43rd International Conference on Conceptual Modeling (ER 2024)

Who hasn't dabbled with popular Artificial Intelligence (AI) Large Language Models such as Gemini or ChatGPT, either as part of a project or just to see what it might produce? The popularity and idea that AI can transform human effort is everywhere in our scientific and industrial communities. AI clearly is a growing business driver.

And AI has value for conceptional modelling. For instance, natural language processing can help in identifying the relational data of entities and generative AI and inverse relationship approaches can help create deeply insightful entity relationship (ER) diagrams and models for the systems. To get the usable, reliable AI systems people want, designers and developers need to learn how to repeatably build sound AI systems. With those systems, we will see improvements in areas such as modeling (e.g., digital twins for smart cyber-physical systems), security engineering to defend large-scale systems, and the use of modern software practices (e.g., in developing threat models for DevSecOps).

Building sound AI systems repeatably, we need to pursue these system qualities: robust, secure, scalable, and human centered. Those quality attributes form the basis for forming an AI Engineering discipline. There is much work to do
to create and implement the AI Engineering discipline in areas such as making a commitment to moving R&D in practice to get ahead of the galloping use of AI tools, recognition that we cannot abandon good practices such as practicing cyber hygiene and the use of modern software engineering practices, and the creation of a new concept of the digital-engineering-aware workforce able to interact effectively with AI models.