Africa's rich linguistic heritage remains underrepresented in NLP, largely due to historical policies that favor foreign languages and create significant data inequities. In this paper, we integrate theoretical insights on Africa's language landscape with an empirical evaluation using Sahara - a comprehensive benchmark curated from large-scale, publicly accessible datasets capturing the continent's linguistic diversity. By systematically assessing the performance of leading large language models (LLMs) on Sahara, we demonstrate how policy-induced data variations directly impact model effectiveness across African languages. Our findings reveal that while a few languages perform reasonably well, many Indigenous languages remain marginalized due to sparse data. Leveraging these insights, we offer actionable recommendations for policy reforms and inclusive data practices. Overall, our work underscores the urgent need for a dual approach - combining theoretical understanding with empirical evaluation - to foster linguistic diversity in AI for African communities.

The Next Biennial Should Be Curated by a Machine A proposition for an intelligent system capable of curating otherwise www.biennial.com The Next Biennial Should be Curated by a Machine is an inquiry into the relationship between curating and artificial intelligence, and a possibility of developing an experimental system capable of curating, based on human-machine learning principles. Unfolding as a series of machine learning experiments, the project is a collaboration between artists UBERMORGEN, digital humanist Leonardo Impett, and curator Joasia Krysa. The first online experiment will be launched at Liverpool Biennial 2020 and is co-commissioned by Liverpool Biennial and the Whitney Museum of American Art for its online gallery space artport. Making reference to'The Next Documenta Should Be Curated by an Artist' (e-flux, 2003),* this project extends the proposition to machines.

AI has pervaded our homes, our cars, and now, our hospitals. They are built into our devices, and into our phones, and are the basis for 24 hour care – and increasingly used in drug discovery. Massive data sets are now used to monitor, detect, and address so many conditions, from heart disease to mental illness to the deterioration of gait in Parkinson's disease. Brain computer interfaces are allowing the disabled to walk, and the blind to navigate. Robots, and NLP tools such as Alexa, let seniors to age in place, gracefully.

Ever struggle to find the right piece of content to share with a client or prospect? Or lose track of links shared in email chains or group chat? We built Team Newsletters to solve these problems and help your team capture industry intelligence at scale. There's no need to copy-paste links or spend time formatting text. Simply activate your newsletters, and Feedly will do the rest.

EyeEm is a photography community and marketplace of over 18 million photographers. It also publishes a magazine, also called EyeEm. For its fourth issue, Machina: A Curation of Real Photography by a Machine, the company turned to an artificial intelligence powered by computer vision, EyeEm Vision, to curate the magazine, selecting the photographs it feels are the best aesthetically and most impactful. Now, before the inner smartphone photographer in you rolls your eyes, understand that it is pretty neat that a machine can, in some ways, learn to identify photographic aesthetics like a human. Sure, an A.I. cannot truly exercise a similar series of complex calculations of why an image might be great or resonant, but it's certainly intriguing to see where humans are in imbuing machines with mental processes.