Most research into wild marine mammals occurs in wealthy countries. Amazingly, in 2018, we have still have very little idea what species are present, let alone the population size / health status / behaviour, etc. in many parts of the world. A solid first step to address this problem is to conduct a rapid assessment survey to determine which species of marine mammals are present in a given area. The idea of a rapid assessment survey is fairly straightforward: you take a boat out and survey the entire coastline of a country using visual observers to record the number and species of any whales and dolphins encountered. As well as being large, surface present and often charismatic animals, and so possible to detect visually at relatively long ranges, dolphins and whales are also highly vocal, using sound to communicate and some species hunt/sense their surroundings with a sophisticated a bio-sonar.
When we think about endangered animals in Afrika at risk of extinction or being poached, we usually think of elephants and rhinos. This can be attributed to various factors including increased publicity around the increasing threats that rhinos and elephants face from poachers. However, there are other endangered animal species in Afrika that also require as much protection and publicity. Take the addax antelopes in Niger as an example. In 2016, the Sahara Conservation Fund (SCF) released their research report which stated there were likely only a handful of addax antelopes, specifically only 3, remaining in the wild in Niger.
A world built from accessible assets that drive human convenience and interaction. This is the future that's powered by the Internet of Things (IoT) and artificial intelligence (AI), two of the planet's hottest topic trends right now for a very good reason. They are also the fuel driving digital transformation in 2019. These are the technologies revolutionising performance, process and productivity. They are also transforming industry challenges across agriculture, retail, health and the public sector and are set to continue on this path well into 2019.
When U.N. member states unanimously adopted the 2030 Agenda in 2015, the narrative around global development embraced a new paradigm of sustainability and inclusion--of planetary stewardship alongside economic progress, and inclusive distribution of income. This comprehensive agenda--merging social, economic and environmental dimensions of sustainability--is not supported by current modes of data collection and data analysis, so the report of the High-Level Panel on the post-2015 development agenda called for a "data revolution" to empower people through access to information.1 Today, a central development problem is that high-quality, timely, accessible data are absent in most poor countries, where development needs are greatest. In a world of unequal distributions of income and wealth across space, age and class, gender and ethnic pay gaps, and environmental risks, data that provide only national averages conceal more than they reveal. This paper argues that spatial disaggregation and timeliness could permit a process of evidence-based policy making that monitors outcomes and adjusts actions in a feedback loop that can accelerate development through learning. Big data and artificial intelligence are key elements in such a process. Emerging technologies could lead to the next quantum leap in (i) how data is collected; (ii) how data is analyzed; and (iii) how analysis is used for policymaking and the achievement of better results. Big data platforms expand the toolkit for acquiring real-time information at a granular level, while machine learning permits pattern recognition across multiple layers of input. Together, these advances could make data more accessible, scalable, and finely tuned. In turn, the availability of real-time information can shorten the feedback loop between results monitoring, learning, and policy formulation or investment, accelerating the speed and scale at which development actors can implement change.
There have been many cases of misdiagnosis in Kenya. The Kenya Medical Practitioners and Dentists Board blames machines for these cases. However, this is misleading since machines do not interpret the results. This is the role of medical personnel. Ironically, machines powered by Artificial Intelligence (AI) are learning fast and becoming so clever that it will be easier for medics to make more accurate decisions.
Given that South African education is in crisis, strategies for improvement and sustainability of high-quality, up-to-date education must be explored. In the migration of education online, inclusion of machine translation for low-resourced local languages becomes necessary. This paper aims to spur the use of current neural machine translation (NMT) techniques for low-resourced local languages. The paper demonstrates state-of-the-art performance on English-to-Setswana translation using the Autshumato dataset. The use of the Transformer architecture beat previous techniques by 5.33 BLEU points. This demonstrates the promise of using current NMT techniques for African languages.
Based on 46 in-depth interviews with scientists, engineers, and CEOs, this document presents a list of concrete machine research problems, progress on which would directly benefit tech ventures in East Africa. The goal of this work is to give machine learning researchers a fuller picture of where and how their efforts as scientists can be useful. The goal is thus not to highlight research problems that are unique to East Africa -- indeed many of the problems listed below are of general interest in machine learning. The problems on the list are united solely by the fact that technology practitioners and organizations in East Africa reported a pressing need for their solution. The author is aware that listing machine learning problems without also providing data for them is not a recipe for getting those problems solved.
Researchers at the MIT Media Lab questioned people around the world about how driverless cars should be programmed to react if their brakes fail. There were stark cultural differences, according to the website Quartz's summary of the research. When forced to choose who survives an accident, people in car-happy Western countries were lukewarm about keeping pedestrians alive at the expense of passengers; they also strongly preferred -- as did people in African countries such as Kenya and South Africa -- to save children over old people. People in Eastern countries, such as China, strongly preferred to save elders over the young and pedestrians over car passengers.