What's so hard about continuous learning? (www.seangoedecke.com)

The article discusses the complexities and challenges of enabling continuous learning in AI models post-deployment, highlighting the significant gap between human learning and current AI capabilities. While technically feasible to update model weights during runtime, the practice is impeded by concerns about model degradation, the necessity for careful oversight, and the inherent risks of training models using live data—a process that could lead to vulnerabilities like weight poisoning. The dream of creating a continuously learning AI that could adapt and refine its understanding of specific tasks, such as coding, remains elusive. Current methods, including fine-tuning on specific codebases, often fail to instill a deep, functional knowledge of the system. Moreover, the article emphasizes that continuous learning could complicate user experiences with upgrades, as insights from a model's previous interactions may not easily transfer to newer iterations. This underscores the need for the AI community to navigate not only the technical hurdles but also the safety and usability challenges associated with advanced learning models.