The ongoing implementation of AI and machine learning is accelerating the creation of smart devices that are contextually aware of their environment. In turn, the demands placed on smart machines will benefit from the growth in multi-sensory data that can compute with increasing precision and greater performance. Edge computing continues to drive the opportunity to transform this AI data into actionable value. The intelligent edge is the next phase in this evolution and will no doubt reinforce the contributions being made by AI technology. The article below offers more insight into how this evolution will unfold.
As the adoption of artificial intelligence (AI) and machine learning (ML) grows, the ability to process large amounts of data in the form of algorithms for computational purposes becomes increasingly important.
To help make the expanding use of data applications across billions of connected devices more efficient and valuable, there is growing momentum to migrate the processing from centralized third-party cloud servers to decentralized and localized on-device, commonly referred to as edge computing. According to SAR Insight & Consulting’s latest artificial intelligence/machine learning embedded chips database, the global number of AI-enabled devices with edge computing will grow at a CAGR of 64.2% during the period 2019-2024.