持续学习时代的8个预测
摘要
作者认为AI需要持续学习才能胜任完整工作，并预言这将根本改变AI监管与技术对齐的方式。
核心要点
- 持续学习是AI承担完整工作的必要条件，仅靠跨会话传递文本笔记无法积累经验
- 现有AI监管假设“训练-部署”两阶段，持续学习使这一阶段区分不再存在
- 监管应转向月度或季度风险检查，而非部署前的一次性审查
- 现有对齐技术几乎都假设权重冻结，缺少对持续更新下模型不变坏的研究
- 用户间聚合学习可能引入后门或恶意倾向，需防止此类注入
原文佐证
- Even if you had an infinite sequence of saxophone-virgin students waiting outside the studio that could write notes to the next guy, there s no sequence of text they could write together that would allow the Nth student outside to play proficiently on their first try.
- Almost all current techniques are focused on the problem of how we make it so that a frozen set of weights behaves well during deployment.
- to the extent that the government really wants to do some kind of safety evaluations on model providers, it would make more sense to do monthly or quarterly risk inspections rather than singling out some special moment that occurs after training is done and before deployment begins
AI 洞察
持续学习若成真，AI安全与监管的底层逻辑将彻底重构。当前以部署为边界的安全评估体系会变得无效，行业需要发展在线监控与动态对齐能力。这也意味着AI能力增长可能从“阶梯式”转向“连绵式”，个体与组织适应周期大幅缩短。