The history of gesture, in other words, bears directly upon the question of AI alignment. Humans across cultures and times intuitively maintain a set of semantic and ethical judgements rooted in our physicality, our learned and instinctive gestures, and the affordances of the natural world. There is just something “knockable” about wood. LLMs don’t have childhood memories of jumping over cracks in pavement or their grandmother teaching them gestures. Moreover, such things are not really in their training data either.
RUN dnf install -y ${BASE_PKG} && \
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It is worth noting, too, that humans often follow a less rigorous process compared to the clean room rules detailed in this blog post, that is: humans often download the code of different implementations related to what they are trying to accomplish, read them carefully, then try to avoid copying stuff verbatim but often times they take strong inspiration. This is a process that I find perfectly acceptable, but it is important to take in mind what happens in the reality of code written by humans. After all, information technology evolved so fast even thanks to this massive cross pollination effect.
近期，DeepSeek 联合北京大学与清华大学悄悄上线了一篇论文，正式发布名为 DualPath 的新技术方案，重点解决了 AI 大模型在执行复杂多轮任务时遭遇的历史数据读取瓶颈。。91视频对此有专业解读