Edge AI technology is already in high demand, but widespread adoption across the enterprise and public sector has been slow until now. Critical pieces of the technology stack are maturing simultaneously and will accelerate edge AI adoption in the coming years.
The data-centric approach to AI consists of systematically optimizing datasets to improve the accuracy of AI systems. Machine learning scientists find this approach promising because refined data generates better results than unrefined data.
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Deep Learning (DL) is a subset of Machine Learning (ML) that involves learning a hierarchy of features to gain meaningful insight from a complex input space. DL networks can be visualized as a neural network with two or more layers of neurons performing calculations.