Human-like Neural Nets by Catapulting

A speculative proposal suggests training extremely overparameterized neural networks with high learning rates to trigger catapulting, achieving human-like generalization. This approach would resolve the bias-variance tradeoff, making models immune to adversarial attacks and enabling sample-efficient learning from small, diverse datasets. The idea could be tested on multi-trillion-parameter models.