{"id":22642,"date":"2020-12-12T23:32:06","date_gmt":"2020-12-12T15:32:06","guid":{"rendered":"https:\/\/swarma.org\/?p=22642"},"modified":"2020-12-12T23:32:06","modified_gmt":"2020-12-12T15:32:06","slug":"%e5%91%a8%e6%97%a5%e7%9b%b4%e6%92%ad%ef%bc%9a%e5%9b%a0%e6%9e%9c%e5%8a%a9%e5%8a%9bcv%e3%80%81nlp%e5%92%8c%e8%ae%a4%e7%9f%a5%e7%a5%9e%e7%bb%8f%e7%a7%91%e5%ad%a6-%e5%9b%a0%e6%9e%9c%e7%a7%91%e5%ad%a6","status":"publish","type":"post","link":"https:\/\/swarma.org\/?p=22642","title":{"rendered":"\u5468\u65e5\u76f4\u64ad\uff1a\u56e0\u679c\u52a9\u529bCV\u3001NLP\u548c\u8ba4\u77e5\u795e\u7ecf\u79d1\u5b66 | \u56e0\u679c\u79d1\u5b66\u4e0eCausal AI\u8bfb\u4e66\u4f1a"},"content":{"rendered":"
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\u660e\u665a8\u70b9\uff0c\u201c\u56e0\u679c\u79d1\u5b66\u4e0eCausal AI\u201d\u8bfb\u4e66\u4f1a\u5c06\u8fdb\u884c\u7b2c\u5341\u4e8c\u671f\u7684\u7ebf\u4e0a\u8bba\u6587\u5206\u4eab\uff0c\u4e3b\u9898\u662f\u201c<\/span>\u56e0\u679c\u52a9\u529bCV\u3001NLP\u548c\u8ba4\u77e5\u795e\u7ecf\u79d1\u5b66<\/span>\u201d\uff0c\u5c06\u7531\u6d59\u6c5f\u5927\u5b66\u5728\u8bfb\u7855\u58eb\u5434\u4ea6\u5168<\/span>\u3001\u6d59\u6c5f\u5927\u5b66\u5728\u8bfb\u535a\u58eb\u5f20\u5723\u5b87<\/span>\u3001\u96c6\u667a\u4ff1\u4e50\u90e8\u5e38\u9a7b\u4f5c\u8005<\/span>\u5341\u4e09\u7ef4\u6765\u8fdb\u884c\u5206\u4eab\uff0c\u672c\u6b21\u5185\u5bb9\u5c06\u5728\u96c6\u667a\u4ff1\u4e50\u90e8B\u7ad9\u76f4\u64ad\u3002<\/section>\n


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\u8bba\u6587\u89e3\u8bfb \u300aDe-Biased Court\u2019s View Generation with Causality\u300b<\/span><\/strong><\/strong><\/span><\/strong><\/strong><\/p>\n<\/section>\n

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\u6570\u636e\u4e0d\u5e73\u8861\u95ee\u9898\u7ecf\u5e38\u51fa\u73b0\u5728\u6587\u672c\u5206\u7c7b\u4efb\u52a1\u4e2d\uff0c\u800c\u5728\u6587\u672c\u751f\u6210\u4efb\u52a1\u4e2d\u88ab\u5ffd\u89c6\u3002\u5728\u6c11\u4e8b\u8bc9\u8bbc\u4e2d\uff0c\u5b58\u5728\u7740\u65e0\u6cd5\u88ab\u89c2\u6d4b\u5230\u7684\u6570\u636e\u751f\u6210\u673a\u5236\uff1a\u539f\u544a\u5f80\u5f80\u5728\u6709\u628a\u63e1\u7684\u60c5\u51b5\u4e0b\u624d\u4f1a\u8d77\u8bc9\u3002\u8fd9\u79cd\u6570\u636e\u751f\u6210\u673a\u5236\u5bfc\u81f4\u7684\u6570\u636e\u4e0d\u5e73\u8861\uff0c\u4f1a\u5bfc\u81f4\u6cd5\u9662\u89c2\u70b9\u751f\u6210\u6a21\u578b\u5728\u7f16\u7801\u65f6\u53ea\u5173\u6ce8\u652f\u6301\u6027\u7684\u4fe1\u606f\uff0c\u4ece\u800c\u751f\u6210\u4e0d\u51c6\u786e\u7684\u6cd5\u9662\u5173\u70b9\u3002\u6587\u7ae0\u901a\u8fc7\u5bf9\u8fd9\u79cd\u73b0\u8c61\u8fdb\u884c\u4e86\u56e0\u679c\u5efa\u6a21\uff0c\u5e76\u5c06\u540e\u95e8\u8c03\u6574\u5de7\u5999\u8fd0\u7528\u5230\u4e86NLP\u4e2d\uff0c\u4ece\u800c\u4f7f\u5f97\u6cd5\u9662\u89c2\u70b9\u751f\u6210\u6a21\u578b\u80fd\u591f\u6d88\u9664\u6570\u636e\u751f\u6210\u5e26\u6765\u7684\u6df7\u6dc6\u504f\u5dee\u3002<\/span><\/section>\n


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Outline<\/span><\/strong><\/strong><\/span><\/p>\n<\/h3>\n

    \n
  1. \n
    Introduction to Court’s View Generation<\/section>\n<\/li>\n
      \n
    1. \n
      Brief Introduction to Legal AI<\/section>\n<\/li>\n
    2. \n
      Task Definition<\/section>\n<\/li>\n
    3. \n
      Challenges<\/section>\n<\/li>\n<\/ol>\n
    4. \n
      Method<\/section>\n<\/li>\n
        \n
      1. \n
        Causal Graph<\/section>\n<\/li>\n
      2. \n
        Backdoor Adjustment<\/section>\n<\/li>\n
      3. \n
        Backdoor in Implementation<\/section>\n<\/li>\n
      4. \n
        Model Arch<\/section>\n<\/li>\n<\/ol>\n
      5. \n
        Experiments<\/section>\n<\/li>\n
          \n
        1. \n
          Dataset<\/section>\n<\/li>\n
        2. \n
          Metrics<\/section>\n<\/li>\n
        3. \n
          Baselines<\/section>\n<\/li>\n
        4. \n
          Results<\/section>\n<\/li>\n<\/ol>\n
        5. \n
          Discussion<\/section>\n<\/li>\n
            \n
          1. \n
            Ethical Issue<\/section>\n<\/li>\n
          2. \n
            Future Work<\/section>\n<\/li>\n<\/ol>\n<\/ol>\n

             <\/span><\/p>\n

            \n

            Reference<\/span><\/strong><\/strong><\/span><\/p>\n<\/h2>\n

            I Sutskever, O Vinyals, and QV Le. 2014. Sequence to sequence learning with neural networks. Advances in NIPS. <\/span><\/p>\n

             <\/span><\/p>\n

            Abigail See, Peter J Liu, and Christopher D Manning. 2017. Get to the point: Summarization with pointer-generator networks. arXiv preprint arXiv:1704.04368. <\/span><\/p>\n

             <\/span><\/p>\n

            Haoxi Zhong, Zhipeng Guo, Cunchao Tu, Chaojun Xiao, Zhiyuan Liu, and Maosong Sun. 2018. Legal judgment prediction via topological learning. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 3540\u20133549. <\/span><\/p>\n

             <\/span><\/p>\n

            Judea Pearl. 2009. Causality. Cambridge university press.<\/span><\/p>\n

             <\/span><\/p>\n

            Judea Pearl, Madelyn Glymour, and Nicholas P Jewell. 2016. Causal inference in statistics: A primer. John Wiley & Sons. <\/span><\/p>\n

             <\/span><\/p>\n

            \n

            \u5206\u4eab\u8005\u4ecb\u7ecd<\/span><\/strong><\/strong><\/span><\/p>\n<\/h2>\n

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            \u5434\u4ea6\u5168\uff0c\u6d59\u6c5f\u5927\u5b66\u5728\u8bfb\u7855\u58eb\uff0c\u7814\u7a76\u65b9\u5411\u4e3a\u81ea\u7136\u8bed\u8a00\u5904\u7406<\/span><\/p>\n

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            \u56e0\u679c\u5728\u53bb\u504f\u5dee\u9884\u8bad\u7ec3\u4e2d\u7684\u5e94\u7528<\/span><\/strong><\/span><\/strong><\/strong><\/p>\n<\/section>\n

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            \u9884\u8bad\u7ec3\u6280\u672f\u65e8\u5728\u53ef\u8f7b\u6613\u5927\u89c4\u6a21\u6536\u96c6\u7684\u65e0\u6807\u7b7e<\/span>\uff08\u6216\u5355\u4e00\u6807\u7b7e\uff09<\/span>\u6570\u636e\u96c6\u4e0a\uff0c\u901a\u8fc7\u5305\u62ec\u81ea\u76d1\u7763\u5b66\u4e60<\/span>\uff08Self-supervised Learning\uff09<\/span>\u5728\u5185\u7684\u624b\u6bb5\u5b66\u4e60\u5230\u548c\u67d0\u4e2a\u5177\u4f53\u4e0b\u6e38\u4efb\u52a1\u65e0\u5173\u7684\u901a\u7528\u8868\u5f81\u3002\u5927\u91cf\u7684\u4e0b\u6e38\u4efb\u52a1\u53ef\u5229\u7528\u8fd9\u79cd\u901a\u7528\u7684\u7279\u5f81\u4f5c\u4e3a\u5148\u9a8c\u77e5\u8bc6\u6216\u989d\u5916\u8f85\u52a9\u77e5\u8bc6\u4ee5\u589e\u5f3a\u6027\u80fd\u548c\u5b9e\u73b0\u51b7\u542f\u52a8\u3002\u76f8\u6bd4\u4e8e\u4e00\u822c\u7684\u6df1\u5ea6\u5b66\u4e60\u4efb\u52a1\uff0c\u9884\u8bad\u7ec3\u4efb\u52a1\u66f4\u52a0\u770b\u91cd\u6a21\u578b\u7684\u6cdb\u5316\u80fd\u529b\uff0c\u5e76\u4e14\u5bf9\u9884\u8bad\u7ec3\u6570\u636e\u4e2d\u5b58\u5728\u7684\u504f\u89c1\u66f4\u4e3a\u654f\u611f\u3002\u8fd9\u662f\u56e0\u4e3a\uff0c\u9886\u57df\u5185\u7684\u504f\u89c1\u77e5\u8bc6\u53ef\u80fd\u5bf9\u9886\u57df\u5185\u7684\u5206\u6790\u5224\u65ad\u6709\u5e2e\u52a9\uff0c\u4f46\u5bf9\u9886\u57df\u5916\u7684\u4efb\u52a1\u53ef\u80fd\u662f\u6709\u5bb3\u7684[1]\u3002\u56e0\u6b64\uff0c\u5982\u4f55\u5b9e\u73b0\u53bb\u504f\u5dee\u9884\u8bad\u7ec3\u548c\u7279\u5f81\u63d0\u51fa\u662f\u91cd\u8981\u7684\u7814\u7a76\u548c\u5e94\u7528\u8bfe\u9898\u3002<\/span><\/section>\n


            <\/span><\/p>\n

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            Outline<\/span><\/strong><\/strong><\/span><\/p>\n<\/h3>\n

            Introduction to Causal Pretraining<\/section>\n
              \n
                \n
              1. \n
                Brief introduction to pretraining<\/section>\n<\/li>\n
              2. \n
                Motivation<\/section>\n<\/li>\n
              3. \n
                Challenges<\/section>\n<\/li>\n<\/ol>\n
              4. \n
                Paper Reading [2-3]<\/section>\n<\/li>\n
              5. \n
                Discussion<\/section>\n<\/li>\n<\/ol>\n

                \n


                <\/p>\n

                Reference<\/span><\/strong><\/strong><\/span><\/p>\n<\/h3>\n

                [1] Kun Kuang, Peng Cui, Susan Athey, Ruoxuan Xiong, and Bo Li. 2018. Stable Pre- diction across Unknown Environments.. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, KDD 2018, Lon- don,UK,August19-23,2018.1617\u20131626. <\/span>https:\/\/doi.org\/10.1145\/3219819.3220082<\/span> <\/span><\/p>\n

                 <\/span><\/p>\n

                [2] Shengyu Zhang, Tan Jiang, Tan Wang, Kun Kuang, Zhou Zhao, Jianke Zhu, Jin Yu, Hongxia Yang, Fei Wu: DeVLBert: Learning Deconfounded Visio-Linguistic Representations. ACM Multimedia 2020: 4373-4382<\/span><\/p>\n

                 <\/span><\/p>\n

                [3] Zekun Yang, Tianlin Liu: Causally Denoise Word Embeddings Using Half-Sibling Regression. AAAI 2020: 9426-9433<\/span><\/p>\n

                 <\/span><\/p>\n

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                \u5206\u4eab\u8005\u4ecb\u7ecd<\/span><\/strong><\/strong><\/span><\/p>\n<\/h2>\n

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                \u5f20\u5723\u5b87\uff0c\u6d59\u6c5f\u5927\u5b66\u5728\u8bfb\u535a\u58eb\uff0c\u7814\u7a76\u65b9\u5411\u4e3a\u591a\u6a21\u6001\u5b66\u4e60\u3001\u63a8\u8350\u7cfb\u7edf\u548c\u7a33\u5b9a\u5b66\u4e60<\/span><\/p>\n

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                \u8bba\u6587\u89e3\u8bfb \u300aCausality in cognitive neuroscience: concepts, challenges, and distributional robustness\u300b<\/span><\/strong><\/strong><\/span><\/strong><\/strong><\/p>\n<\/section>\n

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                \u80cc\u666f\u4ecb\u7ecd<\/span><\/strong><\/strong><\/span><\/p>\n


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                \u6982\u7387\u6a21\u578b\u63cf\u8ff0\u4e86\u89c2\u5bdf\u53d8\u91cf\u4e4b\u95f4\u7684\u4f9d\u8d56\u7ed3\u6784\uff0c\u56e0\u679c\u6a21\u578b\u5219\u66f4\u8fdb\u4e00\u6b65\uff0c\u4f8b\u5982\u53ef\u4ee5\u9884\u6d4b\u8ba4\u77e5\u529f\u80fd\u5982\u4f55\u88ab\u5916\u90e8\u5e72\u9884\u9020\u6210\u7684\u795e\u7ecf\u5143\u6d3b\u52a8\u5f71\u54cd\u3002\u5728\u8fd9\u7bc7\u8bba\u6587\u7efc\u8ff0\u4e2d\uff0c\u4f5c\u8005\u56de\u987e\u4e86\u77e5\u795e\u7ecf\u79d1\u5b66\u80cc\u666f\u4e0b\u7684\u56e0\u679c\u5173\u7cfb\u6982\u5ff5\uff0c\u5e76\u56de\u987e\u4e86\u4ece\u6570\u636e\u4e2d\u63a8\u65ad\u56e0\u679c\u5173\u7cfb\u7684\u73b0\u6709\u65b9\u6cd5\u3002\u56e0\u679c\u63a8\u7406\u662f\u4e00\u9879\u9887\u6709\u91ce\u5fc3\u53c8\u5f02\u5e38\u8270\u5de8\u7684\u4efb\u52a1\uff0c\u5728\u8ba4\u77e5\u795e\u7ecf\u79d1\u5b66\u4e2d\u5c24\u5176\u5177\u6709\u6311\u6218\u6027\u3002\u5728\u6587\u7ae0\u4e2d\uff0c\u4f5c\u8005\u4ece\u5404\u4e2a\u65b9\u9762\u8be6\u7ec6\u8ba8\u8bba\u5f53\u524d\u4e24\u4e2a\u4e3b\u8981\u56f0\u96be\uff1a\u5e72\u9884\u6570\u636e\u7684\u7a00\u7f3a\u6027\u548c\u5bfb\u627e\u6b63\u786e\u53d8\u91cf\u7684\u6311\u6218\uff0c\u4e3b\u5f20\u5c06\u5206\u5e03\u9c81\u68d2\u6027\u4f5c\u4e3a\u89e3\u51b3\u8fd9\u4e9b\u95ee\u9898\u7684\u6307\u5bfc\u539f\u5219\u3002\u9c81\u68d2\u6027\u6216\u4e0d\u53d8\u6027\u662f\u662f\u56e0\u679c\u65b9\u6cd5\u7684\u57fa\u672c\u539f\u5219\uff0c\u53ea\u8981\u76ee\u6807\u73af\u5883\u4e0d\u4ea7\u751f\u56e0\u679c\u4f5c\u7528\uff0c\u4e00\u4e2a\u76ee\u6807\u53d8\u91cf\u7684\u56e0\u679c\u6a21\u578b\u5c31\u53ef\u4ee5\u5728\u4e0d\u540c\u7684\u73af\u5883\u6216\u4e3b\u4f53\u4e2d\u6cdb\u5316\u3002\u5982\u679c\u4e00\u4e2a\u5019\u9009\u56e0\u679c\u6a21\u578b\u4e0d\u80fd\u6cdb\u5316\uff0c\u90a3\u4e48\u8981\u4e48\u5b83\u4e0d\u5305\u542b\u76ee\u6807\u53d8\u91cf\u7684\u6b63\u786e\u539f\u56e0\uff0c\u8981\u4e48\u95ee\u9898\u4e2d\u57fa\u672c\u53d8\u91cf\u6ca1\u6709\u8868\u793a\u4e3a\u6070\u5f53\u7684\u7c92\u5ea6\u3002\u4e3a\u89e3\u51b3\u5e72\u9884\u6570\u636e\u7a00\u7f3a\u548c\u5bfb\u627e\u6b63\u786e\u53d8\u91cf\u95ee\u9898\uff0c\u8ba4\u77e5\u795e\u7ecf\u79d1\u5b66\u540c\u6837\u9700\u8981\u4ece\u6570\u636e\u548c\u6a21\u578b\u8bc4\u4f30\u4e24\u65b9\u9762\u5206\u522b\u53d1\u5c55\u79cd\u79cd\u6280\u672f\u53bb\u5e94\u5bf9\u6311\u6218\u3002<\/span><\/section>\n

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                Outline<\/span><\/strong><\/strong><\/span><\/strong><\/strong><\/span><\/p>\n

                – \u8ba4\u77e5\u795e\u7ecf\u79d1\u5b66\u4e2d\u7684\u56e0\u679c<\/span><\/section>\n
                – \u8ba4\u77e5\u795e\u7ecf\u79d1\u5b66\u7814\u7a76\u5c42\u6b21 <\/span><\/section>\n
                    – \u573a\u666f 1\uff1astimulus \u2192 brain activity \u2192 response<\/span><\/section>\n
                    – \u573a\u666f 2\uff1astimulus \u2192 brain activity<\/span><\/section>\n
                \u8ba4\u77e5\u795e\u7ecf\u79d1\u5b66\u7684\u56e0\u679c\u6311\u6218<\/span><\/section>\n
                    – \uff081\uff09\u5e72\u9884\u6570\u636e\u7f3a\u4e4f\uff08the scarcity of interventional data\uff09<\/span><\/section>\n
                    – \uff082\uff09\u5bfb\u627e\u6b63\u786e\u53d8\u91cf\uff08the challenge of fnding the right variables\uff09<\/span><\/section>\n
                \u7ed3\u6784\u56e0\u679c\u6a21\u578b\u4e2d\u7684\u89c2\u5bdf\u4e0e\u5e72\u9884<\/span><\/section>\n
                    – \u5e72\u9884\u5206\u5e03\u4e0e\u89c2\u5bdf\u5206\u5e03<\/span><\/section>\n
                    – \u56e0\u679c\u6a21\u578b\u7684\u7b49\u6548\u6027<\/span><\/section>\n
                – \u5178\u578b\u56e0\u679c\u6a21\u578b\u548c\u65b9\u6cd5<\/span><\/section>\n
                    – \u968f\u673a\u5316\u5b9e\u9a8c<\/span><\/section>\n
                    – \u57fa\u4e8e\u7ea6\u675f\u7684\u65b9\u6cd5\uff08Constraint-based methods\uff09 <\/span><\/section>\n
                    – \u57fa\u4e8e\u5206\u6570\u7684\u65b9\u6cd5\uff08Score-based methods\uff09<\/span><\/section>\n
                    – \u7ea6\u675f\u7ed3\u6784\u56e0\u679c\u6a21\u578b\uff08Restricted structural causal models\uff09<\/span><\/section>\n
                    – \u52a8\u529b\u56e0\u679c\u6a21\u578b\uff08Dynamic causal modelling (DCM)<\/span><\/section>\n
                    – \u683c\u5170\u6770\u56e0\u679c (Granger causality)<\/span><\/section>\n
                  – \u8de8\u73af\u5883\u4e0d\u53d8\u6027\u4e0e\u56e0\u679c\u6a21\u578b<\/span><\/section>\n
                  – \u5bfb\u627e\u6b63\u786e\u7c92\u5ea6\u53d8\u91cf<\/span><\/section>\n
                  – \u6a21\u578b\u9c81\u68d2\u6027\u548c\u53d8\u91cf\u7ed3\u6784<\/span><\/section>\n
                      – Robust Independent Component Analysis<\/span><\/section>\n
                      – Causal discovery with exogenous variation<\/span><\/section>\n
                      – Anchor regression  <\/span><\/section>\n

                \n


                <\/p>\n

                \u5206\u4eab\u8005\u4ecb\u7ecd<\/span><\/strong><\/strong><\/span><\/strong><\/strong><\/span><\/p>\n<\/h2>\n

                \u5341\u4e09\u7ef4\uff0c\u67d0\u822a\u7a7a\u516c\u53f8\u6570\u636e\u8fd0\u8425\u4e3b\u7ba1\uff0c\u96c6\u667a\u4ff1\u4e50\u90e8\u5e38\u9a7b\u4f5c\u8005\uff0c\u6bd5\u4e1a\u4e8e\u9996\u90fd\u5e08\u8303\u5927\u5b66\u6570\u5b66\u7cfb\uff0c\u957f\u671f\u5173\u6ce8\u590d\u6742\u7cfb\u7edf\u4e0e\u8ba4\u77e5\u79d1\u5b66\u7b49\u8de8\u5b66\u79d1\u7814\u7a76<\/span><\/section>\n

                <\/section>\n

                <\/section>\n
                <\/p>\n
                \n
                \n
                \n
                \n

                \u76f4\u64ad\u4fe1\u606f\u4e0e\u62a5\u540d\u65b9\u5f0f<\/strong><\/p>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n


                <\/p>\n

                <\/span><\/section>\n

                \u76f4\u64ad\u65f6\u95f4\uff1a\u660e\u665a<\/span><\/strong>\uff0812\u670813\u65e5\uff09 20:00-23:00<\/span><\/p>\n


                <\/p>\n

                \u53c2\u4e0e\u65b9\u5f0f 1\uff1a\u817e\u8baf\u4f1a\u8bae\uff08\u9700\u62a5\u540d\u53c2\u52a0\u7cfb\u5217\u8bfb\u4e66\u4f1a\uff09<\/strong><\/p>\n

                \n

                \u53c2\u4e0e\u65b9\u5f0f 2\uff1a\u96c6\u667a\u4ff1\u4e50\u90e8B\u7ad9\u76f4\u64ad<\/strong><\/p>\n


                <\/p>\n

                \u626b\u63cf\u4e0b\u65b9\u4e8c\u7ef4\u7801\uff0c\u70b9\u51fb\u300c\u6211\u8981\u542c\u300d\u62a5\u540d\uff0c\u5185\u9644B\u7ad9\u76f4\u64ad\u5730\u5740\u3002<\/strong><\/p>\n<\/section>\n


                <\/section>\n

                <\/p>\n


                <\/section>\n

                <\/span><\/section>\n

                <\/span>
                <\/section>\n

                <\/p>\n

                \n
                \n
                \n
                \n
                \n

                \u5173\u4e8e\u8bfb\u4e66\u4f1a\u5185\u5bb9\u4ecb\u7ecd\u7684\u66f4\u65b0<\/strong><\/p>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n

                \n


                <\/p>\n<\/section>\n

                \u96c6\u667a\u4ff1\u4e50\u90e8\u8054\u5408\u667a\u6e90\u793e\u533a\u53d1\u8d77\u4e86\u56e0\u679c\u79d1\u5b66\u4e0eCausal AI\u8bfb\u4e66\u4f1a\u53d7\u5230\u4e86\u56e0\u679c\u79d1\u5b66\u9886\u57df\u4e00\u7ebf\u79d1\u7814\u5de5\u4f5c\u8005\u7684\u5e7f\u6cdb\u8ba4\u53ef\uff0c\u6211\u4eec\u4e5f\u6df1\u6df1\u7684\u611f\u53d7\u5230\u4e86\u9752\u5e74\u79d1\u5b66\u5de5\u4f5c\u8005\u63a8\u52a8\u8be5\u9886\u57df\u53d1\u5c55\u7684\u70ed\u60c5\u3002<\/span>\u76ee\u524d\u56e0\u679c\u79d1\u5b66\u4e0eCausal AI\u8bfb\u4e66\u4f1a\u5df2\u7ecf\u6709220+\u7684\u670b\u53cb\u62a5\u540d\uff0c\u5176\u4e2d\u7855\u535a\u7684\u6bd4\u4f8b\u63a5\u8fd180%\u3002<\/span><\/span><\/p>\n

                <\/span><\/p>\n<\/h2>\n

                 <\/span><\/p>\n

                <\/span><\/p>\n

                \u5728\u8bfb\u4e66\u4f1a\u4e2d\u7684\u53c2\u4e0e\u8005\u4ee5\u79d1\u7814\u5de5\u4f5c\u8005\u5c45\u591a\uff0c\u6e05\u534e\u5927\u5b66\u3001\u5317\u4eac\u5927\u5b66\u3001\u56fd\u9632\u79d1\u6280\u5927\u5b66\u3001\u6d59\u6c5f\u5927\u5b66\u3001\u590d\u65e6\u5927\u5b66\u3001\u4e2d\u5c71\u5927\u5b66\u7b49\u56fd\u5185\u9ad8\u6821\u7684\u5b66\u751f\u5c45\u591a\uff0c\u4e5f\u6709\u8bb8\u591a\u4e00\u7ebf\u4e92\u8054\u7f51\u5305\u62ec\u8c37\u6b4cX\u3001FackBook\u3001\u6ef4\u6ef4\u3001\u817e\u8baf\u3001\u963f\u91cc\u3001\u62fc\u591a\u591a\u7b49\u5927\u5382\u7684\u5de5\u7a0b\u5e08\u4e5f\u53c2\u4e0e\u5176\u4e2d\u3002<\/span><\/p>\n


                <\/span><\/p>\n

                <\/span><\/p>\n

                 <\/span>      <\/span>      <\/span><\/p>\n

                \u6b22\u8fce\u5927\u5bb6\u52a0\u5165\u5176\u4e2d\uff0c\u73b0\u5728\u52a0\u5165\u8bfb\u4e66\u4f1a\u7684\u6210\u5458\u5c06\u4f1a\u4f5c\u4e3a\u56e0\u679c\u793e\u533a\u7684\u79cd\u5b50\u7528\u6237\u957f\u671f\u8fd0\u8425\uff0c\u5c06\u53ef\u4ee5\u514d\u8d39\u4eab\u53d7\u96c6\u667a\u4ff1\u4e50\u90e8\u540e\u7eed\u56e0\u679c\u76f8\u5173\u4e3b\u9898\u7684\u8bfb\u4e66\u4f1a<\/strong>\uff0c\u5982\u679c\u4f60\u5fc3\u52a8\u4e86\uff0c\u5c31\u70b9\u51fb\u4e0b\u9762\u7684\u94fe\u63a5\u62a5\u540d\u5427~<\/span><\/p>\n

                 <\/span><\/p>\n

                <\/span><\/span><\/section>\n

                \u65f6\u95f4\uff1a<\/span><\/strong>9\u670820\u65e5\u8d77\uff0c\u6bcf\u5468\u65e5\u665a19:00-21:00\uff0c\u6301\u7eed\u7ea62-3\u4e2a\u6708<\/span><\/section>\n
                \u6a21\u5f0f\uff1a<\/span><\/strong>\u7ebf\u4e0a\u95ed\u95e8\u8bfb\u4e66\u4f1a\uff1b\u6536\u8d39-\u9000\u6b3e\u7684\u4fdd\u8bc1\u91d1\u6a21\u5f0f\uff1b\u8bfb\u4e66\u4f1a\u6210\u5458\u8ba4\u9886\u89e3\u8bfb\u8bba\u6587<\/span><\/section>\n
                \u8d39\u7528\uff1a<\/span><\/strong>299\/\u4eba<\/span><\/section>\n

                 <\/span><\/p>\n

                \u4e86\u89e3\u8bfb\u4e66\u4f1a\u5177\u4f53\u89c4\u5219\u3001\u62a5\u540d\u8bfb\u4e66\u4f1a\u8bf7\u70b9\u51fb\u4e0b\u65b9\u6587\u7ae0\uff1a<\/span><\/p>\n

                \u56e0\u679c\u79d1\u5b66\u4e0e Causal AI \u7cfb\u5217\u8bfb\u4e66\u4f1a | \u4f17\u5305\u51fa\u4e66<\/span><\/a><\/section>\n


                <\/p>\n

                <\/p>\n

                \u76ee\u524d\u8bfb\u4e66\u4f1a\u5df2\u7ecf\u6709\u8d85\u8fc7130\u4f59\u4eba\u7684\u6d77\u5185\u5916\u9ad8\u6821\u79d1\u7814\u9662\u6240\u7684\u4e00\u7ebf\u79d1\u7814\u5de5\u4f5c\u8005\u4ee5\u53ca\u4e92\u8054\u7f51\u4e00\u7ebf\u4ece\u4e1a\u4eba\u5458\u53c2\u4e0e\uff0c\u5982\u679c\u4f60\u4e5f\u5bf9\u8fd9\u4e2a\u4e3b\u9898\u611f\u5174\u8da3\uff0c\u5c31\u5feb\u52a0\u5165\u6211\u4eec\u5427\uff01<\/span><\/p>\n


                <\/span><\/p>\n

                <\/section>\n

                \u9488\u5bf9\u8bfb\u4e66\u4f1a\u7684\u4e3b\u9898\uff0c\u7531\u53d1\u8d77\u4eba\u9f9a\u9e64\u626c\u8bbe\u7f6e\u597d\u4e86\u5185\u5bb9\u6846\u67b6\uff0c\u6bcf\u4e2a\u4e3b\u9898\u4e0b\u6709\u4e00\u4e2a\u8d1f\u8d23\u4eba\u6765\u8d1f\u8d23\u7ef4\u62a4\u7ec4\u7ec7\u76f8\u5173\u5185\u5bb9\uff0c\u76ee\u524d\u5df2\u7ecf\u5b9a\u597d\u7684\u5982\u56fe\u6240\u793a\uff0c\u6b22\u8fce\u5bf9\u4e3b\u9898\u611f\u5174\u8da3\u7684\u8054\u7cfb\u76f8\u5173\u8d1f\u8d23\u4eba\uff0c\u4ee5\u53ca\u6765\u8ba4\u9886\u76f8\u5173\u4e3b\u9898~<\/span><\/p>\n


                <\/span><\/p>\n

                <\/span><\/section>\n


                <\/section>\n
                \u63a8\u8350\u9605\u8bfb<\/span><\/strong><\/strong><\/span><\/section>\n