Across the world on Sunday, examinees will sit the Japanese Language Proficiency Test, a lengthy comprehension test that is the most widely taken Japanese exam by foreign nationals and whose upper levels can open opportunities in employment and education. Many companies seeking to hire foreign workers with Japanese ability specify the top two of the test's five levels among their requirements. But the JLPT's multiple-choice format of passive reading and listening skills, with no sections for speaking or writing, casts doubt over its suitability as a standard for gauging candidates looking to work in a Japanese environment. Established in 1984 and administered jointly by the Japan Foundation and Japan Educational Exchanges and Services, the JLPT boasts huge examinee numbers that were surging before the pandemic. In 2013, 571,075 people in 65 countries and regions took the test, rising to a record 1,168,535 examinees in 87 countries and regions in 2019.

As artificial intelligence (AI) steadily grows, so do concerns about its environmental footprint. Today's emerging natural language processing (NLP) models, such as GPT-3 can consume as much energy as five cars, according to a 2019 study. To reduce their environmental and climate impact, researchers in the United Arab Emirates are proposing a new development approach for these models that takes energy consumption into account at every stage, aiming to boost energy efficiency wherever possible. Last April, Abu Dhabi's Technology Innovation Institute (TII) launched NOOR, the largest Arabic-language NLP model to date. NOOR – Arabic for "light" – is trained on 10 billion parameters including books, poetry, news, and technical information, reinforcing the model's broad applicability, according to its creators.

Throughout the Covid-19 pandemic, a significant amount of effort had been put into developing techniques that predict the number of infections under various assumptions about the public policy and non-pharmaceutical interventions. While both the available data and the sophistication of the AI models and available computing power exceed what was available in previous years, the overall success of prediction approaches was very limited. In this paper, we start from prediction algorithms proposed for XPrize Pandemic Response Challenge and consider several directions that might allow their improvement. Then, we investigate their performance over medium-term predictions extending over several months. We find that augmenting the algorithms with additional information about the culture of the modeled region, incorporating traditional compartmental models and up-to-date deep learning architectures can improve the performance for short term predictions, the accuracy of medium-term predictions is still very low and a significant amount of future research is needed to make such models a reliable component of a public policy toolbox.

Huawei today announced new developments to Huawei Mobile Services ecosystem at the HUAWEI Mate 40 Series launch event, launching Petal Search, Petal Maps, HUAWEI Docs, levelling up global Huawei users' digital experience with other new updates. Huawei's official search engine app, Petal Search now is available in over 170 countries and regions and supports over 50 languages, letting users conveniently and instantly find out the information and services they need. Petal Search offers search capabilities across more than 20 categories, including apps, news, videos, images, shopping, flights, and local business. It also develops and integrates various tools, such as weather, calculator, rate exchange and even paper query to help user easily obtain daily-used information. With the new update, the search experience is now visually richer.

Since COVID-19 was first identified in December 2019, various public health interventions have been implemented across the world. As different measures are implemented at different countries at different times, we conduct an assessment of the relative effectiveness of the measures implemented in 18 countries and regions using data from 22/01/2020 to 02/04/2020. We compute the top one and two measures that are most effective for the countries and regions studied during the period. Two Explainable AI techniques, SHAP and ECPI, are used in our study; such that we construct (machine learning) models for predicting the instantaneous reproduction number ($R_t$) and use the models as surrogates to the real world and inputs that the greatest influence to our models are seen as measures that are most effective. Across-the-board, city lockdown and contact tracing are the two most effective measures. For ensuring $R_t<1$, public wearing face masks is also important. Mass testing alone is not the most effective measure although when paired with other measures, it can be effective. Warm temperature helps for reducing the transmission.

The pace of adoption for AI and cognitive technologies continues unabated with widespread, worldwide, rapid adoption. Adoption of AI by enterprises and organizations continues to grow, as evidenced by a recent survey showing growth across each of the seven patterns of AI. However, with this growth of adoption comes strain as existing regulation and laws struggle to deal with emerging challenges. As a result, governments around the world are moving quickly to ensure that existing laws, regulations, and legal constructs remain relevant in the face of technology change and can deal with new, emerging challenges posed by AI. Research firm Cognilytica recently published a report on Worldwide AI Laws and Regulations that explores the latest legal and regulatory actions taken by countries around the world across nine different AI-relevant areas. Specifically, the report analyzed emerging laws and regulations pertaining to the use of facial recognition and computer vision, operation and development of autonomous vehicles, issues of AI-relevant data privacy, challenges arising from conversational systems and chatbots, the emergence of the possibility of lethal autonomous weapons systems (LAWS), concerns around AI ethics and bias, aspects of AI-supported decision making, the potential for malicious use of AI, and other regulations and laws pertaining to the use, creation, or interaction with AI systems.

The three-day expo opened here on Thursday, attracting more than 200 exhibitors from ten countries and regions, with some 1,000 AI products on show. Visitors watch a concept unmanned vehicle at the Global AI Product Application Expo 2018, in Suzhou of east China's Jiangsu Province, May 10, 2018. The three-day expo opened here on Thursday, attracting more than 200 exhibitors from ten countries and regions, with some 1,000 AI products on show. The three-day expo opened here on Thursday, attracting more than 200 exhibitors from ten countries and regions, with some 1,000 AI products on show. The three-day expo opened here on Thursday, attracting more than 200 exhibitors from ten countries and regions, with some 1,000 AI products on show.

In Southern California, near TFS U.S. headquarters, TIMS will support the development of telematics car insurance services for Toyota customers, as well as new experiences aimed at more fully satisfying customers by working in unison with dealers and distributors. TIMS will contribute to the development of insurance offerings that benefit consumers-including so-called'pay how you drive' insurance, which encourages safer driving. TIMS plans to conduct analysis of big data, and conduct relevant marketing and promotion of the new services to help offer broader insurance options to users. The aim of establishing the new company is to contribute-from the aspect of automobile insurance-to the achievement of Toyota's vision of a mobile society that is safe, secure and convenient. AD and TFS will invest in the new company through their own subsidiaries in the U.S., while investment on the part of TMC will come from Toyota Connected, Inc. (TC) established in the U.S. in January 2016.