Artificial Intelligence (AI) is about to enter the traditional industry segments. When this happens we can start to see the real gains with AI, where the industry becomes radically more efficient and the environment much cleaner. But currently, many are struggling to bring the intelligence to all the small devices that together form the industry, in other words, to bring AI to the edge.
AI keeps entering new fields with great promises at a high pace. So far, the connected and already digital industries such as media and advertising, finance and retail have been exploited the most. There is no doubt that AI has created real value in these segments: There are plenty of convenient services and functions nowadays that make our lives a little bit easier and smoother. However, the big and important problems are still ahead of us. The solution to climate- and environmental problems is to remove old, dirty and inefficient technology and replace it with clean energy and an efficient industry, the latter commonly referred to as Industry 4.0.
A crucial component in Industry 4.0 is by introducing intelligence “on the edge”. The edge is referred to as the part of our world that is outside the range of high speed and large bandwidth connections, which, frankly speaking, is most of our world at the moment. Intelligence on the edge means that even the smallest devices and machines around us are able to sense their environment, learn from it and react on it. This allows for instance the machines in some factory to take higher level decisions, act autonomously and to feedback important flaws or improvements to the user or the cloud.
Practically, the sensing part is achieved by having a sensor of some sort (e.g. accelerometer, data from an engine or any other type of sensor) connected to a small microcontroller unit (MCU). The MCU is loaded with some software that has been pre-trained on the typical scenarios that the device will encounter i.e. some data that has been collected beforehand and fed to the software. This is the learning part, which can also be a continuous process so that the device learns as soon as it encounters new things. The last part, the reaction of the AI (inference), can be some physical actuation on its immediate environment, or it can be a signal to a human being or the cloud for further action and assistance.
To be able to learn from data, the software is based on some machine learning technique. However, some of the most promising machine learning models widely applied in the cloud and digital industries, still use too much computing power and memory to be able to fit the small MCUs on the market.
Developing embedded real-time applications is on its own one...