• Corpus ID: 251320207

On the Learnability of Physical Concepts: Can a Neural Network Understand What's Real?

@inproceedings{Achille2022OnTL,
  title={On the Learnability of Physical Concepts: Can a Neural Network Understand What's Real?},
  author={Alessandro Achille and Stefan 0 Soatto},
  year={2022}
}
We revisit the classic signal-to-symbol barrier in light of the remarkable ability of deep neural networks to generate realistic synthetic data. DeepFakes and spoofing highlight the feebleness of the link between physical reality and its abstract representation, whether learned by a digital computer or a biological agent. Starting from a widely applicable definition of abstract concept , we show that standard feed-forward architectures cannot capture but trivial concepts, regardless of the number… 

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