kaal:claim:4855607-004

Deep learning models adapt to changes in data distribution far less readily than human learning does, which limits their reliability once the operating environment diverges from the training data.

Furthermore, deep learning models are not as adaptable to changes in data distribution as human learning, which can adapt more quickly to new situations and contexts.

Wulf A. Kaal, How AI Models are Optimized Through Web3 Governance (2024), Model Overview: Deep Learning Models, p. 7
https://ssrn.com/abstract=4855607 · source PDF

Wulf A. Kaal, How AI Models are Optimized Through Web3 Governance (2024). SSRN: https://ssrn.com/abstract=4855607

• settings where the data distribution shifts after training

failuresupport: evidencedfailure: Distribution shift brittlenessfamily: ai-model-and-training-failureresearch-methods

• generalizes kaal:claim:5095633-010 When a training dataset disproportionately represents one region or demographic group, the resulting model pro...

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