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Rethinking AI with Small Language Models (SLMs) in the Global South - A Whitepaper
As artificial intelligence reshapes our digital landscape, large language models (LLMs) like ChatGPT, Claude, Gemini, DeepSeek, etc., have become household names. These AI powerhouses can generate text, create images, process speech, and even produce videos, making them seem like the ultimate solution for every technological need.
But there’s a catch. While the world celebrates these breakthroughs, many regions in the Global South face a different reality.
What Africa needs to do to become a major AI player
Researchers who want to make Africa-centric AI don’t face just insufficient local investment and inaccessible data. There are major linguistic challenges, too.
During one discussion at the Indaba, Ife Adebara, a Nigerian computational linguist, posed a question: “How many people can write a bachelor’s thesis in their native African language?”
Zero hands went up…
EqualyzAI at Deep Learning Indaba 2024
From September 1st to 7th, 2024, the vibrant city of Dakar, Senegal, became the epicenter of African AI innovation as it hosted the Deep Learning Indaba 2024 at Amadou Makhtar Mbow University. Our presence at the Deep Learning Indaba 2024 reaffirmed our commitment to equitably provide access to AI by ensuring every language — especially those from underrepresented communities — finds its place in the digital world…
Building Inclusive AI: The Importance of Multilingual Datasets in African Language Models
Inclusivity in AI entails the development of systems that comprehend and cater to the diverse needs of all individuals, regardless of their linguistic or cultural contexts. For Africa, this involves cultivating artificial intelligence that appreciates the diversity of indigenous languages, dialects, and cultural contexts…
Cultural Nuances and AI: Understanding Context in African Language Processing
Consider an AI system attempting to understand this African proverb: “Little by little, the pig’s nose enters the yard.” Without understanding the context, the algorithm may mistakenly interpret this as a behavioral observation involving pigs. However, the true meaning is a powerful lesson: seemingly minor difficulties, if ignored, can grow into major problems…