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The Foresight Institute Podcast

Matjaz Leonardis | Interpretability and Security of AI Models

Dec 15, 2023
Matjaz Leonardis discusses the risks of undetectable back doors in ML models, their impact on model integrity, challenges to interpretability and robustness, and the need for deeper research into vulnerabilities.
09:28

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Quick takeaways

  • The risks of undetectable back doors in ML models for critical decisions, emphasizing the need for increased security measures.
  • The challenges in ensuring model interpretability and robustness due to concealed back doors, highlighting the importance of research in enhancing AI safety.

Deep dives

Backdoors in Machine Learning Models

The concept of backdoors in machine learning models is discussed, highlighting concerns about interpretability and robustness. The potential to manipulate models by inserting hidden elements during training raises questions about the integrity and reliability of AI systems. Researchers have successfully demonstrated the creation of undetectable backdoors in neural networks, challenging the security and trustworthiness of these models. The implications extend to AI safety, with the idea of cryptographically hiding functions within the code for control and potential risks in ensuring transparency and reliability.

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