Reconstructing raw inputs forces models to model irrelevant low-level detail. Predicting in a learned embedding space allows the model to focus on semantically meaningful, causally relevant features.
大多数人认为AI模型需要重建完整的输入数据才能理解世界，但作者认为这种方法迫使模型关注无关的低级细节。相反，在嵌入空间中进行预测可以让模型专注于语义上有意义、因果相关的特征，这是一个反直觉的见解。
these two challenges are fundamentally distinct: the former relies on fuzzy semantic planning, while the latter demands strict logical constraints
主流AI研究通常将语义规划和逻辑验证视为可以统一处理的问题，但作者明确指出它们是根本不同的挑战。这一观点与当前大多数LLM代理方法相悖，暗示了单一神经网络架构的局限性。
内置视频和音乐生成
大多数人认为AI系统需要专门的模块或插件来处理多媒体内容生成，但作者暗示OpenClaw已经将这些功能'内置'，表明其架构已经实现了高度整合，挑战了AI系统模块化设计的传统观念。
Sandboxes made for running tens of thousands of agents
大多数人认为在单个系统中运行数万个AI代理是不现实的，会导致资源竞争和性能下降。Freestyle明确将此作为设计目标，暗示他们的架构可能重新定义了AI代理的规模边界，挑战了关于AI系统可扩展性的主流认知。
Some good pointers to [[Brian Eno c]] work and thinking, to follow up.
Also good anecdote from one of those links on Rem Koolhaas notion of n:: premature sheen Making things look nice early takes away from thinking about other points of quality. Jeremy applies it to AI too, the premature sheen generate awe, but not quality output.
for - Yann Lecun - paper - Yann Lecun - AI - LLMs are dead - language is optional for reasoning - to paper - VL-JEPA: Joint Embedding Predictive Architecture for Vision-language - https://hyp.is/eSxi8OxGEfCF7QMFiWL9Fg/arxiv.org/abs/2512.10942
Comment - That language and reasoning are separate is obvious. - If we look at the diversity of life and its ability to operationalize goal seeking behavior, that already tells you that - Michael Levin's research on goal-seeking behavior of organisms and the framework of multi-scale competency architecture validates Lecun's insight - Orders of magnitude fewer efficiency of Lecun's team's prototype compared to LLM also validates this
summary
Kozyreva, A., Lewandowsky, S., & Hertwig, R. (2019, December 4). Citizens Versus the Internet: Confronting Digital Challenges With Cognitive Tools. https://doi.org/10.31234/osf.io/ky4x8