Without a mechanism for continuous and diverse learning, AI systems will tend to reproduce the dominant patterns already present in their training data. That limitation would make truly creative work difficult.
大多数人认为AI的创造力主要来自模型规模和计算能力的提升，而作者认为缺乏持续学习和多样性机制将限制AI的真正创造力。这一观点挑战了主流AI发展路径，暗示技术规模扩张本身不足以实现真正的科学创新。
A learning system can continuously incorporate real-world data in a way that numerical solvers fundamentally cannot, capturing and compounding the knowledge that is currently trapped out there in the real world.
揭示了AI驱动设计的另一大优势：打通仿真与现实的闭环。传统求解器难以穷尽制造公差等现实复杂因素，而学习系统能持续吸收实测数据，形成越用越聪明的“数据飞轮”。将现实中散落的隐性知识固化为模型能力，这是传统工具无法企及的质变。
Keep in mind that as a software developer, of any degree, learning is continuous. New technologies, new ways to write code, not so new approaches, persisting patterns. Read books, watch online courses, follow tutorials, keep learning!