{"id":42337,"date":"2023-05-06T16:39:33","date_gmt":"2023-05-06T08:39:33","guid":{"rendered":"https:\/\/swarma.org\/?p=42337"},"modified":"2023-05-06T16:39:33","modified_gmt":"2023-05-06T08:39:33","slug":"%e9%87%8d%e7%a3%85%e5%9c%86%e6%a1%8c%ef%bc%9a%e5%9b%a0%e6%9e%9c%e6%8e%a8%e7%90%86%e3%80%81%e7%a7%91%e5%ad%a6%e5%8f%91%e7%8e%b0%e4%b8%8e%e5%a4%a7%e6%a8%a1%e5%9e%8b-%e5%91%a8%e6%97%a5%e7%9b%b4","status":"publish","type":"post","link":"https:\/\/swarma.org\/?p=42337","title":{"rendered":"\u91cd\u78c5\u5706\u684c\uff1a\u56e0\u679c\u63a8\u7406\u3001\u79d1\u5b66\u53d1\u73b0\u4e0e\u5927\u6a21\u578b | \u5468\u65e5\u76f4\u64ad\u00b7\u56e0\u679c\u8868\u5f81 x AI+Science \u8bfb\u4e66\u4f1a\u8054\u52a8"},"content":{"rendered":"
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\u4e3a\u4e86\u4fc3\u8fdb\u56e0\u679c\u79d1\u5b66\u793e\u533a\u3001AI+Science\u793e\u533a\u4ee5\u53ca\u540eChatGPT\u793e\u533a\u7684\u8de8\u5b66\u79d1\u4ea4\u6d41\u548c\u78b0\u649e\uff0c\u6269\u5c55\u79d1\u7814\u601d\u7ef4\uff0c\u6211\u4eec\u7ec4\u7ec7\u4e86\u6b64\u6b21\u5706\u684c\u6d3b\u52a8\uff0c\u5e0c\u671b\u901a\u8fc7\u5206\u4eab+\u5706\u684c\u8ba8\u8bba\u7684\u5f62\u5f0f\uff0c\u5e2e\u52a9\u5927\u5bb6\u66f4\u597d\u5730\u4ea4\u6d41\u3002\u5176\u4e2d\u5206\u4eab\u73af\u8282\u7ec4\u4e3b\u8981\u662f\u7531CMU\u7684\u5f20\u5764\u8001\u5e08\u56e2\u961f\u4ecb\u7ecd\u56e0\u679c\u8868\u5f81\u8bfb\u4e66\u4f1a\u7684\u76f8\u5173\u5185\u5bb9\u3002\u5728\u5706\u684c\u8ba8\u8bba\u73af\u8282\uff0c\u6765\u81ea\u65af\u5766\u798f\u3001MIT\u3001CMU\u7b49\u56fd\u5185\u5916\u7684\u8001\u5e08\u548c\u540c\u5b66\u56f4\u7ed5\u300c\u56e0\u679c\u8868\u5f81\u5b66\u4e60\u300d\u300c\u79d1\u5b66\u53d1\u73b0\u300d\u4ee5\u53ca\u300c\u5927\u6a21\u578b\u300d\u7b49\u8bdd\u9898\u8fdb\u884c\u63a2\u8ba8\u548c\u4ea4\u6d41\u3002<\/span><\/strong><\/p>\n


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\u5f20\u5764\uff1a<\/strong>\u5361\u5185\u57fa\u6885\u9686\u5927\u5b66\u526f\u6559\u6388\u548cMBZUAI\u5927\u5b66\u526f\u6559\u6388\uff0c\u7814\u7a76\u5174\u8da3\u662f\u673a\u5668\u5b66\u4e60\u548c\u4eba\u5de5\u667a\u80fd\uff0c\u5c24\u5176\u662f\u56e0\u679c\u53d1\u73b0\u3001\u57fa\u4e8e\u56e0\u679c\u5173\u7cfb\u7684\u5b66\u4e60\u548c\u901a\u7528\u4eba\u5de5\u667a\u80fd\u3002\u66fe\u62c5\u4efb\u5927\u578b\u673a\u5668\u5b66\u4e60\u6216\u4eba\u5de5\u667a\u80fd\u4f1a\u8bae\u7684\u533a\u57df\u4e3b\u5e2d\u6216\u9ad8\u7ea7\u7a0b\u5e8f\u59d4\u5458\u4f1a\u6210\u5458\uff0c\u5305\u62ecNeurIPS\u3001UAI\u3001ICML\u3001AISTATS\u3001AAAI\u548cIJCAI\u3002<\/p>\n

\u4e2a\u4eba\u4e3b\u9875\uff1ahttps:\/\/www.cmu.edu\/dietrich\/philosophy\/people\/faculty\/zhang.html<\/p>\n


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\u8c22\u5c11\u5b89\uff1a<\/strong>\u5361\u5185\u57fa\u6885\u9686\u5927\u5b66\u5f20\u5764\u6559\u6388\u7684\u535a\u58eb\u7814\u7a76\u751f\uff0c\u7814\u7a76\u5174\u8da3\u662f\u57fa\u4e8e\u56e0\u679c\u63a8\u8bba\u7684\u673a\u5668\u5b66\u4e60\u4e0e\u8ba1\u7b97\u673a\u89c6\u89c9\u3002\u5728\u673a\u5668\u5b66\u4e60\u4ee5\u53ca\u89c6\u89c9\u9886\u57df\u9876\u4f1a\u5982ICML, NeurIPS, ICLR, CVPR\u7b49\u4ee5\u7b2c\u4e00\u4f5c\u8005\u53d1\u8868\u8fc7\u591a\u7bc7\u8bba\u6587\u3002<\/span><\/p>\n

\u4e2a\u4eba\u4e3b\u9875: shaoan.net<\/span><\/p>\n


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\u9ec4\u78a7\u8587\uff1a<\/span><\/strong>\u52a0\u5dde\u5927\u5b66\u5723\u5730\u4e9a\u54e5\u5206\u6821\u52a9\u7406\u6559\u6388\uff0c\u4e3b\u8981\u7814\u7a76\u65b9\u5411\u4e3acausal discovery & causality-related machine learning, \u4ee5\u53ca\u5728\u751f\u7269\u548c\u795e\u7ecf\u79d1\u5b66\u7b49\u9886\u57df\u7684\u5e94\u7528\u3002\u5979\u7684\u7814\u7a76\u8d21\u732e\u5df2\u53d1\u8868\u5728 JMLR\u3001ICML\u3001NeurIPS\u3001KDD\u3001AAAI\u3001IJCAI \u3001UAI\u7b49ML\/AI\u4e3b\u8981\u671f\u520a\u548c\u4f1a\u8bae\u3002\u5979\u662f CMU \u6821\u957f\u5956\u5b66\u91d1\u548cApple AI\/ML\u535a\u58eb\u5956\u5b66\u91d1\u7684\u83b7\u5f97\u8005\u3002\u4e3b\u529e\u4e86NeurIPS 2020 workshop on causal discovery and causality-inspired machine learning \u4ee5\u53ca1st conference on Causal Learning and Reasoning (CLeaR)\u3002 <\/p>\n

\u4e2a\u4eba\u4e3b\u9875\uff1ahttps:\/\/biweihuang.com\/<\/p>\n


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\u5f20\u91d1\u91ce<\/strong>\uff0c\u54c8\u4f5b\u5927\u5b66\u535a\u58eb\u540e\uff0c\u5373\u5c06\u6210\u4e3a\u5361\u5185\u57fa\u6885\u9686\u5927\u5b66\u52a9\u7406\u6559\u6388\uff0c\u4e0eAlkes Price\u6559\u6388\u4e00\u8d77\u7814\u7a76\u7edf\u8ba1\u9057\u4f20\u5b66\uff0c\u5728\u65af\u5766\u798f\u5927\u5b66\u653b\u8bfb\u535a\u58eb\u5b66\u4f4d\uff0c\u5e08\u4eceDavid Tse\u6559\u6388\u548cJames Zou\u6559\u6388\u3002\u7814\u7a76\u5174\u8da3\u662f\u6574\u5408 GWAS \u548c\u5355\u7ec6\u80de RNA-seq \u6570\u636e\uff1b\u4e86\u89e3\u590d\u6742\u75be\u75c5\u548c\u6027\u72b6\u7684\u5e38\u89c1\u548c\u7f55\u89c1\u53d8\u5f02\u9057\u4f20\u7ed3\u6784\uff1b\u591a\u7ec4\u5b66\u6570\u636e\u7684\u56e0\u679c\u63a8\u65ad\u7b49\u3002<\/section>\n
\u4e2a\u4eba\u4e3b\u9875\uff1ahttps:\/\/martinjzhang.github.io\/<\/section>\n

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\u859b\u4e1a\u7fd4<\/strong>\uff0c\u666e\u6e21\u5927\u5b66\u7684\u52a9\u7406\u6559\u6388\uff0c\u5728\u5eb7\u5948\u5c14\u5927\u5b66\u653b\u8bfb\u8ba1\u7b97\u673a\u7cfb\u535a\u58eb\uff0c\u5e08\u4ece\u6559\u6388Carla Gomes\u548cBart Selman\u6559\u6388\u3002\u7814\u7a76\u91cd\u70b9\u662f\u5f00\u53d1\u667a\u80fd\u7cfb\u7edf\uff0c\u5c06\u51b3\u7b56\u5236\u5b9a\u4e0e\u673a\u5668\u5b66\u4e60\u548c\u4e0d\u786e\u5b9a\u6027\u4e0b\u7684\u6982\u7387\u63a8\u7406\u7d27\u5bc6\u7ed3\u5408\u3002\u591a\u4e2a\u79d1\u5b66\u9886\u57df\u505a\u51fa\u4e86\u6838\u5fc3\u8d21\u732e\uff0c\u4ece\u4eba\u5de5\u667a\u80fd\u3001\u673a\u5668\u5b66\u4e60\u3001\u53ef\u518d\u751f\u80fd\u6e90\u3001\u6750\u6599\u79d1\u5b66\u3001\u4f17\u5305\u3001\u516c\u6c11\u79d1\u5b66\u3001\u57ce\u5e02\u8ba1\u7b97\u3001\u751f\u6001\u5b66\u5230\u884c\u4e3a\u8ba1\u91cf\u7ecf\u6d4e\u5b66\u3002<\/section>\n
\u4e2a\u4eba\u4e3b\u9875\uff1a<\/span><\/span><\/section>\n
https:\/\/www.cs.purdue.edu\/homes\/yexiang\/<\/span><\/span><\/span><\/section>\n

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\u5434\u6cf0\u9716<\/strong>\uff0c\u65af\u5766\u798f\u5927\u5b66\u8ba1\u7b97\u673a\u79d1\u5b66\u7cfb\u7684\u535a\u58eb\u540e\u7814\u7a76\u5458\uff0c\u7531Jure Leskovec\u6559\u6388\u6307\u5bfc\u3002\u4ed6\u7684\u7814\u7a76\u5174\u8da3\u4e3aAI+Science\uff0c\u5305\u62ec\u5f00\u53d1\u673a\u5668\u5b66\u4e60\u65b9\u6cd5\u7528\u4e8e\u5927\u89c4\u6a21\u79d1\u5b66\u548c\u5de5\u7a0b\u6a21\u62df\uff0c\u5f00\u53d1\u795e\u7ecf\u7b26\u53f7\u65b9\u6cd5\u7528\u4e8e\u79d1\u5b66\u53d1\u73b0\uff0c\u4ee5\u53ca\u7531\u79d1\u5b66\u95ee\u9898\u542f\u53d1\u7684\u8868\u793a\u5b66\u4e60\uff08\u8fd0\u7528\u56fe\u795e\u7ecf\u7f51\u7edc\u3001\u4fe1\u606f\u7406\u8bba\u548c\u7269\u7406\u7b49\u65b9\u6cd5\uff09\u3002<\/section>\n
\u4e2a\u4eba\u4e3b\u9875\uff1ahttps:\/\/tailin.org\/<\/section>\n

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\u738b\u701a\u5bb8\uff0c<\/strong>\u73b0\u4efb\u65af\u5766\u798f\u8ba1\u7b97\u673a\u79d1\u5b66\u7cfb\u4e0e\u57fa\u56e0\u6cf0\u514b\u65b0\u836f\u7814\u53d1\u90e8\u95e8\u8054\u5408\u535a\u58eb\u540e\uff0c\u5bfc\u5e08\u4e3aJure Leskovec\u6559\u6388\u548cAviv Regev\u9662\u58eb\u3002\u4ed6\u7684\u7814\u7a76\u91cd\u70b9\u5728\u4e8e\u5c06AI\/ML\u7b49\u5148\u8fdb\u8ba1\u7b97\u6280\u672f\u5e94\u7528\u4e8e\u57fa\u56e0\u7ec4\u5b66\u53ca\u65b0\u578b\u7597\u6cd5\u7684\u4e34\u5e8a\u7814\u7a76\uff0c\u5e76\u6d89\u730e\u4e0e\u57fa\u7840\u6a21\u578b\u76f8\u5173\u7684\u9886\u57df\uff08\u5982\u81ea\u76d1\u7763\u5b66\u4e60\u3001\u6301\u7eed\u5b66\u4e60\u3001\u673a\u5668\u5b66\u4e60\u7cfb\u7edf\u7b49\uff09\u3002\u4ed6\u4f5c\u4e3a\u7b2c\u4e00\u4f5c\u8005\u5728Nature\u3001Nature Machine Intelligence\u3001NeurIPS\u3001ICCV\u7b49\u671f\u520a\u548c\u4f1a\u8bae\u53d1\u8868\u6587\u7ae0\uff0c\u4e5f\u5728ICML\u3001NeurIPS\u3001ICLR\u4e0a\u4e3e\u529e\u8fc7\u4e00\u4e9b\u4e0eAI for Science\u76f8\u5173\u7684workshop\u3002\u4ed6\u672c\u79d1\u4fdd\u9001\u81f3\u5357\u4eac\u5927\u5b66\u5321\u4e9a\u660e\u5b66\u9662\u7269\u7406\u7cfb\uff0c\u5bfc\u5e08\u662f\u738b\u6b23\u7136\u6559\u6388\u3002\u4ed6\u5728\u5251\u6865\u5927\u5b66\u83b7\u5f97\u673a\u5668\u5b66\u4e60\u535a\u58eb\u5b66\u4f4d\uff0c\u5bfc\u5e08\u662fJoan Lasenby\u6559\u6388\u3002<\/span><\/section>\n
\u4e2a\u4eba\u4e3b\u9875\uff1ahttps:\/\/www.hanchenw.com<\/span><\/section>\n

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2023\u5e745\u67087\u65e5\uff08\u5468\u65e5\uff09\u4e0a\u53489:00-11:00<\/span><\/span><\/span><\/h3>\n

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\u53c2\u4e0e\u65b9\u5f0f\uff1a<\/span><\/strong><\/section>\n
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\u626b\u7801\u53c2\u4e0e\u8bfb\u4e66\u4f1a\uff0c\u52a0\u5165\u7fa4\u804a\u83b7\u53d6\u672c\u7cfb\u5217\u8bfb\u4e66\u4f1a\u7684\u89c6\u9891\u56de\u653e\u6743\u9650\u3001\u8d44\u6599\u6743\u9650\uff0c\u4e0e\u793e\u533a\u7684\u4e00\u7ebf\u79d1\u7814\u5de5\u4f5c\u8005\u548c\u4f01\u4e1a\u5b9e\u8df5\u8005\u6c9f\u901a\u4ea4\u6d41\u3002<\/span><\/section>\n

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\u62df\u8ba8\u8bba\u7684\u95ee\u9898\u5217\u8868<\/strong><\/strong><\/p>\n<\/section>\n

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

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\u5982\u4f55\u6253\u9020\u4e00\u4e2a\u901a\u7528\u7684\u300cAI\u79d1\u5b66\u5bb6\u300d\u5e73\u53f0\uff0c\u8d4b\u80fd\u79d1\u7814\u5de5\u4f5c\uff1f<\/span><\/p>\n

1.\u5728AI\u79d1\u5b66\u53d1\u73b0\u7684\u9886\u57df\u4e2d\uff0c\u5b58\u5728\u54ea\u4e9b\u666e\u904d\u548c\u57fa\u7840\u7684\u673a\u5668\u5b66\u4e60\u95ee\u9898\uff1f\u8fd9\u4e00\u9886\u57df\u7684\u7814\u7a76\u65b9\u6cd5\u6709\u54ea\u4e9b\u5171\u540c\u7684\u6b65\u9aa4\u548c\u6d41\u7a0b\uff1f<\/span><\/p>\n

2.\u56e0\u679c\u63a8\u65ad\u5728\u5927\u578b\u8bed\u8a00\u6a21\u578b\uff08LLM\uff09\u4e2d\u626e\u6f14\u4ec0\u4e48\u6837\u7684\u89d2\u8272\uff1f\u5b83\u4eec\u4e4b\u95f4\u6709\u4ec0\u4e48\u8054\u7cfb\u548c\u533a\u522b\uff1f<\/span><\/p>\n

3.LLM\u5982\u4f55\u4e0e\u79d1\u5b66\u6570\u636e\u548c\u77e5\u8bc6\u8fdb\u884c\u4ea4\u4e92\u548c\u878d\u5408\uff1fLLM\u4e0e\u79d1\u5b66\u9886\u57df\u4e4b\u95f4\u6709\u4ec0\u4e48\u6837\u7684\u63a5\u53e3\u548c\u6865\u6881\uff1f<\/span><\/p>\n

4.\u9488\u5bf9\u4e0d\u540c\u9886\u57df\u7684\u79d1\u5b66\u95ee\u9898\uff0c\u5982\u4f55\u8bbe\u8ba1\u548c\u6784\u5efa\u4e00\u4e2aAI\u79d1\u5b66\u5bb6\uff0c\u80fd\u591f\u6709\u6548\u5730\u5229\u7528\u65b0\u7684\u6570\u636e\u96c6\u548c\u6570\u636e\u7c7b\u578b\uff0c\u4ece\u800c\u505a\u51fa\u6709\u610f\u4e49\u7684\u79d1\u5b66\u53d1\u73b0\uff1f\u8fd9\u6837\u4e00\u4e2aAI\u79d1\u5b66\u5bb6\u7cfb\u7edf\u9700\u8981\u5177\u5907\u54ea\u4e9b\u5173\u952e\u7684\u7ec4\u4ef6\u548c\u529f\u80fd\uff1f<\/span><\/p>\n

5.\u5728AI\u79d1\u5b66\u53d1\u73b0\u7684\u7814\u7a76\u4e2d\uff0c\u6709\u54ea\u4e9b\u5b50\u9886\u57df\u4e4b\u95f4\u7684\u7ec4\u5408\u662f\u8f83\u5c11\u88ab\u5173\u6ce8\u4f46\u5177\u6709\u91cd\u8981\u4ef7\u503c\u7684\uff1f<\/span><\/p>\n

6.LLM\u5728\u5904\u7406\u79d1\u5b66\u6570\u636e\u548c\u77e5\u8bc6\u65f6\uff0c\u5982\u4f55\u89e3\u51b3\u4e0d\u786e\u5b9a\u6027\u95ee\u9898\uff1f\u4e0d\u786e\u5b9a\u6027\u5bf9LLM\u7684\u6027\u80fd\u548c\u6548\u679c\u6709\u4ec0\u4e48\u5f71\u54cd\uff1f<\/span><\/p>\n

7.\u4e0d\u540c\u7684\u79d1\u5b66\u9886\u57df\uff08\u5982\u751f\u7269\u5b66\u3001\u7269\u7406\u5b66\u7b49\uff09\u5bf9AI\u7684\u4e0d\u540c\u5b50\u9886\u57df\uff08\u5982\u4ee5\u4e0b10\u70b9\uff09\u63d0\u51fa\u4e86\u54ea\u4e9b\u6311\u6218\u548c\u9700\u6c42\uff1f<\/span><\/section>\n

<\/section>\n
\u4ee5\u53ca\u66f4\u591a\u5728\u8fd9\u4e2a\u8fc7\u7a0b\u4e2d\u884d\u751f\u8ba8\u8bba\u51fa\u6765\u7684\u95ee\u9898\u3002<\/span><\/section>\n


<\/h1>\n

\u56e0\u679c\u5173\u7cfb\u548c\u5927\u578b\u8bed\u8a00\u6a21\u578b<\/span><\/strong><\/span><\/h1>\n
\u7531\u9646\u8d85\u8d85\u8001\u5e08\u63a8\u8350<\/span><\/section>\n
    \n
  1. \n
    \u56e0\u679c\u63a8\u7406\uff1a\u5927\u578b\u8bed\u8a00\u6a21\u578b\u662f\u5426\u80fd\u591f\u5b66\u4e60\u8fdb\u884c\u56e0\u679c\u63a8\u7406\uff1f\u5b83\u4eec\u662f\u5426\u80fd\u591f\u533a\u5206\u56e0\u679c\u5173\u7cfb\uff0c\u5e76\u5229\u7528\u8fd9\u79cd\u7406\u89e3\u6765\u9884\u6d4b\u672a\u6765\u4e8b\u4ef6\uff1f<\/span><\/section>\n<\/li>\n
  2. \n
    \u56e0\u679c\u63a8\u65ad\uff1a\u5982\u4f55\u5229\u7528\u5927\u578b\u8bed\u8a00\u6a21\u578b\u8fdb\u884c\u56e0\u679c\u63a8\u65ad\u4efb\u52a1\uff0c\u4f8b\u5982\u8bc6\u522b\u5e72\u9884\u5bf9\u7cfb\u7edf\u7684\u56e0\u679c\u6548\u5e94\u6216\u9884\u6d4b\u653f\u7b56\u53d8\u5316\u7684\u5f71\u54cd\uff1f<\/span><\/section>\n<\/li>\n
  3. \n
    \u56e0\u679c\u53d1\u73b0\uff1a\u5927\u578b\u8bed\u8a00\u6a21\u578b\u662f\u5426\u53ef\u4ee5\u7528\u4e8e\u56e0\u679c\u53d1\u73b0\u4efb\u52a1\uff0c\u4f8b\u5982\u5728\u590d\u6742\u7cfb\u7edf\u4e2d\u8bc6\u522b\u53d8\u91cf\u4e4b\u95f4\u7684\u56e0\u679c\u5173\u7cfb\uff1f<\/span><\/section>\n<\/li>\n
  4. \n
    \u53cd\u4e8b\u5b9e\u63a8\u7406\uff1a\u5927\u578b\u8bed\u8a00\u6a21\u578b\u662f\u5426\u80fd\u591f\u63a8\u7406\u51fa\u53cd\u4e8b\u5b9e\u60c5\u666f\uff0c\u4f8b\u5982\u201c\u5982\u679c\u2026\u2026\u4f1a\u600e\u6837\u201d\uff0c\u5e76\u751f\u6210\u53ef\u4fe1\u7684\u53cd\u4e8b\u5b9e\u7ed3\u679c\uff1f<\/span><\/section>\n<\/li>\n
  5. \n
    \u5e72\u9884\u63a8\u7406\uff1a\u5927\u578b\u8bed\u8a00\u6a21\u578b\u662f\u5426\u80fd\u591f\u63a8\u7406\u51fa\u5e72\u9884\u7684\u5f71\u54cd\uff0c\u4f8b\u5982\u6539\u53d8\u53d8\u91cf\u503c\u5bf9\u7cfb\u7edf\u7ed3\u679c\u7684\u5f71\u54cd\uff1f<\/span><\/section>\n<\/li>\n
  6. \n
    \u504f\u89c1\u548c\u516c\u6b63\uff1a\u5927\u578b\u8bed\u8a00\u6a21\u578b\u662f\u5426\u80fd\u591f\u8bbe\u8ba1\u51fa\u5bf9\u6570\u636e\u548c\u51b3\u7b56\u8fc7\u7a0b\u4e2d\u7684\u504f\u89c1\u548c\u516c\u6b63\u8fdb\u884c\u56e0\u679c\u63a8\u7406\u7684\u65b9\u6cd5\uff0c\u5e76\u751f\u6210\u5e72\u9884\u63aa\u65bd\u6765\u89e3\u51b3\u8fd9\u4e9b\u95ee\u9898\uff1f<\/span><\/section>\n<\/li>\n
  7. \n
    \u53ef\u89e3\u91ca\u6027\uff1a\u5982\u4f55\u4f7f\u5927\u578b\u8bed\u8a00\u6a21\u578b\u5728\u5176\u56e0\u679c\u63a8\u7406\u80fd\u529b\u65b9\u9762\u66f4\u5177\u53ef\u89e3\u91ca\u6027\uff0c\u4ee5\u4fbf\u66f4\u597d\u5730\u7406\u89e3\u548c\u5206\u6790\u5176\u51b3\u7b56\u8fc7\u7a0b\uff1f<\/span><\/section>\n<\/li>\n
  8. \n
    \u65f6\u95f4\u5e8f\u5217\u9884\u6d4b\uff1a\u5927\u578b\u8bed\u8a00\u6a21\u578b\u80fd\u5426\u901a\u8fc7\u5b66\u4e60\u56e0\u679c\u5173\u7cfb\u6765\u8fdb\u884c\u65f6\u95f4\u5e8f\u5217\u9884\u6d4b\uff0c\u4f8b\u5982\u9884\u6d4b\u672a\u6765\u7684\u6c14\u6e29\u3001\u80a1\u7968\u4ef7\u683c\u6216\u6d41\u884c\u75c5\u8d8b\u52bf\uff1f<\/span><\/section>\n<\/li>\n
  9. \n
    \u56e0\u679c\u8bed\u8a00\u751f\u6210\uff1a\u5927\u578b\u8bed\u8a00\u6a21\u578b\u80fd\u5426\u751f\u6210\u5305\u542b\u56e0\u679c\u5173\u7cfb\u7684\u81ea\u7136\u8bed\u8a00\u6587\u672c\uff0c\u4f8b\u5982\u89e3\u91ca\u67d0\u4e2a\u4e8b\u4ef6\u7684\u539f\u56e0\u548c\u7ed3\u679c\uff1f<\/span><\/section>\n<\/li>\n
  10. \n
    \u5bf9\u6297\u5e72\u6270\uff1a\u5927\u578b\u8bed\u8a00\u6a21\u578b\u80fd\u5426\u5e94\u5bf9\u5bf9\u6297\u6027\u653b\u51fb\u548c\u5e72\u6270\uff0c\u4f8b\u5982\u8bc6\u522b\u5bf9\u6297\u6027\u6837\u672c\u5e76\u4fdd\u6301\u5176\u56e0\u679c\u63a8\u7406\u80fd\u529b\uff1f<\/span><\/section>\n<\/li>\n
  11. \n
    \u56e0\u679c\u56fe\u795e\u7ecf\u7f51\u7edc\uff1a\u53ef\u4ee5\u5c06\u56e0\u679c\u56fe\u548c\u795e\u7ecf\u7f51\u7edc\u7ed3\u5408\u8d77\u6765\uff0c\u4ee5\u5b9e\u73b0\u66f4\u51c6\u786e\u548c\u9ad8\u6548\u7684\u56e0\u679c\u63a8\u7406\u548c\u9884\u6d4b\uff1f<\/span><\/section>\n<\/li>\n<\/ol>\n


    <\/h1>\n

    AI for\u79d1\u5b66\u53d1\u73b0\u4e0e\u5927\u6a21\u578b<\/span><\/strong><\/span><\/h1>\n
    \u7531\u5434\u6cf0\u9716\u8001\u5e08\u8d21\u732e<\/span><\/section>\n

    <\/section>\n
    <\/section>\n
    From AI-Descartes [1]<\/span><\/section>\n

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

      \u7b26\u53f7\u56de\u5f52\uff08symbolic regression\uff09\uff1a\u6839\u636e\u8f93\u5165\u548c\u8f93\u51fa\u7684\u6570\u636e\uff0c\u53d1\u73b0\u7b26\u53f7\u8868\u793a\u7684\u65b9\u7a0b\u80fd\u5c3d\u53ef\u80fd\u51c6\u786e\u7b80\u660e\u5730\u628a\u8f93\u5165\u6620\u5c04\u5230\u8f93\u51fa\u3002<\/span><\/p>\n<\/li>\n

    2. \n

      \u6982\u5ff5\u5b66\u4e60\uff08concept learning\uff09\uff1a\u4ece\u6570\u636e\u4e2d\u81ea\u52a8\u5b66\u4e60\u548c\u63d0\u53d6\u51fa\u62bd\u8c61\u6982\u5ff5\u3002\u6bd4\u5982\u4ece\u6570\u636e\u4e2d\u63d0\u53d6\u51fa\u201c\u51c6\u7c92\u5b50\u201d\u3001\u201c\u5bf9\u79f0\u6027\u201d\u3001\u201c\u71b5\u201d\u7b49\u62bd\u8c61\u7684\u6982\u5ff5\u3002<\/span><\/p>\n<\/li>\n

    3. \n

      \u8bcd\u5178\u5b66\u4e60\uff08dictionary learning\uff09\uff1a\u4ece\u6570\u636e\u4e2d\u5b66\u4e60\u4e00\u7ec4\u57fa\u672c\u5143\u7d20\uff08\u79f0\u4e3a\u5b57\u5178\uff09\uff0c\u4f7f\u5f97\u6570\u636e\u53ef\u4ee5\u901a\u8fc7\u8fd9\u4e9b\u57fa\u672c\u5143\u7d20\u7684\u7a00\u758f\u7ebf\u6027\u7ec4\u5408\u6765\u8868\u793a\u3002\u6bd4\u5982\u4ece\u6570\u636e\u4e2d\u5b66\u51fa\u4e00\u7cfb\u5217\u7684\u6570\u5b66\u516c\u7406\uff0c\u53c8\u6bd4\u5982\u4ece\u6570\u636e\u4e2d\u5b66\u4e60\u51fa\u4e00\u7cfb\u5217\u7684\u7269\u7406\u7406\u8bba\u3002<\/span><\/p>\n<\/li>\n

    4. \n

      \u5173\u7cfb\u5b66\u4e60\uff08relation learning\uff09\uff1a\u81ea\u52a8\u5b66\u4e60\u548c\u8bc6\u522b\u7cfb\u7edf\u4e2d\u4e0d\u540c\u5b50\u7cfb\u7edf\u7684\u6f5c\u5728\u5173\u7cfb\u3002<\/span><\/p>\n<\/li>\n

    5. \n

      \u56e0\u679c\u5b66\u4e60\uff08causal learning\uff09\uff1a\u81ea\u52a8\u5b66\u4e60\u548c\u8bc6\u522b\u6570\u636e\u4e2d\u7684\u56e0\u679c\u5173\u7cfb\u3002<\/span><\/p>\n<\/li>\n

    6. \n

      AI\u7528\u4e8e\u63a8\u7406\uff08AI for reasoning\uff09\uff1aAI\u53ef\u4ee5\u5e2e\u52a9\u6f14\u7ece\u63a8\u7406\uff08deductive reasoning\uff09\u548c\u8bf1\u5bfc\u63a8\u7406\uff08abductive reasoning\uff09\u3002\u6f14\u7ece\u63a8\u7406\u662f\u6307\u4e00\u822c\u539f\u5219\u6216\u524d\u63d0\u51fa\u53d1\uff0c\u901a\u8fc7\u903b\u8f91\u63a8\u5bfc\u5f97\u51fa\u7279\u5b9a\u7ed3\u8bba\uff0c\u6bd4\u5982\u5b9a\u7406\u8bc1\u660e\uff08theorem proving\uff09\u3002\u8bf1\u5bfc\u63a8\u7406\u662f\u6307\u6839\u636e\u89c2\u5bdf\u5230\u7684\u73b0\u8c61\u6216\u7ed3\u679c\uff0c\u5c1d\u8bd5\u627e\u5230\u6700\u53ef\u80fd\u7684\u89e3\u91ca\u6216\u539f\u56e0\u3002<\/span><\/p>\n<\/li>\n

    7. \n

      AI\u7528\u4e8e\u5b9e\u9a8c\u8bbe\u8ba1\uff08AI for experimental design\uff09\uff1aAI\u5e2e\u52a9\u79d1\u5b66\u5bb6\u548c\u7814\u7a76\u4eba\u5458\u66f4\u9ad8\u6548\u5730\u8bbe\u8ba1\u5b9e\u9a8c\uff0c\u627e\u5230\u6700\u4f18\u5b9e\u9a8c\u6761\u4ef6\uff0c\u63d0\u9ad8\u6570\u636e\u6536\u96c6\u7684\u8d28\u91cf\uff0c\u5e76\u52a0\u901f\u77e5\u8bc6\u53d1\u73b0\u8fc7\u7a0b\u3002\u5305\u542b\u641c\u7d22\u6700\u4f18\u5b9e\u9a8c\u6761\u4ef6\u3001\u81ea\u52a8\u4f18\u5316\u3001\u591a\u76ee\u6807\u4f18\u5316\u3001\u9ad8\u6548\u7b5b\u9009\u3001\u65e0\u76d1\u7763\u5b66\u4e60\u3001\u4ee5\u53ca\u9884\u6d4b\u548c\u63a8\u7406\u7b49\u65b9\u9762\u3002<\/span><\/p>\n<\/li>\n

    8. \n

      AI\u7528\u4e8e\u79bb\u7fa4\u70b9\u68c0\u6d4b\uff08Outlier Detection\uff09\uff1a\u7528\u4e8e\u8bc6\u522b\u6570\u636e\u96c6\u4e2d\u4e0e\u5927\u591a\u6570\u6570\u636e\u70b9\u663e\u8457\u4e0d\u540c\u7684\u6570\u636e\u70b9\u3002\u5728\u7c92\u5b50\u7269\u7406\u3001\u5b87\u5b99\u5b66\u3001\u6750\u6599\u79d1\u5b66\u7b49\u65b9\u9762\u6709\u91cd\u8981\u5e94\u7528<\/span><\/p>\n<\/li>\n

    9. \n
      AI\u79d1\u5b66\u5bb6\uff1a\u628a\u4ee5\u4e0a\u5404\u90e8\u5206\u6709\u673a\u7ec4\u5408\u8d77\u6765\uff0c \u5f62\u6210\u4e00\u4e2a\u95ed\u73af\u7684AI\u79d1\u5b66\u5bb6\u4ece\u4e8b\u79d1\u5b66\u7814\u7a76\u3002\u6709\u540d\u7684\u5de5\u4f5c\u6709Adam [2], AI Physicist [3]\uff0cAI-Descartes [1], LLM for scientific experiments [4]\u7b49\u3002<\/span><\/section>\n<\/li>\n<\/ol>\n

      <\/section>\n
      [1] Cornelio, Cristina, et al. “Combining data and theory for derivable scientific discovery with AI-Descartes.” Nature Communications 14.1 (2023): 1777.<\/span><\/section>\n
      [2] King, Ross D., et al. “The automation of science.” Science 324.5923 (2009): 85-89.<\/span><\/section>\n
      [3] Wu, Tailin, and Max Tegmark. “Toward an artificial intelligence physicist for unsupervised learning.” Physical Review E 100.3 (2019): 033311.<\/span><\/section>\n
      [4] Boiko, Daniil A., Robert MacKnight, and Gabe Gomes. “Emergent autonomous scientific research capabilities of large language models.” arXiv preprint arXiv:2304.05332 (2023).<\/span><\/section>\n

      <\/section>\n

      \u5927\u6a21\u578b for \u56e0\u679c\u53d1\u73b0\u4e0e\u673a\u5668\u5b66\u4e60<\/span><\/strong><\/span><\/h1>\n
      \u7531\u674e\u5ae3\u7136\u8001\u5e08\u8d21\u732e<\/span><\/section>\n

      <\/section>\n

      [1] MLCopilot: \u91ca\u653e\u5927\u578b\u8bed\u8a00\u6a21\u578b\u89e3\u51b3\u673a\u5668\u5b66\u4e60\u4efb\u52a1\u7684\u80fd\u529b<\/span><\/h3>\n
      \u8bba\u6587\u9898\u76ee\uff1aMLCopilot: Unleashing the Power of Large Language Models in Solving Machine Learning Tasks<\/span><\/section>\n
      \u8bba\u6587\u5730\u5740\uff1ahttps:\/\/arxiv.org\/abs\/2304.14979<\/span><\/section>\n
      \u4f5c\u8005\uff1aL Zhang, Y Zhang, K Ren, D Li, Y Yang[Microsoft Research]<\/span><\/section>\n
      <\/span><\/section>\n
      \u8981\u70b9:<\/span><\/section>\n

       <\/span>\u2022 \u52a8\u673a\uff1a\u673a\u5668\u5b66\u4e60\u5728\u5404\u4e2a\u9886\u57df\u5f97\u5230\u4e86\u5e7f\u6cdb\u7684\u5e94\u7528\uff0c\u4f46\u9488\u5bf9\u7279\u5b9a\u573a\u666f\u81ea\u9002\u5e94\u7684\u673a\u5668\u5b66\u4e60\u4ecd\u7136\u662f\u6602\u8d35\u4e14\u4e0d\u5bb9\u6613\u7684\u3002\u800c\u73b0\u6709\u7684\u81ea\u52a8\u5316\u673a\u5668\u5b66\u4e60\u65b9\u6cd5\u901a\u5e38\u8017\u65f6\u4e14\u96be\u4ee5\u7406\u89e3\uff0c\u672c\u7814\u7a76\u65e8\u5728\u901a\u8fc7\u5f15\u5165\u65b0\u7684\u6846\u67b6MLCopilot\uff0c\u5229\u7528LLM\u6765\u5f00\u53d1\u65b0\u578b\u673a\u5668\u5b66\u4e60\u4efb\u52a1\u7684\u673a\u5668\u5b66\u4e60\u89e3\u51b3\u65b9\u6848\uff0c\u4ee5\u5f25\u5408\u673a\u5668\u667a\u80fd\u548c\u4eba\u7c7b\u77e5\u8bc6\u4e4b\u95f4\u7684\u5dee\u8ddd\u3002<\/span><\/p>\n

       \u2022 \u65b9\u6cd5\uff1a\u63d0\u51fa\u4e00\u79cd\u65b0\u6846\u67b6MLCopilot\uff0c\u5229\u7528\u6700\u5148\u8fdb\u7684\u8bed\u8a00\u6a21\u578b(\u5373LLM)\u4e3a\u65b0\u673a\u5668\u5b66\u4e60\u4efb\u52a1\u5f00\u53d1\u673a\u5668\u5b66\u4e60\u89e3\u51b3\u65b9\u6848\u3002\u8be5\u7814\u7a76\u5c06\u95ee\u9898\u5206\u89e3\u6210\u4e24\u4e2a\u7ec4\u4ef6\uff0c\u5305\u62ec\u79bb\u7ebf\u548c\u5728\u7ebf\u9636\u6bb5\u3002\u79bb\u7ebf\u9636\u6bb5\uff0cMLCopilot\u5c06\u5386\u53f2\u6570\u636e\u89c4\u8303\u5316\uff0c\u5e76\u521b\u5efa\u7ecf\u9a8c\u6c60\u3002LLM\u7528\u4e8e\u4ece\u5386\u53f2\u7ecf\u9a8c\u4e2d\u63d0\u53d6\u6709\u4ef7\u503c\u7684\u77e5\u8bc6\u3002\u5728\u7ebf\u9636\u6bb5\uff0cMLCopilot\u6839\u636e\u76ee\u6807\u4efb\u52a1\u7684\u63cf\u8ff0\u4ece\u7ecf\u9a8c\u6c60\u4e2d\u68c0\u7d22\u6700\u76f8\u5173\u7684\u4efb\u52a1\u7ecf\u9a8c\uff0c\u7136\u540e\u4e0eLLM\u4ea4\u4e92\u4ee5\u4e00\u6b21\u6027\u83b7\u5f97\u591a\u4e2a\u5efa\u8bae\u7684\u673a\u5668\u5b66\u4e60\u89e3\u51b3\u65b9\u6848\u3002<\/span><\/p>\n

       \u2022 \u4f18\u52bf\uff1a\u5c55\u793a\u4e86LLM\u6269\u5c55\u5230\u7406\u89e3\u7ed3\u6784\u5316\u8f93\u5165\u548c\u6267\u884c\u5f7b\u5e95\u63a8\u7406\u7684\u53ef\u80fd\u6027\uff0c\u5e76\u53d1\u73b0\u7ecf\u8fc7\u4e00\u4e9b\u4e13\u95e8\u8bbe\u8ba1\u540e\uff0cLLM\u53ef\u4ee5\u4ece\u73b0\u6709\u7684\u673a\u5668\u5b66\u4e60\u4efb\u52a1\u4e2d\u83b7\u5f97\u7ecf\u9a8c\uff0c\u5e76\u6709\u6548\u5730\u63a8\u7406\u51fa\u6709\u524d\u9014\u7684\u7ed3\u679c\u3002\u751f\u6210\u7684\u89e3\u51b3\u65b9\u6848\u53ef\u4ee5\u76f4\u63a5\u7528\u4e8e\u5b9e\u73b0\u9ad8\u6c34\u5e73\u7684\u7ade\u4e89\u529b\u3002\u8fd8\u63d0\u51fa\u4e86\u4e00\u79cd\u65b0\u7684\u68c0\u7d22\u548c\u63d0\u793a\u6846\u67b6\uff0c\u7528\u4e8e\u51e0\u4e4e\u5373\u65f6\u5730\u89e3\u51b3\u673a\u5668\u5b66\u4e60\u4efb\u52a1\uff0c\u800c\u65e0\u9700\u4efb\u4f55\u8017\u65f6\u7684\u641c\u7d22\u6216\u4f18\u5316\u3002<\/span><\/p>\n

      \u4e00\u53e5\u8bdd\u603b\u7ed3:<\/span><\/section>\n
      \u63d0\u51fa\u4e00\u79cd\u65b0\u7684\u6846\u67b6MLCopilot\uff0c\u5229\u7528\u6700\u5148\u8fdb\u7684\u8bed\u8a00\u6a21\u578b(\u5373LLMs\u4e3a\u65b0\u578b\u673a\u5668\u5b66\u4e60\u4efb\u52a1\u5f00\u53d1\u673a\u5668\u5b66\u4e60\u89e3\u51b3\u65b9\u6848\u3002\u8be5\u7814\u7a76\u5c55\u793a\u4e86LLM\u6269\u5c55\u5230\u7406\u89e3\u7ed3\u6784\u5316\u8f93\u5165\u548c\u6267\u884c\u5f7b\u5e95\u63a8\u7406\u7684\u53ef\u80fd\u6027\uff0c\u5e76\u53d1\u73b0\u7ecf\u8fc7\u4e00\u4e9b\u4e13\u95e8\u8bbe\u8ba1\u540e\uff0cLLM\u53ef\u4ee5\u4ece\u73b0\u6709\u7684\u673a\u5668\u5b66\u4e60\u4efb\u52a1\u4e2d\u83b7\u5f97\u7ecf\u9a8c\uff0c\u5e76\u6709\u6548\u5730\u63a8\u7406\u51fa\u6709\u524d\u9014\u7684\u7ed3\u679c\u3002\u751f\u6210\u7684\u89e3\u51b3\u65b9\u6848\u53ef\u4ee5\u76f4\u63a5\u7528\u4e8e\u5b9e\u73b0\u9ad8\u6c34\u5e73\u7684\u7ade\u4e89\u529b\u3002<\/span><\/section>\n

      <\/section>\n
      <\/span><\/section>\n
      [2]\u56e0\u679c\u63a8\u7406\u4e0e\u5927\u578b\u8bed\u8a00\u6a21\u578b: \u5f00\u8f9f\u56e0\u679c\u5173\u7cfb\u7684\u65b0\u524d\u6cbf<\/span><\/section>\n
      \u8bba\u6587\u9898\u76ee\uff1aCausal Reasoning and Large Language Models: Opening a New Frontier for Causality<\/span><\/section>\n
      \u8bba\u6587\u5730\u5740\uff1ahttps:\/\/arxiv.org\/abs\/2305.00050<\/span><\/section>\n
      \u4f5c\u8005\uff1aE K\u0131c\u0131man, R Ness, A Sharma, C Tan [Microsoft Research & University of Chicago]<\/span><\/section>\n
      <\/span><\/section>\n
      \u8981\u70b9:<\/span><\/section>\n

       \u2022 \u52a8\u673a\uff1a\u5927\u578b\u8bed\u8a00\u6a21\u578b\u7684\u56e0\u679c\u63a8\u7406\u80fd\u529b\u4e00\u76f4\u662f\u4e00\u4e2a\u4e89\u8bae\u6027\u7684\u95ee\u9898\uff0c\u5bf9\u5176\u5728\u533b\u5b66\u3001\u79d1\u5b66\u3001\u6cd5\u5f8b\u548c\u653f\u7b56\u7b49\u9886\u57df\u7684\u5e94\u7528\u5177\u6709\u91cd\u8981\u610f\u4e49\u3002\u672c\u6587\u65e8\u5728\u6df1\u5165\u4e86\u89e3\u5927\u578b\u8bed\u8a00\u6a21\u578b\u7684\u56e0\u679c\u80fd\u529b\uff0c\u5e76\u8003\u8651\u4e0d\u540c\u7c7b\u578b\u56e0\u679c\u63a8\u7406\u4efb\u52a1\u4e4b\u95f4\u7684\u533a\u522b\u4ee5\u53ca\u6784\u9020\u548c\u6d4b\u91cf\u6548\u5ea6\u7684\u5a01\u80c1\u3002<\/span><\/p>\n

       \u2022 \u65b9\u6cd5\uff1a\u91c7\u7528\u591a\u4e2a\u56e0\u679c\u57fa\u51c6\u6d4b\u8bd5\u6570\u636e\u96c6\uff0c\u53d1\u73b0\u57fa\u4e8e\u5927\u578b\u8bed\u8a00\u6a21\u578b\u7684\u65b9\u6cd5\u5728\u591a\u4e2a\u56e0\u679c\u63a8\u7406\u4efb\u52a1\u4e2d\u53d6\u5f97\u4e86\u65b0\u7684\u6700\u9ad8\u51c6\u786e\u7387\uff0c\u5e76\u80fd\u4f7f\u7528\u81ea\u7136\u8bed\u8a00\u8f93\u5165\u7cfb\u7edf\u5730\u5904\u7406\u56e0\u679c\u5173\u7cfb\u3002\u540c\u65f6\u63d0\u4f9b\u4e86\u4e00\u4e9b\u6280\u5de7\u6765\u89e3\u91ca\u5927\u578b\u8bed\u8a00\u6a21\u578b\u7684\u5931\u6548\u6a21\u5f0f\u3002<\/span><\/p>\n

       \u2022 \u4f18\u52bf\uff1a\u5927\u578b\u8bed\u8a00\u6a21\u578b\u53ef\u4ee5\u4f5c\u4e3a\u4eba\u7c7b\u9886\u57df\u77e5\u8bc6\u7684\u4ee3\u7406\uff0c\u4ee5\u51cf\u5c11\u4eba\u7c7b\u5728\u56e0\u679c\u5206\u6790\u4e2d\u7684\u5de5\u4f5c\u91cf\uff0c\u540c\u65f6\u5e2e\u52a9\u5b9e\u73b0\u534f\u53d8\u4e0e\u903b\u8f91\u56e0\u679c\u5206\u6790\u7684\u7edf\u4e00\u3002<\/span><\/p>\n

      \u4e00\u53e5\u8bdd\u603b\u7ed3:<\/span><\/section>\n
      \u63a2\u8ba8\u4e86\u5927\u578b\u8bed\u8a00\u6a21\u578b\u7684\u56e0\u679c\u63a8\u7406\u80fd\u529b\uff0c\u53d1\u73b0\u5b83\u4eec\u53ef\u4ee5\u5728\u591a\u79cd\u56e0\u679c\u63a8\u7406\u4efb\u52a1\u4e2d\u53d6\u5f97\u65b0\u7684\u6700\u9ad8\u51c6\u786e\u7387\uff0c\u5e76\u80fd\u591f\u4f7f\u7528\u81ea\u7136\u8bed\u8a00\u8f93\u5165\u7cfb\u7edf\u5730\u5904\u7406\u56e0\u679c\u5173\u7cfb\u3002\u540c\u65f6\uff0c\u8fd9\u4e9b\u6a21\u578b\u5177\u6709\u4e0d\u53ef\u9884\u6d4b\u7684\u5931\u6548\u6a21\u5f0f\uff0c\u9700\u8981\u7279\u6b8a\u6280\u5de7\u6765\u89e3\u91ca\u5176\u9c81\u68d2\u6027\u3002\u672c\u6587\u8ba4\u4e3a\u5927\u578b\u8bed\u8a00\u6a21\u578b\u53ef\u4ee5\u4f5c\u4e3a\u4eba\u7c7b\u9886\u57df\u77e5\u8bc6\u7684\u4ee3\u7406\uff0c\u4ee5\u51cf\u5c11\u4eba\u5728\u56e0\u679c\u5206\u6790\u4e2d\u7684\u5de5\u4f5c\u91cf\uff0c\u540c\u65f6\u5e2e\u52a9\u5b9e\u73b0\u534f\u53d8\u4e0e\u903b\u8f91\u56e0\u679c\u5206\u6790\u7684\u7edf\u4e00\u3002<\/span><\/section>\n
      https:\/\/arxiv.org\/abs\/2305.00050<\/span><\/section>\n

      <\/section>\n

      [3]\u590d\u6742\u63a8\u7406\uff1a<\/span>\u5927\u8bed\u8a00\u6a21\u578b\u7684\u5317\u6781\u661f\u80fd\u529b<\/span><\/span>
      <\/h3>\n
      \u8bba\u6587\u9898\u76ee\uff1aTowards Complex Reasoning: the Polaris of Large Language Models<\/span><\/section>\n
      \u8bba\u6587\u94fe\u63a5\uff1a<\/span><\/section>\n
      \uff08\u82f1\u6587\uff09https:\/\/yaofu.notion.site\/Towards-Complex-Reasoning-the-Polaris-of-Large-Language-Models-c2b4a51355b44764975f88e6a42d4e75<\/span><\/section>\n
      \uff08\u4e2d\u6587\uff09https:\/\/yaofu.notion.site\/6dafe3f8d11445ca9dcf8a2ca1c5b199<\/span><\/section>\n
      \u4f5c\u8005\uff1a\u7b26\u5c27 [\u82f1\u56fd\u7231\u4e01\u5821\u5927\u5b66]<\/span><\/section>\n
      <\/span><\/section>\n

      \u6211\u4eec\u7814\u7a76\u590d\u6742\u63a8\u7406\u7684\u539f\u56e0\u6709\u4e24\u4e2a\uff1a<\/span><\/p>\n

       \u2022 \u6b63\u5982\u4e0a\u6587\u63d0\u5230\u7684\uff0c\u590d\u6742\u63a8\u7406\u662f\u6807\u5fd7\u7740\u5c0f\u6a21\u578b\u4e0e\u5927\u6a21\u578b\u5dee\u5f02\u7684\u5173\u952e\u56e0\u7d20\uff0c\u8fd9\u4e00\u70b9\u5728 GPT-4 \u53d1\u5e03\u6587\u7ae0\u4e2d\u5df2\u7ecf\u8ba8\u8bba\u8fc7\u3002<\/span><\/p>\n

       \u2022 \u590d\u6742\u63a8\u7406\u662f\u4f7f\u6a21\u578b\u6210\u4e3a\u4e0b\u4e00\u4ee3\u64cd\u4f5c\u7cfb\u7edf\u7684\u6838\u5fc3\u80fd\u529b\u3002<\/span><\/p>\n

      \u5c06\u8bed\u8a00\u6a21\u578b\u89c6\u4e3a\u4e0b\u4e00\u4ee3\u64cd\u4f5c\u7cfb\u7edf\u7684\u613f\u666f\u5c24\u4e3a\u6709\u8da3\uff0c\u56e0\u4e3a\u5b83\u4e3a\u6784\u5efa\u65b0\u5e94\u7528\u7a0b\u5e8f\u548c\u521b\u5efa\u57fa\u4e8e\u8bed\u8a00\u6a21\u578b\u7684\u8ba1\u7b97\u751f\u6001\u7cfb\u7edf\uff08\u53ef\u80fd\u6bd4\u8d85\u7ea7\u5e94\u7528\u7a0b\u5e8f\u5982 ChatGPT \u63d0\u4f9b\u66f4\u5927\u7684\u673a\u4f1a\uff09\u5f00\u8f9f\u4e86\u65e0\u6570\u53ef\u80fd\u6027\u3002\u590d\u6742\u63a8\u7406\u80fd\u529b\u662f\u57fa\u7840\uff0c\u56e0\u4e3a\u5982\u679c\u6211\u4eec\u5e0c\u671b\u6a21\u578b\u6210\u4e3a\u65b0\u7684\u64cd\u4f5c\u7cfb\u7edf\uff0c\u5b83\u9700\u8981\u80fd\u591f\u901a\u8fc7\u4e0e\u5de5\u5177\u3001\u7528\u6237\u548c\u5916\u90e8\u73af\u5883\u7684\u6240\u6709\u5143\u7d20\u4e92\u52a8\u6765\u5b8c\u6210\u590d\u6742\u7684\u6307\u4ee4\u3002<\/span><\/p>\n

      \u672c\u6587\u7814\u7a76\u4e86\u5982\u4f55\u8bad\u7ec3\u5177\u6709\u5f3a\u5927\u590d\u6742\u63a8\u7406\u80fd\u529b\u7684\u6a21\u578b\uff0c\u5982\u4f55\u8fdb\u884c\u63d0\u793a\u5de5\u7a0b\u4ee5\u5145\u5206\u53d1\u6325\u6a21\u578b\u7684\u63a8\u7406\u80fd\u529b\uff0c\u4ee5\u53ca\u5982\u4f55\u8bc4\u4f30\u6a21\u578b\u7684\u63a8\u7406\u6027\u80fd\u3002\u672c\u6587\u7684\u5185\u5bb9\u5206\u4e3a\u4ee5\u4e0b\u51e0\u90e8\u5206\uff1a<\/span><\/p>\n

       \u2022 \u5728\u7b2c 2 \u90e8\u5206\uff0c\u6211\u4eec\u8ba8\u8bba\u4e86\u6784\u5efa\u5177\u6709\u5f3a\u5927\u590d\u6742\u63a8\u7406\u80fd\u529b\u7684\u8bed\u8a00\u6a21\u578b\u7684\u73b0\u6709\u65b9\u6cd5\u3002\u590d\u6742\u63a8\u7406\u7684\u65b9\u6848\u4e0e\u901a\u7528\u5927\u578b\u8bed\u8a00\u6a21\u578b\uff08LLM\uff09\u5f00\u53d1\u7684\u65b9\u6848\u76f8\u4f3c\uff0c\u5305\u62ec\u4e09\u4e2a\u9636\u6bb5\uff1a\u6301\u7eed\u8bad\u7ec3 (continue training)\u3001\u6307\u4ee4\u5fae\u8c03 (instruction finetuning) \u548c\u5f3a\u5316\u5b66\u4e60 (reinforcement learning)\u3002\u6211\u4eec\u8fd8\u4f1a\u8fdb\u4e00\u6b65\u8ba8\u8bba\u4ee3\u7801\u4e0e\u63a8\u7406\u4e4b\u95f4\u4ee4\u4eba\u60ca\u8bb6\u7684\u8026\u5408\u5173\u7cfb\u3002<\/span><\/p>\n

       \u2022 \u5728\u7b2c 3 \u90e8\u5206\uff0c\u6211\u4eec\u8ba8\u8bba\u4e86\u590d\u6742\u63a8\u7406\u7684\u63d0\u793a\u5de5\u7a0b (prompt engineering) \u6280\u672f\u3002\u5f53\u8bed\u8a00\u6a21\u578b\u6210\u4e3a\u65b0\u4e00\u4ee3\u64cd\u4f5c\u7cfb\u7edf\u5185\u6838\u65f6\uff0c\u63d0\u793a\u5de5\u7a0b \/ \u573a\u666f\u5b66\u4e60\u5c06\u6210\u4e3a\u65b0\u4e00\u4ee3\u811a\u672c\u7f16\u7a0b (shell script)\u3002<\/span><\/p>\n

       \u2022 \u5728\u7b2c 4 \u90e8\u5206\uff0c\u6211\u4eec\u8ba8\u8bba\u4e86\u5982\u4f55\u8bc4\u4f30\u5927\u578b\u8bed\u8a00\u6a21\u578b\u7684\u63a8\u7406\u80fd\u529b\u3002\u6211\u4eec\u4ecb\u7ecd Chain-of-thought Hub\uff0c\u8fd9\u662f\u4e00\u4e2a\u5305\u542b100\u591a\u4e2a\u63a8\u7406\u4efb\u52a1\u7684\u6570\u636e\u96c6\u5408\uff0c\u6e05\u695a\u5730\u6807\u793a\u4e86\u5927\u578b\u4e0e\u5c0f\u578b\u6a21\u578b\u7684\u5dee\u5f02\u3002\u6211\u4eec\u91cd\u70b9\u4ecb\u7ecd\u4e86 LLaMA 65B \u7684\u51fa\u8272\u8868\u73b0\u3002\u6211\u4eec\u8ba4\u4e3a\u5b83\u5177\u6709\u975e\u5e38\u5f3a\u5927\u7684\u6f5c\u529b\uff0c\u53ef\u4f5c\u4e3a\u518d\u73b0 ChatGPT-3.5 \u7684\u57fa\u5ea7\u6a21\u578b\u3002<\/span><\/section>\n

      <\/section>\n

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

      \u56e0\u679c\u8868\u5f81\u5b66\u4e60\u8bfb\u4e66\u4f1a\u542f\u52a8<\/strong><\/span><\/p>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n


      <\/section>\n

      \u968f\u7740\u201c\u56e0\u679c\u9769\u547d\u201d\u5728\u4eba\u5de5\u667a\u80fd\u4e0e\u5927\u6570\u636e\u9886\u57df\u5f90\u5f90\u5c55\u5f00\uff0c\u4f5c\u4e3a\u8fde\u63a5\u56e0\u679c\u79d1\u5b66\u4e0e\u6df1\u5ea6\u5b66\u4e60\u6865\u6881\u7684\u56e0\u679c\u8868\u5f81\u5b66\u4e60\uff0c\u6210\u4e3a\u5907\u53d7\u5173\u6ce8\u7684\u524d\u6cbf\u65b9\u5411\u3002\u4ee5\u5f80\u7684\u6df1\u5ea6\u8868\u5f81\u5b66\u4e60\u5728\u6570\u636e\u964d\u7ef4\u4e2d\u4fdd\u7559\u4fe1\u606f\u5e76\u8fc7\u6ee4\u566a\u97f3\uff0c\u65b0\u5174\u7684\u56e0\u679c\u79d1\u5b66\u5219\u5f62\u6210\u4e86\u56e0\u679c\u63a8\u7406\u4e0e\u53d1\u73b0\u7684\u4e00\u7cfb\u5217\u65b9\u6cd5\u3002\u968f\u7740\u4e8c\u8005\u7ed3\u5408\uff0c\u56e0\u679c\u8868\u5f81\u5b66\u4e60\u6709\u671b\u50ac\u751f\u66f4\u5f3a\u5927\u7684\u65b0\u4e00\u4ee3AI\u3002<\/span><\/p>\n


      <\/p>\n

      \u96c6\u667a\u4ff1\u4e50\u90e8\u7ec4\u7ec7\u4ee5\u201c\u56e0\u679c\u8868\u5f81\u5b66\u4e60\u201d\u4e3a\u4e3b\u9898\u3001\u4e3a\u671f\u5341\u5468\u7684\u8bfb\u4e66\u4f1a\uff0c\u805a\u7126\u56e0\u679c\u79d1\u5b66\u76f8\u5173\u95ee\u9898\uff0c\u5171\u5b66\u5171\u7814\u76f8\u5173\u6587\u732e\u3002\u6b22\u8fce\u4ece\u4e8b\u56e0\u679c\u79d1\u5b66\u3001\u4eba\u5de5\u667a\u80fd\u4e0e\u590d\u6742\u7cfb\u7edf\u7b49\u76f8\u5173\u7814\u7a76\u9886\u57df\uff0c\u6216\u5bf9\u56e0\u679c\u8868\u5f81\u5b66\u4e60\u7684\u7406\u8bba\u4e0e\u5e94\u7528\u611f\u5174\u8da3\u7684\u5404\u754c\u670b\u53cb\u62a5\u540d\u53c2\u4e0e\u3002\u96c6\u667a\u4ff1\u4e50\u90e8\u5df2\u7ecf\u7ec4\u7ec7\u4e09\u5b63\u201c\u56e0\u679c\u79d1\u5b66\u201d\u8bfb\u4e66\u4f1a\uff0c\u5f62\u6210\u4e86\u8d85\u8fc7\u5343\u4eba\u7684\u56e0\u679c\u79d1\u5b66\u793e\u533a\uff0c\u56e0\u679c\u8868\u5f81\u5b66\u4e60\u8bfb\u4e66\u4f1a<\/a>\u662f\u5176\u7b2c\u56db\u5b63\uff0c\u73b0\u5728\u52a0\u5165\u8bfb\u4e66\u4f1a\u5373\u53ef\u53c2\u4e0e\u56e0\u679c\u793e\u533a\u5404\u7c7b\u7ebf\u4e0a\u7ebf\u4e0b\u4ea4\u6d41\u5408\u4f5c\u3002<\/span><\/p>\n


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

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


      <\/span><\/section>\n
      \u8be6\u60c5\u8bf7\u89c1\uff1a<\/span><\/section>\n
      \u8fde\u63a5\u56e0\u679c\u79d1\u5b66\u4e0e\u6df1\u5ea6\u5b66\u4e60\u7684\u6865\u6881\uff1a\u56e0\u679c\u8868\u5f81\u5b66\u4e60\u8bfb\u4e66\u4f1a\u542f\u52a8<\/a><\/span>
      <\/span><\/section>\n

      <\/span><\/section>\n
      \u539f\u6587\u94fe\u63a5\uff1ahttps:\/\/pattern.swarma.org\/study_group\/22?from=wechat<\/span><\/section>\n


      <\/p>\n


      <\/p>\n

      \n
      \n
      \n
      \n

      AI+Science \u8bfb\u4e66\u4f1a\u542f\u52a8<\/strong><\/p>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n

      \n


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

      AI+Science \u662f\u8fd1\u5e74\u5174\u8d77\u7684\u5c06\u4eba\u5de5\u667a\u80fd\u548c\u79d1\u5b66\u76f8\u7ed3\u5408\u7684\u4e00\u79cd\u8d8b\u52bf\u3002\u4e00\u65b9\u9762\u662f AI for Science\uff0c\u673a\u5668\u5b66\u4e60\u548c\u5176\u4ed6 AI \u6280\u672f\u53ef\u4ee5\u7528\u6765\u89e3\u51b3\u79d1\u5b66\u7814\u7a76\u4e2d\u7684\u95ee\u9898\uff0c\u4ece\u9884\u6d4b\u5929\u6c14\u548c\u86cb\u767d\u8d28\u7ed3\u6784\uff0c\u5230\u6a21\u62df\u661f\u7cfb\u78b0\u649e\u3001\u8bbe\u8ba1\u4f18\u5316\u6838\u805a\u53d8\u53cd\u5e94\u5806\uff0c\u751a\u81f3\u50cf\u79d1\u5b66\u5bb6\u4e00\u6837\u8fdb\u884c\u79d1\u5b66\u53d1\u73b0\uff0c\u88ab\u79f0\u4e3a\u79d1\u5b66\u53d1\u73b0\u7684\u201c\u7b2c\u4e94\u8303\u5f0f\u201d\u3002\u53e6\u4e00\u65b9\u9762\u662f Science for AI\uff0c\u79d1\u5b66\u5c24\u5176\u662f\u7269\u7406\u5b66\u4e2d\u7684\u89c4\u5f8b\u548c\u601d\u60f3\u542f\u53d1\u673a\u5668\u5b66\u4e60\u7406\u8bba\uff0c\u4e3a\u4eba\u5de5\u667a\u80fd\u7684\u53d1\u5c55\u63d0\u4f9b\u5168\u65b0\u7684\u89c6\u89d2\u548c\u65b9\u6cd5\u3002<\/span><\/section>\n

      <\/section>\n
      \u96c6\u667a\u4ff1\u4e50\u90e8\u8054\u5408\u65af\u5766\u798f\u5927\u5b66\u8ba1\u7b97\u673a\u79d1\u5b66\u7cfb\u535a\u58eb\u540e\u7814\u7a76\u5458\u5434\u6cf0\u9716\uff08Jure Leskovec \u6559\u6388\u6307\u5bfc\uff09\u3001\u54c8\u4f5b\u91cf\u5b50\u8ba1\u5212\u7814\u7a76\u5458\u6248\u9e3f\u4e1a\u3001\u9ebb\u7701\u7406\u5de5\u5b66\u9662\u7269\u7406\u7cfb\u535a\u58eb\u751f\u5218\u5b50\u9e23\uff08Max Tegmark \u6559\u6388\u6307\u5bfc\uff09\uff0c\u5171\u540c\u53d1\u8d77\u4ee5\u201cAI+Science<\/a>\u201d\u4e3a\u4e3b\u9898\u7684\u8bfb\u4e66\u4f1a\uff0c\u63a2\u8ba8\u8be5\u9886\u57df\u7684\u91cd\u8981\u95ee\u9898\uff0c\u5171\u5b66\u5171\u7814\u76f8\u5173\u6587\u732e\u3002\u8bfb\u4e66\u4f1a\u4ece2023\u5e743\u670826\u65e5\u5f00\u59cb\uff0c\u6bcf\u5468\u65e5\u65e9\u4e0a 10:00-12:00 \u7ebf\u4e0a\u4e3e\u884c\uff0c\u6301\u7eed\u65f6\u95f4\u9884\u8ba110\u5468\u3002\u6b22\u8fce\u5bf9\u63a2\u7d22\u8fd9\u4e2a\u6fc0\u52a8\u4eba\u5fc3\u7684\u524d\u6cbf\u9886\u57df\u6709\u5174\u8da3\u7684\u670b\u53cb\u62a5\u540d\u53c2\u4e0e\u3002<\/span><\/section>\n

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

      <\/section>\n
      \u8be6\u60c5\u8bf7\u89c1\uff1a<\/span><\/section>\n
      \u4eba\u5de5\u667a\u80fd\u548c\u79d1\u5b66\u53d1\u73b0\u76f8\u4e92\u8d4b\u80fd\u7684\u65b0\u8303\u5f0f\uff1aAI+Science \u8bfb\u4e66\u4f1a\u542f\u52a8<\/a>
      <\/section>\n

      <\/section>\n

      <\/section>\n

      \u70b9\u51fb\u201c\u9605\u8bfb\u539f\u6587\u201d\uff0c\u62a5\u540d\u8bfb\u4e66\u4f1a<\/span><\/p>\n

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

      \u5bfc\u8bed \u79d1\u5b66\u53d1\u73b0\u4f5c\u4e3a\u4eba\u7c7b\u77e5\u8bc6\u8fdb\u6b65\u7684\u6838\u5fc3\u9a71\u52a8\u529b\uff0c\u4e0e\u79d1\u5b66\u7814\u7a76\u7684\u57fa\u7840\u56e0\u679c\u5173\u7cfb\u5bc6\u5207\u76f8\u5173\u3002\u901a\u8fc7\u5173\u6ce8\u56e0\u679c\u53d1\u73b0\uff0c\u7814\u7a76\u8005\u80fd\u591f\u6316\u6398\u6570\u636e\u4e2d\u6f5c\u5728\u7684\u56e0\u679c\u5173\u7cfb\uff0c\u83b7\u5f97\u65b0\u7684\u7814\u7a76\u601d\u8def\u548c\u65b9\u5411\uff0c\u4ece\u800c\u4fc3\u8fdb\u79d1\u5b66\u53d1\u5c55\u3002\u6b64\u5916\uff0c\u73b0\u4ee3\u79d1\u5b66\u7814\u7a76\u8d8a\u6765\u8d8a\u6ce8\u91cd\u6a21\u578b\u7684\u53ef\u89e3\u91ca\u6027\uff0c\u56e0\u679c\u53d1\u73b0\u6709\u52a9\u4e8e\u63ed\u793a\u6570\u636e\u80cc\u540e\u7684\u771f\u5b9e\u673a\u5236\uff0c\u4e3a\u79d1\u5b66\u5bb6\u63d0\u4f9b\u66f4\u5177\u89e3\u91ca\u529b\u7684\u6a21\u578b\uff0c\u8fdb\u800c\u6df1\u5316\u5bf9\u76f8\u5173…<\/p>\n","protected":false},"author":0,"featured_media":42324,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":[],"categories":[1],"tags":[],"special":[],"_links":{"self":[{"href":"https:\/\/swarma.org\/index.php?rest_route=\/wp\/v2\/posts\/42337"}],"collection":[{"href":"https:\/\/swarma.org\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/swarma.org\/index.php?rest_route=\/wp\/v2\/types\/post"}],"replies":[{"embeddable":true,"href":"https:\/\/swarma.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=42337"}],"version-history":[{"count":0,"href":"https:\/\/swarma.org\/index.php?rest_route=\/wp\/v2\/posts\/42337\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/swarma.org\/index.php?rest_route=\/wp\/v2\/media\/42324"}],"wp:attachment":[{"href":"https:\/\/swarma.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=42337"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/swarma.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=42337"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/swarma.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=42337"},{"taxonomy":"special","embeddable":true,"href":"https:\/\/swarma.org\/index.php?rest_route=%2Fwp%2Fv2%2Fspecial&post=42337"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}