$ sudo screen /dev/ttyUSB0 115200

Edge technology as a catalyst for AI adoption

Edge AI is a combination of AI and Edge Computing. It enables machines to make decisions on locally harvested data, instead of sending it to a centralized cloud server. It enables the deployment of machine learning algorithms to the edge of the device where the data is generated. Edge AI has the potential to provide artificial intelligence for every use case at any place. The more human tasks we transfer to computers and machines, the more abundant and pervasive the data and the wider the demand for artificial intelligence processing becomes. This increasing demand is no longer satisfied by centralized processing in data centres. The recent availability of small and low power supercomputers makes edge intelligence attractive for a lot of applications, and this will give a boost to ai adoption in many industries with real-life use cases (and real ROI)

The edge as an expansion of cloud capabilities

Edge & cloud technology go hand in hand. While in the last decade a huge shift towards the cloud has happened, we experienced that for specific use cases it’s better to run (part of) the intelligence locally. By doing so, we can have the best of both worlds. We’re listing the main contextual elements where edge computing can offer a solution :

-          Network capacity

o   Moving a tremendous amount of data across the network poses serious challenges to network capacity and the computing power of cloud computing infrastructure. By using edge technology a part of the processing is done locally, which avoids abundant network traffic

-          Latency

o   For cloud-based computing, the transmission delay can be prohibitively high. Many new types of applications have challenging delay requirements that the cloud would have difficulty meeting consistently (e.g. autonomous driving)

-          Security

o   Most cloud computing applications depend on wireless communications and backbone networks for connecting users to services. For many industrial scenarios, intelligent services must be highly reliable, even when network connections are lost.

-          Data privacy :

o   Deep Learning often involves a huge massive amount of private information. Privacy issues are critical to areas such as smart homes, smart cities, hospitals, etc... In some cases, even the transmission of sensitive data may not be possible.

In conclusion, edge intelligence has a role to play in the future infrastructure, but requires specific hardware and this makes the rollout of new edge AI applications challenging. Find out how the modular Blox system can make edge technology easier & accessible for you. 

in Blog
# Blog
$ sudo screen /dev/ttyUSB0 115200

Why we do what we do.
A word from our co-founder Hans Stevens