As artificial intelligence (AI) matures, adoption continues to increase. According to recent research, 35% of organizations are using AI, with 42% exploring its potential. While AI is well-understood and heavily deployed in the cloud, it remains nascent at the edge and has some unique challenges.
Many use AI throughout the day, from navigating in cars to tracking steps to speaking to digital assistants. Even though a user accesses these services often on a mobile device, the compute results reside in cloud usages of AI. More specifically, a person requests information, and that request is processed by a central learning model in the cloud, which then sends results back to the person’s local device.
AI at the edge is less understood and less frequently deployed than AI in the cloud. From its inception, AI algorithms and innovations relied on a fundamental assumption—that all data can be sent to one central location. In this central location, an algorithm has complete access to the data. This allows the algorithm to build its intelligence like a brain or central nervous system, with full authority on compute and data.
But, AI at the edge is different. It distributes the intelligence across all the cells and nerves. By pushing intelligence to the edge, we give these edge devices agency. That is essential in many applications and domains such as healthcare and industrial manufacturing.
Reasons to deploy AI at the edge
There are three primary reasons to deploy AI at the edge.
Protecting personally identifiable information (PII)
First, some organizations that deal with PII or sensitive IP (intellectual property) prefer to leave the data where it originates—in the imaging machine at the hospital or on a manufacturing machine on the factory floor. This can reduce the risk of “excursions” or “leakage” that can occur when transmitting data over a network.
Minimizing bandwidth usage
Second is a bandwidth issue. Shipping large quantities of data from the edge to the cloud can clog the network and, in some cases, is impractical. It is not uncommon for an imaging machine in a health setting to generate files that are so massive that it is either not possible to transfer them to the cloud or would take days to complete such a transfer.
Continue reading: https://www.techrepublic.com/article/edge-ai-tips/
Many use AI throughout the day, from navigating in cars to tracking steps to speaking to digital assistants. Even though a user accesses these services often on a mobile device, the compute results reside in cloud usages of AI. More specifically, a person requests information, and that request is processed by a central learning model in the cloud, which then sends results back to the person’s local device.
AI at the edge is less understood and less frequently deployed than AI in the cloud. From its inception, AI algorithms and innovations relied on a fundamental assumption—that all data can be sent to one central location. In this central location, an algorithm has complete access to the data. This allows the algorithm to build its intelligence like a brain or central nervous system, with full authority on compute and data.
But, AI at the edge is different. It distributes the intelligence across all the cells and nerves. By pushing intelligence to the edge, we give these edge devices agency. That is essential in many applications and domains such as healthcare and industrial manufacturing.
Reasons to deploy AI at the edge
There are three primary reasons to deploy AI at the edge.
Protecting personally identifiable information (PII)
First, some organizations that deal with PII or sensitive IP (intellectual property) prefer to leave the data where it originates—in the imaging machine at the hospital or on a manufacturing machine on the factory floor. This can reduce the risk of “excursions” or “leakage” that can occur when transmitting data over a network.
Minimizing bandwidth usage
Second is a bandwidth issue. Shipping large quantities of data from the edge to the cloud can clog the network and, in some cases, is impractical. It is not uncommon for an imaging machine in a health setting to generate files that are so massive that it is either not possible to transfer them to the cloud or would take days to complete such a transfer.
Continue reading: https://www.techrepublic.com/article/edge-ai-tips/
