However, existing evaluations fall short: they lack flexible tool integration, test visual and search tools separately, and evaluate primarily by final answers.
大多数人认为现有的多模态评估方法已经足够全面，能够有效衡量AI代理的能力。但作者指出这些评估方法存在根本性缺陷：缺乏工具集成能力、单独测试不同工具、仅关注最终答案而非过程。这一观点挑战了当前AI评估领域的共识，暗示我们需要重新思考如何真正衡量AI代理的能力。
It has a panel of critics who tear my work apart from different angles—skills I wrote to invoke certain kinds of feedback, whether it's for length, pacing, or the soundness of the argument.
大多数人认为AI写作缺乏批判性视角和严格编辑，但作者展示了一个由AI驱动的批评者团队，专门从不同角度撕碎她的作品。这挑战了人们对AI写作质量的担忧，表明AI可以被训练提供比传统编辑更全面、更严格的反馈，甚至可能超越人类编辑的一致性和广度。
for - progress trap - AI music - critique - Folia Sound Studio - to - P2P Foundation - Michel Bauwens - Commons Transition Plan - Netarchical Capitalism - Predatory Capitalism
to - P2P Foundation - Michel Bauwens - Commons Transition Plan - Netarchical Capitalism - Predatory Capitalism https://hyp.is/o-Hp-DCAEe-8IYef613YKg/wiki.p2pfoundation.net/Commons_Transition_Plan
from - youtube - Folia Sound Studio critique of AI music tools - Suno - Udio https://hyp.is/NGScyjB_Ee-rSYNhe9Fuug/docdrop.org/video/wEQ9Vg2YKcU/
Recommendations DON'T use shifted PPMI with SVD. DON'T use SVD "correctly", i.e. without eigenvector weighting (performance drops 15 points compared to with eigenvalue weighting with (p = 0.5)). DO use PPMI and SVD with short contexts (window size of (2)). DO use many negative samples with SGNS. DO always use context distribution smoothing (raise unigram distribution to the power of (lpha = 0.75)) for all methods. DO use SGNS as a baseline (robust, fast and cheap to train). DO try adding context vectors in SGNS and GloVe.