hopkins University have a very different view on how to manage and distribute resources for data scientists. It does feature containers, but it doesn't involve Kubernetes. To hear Whitehouse tell it, the whole data science community has zigged, without ever considering whether they should have zagged.

As cyber security becomes even more crucial amidst Covid19 pandemic, artificial intelligence and machine learning backed solutions are helping several insurance companies tackle these threats, an EY report said. In a recent case, EY had helped an insurance company tackle the cybersecurity threat and this could be replicated throughout the industry. An Indian insurance company was looking to augment its internal security to protect its data, systems, and infrastructure from potential cyber security threats. In addition, the company determined that it needed a 24 7 security log monitoring system that could operate 365 days a year along with the capability to conduct analytics, threat profiling, correlation and alerting, EY report said. The company is now not only able to prevent active threats, but also conduct analysis on potential vulnerabilities.

In a computing context, cybersecurity is undergoing massive shifts in technology and its operations in recent days, and data science is driving the change. Extracting security incident patterns or insights from cybersecurity data and building corresponding data-driven model, is the key to make a security system automated and intelligent. To understand and analyze the actual phenomena with data, various scientific methods, machine learning techniques, processes, and systems are used, which is commonly known as data science. In this paper, we focus and briefly discuss on cybersecurity data science, where the data is being gathered from relevant cybersecurity sources, and the analytics complement the latest data-driven patterns for providing more effective security solutions. The concept of cybersecurity data science allows making the computing process more actionable and intelligent as compared to traditional ones in the domain of cybersecurity. We then discuss and summarize a number of associated research issues and future directions. Furthermore, we provide a machine learning based multi-layered framework for the purpose of cybersecurity modeling. Overall, our goal is not only to discuss cybersecurity data science and relevant methods but also to focus the applicability towards data-driven intelligent decision making for protecting the systems from cyber-attacks.

A Finnish soldier stands in front of a seized BA-10 armored vehicle. In a new international cross disciplinary study, researchers have used artificial intelligence to analyse large amounts of historical photos from WW2. Among other things, the study shows that artificial intelligence can recognise the identity of photographers based on the content of photos taken by them. Artificial Intelligence (AI) is now able to identify photographers based on the content of images they've taken. This is the conclusion of a new study at AU Engineering, Aarhus University, where, in collaboration with Tampere University and the Finnish Environment Institute, researchers have used state-of-the-art artificial intelligence to trawl through photographs taken by 23 well-known Finnish photographers during the Second World War.

Roof falls due to geological conditions are major safety hazards in mining and tunneling industries, causing lost work times, injuries, and fatalities. Several large-opening limestone mines in the Eastern and Midwestern United States have roof fall problems caused by high horizontal stresses. The typical hazard management approach for this type of roof fall hazard relies heavily on visual inspections and expert knowledge. In this study, we propose an artificial intelligence (AI) based system for the detection roof fall hazards caused by high horizontal stresses. We use images depicting hazardous and non-hazardous roof conditions to develop a convolutional neural network for autonomous detection of hazardous roof conditions. To compensate for limited input data, we utilize a transfer learning approach. In transfer learning, an already-trained network is used as a starting point for classification in a similar domain. Results confirm that this approach works well for classifying roof conditions as hazardous or safe, achieving a statistical accuracy of 86%. However, accuracy alone is not enough to ensure a reliable hazard management system. System constraints and reliability are improved when the features being used by the network are understood. Therefore, we used a deep learning interpretation technique called integrated gradients to identify the important geologic features in each image for prediction. The analysis of integrated gradients shows that the system mimics expert judgment on roof fall hazard detection. The system developed in this paper demonstrates the potential of deep learning in geological hazard management to complement human experts, and likely to become an essential part of autonomous tunneling operations in those cases where hazard identification heavily depends on expert knowledge.

In this paper--as a case study--we present a systematic study of gender bias in machine translation with Google Translate. We translated sentences containing names of occupations from Hungarian, a language with gender-neutral pronouns, into English. Our aim was to present a fair measure for bias by comparing the translations to an optimal non-biased translator. When assessing bias, we used the following reference points: (1) the distribution of men and women among occupations in both the source and the target language countries, as well as (2) the results of a Hungarian survey that examined if certain jobs are generally perceived as feminine or masculine. We also studied how expanding sentences with adjectives referring to occupations effect the gender of the translated pronouns. As a result, we found bias against both genders, but biased results against women are much more frequent. Translations are closer to our perception of occupations than to objective occupational statistics. Finally, occupations have a greater effect on translation than adjectives.

For strategic problems, intelligent systems based on Deep Reinforcement Learning (DRL) have demonstrated an impressive ability to learn advanced solutions that can go far beyond human capabilities, especially when dealing with complex scenarios. While this creates new opportunities for the development of intelligent assistance systems with groundbreaking functionalities, applying this technology to real-world problems carries significant risks and therefore requires trust in their transparency and reliability. With superhuman strategies being non-intuitive and complex by definition and real-world scenarios prohibiting a reliable performance evaluation, the key components for trust in these systems are difficult to achieve. Explainable AI (XAI) has successfully increased transparency for modern AI systems through a variety of measures, however, XAI research has not yet provided approaches enabling domain level insights for expert users in strategic situations. In this paper, we discuss the existence of superhuman DRL-based strategies, their properties, the requirements and challenges for transforming them into real-world environments, and the implications for trust through explainability as a key technology.

Federated learning enables training a global model from data located at the client nodes, without data sharing and moving client data to a centralized server. Performance of federated learning in a multi-access edge computing (MEC) network suffers from slow convergence due to heterogeneity and stochastic fluctuations in compute power and communication link qualities across clients. We propose a novel coded computing framework, CodedFedL, that injects structured coding redundancy into federated learning for mitigating stragglers and speeding up the training procedure. CodedFedL enables coded computing for non-linear federated learning by efficiently exploiting distributed kernel embedding via random Fourier features that transforms the training task into computationally favourable distributed linear regression. Furthermore, clients generate local parity datasets by coding over their local datasets, while the server combines them to obtain the global parity dataset. Gradient from the global parity dataset compensates for straggling gradients during training, and thereby speeds up convergence. For minimizing the epoch deadline time at the MEC server, we provide a tractable approach for finding the amount of coding redundancy and the number of local data points that a client processes during training, by exploiting the statistical properties of compute as well as communication delays. We also characterize the leakage in data privacy when clients share their local parity datasets with the server. We analyze the convergence rate and iteration complexity of CodedFedL under simplifying assumptions, by treating CodedFedL as a stochastic gradient descent algorithm. Furthermore, we conduct numerical experiments using practical network parameters and benchmark datasets, where CodedFedL speeds up the overall training time by up to $15\times$ in comparison to the benchmark schemes.