The Internet of Things was just the beginning. With “Edge AI,” sensors and end devices start to think and thus offer companies from different areas new possibilities. Artificial intelligence is one of the most discussed technological innovations in recent years. The possible uses seem – at least if you believe the media tenor – to be endless.
Whether Alexa or Google Home, online shopping, in the healthcare sector, online marketing, or in the industry – learning and networked IT systems play an increasingly important role in companies. With the help of ever more robust algorithms, vast amounts of data can be analyzed, patterns or errors can be identified, and processes can be automated.
Cloud Computing Is Not Always Suitable
Currently, most use cases that contain artificial intelligence are based on cloud computing functionality. This means, for example, that a smartwatch must always communicate with the cloud to be able to map AI functions according to the current state of the art. The problem: To reach the next evolutionary level of artificial intelligence, data processing is required much faster than the cloud could offer – the cloud is becoming more, and more of a bottleneck for advanced
AI use cases. An example: A motorcyclist with a sensor-equipped helmet and object recognition drives towards a pothole. The helmet receives the signal from the sensor that has detected the potential hazard and has to process it within a few seconds. It doesn’t seem to make much sense here To first send the data to the cloud due to the high latency, let alone save it for the long term – an alternative method of data processing is needed.
As a result, the topic “Edge computing” is becoming more and more important. The difference to cloud computing: With this approach, data is recorded, analyzed, and processed decentrally on the “edge of the network” – that is, on smart devices and sensors of various types.
The Advantages Of Decentralized Data Processing
The data are processed where they are collected. Decentralization offers advantages in many areas. Because if there is no communication with the cloud, the individual device is not dependent on a stable network connection for processing. This is particularly important in rural regions with inadequate broadband coverage.
But also for extreme weather situations. For example, data processing via Edge AI makes far more sense at weather stations or in the mountains. There, but also in cities, there is also the issue of energy efficiency. Long-term communication skills require energy, and if batteries or a complex power supply are needed, this drives up the price. For example, this makes areas of application, for example, from the retail sector, uninteresting due to tight margins. The same applies to the communication costs themselves.
Last but not least, data protection and IT security also play a not insignificant role. If data is always sent to the cloud to be processed there, the risk increases that unauthorized persons will intercept or read it. If, on the other hand, the original information is evaluated and processed directly on-site, it can then be deleted immediately, and only the corresponding metadata is passed on at defined intervals. Image and video processing is a particularly critical case here.
When it comes to IT security, the vulnerability of the cloud comes to the fore. In large companies, in which system-critical AI solutions are often organized in a centralized manner, there is high latency and the risk of bundled attacks via IoT devices. If any of these devices are in use and constantly communicate with the cloud, a distributed denial-of-service attack (DDoS) can quickly become a problem.
This problem does not usually exist with Edge AI devices, as the devices do not constantly communicate with each other or with the cloud. And the risk of the “single point of failure,” in which the failure of one component means the collapse of the entire system, does not exist with Edge AI devices.
Also Read : With IoT Edge Computing To The Autonomous Digital Company Of The Future
The Implementation Of Edge AI Projects
At the beginning of every Edge AI project, it is essential to define the fundamental problem clearly and outline the solution. Since we are talking about very new technology at Edge AI, it remains to be seen how individual aspects will develop and establish themselves in business practice.
Above all, this includes how far artificial intelligence will migrate from the cloud to the edge. This depends not least on the performance of the processors. Questions that will also be relevant are which price-performance combinations will prevail in chip design and how disposable the future chips will be.
The second step to the Edge AI project is to create a system footprint and define the areas in which machine learning should support. At the center of this consideration are the questions: Which target group do I want to address with the product, and which value proposition do I want to give to the end product users? Key questions that also need to be considered are:
- Which functions should the product fulfill?
- Which use cases should the product support?
- Which key deliverables does the project have to provide, and when do they have to be delivered?
- What specific technical components (building blocks) should the project consist of?
- Which specialized interfaces does the project need for each system or user involved?
- What are possible technical restrictions?
- What are possible conditions from the stakeholders involved?
Once these central questions have been clarified, the third step of the Edge AI project is collecting and labeling data and then developing digital test scenarios that will later prove the use case in practice. So here, it is essential to build models, feed them with the collected data, and train them in this way.
The data quality is of integral importance for this project – you should therefore work cleanly, especially when it comes to labeling, as the subsequent steps are based on this. Other questions that should be asked in advance are: Which mechanisms do I use to collect the data? Which data format do I choose, and which real-life conditions do I define for my project?
If this step has also been completed, it is ultimately essential to check the implementation carried out and integrate the machine learning model on the intelligent device.
Outlook And Challenges
As already mentioned, Edge AI is a new technology-fueled by technical innovations and is only just becoming feasible – but already promises enormous potential for use cases in various industries. A problem that is already emerging today is the case in the Internet of Things – that there is no uniform standard for frameworks. Companies should work together to create a “common ground” from which all actors can benefit.
Also Read : The Internet Of Things In Insurance
