Reportedly, memory and continual learning could replace scaling for human-like AI

A viral research paper argues that achieving human-like AI (dubbed "A-GEE-I") may not require endless scaling of compute and data, but instead a fundamental shift toward memory systems and continual learning. The core idea is moving from static, one-shot training to systems that continuously update without forgetting—mirroring biological intelligence's plasticity. This approach could reduce dependence on massive retraining cycles and create more adaptive, context-aware agents embedded in real environments.