Despite the myriad peer-reviewed papers demonstrating novel Artificial Intelligence (AI)-based solutions to COVID-19 challenges during the pandemic, few have made significant clinical impact. The impact of artificial intelligence during the COVID-19 pandemic was greatly limited by lack of model transparency. This systematic review examines the use of Explainable Artificial Intelligence (XAI) during the pandemic and how its use could overcome barriers to real-world success. We find that successful use of XAI can improve model performance, instill trust in the end-user, and provide the value needed to affect user decision-making. We introduce the reader to common XAI techniques, their utility, and specific examples of their application. Evaluation of XAI results is also discussed as an important step to maximize the value of AI-based clinical decision support systems. We illustrate the classical, modern, and potential future trends of XAI to elucidate the evolution of novel XAI techniques. Finally, we provide a checklist of suggestions during the experimental design process supported by recent publications. Common challenges during the implementation of AI solutions are also addressed with specific examples of potential solutions. We hope this review may serve as a guide to improve the clinical impact of future AI-based solutions.

Artificial intelligence is a rapidly evolving field of technology. Kenya as a third world country has not lagged behind in making a comprehensive step towards promoting and implementing AI technology although it will take time to fully embrace it. The Kenyan government has used this technology to improve health, agriculture and government services. As an AI engineer, I firmly believe that these emerging technologies have a significant impact on national security. As NIS mission is to gather intelligence, analyse and apply results to predict the various outcomes and actions that need to be taken, it follows new integration tools provided by the machine learning that will significantly reduce the time NIS analysts and operators may use to analyze Intelligence data. it draws conclusions in it, and advises the Government accordingly on appropriate intelligence reports.

At the end of a tumultuous year for both her professional life and her country of ancestry, Dr Timnit Gebru decided to do more than complain about the impact that technology was having on political discourse and established an institute specifically to address the harms that artificial intelligence (AI) causes on marginalised groups. Not everyone would have the courage to take on a company so large that it's name is a verb -- over both its human resources record and on its policies -- but in a year in which the choices of tech companies have dominated political discourse, it is one of the more urgent questions of our time, and Gebru is on it. Through her public criticism of Google, Gebru has highlighted an important and developing debate in tech policy research. Her public confrontations online with senior management at Google underscore the ways in which tech platforms claiming to encourage research on their own systems end up producing hackneyed and partial accounts because they are unwilling to allow their work to stand up to true, rigorous, academic scrutiny. This is the challenge faced by researchers trying to understand the impact that algorithms are having on the way we receive, consume and respond to political information curated by proprietary AI models: How can we truly understand the impact that technology is having on our public sphere if the tech companies won't let anyone see what's under the bonnet?

BENGALURU: Bengaluru has emerged among the top five cities in the world for Artificial Intelligence (AI), ranked at No. 5, with the first four being cities in the USA. The ranking is among top 50 AI cities, measured by the TIDE Framework and listed by Harvard Business Review (HBR). The top four cities are San Francisco, New York, Boston and Seattle. Reviewers have noted that Bengaluru also has the world's second-largest AI talent pool and is ranked fifth for diversity among AI workers, as measured by data from Fletcher school, Tufts University, and derived at based on a framework of indicators such as talent pool, investments, diversity of talent, evolution of the country's digital foundations (TIDE). Another feather in Bengaluru's cap is that it is also among cities on HBR's list of AI hotspots in the developing world -- these cities also score favourably on the cost of living, which could be a powerful draw for diverse talent, the reviewers noted.

Computers have become amazingly precise at translating spoken words to text messages and scouring huge troves of information for answers to complex questions. At least, that is, so long as you speak English or another of the world's dominant languages. But try talking to your phone in Yoruba, Igbo or any number of widely spoken African languages and you'll find glitches that can hinder access to information, trade, personal communications, customer service and other benefits of the global tech economy. "We are getting to the point where if a machine doesn't understand your language it will be like it never existed," said Vukosi Marivate, chief of data science at the University of Pretoria in South Africa, in a call to action before a December virtual gathering of the world's artificial intelligence researchers. American tech giants don't have a great track record of making their language technology work well outside the wealthiest markets, a problem that's also made it harder for them to detect dangerous misinformation on their platforms.

The administration of President Joe Biden entered office on January 20, 2021, pledging a broad-strokes overhaul of how Washington interacts with the world, promising to be a distinct counterpoint to the disruptive, go-it-alone posture of former President Donald Trump, and tying stability and prosperity at home to US interests abroad in his so-called "foreign policy for the middle class". As 2021 ends, the administration has indeed sought to re-up relations with key allies and position itself as a central player in combating global crises, but has faced criticism for failing to live up to vows of a human rights-leading foreign policy and for what some have described as an over-emphasis on sweeping ideological differences at a time when global cooperation -- particularly between superpowers -- is sorely needed. "2021 was a year of transition. President Biden replaced Trump's impetuousness with pragmatism and realism. There is a greater understanding of what US policy actually is," PJ Crowley, the former US assistant secretary of state for public affairs under President Barack Obama, told Al Jazeera.

The coronavirus pandemic has tested public health systems globally. Few novel and infectious diseases around the world have ever posed such dramatic challenges as the novel coronavirus SARS-CoV-2, which causes COVID-19. With highly efficient human-to-human transmission and high mortality rates, COVID19 led the World Health Organization to declare a public health emergency of international concern and caused countries around the world to reassess their public health capabilities. The United Arab Emirates, like other members of the international community, faced the unprecedented challenge of ensuring public health and safety while minimizing economic fallout. These efforts by the U.A.E.'s leadership allowed the U.A.E. to be globally ranked as one of the top countries, and the highest in the Arab world, in terms of its COVID-19 response. VPS Healthcare is an integrated healthcare service provider with 22 operational hospitals, over 125 healthcare centres, 13000 employees, one of the largest pharmaceutical manufacturing plants in Dubai and medical support services spread across the Middle East, Europe and India. By providing comprehensive patient management at international quality standards across the MENA Region and beyond and to the entire strata of community, VPS Healthcare reflects a brand image of excellence in healthcare delivery system.

A miniature robot treads stealthily in a vast desert, navigating the treacherously empty terrain in pursuit of fertile areas. When it identifies one, it reports the findings and then plants seeds based on the data retrieved from its sensors and navigation system. The autonomous robot, A'seedbot, could transform desert soil into a lush landscape, reducing the percentage of the desert to a large extent in the UAE. This tiny robot farmer is the graduation project of Mazyar Etehadi from the Dubai Institute of Design and Innovation. A'seedbot was unveiled at the Global Grad Show, an event that encourages designers to unveil innovative solutions to today's pressing social and environmental problems.

Global food insecurity is expected to worsen in the coming decades with the accelerated rate of climate change and the rapidly increasing population. In this vein, it is important to remove inefficiencies at every level of food production. The recent advances in deep learning can help reduce such inefficiencies, yet their application has not yet become mainstream throughout the industry, inducing economic costs at a massive scale. To this point, modern techniques such as CNNs (Convolutional Neural Networks) have been applied to RPQD (Raw Produce Quality Detection) tasks. On the other hand, Transformer's successful debut in the vision among other modalities led us to expect a better performance with these Transformer-based models in RPQD. In this work, we exclusively investigate the recent state-of-the-art Swin (Shifted Windows) Transformer which computes self-attention in both intra- and inter-window fashion. We compare Swin Transformer against CNN models on four RPQD image datasets, each containing different kinds of raw produce: fruits and vegetables, fish, pork, and beef. We observe that Swin Transformer not only achieves better or competitive performance but also is data- and compute-efficient, making it ideal for actual deployment in real-world setting. To the best of our knowledge, this is the first large-scale empirical study on RPQD task, which we hope will gain more attention in future works.

Modern neural language models widely used in tasks across NLP risk memorizing sensitive information from their training data. As models continue to scale up in parameters, training data, and compute, understanding memorization in language models is both important from a learning-theoretical point of view, and is practically crucial in real world applications. An open question in previous studies of memorization in language models is how to filter out "common" memorization. In fact, most memorization criteria strongly correlate with the number of occurrences in the training set, capturing "common" memorization such as familiar phrases, public knowledge or templated texts. In this paper, we provide a principled perspective inspired by a taxonomy of human memory in Psychology. From this perspective, we formulate a notion of counterfactual memorization, which characterizes how a model's predictions change if a particular document is omitted during training. We identify and study counterfactually-memorized training examples in standard text datasets. We further estimate the influence of each training example on the validation set and on generated texts, and show that this can provide direct evidence of the source of memorization at test time.