In the 2nd series of talks organised by CAIDAS, members and selected guests present their research with exciting talks on current activities and projects. The talks will take place every Friday in the middle of the month at 15:00 (st) starting from December 2021. The talks will be 45 minutes, followed by a 15-minute discussion.
Due to the pandemic contact restrictions, the series of talks are held as a zoom meeting: https://go.uniwue.de/ai-talks-zoom
See below for details.
What makes teams successful? Insights from Repository Mining, Network Science, and Empirical Software Engineering
The convergence of social and technical systems provides us with a wealth of log data that capture the structure and dynamics of social organizations. It is tempting to utilize these data to better understand how social systems evolve, how collaboration patterns in teams are related to their "success" or "failure", and how the position of individuals in social networks affects their performance, motivation, and productivity.
Focusing on the empirical study of collaborative software projects, in this talk I will show how massive repository data from publicly available online platforms can be used to better understand human and social aspects in software development. Addressing an ongoing debate about the influence of team size on developer productivity, I specifically argue how we can use high-resolution time-stamped data on code editing to automatically construct meaningful collaboration networks. I further show how we can creatively use network models to test hypotheses at the intersection of software engineering, organizational theory, and network science, and which pitfalls await us in the analysis of massive data from online systems.
AI in Engineering Design: From Tool to Partner
The role of AI in Engineering and particularly in Engineering Design has made significant progress in the last years. In the first part of my presentation, I will outline the CAE/AI enhanced approach to engineering design from an industrial perspective. This will include examples from design and topology optimization and concludes with some of the remaining challenges like robustness and many-objective optimization.
In the second part of my presentation, I will introduce approaches to go beyond the tool-based AI in the engineering design process chain and enable the AI methods to improve their performance over time. Experience-based Computation: learning to optimise is an EU Horizon 2020 project that addresses the issue on how optimization can be improved through learning just like the engineer becomes more and more experienced over time. I will look at one approach inspired from data mining and knowledge extraction and one from transfer learning and the advantage of multi-task optimization.
AI as a cooperative partner in the engineering design process will be the subject of the last part of my presentation. I will briefly introduce the general concept of cooperative intelligence and then outline some of the challenges in understanding the engineer for optimal support. Many if not most engineering design decisions are made in a team, therefore, it is necessary to go beyond the cooperative interaction between the engineer and AI as a partner, but to also study the effect that an AI system can have on the decision dynamics in a team.
The presentation will conclude with a summary and some additional issues that have to be addressed to evolve AI from a tool to a partner in Engineering and in Engineering Design.