There exists no set model for the successful implementation of AI at the edge – it is instead a case of flexibility, writes Canonical’s Carmine Rimi
As individual technologies, both artificial intelligence (AI) and edge computing have grown in stature over recent years. The connected home has brought a vast array of sensor-led solutions to the fore – remote-controlled heating, lighting and entertainment – while many laptops and tablets already possess the AI necessary to convert hand-written notes into text, creating instant designs on the screen.
While the convergence of the two is still in its infancy, together they hold the potential to revolutionise the lives of consumers and businesses alike. But it is not a marriage without its challenges, especially when it comes to practical implementation.
Laying the foundations
AI at the edge is not a distant dream. The ability to unlock a phone with your face, ask a smart assistant what the weather will be like tomorrow, or automatically adjust a camera to take the perfect low-light shots are all examples of AI in our pockets, right at the edge.
These use cases remain small scale, however, and relate to individuals rather than a technology that empowers groups or organisations across a variety of mutually-reinforcing applications. The challenge here is getting the infrastructure right to begin with, and to support the growing complexity at the extremities, because more network capacity locally means greater density at the edge.
The right mix of compute, AI accelerators, storage, and networking will allow AI at the edge to thrive and evolve. Similarly, bringing in the capacity of a core data centre, to offload any passive edge applications, will help streamline operations even further.
Although the edge is where the innovation appears to happen – where people actually interact with technology – balancing the workload between the locality and the cloud is the key to successful implementation. Get these foundations right, and the potential of AI at the edge will begin to be fully realised.
Harmonious infrastructure is the difference between one off examples of edge-based AI and much more expansive opportunities that connect larger patterns of data. Pooled from multiple devices, AI at the edge will offer intelligence greater than any individual piece of data can provide.