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Context and background Multidimensional Poverty (MP) considers poverty in multiple dimensions of deprivations such as health, education, energy, the standard of living and access to basic services. MP remains a major challenge in Africa, with a large proportion of the population living in MP. According to United Nations Development Programme (UNDP), Africa has shown the highest Multidimensional Poverty Index (MPI) having over 40% of its population living in MP. Goal and Objectives: This paper is a review, aimed at assessing the potential of the integration of satellite and Artificial Intelligence (AI) in mapping MP, with a specific focus on Africa. Methodology: Based on the reviews of past studies, the combination of satellite data such as nighttime light, daytime satellite imagery and high-resolution settlement data in combination with techniques such as field surveys, statistical correlation models (transfer learning) and AI (deep learning) has been applied in mapping MP. Results: The findings from studies show that the combination of satellite data and AI has the capability of providing more accurate and granular MP maps, compared to the traditional approach. Again, this paper explains the concept of MP with a specific focus on Africa and presents a map depicting the current MPI in African countries. Finally, pitfalls especially in the accuracy, granularity and frequency of MP data were identified. Consequently, the satellite and AI approaches are recommended for more accurate, frequent, cost-effective and granular data, required in mapping poverty and design of interventions that effectively address the needs of the vulnerable populations in Africa. [ABSTRACT FROM AUTHOR]

Context and background The incorporation of artificial intelligence (AI) into businesses has become increasingly widespread in recent years, particularly in developed countries, fundamentally reshaping the landscape of entrepreneurship and innovation. AI presents numerous opportunities for small businesses, including those involved in cross-border trade. It has the potential to transform various aspects of business operations, such as customer interactions, marketing research, understanding consumer behavior, optimizing routes, and forecasting sales and marketing trends. Additionally, AI can contribute to the formalization of business processes, and assist these businesses in adapting more effectively to the ever-changing dynamics of the market. However, the successful implementation of AI necessitates a meticulous assessment of AI applications to ensure alignment with the objectives and resources of each individual small business. Goal and Objectives: The primary focus of this paper is to explore the potential of Artificial Intelligence in enhancing Africa's cross-border trade. The paper will address the following research questions: What are the effective applications of artificial intelligence in cross-border informal trade in Africa, and what are the limitations? What does the future hold for African countries in terms of utilizing artificial intelligence for cross-border trade? How can African informal cross-border traders and their stakeholders benefit from incorporating artificial intelligence in their activities? Methodology: The research is purely empirical research based on observation and measurement of phenomena, as directly experienced by the researcher. Results: AI can be effectively applied by Informal Cross-Border Traders (ICBT) to enhance their businesses and enhance competitiveness. There are several AI applications accessible to ICBT within their operational context. Although the adoption and utilization of AI in Africa are still in their infancy, there is considerable promise for the future. Africans must address the challenges hindering the adoption and utilization of AI, as technology is advancing rapidly, and opportunities await those who embrace it. [ABSTRACT FROM AUTHOR]

The main purpose of this paper is to investigate the level of adoption of artificial intelligence(AI) to support library services delivery in university libraries in Africa. Qualitative research method was adopted to collect data. A preliminary survey of 102 university libraries in English speaking countries in Africa was conducted to identify the university libraries that have adopted AI in their libraries. Content analysis was used to analyse the responses. The study found that the only few university libraries in Africa have adopted AI technologies such as Chatbot, ChatGPT, LibKey from ThirdIron, robots, RFID technology and Grammarly. These AI technologies are used to render different library services like answering of directional and ready reference questions posed by library users, serving as a knowledge base for cataloguing information of library materials, self-check-out machine for books, used as a marketing tool for the library, tool for statistics evaluation and recommendations, assisting in the charging and discharging of library materials, etc. Lack of funds to acquire the AI tools, training of librarians, and lack of full knowledge of AI were the most mentioned challenges associated with adoption of AI in the libraries. The study recommended a formulation of policy to guide the adoption of new technologies such as AI, and training and re-training of librarians through workshops equip librarians with skills needed to effectively use the AI technologies. [ABSTRACT FROM AUTHOR]

The Nguni term inkaba refers both to the umbilical cord and the navel. In this paper, we use the idea of the umbilical cord to explain an enduring intellectual connection that many African scholars feel towards African epistemes. By drawing on the term inkaba, the paper explores the idea of "umbilical sense" as an intellectual instinct that one develops in relation to a range of knowledges that have been marginalised. This sense speaks to the intellectual orientation drawn from growing and developing as persons within indigenous communities. We argue that the desire and ability to respond to the conceptual shortcomings in Western academe stems from an umbilical sense, or a navel sense – ukuzwa ngenkaba in Nguni languages. Drawing on African writers such as Mazisi Kunene and Ifeanyi Menkiti, we propose that the acquisition of knowledge forms part of an African conception of personhood that links one to many corpuses of historical knowledges. Knowledge thus forms part of how we emerge as persons and, in turn, make other persons. We explore the implications of our concept for current debates on the decolonisation of knowledge and the meaning of post-human knowledge with the emergence of artificial intelligence. [ABSTRACT FROM AUTHOR]

The role of libraries in preparing students to thrive during their studies and innovate after university is growing in importance. Information is more easily accessible through digital channels and is increasingly abundant. Generative Artificial intelligence (AI) adds to this reality and increases the need for digitally driven information literacy skills. This paper aims to guide librarians by discussing the digitalization of information creation, retrieval, and use. It recommends the training of both digital and information literacy for students. Librarians are called upon to provide clear guidelines to their universities to steer the use of generative AI. The implications of digital information sources and generative AI are discussed with the role of librarians in context. Information literacy and digital literacy are related. Academic libraries should include digital topics in information literacy training. Information literacy should be trained at the start of university education and before students begin dissertation writing. University libraries should propose guidelines for the use of generative artificial intelligence tools by students. [ABSTRACT FROM AUTHOR]

Large scale databases are critical for helping scientists decipher long-term patterns in human evolution. This paper describes the conception and development of such a research database and illustrates how big data can be harnessed to formulate new ideas about the past. The Role of Culture in Early Expansions of Humans (ROCEEH) is a transdisciplinary research center whose aim is to study the origins of culture and the multifaceted aspects of human expansions across Africa and Eurasia over the last three million years. To support its research, the ROCEEH team developed an online tool named the ROCEEH Out of Africa Database (ROAD) and implemented its web-based applications. ROAD integrates geographical data as well as archaeological, paleoanthropological, paleontological and paleobotanical content within a robust chronological framework. In fact, a unique feature of ROAD is its ability to dynamically link scientific data both spatially and temporally, thereby allowing its reuse in ways that were not originally conceived. The data stem from published sources spanning the last 150 years, including those generated by the research team. Descriptions of these data rely on the development of a standardized vocabulary and profit from online explanations of each table and attribute. By synthesizing legacy data, ROAD facilitates the reuse of heritage data in novel ways. Database queries yield structured information in a variety of interoperable formats. By visualizing data on maps, users can explore this vast dataset and develop their own theories. By downloading data, users can conduct further quantitative analyses, for example with Geographic Information Systems, modeling programs and artificial intelligence. In this paper, we demonstrate the innovative nature of ROAD and show how it helps scientists studying human evolution to access datasets from different fields, thereby connecting the social and natural sciences. Because it permits the reuse of "old" data in new ways, ROAD is now an indispensable tool for researchers of human evolution and paleogeography. [ABSTRACT FROM AUTHOR]

AI technology is capturing the African imaginations as a gateway to progress and prosperity. There is a growing interest in AI by different actors across the continent including scientists, researchers, humanitarian and aid organizations, academic institutions, tech start-ups, and media organizations. Several African states are looking to adopt AI technology to capture economic growth and development opportunities. On the other hand, African researchers highlight the gap in regulatory frameworks and policies that govern the development of AI in the continent. They argue that this could lead to AI technology exacerbating problems of inequalities and injustice in the continent. However, most of the literature on AI ethics is biased toward Euro-American perspectives and lack the understanding of how AI development is apprehended in the Global South, and particularly Africa. Drawing on the case study of the first African Master's in Machine Intelligence program, this paper argues for looking beyond the question of ethics in AI and examining AI governance issues through the analytical lens of the raciality of computing and the political economy of technoscience to understand AI development in Africa. By doing so, this paper seeks a different theorization for AI ethics from the South that is based on lived experiences of those in the margins and avoids the framings of technological futures that simplistically pathologize or celebrate Africa. [ABSTRACT FROM AUTHOR]

This paper offers an overview of some of the highlights of the 2023 NISO Plus Annual Conference that was held virtually from February 14–February 16, 2023. This was the fourth such conference and the third to be held in a completely virtual format due to the global impact of COVID-19. These conferences have resulted from the merger of NISO and the National Federation of Abstracting and Information Services (NFAIS) in June 2019, replacing the NFAIS Annual Conferences and offering a new, more interactive format. As with prior years, there was no general topical theme (although the importance of metadata was a common thread throughout), but there were topics of interest for everyone working in the information ecosystem - from the practical subjects of standards, archiving, digital preservation, Open Science, and Open Access. With speakers and attendees from around the world and across multiple time zones and continents, it truly was a global conversation! [ABSTRACT FROM AUTHOR]

Solar energy-based technologies have developed rapidly in recent years, however, the inability to appropriately estimate solar energy resources is still a major drawback for these technologies. In this study, eight different artificial intelligence (AI) models namely; convolutional neural network (CNN), artificial neural network (ANN), long short-term memory recurrent model (LSTM), eXtreme gradient boost algorithm (XG Boost), multiple linear regression (MLR), polynomial regression (PLR), decision tree regression (DTR), and random forest regression (RFR) are designed and compared for solar irradiance prediction. Additionally, two hybrid deep neural network models (ANN-CNN and CNN-LSTM-ANN) are developed in this study for the same task. This study is novel as each of the AI models developed was used to estimate solar irradiance considering different timesteps (hourly, every minute, and daily average). Also, different solar irradiance datasets (from six countries in Africa) measured with various instruments were used to train/test the AI models. With the aim to check if there is a universal AI model for solar irradiance estimation in developing countries, the results of this study show that various AI models are suitable for different solar irradiance estimation tasks. However, XG boost has a consistently high performance for all the case studies and is the best model for 10 of the 13 case studies considered in this paper. The result of this study also shows that the prediction of hourly solar irradiance is more accurate for the models when compared to daily average and minutes timestep. The specific performance of each model for all the case studies is explicated in the paper. [ABSTRACT FROM AUTHOR]

Changes in average wage are usually attributed to technological change in the past industrial revolutions. With the emergence and gradual explosion of artificial intelligence (AI) in the 4th industrial revolution, this paper applies panel vector autoregressive technique, with annual data from the World Bank and global economy from 2004 to 2017, to examine the effect of artificial intelligence on average wages in Southern Africa. Findings from the study show that artificial intelligence has a significant negative relationship with average wages but is positively associated with gross domestic product per capita (GDPC), unemployment and inflation. The study also finds inflation and GDPC to be positively associated with average wage. Based on the finding, policy direction focusing on wage stabilisation, redistribution of income, advance learning and skill development training that promote competitiveness to computerisation is recommended. [ABSTRACT FROM AUTHOR]

Through document analysis and interviews, this paper examines the patent narratives of Transsion, a Chinese company dominating Africa's smartphone market and a leading innovator in facial recognition technologies (FRTs) optimized for darker skin tones. We identify two major narratives concerning Transsion's FRT patenting practice. First is 'empowerment', through which Transsion argues that there are 'blind spots' in conventional AI technologies and presents its AI camera as a remedy and an empowerment tool for dark-skinned users by 'seeing' their beauty. Second is the 'warfare' narrative, which is shaped by the heightening market competition and accelerating patent races among Chinese phone makers. As the battle for tech supremacy intensifies in Africa, Transsion expresses a strong sense of crisis and considers its FRT patents as 'weapons of competition' in preparation for a future smartphone warfare in Africa. This study makes two contributions. Empirically, through analyzing patents, we examine a relatively less-known Chinese tech company that has tremendous impact in the Global South. Theoretically, we interrogate the possibility of algorithmic empowerment against racist AI and technological independence through patents, although developing AI as weaponry, in the China-Africa context, also hampers the politics of decolonization. [ABSTRACT FROM AUTHOR]

• Agriculture productivity in Africa and most developing countries are far from optimum. • Digitalization is a potential game changer for agricultural productivity. • Sensors, IoT, AI, and smartphones are key for Africa's agricultural digitalization. • Digitalization could strengthen African food security and globally. • Infrastructure and policy challenges digitalization on the continent. Africa's agriculture has over time, improved and expanded in scale and efficiency, yet its productivity is far from its optimum, and the majority of the continent is still food insecure. To harness its potential for a drastic increase to avoid continued mass hunger and support the bioeconomy's growth, farm productivity in Africa must be retooled. This paper examines the role of digitalization as a potential game changer and an impetus for a transformational future for large-scale and smallholder farming and livelihoods through driving technological and methodological innovation and automation. Digitalization is seen here as consisting of four supporting elements: sensors (in situ or remote), wireless transmission of data, artificial intelligence (AI), and transmission of diagnoses and or recommendations to the end user. In addition to strengthening food security on the continent and globally and the ensuing economic benefits, other cross-cutting effects are presented. These include risk management through early warning systems for natural disasters, improved decision-making with big data, reduction in rural-urban migration by the attraction of youth towards agriculture, and the improvement in environmental stewardship and building climate resilience. Challenges to realizing the benefits of digitalization, including lack of infrastructure and human resource expertise and the role of public-private partnerships in developing solutions, are also addressed. [ABSTRACT FROM AUTHOR]