The thing that we’re seeing is the early glimpse of scaling law in simulations. The more data about humans and more compute you ingest, you start to get predictive and predictable gains of the model performance in simulating it, simulating people.
"模拟的 Scaling Law"目前只是内部观察，没有公开曲线和评测口径。真正该追问的是纵轴测什么：拟合已有人群的既往回答，还是预测尚未发生的行为？前者随数据变多必然变好，那不算 Scaling Law。
A new crop of AI labs are focused on recursive self-improvement — but the goal is proving elusive.
文章暗示AI实验室专注于递归自我改进，但缺乏具体证据支持这一说法。这是一个未经证实的概括，可能忽略了其他研究方向。文章应该提供具体例子和数据来支持这一论点，而不是做出笼统的断言。
By embedding our technical security rules directly into the agent workflow, we transformed those early near-misses into a secure, production-ready platform
文章声称通过嵌入安全规则解决了安全问题，但没有提供足够的证据证明这种方法的实际效果或安全性。这是一种未经充分验证的因果关系断言。改进方法应包括具体的测试结果、安全审计数据或第三方验证，以支持这一论断的有效性。
Karan, A. (2022). We cannot afford to repeat these four pandemic mistakes. BMJ, 376, o631. https://doi.org/10.1136/bmj.o631