Shades of 40 years ago, when we shifted from mainframes to desktop computing. AI is now moving from the cloud to the edge, and companies like Microsoft and Apple are at the forefront of this shift.
It’s not an easy transference, mind you.
As noted, artificial intelligence has traditionally been deployed in the cloud for the simple reason that AI algorithms require massive levels of number crunching that consumes immense computing resources. Something only the cloud can truly provide.
But times are quickly changing.
The AI of today does not just live in the cloud.
In many situations, AI-based data decisions need to be made locally, on devices that are close to the edge of the network. Particularly in cases where latency of moving the data to the cloud, processing it, and then transmitting the results back over the network can have a severe impact.
These delays can even have serious consequences. Examples being sensors in a chemical plant that predict an imminent explosion, or security cameras at airports or factories.
It is why technology giants are investing in technologies to move AI to the very edge of the network.
Apple, for example, spent $200 million earlier this month to acquire Xnor.ai, an artificial intelligence startup focused on low-power machine learning hardware and software. Microsoft, meanwhile, already offers a comprehensive toolkit called Azure IoT Edge to move AI workloads to the edge.
Yes, challenges do remain, most notably processing and performance.
But in a connected world where intelligence at the edge will compliment intelligence in the cloud, AI will live where it needs to live.