Over the last few years, responsible AI has gone from a niche concept to a constant headline. Responsible, trustworthy AI is the subject of several documentaries, books, and conferences. The more we make responsible AI an expectation and a known commodity, the more likely we are to make it our reality. This enables us to flourish with more accessible AI. This is our shared goal through the Responsible AI Badge certification programme for senior executives.

The ELEXIS showcase event invites representatives of institutions that have become observers, as well as people from the industry, operating in fields such as Language Technology, Machine Translation, language learning, Dictionary Publishing, etc.

According to Deloitte, compliance costs have risen by 60% for banks and other financial institutions since the 2008 recession. The situation is not much different in other industries as well. As a result, enterprises across the globe are struggling to minimize the cost of compliance under control. Even though there are many ways to keep compliance costs in check, none are as effective as using automation and artificial intelligence. Artificial intelligence and automation can not only increase your efficiency of compliance operations but can also minimize costs.

Due to the COVID-19 pandemic, the global supply chain is disrupted at an unprecedented scale under uncertain and unknown trends of labor shortage, high material prices, and changing travel or trade regulations. To stay competitive, enterprises desire agile and dynamic response strategies to quickly react to disruptions and recover supply-chain functions. Although both centralized and multi-agent approaches have been studied, their implementation requires prior knowledge of disruptions and agent-rule-based reasoning. In this paper, we introduce a model-based multi-agent framework that enables agent coordination and dynamic agent decision-making to respond to supply chain disruptions in an agile and effective manner. Through a small-scale simulated case study, we showcase the feasibility of the proposed approach under several disruption scenarios that affect a supply chain network differently, and analyze performance trade-offs between the proposed distributed and centralized methods.

In this paper, we introduce a decentralized digital twin (DDT) framework for dynamical systems and discuss the prospects of the DDT modeling paradigm in computational science and engineering applications. The DDT approach is built on a federated learning concept, a branch of machine learning that encourages knowledge sharing without sharing the actual data. This approach enables clients to collaboratively learn an aggregated model while keeping all the training data on each client. We demonstrate the feasibility of the DDT framework with various dynamical systems, which are often considered prototypes for modeling complex transport phenomena in spatiotemporally extended systems. Our results indicate that federated machine learning might be a key enabler for designing highly accurate decentralized digital twins in complex nonlinear spatiotemporal systems.

Directorate General Defence Estates (DGDE) has developed an indigenous AI-based software that will detect illegal constructions or trespassing on the defence land using satellite imaging. The software was developed by the Centre of Excellence on Satellite and Unmanned Remote VEhicle Initiative (CoE-SURVEI), along with Bhabha Atomic Research Centre (BARC), at Meerut Cantonment in Uttar Pradesh. Currently, the technology employs trained software and Cartosat-3 imagery from the National Remote Sensing Centre (NRSC). This software can detect any alterations made to the land by comparing satellite images in a time series. It allows the CEOs of Cantonment Boards to keep track of changes being made to the area, whether or not these changes are authorized, and when to take any action if it turns out to be unauthorized.

What's the news?: Deep learning is behind machine learning's most high-profile successes. But this incredible performance comes at a cost: training deep-learning models requires huge amounts of energy. Now, new research shows how scientists who use cloud platforms to train algorithms can dramatically reduce the energy they use, and therefore the emissions they create. How can they do it?: Simple changes to cloud settings are the key. Researchers created a tool that measures the electricity usage of any machine-learning program that runs on Azure, Microsoft's cloud service, during every phase of their project.

Singapore, 8 June 2022 – The Singapore National Parks Board (NParks), Microsoft and Conservation International announced the launch of Fin Finder, Asia's first mobile application that employs artificial intelligence (AI) to visually identify illegally traded shark and ray species. Through the tripartite collaboration, the mobile app was created by a Singapore-led team from Conservation International in consultation with NParks with support from the Microsoft AI for Earth program. The app will be used by officers from the Singapore National Parks Board to combat illegal wildlife trade. According to the Convention on International Trade in Endangered Species of Wild Fauna and Flora (CITES) Appendix II [1], there are approximately 1,000 species of sharks and rays in the world, of which over 30 species are listed under CITES Appendix II for regulated trade. In Singapore, more than 160,000 kilograms of fins from CITES-listed sharks and rays have entered the borders between 2012 and 2020 [2].

When Roe v. Wade was overturned, Team Meat, creator of classic platformer Super Meat Boy, had one thing to say: "The Supreme Court can go fuck itself." It's been little more than a week since the court handed down its landmark ruling in Dobbs v. Jackson Women's Health Organization, ending the legal right to abortion in the United States. A person's ability to get the healthcare they need will now be determined by a patchwork of state-by-state laws and policies. Team Meat's tweet, composed by the company's social media manager, is the organization's official stance on the matter. "Everyone at Team Meat stands by this fully," cofounder Tommy Refenes tells WIRED.

Abstract: Multilevel linear models allow flexible statistical modelling of complex data with different levels of stratification. Identifying the most appropriate model from the large set of possible candidates is a challenging problem. In the Bayesian setting, the standard approach is a comparison of models using the model evidence or the Bayes factor. However, in all but the simplest of cases, direct computation of these quantities is impossible. Markov Chain Monte Carlo approaches are widely used, such as sequential Monte Carlo, but it is not always clear how well such techniques perform in practice.