TinyML, a cutting-edge technology designed to run machine learning models on low-power devices, is gaining traction as a more sustainable and cost-effective alternative to traditional AI. This approach allows powerful AI applications to function on small, energy-efficient devices without relying on massive computing power or expensive infrastructure, making it particularly beneficial for the Global South.

TinyML’s ability to operate on small, low-cost hardware enables it to bring AI to areas with limited resources. This technology is being applied in various sectors, such as agriculture, healthcare, and energy, where access to power and connectivity is often scarce. It opens up possibilities for smart devices that can operate in remote areas, providing real-time data analysis and decision-making capabilities that were previously out of reach.

As TinyML continues to evolve, it promises to bridge the digital divide, offering AI-powered solutions that are affordable, energy-efficient, and tailored for regions with limited access to traditional AI infrastructure.

For more details, read the full article at Science.org.