On May 15, I presented about the future of configuration management enhanced by AI at the IpX International CM2 Congress. The presentation highlighted a fictitious day with an AI assistant, Chrissy, illustrating its role in tasks like meeting organization, impact analysis, and change planning, while addressing data quality and bias concerns in AI systems.
This article discusses enhancing the CM Baseline’s knowledge graph by integrating product and event data, creating a comprehensive ‘product 360.’ It emphasizes the significance of Event Knowledge Graphs to uncover insights overlooked by traditional analysis. By modeling relationships between events and states, it proposes improved predictive capabilities and behavior understanding in product development.
Recent incidents involving autonomous vehicles highlight safety concerns and the complexities of AI development. As AVs face challenges in predictable behavior, the need for Explainable AI becomes crucial. Ensuring accountability and transparency in AI decision-making can build public trust while addressing the risks associated with testing on public roads.
OpenAI’s release of GPT-4 enhances Large Language Models (LLMs) capabilities for impact analysis in configuration management. By leveraging LLMs, knowledge graphs can be utilized to reveal implicit relationships and support decision-making during changes. Upcoming tools, like NeoChat, hint at significant advancements in data exploration for engineers and managers.
The emergence of Large Language Models (LLMs) like Bard, ChatGPT, and Ernie Bot has raised questions about their influence on Configuration Management. Potential applications include improving documentation, assisting audits, and enhancing search capabilities. Despite their promise, human oversight remains essential in decision-making and compliance due to accountability concerns surrounding AI.
I joined Thom Singer and Craig Brown for a second time on the Digital Enterprise Society Podcast. I talk about the Future of Configuration Management. Topics like the CM2 Baseline, Machine Learning and Software over-the-air updates were discussed.
This article examines the complexities of managing product configurations in AI integration, focusing on how evolving AI capabilities, training datasets, and regulatory changes like Export Control influence this process. It raises critical questions about accountability, impact analysis, and certification requirements when AI alters a product’s functionality or classification.
In a two-part podcast, Arnaud Hubaux from ASML and Max Gravel from IpX explore the intersection of export control and machine learning. They discuss complexities arising from datasets, algorithms, and federated learning, emphasizing the need for legal expertise in navigating export regulations related to AI technologies and their evolving configurations.
Rob McAveney, Aras CTO, discussed the transformative impact of AI and machine learning on Impact Analysis at the Aras Digital 2020 event. His insights emphasize using these technologies to enhance decision-making in change management by identifying patterns in impact matrices, ultimately improving quality, reducing rework, and minimizing delays.
Industry 4.0, introduced by the German government, emphasizes the integration of advanced technologies like IoT into manufacturing. A survey showed many companies focus on initiatives like Big Data and Augmented Reality, with Configuration Management (CM) being crucial for handling increased product complexity. However, insufficient CM maturity poses risks as companies embrace these changes.