Artificial intelligence (AI) may soon know more about you than you think. A startup called Hume AI claims to use algorithms to measure emotions from facial, vocal, and verbal expressions. It's one of a growing number of companies that purport to read human emotions using computers. But some experts say that the concept raises privacy issues. "Whoever controls these systems and platforms are going to have a lot of information on individuals," Bob Bilbruck, a tech startup advisor, told Lifewire in an email interview.

Neonatal respiratory distress is a common condition that if left untreated, can lead to short- and long-term complications. This paper investigates the usage of digital stethoscope recorded chest sounds taken within 1min post-delivery, to enable early detection and prediction of neonatal respiratory distress. Fifty-one term newborns were included in this study, 9 of whom developed respiratory distress. For each newborn, 1min anterior and posterior recordings were taken. These recordings were pre-processed to remove noisy segments and obtain high-quality heart and lung sounds. The random undersampling boosting (RUSBoost) classifier was then trained on a variety of features, such as power and vital sign features extracted from the heart and lung sounds. The RUSBoost algorithm produced specificity, sensitivity, and accuracy results of 85.0%, 66.7% and 81.8%, respectively.

With increasing interest in procedural content generation by academia and game developers alike, it is vital that different approaches can be compared fairly. However, evaluating procedurally generated video game levels is often difficult, due to the lack of standardised, game-independent metrics. In this paper, we introduce two simulation-based evaluation metrics that involve analysing the behaviour of an A* agent to measure the diversity and difficulty of generated levels in a general, game-independent manner. Diversity is calculated by comparing action trajectories from different levels using the edit distance, and difficulty is measured as how much exploration and expansion of the A* search tree is necessary before the agent can solve the level. We demonstrate that our diversity metric is more robust to changes in level size and representation than current methods and additionally measures factors that directly affect playability, instead of focusing on visual information. The difficulty metric shows promise, as it correlates with existing estimates of difficulty in one of the tested domains, but it does face some challenges in the other domain. Finally, to promote reproducibility, we publicly release our evaluation framework.

We study the problem of learning the preferences of drivers and planners in the context of last mile delivery. Given a data set containing historical decisions and delivery locations, the goal is to capture the implicit preferences of the decision-makers. We consider two ways to use the historical data: one is through a probability estimation method that learns transition probabilities between stops (or zones). This is a fast and accurate method, recently studied in a VRP setting. Furthermore, we explore the use of machine learning to infer how to best balance multiple objectives such as distance, probability and penalties. Specifically, we cast the learning problem as a structured output prediction problem, where training is done by repeatedly calling the TSP solver. Another important aspect we consider is that for last-mile delivery, every address is a potential client and hence the data is very sparse. Hence, we propose a two-stage approach that first learns preferences at the zone level in order to compute a zone routing; after which a penalty-based TSP computes the stop routing. Results show that the zone transition probability estimation performs well, and that the structured output prediction learning can improve the results further. We hence showcase a successful combination of both probability estimation and machine learning, all the while using standard TSP solvers, both during learning and to compute the final solution; this means the methodology is applicable to other, real-life, TSP variants, or proprietary solvers.

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.