{"id":50692,"date":"2024-06-01T17:59:56","date_gmt":"2024-06-01T09:59:56","guid":{"rendered":"https:\/\/swarma.org\/?p=50692"},"modified":"2024-06-01T17:59:56","modified_gmt":"2024-06-01T09:59:56","slug":"%e5%a4%8d%e6%9d%82%e6%80%a7%e6%80%8e%e6%a0%b7%e9%87%8f%e5%8c%96%e5%92%8c%e9%a9%b1%e5%8a%a8%e4%b8%8b%e4%b8%80%e4%bb%a3ai%e7%b3%bb%e7%bb%9f%ef%bc%9fai-by-complexity%e7%99%be%e7%af%87%e5%89%8d%e6%b2%bf","status":"publish","type":"post","link":"https:\/\/swarma.org\/?p=50692","title":{"rendered":"\u590d\u6742\u6027\u600e\u6837\u91cf\u5316\u548c\u9a71\u52a8\u4e0b\u4e00\u4ee3AI\u7cfb\u7edf\uff1fAI by Complexity\u767e\u7bc7\u524d\u6cbf\u6587\u732e\u6c47\u603b"},"content":{"rendered":"
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\u672c\u89c6\u89d2\u4e0b\u5173\u6ce8\u7684\u6838\u5fc3\u95ee\u9898\uff1a<\/strong>AI\u5927\u6a21\u578b\u9996\u5148\u662f\u4e00\u4e2a\u5178\u578b\u7684\u590d\u6742\u7cfb\u7edf\u3002\u5982\u4f55\u7528\u590d\u6742\u7cfb\u7edf\u7684\u89c6\u89d2\u53bb\u7814\u7a76\u73b0\u5728\u7684AI\u7cfb\u7edf\uff0c\u6709\u54ea\u4e9b\u65b9\u6cd5\u53ef\u4ee5\u501f\u9274\uff0c\u6709\u54ea\u4e9b\u89c4\u5f8b\u53ef\u4ee5\u5e2e\u52a9\u6df1\u5165\u7406\u89e3AI\u5927\u6a21\u578b\uff0c\u53ef\u4ee5\u4ece\u590d\u6742\u7f51\u7edc\u3001\u590d\u6742\u52a8\u529b\u7cfb\u7edf\u7684\u65b9\u6cd5\u6765\u5bf9\u5927\u6a21\u578b\u8fdb\u884c\u7814\u7a76\uff0c\u540c\u65f6\u4e5f\u80fd\u591f\u53bb\u6df1\u5165\u7406\u89e3AI\u5927\u6a21\u578b\u7684\u6d8c\u73b0\u73b0\u8c61\u548c\u89c4\u6a21\u6cd5\u5219\u3002<\/span><\/p>\n

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https:\/\/pattern.swarma.org\/article\/224<\/span><\/p>\n


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\u672c\u89c6\u89d2\u4e0b\u8ba8\u8bba\u7684\u6838\u5fc3\u7406\u8bba\uff1a\u7f51\u7edc\u79d1\u5b66\u3001\u89c4\u6a21\u6cd5\u5219\u3001\u6d8c\u73b0<\/strong><\/span><\/p>\n

1. Jason Wei, Yi Tay, Rishi Bommasani, et al. Emergent Abilities of Large Language Models. arXiv:2206.07682<\/em>, 2022<\/span><\/p>\n

\u5927\u8bed\u8a00\u6a21\u578b\u4e2d\u7684\u6d8c\u73b0\uff1a\u5927\u8bed\u8a00\u6a21\u578b\u7684\u6d8c\u73b0\u73b0\u8c61\u4e5f\u662f\u8fd1\u5e74\u6765\u7814\u7a76\u7684\u70ed\u70b9\u95ee\u9898\uff0c\u4f46\u662f\u4e0e\u4f20\u7edf\u590d\u6742\u7cfb\u7edf\u4e2d\u7684\u6d8c\u73b0\u5b9a\u4e49\u4e0d\u540c\uff0c\u5927\u8bed\u8a00\u6a21\u578b\u7684\u6d8c\u73b0\u80fd\u529b\uff0c\u6307\u7684\u662f\u968f\u7740\u6a21\u578b\u89c4\u6a21\u7684\u589e\u957f\uff0c\u4e00\u4e9b\u80fd\u529b\u4f1a\u7a81\u7136\u53d8\u5f3a\uff0c\u7a81\u53d8\u5f0f\u5730\u62e5\u6709\u4e86\u5c0f\u8bed\u8a00\u6a21\u578b\u6240\u4e0d\u5177\u6709\u7684\u65b0\u80fd\u529b\u3002<\/span><\/p>\n

2. Jared Kaplan, Sam McCandlish, Tom Henighan, et al. Scaling Laws for Neural Language Models. arXiv:2001.08361<\/em>, 2020<\/span><\/p>\n

\u5927\u8bed\u8a00\u6a21\u578b\u7684\u89c4\u6a21\u6cd5\u5219\uff1a\u4e0e\u8bb8\u591a\u590d\u6742\u7cfb\u7edf\u4e00\u6837\uff0c\u5927\u8bed\u8a00\u6a21\u578b\u4e5f\u9075\u5faa\u7740\u89c4\u6a21\u6cd5\u5219\uff08Scaling law\uff09\uff0c\u4e5f\u5c31\u662f\u5b83\u7684\u5404\u9879\u5b8f\u89c2\u6307\u6807\u4f1a\u5f62\u6210\u4e24\u4e24\u7684\u5e42\u5f8b\u5173\u7cfb\u3002<\/span><\/p>\n

3. Emanuele La Malfa, Gabriele La Malfa, Giuseppe Nicosia, et al. Characterizing Learning Dynamics of Deep Neural Networks via Complex Networks. arXiv:2110.02628<\/em>, 2021<\/span><\/p>\n

\u5c55\u793a\u4e86\u795e\u7ecf\u7f51\u7edc\u8bad\u7ec3\u8fc7\u7a0b\u4e2d\u6743\u91cd\u5206\u5e03\u7684\u5f02\u8d28\u5316\u8d8b\u52bf\uff0c\u5373\u8d62\u8005\u901a\u5403\u7684\u5c40\u9762\u3002<\/span><\/p>\n

4. Matteo Zambra, Alberto Testolin, Amos Maritan. Emergence of Network Motifs in Deep Neural Networks. arXiv:1912.12244<\/em>, 2019<\/span><\/p>\n

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5. Emanuele La Malfa, Gabriele La Malfa, Claudio Caprioli, et al. Deep Neural Networks as Complex Networks. arXiv:2209.05488<\/em>, 2022<\/span><\/p>\n

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\u4e8c\u3001\u795e\u7ecf\u79d1\u5b66\u89c6\u89d2<\/span><\/strong><\/span><\/h3>\n

\u672c\u89c6\u89d2\u4e0b\u5173\u6ce8\u7684\u6838\u5fc3\u95ee\u9898\uff1a<\/strong>bio-inspired machine learning\u662f\u4e00\u4e2a\u975e\u5e38\u5e7f\u9614\u7684\u9886\u57df\uff0c\u901a\u8fc7\u4e00\u671f\u8bfb\u4e66\u4f1a\u6765\u56ca\u62ec\u8bf8\u591a\u4ee3\u8868\u6027\u7814\u7a76\u662f\u4e0d\u591f\u7684\uff0c\u56e0\u6b64\uff0c\u6211\u4eec\u5e0c\u671b\u5728\u8fd9\u4e2a\u90e8\u5206\u53ea\u8282\u9009\u751f\u7269\u542f\u53d1\u673a\u5668\u5b66\u4e60\u4e2d\u5177\u6709\u590d\u6742\u6027\u5473\u9053\u7684\u7814\u7a76\u8303\u4f8b\u8fdb\u884c\u5b66\u4e60\u4e0e\u8ba8\u8bba\u3002\u4f8b\u5982\uff1aHebbian Learning\u548cAnti-Hebbian Learning\u3002<\/span><\/p>\n


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\u672c\u89c6\u89d2\u4e0b\u8ba8\u8bba\u7684\u6838\u5fc3\u7406\u8bba\uff1aHebbian Learning\u3001Anti-Hebbian Learning\u3001<\/strong>\u8109\u51b2\u795e\u7ecf\u7f51\u7edc<\/strong><\/span><\/p>\n

\u63a8\u8350\u4eba\uff1a\u5f20\u7ae0<\/span><\/p>\n

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  1. \n

    Alemanno, Francesco, et al. “Supervised hebbian learning.” Europhysics Letters<\/em> 141.1 (2023): 11001. https:\/\/iopscience.iop.org\/article\/10.1209\/0295-5075\/aca55f\/meta<\/span><\/p>\n<\/li>\n<\/ol>\n

    Hebbian Learning\u6307\u4eba\u4eec\u4ece\u751f\u7269\u795e\u7ecf\u7f51\u7edc\u4e2d\u53d1\u73b0\u4e86\u4e00\u6761\u7b80\u5355\u6709\u8da3\u7684\u89c4\u5219\uff0c\u5373Fire together\uff0cwire together\u3002\u8fd9\u4e2a\u7b80\u5355\u7684\u89c4\u5219\u53ef\u4ee5\u7528\u4e8e\u6307\u5bfc\u795e\u7ecf\u7f51\u7edc\u7684\u7ed3\u6784\u8bbe\u8ba1\u548c\u8bad\u7ec3\u3002<\/span><\/p>\n

      \n
    1. \n

      Choe, Yoonsuck. “Anti-hebbian learning.” Encyclopedia of Computational Neuroscience<\/em>. New York, NY: Springer New York, 2022. 213-216. https:\/\/link.springer.com\/referenceworkentry\/10.1007\/978-1-0716-1006-0_675<\/span><\/p>\n<\/li>\n<\/ol>\n

      Anti hebbian learning\u5219\u662f\u53e6\u4e00\u6761\u4e0e\u4e4b\u76f8\u53cd\u7684\u89c4\u5219\uff0c\u5373\u5c3d\u53ef\u80fd\u51cf\u5c11\u795e\u7ecf\u5143\u7684\u534f\u540c\u6fc0\u6d3b\uff0c\u4ece\u800c\u8d77\u5230\u8282\u7701\u80fd\u91cf\u7684\u4f5c\u7528\uff0c\u76f8\u6bd4\u4e8eHebbian Leraning\uff0cAnti Hebbian Leraning\u662f\u4e00\u4e2a\u66f4\u665a\u53d1\u73b0\u7684\u89c4\u5219\uff0c\u4f46\u540c\u6837\u662f\u7b80\u5355\u89c4\u5219\u6307\u5bfc\u795e\u7ecf\u7f51\u7edc\u7ed3\u6784\u548c\u52a8\u529b\u5b66\u7684\u53ef\u5b9e\u8df5\u7684\u8303\u4f8b\u3002<\/span><\/p>\n


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      \u4e09\u3001\u7edf\u8ba1\u7269\u7406\u89c6\u89d2<\/span><\/strong><\/span><\/h3>\n

      \u672c\u89c6\u89d2\u4e0b\u5173\u6ce8\u7684\u6838\u5fc3\u95ee\u9898\u4e00\uff1a<\/strong><\/span><\/p>\n

      \u6240\u6709\u7814\u7a76\u5173\u6ce8\u7684\u6838\u5fc3\u95ee\u9898\u90fd\u662f\u5982\u4f55\u7406\u89e3\u795e\u7ecf\u7f51\u7edc\u5728\u521d\u59cb\u5316\u3001\u4f18\u5316\uff08\u5373\u8bad\u7ec3\uff09\u548c\u90e8\u7f72\u9636\u6bb5\u7684\u884c\u4e3a\u6240\u7b26\u5408\u7684\u7269\u7406\u89c4\u5f8b\u3002\u4f8b\u5982\uff0c\u5728\u521d\u59cb\u5316\u65f6\u662f\u5426\u6709\u53ef\u80fd\u4f7f\u5f97\u795e\u7ecf\u7f51\u7edc\u907f\u514d\u68af\u5ea6\u7206\u70b8\u6216\u68af\u5ea6\u6d88\u5931\uff1f\u5728\u8bad\u7ec3\u8fc7\u7a0b\u4e2d\uff0c\u795e\u7ecf\u7f51\u7edc\u662f\u5426\u53ef\u4ee5\u5728\u7279\u70b9\u4f18\u5316\u5668\u7684\u4f5c\u7528\u4e0b\u907f\u5f00\u5c40\u90e8\u6781\u5c0f\u503c\uff0c\u5e94\u8be5\u5982\u4f55\u6cdb\u5316\uff1f\u5728\u90e8\u7f72\u9636\u6bb5\uff0c\u662f\u5426\u53ef\u4ee5\u4f9d\u636e\u7279\u5b9a\u7edf\u8ba1\u7269\u7406\u89c4\u5219\u5bf9\u795e\u7ecf\u7f51\u7edc\u53c2\u6570\u8fdb\u884c\u7b5b\u9009\u548c\u91cf\u5316\u4ee5\u964d\u4f4e\u8ba1\u7b97\u91cf\uff1f<\/span><\/p>\n


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

      \u672c\u89c6\u89d2\u4e0b\u8ba8\u8bba\u7684\u6838\u5fc3\u7406\u8bba\uff1a\u5e73\u5747\u573a\u7406\u8bba\u3001\u91cd\u6574\u5316\u7fa4<\/strong><\/span><\/p>\n

      \u63a8\u8350\u4eba\uff1a\u7530\u6d0b<\/span><\/p>\n

      \u795e\u7ecf\u7f51\u7edc\u7684\u5e73\u5747\u573a\u7406\u8bba\u7684\u6838\u5fc3\u76ee\u7684\u5728\u4e8e\u7406\u89e3\u795e\u7ecf\u7f51\u7edc\u5728\u65e0\u9650\u5bbd\u7684\u6761\u4ef6\u4e0b\u7684\u52a8\u529b\u5b66\u884c\u4e3a\uff0c\u786e\u5b9a\u795e\u7ecf\u7f51\u7edc\u5728\u521d\u59cb\u5316\u548c\u8bad\u7ec3\u9636\u6bb5\u7684\u6700\u4f18\u6761\u4ef6\uff08\u4f8b\u5982\u6700\u4f18\u521d\u59cb\u5316\u65b9\u6848\u7b49\uff09\u3002\u4ee5\u4e0b\u662f\u5173\u4e8e\u4eba\u5de5\u795e\u7ecf\u7f51\u7edc\u7684\u5e73\u5747\u573a\u7406\u8bba<\/strong>\u7684\u8bba\u6587\uff0c\u524d\u9762\u4e3a\u4e09\u7bc7\u6838\u5fc3\u6587\u732e\uff0c\u5206\u522b\u5bf9\u5e94\u5e73\u5747\u573a\u7406\u8bba\u3001\u5b9e\u9a8c\u6548\u679c\u3001\u7ecf\u5178\u5e73\u5747\u573a\u7406\u8bba\u7684\u5c40\u9650\u548c\u63a8\u5e7f\uff1b\u540e\u9762\u4e24\u7bc7\u4e3a\u8865\u5145\u6587\u732e\u3002<\/span><\/p>\n

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        Pennington, J., Schoenholz, S., & Ganguli, S. (2018, March). The emergence of spectral universality in deep networks. In International Conference on Artificial Intelligence and Statistics<\/em> (pp. 1924-1932). PMLR. https:\/\/proceedings.mlr.press\/v84\/pennington18a.html<\/span><\/p>\n<\/li>\n

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        Bahri, Y., Kadmon, J., Pennington, J., Schoenholz, S. S., Sohl-Dickstein, J., & Ganguli, S. (2020). Statistical mechanics of deep learning. Annual Review of Condensed Matter Physics<\/em>, 11<\/em>, 501-528. https:\/\/www.annualreviews.org\/content\/journals\/10.1146\/annurev-conmatphys-031119-050745<\/span><\/p>\n<\/li>\n<\/ol>\n


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

        \u91cd\u6574\u5316\u7fa4\u8fd9\u4e00\u4e2a\u65b9\u5411\u7684\u6838\u5fc3\u7814\u7a76\u76ee\u7684\u5728\u4e8e\u4ece\u4fe1\u606f\u8bba\u6216\u6982\u7387\u8bba\u7684\u89d2\u5ea6\u7406\u89e3\u91cd\u6574\u5316\u7fa4\u7684\u8bbe\u8ba1\u539f\u7406\uff0c\u5e76\u7ed3\u5408\u65b0\u7684\u6570\u5b66\u5de5\u5177\u8bbe\u8ba1\u66f4\u5177\u6709\u6cdb\u7528\u6027\u6216\u8ba1\u7b97\u6027\u80fd\u66f4\u52a0\u9ad8\u7684\u91cd\u6574\u5316\u7fa4\uff0c\u4ee5\u5728\u539f\u59cb\u7406\u8bba\u65e0\u6cd5\u6db5\u76d6\u7684\u573a\u666f\u4e2d\u9a8c\u8bc1\u7cfb\u7edf\u662f\u5426\u5904\u4e8e\u4e34\u754c\u70b9\u6216\u662f\u5426\u7b26\u5408\u7279\u5b9a\u6807\u5ea6\u5173\u7cfb\u3002\u4e0b\u97625\u7bc7\u6587\u7ae0\u662f\u91cd\u6574\u5316\u7fa4<\/strong>\u548c\u673a\u5668\u5b66\u4e60\u60f3\u6cd5\u4ea4\u878d\u6216\u7528\u4e8e\u89e3\u51b3\u6570\u636e\u79d1\u5b66\u95ee\u9898\u7684\u4ee3\u8868\u3002<\/span><\/p>\n

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          Xu, Yizhou, Yang Tian, and Pei Sun. “Fast renormalizing the structures and dynamics of ultra-large systems via random renormalization group.” (2024). https:\/\/www.researchsquare.com\/article\/rs-3888068\/v1<\/span><\/p>\n<\/li>\n

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

          \u672c\u89c6\u89d2\u4e0b\u5173\u6ce8\u7684\u6838\u5fc3\u95ee\u9898\u4e8c\uff1a<\/strong>\u5229\u7528\u7edf\u8ba1\u7269\u7406\u5bf9\u590d\u6742\u7cfb\u7edf\u8fdb\u884c\u5ea6\u91cf\u8fd9\u4e2a\u7814\u7a76\u65b9\u5411\u4e0a\u4e5f\u6709\u4e86\u5f88\u591a\u65b0\u7684\u8fdb\u5c55\uff0c\u5982\u4f7f\u7528\u81ea\u7531\u80fd\u5ea6\u91cf\u590d\u6742\u7f51\u7edc\u7684\u4fe1\u606f\u4f20\u64ad\u901f\u5ea6\uff0c\u4f7f\u7528\u71b5\u5ea6\u91cf\u590d\u6742\u7f51\u7edc\u5bf9\u6297\u6270\u52a8\u7684\u80fd\u529b\uff0c\u4f7f\u7528\u70ed\u673a\u6548\u7387\u5ea6\u91cf\u590d\u6742\u7f51\u7edc\u7684\u6548\u7387\u7b49\u3002\u800c\u56e0\u4e3a\u6211\u4eec\u6709\u4e00\u4e2a\u4fe1\u4ef0\uff0c\u201c\u4e00\u4e2a\u597d\u7684AI\u7cfb\u7edf\u9996\u5148\u662f\u4e00\u4e2a\u597d\u7684\u590d\u6742\u7cfb\u7edf\u201d\uff0c\u56e0\u6b64\u8fd9\u90e8\u5206\u5ea6\u91cf\u65b9\u5f0f\u4e5f\u53ef\u4ee5\u53bb\u5b66\u4e60\uff0c\u5e76\u6709\u52a9\u4e8e\u6307\u5bfcAI\u7cfb\u7edf\u7684\u8bbe\u8ba1\u3002<\/span><\/p>\n


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

          \u672c\u89c6\u89d2\u4e0b\u8ba8\u8bba\u7684\u6838\u5fc3\u7406\u8bba\uff1a\u71b5\u3001\u81ea\u7531\u80fd\u539f\u7406<\/strong><\/span><\/p>\n

          \u63a8\u8350\u4eba\uff1a\u5f20\u7ae0\u3001\u725f\u7267\u4e91<\/span><\/p>\n

          \u63a8\u8350\u8bba\u6587\uff1a9\u7bc7<\/span><\/p>\n

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            Ghavasieh, Arsham, Carlo Nicolini, and Manlio De Domenico. “Statistical physics of complex information dynamics.” Physical Review E<\/em> 102.5 (2020): 052304. https:\/\/journals.aps.org\/pre\/abstract\/10.1103\/PhysRevE.102.052304<\/span><\/p>\n<\/li>\n

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            Ghavasieh, Arsham, and Manlio De Domenico. “Generalized network density matrices for analysis of multiscale functional diversity.” Physical Review E<\/em> 107.4 (2023): 044304. https:\/\/journals.aps.org\/pre\/abstract\/10.1103\/PhysRevE.107.044304<\/span><\/p>\n<\/li>\n

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            Ghavasieh, Arsham, and Manlio De Domenico. “Enhancing transport properties in interconnected systems without altering their structure.” Physical Review Research<\/em> 2.1 (2020): 013155. https:\/\/journals.aps.org\/prresearch\/abstract\/10.1103\/PhysRevResearch.2.013155<\/span><\/p>\n<\/li>\n

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

            \u81ea\u7531\u80fd\u7684\u6982\u5ff5\u8d77\u6e90\u4e8e\u7edf\u8ba1\u7269\u7406\uff0c\u540e\u88ab\u5f15\u5165\u673a\u5668\u5b66\u4e60\u548c\u4fe1\u606f\u52a0\u5de5\u9886\u57df\uff0c\u8868\u793a\u5bf9\u4e16\u754c\u8868\u5f81\u72b6\u6001\u4e0e\u5176\u771f\u5b9e\u72b6\u6001\u95f4\u7684\u5dee\u5f02\u3002\u81ea\u7531\u80fd\u539f\u7406\u8ba4\u4e3a\uff0c\u6240\u6709\u53ef\u53d8\u7684\u91cf\uff0c\u53ea\u8981\u4f5c\u4e3a\u7cfb\u7edf\u7684\u4e00\u90e8\u5206\uff0c\u90fd\u4f1a\u4e3a\u6700\u5c0f\u5316\u81ea\u7531\u80fd\u800c\u53d8\u5316\u3002\u672c\u8d28\u4e0a\uff0c\u81ea\u7531\u80fd\u539f\u7406\u5e0c\u671b\u9610\u660e\u5728\u751f\u7269\u7cfb\u7edf\u4e2d\u5b9e\u73b0\u81ea\u7ec4\u7ec7\u7684\u53ef\u80fd\u6027\u3002\u5728\u96c6\u667a\u201c\u81ea\u7531\u80fd\u539f\u7406\u4e0e\u5f3a\u5316\u5b66\u4e60\u201d\u8bfb\u4e66\u4f1a\u4e2d\u5bf9\u4ee5\u81ea\u7531\u80fd\u539f\u7406\u4e3a\u57fa\u7840\u7684\u4e3b\u52a8\u63a8\u65ad\u6846\u67b6\u8fdb\u884c\u4e86\u8be6\u7ec6\u7684\u4ecb\u7ecd\uff0c\u66f4\u591a\u504f\u5411\u4ece\u8d1d\u53f6\u65af\u5b9a\u7406\u51fa\u53d1\u7684\u8ba4\u77e5\u7684\u89c6\u89d2\u3002\u5728\u672c\u6b21AI by Complexity\u8bfb\u4e66\u4f1a\u4e2d\u6211\u4eec\u5c06\u66f4\u591a\u4ece\u968f\u673a\u52a8\u529b\u7cfb\u7edf\u4ee5\u53ca\u975e\u5e73\u8861\u7a33\u6001\u7b49\u89c6\u89d2\u51fa\u53d1\u63a2\u7a76\u81ea\u7531\u80fd\u539f\u7406\u4e0e\u667a\u80fd\u4f53\u611f\u77e5\u548c\u884c\u52a8\u4e4b\u95f4\u7684\u8054\u7cfb\uff0c\u8fd9\u5bf9\u4e8e\u6784\u5efa\u51b3\u7b56AI\u667a\u80fd\u4f53\u6216\u8bb8\u80fd\u591f\u5e26\u6765\u65b0\u7684\u542f\u53d1\u3002\u4ee5\u4e0b5\u7bc7\u8bba\u6587\u4e0e\u81ea\u7531\u80fd\u539f\u7406\u76f8\u5173\u3002<\/span><\/p>\n

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              \u751f\u547d\u4f55\u4ee5\u5b58\u5728\uff1f\u8fd9\u7bc7\u6587\u7ae0\u4ece\u968f\u673a\u52a8\u529b\u7cfb\u7edf\u7684\u89c6\u89d2\u51fa\u53d1\uff0c\u57fa\u4e8e\u53d8\u5206\u81ea\u7531\u80fd<\/strong>\u7684\u6700\u5c0f\u4f5c\u7528\u91cf\u539f\u7406\uff0c\u8bd5\u56fe\u89e3\u91ca\u751f\u547d\u4f53\u80fd\u591f\u62b5\u6297\u73af\u5883\u4e2d\u65e0\u5e8f\u7684\u6ce2\u52a8\u7684\u5185\u5728\u903b\u8f91\uff0c\u5e76\u5efa\u7acb\u4e86\u5b83\u4e0e\u4fe1\u606f\u74f6\u9888\u65b9\u6cd5\u5728\u5f62\u5f0f\u4e0a\u7b49\u4ef7\u7684\u6761\u4ef6\u3002<\/span><\/p>\n

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                \u81ea\u7531\u80fd\u539f\u7406<\/strong>\u8ba4\u4e3a\u884c\u52a8\u548c\u611f\u77e5\u53ef\u4ee5\u88ab\u7406\u89e3\u4e3a\u6700\u5c0f\u5316\u611f\u89c9\u6837\u672c\u7684\u81ea\u7531\u80fd\uff0c\u800c\u5f3a\u5316\u5b66\u4e60<\/strong>\u6e90\u4e8e\u884c\u4e3a\u4e3b\u4e49\u548c\u5de5\u7a0b\u5b66\uff0c\u5047\u5b9a\u4ee3\u7406\u8005\u4f18\u5316\u7b56\u7565\u4ee5\u6700\u5927\u5316\u672a\u6765\u7684\u5956\u52b1\u3002\u8fd9\u7bc7\u6587\u7ae0\u8bd5\u56fe\u5c06\u81ea\u7531\u80fd\u539f\u7406\u4ee5\u53ca\u5f3a\u5316\u5b66\u4e60\u9488\u5bf9\u884c\u52a8\u548c\u611f\u77e5\u7684\u4e24\u79cd\u8868\u8ff0\u8054\u7cfb\u8d77\u6765\u3002<\/span><\/p>\n

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                  \u8fd9\u7bc7\u8bba\u6587\u63d0\u4f9b\u4e86\u81ea\u7531\u80fd\u539f\u7406<\/strong>\u7684\u7b80\u6d01\u63a8\u5bfc\u3002\u81ea\u7531\u80fd\u91cf\u539f\u7406\u662f\u5173\u4e8e\u81ea\u7ec4\u7ec7\u548c\u611f\u77e5\u884c\u4e3a\u7684\u89c4\u8303\u6027\u63cf\u8ff0\uff0c\u5b83\u5c06\u81ea\u7ec4\u7ec7\u63cf\u8ff0\u4e3a\u6700\u5927\u5316\u8d1d\u53f6\u65af\u6a21\u578b\u8bc1\u636e\uff0c\u5c06\u611f\u77e5\u884c\u4e3a\u63cf\u8ff0\u4e3a\u6700\u4f18\u8d1d\u53f6\u65af\u8bbe\u8ba1\u548c\u51b3\u7b56\u3002\u8fd9\u7bc7\u6587\u7ae0\u968f\u673a\u52a8\u529b\u7cfb\u7edf\u7684\u6717\u4e4b\u4e07\u65b9\u7a0b\u63cf\u8ff0\u5f00\u59cb\uff0c\u6700\u7ec8\u5f97\u51fa\u53ef\u4ee5\u88ab\u770b\u4f5c\u662f\u6709\u611f\u77e5\u7684\u7269\u7406\u5b66\u7684\u8d1d\u53f6\u65af\u529b\u5b66\u3002<\/span><\/p>\n

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                    Mazzaglia, Pietro, et al. “The free energy principle for perception and action: A deep learning perspective.” Entropy<\/em> 24.2 (2022): 301. https:\/\/www.mdpi.com\/1099-4300\/24\/2\/301<\/span><\/p>\n<\/li>\n<\/ol>\n

                    \u8fd9\u7bc7\u6587\u7ae0\u5efa\u7acb\u4e86\u81ea\u7531\u80fd\u539f\u7406<\/strong>\u4e0e\u6df1\u5ea6\u5b66\u4e60\u4e16\u754c\u6a21\u578b\u4e4b\u95f4\u7684\u8054\u7cfb\uff0c\u5e76\u5bf9\u5f3a\u5316\u5b66\u4e60\u4e16\u754c\u6a21\u578b\u67b6\u6784\u8bbe\u8ba1\u7684\u4e0d\u540c\u65b9\u9762\u8fdb\u884c\u4e86\u8ba8\u8bba\u3002<\/span><\/p>\n

                      \n
                    1. \n

                      Hafner, Danijar, et al. “Action and perception as divergence minimization.” arXiv preprint arXiv:2009.01791<\/em> (2020). https:\/\/arxiv.org\/abs\/2009.01791<\/span><\/p>\n<\/li>\n<\/ol>\n

                      \u57fa\u4e8e\u81ea\u7531\u80fd\u539f\u7406<\/strong>\u4ee5\u53ca\u81ea\u7531\u80fd\u539f\u7406\u7684\u5177\u4f53\u5b9e\u73b0\u4e3b\u52a8\u63a8\u65ad\u6846\u67b6\uff0c\u542f\u53d1\u4e86\u5dee\u5f02\u6700\u5c0f\u5316\uff08divergence minimization\uff09\u7684\u60f3\u6cd5\uff0c\u4ece\u5dee\u5f02\u6700\u5c0f\u5316\u7684\u89d2\u5ea6\u51fa\u53d1\uff0c\u5f88\u591a\u6df1\u5ea6\u5b66\u4e60\u5f3a\u5316\u5b66\u4e60\u7b97\u6cd5\u53ef\u4ee5\u770b\u4f5c\u662f\u667a\u80fd\u4f53\u5728\u6700\u5c0f\u5316\u5185\u5728\u6a21\u578b\u5206\u5e03\u4e0e\u76ee\u6807\u5206\u5e03\u4e4b\u95f4\u7684\u5dee\u5f02\u3002\u4ece\u8fd9\u4e2a\u89d2\u5ea6\u51fa\u53d1\u53ef\u4ee5\u5c06\u5927\u91cf\u5df2\u6709\u7684\u7684\u6df1\u5ea6\u5b66\u4e60\u5f3a\u5316\u5b66\u4e60\uff08\u66f4\u591a\u9488\u5bf9\u5f3a\u5316\u5b66\u4e60\uff09\u7b97\u6cd5\u8fdb\u884c\u4e00\u4e2a\u7edf\u4e00\u7684\u5206\u7c7b\uff0c\u5e76\u4e14\u80fd\u591f\u542f\u53d1\u6211\u4eec\u8bbe\u8ba1\u65b0\u7684\u5f3a\u5316\u5b66\u4e60\u7b97\u6cd5\u3002<\/span><\/p>\n


                      <\/strong><\/span><\/section>\n
                      \u672c\u89c6\u89d2\u4e0b\u5173\u6ce8\u7684\u6838\u5fc3\u95ee\u9898\u4e09\uff1a<\/strong>PMI LAB\u81f4\u529b\u4e8e\u5bfb\u627e\u751f\u7269\u548c\u4eba\u5de5\u667a\u80fd\u7684\u7269\u7406\u539f\u7406\uff0c\u575a\u4fe1\u8d4b\u4e88\u6570\u5b66\u4f18\u7f8e\u6027\u7684\u7406\u8bba\u53ef\u4ee5\u5e2e\u52a9\u4eba\u4eec\u6d1e\u5bdf\u4e8b\u7269\u6df1\u5c42\u6b21\u7684\u672c\u8d28\u3002\u8fd1\u51e0\u5e74\u6765\u4eba\u5de5\u667a\u80fd\u7684\u6d8c\u73b0\u80fd\u529b\u53d7\u5230\u793e\u4f1a\u5404\u754c\u7684\u9ad8\u5ea6\u5173\u6ce8\uff0c\u6b63\u6210\u4e3a\u65b0\u4e00\u8f6e\u79d1\u6280\u9769\u547d\u7684\u9a71\u52a8\u529b\u3002\u5982\u4f55\u66f4\u597d\u5730\u7406\u89e3\u8fd9\u79cd\u6d8c\u73b0\u80fd\u529b\uff1f\u5982\u4f55\u57fa\u4e8e\u7269\u7406\u5b66\u7b2c\u4e00\u6027\u539f\u7406\u521b\u9020\u66f4\u5b89\u5168\u3001\u53ef\u9760\u3001\u901a\u7528\u7684\u4eba\u5de5\u667a\u80fd\uff0c\u8fdb\u800c\u7406\u89e3\u4eba\u7c7b\u5fc3\u667a\u7684\u5f62\u6210\uff1f\u672c\u5b9e\u9a8c\u5ba4\u4ece\u7edf\u8ba1\u7269\u7406\u7684\u89c6\u89d2\uff0c\u5229\u7528\u5e73\u5747\u573a\u7406\u8bba\u7684\u601d\u60f3\u6765\u5bfb\u627e\u80fd\u591f\u8054\u7cfb\u5fae\u89c2\u795e\u7ecf\u6d3b\u52a8\u548c\u5b8f\u89c2\u667a\u80fd\u7684\u7269\u7406\u91cf\uff0c\u5e76\u63ed\u793a\u667a\u80fd\u6a21\u578b\u4e2d\u54ea\u4e9b\u53c2\u6570\u53d8\u5316\u4f1a\u5bfc\u81f4\u7cfb\u7edf\u72b6\u6001\u7684\u663e\u8457\u5f71\u54cd\u3002\u5177\u4f53\u800c\u8a00\uff0c\u672c\u5b9e\u9a8c\u5ba4\u4ece\u73a9\u5177\u6a21\u578b\u7684\u5e73\u8861\u7cfb\u7efc\u5206\u5e03\u548c\u975e\u5e73\u8861\u52a8\u529b\u5b66\u4e24\u4e2a\u65b9\u5411\u7740\u624b\u7814\u7a76\uff1a\u5e73\u8861\u6001\u65b9\u6cd5\u6709\u52a9\u4e8e\u523b\u753b\u795e\u7ecf\u7f51\u7edc\u96c6\u4f53\u884c\u4e3a\u7684\u76f8\u53d8\uff0c\u5373\u5bf9\u79f0\u6027\u7834\u7f3a\uff0c\u4ee5\u53ca\u4f18\u5316\u7b97\u6cd5\u80cc\u540e\u7684\u80fd\u91cf\u666f\u89c2\uff1b\u800c\u975e\u5e73\u8861\u52a8\u529b\u5b66\u6709\u52a9\u4e8e\u7814\u7a76\u7f51\u7edc\u5b66\u4e60\u548c\u4fe1\u606f\u4f20\u64ad\u7684\u975e\u5e73\u8861\u8fc7\u7a0b\uff0c\u4ee5\u6b64\u63ed\u793a\u7f51\u7edc\u52a8\u529b\u5b66\u7684\u7269\u7406\u672c\u8d28\u3002<\/span><\/section>\n

                      <\/span><\/section>\n
                      \u672c\u89c6\u89d2\u4e0b\u8ba8\u8bba\u7684\u6838\u5fc3\u7406\u8bba\uff1a\u8d39\u7c73\u73bb\u8272\u673a\u7f51\u7edc\u3001\u968f\u673a\u77e9\u9635\u8c31\u8ba1\u7b97\u3001\u7406\u8bba\u673a\u5668\u5b66\u4e60\u3001\u8fde\u7eed\u5b66\u4e60\u3001\u5e73\u5747\u573a\u7406\u8bba<\/strong><\/span><\/section>\n
                      \u63a8\u8350\u4eba\uff1aPMI Lab<\/span><\/section>\n
                      \u63a8\u8350\uff1a14\u7bc7\u8bba\u6587\u30011\u672c\u4e66\u7c4d<\/span><\/section>\n
                      \u3010\u795e\u7ecf\u7f51\u7edc\u7684\u7edf\u8ba1\u7269\u7406(\u4e66\u7c4d)\u3011<\/span><\/section>\n
                      Huang H. Statistical mechanics of neural networks[M]. Springer, 2021.<\/span><\/section>\n
                      \u4ee5\u4e0b\u5206\u7c7b\u6309\u7167\u2ee9\u6d77\u5e73\u8001\u5e08\u5bf9\u667a\u80fd\u539f\u7406\u7684\u516b\u5927\u6311\u6218(Eight challenges in developing theory of intelligence, ArXiv: 2306.11232)\u8fdb\u884c\u5206\u7c7b\uff0c\u5176\u4e2d\u5173\u4e8e\u56e0\u679c\u5b66\u4e60\u3001\u610f\u8bc6\u7406\u8bba\u8fd8\u672a\u6709\u6b63\u5f0f\u6210\u679c\u3002<\/span><\/section>\n
                      \u3010\u4e00\u3001\u8868\u5f81\u5b66\u4e60 Representation learning\u3011<\/span><\/section>\n
                      Huang H. Mechanisms of dimensionality reduction and decorrelation in deep neural networks[J]. Physical<\/span><\/section>\n
                      Review E, 2018, 98(6): 062313. https:\/\/journals.aps.org\/pre\/abstract\/10.1103\/PhysRevE.98.062313<\/span><\/section>\n
                      J. Zhou and H. Huang, Weakly correlated synapses promote dimension reduction in deep neural networks<\/em>, Phys. Rev. E 103, 012315 (2021). https:\/\/journals.aps.org\/pre\/abstract\/10.1103\/PhysRevE.103.012315<\/span><\/section>\n
                      \u3010\u4e8c\u3001\u6cdb\u5316\u7406\u8bba Generalization\u3011<\/span><\/section>\n
                      Huang H, Kabashima Y. Origin of the computational hardness for learning with binary synapses[J]. Physical Review E, 2014, 90(5): 052813. https:\/\/journals.aps.org\/pre\/abstract\/10.1103\/PhysRevE.90.052813<\/span><\/section>\n
                      C. Li and H. Huang, Learning credit assignment<\/em>, Phys. Rev. Lett. 125, 178301(2020).https:\/\/journals.aps.org\/prl\/abstract\/10.1103\/PhysRevLett.125.178301<\/span><\/section>\n
                      C. Li, and H. Huang, Emergence of hierarchical modes from deep learning<\/em>, Phys. Rev. Research 5, L022011 (2023)  <\/span><\/section>\n
                      https:\/\/journals.aps.org\/prresearch\/abstract\/10.1103\/PhysRevResearch.5.L022011<\/span><\/section>\n
                      \u3010\u4e09\u3001\u5bf9\u6297\u6027\u653b\u51fb Adersarial vulnerality\u3011<\/span><\/section>\n
                      Xie M, Wang Y, Huang H. Fermi-Bose Machine[J]. arXiv preprint arXiv:2404.13631, 2024.https:\/\/arxiv.org\/abs\/2404.13631<\/span><\/section>\n
                      \u3010\u56db\u3001\u8fde\u7eed\u5b66\u4e60 Continual learning\u3011<\/span><\/section>\n
                      Li C, Huang Z, Zou W, et al. Statistical mechanics of continual learning: Variational principle and mean-field<\/span><\/section>\n
                      potential[J]. Physical Review E, 2023, 108(1): 014309.<\/span><\/section>\n
                      https:\/\/journals.aps.org\/pre\/abstract\/10.1103\/PhysRevE.108.014309<\/span><\/section>\n
                      \u3010\u4e94\u3001\u5927\u8111\u7684\u5185\u7980\u6a21\u578b Internal model of the brain\u3011<\/span><\/section>\n
                      Hou T, Huang H. Statistical physics of unsupervised learning with prior knowledge in neural networks[J]. Physical Review Letters, 2020, 124(24): 248302. https:\/\/journals.aps.org\/prl\/abstract\/10.1103\/PhysRevLett.124.248302<\/span><\/section>\n
                      Zou W, Huang H. Introduction to dynamical mean-field theory of randomly connected neural networks with<\/span><\/section>\n
                      bidirectionally correlated couplings[J]. SciPost Physics Lecture Notes, 2024: 079. https:\/\/www.scipost.org\/SciPostPhysLectNotes.79?acad_field_slug=astronomy<\/span><\/section>\n
                      W. Zou, C. Li, and H. Huang, Ensemble perspective for understanding temporal credit assignment<\/em>, Phys. Rev. E 107, 024307 (2023). https:\/\/journals.aps.org\/pre\/abstract\/10.1103\/PhysRevE.107.024307<\/span><\/section>\n
                      Z. Jiang, Z. Chen, T. Hou, and H. Huang, Spectrum of non-Hermitian deep-Hebbian neural networks<\/em>, Phys. Rev. Research 5, 013090 (2023). https:\/\/journals.aps.org\/prresearch\/abstract\/10.1103\/PhysRevResearch.5.013090<\/span><\/section>\n
                      J. Qiu and H. Huang, An optimization-based equilibrium measure describes non-equilibrium steady<\/em><\/span><\/section>\n
                      state dynamics: application to edge of chaos<\/em>, arXiv:2401.10009 (2024). https:\/\/arxiv.org\/abs\/2401.10009<\/span><\/section>\n
                      \u3010\u516d\u3001\u5927\u89c4\u6a21\u8bed\u8a00\u6a21\u578b Large language model\u3011<\/span><\/section>\n
                      Li C, Qiu J, Huang H. Meta predictive learning model of languages in neural circuits[J]. Physical Review E, 2024, 109(4): 044309. https:\/\/journals.aps.org\/pre\/abstract\/10.1103\/PhysRevE.109.044309<\/span><\/section>\n


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

                      \u56db\u3001\u590d\u6742\u7f51\u7edc\u89c6\u89d2<\/span><\/strong><\/span><\/h3>\n

                      \u672c\u89c6\u89d2\u4e0b\u5173\u6ce8\u7684\u6838\u5fc3\u95ee\u9898\uff1a<\/strong><\/span>\u795e\u7ecf\u7f51\u7edc\u6a21\u578b\u4f5c\u5728\u673a\u5668\u5b66\u4e60\u7b97\u6cd5\u4e2d\u5904\u4e8e\u6838\u5fc3\u5730\u4f4d\uff0c\u800c\u73b0\u6709\u7684\u795e\u7ecf\u7f51\u7edc\u6a21\u578b\u4ee5\u5168\u8fde\u63a5\u3001CNN\u3001RNN\u3001Transformer\u7b49\u4e3a\u4ee3\u8868\uff0c\u5176\u672c\u8eab\u5e76\u6ca1\u6709\u548c\u7f51\u7edc\u79d1\u5b66\u76f4\u63a5\u5173\u8054\u3002\u800c\u6211\u4eec\u77e5\u9053\u4e00\u4e2a\u5168\u8fde\u63a5\u7684\u590d\u6742\u7f51\u7edc\u5728\u73b0\u5b9e\u4e2d\u662f\u5f88\u5c11\u89c1\u7684\u3002\u7f51\u7edc\u79d1\u5b66\u544a\u8bc9\u6211\u4eec\uff0c\u5168\u8fde\u63a5\u7684\u7f51\u7edc\u5728\u6210\u672c\uff0c\u9c81\u68d2\u6027\uff0c\u4fe1\u606f\u4f20\u9012\u6548\u7387\u7b49\u65b9\u9762\u90fd\u4e0d\u662f\u6700\u4f18\u7684\u3002\u800c\u73b0\u5728\u5927\u591a\u6570\u7a00\u758f\u795e\u7ecf\u7f51\u7edc\u7684\u7814\u7a76\u4e5f\u53ea\u8003\u8651\u4e86\u6210\u672c\u7684\u964d\u4f4e\uff0c\u800c\u5e76\u4e0d\u4ece\u590d\u6742\u7f51\u7edc\u7684\u89c6\u89d2\u8003\u8651\u4e00\u4e2a\u7a00\u758f\u7684\u7f51\u7edc\u5e26\u6765\u7684\u5176\u4ed6\u6027\u8d28\u3002\u56e0\u6b64\u6211\u4eec\u60f3\u5728\u8fd9\u4e00\u90e8\u5206\u4ecb\u7ecd\u7ed3\u5408\u7f51\u7edc\u79d1\u5b66\u548c\u795e\u7ecf\u7f51\u7edc\u6a21\u578b\u7684\u8bba\u6587\uff0c\u4e00\u4e9b\u5bb9\u6613\u60f3\u5230\u7684\u8bba\u6587\u662f\u5728\u5e93\u8ba1\u7b97\u9886\u57df\uff08reservoir computing\uff09\u4e2d\uff0c\u7814\u7a76\u5e93\u4e2d\u4e0d\u540c\u7f51\u7edc\u7ed3\u6784\u5bf9\u6548\u679c\u7684\u5f71\u54cd\u3002<\/span><\/p>\n


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

                      \u672c\u89c6\u89d2\u4e0b\u8ba8\u8bba\u7684\u6838\u5fc3\u7406\u8bba\uff1a\u5e93\u8ba1\u7b97<\/strong><\/span><\/p>\n

                      \u63a8\u8350\u4eba\uff1a\u5f20\u7ae0<\/span><\/p>\n

                      \u63a8\u8350\u8bba\u6587\uff1a4\u7bc7<\/span><\/p>\n

                        \n
                      1. \n

                        Klickstein, Isaac, Louis Pecora, and Francesco Sorrentino. “Symmetry induced group consensus.” Chaos: An Interdisciplinary Journal of Nonlinear Science<\/em> 29.7 (2019). https:\/\/pubs.aip.org\/aip\/cha\/article\/29\/7\/073101\/1059583<\/span><\/p>\n<\/li>\n

                      2. \n

                        Dale, Matthew, et al. “Reservoir computing quality: connectivity and topology.” Natural Computing<\/em> 20 (2021): 205-216. https:\/\/link.springer.com\/article\/10.1007\/s11047-020-09823-1<\/span><\/p>\n<\/li>\n

                      3. \n

                        Kawai, Yuji, Jihoon Park, and Minoru Asada. “A small-world topology enhances the echo state property and signal propagation in reservoir computing.” Neural Networks<\/em> 112 (2019): 15-23. https:\/\/www.sciencedirect.com\/science\/article\/abs\/pii\/S0893608019300115<\/span><\/p>\n<\/li>\n

                      4. \n

                        Zador, Anthony M. “A critique of pure learning and what artificial neural networks can learn from animal brains.” Nature communications<\/em> 10.1 (2019): 3770. https:\/\/www.nature.com\/articles\/s41467-019-11786-6<\/span><\/p>\n<\/li>\n<\/ol>\n


                        <\/h3>\n

                        \u4e94\u3001\u4fe1\u606f\u8bba\u89c6\u89d2<\/span><\/strong><\/span><\/h3>\n

                        \u672c\u89c6\u89d2\u4e0b\u5173\u6ce8\u7684\u6838\u5fc3\u95ee\u9898\u4e00\uff1a<\/strong><\/span>\u76ee\u524dAI\u88ab\u62d3\u5c55\u5230\u5404\u4e2a\u9886\u57df\uff0c\u968f\u7740LLM\u7684\u5174\u8d77\u548c\u5404\u79cd\u53c2\u6570\u89c4\u6a21\u7684\u589e\u5927\uff08AI\u672c\u8eab\u53d8\u4e3a\u590d\u6742\u7cfb\u7edf\uff09\u4ee5\u53ca\u9700\u8981\u5904\u7406\u7684\u4efb\u52a1\u8d8a\u6765\u8d8a\u590d\u6742\uff08AI\u9762\u5bf9\u7684\u5bf9\u8c61\u662f\u590d\u6742\u7cfb\u7edf\uff09\uff0c\u590d\u6742\u7cfb\u7edf\u6240\u7279\u6709\u7684\u6d8c\u73b0\u73b0\u8c61\u4e00\u5b9a\u4f1a\u662fAI\u7814\u7a76\u4e2d\u4e0d\u53ef\u907f\u514d\u4f1a\u9047\u5230\u7684\u95ee\u9898\u3002\u90a3\u4e48\u95ee\u9898\u6765\u4e86\uff1a\u4ec0\u4e48\u662fAI\u81ea\u8eab\u7684\u6d8c\u73b0\uff1f\u56e0\u679c\u6d8c\u73b0\u7406\u8bba\u5982\u4f55\u89e3\u91ca\u751a\u81f3\u4f18\u5316\u51fa\u66f4\u52a0\u5177\u6709\u6d8c\u73b0\u7279\u5f81\u7684AI\uff1f\u53e6\u5916\uff0cAI\u80fd\u5426\u201c\u7406\u89e3\u201d\u6d8c\u73b0\uff1f\u4ee5\u8bc6\u522b\u751a\u81f3\u63a7\u5236\u6d8c\u73b0\u4e3a\u4f18\u5316\u76ee\u6807\uff0c\u80fd\u5426\u8ba9AI\u8868\u73b0\u66f4\u51fa\u8272\uff1f<\/span><\/p>\n


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

                        \u56e0\u679c\u6d8c\u73b0\u6700\u65e9\u7531Erik Hoel\u7b49\u4eba\u63d0\u51fa\uff0c\u5373\u5bf9\u4e00\u4e2a\u7cfb\u7edf\u5efa\u7acb\u591a\u5c3a\u5ea6\u5efa\u6a21\u7684\u89c6\u89d2\uff0c\u5728\u4e0d\u540c\u5c3a\u5ea6\u4e0b\uff0c\u90fd\u53ef\u4ee5\u627e\u5230\u7cfb\u7edf\u80cc\u540e\u7684\u52a8\u529b\u5b66\u3002\u5176\u4e2d\uff0c\u4ece\u5fae\u89c2\u53d8\u91cf\u5230\u5b8f\u89c2\u53d8\u91cf\u4f1a\u7ecf\u5386\u4e00\u4e2a\u7c97\u7c92\u5316\u7684\u6620\u5c04\uff0c\u51cf\u5c11\u6211\u4eec\u5904\u7406\u53d8\u91cf\u7684\u7ef4\u5ea6\u3002\u5b8f\u89c2\u53d8\u91cf\u56fa\u7136\u4f1a\u635f\u5931\u4e00\u4e9b\u4fe1\u606f\uff0c\u4f46\u5f80\u5f80\u5176\u52a8\u529b\u5b66\u8fc7\u7a0b\u66f4\u4e3a\u7b80\u6d01\u3002\u5bf9\u6bcf\u4e00\u4e2a\u5c3a\u5ea6\u4e0b\u7684\u52a8\u529b\u5b66\uff0c\u6211\u4eec\u90fd\u53ef\u4ee5\u6d4b\u91cf\u5176\u56e0\u679c\u6548\u5e94\u5f3a\u5ea6\uff0c\u6bd4\u5982\u8ba1\u7b97\u6709\u6548\u4fe1\u606f\uff08effective information, EI\uff09\u7684\u5927\u5c0f\uff0c\u5173\u4e8e\u8fd9\u4e2a\u5177\u4f53\u5ea6\u91cf\u6307\u6807\u7684\u4ecb\u7ecd\u53ef\u4ee5\u53c2\u8003\u96c6\u667a\u767e\u79d1\u8bcd\u6761\u6709\u6548\u4fe1\u606f\u3002\u6211\u4eec\u53ef\u4ee5\u6bd4\u8f83\u4e0d\u540c\u5c3a\u5ea6\u4e0b\u52a8\u529b\u5b66EI\u7684\u5927\u5c0f\uff0c\u5982\u679c\u5b8f\u89c2\u52a8\u529b\u5b66EI\u5927\u4e8e\u5fae\u89c2\u52a8\u529b\u5b66EI\uff0c\u6211\u4eec\u8ba4\u4e3a\u8be5\u7cfb\u7edf\u53d1\u751f\u56e0\u679c\u6d8c\u73b0\u3002<\/span><\/section>\n

                        <\/span><\/section>\n
                        \u57fa\u4e8eHoel\u7b49\u4eba\u7684\u591a\u5c3a\u5ea6\u56e0\u679c\u6d8c\u73b0\u6846\u67b6\u6765\u6307\u5bfcAI\u7814\u7a76\u7684\u5de5\u4f5c\u76ee\u524d\u53ea\u6709NIS\uff08Neural Information Squeezer\uff09\u7cfb\u5217\uff0c\u5176\u4e2dNIS+\u7684\u6846\u67b6\u53ef\u4ee5\u901a\u8fc7\u8ba9\u673a\u5668\u8bc6\u522b\u6d8c\u73b0\uff08\u6700\u5927\u5316\u6709\u6548\u4fe1\u606f\uff09\u6765\u589e\u5f3a\u5206\u5e03\u5916\u6cdb\u5316\u9884\u6d4b\u7684\u80fd\u529b\uff1a<\/span><\/section>\n
                        \u672c\u89c6\u89d2\u4e0b\u8ba8\u8bba\u7684\u6838\u5fc3\u7406\u8bba\uff1a\u56e0\u679c\u6d8c\u73b0<\/strong><\/section>\n

                        \u63a8\u8350\u4eba\uff1a\u6768\u660e\u54f2<\/span><\/p>\n

                          \n
                        1. \n

                          Zhang, J., & Liu, K. (2022). Neural Information Squeezer for Causal Emergence. Entropy<\/em>, 25<\/em>(1), 26. https:\/\/doi.org\/10.3390\/e25010026<\/span><\/p>\n<\/li>\n

                        2. \n

                          Yang, M., Wang, Z., Liu, K., Rong, Y., Yuan, B., & Zhang, J. (2023). Finding emer<\/em>gence in data by maximizing effective information<\/em> (arXiv:2308.09952). arXiv. http:\/\/arxiv.org\/abs\/2308.09952<\/span><\/p>\n<\/li>\n<\/ol>\n


                          <\/p>\n

                          \u672c\u89c6\u89d2\u4e0b\u5173\u6ce8\u7684\u6838\u5fc3\u95ee\u9898\u4e8c\uff1a<\/strong>\u81ea\u53bb\u5e74\u4ee5\u6765\uff0c\u5305\u62ecIlya Sutskever\u3001Marcus Hutter\u3001Jack Rae\u7b49\u4f17\u591a\u7814\u7a76\u8005\u5747\u5bf9\u538b\u7f29\u4e0e\u667a\u80fd\u4e4b\u95f4\u7684\u5173\u7cfb\u8fdb\u884c\u4e86\u63a2\u8ba8\uff0c\u6709\u89c2\u70b9\u751a\u81f3\u5c06\u8fd9\u4e00\u7406\u8bba\u89c6\u4e3aOpenAI\u7684\u6838\u5fc3\u54f2\u5b66\u3002\u5b9e\u9645\u4e0a\uff0c\u63a2\u7a76\u538b\u7f29\u548c\u667a\u80fd\u7684\u8054\u7cfb\u5df2\u6709\u60a0\u4e45\u5386\u53f2\uff0c\u6700\u65e9\u53ef\u8ffd\u6eaf\u81f3\u56fe\u7075\u5173\u4e8e\u53ef\u8ba1\u7b97\u6027\u7684\u7814\u7a76\u3002\u6b64\u540e\uff0c1960\u5e74\u4ee3Solomonoff\u3001Kolmogorov \u548c Chaitin\u72ec\u7acb\u63d0\u51fa\u5e76\u53d1\u5c55\u7b97\u6cd5\u4fe1\u606f\u8bba\uff08\u7d27\u968f\u9999\u519c\u7684\u4fe1\u606f\u8bba\u4e4b\u540e\uff09\uff0c\u8be5\u9886\u57df\u9010\u6e10\u5f15\u5165\u4e86\u8bf8\u5982\u67ef\u5f0f\u590d\u6742\u5ea6\u8fd9\u6837\u7684\u91cd\u8981\u6982\u5ff5\uff1a\u5373\u590d\u6742\u5ea6\u8861\u91cf\u7684\u662f\u5728\u901a\u7528\u56fe\u7075\u673a\u4e0a\u751f\u6210\u67d0\u4e2a\u5bf9\u8c61\u7684\u6700\u77ed\u7a0b\u5e8f\u957f\u5ea6\u300290\u5e74\u4ee3\uff0c\u7814\u7a76\u8005\u5df2\u7ecf\u5f00\u59cb\u5229\u7528\u538b\u7f29\u65b9\u6cd5\u6709\u6548\u5730\u6267\u884c\u5206\u7c7b\u7b49\u4efb\u52a1\uff0c\u8fd9\u4e00\u5e94\u7528\u5982\u4eca\u91cd\u65b0\u53d7\u5230\u5173\u6ce8\uff0c\u4eff\u4f5b\u5b83\u662f\u4e00\u4e2a\u5168\u65b0\u7684\u53d1\u73b0\u3002\u968f\u7740\u5927\u578b\u6a21\u578b\u7684\u5174\u8d77\uff0c\u8fd9\u4e00\u9886\u57df\u7684\u8054\u7cfb\u548c\u91cd\u8981\u6027\u91cd\u65b0\u53d7\u5230\u5173\u6ce8\uff0c\u8fd9\u53ef\u80fd\u4f7f\u5f97\u8fd9\u4e00\u5b66\u79d1\u4ece\u9ed8\u9ed8\u53d1\u5c55\u5230\u518d\u6b21\u6210\u4e3a\u7126\u70b9\u3002\u5728\u5f53\u524d\u7684\u4eba\u5de5\u667a\u80fd\u7814\u7a76\u4e2d\uff0c\u867d\u7136\u6570\u636e\u89c4\u6a21\u548c\u6a21\u578b\u89c4\u6a21\u53d7\u5230\u4e86\u6781\u5927\u5173\u6ce8\uff0c\u4f46\u5404\u79cdMagic\u795e\u7ecf\u7f51\u7edc\u67b6\u6784\u80cc\u540e\uff0c\u5fc5\u7136\u6709\u66f4\u6df1\u5c42\u7684\u539f\u7406\u5728\u6307\u5bfc\u6216\u63a8\u52a8\u5176\u53d1\u5c55\uff0c\u8fd9\u53ef\u80fd\u5c31\u50cf\u5361\u8bfa\u5faa\u73af\u3001\u5185\u71c3\u673a\u3001\u70ed\u529b\u5b66\u7b2c\u4e8c\u5b9a\u5f8b\u4ee5\u53ca\u70ed\u529b\u5b66\u7edf\u8ba1\u4e4b\u95f4\u7684\u5fae\u5999\u5173\u7cfb\u3002<\/span><\/p>\n


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

                          \u672c\u89c6\u89d2\u4e0b\u8ba8\u8bba\u7684\u6838\u5fc3\u7406\u8bba\uff1a\u7b97\u6cd5\u4fe1\u606f\u8bba<\/strong><\/span><\/p>\n

                          \u63a8\u8350\u4eba\uff1a\u5218\u5b87<\/span><\/p>\n

                            \n
                          1. \n

                            Cilibrasi, Rudi, and Paul MB Vit\u00e1nyi. “Clustering by compression.” IEEE Transactions on Information theory<\/em> 51.4 (2005): 1523-1545. https:\/\/ieeexplore.ieee.org\/abstract\/document\/1412045<\/span><\/p>\n<\/li>\n<\/ol>\n

                            \u5728\u7b97\u6cd5\u4fe1\u606f\u8bba\u9886\u57df\u975e\u5e38\u6709\u5f71\u54cd\u529b\u7684\u5de5\u4f5c\uff1a\u5982\u4f55\u5229\u7528\u538b\u7f29\u673a\u5b9a\u4e49\u8ddd\u79bb\uff0c\u8fdb\u800c\u7528\u6765\u5206\u7c7b\u3001\u6784\u9020\u7cfb\u7edf\u53d1\u751f\u6811\u7b49\u3002<\/span><\/p>\n

                              \n
                            1. \n

                              Del\u00e9tang, Gr\u00e9goire, et al. “Language modeling is compression.” arXiv preprint arXiv:2309.10668<\/em> (2023). https:\/\/arxiv.org\/abs\/2309.10668<\/span><\/p>\n<\/li>\n<\/ol>\n

                              DeepMind\u7684\u6700\u65b0\u5de5\u4f5c\uff0c\u9610\u8ff0\u5927\u6a21\u578b\u548c\u538b\u7f29\u7684\u5173\u7cfb\uff0c\u4f7f\u5f97\u7b97\u6cd5\u4fe1\u606f\u8bba\u518d\u6b21\u5f15\u8d77\u4eba\u5de5\u667a\u80fd\u9886\u57df\u7684\u5173\u6ce8\u3002<\/span><\/p>\n

                                \n
                              1. \n

                                Johnston, Iain G., et al. “Symmetry and simplicity spontaneously emerge from the algorithmic nature of evolution.” Proceedings of the National Academy of Sciences<\/em> 119.11 (2022): e2113883119. https:\/\/www.pnas.org\/doi\/abs\/10.1073\/pnas.2113883119<\/span><\/p>\n

                                \u4ece\u7b97\u6cd5\u4fe1\u606f\u8bba\u89d2\u5ea6\u53bb\u63cf\u8ff0\u5206\u5b50\u7b49\u7ed3\u6784\u7684\u590d\u6742\u6027\uff0c\u8fde\u63a5\u8d77\u6765\u6f14\u5316\u548c\u538b\u7f29\u7684\u5173\u7cfb\u3002<\/span><\/p>\n<\/li>\n

                              2. \n

                                Sharma, Abhishek, et al. “Assembly theory explains and quantifies selection and evolution.” Nature<\/em> 622.7982 (2023): 321-328. https:\/\/www.nature.com\/articles\/s41586-023-06600-9:<\/span><\/p>\n

                                \u8fd1\u671fNature\u6b63\u520a\u53d1\u8868\u7684\u5145\u6ee1\u4e89\u8bae\u7684Assembly Theory<\/span><\/p>\n<\/li>\n

                              3. \n

                                Zecheng Zhang, Chunxiuzi Liu, Yingjun Zhu, et al. Evolutionary Tinkering Enriches the Hierarchical and Interlaced Structures in Amino Acid Sequences \uff08\u8fd1\u671f\u5c06\u5728Physical Review Research\u520a\u51fa\uff09https:\/\/www.researchsquare.com\/article\/rs-3440555\/v2<\/span><\/p>\n

                                \u7b97\u6cd5\u4fe1\u606f\u8bba\u9886\u57df\u6240\u63d0\u51fa\u7684\u65b0\u65b9\u6cd5-\u68af\u5f84\u65b9\u6cd5\uff0c\u5e76\u628a\u5b83\u5e94\u7528\u4e8e\u86cb\u767d\u8d28\u5e8f\u5217\u7684\u5206\u6790\uff0c\u6316\u6398\u5e8f\u5217\u4e2d\u4e9a\u7ed3\u6784\u7684\u91cd\u590d\u5d4c\u5957\u5173\u7cfb\u4ee5\u53ca\u6f14\u5316\u5173\u7cfb\u3002<\/span><\/p>\n<\/li>\n<\/ol>\n


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

                                \u672c\u89c6\u89d2\u4e0b\u5173\u6ce8\u7684\u6838\u5fc3\u95ee\u9898\u4e09\uff1a<\/span><\/strong>\u4fe1\u606f\u74f6\u9888\u7684\u6838\u5fc3\u76ee\u7684\u5728\u4e8e\u63a2\u7d22\u5982\u4f55\u4f7f\u5f97\u795e\u7ecf\u7f51\u7edc\u80fd\u5728\u65e0\u76d1\u7763\u6216\u5f31\u76d1\u7763\u7684\u6761\u4ef6\u4e0b\u8fdb\u884c\u5bf9\u6bd4\u5b66\u4e60\uff0c\u5b9e\u73b0\u5bf9\u6570\u636e\u7684\u6700\u4f18\u4f4e\u7ef4\u8868\u5f81\uff08\u5f53\u7136\uff0c\u76f8\u5173\u7b97\u6cd5\u4e5f\u9002\u7528\u4e8e\u4f20\u7edf\u7684\u76d1\u7763\u5b66\u4e60\uff09\u3002<\/span><\/p>\n


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

                                \u672c\u89c6\u89d2\u4e0b\u8ba8\u8bba\u7684\u6838\u5fc3\u7406\u8bba\uff1a\u7b97\u6cd5\u74f6\u9888<\/span><\/strong><\/p>\n

                                \u63a8\u8350\u4eba\uff1a\u7530\u6d0b<\/span><\/p>\n

                                \u4ee5\u4e0b\u4e24\u7bc7\u4e3a\u4fe1\u606f\u74f6\u9888\uff08information bottleneck\uff09<\/strong>\u5728\u6df1\u5ea6\u5b66\u4e60\u91cc\u6700\u7ecf\u5178\u7684\u8bba\u6587\u3002<\/span><\/p>\n

                                  \n
                                1. \n

                                  Painsky, A., & Tishby, N. (2018). Gaussian lower bound for the information bottleneck limit. Journal of Machine Learning Research<\/em>, 18<\/em>(213), 1-29. https:\/\/www.jmlr.org\/papers\/v18\/17-398.html<\/span><\/p>\n<\/li>\n

                                2. \n

                                  Hjelm, R. D., Fedorov, A., Lavoie-Marchildon, S., Grewal, K., Bachman, P., Trischler, A., & Bengio, Y. (2018). Learning deep representations by mutual information estimation and maximization. arXiv preprint arXiv:1808.06670<\/em>. https:\/\/arxiv.org\/abs\/1808.06670<\/span><\/p>\n<\/li>\n<\/ol>\n


                                  <\/p>\n

                                  \u516d\u3001\u590d\u6742\u7cfb\u7edf\u7684\u4e34\u754c\u6027\u89c6\u89d2<\/strong><\/span><\/h3>\n

                                  \u672c\u89c6\u89d2\u4e0b\u5173\u6ce8\u7684\u6838\u5fc3\u95ee\u9898\uff1a<\/strong>\u5728\u751f\u7269\u7cfb\u7edf\u4e2d\uff0c\u7528\u4ec0\u4e48\u91cf\u5316\u65b9\u6cd5\u5ea6\u91cf\u4e00\u4e2a\u751f\u7269\u7cfb\u7edf\uff08\u5982\u8111\u7f51\u7edc\uff09\u7cfb\u7edf\u662f\u5426\u5904\u4e8e\u4e34\u754c\u6001\uff1f\u4e34\u754c\u6001\u5c06\u4e3a\u8fd9\u4e2a\u7cfb\u7edf\u5e26\u6765\u54ea\u4e9b\u597d\u5904\uff1f\u8fd9\u4e9b\u95ee\u9898\u662f\u5426\u5728\u4eba\u5de5\u795e\u7ecf\u7f51\u7edc\u4e2d\u4e5f\u6210\u7acb\uff1f<\/span><\/p>\n


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

                                  \u672c\u89c6\u89d2\u4e0b\u8ba8\u8bba\u7684\u6838\u5fc3\u7406\u8bba\uff1a\u81ea\u7ec4\u7ec7\u4e34\u754c<\/strong><\/span><\/p>\n

                                  \u63a8\u8350\u4eba\uff1a\u5f20\u7ae0<\/span><\/p>\n

                                    \n
                                  1. \n

                                    Bak, Per. How nature works: the science of self-organized criticality<\/em>. Springer Science & Business Media, 2013.<\/span><\/p>\n<\/li>\n<\/ol>\n

                                    \u5173\u4e8e\u81ea\u7ec4\u7ec7\u4e34\u754c\u6027\u7684\u7ecf\u5178\u4e1b\u4e66\uff0c\u4e2d\u6587\u8bd1\u672c\u4e3a\u300a\u5927\u81ea\u7136\u5982\u4f55\u8fd0\u4f5c\u300b<\/a>\uff0c\u7531\u81ea\u7ec4\u7ec7\u4e34\u754c\u6027\u7684\u63d0\u51fa\u4ebaper bak\u64b0\u5199\u3002<\/span><\/p>\n

                                      \n
                                    1. \n

                                      Tetzlaff, Christian, et al. “Self-organized criticality in developing neuronal networks.” PLoS computational biology<\/em> 6.12 (2010): e1001013. https:\/\/journals.plos.org\/ploscompbiol\/article?id=10.1371\/journal.pcbi.1001013<\/span><\/p>\n<\/li>\n<\/ol>\n

                                      \u8fd9\u662f\u4e00\u7bc7\u7eaf\u751f\u7269\u6587\u7ae0\uff0c\u76d1\u6d4b\u4e86\u4e00\u4e2adeveloping\u7684\u795e\u7ecf\u7f51\u7edc\u7cfb\u7edf\u662f\u5982\u4f55\u9010\u6e10\u4ece\u4f4e\u6d3b\u6027\u72b6\u6001\uff0c\u7ecf\u8fc7\u8d85\u4e34\u754c\uff0c\u4e9a\u4e34\u754c\u7684\u72b6\u6001\uff0c\u6700\u7ec8\u8fbe\u5230\u4e34\u754c\u6001\u7684\u3002\u4f5c\u8005\u8fd8\u901a\u8fc7\u6570\u5b66\u6a21\u578b\u6765\u89e3\u91ca\u4e86\u8fd9\u4e00\u795e\u7ecf\u7f51\u7edcdeveloping\u7684\u8fc7\u7a0b\u3002<\/span><\/p>\n

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                                      1. \n

                                        Katsnelson, Mikhail I., Vitaly Vanchurin, and Tom Westerhout. “Self-organized criticality in neural networks.” arXiv preprint arXiv:2107.03402<\/em> (2021). https:\/\/arxiv.org\/abs\/2107.03402<\/span><\/p>\n<\/li>\n<\/ol>\n

                                        \u901a\u8fc7\u5b9e\u9a8c\uff08MNIST\u624b\u5199\u4f53\u6570\u5b57\u8bc6\u522b\uff09\u548c\u5206\u6790\u8bc1\u660e\uff0c\u4eba\u5de5\u795e\u7ecf\u7f51\u7edc\u518d\u8bad\u7ec3\u4e2d\u4f1a\u9010\u6e10\u8d8b\u5411\u4e8e\u4e00\u4e2a\u81ea\u7ec4\u7ec7\u4e34\u754c\u6001\u3002<\/span><\/section>\n

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                                        \u63a8\u8350\u9605\u8bfb<\/span><\/strong><\/strong><\/strong><\/section>\n
                                        1. <\/strong>\u590d\u6742\u6027\u7684\u8de8\u5b66\u79d1\u5173\u8054\uff1a\u5206\u6570\u9636\u5fae\u79ef\u5206\u3001\u91cd\u6574\u5316\u7fa4\u4e0e\u673a\u5668\u5b66\u4e60<\/a><\/strong><\/span><\/strong><\/section>\n
                                        2. <\/strong>\u601d\u8003\u751f\u547d\u590d\u6742\u6027\u7684\u65b0\u89c6\u89d2\uff1a\u4ece\u7ec4\u88c5\u7406\u8bba\u5230\u68af\u5f84\u7406\u8bba<\/a><\/strong><\/span><\/strong><\/section>\n
                                        3.<\/strong> AI\u4f55\u4ee5\u6d8c\u73b0\uff1a\u590d\u6742\u9002\u5e94\u7cfb\u7edf\u89c6\u89d2\u7684ChatGPT\u548c\u5927\u8bed\u8a00\u6a21\u578b<\/a><\/strong><\/span><\/section>\n
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                                        4.<\/strong> \u81ea\u7531\u80fd\u539f\u7406\uff1a\u751f\u547d\u3001\u610f\u8bc6\u4e0e\u667a\u80fd\u7684\u7edf\u4e00\u539f\u7406<\/a><\/strong><\/span><\/strong><\/span><\/section>\n
                                        5. <\/strong><\/span>\u5f20\u6c5f\uff1a\u7b2c\u4e09\u4ee3\u4eba\u5de5\u667a\u80fd\u6280\u672f\u57fa\u7840\u2014\u2014\u4ece\u53ef\u5fae\u5206\u7f16\u7a0b\u5230\u56e0\u679c\u63a8\u7406 | \u96c6\u667a\u5b66\u56ed\u5168\u65b0\u8bfe\u7a0b<\/a><\/strong><\/strong><\/span><\/section>\n
                                        6.<\/strong> \u9f99\u5e74\u5927\u8fd0\u8d77\uff0c\u5b66\u4e60\u6b63\u5f53\u65f6\uff01\u89e3\u9501\u96c6\u667a\u5168\u7ad9\u5185\u5bb9\uff0c\u5f00\u542f\u65b0\u5e74\u5b66\u4e60\u8ba1\u5212<\/a><\/strong><\/strong><\/strong><\/strong><\/span><\/strong><\/strong><\/section>\n
                                        7. <\/strong>\u52a0\u5165\u96c6\u667a\uff0c\u4e00\u8d77\u590d\u6742\uff01<\/strong><\/span><\/a><\/section>\n


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

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

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