Moving AI to the Edge

Nicely put,  mostly non technical overview of what it means to have AI move to the Edge.  Ssing it in places like the Smart Home.  Favorite topic of mine.  Needs more discussion of security .... more to follow on that.

From cloud to device
The future of AI and machine learning on the Edge   in Feedly
A brief overview of the state-of-the-art in training ML models on devices. For a more comprehensive survey, read our full paper on this topic.  ( https://arxiv.org/abs/1911.00623 ) <- good overview 

We are surrounded by smart devices: from mobile phones and watches to glasses, jewelry, and even clothes. But while these devices are small and powerful, they are merely the tip of a computing iceberg that starts at your fingertips and ends in giant data and compute centers across the world. Data is transmitted from devices to the cloud where it is used to train models that are then transmitted back to be deployed back on the device. Unless used for learning simple concepts like wake words or recognizing your face to unlock your phone, machine learning is computationally expensive and data has no choice but to travel these thousands of miles before it can be turned into useful information.

This journey from device to data center and back to device has its drawbacks. The privacy and security of user data is probably the most obvious as this data needs to be transmitted to the cloud and stored there, most often, indefinitely. Transmission of user data is open to interference and capture, and stored data leaves open the possibility of unauthorized access. But there are other significant drawbacks. Cloud-based AI and ML models have higher latencies, cost more to implement, lack autonomy, and, depending on the frequency of model updates, are often less personalized. .... "