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公开发言
3原话逐字核对过；转述不加引号这一时期的绝大多数视觉 AI 仅仅是靠像素效果来被评判的。如果你生成了漂亮的像素，你就做出了看似精美的软件，但这些像素仅仅是在尝试模拟观测现象。它们并不实际具备或表征现实世界背后的任何语义。因此，它们根本无法带来真正的真实性，无法作为规划的依据，也无法作为对真实物理世界的稳健仿真基础设施。
most of visual AI for this period has been just judged by the pixels. If you've got beautiful pixels, you've got a beautiful piece of software, but these pixels are trying to simulate observations. They don't actually have or represent any of the semantics behind the world. And therefore, they aren't good at realism. They aren't good for giving a basis to plan. They aren't good for actually having a robust simulation infrastructure of the real world.
语境：Christopher Manning 尖锐批评视觉生成与世界模型混为一谈的现状：像素级的逼真无法替代底层因果规律，缺乏因果表征的模型无法用于智能体规划。
这种神经符号方法你可以称之为保守路线：在动作的生成与控制中明确借助物理引擎和物理学知识。当你面对一张水的图像并希望理解其运动规律时，你是通过物理模型来预测其行为表现的。
this neurosyolic approach is you know you call a conservative approach if you will you know that this is absolutely using physics engines and knowledge of physics um to in its generation and control of movement, right? That if you're what you know when you've just started off with an image of water or and you want to understand how that's behaves, you're using a physics model to predict how it's going to behave.
语境：Christopher Manning 阐述神经符号系统在物理建模中的必要性：将已有物理知识显式嵌入生成与控制循环，而非寄希望于黑箱网络完全隐式重学物理学规律。
缩小仿真与现实鸿沟的关键在于神经优化：通过将仿真器输出与真实世界视频中的物理动态进行端到端对比学习，系统能自动迭代校准物理参数，彻底超越人工手写物理规则的精度极限。
语境：Christopher Manning 提出自动闭合 Sim-to-Real 鸿沟的工程方案：以端到端神经网络对物理模拟器进行自适应校准。