Collaborating Authors


Working Towards Explainable and Data-efficient Machine Learning Models via Symbolic Reasoning


In recent years, we have experienced the success of modern machine learning (ML) models. Many of them have led to unprecedented breakthroughs in a wide range of applications, such as AlphaGo beating a world champion human player or the introduction of autonomous vehicles. There has been a continuous effort, both from industry and academia, in bringing such advances into solving real-life problems. However, converting a successful ML model into a real-world product that leads to improved productivity is still a nontrivial task. First, modern ML methods are known for being data-hungry and inefficient.

UN says aid truck hit by debris from Ethiopian drone strike

Al Jazeera

Debris from a drone strike in northern Ethiopia's Tigray region has damaged a truck carrying humanitarian aid and belonging to the World Food Programme (WFP) and injured the truck's driver, the United Nations agency said on Monday. The WFP said the drone strike on Sunday hit near an area called Zana Woreda in northwestern Tigray, as two trucks were delivering relief supplies to families displaced by the nearly two-year long conflict. "Flying debris from the strike injured a driver contracted by WFP and caused minor damage to a WFP fleet truck," the spokesperson said, adding it was not possible to say yet whether further distributions would be suspended in the area. "WFP calls on all parties to respect and adhere to international humanitarian laws and to commit to safeguarding humanitarian workers, premises and assets." The WFP truck was delivering food to internally displaced people as hundreds of thousands have been uprooted by renewed fighting since August 24 after a five-month ceasefire broke down.

Helicobacter pylori (H. pylori) risk factor analysis and prevalence prediction: a machine learning-based approach - BMC Infectious Diseases


Although previous epidemiological studies have examined the potential risk factors that increase the likelihood of acquiring Helicobacter pylori infections, most of these analyses have utilized conventional statistical models, including logistic regression, and have not benefited from advanced machine learning techniques. We examined H. pylori infection risk factors among school children using machine learning algorithms to identify important risk factors as well as to determine whether machine learning can be used to predict H. pylori infection status. We applied feature selection and classification algorithms to data from a school-based cross-sectional survey in Ethiopia. The data set included 954 school children with 27 sociodemographic and lifestyle variables. We conducted five runs of tenfold cross-validation on the data. We combined the results of these runs for each combination of feature selection (e.g., Information Gain) and classification (e.g., Support Vector Machines) algorithms. The XGBoost classifier had the highest accuracy in predicting H. pylori infection status with an accuracy of 77%—a 13% improvement from the baseline accuracy of guessing the most frequent class (64% of the samples were H. Pylori negative.) K-Nearest Neighbors showed the worst performance across all classifiers. A similar performance was observed using the F1-score and area under the receiver operating curve (AUROC) classifier evaluation metrics. Among all features, place of residence (with urban residence increasing risk) was the most common risk factor for H. pylori infection, regardless of the feature selection method choice. Additionally, our machine learning algorithms identified other important risk factors for H. pylori infection, such as; electricity usage in the home, toilet type, and waste disposal location. Using a 75% cutoff for robustness, machine learning identified five of the eight significant features found by traditional multivariate logistic regression. However, when a lower robustness threshold is used, machine learning approaches identified more H. pylori risk factors than multivariate logistic regression and suggested risk factors not detected by logistic regression. This study provides evidence that machine learning approaches are positioned to uncover H. pylori infection risk factors and predict H. pylori infection status. These approaches identify similar risk factors and predict infection with comparable accuracy to logistic regression, thus they could be used as an alternative method.

Meta's massive multilingual translation opus still stumbles on Greek, Armenian, Oromo


"Broadly accessible machine translation systems support around 130 languages; our goal is to bring this number up to 200," the authors write as their mission statement. Meta Properties, owner of Facebook, Instagram and WhatsApp, on Wednesday unveiled its latest effort in machine translation, a 190-page opus describing how it has used deep learning forms of neural nets to double state-of-the-art translation for languages to 202 languages, many of them so-called "low resource" languages such as West Central Oromo, a language of the Oromia state of Ethiopia, Tamasheq, spoken in Algeria and several other parts of Northern Africa, and Waray, the language of the Waray people of the Philippines. The report by a team of researchers at Meta, along with scholars at UC Berkeley and Johns Hopkins, "No Language Left Behind: Scaling Human-Centered Machine Translation," is posted on Facebook's AI research Web site, along with a companion blog post, and both should be required reading for the rich detail on the matter. "Broadly accessible machine translation systems support around 130 languages; our goal is to bring this number up to 200," they write as their mission statement. As Stephanie relates, Meta is open-sourcing its data sets and neural network model code on GitHub, and also offering $200,000 I'm awards to outside uses of the technology.

Timnit Gebru, AI researcher fired by Google thinks a new law is needed


Born to Eritrean parents in Ethiopia, Gebru spoke with The Associated Press recently about how poorly Big Tech's AI priorities -- and its AI-fueled social media platforms -- serve Africa and elsewhere. The new institute focuses on AI research from the perspective of the places and people most likely to experience its harms. She's also co-founder of the group Black in AI, which promotes Black employment and leadership in the field. And she's known for co-authoring a landmark 2018 study that found racial and gender bias in facial recognition software. The interview has been edited for length and clarity.

The Jeffrey Epstein VI Foundation Helps Launch Artificial Intelligence in Ethiopia


When it comes to Sub-Saharan Africa, the media always focuses on war, famine and disease. Little is known about the innovation and positive attributes stemming from the region. One such attribute, is the growth of computer science and artificial intelligence. Indeed, the field of AI has skyrocketed in developing nations with the advent of the home computer, but has recently found burgeoning roots, not in Silicon Valley, India or China, but in the bustling capital of Ethiopia. Thanks to a leading AI group in Hong Kong called OpenCog Foundation, funding from the Jeffrey Epstein VI Foundation based in New York, and the Hong Kong government, the AI lab in Sidist Killo, Ethiopia called Addis AI Lab, has become the computer science pioneer in Sub-Saharan Africa.

How armed drones may have helped turn tide in Ethiopia's conflict

Al Jazeera

Ethiopia's 13-month war has seen yet another dramatic turn as the federal government's counteroffensive against fighters from the northern Tigray region has made substantial advances, reversing the spectacular gains made recently by the Tigrayan forces in their push southwards. State media said this week the country's "joint gallant security forces" had retaken the strategic towns of Dessie and Kombolcha, the latest in a series of battleground victories since Prime Minister Abiy Ahmed said last month he would head to the front line and urged Ethiopians to join the fight. As fighting drags on, the government, with its tiny air force of 22 combat-capable aircraft, seems to have also realised that air power and timely intelligence can make all the difference in a conflict – especially one fought over vast and often mountainous areas like in Ethiopia's north. Although there has been no official confirmation, analysts have pointed to credible reports saying Ahmed's government has reached out in recent months to manufacturers of cheap and efficient armed drones hoping that air power will turn the tide in its way. Photographic evidence has pointed to the presence of Chinese Wing Loong 2 Unarmed Aerial Vehicles or UAVs at Ethiopian military bases, while a Bellingcat investigation in August found strong indications that Iranian armed drones, along with their ground control stations, had been spotted at Semera Airport.

Facebook Says Its New AI Can Identify More Problems Faster


A recent trove of documents leaked from Facebook demonstrated how the social network struggles to moderate dangerous content in places far from Silicon Valley. Internal discussions revealed worries that moderation algorithms for the languages spoken in Pakistan and Ethiopia were insufficient, and that the company lacked adequate training data to tune systems to different dialects of Arabic. Meta Platforms, Facebook's owner, now says it has deployed a new artificial intelligence moderation system for some tasks that can be adapted to new enforcement jobs more quickly than its predecessors because it requires much less training data. The company says the system, called Few-Shot Learner, works in more than 100 languages and can operate on images as well as text. Facebook says Few-Shot Learner makes it possible to automate enforcement of a new moderation rule in about six weeks, down from around six months.

MatES: Web-based Forward Chaining Expert System for Maternal Care Artificial Intelligence

The solution to prevent maternal complications are known and preventable by trained health professionals. But in countries like Ethiopia where the patient to physician ratio is 1 doctor to 1000 patients, maternal mortality and morbidity rate is high. To fill the gap of highly trained health professionals, Ethiopia introduced health extension programs. Task shifting to health extension workers (HEWs) contributed in decreasing mortality and morbidity rate in Ethiopia. Knowledge-gap has been one of the major challenges to HEWs. The reasons are trainings are not given in regular manner, there is no midwife, gynecologists or doctors around for consultation, and all guidelines are paper-based which are easily exposed to damage. In this paper, we describe the design and implementation of a web-based expert system for maternal care. We only targeted the major 10 diseases and complication of maternal health issues seen in Sub-Saharan Africa. The expert system can be accessed through the use of web browsers from computers as well as smart phones. Forward chaining rule-based expert system is used in order to give suggestions and create a new knowledge from the knowledge-base. This expert system can be used to train HEWs in the field of maternal health. Keywords: expert system, maternal care, forward-chaining, rule-based expert system, PHLIPS

An Amharic News Text classification Dataset Artificial Intelligence

In NLP, text classification is one of the primary problems we try to solve and its uses in language analyses are indisputable. The lack of labeled training data made it harder to do these tasks in low resource languages like Amharic. The task of collecting, labeling, annotating, and making valuable this kind of data will encourage junior researchers, schools, and machine learning practitioners to implement existing classification models in their language. In this short paper, we aim to introduce the Amharic text classification dataset that consists of more than 50k news articles that were categorized into 6 classes. This dataset is made available with easy baseline performances to encourage studies and better performance experiments.