Matjaz Leonardis | Interpretability and Security of AI Models

Matjaž Leonardis, in collaboration with David Deutsch at the University of Oxford, is advancing the understanding of a theorem by Popper and Miller from 1983, offering new insights into Bayesian updating and its standard interpretations. His research interests span learning and creativity in both humans and machines. Beyond his academic pursuits, Matjaž engages with a wider audience through social media on topics ranging from education to computation.

Key Highlights

• The use of ML models for critical decisions and the inherent risks of undetectable back doors influencing results.
• Matjaz's paper on the practicality of inserting concealed back doors during ML training and their impact on model integrity.
• Challenges to model interpretability and robustness posed by these back doors, alongside their relevance to AI safety.
• The call for deeper research into complex ML model vulnerabilities, integrating cryptography to enhance computational clarity.

Dive deeper into the session: Full Summary

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