Tanzania


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.


Blood test allows for rapid TB diagnosis

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Tuberculosis (TB) can now be identified in less than an hour thanks to a new blood test. The test procedure -- developed by The University of Queensland's Emeritus Professor Ian Riley in collaboration with researchers in Tanzania, India, Mexico and the Philippines -- is hoped to positively impact TB diagnosis in adults living in remote areas. "TB has been difficult to control because its symptoms are similar to many other diseases," Prof Riley said. "Other challenges include drug resistance to the disease and the high burden of HIV-positive cases in developing countries." Prof Riley explained that the discovery of the testing procedure came from using machine learning techniques to study three groups of adults who had a persistent cough for more than three weeks.


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.


New chatbot provides smoother, unified travel planning - Springwise

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Spotted: Eddy Travels is an AI-enabled personal travel assistant that operates within popular chat applications, such as WhatsApp, Facebook Messenger, Viber, Slack and Telegram. Based on the user's chat conversations, Eddy uses a language processing system that makes tailored travel recommendations. This enables to unify all booking needs to one place, from flights to hotels. Eddy even recommends personalized activities. For example, if your friend mentions taking a trip to Tanzania, Eddy could recommend a safe area to stay in, accommodation, the best travel insurance, tours, etc.


New chatbot provides smoother, unified travel planning - Springwise

#artificialintelligence

Spotted: Eddy Travels is an AI-enabled personal travel assistant that operates within popular chat applications, such as WhatsApp, Facebook Messenger, Viber, Slack and Telegram. Based on the user's chat conversations, Eddy uses a language processing system that makes tailored travel recommendations. This enables to unify all booking needs to one place, from flights to hotels. Eddy even recommends personalized activities. For example, if your friend mentions taking a trip to Tanzania, Eddy could recommend a safe area to stay in, accommodation, the best travel insurance, tours, etc.


DeepMind Loses $572M; KDD 2019 Best Papers; AI for Wildlife Conservation

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DeepMind's New AI Tracks Serengeti Herds from Images Alone DeepMind, the U.K.-based AI research subsidiary acquired by Alphabet in 2014 for $500 million, today detailed ecological research its science team is conducting to develop AI systems that'll help study the behavior of animal species in Tanzania's Serengeti National Park. They extend the popular BERT architecture to a multi-modal two-stream model, processing both visual and textual inputs in separate streams that interact through co-attentional transformer layers.


Artificial Intelligence (AI) Stats News: AI Augmentation To Create $2.9 Trillion Of Business Value

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The recent surveys, studies, forecasts and other quantitative assessments of the health and progress of AI estimated the impact on productivity of human-machine collaboration, the number of jobs that could be automated in major U.S. cities, and the size of the future AI in retail and healthcare markets; and found AI optimism among the general population, algorithms outperforming (again) pathologists, and that our very limited understanding of how our brains learn may improve machine learning. Do you think securing your devices and personal data will become more or less complicated over the next 12 months? DeepMind has developed a machine learning model that can label most animals at Tanzania's Serengeti National Park at least as well as humans while shortening the process by up to 9 months (it normally takes up to a year for volunteers to return labeled photos) [Engadget] In a simulation, biological learning algorithms outperformed state-of-the-art optimal learning curves in supervised learning of feedforward networks, indicating "the potency of neurobiological mechanisms" and opening "opportunities for developing a superior class of deep learning algorithms" [Scientific Reports] The AI in retail market is estimated to reach $4.3 billion by 2024 [P&S Intelligence] [e.g., Nike acquires Celect, August 6, 2019] The AI in healthcare market is estimated to reach $12.2 billion by 2023 [Market Research Future] [e.g., BlueDot has raised $7 million in Series A funding, August 7, 2019] AI companies funded in the last 3 months: 417 for total funding of $8.7 billion Data is eating the world quote of the week: "Although it is fashionable to say that we are producing more data than ever, the reality is that we always produced data, we just didn't know how to capture it in useful ways"--Subbarao Kambhampati, Arizona State University AI is eating the world quote of the week: "We advocate for a new perspective for designing benchmarks for measuring progress in AI. Unlike past decades where the community constructed a static benchmark dataset to work on for the next decade or two, we propose that future benchmarks should dynamically evolve together with the evolving state-of-the-art"--Keisuke Sakaguchi, Ronan Le Bras, Chandra Bhagavatula, Yejin Choi, Allen Institute for Artificial Intelligence and the University of Washington


DeepMind's new AI tracks Serengeti herds from images alone

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DeepMind, the U.K.-based AI research subsidiary acquired by Alphabet in 2014 for $500 million, today detailed ecological research its science team is conducting to develop AI systems that'll help study the behavior of animal species in Tanzania's Serengeti National Park. It hopes to expedite the analysis of data from hundreds of motion-detecting field cameras, which have captured millions of images since they were deployed by the Serengeti Lion Research program over nine years ago. "The Serengeti is one of the last remaining sites in the world that hosts an intact community of large mammals … As human encroachment around the park becomes more intense, these species are forced to alter their behaviours in order to survive," wrote DeepMind in a blog post. "Increasing agriculture, poaching, and climate abnormalities contribute to changes in animal behaviors and population dynamics, but these changes have occurred at spatial and temporal scales which are difficult to monitor using traditional research methods." For nearly a decade, conservationists have tapped the aforementioned cameras to keep tabs on animals within the park's core, enabling them to study their distribution and demography.


DeepMind uses AI to track Serengeti wildlife with photos

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DeepMind has joined the ranks of those using AI to save fragile wildlife populations, and it's doing that on a grand scale. The company is partnering with conservationists and ecologists on a project that uses machine learning to speedily detect and count animals in "millions" of photos taken over the past nine years in Tanzania's Serengeti National Park. Where it normally takes up to a year for volunteers to return labeled photos, DeepMind has developed a model that can label most animals at least as well as humans while shortening the process by up to nine months That's no small challenge when animals seldom cooperate with motion-sensitive cameras -- the AI can recognize out-of-focus cheetahs or fast-moving ostriches. The technology should also be viable in the wild. DeepMind is developing a pre-trained version of its AI model that would need only "modest" hardware and little internet connectivity -- important when a powerful computer and fast internet access could be disruptive to wildlife and expensive to deploy.


The Importance of Predictive Maintenance: Using AI to Increase Operational Efficiency

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Tuesday of this past week was quite fortuitous: In my Data Science Cohort at Lambda School, we are working a predictive maintenance competition on Kaggle regarding Water pumps in Tanzania. And, I went to a Data Science networking event at a defense contractor who spoke of the importance of Predictive Maintenance Solutions -- in their case, they were predicting the failure rates of parts of the F35 Joint Strike Fighter. According to IoT world, The Predictive Maintenance report forecasts a compound annual growth rate for Predictive Maintenance of 39% between 2016–2022, with annual technology spending reaching US$10.96 This has a large positive impact on Data Science and Machine Learning if the industry can keep up with the needs of predictive maintenance problems. What is predictive maintenance and why is it so important to different domains?