- The landscape of artificial intelligence (AI) applications has traditionally been dominated by the use of resource-intensive servers centralised in industrialised nations.
- However, recent years have witnessed the emergence of small, energy-efficient devices for AI applications, a concept known as tiny machine learning (TinyML).
Small size, big impact
- This is possible because TinyML doesn’t require a laptop computer or even a mobile phone.
- In fact, given that there are already 250 billion microcontrollers deployed globally, devices that support TinyML are already available at scale.
How does it work?
- Like classical machine learning, TinyML involves data collection – often from Internet of Things (IoT) devices – and cloud-based training.
- Let’s consider an outdoor object-detection application – for example, counting the number of cars on a street to see how heavy the traffic there is.
- In the classical ML process, images have to be gathered using a webcam and sent to a cloud server where the training takes place.
Affordability: the technology’s low cost makes these devices accessible to a wide range of users including educational institutions and students in the developing world.
Sustainability: the modest energy consumption produces a low carbon footprint, reducing impact on the environment.
Flexibility and scalability: it enables the development of applications that address the needs of local communities rather than global agendas.
Internet independent: Because everything is embedded, TinyML devices can operate without online connectivity. This is particularly beneficial for the third of the world that still does not have Internet access.
TinyML applications already power personalised sensors for athletics and provide localisation where GPS isn’t available. They’re also employed by startups such as Useful Sensors, which offers privacy-conserving conversational agents, QR code scanners, and person-detection hardware. Only through the use of TinyML could these smart devices run on the low-cost, low-power microcontrollers.
Developing in the Global South
- It already includes more than 40 countries spanning the Global South from Columbia to Ethiopia to Malaysia.
- Its aim is to develop a community of educators, researchers and practitioners focused on both improving access to TinyML education, and developing innovative solutions to address the unique challenges faced by developing countries.
- Initial efforts included distributing TinyML hardware kits to selected universities with budgetary challenges.
- We also organised global and regional (Africa, Latin America, and Asia) workshops and training sessions.
- These collaborations have led to multiple peer-reviewed papers on TinyML applications.
- They’re also used by Cornell University’s “Elephant Listening Project” as well monitoring water quality in aquaculture to help make it more sustainable, a project supported by EU’s Horizon 2020 programme.
Looking forward
- It offers a sustainable path toward democratising AI technology, fostering local innovation, and addressing regional challenges.
- The growth of TinyML devices and applications is not without potential challenges and risks, however.
- There’s also the risk of embedded biases in critical ML models – because they operate standalone, there’s no option for updates.
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