When COVID-19 hit, an enterprising group of pupils in Abuja, Nigeria, used their robotics class to design and build a simple robot to cut down on interpersonal contact in hospitals. Using only scraps they found around the classroom, they each contributed to the ideas, concept, mechanics and AI elements of their robot "Mairabot" – which earned praise from health officials and their teachers alike. Mairabot, by filmmaker Philip Okpokoro, introduces us to Nabila Abbas and her fellow students in this short, inspiring film. Philip Okpokoro is a Nigerian director and cinematographer with an impressive record in both documentary and live TV directing. He has directed a wide array of film projects from high-end live TV to intimate documentaries for global broadcasters, and has been awarded for best director of photography.

Eventbrite - Jayne Bullock, Department of Computer Science, University of Oxford (jayne.bullock@cs.ox.ac.uk) presents Strachey Lecture: Professor Neil Lawrence (University of Cambridge) - Tuesday, 3 May 2022 at Mathematical Institute, University of Oxford, Andrew Wiles Building, Radcliffe Observatory Quarter, Woodstock Road, Oxford, OX2 6GG, Oxford, England. Find event and ticket information.

South Africa's Apollo Fintech has tapped ThetaRay for its AI-powered cross-border payments monitoring technology Sonar. The tech will be deployed to monitor and screen transactions on its new Knox Wire service, a bank-to-bank cross-border payments system. Apollo Fintech CEO Stephen McCullah says utilising ThetaRay's anti-money laundering (AML) solution will allow the firm "to expand our network of partner financial institutions". Knox Wire enables 30,000 financial institutions to offer their clients near-instant cross-border payments to over 200 countries in 150 currencies with lower transactions costs. "Knox Wire was built to set a new global standard for network size and settlement speed," McCullah adds.

Different parts of the globe are experiencing distinct climate challenges -- severe drought, dangerous flooding, reduced biodiversity or dense air pollution. The challenges are so great that no country can solve them on their own. But innovative startups worldwide are lighting the way, demonstrating how these daunting challenges can be better understood and addressed with AI. Here's how five -- all among the 10,000 members of NVIDIA Inception, a program designed to nurture cutting-edge startups -- are looking out for the environment using NVIDIA-accelerated applications: India-based Blue Sky Analytics is building a geospatial intelligence platform that harnesses satellite data for environmental monitoring and climate risk assessment. The company provides developers with climate datasets to analyze air quality and estimate greenhouse gas emissions from fires -- with additional datasets in the works to forecast future biomass fires and monitor water capacity in lakes, rivers and glacial melts.

The tremendous achievements of Artificial Intelligence (AI) in computer vision, natural language processing, games and robotics, has extended the reach of the AI hype to other fields: in telecommunication networks, the long term vision is to let AI fully manage, and autonomously drive, all aspects of network operation. In this industry vision paper, we discuss challenges and opportunities of Autonomous Driving Network (ADN) driven by AI technologies. To understand how AI can be successfully landed in current and future networks, we start by outlining challenges that are specific to the networking domain, putting them in perspective with advances that AI has achieved in other fields. We then present a system view, clarifying how AI can be fitted in the network architecture. We finally discuss current achievements as well as future promises of AI in networks, mentioning a roadmap to avoid bumps in the road that leads to true large-scale deployment of AI technologies in networks.

The Middle East, like the rest of the world, is moving towards a post-Covid-19 future. As things return to normal, companies are turning their attention back to issues such as how to optimise digital experiences. The Gulf Co-operation Council (GCC) nations are home to people from diverse cultures. Expatriates make up more than 85 per cent of the populations of many Gulf countries. Two hundred nationalities reside in the UAE alone.

Editor's note: This article was originally published on March 8, 2021. Wired reports a gender bias exists in AI and, in 2018, found that only 12% of AI researchers are women. When I started my career as a Data Analyst, a Data Science engineer position was not widely available in Ukraine. Self-education and getting acquainted with ML algorithms took me some time and a lot of effort. Nowadays, I work as an AI engineer at MobiDev, and the more experience I get, the more willing I am to share my experiences with people in my articles and webinars.

The graph represents a network of 1,859 Twitter users whose tweets in the requested range contained "#FinServ", or who were replied to or mentioned in those tweets. The network was obtained from the NodeXL Graph Server on Sunday, 24 April 2022 at 02:01 UTC. The requested start date was Sunday, 24 April 2022 at 00:01 UTC and the maximum number of days (going backward) was 14. The maximum number of tweets collected was 7,500. The tweets in the network were tweeted over the 5-day, 15-hour, 35-minute period from Monday, 18 April 2022 at 08:25 UTC to Sunday, 24 April 2022 at 00:00 UTC.

Getting a census count wrong can cost communities big. A March 10 report from the US Census Bureau showed an overcount of white and Asian people and an undercount of people who identify as Black, Hispanic or Latino, or multiracial in 2020, a failure that has led to renewed calls to modernize the census. Progress reaching historically undercounted groups has been slow, and the stakes are high. The once-a-decade endeavor informs the distribution of federal tax dollars and apportions members of the House of Representatives for each state, potentially redrawing the political map. According to emails obtained through a records request, Trump administration officials interfered in the population count to produce outcomes beneficial to Republicans, but problems with the census go back much further.

Although machine learning (ML) has shown promise in numerous domains, there are concerns about generalizability to out-of-sample data. This is currently addressed by centrally sharing ample, and importantly diverse, data from multiple sites. However, such centralization is challenging to scale (or even not feasible) due to various limitations. Federated ML (FL) provides an alternative to train accurate and generalizable ML models, by only sharing numerical model updates. Here we present findings from the largest FL study to-date, involving data from 71 healthcare institutions across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, utilizing the largest dataset of such patients ever used in the literature (25, 256 MRI scans from 6, 314 patients). We demonstrate a 33% improvement over a publicly trained model to delineate the surgically targetable tumor, and 23% improvement over the tumor's entire extent. We anticipate our study to: 1) enable more studies in healthcare informed by large and diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further quantitative analyses for glioblastoma via performance optimization of our consensus model for eventual public release, and 3) demonstrate the effectiveness of FL at such scale and task complexity as a paradigm shift for multi-site collaborations, alleviating the need for data sharing.