Join Testnet

Here’s What’s Still Lacking in Artificial Intelligence

How does ArtificiaI Intelligence(AI) Currently Work?

Imagine having to run to a nearby library and flip through pages of encyclopedias every time you saw a dog in the street and wanted to know what breed it is. That’s essentially how artificial applications function today. Even though Artificial Intelligence can do many tasks at par, or better than humans, humans still have an advantage: we, as humans, have intelligence at the edge.

The majority of our daily tasks are processed and performed by our own brain. When our processing power and memory aren’t enough to solve a problem, we tap into knowledge located at an external location such as a library, google, etc.

Machine learning algorithms are both computational and data-intensive. And therein lies a problem. These characteristics limit the number of environments where artificial intelligence can be deployed. As we gradually move toward a world where computation is moving from information technology to operation technology, we need to develop and deploy technologies for fog computing. These will bring the performance of AI functions to the edge.

Where AI Can’t Reach

Another limitation that cloud-based AI poses is for environments where there’s limited or no connectivity. The only alternative to cloud servers are proprietary data centers that cost heavily to set up and maintain.

Remote locations such as countryside farms can benefit immensely from artificial intelligence. Yet they will have limited access to AI applications because of their poor connectivity. As IoT moves into more disconnected environments, the need for edge or fog computing will only accelerate.

Artificial Intelligence Needs Adequate Network Solutions

If AI is the brain in the cloud, then networks are the neurons delivering all the necessary information. Actually, the network is the only entity that interacts with all elements of the cloud — the cloud data centers, services, and applications.

Currently, network architectures are simply incapable of meeting the demands of AI and machine learning as it involves enormous volumes of raw and processed data. An advanced network such as NOIA Network, will have the ability to deliver content at the edge. Just such a network will be required in order to provide cloud services with the necessary availability, security, performance, and scalability needed to drive the adoption of AI and machine learning applications.

Therefore, high-bandwidth edge internet connectivity is as imperative for cloud implementation as it is for bandwidth-intensive enterprise applications that rely on the cloud’s compute and storage power. Given that vast amounts of data will need to be analyzed quickly and at scale, cloud computing and edge networks such as NOIA will become essential for the delivery of AI services.