We introduce a framework for the use of large language models (LLMs) in Building Understandable Messaging for Policy and Evidence Review (BUMPER). LLMs are proving capable of providing interfaces for understanding and synthesizing large databases of diverse media. This presents an exciting opportunity to supercharge the translation of scientific evidence into policy and action, thereby improving livelihoods around the world. However, these models also pose challenges related to access, trust-worthiness, and accountability. The BUMPER framework is built atop a scientific knowledge base (e.g., documentation, code, survey data) by the same scientists (e.g., individual contributor, lab, consortium). We focus on a solution that builds trustworthiness through transparency, scope-limiting, explicit-checks, and uncertainty measures. LLMs are rapidly being adopted and consequences are poorly understood. The framework addresses open questions regarding the reliability of LLMs and their use in high-stakes applications. We provide a worked example in health policy for a model designed to inform measles control programs. We argue that this framework can facilitate accessibility of and confidence in scientific evidence for policymakers, drive a focus on policy-relevance and translatability for researchers, and ultimately increase and accelerate the impact of scientific knowledge used for policy decisions.

Kashef has not had the best few days in the office. Unfortunately for our artificial intelligence (AI) predictor, the adrenaline-fuelled, high-octane football being played in the opening set of fixtures has resulted in several major upsets. The good news for us sentient beings is that every time Kashef has got it wrong, we have been treated to a veritable feast of World Cup magic. Just take Saudi Arabia's historic victory over Argentina as a case in point. Today, Kashef has processed the historical data and performances of all the teams who are in action to predict the results of each game.

In this paper, we present a seq2seq approach for transliterating missionary Basaa orthographies into the official orthography. Our model uses pre-trained Basaa missionary and official orthography corpora using BERT. Since Basaa is a low-resource language, we have decided to use the mT5 model for our project. Before training our model, we pre-processed our corpora by eliminating one-to-one correspondences between spellings and unifying characters variably containing either one to two characters into single-character form. Our best mT5 model achieved a CER equal to 12.6747 and a WER equal to 40.1012.

This webinar brings together a diverse group of scholars and experts to discuss some of the inequity and systemic vulnerabilities of covid-19 pandemic. Nathaniel Osgood serves as Professor in the Department of Computer Science at the University of Saskatchewan, and Director of the Computational Epidemiology and Public Health Informatics Laboratory. His research focuses on combining tools from Systems Science, Data Science, Computational Science and Mathematics to inform decision making in health & health care. Dr. Osgood serves as Chief Research Advisor for the Saskatchewan Centre for Patient Oriented Research and has contributed to or co-led over a dozen initiatives involving people with lived experience with dynamic modeling, machine learning and/or big data collection efforts. Dr. Osgood served as the technical director of COVID-19 modeling for the Province of Saskatchewan from March 2020-April 2021.

Berlin, Germany: A global early warning centre in Berlin will use artificial intelligence to predict the next pandemic. But AI's already helped us fight COVID-19. If artificial intelligence is the future, then the future is now. This pandemic has shown us just how fast artificial intelligence (AI) works and what it can do in so many different ways. It may also help us predict the next pandemic.

"It's about saving as many lives as we possibly can," Tim Wood said. Wood spoke to Industrious en route to a meeting with USAID about its Global Health Supply Chain Program-Procurement and Supply Management project, implemented by Chemonics, a development contractor, and a consortium of partners, including IBM. Getting bed nets, HIV medication and other health supplies from medical storage facilities in Washington DC to remote parts of Africa is no small feat. But Wood, a global supply chain VP at IBM, and his GHSC-PSM consortium partners are doing just that. Global supply chains are crucial to any business or operation.

DAKAR, Senegal – The U.S. military in Africa has taken steps to increase the security of troops on the ground, adding armed drones and armored vehicles and taking a harder look at when American forces go out with local troops, the head of the U.S. Africa Command says. Gen. Thomas D. Waldhauser told reporters on Monday the U.S. also has cut the response time needed for medical evacuations -- the result of a broad review in the wake of last year's ambush in Niger that killed four U.S. soldiers and four of their Niger counterparts. "Since that happened, there were significant things to change and learn," Waldhauser said. "We've done a thorough scrub really on every level, whether it's at a tactical level ... or how we conduct business at AFRICOM." A report is due in mid-August on actions taken in response to the findings, Waldhauser said.

DAKAR – The U.S. military in Africa has taken steps to increase the security of troops on the ground, adding armed drones and armored vehicles and taking a harder look at when American forces go out with local troops, the head of the U.S. Africa Command said Monday. Gen. Thomas D. Waldhauser told reporters the U.S. also has cut the response time needed for medical evacuations -- the result of a broad review in the wake of last year's ambush in Niger that killed four U.S. soldiers and four of their Niger counterparts. "Since that happened, there were significant things to change and learn," Waldhauser said. "We've done a thorough scrub really on every level, whether it's at a tactical level … or how we conduct business at AFRICOM." A report is due in mid-August on actions taken in response to the findings, Waldhauser said.

YAOUNDE – With a flash of green light, a robot sputters and whizzes across the room, obeying the remote control commands 15-year-old Xaviera Nguefo and her team send its way. It is a scene that would not look out of place in a futuristic sci-fi fantasy, but is instead playing out in Yaounde, the dusty capital of Cameroon with its potholed streets and frequent power outages. In a country where 1 in 4 girls do not even learn to read, Xaviera, one of about 20 young Cameroonians studying at the NextGen Technology Center in Yaounde, is picking up the basics of artificial intelligence. "I love doing that because the physics that they teach us (at school) is all applied here," she said. "And it makes me a little bit smarter!"

Artificial intelligence is a fascinating but not particularly accessible technology. Project DataREACH, currently in trial mode in Cameroon, wants to change that by giving doctors in all corners of the globe access to advanced AI to help diagnose illnesses and spot troubling health trends. "Our goal is to bridge the gap between the medical data now being gathered on the ground in developing nations, and the cutting-edge [AI] research and application...from the West," Project DataREACH founder and CEO Vikash Singh told PCMag. Singh's app allows clinicians to collect patient data like height, weight, blood pressure, cholesterol, family history, and location. That data is then analyzed via machine learning to assist physicians in evaluating the risk of noncommunicable diseases, including diabetes and cardiovascular issues.