Dynamic Deep Learning
Letitia and Melanie discuss the limitations of current deep learning models, emphasizing the lack of dynamic, iterative capabilities. They explore the potential of integrating temporal, dynamic qualities into deep neural networks and other innovative approaches like probabilistic program induction. The conversation delves into the challenges of combining various ideas into a cohesive architecture for more advanced learning systems.In this clip
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Machine Learning Street Talk (MLST)
#57 - Prof. Melanie Mitchell - Why AI is harder than we think
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