Balancing AI Safety and Utility

This chapter explores the intricate dynamics of AI model behavior, focusing on the tension between robustness, corrigibility, and the ethical implications of adversarial techniques. It emphasizes the need for a thoughtful approach to AI design that balances safety and utility while addressing historical cybersecurity lessons. Additionally, the chapter raises critical questions about the governance of powerful open-source AI models and their societal impact.

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In this episode, security researcher Nicholas Carlini of Google DeepMind delves into his extensive work on adversarial machine learning and cybersecurity. He discusses his pioneering contributions, which include developing attacks that have challenged the defenses of image classifiers and exploring the robustness of neural networks. Carlini details the inherent difficulties of defending against adversarial attacks, the role of human intuition in his work, and the potential of scaling attack methodologies using language models. He also addresses the broader implications of open-source AI and the complexities of balancing security with accessibility in emerging AI technologies.

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