Online Social Networks (OSN) are privileged observation channels for understanding the geospatial facets of many real-world phenomena [1]. Unfortunately, in most cases OSN content lacks explicit and structured geographic information, as in the case of Twitter, where only a minimal fraction (1% to 4%) of messages are natively geotagged [2]. This shortage of explicit geographic information drastically limits the exploitation of OSN data in geospatial Decision Support Systems (DSS) [3]. Conversely, the prompt availability of geotagged content would empower existing systems and would open up the possibility to develop new and better geospatial services and applications [4, 5]. As a practical example of this kind, several social media-based systems have been proposed in recent years for mapping and visualizing situational information in the aftermath of mass disasters - a task dubbed as crisis mapping - in an effort to augment emergency response [6, 7]. These systems, however, demand geotagged data to be placed on crisis maps, which in turn imposes to perform the geoparsing task on the majority of social media content. Explicit geographic information is not only needed in early warning [8, 9] and emergency response systems [10, 11, 12, 13, 14], but also in systems and applications for improving event promotion [15, 16], touristic planning [17, 18, 19], healthcare accessibility [20], news aggregation [21] Post-print of the article published in Decision Support Systems 136, 2020. Please refer to the published version: doi.org/10.1016/j.dss.2020.113346

In the last 5 years, the availability of large audio datasets in African countries has opened unlimited opportunities to build machine intelligence (MI) technologies that are closer to the people and speak, learn, understand, and do businesses in local languages, including for those who cannot read and write. Unfortunately, these audio datasets are not fully exploited by current MI tools, leaving several Africans out of MI business opportunities. Additionally, many state-of-the-art MI models are not culture-aware, and the ethics of their adoption indexes are questionable. The lack thereof is a major drawback in many applications in Africa. This paper summarizes recent developments in machine intelligence in Africa from a multi-layer multiscale and culture-aware ethics perspective, showcasing MI use cases in 54 African countries through 400 articles on MI research, industry, government actions, as well as uses in art, music, the informal economy, and small businesses in Africa. The survey also opens discussions on the reliability of MI rankings and indexes in the African continent as well as algorithmic definitions of unclear terms used in MI.

Africa is home to the world's fastest growing population, which is expected to double by 2050. This growth is directly linked to the increase in demand for energy – indeed the African Energy Chamber projects that the continent's demand for power will keep rising between 4-5% per year, possibly doubling by 2050. A reversal of fortune for the world's unelectrified population is one of the Sustainable Development Goals of the United Nations (SDG7). African governments have traditionally relied on centralised grid expansion to improve electricity access. This requires significant capital expenditure and is often not time or cost effective, especially in rural areas where much of Africa's unelectrified population live. At the same time, the Paris Agreement enshrines the global aim to achieve Net Zero in the next 3 decades in order to meet the goal of keeping global temperature rise well below 2 degrees Celsius above pre-industrial levels.

The BBC is launching 11 new language services and one of them is English-based Pidgin, which is one of the most widely spoken languages across West Africa, even though it is not officially recognised. The Oxford English Dictionary definition of Pidgin is: A language containing lexical and other features from two or more languages, characteristically with simplified grammar and a smaller vocabulary than the languages from which it is derived, used for communication between people not having a common language; a lingua franca. Simply put, Pidgin English is a mixture of English and local languages which enables people who do not share a common language to communicate. Most African countries are made up of numerous different ethnic groups who do not necessarily have a lingua franca, so Pidgin has developed. It is widely spoken in Nigeria, Ghana, Equatorial Guinea and Cameroon.