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  1. \n
    Belfiore, Jean-Claude, and Daniel Bennequin. “Topos and stacks of deep neural networks.” arXiv preprint arXiv:2106.14587<\/em> (2021). https:\/\/arxiv.org\/pdf\/2106.14587.pdf   https:\/\/arxiv.org\/abs\/2106.14587<\/span><\/section>\n<\/li>\n
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    https:\/\/www.laurentlafforgue.org\/Expose_Lafforgue_topos_AI_ETH_sept_2022.pdf<\/span><\/section>\n<\/li>\n
  3. \n
    Bradley, Tai-Danae, John Terilla, and Yiannis Vlassopoulos. “An enriched category theory of language: from syntax to semantics.” La Matematica<\/em> 1.2 (2022): 551-580. https:\/\/link.springer.com\/article\/10.1007\/s44007-022-00021-2<\/span><\/section>\n<\/li>\n
  4. \n
    Shiebler, Dan, Bruno Gavranovi\u0107, and Paul Wilson. “Category theory in machine learning.” arXiv preprint arXiv:2106.07032<\/em> (2021). https:\/\/arxiv.org\/abs\/2106.07032<\/span><\/section>\n<\/li>\n
  5. \n
    Seven Sketches in Compositionality: An Invitation to Applied Category Theory https:\/\/math.mit.edu\/~dspivak\/teaching\/sp18\/7Sketches.pdf<\/span><\/section>\n<\/li>\n<\/ol>\n

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      \u8303\u7574\u8bba\u5165\u95e8\u7cfb\u5217\u8bfe\u7a0b\uff0c<\/strong>\u7b2c\u4e00\u5b63\u8bfe\u7a0b<\/a><\/strong>\u6211\u4eec\u4ee5 \u201c\u4eba\u4eba\u53ef\u5b66\u8303\u7574\u8bba\u201d \u4e3a\u5b97\u65e8\uff0c\u4ece\u76f8\u5f53\u4f4e\u7684\u8d77\u70b9\u7ec4\u7ec7\u5185\u5bb9\uff0c\u5e2e\u52a9\u5927\u5bb6\u7528\u8303\u7574\u7684\u89d2\u5ea6\u770b\u5f85\u719f\u77e5\u7684\u95ee\u9898\uff0c\u611f\u53d7\u8303\u7574\u8bba\u7684\u601d\u7ef4\u65b9\u5f0f\uff0c\u83b7\u5f97\u4ece\u4f8b\u5b50\u4e2d\u5b66\u4e60\u62bd\u8c61\u7684\u8303\u7574\u8bba\u77e5\u8bc6\u7684\u80fd\u529b\u3002\u8bfe\u7a0b\u4e2d\u8865\u5145\u4e86\u5fc5\u8981\u7684\u524d\u7f6e\u6570\u5b66\u77e5\u8bc6\uff0c\u4ee5\u4fbf\u66f4\u591a\u7684\u5b66\u5458\u7834\u9664\u95e8\u69db\u767b\u5802\u5165\u5ba4\u3002\u7b2c\u4e8c\u5b63\u8bfe\u7a0b<\/a><\/strong>\u7ad9\u5728\u65b0\u7684\u8d77\u70b9\u4e0a\uff0c\u66f4\u591a\u5173\u6ce8\u8303\u7574\u8bba\u5185\u5728\u7684\u95ee\u9898\uff0c\u800c\u4e0d\u7528\u8fc7\u4e8e\u5173\u6ce8\u5177\u4f53\u7684\u95ee\u9898\u80cc\u666f\u3002\u4f8b\u5982\u5728\u8303\u7574\u8bba\u4e2d\uff0c\u53ef\u4ee5\u76f4\u63a5\u62bd\u8c61\u5730\u8ba8\u8bba\u4e00\u4e2a\u4f5c\u4e3a\u51fd\u5b50\u7684\u7bad\u5934\u5982\u4f55\u8fd0\u52a8\u5230\u53e6\u4e00\u4e2a\u51fd\u5b50\u7bad\u5934\uff0c\u8fd9\u6837\u9700\u8981\u7406\u89e3\u51fd\u5b50\u8303\u7574\u548c\u81ea\u7136\u53d8\u6362\u7684\u6982\u5ff5\u3002\u901a\u8fc7\u638c\u63e1\u8fd9\u4e9b\u8d8a\u6765\u8d8a\u62bd\u8c61\u7684\u601d\u7ef4\u5de5\u5177\uff0c\u5b66\u5458\u5c06\u9010\u6e10\u611f\u53d7\u5230\u8303\u7574\u8bba\u7684\u5f3a\u5927\u62bd\u8c61\u7b80\u5316\u80fd\u529b\uff0c\u611f\u53d7\u5230\u4e3a\u4f55\u4e0d\u540c\u9886\u57df\u7684\u7814\u7a76\u524d\u6cbf\u4e0d\u7ea6\u800c\u540c\u5730\u5e94\u7528\u8fd9\u4e9b\u5de5\u5177\u3002<\/span><\/section>\n
      \u7b2c\u4e00\u5b63\u8be6\u60c5\u53ca\u8bd5\u770b\uff1ahttps:\/\/campus.swarma.org\/course\/2723<\/span><\/section>\n
      \u7b2c\u4e8c\u5b63\u8be6\u60c5\u53ca\u8bd5\u770b\uff1ahttps:\/\/campus.swarma.org\/course\/3456<\/span><\/section>\n

      <\/span><\/section>\n
      AI+Science \u8bfb\u4e66\u4f1a\u7b2c\u4e09\u5b63\uff1a\u4eba\u5de5\u667a\u80fd\u4e0e\u6570\u5b66<\/a><\/strong><\/span><\/section>\n
      \u4eceAI for math\u548cmath for AI\u4e24\u4e2a\u65b9\u9762\u6df1\u5165\u63a2\u8ba8\u4e86\u4eba\u5de5\u667a\u80fd\u4e0e\u6570\u5b66\u7684\u5bc6\u5207\u8054\u7cfb\u3002\u8bfb\u4e66\u4f1a\u6982\u8ff0\u4e86\u4eba\u5de5\u667a\u80fd\u5728\u6570\u5b66\u7684\u5e94\u7528\uff0c\u5e76\u6df1\u5165\u63a2\u8ba8\u5927\u6a21\u578b\u4e0e\u6570\u5b66\u63a8\u7406\uff0c\u5b9a\u7406\u81ea\u52a8\u8bc1\u660e\uff0c AI\u53d1\u73b0\u6570\u5b66\u89c4\u5f8b\uff0c\u7b26\u53f7\u8ba1\u7b97\u7b49\u65b9\u5411\u7684\u7814\u7a76\u5de5\u4f5c\u3002\u8bfb\u4e66\u4f1a\u8fd8\u5c55\u5f00\u4e86\u5927\u6a21\u578b\u4e0e\u795e\u7ecf\u7f51\u7edc\u7684\u6570\u5b66\u57fa\u7840\u7684\u68b3\u7406\uff0c\u5e76\u6df1\u5165\u63a2\u8ba8\u51e0\u4f55\u4e0e\u62d3\u6251\u5728\u673a\u5668\u5b66\u4e60\u7684\u5e94\u7528\u3002\u8be6\u60c5\u53ca\u8bd5\u770b\uff1a<\/span><\/section>\n
      https:\/\/pattern.swarma.org\/study_group\/32<\/span><\/section>\n

      <\/span><\/section>\n
      \u901a\u7528\u4eba\u5de5\u667a\u80fd\uff08AGI\uff09\u8bfb\u4e66\u4f1a<\/a><\/strong><\/span><\/section>\n
      \u56f4\u7ed5\u901a\u7528\u4eba\u5de5\u667a\u80fd\u7684\u57fa\u7840\u7406\u8bba\u4e0e\u53d1\u5c55\u8def\u7ebf\uff0c\u68b3\u7406\u91cc\u667a\u80fd\u7684\u5b9a\u4e49\u4e0e\u5ea6\u91cf\u3001\u667a\u80fd\u7684\u539f\u7406\u3001\u5927\u8bed\u8a00\u6a21\u578b\u4e0e\u4fe1\u606f\u4e16\u754c\u7684\u667a\u80fd\u3001\u611f\u77e5\u4e0e\u5177\u8eab\u667a\u80fd\u3001\u591a\u89c6\u89d2\u4e0b\u7684\u4eba\u5de5\u667a\u80fd\u3001\u5bf9\u9f50\u6280\u672f\u4e0eAGI\u5b89\u5168\u6027\u3001AGI\u65f6\u4ee3\u7684\u672a\u6765\u793e\u4f1a\u4e03\u4e2a\u677f\u5757\u7684\u5185\u5bb9\u3002\u91cd\u70b9\u68b3\u7406\u4e86\u5927\u8bed\u8a00\u6a21\u578b\u7684\u5e95\u5c42\u673a\u7406\uff0c\u5e76\u9488\u5bf9\u591a\u6a21\u6001\u611f\u77e5\u3001\u5177\u8eab\u667a\u80fd\u7b49\u5b9e\u73b0\u901a\u7528\u4eba\u5de5\u667a\u80fd\u7684\u8def\u5f84\u8fdb\u884c\u4e86\u63a2\u8ba8\u3002\u8be6\u60c5\u53ca\u8bd5\u770b\uff1a<\/span><\/section>\n
      https:\/\/pattern.swarma.org\/study_group\/33<\/span><\/section>\n

      <\/section>\n

      <\/section>\n

      \n
      \n
      \n
      \n
      \n

      <\/section>\n

      <\/section>\n
      \n
      <\/svg><\/section>\n<\/section>\n<\/section>\n
      \n

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

      \n

      <\/section>\n

      <\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/h3>\n

      <\/section>\n
        \n
      1. \n

        \u5927\u6a21\u578b\u7684\u80fd\u529b\u8fb9\u754c\u5728\u54ea\u91cc\uff1f\u6765\u81ea\u8303\u7574\u8bba\u89c6\u89d2\u7684\u7b54\u6848<\/span><\/a><\/p>\n<\/li>\n

      2. \n

        AI \u4e3a\u4ec0\u4e48\u4f1a\u6709\u521b\u9020\u529b\uff1f\u8303\u7574\u8bba\u523b\u753b\u5927\u6a21\u578b\u521b\u9020\u529b\u7684\u6765\u6e90<\/span><\/a><\/p>\n<\/li>\n

      3. \n

        \u667a\u80fd\u662f\u4ec0\u4e48\uff1f\u8303\u7574\u8bba\u4e3a\u901a\u7528\u4eba\u5de5\u667a\u80fd\u63d0\u4f9b\u666e\u9002\u6846\u67b6<\/span><\/a><\/p>\n<\/li>\n

      4. \n

        \u5f53\u81ea\u7136\u8bed\u8a00\u5904\u7406\u9047\u4e0a\u91cf\u5b50\u8ba1\u7b97\u4e0e\u8303\u7574\u8bba\uff1a\u4eba\u5de5\u667a\u80fd\u5f00\u542f\u66f4\u5927\u60f3\u8c61\u7a7a\u95f4<\/span><\/a><\/p>\n<\/li>\n

      5. \n

        \u8303\u7574\u8bba\uff1a\u6570\u5b66\u7684\u6570\u5b66<\/span><\/a><\/p>\n<\/li>\n

      6. \n

        \u4e00\u5206\u949f\u6570\u5b66\uff1a\u8303\u7574\u8bba<\/span><\/a><\/p>\n<\/li>\n

      7. \n

        \u6784\u5efa\u6570\u5b66\u548c\u7269\u7406\u57fa\u7840\u7684\u8303\u7574\u8bba\uff1a\u7528\u300c\u7b49\u4ef7\u300d\u53d6\u4ee3\u300c\u76f8\u7b49\u300d<\/span><\/a><\/p>\n<\/li>\n

      8. \n

        \u901a\u7528\u4eba\u5de5\u667a\u80fd\uff08AGI\uff09\u5df2\u7ecf\u5230\u6765\uff1f\u6df1\u5ea6\u89e3\u6790 ChatGPT \u83b7\u5f97\u667a\u80fd\u7684\u6570\u5b66\u7269\u7406\u673a\u7406<\/span><\/a><\/p>\n<\/li>\n

      9. \n

        \u601d\u7ef4\u7684\u6781\u9650\uff1a\u79d1\u5b66\u7684\u8fdb\u6b65\u5c06\u53d6\u51b3\u4e8e\u4eba\u7c7b\u8fd8\u662fAI \uff1f<\/span><\/a><\/p>\n<\/li>\n

      10. \n

        \u4eba\u4eba\u53ef\u5b66\u7684\u8303\u7574\u8bba\u2014\u2014\u8de8\u9886\u57df\u7684\u79d1\u5b66\u65b9\u6cd5\u8bba | \u7cbe\u54c1\u5165\u95e8\u7cfb\u5217\u8bfe<\/span><\/a><\/p>\n<\/li>\n

      11. \n
        \u4eba\u4eba\u53ef\u5b66\u7684\u8303\u7574\u8bba\u7b2c\u4e8c\u5b63\u2014\u2014\u8de8\u5b66\u79d1\u7684\u79d1\u5b66\u65b9\u6cd5\u8bba | \u7cbe\u54c1\u5165\u95e8\u7cfb\u5217\u8bfe<\/span><\/a><\/section>\n<\/li>\n<\/ol>\n

        <\/section>\n

        <\/section>\n
        \u70b9\u51fb\u201c\u9605\u8bfb\u539f\u6587\u201d\uff0c\u586b\u8868\u52a0\u5165<\/span><\/section>\n

        <\/mp-style-type><\/p>\n<\/div>\n","protected":false},"excerpt":{"rendered":"

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