Large language models (LLM) have revolutionized the processing of natural language. Although first benchmarks of the process modeling abilities of LLM are promising, it is currently under debate to what extent an LLM can generate good process models. In this contribution, we argue that the evaluation of the process modeling abilities of LLM is far from being trivial. Hence, available evaluation results must be taken carefully. For example, even in a simple scenario, not only the quality of a model should be taken into account, but also the costs and time needed for generation. Thus, an LLM does not generate one optimal solution, but a set of Pareto-optimal variants. Moreover, there are several further challenges which have to be taken into account, e.g. conceptualization of quality, validation of results, generalizability, and data leakage. We discuss these challenges in detail and discuss future experiments to tackle these challenges scientifically.

The Internet of Things' killer app might be artificial intelligence. While it may be a stretch to classify artificial intelligence (AI) and its multifaceted offshoot machine learning as true applications, these techs can profoundly change IoT operations. AI makes IoT networks smarter and able to scale as needed without the risk of uncontrollable growth. IoT operations is an ongoing struggle to try to ensure that the thousands or more devices run properly and safely on an enterprise network and that the data that's being collected is both accurate and timely. While the sophisticated back-end analytics engines do the heavy lifting of processing the steady stream of data, ensuring the quality of the data itself is often left to somewhat archaic methodologies.

Between 2014 and 2017 Amazon tried to build an algorithmic system to analyze resumes and suggest the best hires. An anonymous Amazon employee called it the "holy grail" if it actually worked. After the company trained the algorithm on 10 years of its own hiring data, the algorithm reportedly became biased against female applicants. The word "women," like in women's sports, would cause the algorithm to specifically rank applicants lower. After Amazon engineers attempted to fix that problem, the algorithm still wasn't up to snuff and the project was ended.