Scaling and Symbolic Computation
Laura discusses the potential of scaling current machine learning approaches while highlighting the need for more data-efficient methods. She reflects on the enduring critique of connectionist models and their ability to perform symbolic computation, suggesting that while neural networks may not explicitly represent rules, they can implicitly learn them. The conversation hints at the importance of agency in learning, setting the stage for deeper exploration of active versus passive learning strategies.In this clip
From this podcast
Machine Learning Street Talk (MLST)
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