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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