Uganda


Machine Learning for a Low-cost Air Pollution Network

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We consider the example of a deployment of an air pollution monitoring network in Kampala, an East African city. Air pollution contributes to over three million deaths globally each year(Lelieveld and others, 2015). Kampala has one of the highest concentrations of fine particulate matter (PM 2.5) of any African city Mead (2017) Hence we know little about its distribution or extent. Lower cost devices do exist, but these do not, on their own, provide the accuracy required for decision makers. In our case study, the Kampala network of sensors consists largely of low cost optical particle counters (OPCs) that give estimates of the PM2.5 particulate concentration.


World's first AI health app in Swahili launches to tackle doctor shortages

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An innovative chat-bot that helps patients and doctors diagnose diseases ranging from malaria to diabetes has become the first health app to launch in Swahili. Developed by Ada Health, the app relies on artificial intelligence, large medical databases and personalised responses to assess an individual's symptoms, suggest a cause and recommend the next stage of treatment. The smartphone chat-bot is already used by roughly eight million people in more than 130 countries across the globe – published in languages including English, French and Spanish. But it has now become the first AI health application to launch in Swahili, a language spoken by almost 100 million people across East Africa – predominantly in Tanzania, Uganda and Kenya. According to Hila Azadzoy, the managing director of Ada's global health initiative, the expansion will help tackle a shortage of doctors and nurses in the region, where countries have fewer than one physician per 1,000 people on average.


Powered by Artificial Intelligence, smartphones can now ward off banana pests

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Banana, a nutritionally-rich, delicious fruit, is a widely-cultivated crop across the world and is a staple diet of people living in parts of Africa, Asia and Latin America. Due to pests and diseases, only 13% of the global production is traded, and often, farmers in India experience severe loss due to fusarium wilt or Panama disease. A novel innovation now aims to change the fortunes of banana growers by helping them detect diseases and pests with their smartphone. In a recent study, researchers from the USA, Democratic Republic of Congo, Uganda, Ethiopia and India have developed a banana pest detection app powered by artificial intelligence (AI). Artificial Intelligence is an emerging arena in computer science where machines are programmed to simulate human intelligence and perform tasks like speech recognition, visual perception, language translation and decision-making.


Google awards $25 million in global AI impact grants

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Google today awarded $25 million in grants to a range of organizations to help them apply machine learning to fight some of the world's biggest challenges. Recipients range from New York City's fire department, which wants to find ways to reduce emergency call response time, to an experiment to track air quality with sensors attached to mopeds in Uganda, information that may shape public policy. The program is also an extension of Google's AI for Social Good program, which provides flood forecasting to communities in India and is researching how to provide speech recognition for more people with disabilities. More than 2,600 applications were received since the contest was announced in October from 119 countries around the world, Google.org The news was announced today onstage at the Google I/O developer conference by CEO Sundar Pichai and AI head Jeff Dean.


App developers in Uganda use TensorFlow to spot armyworm damage in maize

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Fall armyworm, the larval life stage of a fall armyworm moth, impacts maize crops worldwide but particularly in countries like Uganda, where agricultural businesses employ 70% of the population. Studies show the potential impact is between 8.3 and 20.6 million tons per year, with the fallout amounting to between $2.48 million and $6.19 million per year. The threat of devastating losses prompted developers participating in a Google Developer Group in Mbale to create an Android app -- FlatButter -- that identifies signs of fall armyworm damage in maize crops. It's been featured on a national TV station in Uganda and highlighted by the Food Agricultural Organization of the United Nations, as well as by Google in a short film published today. "The vast damage and yield losses in maize production, due to FAW, got the attention of global organizations, who are calling for innovators to help," wrote Hansu Mobile and Intelligent Innovations CEO Nsubuga Hassan, who led the team that developed the app.


Nazirini's story - using machine learning to tackle crop disease

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Sign in to report inappropriate content. An incredible human story of the Ugandan developer, Nazirini Siraji, and the app created by her small team in Mbale, Uganda, with learning from their local Google Developer Group community. Harnessing the power of TensorFlow and Machine Learning, this free app helps farmers identify and treat Fall Armyworm, reducing the massive crop devastation currently impacting Uganda and Africa. Find your local GDG chapter https://goo.gle/33ER1Kd


Machine learning to identify persons at high-risk of HIV acquisition in rural Kenya and Uganda

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Between 2013-2017, 75% of residents in 16 communities in the SEARCH Study tested annually for HIV. In this population, we evaluated three strategies for using demographic factors to predict the one-year risk of HIV seroconversion: (1) membership in 1 known "Risk Group" (e.g., young woman or HIV-infected spouse); (2) a "Model-based" risk score constructed with logistic regression; (3) a "Machine Learning" risk score constructed with the Super Learner algorithm. We hypothesized Machine Learning would identify high-risk individuals more efficiently (fewer persons targeted for a fixed sensitivity) and with higher sensitivity (for a fixed number of persons targeted) than either other approach.


Machine Learning Engineer in London - WeFarm

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We are a unique social enterprise providing a vital service for the world's 500 million smallholder farmers who live and work without internet access. This pioneering, peer-to-peer platform enables farmers to access crowdsourced information by SMS, creating social impact on a groundbreaking scale and generating a game-changing data feed through the use of cutting edge AI techniques. In just one year WeFarm has scaled to more than 72,000 farmers across Kenya, Uganda and Peru, has facilitated over 11.5 million interactions and featured in the FT, Forbes, Wired.co.uk, as well as winning awards from Google's Impact Challenge, The Venture and the European Commission's Ideas From Europe. Would you like to change the world and create social impact on a global scale? Do you want every LOC you write to save livelihoods?


Microsoft releases 18M building footprints in Uganda and Tanzania to enable AI Assisted Mapping

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In the last ten years, 2 billion people were affected by disasters according to the World Disasters report 2018. In 2017, 201 million people needed humanitarian assistance and 18 million were displaced due to weather related disasters. Many of these disaster-prone areas are literally "missing" from the map, making it harder for first responders to prepare and deliver relief efforts. Since the inception of Tasking Manager, the Humanitarian OpenStreetMap Team (HOT) community has mapped at an incredible rate with 11 million square kilometers mapped in Africa alone. However, large parts of Africa with populations prone to disasters still remain unmapped -- 60% of the 30 million square kilometers.


Good for AI - Data Matters

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Artificial Intelligence is the biggest threat to mankind, right? Even if robots aren't taking over the planet by force, the yarn goes, computers will surely push us all into unemployment in the next decade or so. Let's meet someone who can give us a slightly different perspective. This is Joel, standing in front of his house, a few kilometers outside Gulu, Uganda, where he lives with his 14 brothers and sisters. Joel works for Zillow, the leading online real estate marketplace in the US with 1.1B of revenue in 2017.