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Will developers become redundant due to AI?
Andreas Pfeiffer from customer service talks about experiences with programming embedded systems using AI
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Have you ever tried to create programs using AI? I have to admit, I was amazed. The result was much better than expected and worked straight away. But does it also work when programming sophisticated embedded systems that require a high degree of precision, efficiency and know-how? What challenges and advantages does the use of AI bring in this area?
Accelerated development process
Traditionally, programming embedded systems required extensive manual work, from analyzing the hardware to writing low-level code and optimizing performance. AI tools have noticeably accelerated the development process. These tools can recognize patterns in data and code, make suggestions for optimization, and automate repetitive tasks.
One tool that is frequently used is AI-based code generators, which can create executable code from hardware descriptions or abstract specifications. Developers report that this not only reduces the time required, but also minimizes the likelihood of errors in the code. AI-based optimizations are particularly valuable for real-time applications where every millisecond counts.
AI tools enable developers to focus more on system architecture and testing while AI takes over routine tasks. Automatic detection of errors and vulnerabilities in the code significantly improves quality. AI algorithms can analyze code and the hardware environment to make suggestions for improving efficiency.
Despite the obvious advantages, there are also challenges. Some developers report a considerable learning curve in order to be able to use the new tools productively. In addition, AI-based tools are often designed for specific frameworks or hardware platforms, which limits their universal applicability.
Another problem is that developers do not know exactly how the AI reaches its decisions. In safety-critical and regulated applications, such as in medical technology or the automotive industry, this can lead to concerns regarding traceability.
Rethink required
The experience with programming embedded systems using AI tools is overwhelmingly positive. The technology saves time, reduces errors, and facilitates the implementation of complex functions. However, using these tools also requires an adjustment in approach and a change in thinking during development. Without further analysis and testing, I cannot say whether the program I mentioned at the beginning will function robustly and error-free in all cases.
AI will certainly not replace experienced embedded developers, but it is proving to be a powerful partner that can significantly increase productivity and efficiency. The future of embedded programming will undoubtedly be characterized by close collaboration between humans and machines.
