The computer algorithm, which was trained using mammography images from almost 29,000 women, was shown to be as effective as human radiologists in spotting cancer. At a time when health services around the world are stretched as they deal with long backlogs of patients following the pandemic, this sort of technology can help ease bottlenecks and improve treatment. For malaria, a handheld lab-on-a-chip molecular diagnostics systems developed with AI could revolutionize how the disease is detected in remote parts of Africa. The project, which is led by the Digital Diagnostics for Africa Network, brings together collaborators such as MinoHealth AI Labs in Ghana and Imperial's Global Development Hub. This technology could help pave the way for universal health coverage and push us towards achieving SDG3.

This book constitutes the refereed post-conference proceedings of the First International Conference on Pan-African Intelligence and Smart Systems, PAAISS 2021, which was held in Windhoek, Namibia, in September 2021. The 17 revised full papers presented were carefully selected from 41 submissions. The theme of PAAISS 2021 was "Advancing AI research in Africa" and the papers are arranged according to subject areas: Deep Learning; Classification and Pattern Recognition; Neural Networks and Support Vector Machines; Smart Systems.

Givaudan has developed Consumer Foresight, a new tool that aims to help its customers co-create and innovate. This'futurescaping' platform will leverage big data, artificial intelligence and Givaudan's'deep expertise' of the food and beverage sector. It is a step beyond the trend forecasting models of today, Taste & Wellbeing President Louie D'Amico believes. "Most trend forecasting models largely focus on understanding the past and the present. Customer Foresight will be more predictive, with an ability to create potential future scenarios of what should be possible tomorrow to shape the future of food," he told FoodNavigator.

A doctor can't tell if somebody is Black, Asian, or white, just by looking at their X-rays. The study found that an artificial intelligence program trained to read X-rays and CT scans could predict a person's race with 90 percent accuracy. But the scientists who conducted the study say they have no idea how the computer figures it out. "When my graduate students showed me some of the results that were in this paper, I actually thought it must be a mistake," said Marzyeh Ghassemi, an MIT assistant professor of electrical engineering and computer science, and coauthor of the paper, which was published Wednesday in the medical journal The Lancet Digital Health. "I honestly thought my students were crazy when they told me."

The Burrows-Wheeler Transform (BWT) is an invertible text transformation that permutes symbols of a text according to the lexicographical order of its suffixes. BWT is the main component of popular lossless compression programs (such as bzip2) as well as recent powerful compressed indexes (such as the r-index7), central in modern bioinformatics. The compressibility of BWT is quantified by the number r of equal-letter runs in the output. Despite the practical significance of BWT, no nontrivial upper bound on r is known. By contrast, the sizes of nearly all other known compression methods have been shown to be either always within a poly-log n factor (where n is the length of the text) from z, the size of Lempel–Ziv (LZ77) parsing of the text, or much larger in the worst case (by an nε factor for ε 0). In this paper, we show that r (z log2 n) holds for every text. This result has numerous implications for text indexing and data compression; in particular: (1) it proves that many results related to BWT automatically apply to methods based on LZ77, for example, it is possible to obtain functionality of the suffix tree in (z polylog n) space; (2) it shows that many text processing tasks can be solved in the optimal time assuming the text is compressible using LZ77 by a sufficiently large polylog n factor; and (3) it implies the first nontrivial relation between the number of runs in the BWT of the text and of its reverse. In addition, we provide an (z polylog n)-time algorithm converting the LZ77 parsing into the run-length compressed BWT. To achieve this, we develop several new data structures and techniques of independent interest. In particular, we define compressed string synchronizing sets (generalizing the recently introduced powerful technique of string synchronizing sets11) and show how to efficiently construct them. Next, we propose a new variant of wavelet trees for sequences of long strings, establish a nontrivial bound on their size, and describe efficient construction algorithms. Finally, we develop new indexes that can be constructed directly from the LZ77 parsing and efficiently support pattern matching queries on text substrings. Lossless data compression aims to exploit redundancy in the input data to represent it in a small space.

This is my last editorial as Editor-in-Chief of Communications,a so it is a moment to share learnings and, of course, to reflect on accomplishments. First, we launched the Regional Special Sections (RSS) in November 2018 with a spotlight on computing in the China Region. With 40 pages of articles, spanning tech idols to gaming to computing culture to fintech and "superAI," the first RSS created an excitement that inspired and challenged co-hosts of the Europe, India, East Asia and Oceania, Latin America, and Arabia Regions. In just three years, we have circumnavigated the globe,b and with the second Europe Region Section (April 2022) and India Region Section (November 2022), a new circuit is well under way! The RSS are an exciting read for the ACM community (great job by the co-hosts and authors), delivering news insights and perspectives into how computing is shaping and being shaped around the world.

I always thought of history more like a movie than a series of real events. I'd seen people all over the internet creating similar images – colorized Roman statues, colorized photos; and I wanted to give it a shot," Saladin wrote on her blog.

Seven years have passed since world leaders met in New York and agreed on 17 Sustainable Development Goals (SDGs) to solve major challenges such as poverty, hunger, inequality, climate change and health. The pandemic has undoubtedly diverted attention from some of these issues in the last couple of years. But even before COVID-19, the UN was warning that progress in meeting the SDGs was not advancing at the speed or scale needed. Greeting them in 2030 will be difficult. The pandemic has demonstrated like nothing else the power of working collaboratively, across borders, for the benefit of society.

InstaDeep is an EMEA leader in delivering decision-making AI products. Leveraging their extensive know-how in GPU-accelerated computing, deep learning, and reinforcement learning, they have built products, such as the novel DeepChain platform, to tackle the most complex challenges across a range of industries. InstaDeep has also developed collaborations with global leaders in the AI ecosystem, such as Google DeepMind, NVIDIA, and Intel. They are part of Intel's AI Builders program and are one of only 2 NVIDIA Elite Service Delivery Partners across EMEA. The InstaDeep team is made up of approximately 155 people working across its network of offices in London, Paris, Tunis, Lagos, Dubai, and Cape Town, and is growing fast.

Air pollution is a major public health problem worldwide although the lack of data is a global issue for most low and middle income countries. Ambient air pollution in the form of fine particulate matter (PM2.5) exceeds the World Health Organization guidelines in Rwanda with a daily average of around 42.6 microgram per meter cube. Monitoring and mitigation strategies require an expensive investment in equipment to collect pollution data. Low-cost sensor technology and machine learning methods have appeared as an alternative solution to get reliable information for decision making. This paper analyzes the trend of air pollution in Rwanda and proposes forecasting models suitable to data collected by a network of low-cost sensors deployed in Rwanda.