Additional reporting by Usman Zahid and Mahfouz Zubaide
主干道被乱停的车辆堵得水泄不通，路边散落着蔫掉的菜叶和塑料垃圾，地面上散布着不少干涸的痰迹。街道的脏乱仿佛成了一种默许，违停与垃圾不再被视为问题。鸣笛声此起彼伏，人们谈论的多是家长里短，而非“头上的星空与心中的道德律”。在这样的环境里，我们也不自觉地爱上议论是非、拆解他人。
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「人們只要追蹤環境中的統計資訊，就能學得非常、非常快，」雷布夏特說。「這類任務旨在模擬真實世界中的沉浸式學習情境，那裡的一切往往含糊不清，而且我們很少能立即得到回饋。」
The converse is also worth asking — whether simulating artificial environments (for instance a 3d representation of a Youtube video) might have unintended negative consequences. Fei-Fei Li’s startup World Labs, which aims to make the leading “world model” — an alternative to language models based on tokenizing physical space rather than words — recently raised a substantial amount of money. As consumer-facing robots become more plausible, the business case for such a model is obvious. But what physical spaces are “world” models actually being trained on? The contemporary physical environment, sound-proofed, plastic-coated, and artificially-colored, is radically different from the environment that Homo sapiens evolved to excel in.