Ethiopia


ICLR 2020 Accepted Papers Announced

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The International Conference on Learning Representations ICLR 2020 is four months away but has already attracted more than its share of drama with a deluge of submissions and doubts about the qualifications of some reviewers. Yesterday the conference programme chairs finally put the selection process behind them, announcing 687 out of 2594 papers had made it to ICLR 2020 -- a 26.5 percent acceptance rate. ICLR 2020 will be held in Addis Ababa, Ethiopia from April 26 to 30. This will be the first trip to Africa for a major AI conference, a move long-encouraged by many leading AI researchers. All accepted papers will be presented as posters as usual, while 23 percent will have an oral presentation.


Chinese firm to help build artificial intelligence infrastructure in Ethiopia - Xinhua

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A Chinese firm has signed a Memorandum of Understanding (MoU) with Ethiopia authorities on establishing a National Artificial Intelligence Infrastructure (NAIF) in Ethiopia, reported state media outlet Ethiopia News Agency (ENA) on Saturday. The MoU was signed between Ethiopia Innovation and Technology State Minister, Sisay Tola and Chen Kuan, the founder and CEO of Chinese firm Infervision Technology Corporation in Ethiopia's capital Addis Ababa on Friday evening, reported ENA. Ethiopia hopes the partnership with Infervision will boost the technological capacity of its education, health care and medical services. Ethiopia also hopes the partnership will facilitate a platform for exchange of ideas and investment opportunities between enterprises of both countries in various sectors including energy, textile, agriculture, construction and information technology. Ethiopia and China have recently signed various agreements in the Information Communication and Technology (ICT), as Ethiopia looks to modernize its largely agrarian economy.


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.


Dealing With Bias in Artificial Intelligence

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Timnit Gebru is a research scientist at Google on the ethical A.I. team and a co-founder of Black in AI, which promotes people of color in the field. Dr. Gebru has been instrumental in moving a major international A.I. conference, the International Conference on Learning Representations, to Ethiopia next year after more than half of the Black in AI speakers could not get visas to Canada for a conference in 2018. She talked about the foundational origins of bias and the larger challenge of changing the scientific culture. Their comments have been edited and condensed. You could mean bias in the sense of racial bias, gender bias.


We are finally getting better at predicting organized conflict

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Incidents of conflict and protest, along with many other structural variables, are fed into constituent models. Input variables would include things like population density, GDP growth, travel time to the nearest city, proportion of barren land, years since independence, and type of government. Several different models, each of which uses a different method, compute a probability of conflict. Constituent models could be a conflict history regression model, natural resources model, and an aggregate machine learning model. The results from the constituent models get combined to produce a final risk score.


Google AI's ALBERT claims top spot in multiple NLP performance benchmarks

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Researchers from Google AI (formerly Google Research) and Toyota Technological Institute of Chicago have created ALBERT, an AI model that achieves state-of-the-art results that exceed human performance. ALBERT now claims first place on major NLP performance leaderboards for benchmarks like GLUE and SQuAD 2.0, and high RACE performance score. On the Stanford Question Answering Dataset benchmark (SQUAD), ALBERT achieves a score of 92.2, on General Language Understanding Evaluation (GLUE) benchmark, ALBERT achieves a score of 89.4, and on ReAding Comprehension from English Examinations (RACE) benchmark, ALBERT gets a score of 89.4%. ALBERT is a version of Transformer-based BERT that "uses parameter reduction techniques to lower memory consumption and increase the training speed of BERT," according to a paper published on OpenReview.net The paper was published alongside other papers being considered for publication as part of the International Conference of Learning Representations, which will take place in April 2020 in Addis Ababa, Ethiopia.


Handwritten Amharic Character Recognition Using a Convolutional Neural Network

arXiv.org Machine Learning

Amharic is the official language of the Federal Democratic Republic of Ethiopia. There are lots of historic Amharic and Ethiopic handwritten documents addressing various relevant issues including governance, science, religious, social rules, cultures and art works which are very reach indigenous knowledge. The Amharic language has its own alphabet derived from Ge'ez which is currently the liturgical language in Ethiopia. Handwritten character recognition for non Latin scripts like Amharic is not addressed especially using the advantages of the state of the art techniques. This research work designs for the first time a model for Amharic handwritten character recognition using a convolutional neural network. The dataset was organized from collected sample handwritten documents and data augmentation was applied for machine learning. The model was further enhanced using multi-task learning from the relationships of the characters. Promising results are observed from the later model which can further be applied to word prediction.


Tackling climate change with artificial intelligence

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In an bid to reduce the effects of food waste further, millions of trees are being planted across the globe, with Ethiopia recently setting a record of 30 million trees in a day. But while global efforts to plant new trees are necessary, British start-up Biocarbon Engineering have identified issues. Planting by hand isn't the most effective; it's extremely time consuming, and therefore the number of trees that can be planted is limited. This is where artificial intelligence comes in.


Predicting Crop Losses using Machine Learning CGIAR Platform for Big Data in Agriculture

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Timely and accurate agricultural impact assessments for droughts are critical for designing appropriate interventions and policy. These assessments are often ad hoc, late, or spatially imprecise, with reporting at the zonal or regional level. This is problematic as we find substantial variability in losses at the village-level, which is missing when reporting at the zonal level. In this paper, we propose a new data fusion method--combining remotely sensed data with agricultural survey data--that might address these limitations. We apply the method to Ethiopia, which is regularly hit by droughts and is a substantial recipient of ad hoc imported food aid.


Boeing defends 'fundamental safety' of 737 Max after crash report but admits system error

The Japan Times

WASHINGTON - Embattled U.S. aviation giant Boeing on Thursday insisted on the "fundamental safety" of its 737 Max aircraft but pledged to take all necessary steps to ensure the jets' airworthiness. The statements came hours after Ethiopian officials said pilots of a doomed plane that crashed last month, leaving 157 people dead, had followed the company's recommendations. The preliminary findings released Thursday by transportation authorities in Addis Ababa put the American aircraft giant under even greater pressure to restore public trust amid mounting signs the company's onboard anti-stall systems were at fault in crashes involving its formerly top-selling 737 Max aircraft -- incidents that left nearly 350 people dead in less than five months. "We remain confident in the fundamental safety of the 737 Max," CEO Dennis Muilenburg said in a statement, adding that impending software fixes would make the aircraft "among the safest airplanes ever to fly." Muilenburg also acknowledged, however, that an "erroneous activation" of Boeing's Maneuvering Characteristics Augmentation System had occurred. The system is designed to prevent stalls but may have forced the Ethiopian and Indonesian jets into the ground.