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Africa : IDRC to catalyze the ecosystem of AI innovators through research grants - Actu IA

#artificialintelligence

In 2020, IDRC and the Swedish International Development Cooperation Agency (Sida) launched the Artificial Intelligence for Development in Africa (IAPD Africa) program. This program aims to support the AI community and policymakers in developing responsible, ethical, and equitable AI that meets the continent's challenges, under the leadership of Africa. IDRC, the International Development Research Centre, was established in Canada in 1970 with a mission "to initiate, encourage, support and conduct research into the problems of the developing regions of the world and into the application of scientific, technical and other knowledge for the economic and social advancement of those regions . IDRC sees climate change and inequality, combined with the HIV/AIDS pandemic, as major obstacles to achieving the UN's sustainable development goals, and it is these challenges that it helps to address. While the center is headquartered in Ottawa, Canada, its five regional offices are located in India, Jordan, Kenya, Senegal, and Uruguay to be as close as possible to the researchers and projects it funds.


State of AI Ethics Report (Volume 6, February 2022)

arXiv.org Artificial Intelligence

This report from the Montreal AI Ethics Institute (MAIEI) covers the most salient progress in research and reporting over the second half of 2021 in the field of AI ethics. Particular emphasis is placed on an "Analysis of the AI Ecosystem", "Privacy", "Bias", "Social Media and Problematic Information", "AI Design and Governance", "Laws and Regulations", "Trends", and other areas covered in the "Outside the Boxes" section. The two AI spotlights feature application pieces on "Constructing and Deconstructing Gender with AI-Generated Art" as well as "Will an Artificial Intellichef be Cooking Your Next Meal at a Michelin Star Restaurant?". Given MAIEI's mission to democratize AI, submissions from external collaborators have featured, such as pieces on the "Challenges of AI Development in Vietnam: Funding, Talent and Ethics" and using "Representation and Imagination for Preventing AI Harms". The report is a comprehensive overview of what the key issues in the field of AI ethics were in 2021, what trends are emergent, what gaps exist, and a peek into what to expect from the field of AI ethics in 2022. It is a resource for researchers and practitioners alike in the field to set their research and development agendas to make contributions to the field of AI ethics.


inequity

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This webinar brings together a diverse group of scholars and experts to discuss some of the inequity and systemic vulnerabilities of covid-19 pandemic. Nathaniel Osgood serves as Professor in the Department of Computer Science at the University of Saskatchewan, and Director of the Computational Epidemiology and Public Health Informatics Laboratory. His research focuses on combining tools from Systems Science, Data Science, Computational Science and Mathematics to inform decision making in health & health care. Dr. Osgood serves as Chief Research Advisor for the Saskatchewan Centre for Patient Oriented Research and has contributed to or co-led over a dozen initiatives involving people with lived experience with dynamic modeling, machine learning and/or big data collection efforts. Dr. Osgood served as the technical director of COVID-19 modeling for the Province of Saskatchewan from March 2020-April 2021.


Machine Learning: Algorithms, Models, and Applications

arXiv.org Artificial Intelligence

Recent times are witnessing rapid development in machine learning algorithm systems, especially in reinforcement learning, natural language processing, computer and robot vision, image processing, speech, and emotional processing and understanding. In tune with the increasing importance and relevance of machine learning models, algorithms, and their applications, and with the emergence of more innovative uses cases of deep learning and artificial intelligence, the current volume presents a few innovative research works and their applications in real world, such as stock trading, medical and healthcare systems, and software automation. The chapters in the book illustrate how machine learning and deep learning algorithms and models are designed, optimized, and deployed. The volume will be useful for advanced graduate and doctoral students, researchers, faculty members of universities, practicing data scientists and data engineers, professionals, and consultants working on the broad areas of machine learning, deep learning, and artificial intelligence.


Defying the odds!

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The phrase "overcoming the odds" is an understatement for 24-year-old Joshua Burgess. Though born with congenital rubella syndrome, which has caused him to suffer from a number of health challenges over the years, he continues to break barriers. On September 28, Burgess participated in the prestigious UNESCO Information for All Programme's (IFAP) Second Artificial Intelligence for Information Accessibility (AI4IA) Conference, where he spoke about'Openness and Inclusivity for the Disabled Community in a New Era'. "My presentation reflected my views as a young, blind Jamaican also living with chronic hearing loss. It was important for me to note that, while I have benefited from artificial intelligence's (AI) ability to help me integrate into society, it is also important for us to recognise that it is not a one-size-fits-all. We must collaborate with key stakeholders to ensure openness, inclusivity, fairness, and accessibility for everyone," said Burgess.


COVID-19: quality of life and artificial intelligence

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Bongs Lainjo Cybermatic International, Montréal, QC, Canada Correspondence: Bongs Lainjo Email [email protected] Abstract: The objective of the study is to conduct an exploratory review of the Covid-19 pandemic by focusing on the theme of Covid-19 pandemic morbidity and mortality, considering the dynamics of artificial intelligence and quality of life (QOL). The methods used in this research paper include a review of literature, anecdotal evidence, and reports on the morbidity of COVID-19, including the scope of its devastating effects in different countries such as the US, Africa, UK, China, and Brazil, among others. The findings of this study suggested that the devastating effects of the coronavirus are felt across different vulnerable populations. These include the elderly, front-line workers, marginalized communities, visible minorities, and more. The challenge in Africa is especially daunting because of inadequate infrastructure, and financial and human resources, among others. Besides, AI technology is being successfully used by scientists to enhance the development process of vaccines and drugs. However, its usage in other stages of the pandemic has not been adequately explored. Ultimately, it has been concluded that the effects of the Covid-19 are producing unprecedented and catastrophic outcomes in many countries. With a few exceptions, the common and current intervention approach is driven by many factors, including the compilation of relevant reliable and compelling data sets. On a positive note, the compelling trailblazing and catalytic contributions of AI towards the rapid discovery of COVID-19 vaccines are a good indication of future technological innovations and their effectiveness. History has a way of reminding us that while the good times are great, a business as usual comes with many unforeseen risks and challenges. On a positive note, stress, anxiety, and other mental health issues have turned around many mindsets in certain groups. There are now significant and unprecedented levels of compassion, empathy, and more, originating from many populations. One such instance, wherein significant challenges were posed to the community is at the time of the First World War. Besides, there was the Spanish plague, there was the second world war and for the last 60 plus years, we have had to live in a world of misgivings; ranging from populism to political unrests and instability in several parts of the world, primarily the Middle East and some parts of Asia.


Israeli startup wins IBM top prize to Zzapp out malaria by mapping water sources

#artificialintelligence

ZzappMalaria, a Jerusalem-based startup whose mobile app aims to help identify potential sources of malaria, has won a first prize of $3 million in the IBM Watson AI XPRIZE competition. The firm was also selected as the "Most Inspiring Team" in the People's Choice Award. The IBM Watson AI XPRIZE Challenge was launched in 2016 to promote the use of AI to solve the world's most pressing problems. Aifred Health, a Montreal-based digital health company focused on providing support for clinical decisions for mental health, won second place, getting a $1 million prize. Marinus Analytics, a Pittsburg, US-based firm that uses AI to quickly turn big data into actionable intelligence, helps fight human trafficking by saving hours and sometimes days of investigative time to find traffickers and recover victims.


Deep Learning Models for Early Detection and Prediction of the spread of Novel Coronavirus (COVID-19)

arXiv.org Machine Learning

SARS-CoV2, which causes coronavirus disease (COVID-19) is continuing to spread globally and has become a pandemic. People have lost their lives due to the virus and the lack of counter measures in place. Given the increasing caseload and uncertainty of spread, there is an urgent need to develop machine learning techniques to predict the spread of COVID-19. Prediction of the spread can allow counter measures and actions to be implemented to mitigate the spread of COVID-19. In this paper, we propose a deep learning technique, called Deep Sequential Prediction Model (DSPM) and machine learning based Non-parametric Regression Model (NRM) to predict the spread of COVID-19. Our proposed models were trained and tested on novel coronavirus 2019 dataset, which contains 19.53 Million confirmed cases of COVID-19. Our proposed models were evaluated by using Mean Absolute Error and compared with baseline method. Our experimental results, both quantitative and qualitative, demonstrate the superior prediction performance of the proposed models.


Integrated Time Series Summarization and Prediction Algorithm and its Application to COVID-19 Data Mining

arXiv.org Machine Learning

This paper proposes a simple method to extract from a set of multiple related time series a compressed representation for each time series based on statistics for the entire set of all time series. This is achieved by a hierarchical algorithm that first generates an alphabet of shapelets based on the segmentation of centroids for clustered data, before labels of these shapelets are assigned to the segmentation of each single time series via nearest neighbor search using unconstrained dynamic time warping as distance measure to deal with non-uniform time series lenghts. Thereby, a sequence of labels is assigned for each time series. Completion of the last label sequence permits prediction of individual time series. Proposed method is evaluated on two global COVID-19 datasets, first, for the number of daily net cases (daily new infections minus daily recoveries), and, second, for the number of daily deaths attributed to COVID-19 as of April 27, 2020. The first dataset involves 249 time series for different countries, each of length 96. The second dataset involves 264 time series, each of length 96. Based on detected anomalies in available data a decentralized exit strategy from lockdowns is advocated.


Large expert-curated database for benchmarking document similarity detection in biomedical literature search

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Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations.