Focusing on solutions within Azafran’s Thesis, following are a number of Augmented AI and Tiny ML use cases already out there in the world:
One recent event in the advancement of TinyML was Apple’s acquisition of Xnor.ai, a Seattle startup spun off from the Allen Institute specializing in low-power, edge-based ML tools. Xnor.ai’s technology embeds ML on the edge, enabling facial recognition, natural language processing, augmented reality, and other ML-driven capabilities to be executed on low-power devices rather than relying on the cloud.
Another recent key milestone in development of TinyML was Amazon Web Services’ recent release of the open-source AutoGluon toolkit. This is a ML pipeline automation tool that includes a feature known as “neural architecture search” which finds the most compact, efficient structure of a neural net for a specific ML inferencing task.
There are also commercial implementations of neural architecture search tools on the market. A solution from Montreal-based startup Deeplite (under LOI with Azafran) can automatically optimize a neural network for high-performance inferencing on a range of edge-device hardware platforms. It does this without requiring manual inputs or guidance from scarce, expensive data scientists.
Radiologists are already using machine learning and assistive technologies as diagnostic tools. Retinal scanners in smartphone apps can detect “white eye,” or retinal reflections, in infants – often a sign of cancer or cataracts.
Facial recognition technology is also being used to diagnose infants. Boston-based company FDNA developed an AI that cross-references photos of infant faces to flag potential genetic conditions.
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