As artificial intelligence (AI) becomes a cornerstone of the global economy, it is imperative that its foundations are laid in the fertile soil of community-driven data, knowledge, and wisdom.
This approach must be sustainable, transparent, and universally beneficial. Herein lies the essence of 'bottom-up AI’ — an AI that grows from the grassroots of society.
Kenya, with its innovative bottom-up economic strategy, could play a pioneering role in new AI developments. Bottom-up AI could give farmers, traders, teachers, and the local and business communities the power to use and protect AI systems that contain their knowledge and skills that have been honed over generations.
Kenya’s digital landscape is ripe for such innovation. It is home to a dynamic tech community and has been the cradle of numerous technological breakthroughs, such as the widely known M-Pesa mobile payment service and the Ushahidi crowdsourcing platforms.
However, there is a prevailing notion, fuelled by media narratives, that AI development is the exclusive domain of massive investments and powerful computational centres.
Is it possible for Kenya to circumvent these limitations using its indigenous 'Jua Kali’ – an informal, resourceful approach – to cultivate bottom-up AI?
The answer is a resounding yes, as exemplified by the advent of open-source platforms and the strategic utilisation of concise, high-quality datasets.
Contrary to the dominant belief that AI necessitates colossal AI systems — as leveraged by prominent language models like ChatGPT and Bard —open-source AI platforms are challenging this paradigm.
A purported internal document from Google candidly acknowledges this competitive edge: “They are doing things with $100 and 13 billion parameters that we struggle with at $10 million and 540 billion parameters. And they are doing so in weeks, not months.”
Names like Vicuna, Alpaca, LLama, and Falcon now appear alongside ChatGPT and Bard, demonstrating that open-source platforms can deliver comparable performance without extravagant costs. Moreover, they are faster, more adaptable, and environmentally friendly – requiring significantly less energy for data processing.
As open-source algorithms become more accessible, the emphasis of bottom-up AI naturally shifts to data quality, which depends on data labelling, a human-intensive activity. A lot of data labelling for Chat GPT has been done in Kenya, which triggered numerous labour criticism.
Alternative approaches are feasible. As a matter of fact, at Diplo, the organisation I am part of, we’ve pioneered integrating data labelling into our regular activities, from research to training to project development. This is akin to using digital highlighters and sticky notes within our interactive frameworks, thus organically fostering bottom-up AI.
Text is not the sole medium for knowledge codification. We can also digitally annotate videos and voice recordings. Imagine farmers sharing their insights on agriculture and market strategies through narratives, enhancing the AI’s knowledge base with lived experiences.
The primary hurdle for bottom-up AI is not technological but organisational and revolves around societal and policy priorities. Building on its digital dynamism, Kenya has the potential to lead by marrying technological advances with practical, citizen-focused applications.
Kenya’s bottom-up AI could contribute to preserving our knowledge and wisdom as a global public good, which we should pass on to future generations as the common heritage of humanity.
Kurbalija, PhD, is the executive director at Diplo Foundation & head, the Geneva Internet Platform
