Human-like Neural Nets by Catapulting

A new proposal suggests that AI neural networks could become far more human-like by training massively oversized models at very high learning rates on small, carefully filtered datasets. This approach, called "catapulting," could push AI into a better-generalizing state, making it resistant to adversarial attacks and potentially safer, while also explaining why humans learn so efficiently compared to current AI systems.