Electroencephalography (EEG) recordings of rhythm perception might contain enough information to distinguish different rhythm types/genres or even identify the rhythms themselves. We apply convolutional neural networks (CNNs) to analyze and classify EEG data recorded within a rhythm perception study in Kigali, Rwanda which comprises 12 East African and 12 Western rhythmic stimuli – each presented in a loop for 32 seconds to 13 participants. We investigate the impact of the data representation and the pre-processing steps for this classification tasks and compare different network structures. Using CNNs, we are able to recognize individual rhythms from the EEG with a mean classification accuracy of 24.4% (chance level 4.17%) over all subjects by looking at less than three seconds from a single channel. Aggregating predictions for multiple channels, a mean accuracy of up to 50% can be achieved for individual subjects.

Internet connectivity in schools is critical to provide students with the digital literary skills necessary to compete in modern economies. In order for governments to effectively implement digital infrastructure development in schools, accurate internet connectivity information is required. However, traditional survey-based methods can exceed the financial and capacity limits of governments. Open-source Earth Observation (EO) datasets have unlocked our ability to observe and understand socio-economic conditions on Earth from space, and in combination with Machine Learning (ML), can provide the tools to circumvent costly ground-based survey methods to support infrastructure development. In this paper, we present our work on school internet connectivity prediction using EO and ML. We detail the creation of our multi-modal, freely-available satellite imagery and survey information dataset, leverage the latest geographically-aware location encoders, and introduce the first results of using the new European Space Agency phi-lab geographically-aware foundational model to predict internet connectivity in Botswana and Rwanda. We find that ML with EO and ground-based auxiliary data yields the best performance in both countries, for accuracy, F1 score, and False Positive rates, and highlight the challenges of internet connectivity prediction from space with a case study in Kigali, Rwanda. Our work showcases a practical approach to support data-driven digital infrastructure development in low-resource settings, leveraging freely available information, and provide cleaned and labelled datasets for future studies to the community through a unique collaboration between UNICEF and the European Space Agency phi-lab.

Electroencephalography (EEG) recordings of rhythm perception might contain enough information to distinguish different rhythm types/genres or even identify the rhythms themselves. We apply convolutional neural networks (CNNs) to analyze and classify EEG data recorded within a rhythm perception study in Kigali, Rwanda which comprises 12 East African and 12 Western rhythmic stimuli - each presented in a loop for 32 seconds to 13 participants. We investigate the impact of the data representation and the pre-processing steps for this classification tasks and compare different network structures. Using CNNs, we are able to recognize individual rhythms from the EEG with a mean classification accuracy of 24.4% (chance level 4.17%) over all subjects by looking at less than three seconds from a single channel. Aggregating predictions for multiple channels, a mean accuracy of up to 50% can be achieved for individual subjects.

The Democratic Republic of the Congo has accused Rwanda of carrying out a drone attack that damaged a civilian aircraft at the airport in the strategic eastern city of Goma, the capital of North Kivu province. Fighting has flared in recent days around the town of Sake, 20km (12 miles) from Goma, between M23 rebels – which Kinshasa says are backed by Kigali – and Congolese government forces. "On the night of Friday to Saturday, at 2-o-clock in the morning local time, there was a drone attack by the Rwandan army," said Lieutenant-Colonel Guillaume Ndjike Kaito, army spokesperson for North Kivu province. "It had obviously come from the Rwandan territory, violating the territorial integrity of the Democratic Republic of the Congo," he added in a video broadcast by the governorate. The drones "targeted aircraft of DRC armed forces".

'It feels like a gold rush," says Timnit Gebru. "In fact, it is a gold rush. And a lot of the people who are making money are not the people actually in the midst of it. But it's humans who decide whether all this should be done or not. We should remember that we have the agency to do that." Gebru is talking about her specialised field: artificial intelligence. On the day we speak via a video call, she is in Kigali, Rwanda, preparing to host a workshop and chair a panel at an international conference on AI. It will address the huge growth in AI's capabilities, as well as something that the frenzied conversation about AI misses out: the fact that many of its systems may well be built on a huge mess of biases, inequalities and imbalances of power. This gathering, the clunkily titled International Conference on Learning Representations, marks the first time people in the field have come together in an African country – which makes a powerful point about big tech's neglect of the global south. When Gebru talks about the way that AI "impacts people all over the world and they don't get to have a say on how they should shape it", the issue is thrown into even sharper relief by her backstory. In her teens, Gebru was a refugee from the war between Ethiopia, where she grew up, and Eritrea, where her parents were born. After a year in Ireland, she made it to the outskirts of Boston, Massachusetts, and from there to Stanford University in northern California, which opened the way to a career at the cutting edge of the computing industry: Apple, then Microsoft, followed by Google. But in late 2020, her work at Google came to a sudden end. As the co-leader of Google's small ethical AI team, Gebru was one of the authors of an academic paper that warned about the kind of AI that is increasingly built into our lives, taking internet searches and user recommendations to apparently new levels of sophistication and threatening to master such human talents as writing, composing music and analysing images. The clear danger, the paper said, is that such supposed "intelligence" is based on huge data sets that "overrepresent hegemonic viewpoints and encode biases potentially damaging to marginalised populations". Put more bluntly, AI threatens to deepen the dominance of a way of thinking that is white, male, comparatively affluent and focused on the US and Europe. In response, senior managers at Google demanded that Gebru either withdraw the paper, or take her name and those of her colleagues off it. This triggered a run of events that led to her departure. Google says she resigned; Gebru insists that she was fired. What all this told her, she says, is that big tech is consumed by a drive to develop AI and "you don't want someone like me who's going to get in your way.

Figure from Use of Deep Learning to Examine the Association of the Built Environment With Prevalence of Neighborhood Adult Obesity, Adyasha Maharana and Elaine Okanyene Nsoesie. Image on the right represents actual obesity prevalence; on the left, cross-validated estimates of obesity prevalence based on features of the built environment extracted from satellite images. Figure reproduced under CC-BY licence. The 11th International Conference on Learning Representations (ICLR) took place last week in Kigali, Rwanda, the first time a major AI conference has taken place in-person in Africa. The program included workshops, contributed talks, affinity group events, and socials.

KIGALI, Rwanda--Amid growing talk of the promise and peril of artificial intelligence, more than 2,000 researchers and engineers from around the world gathered in Rwanda this week to debate contrasting visions for the technology's future. One vision is to build ever-more-powerful systems such as ChatGPT that aim to exceed human intelligence to boost worker productivity and economic growth. The other is to create more-targeted, small-scale AI solutions to local and global challenges, including tackling climate change, improving healthcare and preserving biodiversity.

The 11th International Conference on Learning Representations (ICLR) is taking place this week in Kigali, Rwanda, the first time a major AI conference has taken place in-person in Africa. The program includes workshops, contributed talks, affinity group events, and socials. In addition, a total of six invited talks covered a broad range of topics. In this post we give a flavour of the first two of these presentations. Sofia Crepso is an artist who explores the interaction between biological systems and AI.

Atmospheric pollution remains one of the major public health threat worldwide with an estimated 7 millions deaths annually. In Africa, rapid urbanization and poor transport infrastructure are worsening the problem. In this paper, we have analysed spatio-temporal variations of PM2.5 across different geographical regions in Africa. The West African region remains the most affected by the high levels of pollution with a daily average of 40.856 $\mu g/m^3$ in some cities like Lagos, Abuja and Bamako. In East Africa, Uganda is reporting the highest pollution level with a daily average concentration of 56.14 $\mu g/m^3$ and 38.65 $\mu g/m^3$ for Kigali. In countries located in the central region of Africa, the highest daily average concentration of PM2.5 of 90.075 $\mu g/m^3$ was recorded in N'Djamena. We compare three data driven models in predicting future trends of pollution levels. Neural network is outperforming Gaussian processes and ARIMA models.

With 314 confirmed cases of the virus as of May 22, the East African country has enlisted the help of five anti-epidemic robots to battle the virus. The robots were donated by the United Nations Development Program (UNDP) to the Kanyinya treatment center that treats Covid-19 patients in the capital city, Kigali. The robots -- named Akazuba, Ikirezi, Mwiza, Ngabo, and Urumuri -- were received by the country's Minister of Health and Minister of ICT and Innovation last week. They will be used for mass temperature screening, monitoring patient status, and keeping medical records of Covid-19 patients, according to Rwanda's Ministry of ICT and Innovation. Keeping healthworkers safeThe robots perform a number of tasks relating to managing coronavirus.