Human-Like Neural Nets by Catapulting

This research proposes a fundamentally different neural network training paradigm—using extremely high learning rates on heavily overparameterized models with small, diverse datasets—that could achieve human-like generalization, adversarial robustness, and sample efficiency while dramatically reducing compute requirements. If validated, this "catapulted" approach would reshape AI economics, improve model safety and alignment, and challenge current scaling assumptions that dominate enterprise AI strategy. Technology leaders should monitor this work as it could influence future investments in model training infrastructure, data strategy, and AI risk mitigation.