11th edition AI lunch meeting (TU/e internal)

Event Details

Friday July 5, 2019 from 12:00 PM to 1:30 PM
MetaForum building - MF 11/12
Data Science

About the event

Many TU/e researchers are advancing or using Artificial Intelligence in their research projects. To support cross disciplinary learning and to strengthen the TU/e AI network, we organize a series of (internal) lunch meetings where various researchers talk about their projects.






   Johan Lukkien (Dean M&CS)



   Jack van Wijk, Visualization (M&CS)

Visualization for explaining predictive analytics


   Chis Snijders, Human Technology Interaction (IE&IS)                 

Talking to a super model


   Vincent Müller, Philosophy & Ethics (IE&IS)

Ethics of Artificial Intelligence and Robotics



Wrap up


Chris Snijders
Predictive models are getting better and smarter, but often still have to relay their findings to humans. Given that models can be complex and might still make mistakes, a human's trust in the model's output is neither automatic nor straightforward. Our research focuses on ways in which we can better understand and improve the connection between model output and human receiver.

Jack van Wijk
Predictive analytics concerns the use of algorithms for decision making and decision support. Typically, these algorithms are based on machine learning, leading to complex models that are often hard to understand. Our research focuses on developing new methods to provide more insight using interactive visualization, such that users are enabled to understand and judge the results. After an overview of the background, a number of examples from different domains are shown.

Vincent Müller
Artificial intelligence and robotics are technologies that seem to be of major importance for the development of humanity in the near future. They have raised fundamental questions about what we should do with these systems, what the systems themselves should do, and what risks they have in the long term. In this talk I will discuss the difference between viewing AI systems as objects, i.e. tools used by humans, vs. AI systems as autonomous subjects.


Data Science

Data Science is an interdisciplinary field that uses a variety of techniques to create value based on extracting knowledge and insights from available data. The successful and responsible application of these methods highly depends on a good understanding of the application domain, taking into account ethics, business models, and human behavior.