{"id":1153083,"date":"2025-11-12T04:00:00","date_gmt":"2025-11-12T12:00:00","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/?post_type=msr-story&p=1153083"},"modified":"2026-01-30T06:46:11","modified_gmt":"2026-01-30T14:46:11","slug":"advancing-ai-to-meet-needs-of-the-global-majority","status":"publish","type":"msr-story","link":"https:\/\/www.microsoft.com\/en-us\/research\/story\/advancing-ai-to-meet-needs-of-the-global-majority\/","title":{"rendered":"Advancing AI to meet needs of the global majority"},"content":{"rendered":"\n
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Advancing AI to meet needs of the global majority<\/h1>\n\n\n\n
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Generative AI powers apps and tools that boost productivity and knowledge in much of the world.<\/h2>\n\n\n\n

But these systems don\u2019t work equally well for all communities\u2014especially those under-represented online, where most AI training data originates. As a result, generative AI performs poorly in many languages and does not reflect the social and cultural realities of every population. Infrastructure challenges are partly to blame, but in nations where low-resource languages dominate, adoption of AI is lower<\/a>, even after adjusting for GDP and internet access.<\/p>\n\n\n\n

That’s where Project Gecko<\/strong><\/a> comes in. This Microsoft Research-led initiative is designed to close these equity gaps by creating cost-effective, tailorable AI systems that deliver vital expertise to the global majority. It uses local languages, culturally sensitive content, and multimodal engagement through text, voice, and video. It brings together researchers from Microsoft Research Africa, Nairobi<\/a>, Microsoft Research India<\/a>, and the Microsoft Research Accelerator in the United States, along with Digital Green (opens in new tab)<\/span><\/a>\u2014a global development organization that builds community-driven digital infrastructure for agriculture\u2014and several contributors in agri-tech, philanthropy, and academia.<\/p>\n\n\n\n

A critical advance is a new AI system called MMCTAgent<\/strong>, which analyzes inputs from speech, images, and videos and provides relevant, context-aware responses. MMCTAgent is now available on Azure AI Foundry Labs (opens in new tab)<\/span><\/a>, and the code may be downloaded from GitHub (opens in new tab)<\/span><\/a><\/strong>.<\/p>\n\n\n\n

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MMCTAgent on Azure AI Foundry Labs<\/a><\/div>\n\n\n\n
Microsoft Research Early Access Program – MMCTAgent<\/a><\/div>\n\n\n\n
MMCTAgent: Enabling multimodal reasoning over large video and image collections<\/a><\/div>\n<\/div>\n\n\n\n

This work reflects Microsoft\u2019s mission to empower every person and every organization on the planet to achieve more<\/a><\/strong>. Developing globally equitable generative AI that reflects the culturally nuanced lived experiences of the communities it serves helps to advance AI in a responsible, inclusive way.<\/p>\n\n\n\n

The following researchers played an integral role in this research: Najeeb Abdulhamid, Liz Ankrah, Kalika Bali, Kevin Chege, Arnab Paul Choudhury, Kavyansh Chourasia, Soumya De, Ogbemi Ekwejunor-Etchie, Ignatius Ezeani, Ade Famoti, Tanuja Ganu, Prashant Kodali, Antonis Krasakis, Mercy Kwambai, Samuel Maina, Muchai Mercy, Danlami Mohammed, Nick Mwangi, Martin Mwiti, Akshay Nambi, Stephanie Nyario, Millicent Ochieng, Jacki O\u2019Neill, Aman Patkar, and Sunayana Sitaram.<\/p>\n\n\n\n

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\u201cBuilding AI systems from the ground up, shaped by the knowledge, languages, and modalities of the global majority, yields more innovative, useful solutions for a great number of people. This is a crucial step in our progress toward adapting and deploying AI widely in low-resource settings.” <\/p>\n\u2014 Ashley Llorens<\/a>, Corporate Vice President and Managing Director, Microsoft Research Accelerator<\/cite><\/blockquote>\n\n\n\n

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Microsoft researcher Stephanie Nyairo (center) works with local collaborators in Kenya to test how accurately speech models recognize farmers\u2019 spoken questions.<\/figcaption><\/figure>\n\n\n\n
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There is no shortage of opportunities to extend AI\u2019s benefits to people who cannot fully access them today, and the Project Gecko team plans to expand their work into healthcare, education, and retail in the future. They began with agriculture because the sector acts as a strategic multiplier, where investments can simultaneously advance climate, health, and education outcomes. The initial focus is on small farms in India and Kenya, where millions of people could benefit from technology that can help boost crop yields and bolster resilience in an increasingly volatile climate.<\/p>\n\n\n\n

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VeLLM: The foundation<\/h2>\n\n\n\n

Project Gecko is built on VeLLM<\/a> (uniVersal Empowerment with LLMs), a platform developed by Microsoft Research India to support AI systems that create multilingual, multimodal content grounded in culturally relevant data. VeLLM uses community-contributed data and principled evaluation to improve LLM performance in non-English languages. For example, researchers from Microsoft used VeLLM to develop Shiksha copilot<\/a>, which helps teachers draft lesson plans faster and improves educational outcomes in rural India. Project Gecko affirms one of the original goals of VeLLM\u2014that AI created in one context would also translate to a different context, like agricultural information in Kenya.<\/p>\n\n\n\n

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“If we want to build AI for everyone everywhere, we need to develop new methods of human-centered AI. This involves forging new and deeper connections among disciplines such as machine learning, linguistics, and the social sciences, as well as the communities the AI is to serve. We all must work hand-in-hand to establish new methods for fine-tuning, model optimization, and evaluation so that AI can represent the richness and complexity of a wide range of culturally and linguistically diverse communities. Project Gecko is a great example of how we might begin to do this.”<\/p>\n\u2014<\/em> Jacki O\u2019Neill<\/a>, Lab Director, Microsoft Research Africa, Nairobi <\/cite><\/blockquote>\n<\/blockquote>\n\n\n\n

AI-powered agriculture in emerging economies<\/h2>\n\n\n\n

Agriculture accounts for 35% of GDP in Africa (opens in new tab)<\/span><\/a>. In Kenya, it accounts for 20% of GDP (opens in new tab)<\/span><\/a> and employs more than 40% of the population. Similarly in India, agriculture along with forestry and fisheries accounts for one-third of GDP (opens in new tab)<\/span><\/a> and supports over\u202f70% of rural households (opens in new tab)<\/span><\/a>. Most of these farms are run by smallholder farmers, families working on less than five acres of land. They are the backbone of rural communities, directly employing millions of people and providing crucial food security.<\/p>\n\n\n\n

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AI systems that reflect local cultural and agricultural contexts are essential to supporting farmers in their daily work. <\/figcaption><\/figure>\n\n\n\n
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Several digital services and AI-powered tools help farm workers address challenges like weather, pests, and livestock health. But since the underlying large language models (LLMs) are mostly trained on English and other Western languages, farmers struggle to get the right answers using local language and cultural terms, leading to a drop in usage.<\/p>\n\n\n\n

\u201cAgriculture has very specific terms, which may change from language to language, and sometimes from district to district. There might be two different words being used for the same thing as location changes. So, all those domain-specific nuances need to be understood,\u201d said Tanuja Ganu, Director of Research Engineering at Microsoft India, who leads the Center for Societal Impact through Cloud and Artificial Intelligence<\/a>.<\/p>\n\n\n\n

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In Kenya, a farmer tends to her livestock as AI models adapted for local languages make agricultural guidance more accessible.<\/figcaption><\/figure>\n\n\n\n
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The local language landscape can be rather complicated. In Kenya, for example, a farmer might write in English, speak in local languages like Kikuyu or Kalenjin, and use spoken Swahili as a common language across communities. Both Kenya and India have strong oral culture, so voice communication and video answers can help with information sharing, understanding, and recall. Visual representation provides a quick way to convey information without relying on text, while limited internet connectivity means that any system must run on low bandwidth and minimal computing power to deliver timely guidance to smallholder farmers.<\/p>\n\n\n\n

FarmerChat (opens in new tab)<\/span><\/a> is a speech-first AI-powered assistant provided by Digital Green (opens in new tab)<\/span><\/a>, an organization that began as a project within Microsoft Research India (opens in new tab)<\/span><\/a>. It helps agricultural extension workers advise millions of farmers with trusted agricultural recommendations. For nearly two decades, Digital Green has curated a library of more than 10,000 videos in over 40 languages and dialects, including Kiswahili, Hindi, and Kikuyu. This is significant because, in many developing regions, the knowledge from people working in the field is often shared through audio and video conversations rather than written documents. As a result, multimodal approaches are essential to unlock this vast reservoir of knowledge.<\/p>\n\n\n\n

Digial Green’s video library is continuously refreshed with input from farmers, extension workers, and researchers. But the full value of their impressive video collection was unrealized amid technical and linguistic challenges. The app needed to evolve from a Q&A engine into a trusted farming companion.<\/p>\n\n\n\n

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\u201cUnlocking this knowledge will support even more farmers to get real-time responses to their queries in their own local language and preferred modality, whenever and wherever they need it. This will boost the effectiveness of public extension and help reach farmers with locally tailored advice.\u201d<\/p>\n\u2014 Rikin Gandhi (opens in new tab)<\/span><\/a>, CEO, Digital Green<\/cite><\/blockquote>\n\n\n\n

Microsoft’s Project Gecko team envisioned farmers using speech or text to submit a query, receiving an actionable answer with step-by-step instructions in text, voice, and relevant video\u2014each of these in the farmers\u2019 preferred language. For example, in Nyeri County, Kenya, farmers may type a question in English or ask verbally in Kikuyu and receive the text answer in English and the voice and video answer in Kikuyu. The video would begin playing from the precise spot where a specific solution is presented.<\/p>\n\n\n\n

\u201cSo, if the video is, let’s say, 30 minutes long, the user does not have to go through the entire video, but we can take the user to, let’s say, 3 minutes 50 seconds, and they can watch it from there for 2 minutes 5 seconds to get the answer. So, it’s efficient. It\u2019s extremely time-effective for the users,\u201d Ganu said.<\/p>\n\n\n\n

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Project Gecko: Building globally equitable generative AI<\/h3>\n<\/div>