The role of artificial intelligence in vaccine distribution will be very critical in vaccinating the global population against COVID-19. Vaccine distribution is one of the biggest logistical challenges humanity has faced so far and I think AI can be leveraged to help us with the equitable distribution of the vaccine. In the United States, as of now the rollout of the vaccine has been painfully slow with a lot of logistical issues from distribution to inoculations. Worldwide, the progress is even more sluggish, with some countries yet to start the journey of inoculations. The role of artificial intelligence in vaccine distribution involves the following challenges that AI can help with provided we have quality and accurate data.

Supply chains have taken a battering this year from the coronavirus pandemic and other extreme events--and artificial intelligence has emerged as a critical tool for navigating everyday business in this environment. The use of AI and its various subsets, such as machine learning, is enabling these companies to forecast demand with increasing accuracy and to optimize their supply chains, executives say. "As a supply-chain provider, as a logistics provider, we are very much in the data business," said Mario Harik, chief information officer at XPO Logistics Inc., while speaking Wednesday on a virtual panel at the WSJ Pro AI Executive Forum Mr. Harik said that events such as the accidental blocking of the Suez Canal by a shipping vessel this week demonstrate how supply-chain optimization and diversification have become essential. AI is a useful tool to quickly figure out how to reroute shipments and plan for extreme events by building redundancy into operations through multiple distribution facilities, he said. AI can help optimize the placement of these facilities, he said, as well as the "intake flow and the outbound flow at facilities as well."

Its widely accepted that artificial intelligence (AI) technologies will add trillions to global GDP in the next 20 years, making it the one of the world's most powerful technology trends on par with the disruption and opportunities being created by cloud computing and blockchain. So is Africa getting a slice of the lucrative artificial intelligence pie and what are the current AI adoption trends in the region? Although Africa's AI industry is still relatively small compared to the US, Europe and Asia, this has not stopped some of the continent's most innovative start-ups from developing solutions that demonstrate how promising the technology can be for the African economy. However, AI innovation in Africa is often ignored or overlooked because the number of patents applied for and the amount of research funding available is not well aligned with local contexts, data is missing, and the map still looks essentially bleak. That said, the prospects for AI in Africa are positive, as the potential for innovation and growth in artificial intelligence (AI) adoption is increasing.

Every day, militaristic and violent metaphors are used by journalists and political actors alike to communicate and mobilize action. These word choices may seem effective yet, these metaphors, imbued with violent imagery, can be dangerous. From a policy standpoint, they are also ineffective (and potentially harmful). One example is how the global "war on drugs" terminology victimized, stigmatized, and misplaced blame. As noted by others, as with any war, there are always civil rights abuses.

Welcome to our March 2021 monthly digest. Our digests are designed to keep you up-to-date with the latest happenings in the AI world. You can catch up with any AIhub stories you may have missed, get the low-down on recent conferences, and generally immerse yourself in all things AI. This month, our attention turned to education, and we considered both the use of AI in teaching, and the teaching of AI. Carles Sierra wrote about team formation techniques in education, describing how AI methods can be used to facilitate collaborative learning.

Father Paolo Benanti is an expert in ethics, digital ethics, and technology. He is a Franciscan monk and Professor of Moral Theology, Bioethics, and Neuroethics at the Gregorian Pontifical University in Rome. I discuss with Father Benanti the controversial aspects of AI in healthcare and how the digital transformation changes us – human beings. Father Benanti, two years ago, there was a morally ambiguous case in the USA – a doctor used a virtual presence system to tell a patient he would die. With the broad adoption of telemedicine and medical workforce shortages, this practice may become an everyday reality. From the beginning of human history, we have understood medicine as a scientific discipline. There was a time when a priest and doctor was the same person. We've always picked up someone special from the human community to hold the position of a doctor.

We propose a novel interpretable framework for cross-lingual content flagging, which significantly outperforms prior work both in terms of predictive performance and average inference time. The framework is based on a nearest-neighbour architecture and is interpretable by design. Moreover, it can easily adapt to new instances without the need to retrain it from scratch. Unlike prior work, (i) we encode not only the texts, but also the labels in the neighbourhood space (which yields better accuracy), and (ii) we use a bi-encoder instead of a cross-encoder (which saves computation time). Our evaluation results on ten different datasets for abusive language detection in eight languages shows sizable improvements over the state of the art, as well as a speed-up at inference time.

Artificial Intelligence is one of the fastest growing technologies of the 21st century and accompanies us in our daily lives when interacting with technical applications. However, reliance on such technical systems is crucial for their widespread applicability and acceptance. The societal tools to express reliance are usually formalized by lawful regulations, i.e., standards, norms, accreditations, and certificates. Therefore, the T\"UV AUSTRIA Group in cooperation with the Institute for Machine Learning at the Johannes Kepler University Linz, proposes a certification process and an audit catalog for Machine Learning applications. We are convinced that our approach can serve as the foundation for the certification of applications that use Machine Learning and Deep Learning, the techniques that drive the current revolution in Artificial Intelligence. While certain high-risk areas, such as fully autonomous robots in workspaces shared with humans, are still some time away from certification, we aim to cover low-risk applications with our certification procedure. Our holistic approach attempts to analyze Machine Learning applications from multiple perspectives to evaluate and verify the aspects of secure software development, functional requirements, data quality, data protection, and ethics. Inspired by existing work, we introduce four criticality levels to map the criticality of a Machine Learning application regarding the impact of its decisions on people, environment, and organizations. Currently, the audit catalog can be applied to low-risk applications within the scope of supervised learning as commonly encountered in industry. Guided by field experience, scientific developments, and market demands, the audit catalog will be extended and modified accordingly.

This paper surveys state-of-the-art methods and models dedicated to time series analysis and modeling, with the final aim of prediction. This review aims to offer a structured and comprehensive view of the full process flow, and encompasses time series decomposition, stationary tests, modeling and forecasting. Besides, to meet didactic purposes, a unified presentation has been adopted throughout this survey, to present decomposition frameworks on the one hand and linear and nonlinear time series models on the other hand. First, we decrypt the relationships between stationarity and linearity, and further examine the main classes of methods used to test for weak stationarity. Next, the main frameworks for time series decomposition are presented in a unified way: depending on the time series, a more or less complex decomposition scheme seeks to obtain nonstationary effects (the deterministic components) and a remaining stochastic component. An appropriate modeling of the latter is a critical step to guarantee prediction accuracy. We then present three popular linear models, together with two more flexible variants of the latter. A step further in model complexity, and still in a unified way, we present five major nonlinear models used for time series. Amongst nonlinear models, artificial neural networks hold a place apart as deep learning has recently gained considerable attention. A whole section is therefore dedicated to time series forecasting relying on deep learning approaches. A final section provides a list of R and Python implementations for the methods, models and tests presented throughout this review. In this document, our intention is to bring sufficient in-depth knowledge, while covering a broad range of models and forecasting methods: this compilation spans from well-established conventional approaches to more recent adaptations of deep learning to time series forecasting.

The damage and the impact of natural disasters are becoming more destructive with the increase of urbanization. Today's metropolitan cities are not sufficiently prepared for the pre and post-disaster situations. Digital Twin technology can provide a solution. A virtual copy of the physical city could be created by collecting data from sensors of the Internet of Things (IoT) devices and stored on the cloud infrastructure. This virtual copy is kept current and up to date with the continuous flow of the data coming from the sensors. We propose a disaster management system utilizing machine learning called DT-DMS is used to support decision-making mechanisms. This study aims to show how to educate and prepare emergency center staff by simulating potential disaster situations on the virtual copy. The event of a disaster will be simulated allowing emergency center staff to make decisions and depicting the potential outcomes of these decisions. A rescue operation after an earthquake is simulated. Test results are promising and the simulation scope is planned to be extended.