Artificial intelligence

Related lectures (31)

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Covers the basic properties of limits and provides illustrative examples.

Explores advancements in generative AI and reinforcement learning, focusing on their applications, safety, and future research directions.

Delves into different ethical theories and thought experiments in the context of AI, exploring the challenges of defining AI.

Explores Taylor approximation for function approximation and error control.

Explores building OS for heterogeneous hardware, data movement efficiency, AI advancements, and NLP challenges.

Covers the foundations of artificial intelligence, machine learning, the data revolution, and research examples in AI and machine learning.

Covers the role of Transformers in unifying various machine learning fields.

Explores matrix similarity, diagonalization, characteristic polynomials, eigenvalues, and eigenvectors in linear algebra.

Explores challenges in developing normative news recommender systems and the ethical considerations in AI, journalism, and diversity.

Delves into the continual learning of representational models after deployment, highlighting the limitations of current artificial neural networks.