Why is edge AI important?
Imagine having to run to your local library and flip through the pages of an encyclopedia every time you saw a dog or cat in the street and wanted to know what its species is. That is pretty much how artificial applications function currently.
Artificial intelligence can predict stocks, diagnose patients, hire job applicants, play the games of chess and go, and do many more tasks on par or better than humans. Humans still have an advantage however: They have intelligence at the edge.
Most of the things humans do are processed and performed by their brain, the source of computational power that is within the direct proximity of their limbs. Where their own processing power and memory aren’t enough to solve a problem, they can tap into knowledge that is located at a further location. This can be anything such as visiting the library or sitting behind a computer and googling an unknown term. In case of the cat and dog premise, we’d eventually learn the names of species and refer to our own memory when seeing a new animal.
In contrary, most mobile apps, Internet of Things and other applications that work with AI and machine learning algorithms applications must rely on processing power sitting in the cloud or at a datacenter at thousands of miles away, and have little intelligence to apply at the edge. Even if you show your favorite yogurt to your smart fridge a thousand times, it’s still have to look it up in its cloud server in order to recognize it the 1001st time:
The do this very efficiently, faster than any human could possibly run through a catalog of products. But they still don’t have a fraction of the processing power that humans do at the edge.
The problem with machine learning algorithms is that they are both computational- and data-intensive. This limits the environments where artificial intelligence can be deployed, especially as we gradually move toward a world where computation is gradually moving from information technology into operation technology, and this is why we need to see the development and deployment of technologies for fog computing, which enable the performance of AI functions at the edge.
Thanks to broadband internet connectivity, web APIs for AI engines have sub-second response times.
