The maritime industry is experiencing a technological revolution that affects shipbuilding, operation of both seagoing and inland vessels, cargo management, and working practices in harbors. This ongoing transformation is driven by the ambition to make the ecosystem more sustainable and cost-efficient. Digitalization and automation help achieve these goals by transforming shipping and cruising into a much more cost- and energy-efficient, and decarbonized industry segment. The key enablers in these processes are always-available connectivity and content delivery services, which can not only aid shipping companies in improving their operational efficiency and reducing carbon emissions but also contribute to enhanced crew welfare and passenger experience. Due to recent advancements in integrating high-capacity and ultra-reliable terrestrial and non-terrestrial networking technologies, ubiquitous maritime connectivity is becoming a reality. To cope with the increased complexity of managing these integrated systems, this article advocates the use of artificial intelligence and machine learning-based approaches to meet the service requirements and energy efficiency targets in various maritime communications scenarios.

Metaverse is one of the hottest buzzwords of the moment. It's basically a virtual world created by combining different technologies, including virtual and augmented reality. While it doesn't technically exist yet, companies like Facebook hope the metaverse will become a place where we go to meet, work, play, study and shop. This'extended reality' is predicted to be the next evolution of the internet and will blur the lines between physical and digital life. Think in-game purchases, where computer gamers can buy virtual goods and services using real money. Jobs in the metaverse might include personalised avatar creator or metaverse research scientist.

Under the theme "Ensuring AI as a Common Good to Transform Education", the 2021 International Forum on Artificial Intelligence (AI) and Education convened policy-makers and practitioners from around the world on 7 and 8 December 2021. The goal was to share knowledge on how governance can be aligned to direct AI towards the common good for education and humanity, and how countries are leveraging AI to deliver the unfulfilled promises and enable the futures of learning. The Forum was co-organized by UNESCO and China with the support of the Inter-UN-Agency Working Group on Artificial Intelligence. It convened approximately 74 speakers including 17 Ministers or Vice Ministers, from UN agencies, international organizations and more than 40 countries around the world. During the two-day event, the Forum attracted more than 9,000 real-time participants and viewers from more than 100 countries.

I feel sorry for Tom Cruise. The next Top Gun movie will probably star an Apple chip designer and a ... [ ] team of LISP programmers. In August this year, eight teams gathered for the three-day final of DARPA's AlphaDogfight trials. The teams had developed Artificial Intelligence (AI) pilots to control F-16 fighter aircraft in simulated dogfights. The winner beat the human USAF pilot in five dogfights out of five.

In a recent study posted to the medRxiv* preprint server, a team of researchers predicts the evolution of coronavirus disease 2019 (COVID-19) mortality rates across countries using a biological science-guided machine learning-based approach. However, a study exploring multiple factors affecting COVID-19 mortality rates individually and interdependently is needed. In the current study, researchers used a novel Fast Fourier Transformation (FFT) driven machine-learning algorithm to analyze the publically available data of COVID-19 mortality rate from 141 countries. They assessed the impact of eight biological and socioeconomic factors such as alcohol consumption, diabetes prevalence, gross domestic product (GDP) per capita, the global health index, meat consumption, milk consumption, PM2.5, and population density on the COVID-19 mortality rates. The 141 countries assessed in the current study varied in size and population and spanned across five continents.

This webinar brings together a diverse group of scholars and experts to discuss some of the inequity and systemic vulnerabilities of covid-19 pandemic. Nathaniel Osgood serves as Professor in the Department of Computer Science at the University of Saskatchewan, and Director of the Computational Epidemiology and Public Health Informatics Laboratory. His research focuses on combining tools from Systems Science, Data Science, Computational Science and Mathematics to inform decision making in health & health care. Dr. Osgood serves as Chief Research Advisor for the Saskatchewan Centre for Patient Oriented Research and has contributed to or co-led over a dozen initiatives involving people with lived experience with dynamic modeling, machine learning and/or big data collection efforts. Dr. Osgood served as the technical director of COVID-19 modeling for the Province of Saskatchewan from March 2020-April 2021.

Artificial Intelligence (AI) is growing by leaps and bounds, with estimated market size of 7.35 billion US dollars. Machine learning (ML) is a field of AI that improves our daily living in various ways. ML involves a group of algorithms that allow software systems to become more accurate and precise in predicting outcomes. Machine learning has been at the forefront of recent years due to impressive advances in computer science, statistics, the development of neural networks, and the improved quality and quantity of datasets. Here we take a deep dive into machine learning examples to give you a better perspective.

In today's China, behemoths like Alibaba Group Holding Ltd. and Tencent Holdings Ltd. are out of favor, but "little giants" are on the rise. That's the designation for a new generation of startups that have been selected under an ambitious government program aimed at fostering a technology industry that can compete with Silicon Valley. These often-obscure companies have demonstrated they're doing something innovative and unique, and they're targeting strategically important sectors like robotics, quantum computing and semiconductors. Wu Gansha won the little giants title for his autonomous driving startup after a government review of his technology. That gave the Beijing company, Uisee, an extra dose of credibility and financial benefits.

Natural disasters ravage the world's cities, valleys, and shores on a regular basis. Deploying precise and efficient computational mechanisms for assessing infrastructure damage is essential to channel resources and minimize the loss of life. Using a dataset that includes labeled pre- and post- disaster satellite imagery, we take a machine learning-based remote sensing approach and train multiple convolutional neural networks (CNNs) to assess building damage on a per-building basis. We present a novel methodology of interpretable deep learning that seeks to explicitly investigate the most useful modalities of information in the training data to create an accurate classification model. We also investigate which loss functions best optimize these models. Our findings include that ordinal-cross entropy loss is the most optimal criterion for optimization to use and that including the type of disaster that caused the damage in combination with pre- and post-disaster training data most accurately predicts the level of damage caused. Further, we make progress in the qualitative representation of which parts of the images that the model is using to predict damage levels, through gradient-weighted class activation mapping (Grad-CAM). Our research seeks to computationally contribute to aiding in this ongoing and growing humanitarian crisis, heightened by anthropogenic climate change.

Federated Learning (FL) has emerged as a result of data ownership and privacy concerns to prevent data from being shared between multiple parties included in a training procedure. Although issues, such as privacy, have gained significant attention in this domain, not much attention has been given to satisfying statistical fairness measures in the FL setting. With this goal in mind, we conduct studies to show that FL is able to satisfy different fairness metrics under different data regimes consisting of different types of clients. More specifically, uncooperative or adversarial clients might contaminate the global FL model by injecting biased or poisoned models due to existing biases in their training datasets. Those biases might be a result of imbalanced training set (Zhang and Zhou 2019), historical biases (Mehrabi et al. 2021a), or poisoned data-points from data poisoning attacks against fairness (Mehrabi et al. 2021b; Solans, Biggio, and Castillo 2020). Thus, we propose a new FL framework that is able to satisfy multiple objectives including various statistical fairness metrics. Through experimentation, we then show the effectiveness of this method comparing it with various baselines, its ability in satisfying different objectives collectively and individually, and its ability in identifying uncooperative or adversarial clients and down-weighing their effect