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Artificial Intelligence in Africa: These are the top 5 in-demand technologies in 2021 – AfricaBusiness.com

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Its widely accepted that artificial intelligence (AI) technologies will add trillions to global GDP in the next 20 years, making it the one of the world's most powerful technology trends on par with the disruption and opportunities being created by cloud computing and blockchain. So is Africa getting a slice of the lucrative artificial intelligence pie and what are the current AI adoption trends in the region? Although Africa's AI industry is still relatively small compared to the US, Europe and Asia, this has not stopped some of the continent's most innovative start-ups from developing solutions that demonstrate how promising the technology can be for the African economy. However, AI innovation in Africa is often ignored or overlooked because the number of patents applied for and the amount of research funding available is not well aligned with local contexts, data is missing, and the map still looks essentially bleak. That said, the prospects for AI in Africa are positive, as the potential for innovation and growth in artificial intelligence (AI) adoption is increasing.


Global Deep Learning Market Research Report – SoccerNurds

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WMR-Western Market Research has recently published a comprehensive and exclusive research report, which is an intelligent study covering all key segments. This research report provides breakthrough inputs and insights on market related factors like size, competition, trends, analysis, forecasts etc. The study encompasses primary and secondary data sources along with quantitative and qualitative practices thus assuring data accuracy. Introspective Market Research Predicts that Deep Learning Market was valued USD xxxx unit in 2020 and is expected to reach USD xxxx Unit by the year 2025, growing at a CAGR of xx% globally. Global Deep Learning Market Overview: Global Deep Learning Market Report 2020 comes with the extensive industry analysis of development components, patterns, flows and sizes.


Artificial Intelligence and energy justice in Africa

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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.


Building AI for the Global South

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Harm wrought by AI tends to fall most heavily on marginalized communities. In the United States, algorithmic harm may lead to the false arrest of Black men, disproportionately reject female job candidates, or target people who identify as queer. In India, those impacts can further impact marginalized populations like Muslim minority groups or people oppressed by the caste system. And algorithmic fairness frameworks developed in the West may not transfer directly to people in India or other countries in the Global South, where algorithmic fairness requires understanding of local social structures and power dynamics and a legacy of colonialism. That's the argument behind "De-centering Algorithmic Power: Towards Algorithmic Fairness in India," a paper accepted for publication at the Fairness, Accountability, and Transparency (FAccT) conference, which begins this week. Other works that seek to move beyond a Western-centric focus include Shinto or Buddhism-based frameworks for AI design and an approach to AI governance based on the African philosophy of Ubuntu.


Global Lega-Tech Artificial Intelligence Market Economic Outlook, Market Structure Analysis,Forecast from 2021-2025 – NeighborWebSJ

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The information presented in Lega-Tech Artificial Intelligence Market Report 2021 includes qualitative and quantitative insights. Under the qualitative analysis part, manufacturing base, raw materials data, Lega-Tech Artificial Intelligence status, trends, SWOT analysis, PESTEL Analysis, distribution channels, driving factors, and a competitive structure is presented. Under the qualitative analysis part, market value/volume, production analysis, consumption data, import-export data, or each region and country are explained. Also, industry size by Lega-Tech Artificial Intelligence type, application, demand and supply scenario, and economic status are explained. Also, comprehensive information on the latest product development, growth opportunities, industry strategies, cost structures, and recent policies are enlightened in the Lega-Tech Artificial Intelligence report.


Extinction of larger animals led to the human brain doubling in size around 30,000 years ago

Daily Mail - Science & tech

The extinction of large animals led to the human brain growing, a new study reveals. When humans first emerged in Africa 2.6 million years ago the average animal size was more than 1,000 pounds, making them easy prey. Throughout the Pleistocene era, creatures' sizes decreased by 90 percent, which forced our ancient ancestors to developing cunning and bold methods to capture their next meal. As they shifted to hunting small, swift prey animals, humans developed higher cognitive abilities and experienced a growth of brain volume from 650cc to 1,500cc. When humans first emerged in Africa 2.6 million years ago the average animal size was more than 1,000 pounds, making them easy prey Previous research shows that early humans survived by hunting large game, which provided them with the necessary fat and sources of energy to survive.


Developing an Artificial Intelligence for Africa strategy

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Africa has a unique opportunity to develop its competitiveness through artificial intelligence (AI). From agriculture and remote health to translating the 2,000-odd languages spoken across the continent, AI can help tackle the economic problems that Africa faces. Africa faces several known challenges in developing AI such as a dearth of investment, a paucity of specialised talent, and a lack of access to the latest global research. These hurdles are being whittled down, albeit slowly, thanks to African ingenuity and to investments by multinational companies such as IBM Research, Google, Microsoft, and Amazon, which have all opened AI labs in Africa. Innovative forms of trans-continental collaboration such as Deep Learning Indaba (a Zulu word for gathering), which is fostering a community of AI researchers in Africa, and Zindi, a platform that challenges African data scientists to solve the continent's toughest challenges, are gaining ground, buoyed by the recent "homecoming" of several globally-trained African experts in AI.


AI researchers detail obstacles to data sharing in Africa

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AI researchers say data sharing is a key part of economic growth in Africa but that it faces a number of common obstacles, including the threat of data colonialism. The African data market is expected to grow steadily in the coming years, and the African Data Centre trade organization predicts the African data market will need hundreds of new datacenters to meet demand in the coming decade. In a paper titled "Narratives and Counternarratives on Data Sharing in Africa," the research team lays out structural problems including but limited to financial or infrastructure problems. Coauthors argue that failure to consider ethical concerns associated with those obstacles could cause irreparable harm. "Currently, a significant proportion of Africa's digital infrastructure is controlled by Western technology powers, such as Amazon, Google, Facebook, and Uber," the paper reads.


Artificial intelligence presents a moral dilemma - The Mail & Guardian

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Since the outbreak of the pandemic, the world has grown increasingly reliant on artificial intelligence (AI) technologies. Thousands of new innovations -- from contact-tracing apps to the drones delivering medical equipment -- sprang up to help us meet the challenges of Covid-19 and life under lockdown. The unprecedented speed with which a vaccine for Covid-19 was discovered can partly be attributed to the use of AI algorithms which rapidly crunched the data from thousands of clinical trials, allowing researchers around the world to compare notes in real time. As Satya Nadella, the chief executive of Microsoft observed, in just two months, the world witnessed a rate of digital transition we'd usually only see in two years. In 2017, PWC published a study showing that adoption of AI technologies could increase global GDP by 14% by 2030. In addition to creating jobs and boosting economies, AI technologies have the potential to drive sustainable development and even out inequalities, democratising access to healthcare and education, mitigating the effects of climate change and making food production and distribution more efficient.


Seroprevalence of anti-SARS-CoV-2 IgG antibodies in Kenyan blood donors

Science

By the end of July 2020, Kenya h ad reported only 341 deaths and ∼20,000 cases of COVID-19. This is in marked contrast to the tens of thousands of deaths reported in many higher-income countries. The true extent of COVID-19 in the community was unknown and likely to be higher than reports indicated. Uyoga et al. found an overall seroprevalence among blood donors of 4.3%, peaking in 35- to 44-year-old individuals (see the Perspective by Maeda and Nkengasong). The low mortality can be partly explained by the steep demographics in Kenya, where less than 4% of the population is 65 or older. These circumstances combine to result in Kenyan hospitals not currently being overwhelmed by patients with respiratory distress. However, the imposition of a strict lockdown in this country has shifted the disease burden to maternal and child deaths as a result of disruption to essential medical services. Science , this issue p. [79][1]; see also p. [27][2] The spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in Africa is poorly described. The first case of SARS-CoV-2 in Kenya was reported on 12 March 2020, and an overwhelming number of cases and deaths were expected, but by 31 July 2020, there were only 20,636 cases and 341 deaths. However, the extent of SARS-CoV-2 exposure in the community remains unknown. We determined the prevalence of anti–SARS-CoV-2 immunoglobulin G among blood donors in Kenya in April–June 2020. Crude seroprevalence was 5.6% (174 of 3098). Population-weighted, test-performance-adjusted national seroprevalence was 4.3% (95% confidence interval, 2.9 to 5.8%) and was highest in urban counties Mombasa (8.0%), Nairobi (7.3%), and Kisumu (5.5%). SARS-CoV-2 exposure is more extensive than indicated by case-based surveillance, and these results will help guide the pandemic response in Kenya and across Africa. [1]: /lookup/doi/10.1126/science.abe1916 [2]: /lookup/doi/10.1126/science.abf8832