Artificial intelligence is often associated with prophecies of job destruction. Yet an army of workers in the global south is being pressed into action. In discussions about the locations comprising the key productive nodes of artificial intelligence and other next-generation digital technologies, African workers rarely get a mention. Autonomous vehicles, machine-learning systems, next-generation search engines and recommendations systems--how many of these technologies are'made in Africa'? The answer, actually, is'all of them'.

U.S. President Donald Trump's broadside against the World Health Organization is another blow to international institutions designed to help nations confront global crises -- and may leave countries even less prepared for the next one. Trump's move on Tuesday to suspend WHO funding amid a pandemic that has cost at least 130,000 lives is the latest salvo in a broader struggle between the U.S. and China over global leadership. Both countries are courting other nations and public opinion as they cover up their own shortcomings in the pandemic and position themselves for the post-virus world. China -- widely criticized for missteps early in the outbreak -- has ramped up efforts to send medical supplies to hard-hit nations, even as reports emerged that much of that gear was faulty or expired. The U.S., meanwhile, announced $300 million in aid to countries fighting the virus but rebuffed requests for the most essential gear while receiving donations from the governments of Egypt, Taiwan and Vietnam among others.

Rabat – Moroccan-born professor of computer science at New York University (NYU) Dr. Anasse Bari has designed an artificial intelligence (AI) tool to analyze and curb the evolution of the COVID-19 pandemic. Managing a team of researchers at NYU, Bari helped create and study the efficacy of an AI instrument to predict patients vulnerable to coronavirus and determine the seriousness of COVID-19 infections. "Our goal was to design and deploy a decision-support tool using AI capabilities--mostly predictive analytics--to flag future clinical coronavirus severity," Bari said. "We hope that the tool, when fully developed, will be useful to physicians as they assess which moderately ill patients really need beds and who can safely go home, with hospital resources stretched thin," the computer scientist added, in light of the fact that hospital resources are limited as the COVID-19 outbreak continues. The Moroccan professor holds a bachelor's degree in Computer Engineering from Al Akhawayn University in Ifrane (AUI), and is establishing negotiations between NYU and AUI to use the newly developed technology in tackling the spread of COVID-19 in Morocco.

Manual evaluation of tissue sections using a microscope is a very time-consuming process. The adoption of AI solutions which can automatically recognize and count visual information could help increase the speed and accuracy of image analysis, whilst also freeing up time for pathologists. Technology Networks recently spoke with Dr Gillian Beamer, a pathologist and assistant professor at Tufts University and Thomas Westerling-Bui, Director, Scientific Strategy and Business Development at Aiforia, to learn how the implementation of a cloud-based platform is helping to advance scientific research on Mycobacterium tuberculosis. Anna MacDonald (AM): Can you provide an overview of what your typical daily work involves? What were some of the challenges you faced doing this manually?

In part I of the blog I wrote about the most acute problem our society faces today - the Climate Crisis, and how we can leverage the pandemic-caused lockdown to analyze the consequences as data points for the "what-if" scenario to make better decisions in the future. While Climate Crisis should be addressed timely and aggressively, COVID-19 posed a health crisis for the governments to deal with the projection of 80% of the population being infected in the short term. How will health systems manage the prevention, diagnostics, and treatment of the pandemic in parallel to provide the ongoing services and treatments? In this part, I will present a few developments in telemedicine, personalized medicine and drug development powered by AI/ML and how they better equipped us in this fight and could be used routinely in the future. Telemedicine is a buzzword we used to hear in the context of highly populated countries with a lack of trained personnel trying to bridge the supply and demand with remote resourcing.

In the midst of South Africa's five-week extended lockdown, footage has emerged of a drone collecting data and enforcing social distancing. It offers a fascinating first-person view of AI-based software in action and yet another example of drones fighting against COVID-19. The footage comes from local South African publication Sowetan Live. A mayor within the largely rural Limpopo Province of South Africa has employed drone technology to monitor social distancing and enforce lockdown rules. Similar to implementations in other countries, this drone uses a public address system.

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Artificial Intelligence algorithms have been steadily increasing in popularity and usage. Deep Learning, allows neural networks to be trained using huge datasets and also removes the need for human extracted features, as it automates the feature learning process. In the hearth of training deep neural networks, such as Convolutional Neural Networks, we find backpropagation, that by computing the gradient of the loss function with respect to the weights of the network for a given input, it allows the weights of the network to be adjusted to better perform in the given task. In this paper, we propose a hybrid method that uses both backpropagation and evolutionary strategies to train Convolutional Neural Networks, where the evolutionary strategies are used to help to avoid local minimas and fine-tune the weights, so that the network achieves higher accuracy results. We show that the proposed hybrid method is capable of improving upon regular training in the task of image classification in CIFAR-10, where a VGG16 model was used and the final test results increased 0.61%, in average, when compared to using only backpropagation.

Existing question answering datasets focus on dealing with homogeneous information, based either only on text or KB/Table information alone. However, as human knowledge is distributed over heterogeneous forms, using homogeneous information might lead to severe coverage problems. To fill in the gap, we present \dataset, a new large-scale question-answering dataset that requires reasoning on heterogeneous information. Each question is aligned with a structured Wikipedia table and multiple free-form corpora linked with the entities in the table. The questions are designed to aggregate both tabular information and text information, i.e. lack of either form would render the question unanswerable. We test with three different models: 1) table-only model. 2) text-only model. 3) a hybrid model \model which combines both table and textual information to build a reasoning path towards the answer. The experimental results show that the first two baselines obtain compromised scores below 20\%, while \model significantly boosts EM score to over 50\%, which proves the necessity to aggregate both structure and unstructured information in \dataset. However, \model's score is still far behind human performance, hence we believe \dataset to an ideal and challenging benchmark to study question answering under heterogeneous information. The dataset and code are available at \url{https://github.com/wenhuchen/HybridQA}.

In 1950, 746 Million people lived in cities but just one hundred years later in 2050, this is anticipated to surpass 6 Billion – some 66% of the world's population. In this increasingly urban, data-driven and hybrid world that integrates the physical and the virtual, the concept of'smartness' comes to the fore. The vision of a'smart city' has been in existence for many years but it is only recently that advances in technology have enabled tangible progress towards its real-world actualization. I believe this is also critical to the successful implementation of the United Nation's 2030 Agenda for Sustainable Development (SDGS). So, what does a smart city mean to you?