We recently published a paper on Neuroscience and Machine Learning (ML).
In it we highlight some ways that ML utterly fails but brains succeed. This is a question of generalization, specifically how a network learns properties in data that generalizes well. In writing this it became helpful to distinguish between model bias and inductive bias. So I wrote a short blog post about it here:
Thoughts are welcome.