Code and Abstraction
The discussion explores how incorporating code into training datasets can enhance model learning by enabling step-by-step reasoning and procedural understanding. Laura highlights the robustness gained from diverse expressions of the same problem, while Yannic draws parallels between human cognitive processes and neural network functioning, suggesting that our understanding of abstractions may be more complex than it seems.In this clip
From this podcast
Machine Learning Street Talk (MLST)
How Do AI Models Actually Think? - Laura Ruis
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