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Related lectures (29)

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Explores Brun's Sieve, Martingales, and Conditional Expectations in probability theory.

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Explores provably beneficial AI, aligning AI goals with human preferences and behaviors, illustrating complexities through examples like image classification and fetching coffee.

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Provides an in-depth exploration of AI ethics regulation, covering legal questions, EU regulation, GDPR, UNESCO texts, and the proposal for AI regulation.

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Provides an overview of deep learning concepts, focusing on data, model architecture, and challenges in handling large datasets.

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Examines historical information revolutions, ethical data concerns, automation impact, and AI ethics.

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Explores legal and ethical aspects of machine learning, including data protection laws, AI risks, and ethical considerations.

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Discusses student feedback, challenges in balancing laws and ethics, and the need for more critical thinking in AI ethics.

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