In today's rapidly evolving technological landscape, artificial intelligence, specifically deep learning, becomes able to solve increasingly complex tasks. Their increasing capabilities make them useful for real-world problems and deployed even for critical tasks such as making hiring decisions, medical diagnostics, or safety-critical operations. However, deep learning algorithms face new challenges, constraints, and threats when deployed outside well-controlled test environments to solve real-world problems.
In this seminar, students will summarize and critically analyze the recent literature on the assigned topic in the form of a report. Additionally, each student will present his work in front of the group at the end of the semester.
- Dozent*in: Phillip Rieger