The new'AI-powered' healthcare system appears to penalise the poorest. The new'AI-powered' healthcare system appears to penalise the poorest. An AI system used to predict how much Kenyans can afford to pay for access to healthcare, has systemically driven up costs for the poor, an investigation has found. The healthcare system being rolled out across the country, a key electoral promise of President William Ruto, was launched in October 2024 and intended to replace Kenya's decades-old national insurance system. Billed as " accelerating digital transformation ", it aimed to expand access to care to Kenya's large informal economy: the day labourers, hawkers, farmers and non-salaried workers that make up 83% of its workforce.

Meta is under pressure to explain why it cancelled a major contract with a company it was using to train AI, shortly after some of its Kenya-based workers alleged they had to view graphic content captured by Meta smart glasses. In February, workers at the company, Sama, told two Swedish newspapers they had witnessed glasses users going to the toilet and having sex . Less than two months later, Meta ended its contract with Sama, which Sama said would result in 1,108 workers being made redundant. Meta says it's because Sama did not meet its standards, a criticism Sama rejects. A Kenyan workers' organisation alleges Meta's decision was caused by the staff speaking out.

The Download: DeepSeek's latest AI breakthrough, and the race to build world models Plus: China has blocked Meta's $2 billion acquisition of AI startup Manus. On Friday, Chinese AI firm DeepSeek released a preview of V4, its long-awaited new flagship model. Notably, the model can process much longer prompts than its last generation, thanks to a new design that handles large amounts of text more efficiently. While the model remains open source, its performance matches leading closed-source rivals from Anthropic, OpenAI, and Google. Here are three ways V4 could shake up AI . AI systems have already gained impressive mastery over the digital world, but the physical world remains humanity's domain.

The Download: Kenya's Great Carbon Valley, and the AI terms that were everywhere in 2025 Welcome to Kenya's Great Carbon Valley: a bold new gamble to fight climate change In June last year, startup Octavia Carbon began running a high-stakes test in the small town of Gilgil in south-central Kenya. It's harnessing some of the excess energy generated by vast clouds of steam under the Earth's surface to power prototypes of a machine that promises to remove carbon dioxide from the air in a manner that the company says is efficient, affordable, and--crucially--scalable. The company's long-term vision is undoubtedly ambitious--it wants to prove that direct air capture (DAC), as the process is known, can be a powerful tool to help the world keep temperatures from rising to ever more dangerous levels. But DAC is also a controversial technology, unproven at scale and wildly expensive to operate. On top of that, Kenya's Maasai people have plenty of reasons to distrust energy companies. This article is also part of the Big Story series: 's most important, ambitious reporting.

Abstract--This paper presents a novel framework for speech transcription and synthesis, leveraging edge-cloud parallelism to enhance processing speed and accessibility for Kinyarwanda and Swahili speakers. It addresses the scarcity of powerful language processing tools for these widely spoken languages in East African countries with limited technological infrastructure. The framework utilizes the Whisper and SpeechT5 pre-trained models to enable speech-to-text (STT) and text-to-speech (TTS) translation. The architecture uses a cascading mechanism that distributes the model inference workload between the edge device and the cloud, thereby reducing latency and resource usage, benefiting both ends. On the edge device, our approach achieves a memory usage compression of 9.5% for the SpeechT5 model and 14% for the Whisper model, with a maximum memory usage of 149 MB. Experimental results indicate that on a 1.7 GHz CPU edge device with a 1 MB/s network bandwidth, the system can process a 270-character text in less than a minute for both speech-to-text and text-to-speech transcription. Using real-world survey data from Kenya, it is shown that the cascaded edge-cloud architecture proposed could easily serve as an excellent platform for STT and TTS transcription with good accuracy and response time. I. INTRODUCTION In today's digital age, the need for accurate and efficient speech transcription and synthesis models has been increasing rapidly. These models play an important role in a variety of applications, such as learning new language(s), accessibility tools for people with difficulties in reading and hearing, as well as automated voice assistants [1]. Kinyarwanda and Swahili are two of the local languages spoken in East Africa. While Swahili is the most widely spoken language in Eastern Africa, the speakers range from 60 million to over 150 million [2].

Researchers in Kenya excavated the first hand and foot bones belonging to'Paranthropis boisei.' Breakthroughs, discoveries, and DIY tips sent every weekday. When it comes to our evolutionary cousins, Neanderthals get most of the attention. Part of this is understandable, since there was a time in Earth's history when the role of dominant primate was up for grabs . Tweak any number of environmental factors, and the tool-wielding, yarn-weaving may have outlasted its cousins (aka humans) instead of going extinct around 40,000 years ago.

Traditional methods for species distribution models (SDMs) use environmental data to predict the distribution of species across geographic space.

Plus: America's first AI law is here Scientists can see Earth's permafrost thawing from space Something is rotten in the city of Nunapitchuk. In recent years, sewage has leached into the earth. The ground can feel squishy, sodden. This small town in northern Alaska is experiencing a sometimes overlooked consequence of climate change: thawing permafrost. And Nunapitchuk is far from the only Arctic town to find itself in such a predicament. Now scientists think they may be able to use satellite data to delve deep beneath the ground's surface and get a better understanding of how the permafrost thaws, and which areas might be most severely affected.

Large Language Models (LLMs) hold promise for improving healthcare access in low-resource settings, but their effectiveness in African primary care contexts remains under-explored. We present a rigorous methodology for creating a benchmark dataset and evaluation framework focused on Kenyan Level 2-3 (dispensary and health center) clinical care. Our approach leverages retrieval-augmented generation (RAG) to ground questions and answers in Kenya's national clinical guidelines, ensuring content aligns with local standard-of-care. The guidelines were digitised, chunked, and indexed for efficient semantic retrieval. Gemini Flash 2.0 Lite was then prompted with relevant guideline excerpts to generate realistic clinical questions, multiple - choice answers, and reasoning scenarios with source citations in English and Swahili. We engaged Kenyan physicians in a co - creation process to refine the dataset's relevance and fairness, and instituted a blinded expert validation pipeline to review for clinical accuracy, clarity, and cultural appropriateness. The resulting Alama Health QA dataset comprises thousands of regulator-aligned question-answer pairs spanning common outpatient conditions in English and Swahili. Beyond standard accuracy metrics, we propose innovative evaluation measures targeting clinical reasoning, safety, and adaptability (e.g. Initial results highlight significant performance gaps in state - of-the - art LLMs when confronted with localized scenarios, echoing recent findings that LLM accuracy on African medical questions lags behind performance on U.S. benchmarks. Our work demonstrates a pathway for dynamic, locally-grounded benchmarks that can evolve with guidelines, providing a crucial tool for safe and effective deployment of AI in African healthcare. Advances in large language models have spurred interest in their potential to augment medical services, especially in low-and middle -income countries facing clinician shortages(Bekbolatova et al., 2024). By handling routine queries or providing decision support, LLMs might help bridge gaps in healthcare access across Africa.