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Event Date Thu May 27 CEST (over 1 year ago)
In your timezone (EST): Thu May 27 2:00am - Thu May 27 11:00am
Location Webinar
Region EMEA

Artificial Intelligence (AI) systems have steadily grown in complexity, gaining predictivity often at the expense of interpretability, robustness and trustworthiness. Deep neural networks are a prime example of this development. While reaching “superhuman” performances in various complex tasks, these models are susceptible to errors when confronted with tiny (adversarial) variations of the input – variations which are either not noticeable or can be handled reliably by humans. This expert talk series will discuss these challenges of current AI technology and will present new research aiming at overcoming these limitations and developing AI systems which can be certified to be trustworthy and robust.

The expert talk series will cover the following topics:
• Measuring Neural Network Robustness
• Auditing AI Systems
• Adversarial Attacks and Defences
• Explainability & Trustworthiness
• Poisoning Attacks on AI
• Certified Robustness
• Model and Data Uncertainty
• AI Safety and Fairness


2021 Speaker

Gregoire Montavon
Senior Researcher, Machine Learning Group, TU Berlin

Head of Department of Artificial Intelligence, Fraunhofer Heinrich Hertz Institute