Abstract--In Kigali, Rwanda, motorcycle taxis are a primary mode of transportation, often navigating unpredictably and disregarding traffic rules, posing significant challenges for autonomous driving systems. This study compares four object detection models--YOLOv5, Faster R-CNN, SSD, and RetinaNet--for motorbike detection using a custom dataset of 198 images collected in Kigali. Implemented in PyT orch with transfer learning, the models were evaluated for accuracy, localization, and inference speed to assess their suitability for real-time navigation in resource-constrained settings. We identify implementation challenges, including dataset limitations and model complexities, and recommend simplified architectures for future work to enhance accessibility for autonomous systems in developing countries like Rwanda. In developing countries like Rwanda, motorcycle taxis, locally known as "moto taxis," dominate urban transportation, particularly in Kigali.

The proliferation of large language models (LLMs) can influence how historical narratives are disseminated and perceived. This study explores the implications of LLMs' responses on the representation of mass atrocity memory, examining whether generative AI systems contribute to prosthetic memory, i.e., mediated experiences of historical events, or to what we term "prosthetic denial," the AI-mediated erasure or distortion of atrocity memories. We argue that LLMs function as interfaces that can elicit prosthetic memories and, therefore, act as experiential sites for memory transmission, but also introduce risks of denialism, particularly when their outputs align with contested or revisionist narratives. To empirically assess these risks, we conducted a comparative audit of five LLMs (Claude, GPT, Llama, Mixtral, and Gemini) across four historical case studies: the Holodomor, the Holocaust, the Cambodian Genocide, and the genocide against the Tutsis in Rwanda. Each model was prompted with questions addressing common denialist claims in English and an alternative language relevant to each case (Ukrainian, German, Khmer, and French). Our findings reveal that while LLMs generally produce accurate responses for widely documented events like the Holocaust, significant inconsistencies and susceptibility to denialist framings are observed for more underrepresented cases like the Cambodian Genocide. The disparities highlight the influence of training data availability and the probabilistic nature of LLM responses on memory integrity. We conclude that while LLMs extend the concept of prosthetic memory, their unmoderated use risks reinforcing historical denialism, raising ethical concerns for (digital) memory preservation, and potentially challenging the advantageous role of technology associated with the original values of prosthetic memory.

Acquiring, processing, and visualizing geospatial data requires significant computing resources, especially for large spatio-temporal domains. This challenge hinders the rapid discovery of predictive features, which is essential for advancing geospatial modeling. To address this, we developed Similarity Search (Sims), a no-code web tool that allows users to perform clustering and similarity search over defined regions of interest using Google Earth Engine as a backend. Sims is designed to complement existing modeling tools by focusing on feature exploration rather than model creation. We demonstrate the utility of Sims through a case study analyzing simulated maize yield data in Rwanda, where we evaluate how different combinations of soil, weather, and agronomic features affect the clustering of yield response zones. Sims is open source and available at https://github.com/microsoft/Sims

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.

High-resolution satellite-based crop yield mapping offers enormous promise for monitoring progress towards the SDGs. Across 15,000 villages in Rwanda we uncover areas that are on and off track to double productivity by 2030. This machine learning enabled analysis is used to design spatially explicit productivity targets that, if met, would simultaneously ensure national goals without leaving anyone behind.

As world leaders gather in New York City for the United Nations General Assembly, there's a lot to get done, with just six years left to achieve the bold ambitions laid out for the world's 2030 agenda. When world governments agreed to the 2030 plan back in 2015, a decade and a half seemed like plenty of time to achieve the 17 Sustainable Development Goals (SDGs) designed to create a more prosperous, safe and fair global society. While amazing progress has been made, we are in danger of falling short. I believe the U.N.'s goals can be attained through a collaborative commitment to make digital networks available to everybody in the world. Mobility, broadband and the cloud are the infrastructure of 21st century life and everybody should have that opportunity.

The United States has condemned growing violence in the Democratic Republic of the Congo (DRC), blaming an armed group it says is backed by neighbouring Rwanda. Fighting has flared in recent days in the eastern part of the DRC between the M23 rebel group and government forces, resulting in dozens of soldiers and civilians being killed or wounded. The fighting has also pushed tens of thousands of civilians to flee towards the eastern city of Goma, which is located between Lake Kivu and the border with Rwanda. "This escalation has increased the risk to millions of people already exposed to human rights abuses including displacement, deprivation, and attacks," US State Department spokesman Matthew Miller said in a statement. "The United States condemns Rwanda's support for the M23 armed group and calls on Rwanda to immediately withdraw all Rwanda Defense Force personnel from the DRC and remove its surface-to-air missile systems, which threaten the lives of civilians, UN and other regional peacekeepers, humanitarian actors, and commercial flights in eastern DRC," Miller added.

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".

Interacting with a contemporary LLM-based conversational agent can create an illusion of being in the presence of a thinking creature. Yet, in their very nature, such systems are fundamentally not like us.

Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. An unidentified armed group attacked a U.N. peacekeeping patrol Monday in the Central African Republic, killing a peacekeeper from Rwanda, the United Nations said. U.N. spokesman Stephane Dujarric said initial reports indicated the U.N. patrol returned fire and killed three of the assailants. The attack happened as the peacekeepers were providing a protective presence around the town of Sam-Ouandja, in the Haute Kotto prefecture in the Central African Republic's east, Dujarric said. Peacekeepers were deployed to Sam-Ouandja last week in response to an attack on the town by an armed group, which fled after the peacekeepers intervened, he said.