{"id":51651,"date":"2024-07-19T21:32:11","date_gmt":"2024-07-19T13:32:11","guid":{"rendered":"https:\/\/swarma.org\/?p=51651"},"modified":"2024-07-19T21:32:11","modified_gmt":"2024-07-19T13:32:11","slug":"%e4%b8%ba%e4%bb%80%e4%b9%88%e5%8e%8b%e7%bc%a9%e5%8d%b3%e6%99%ba%e8%83%bd%ef%bc%9f%e7%ae%97%e6%b3%95%e4%bf%a1%e6%81%af%e8%ae%ba%e4%b8%8e%e5%a4%a7%e6%a8%a1%e5%9e%8b%e3%80%81%e7%94%9f","status":"publish","type":"post","link":"https:\/\/swarma.org\/?p=51651","title":{"rendered":"\u4e3a\u4ec0\u4e48\u201c\u538b\u7f29\u5373\u667a\u80fd\u201d\uff1f\u7b97\u6cd5\u4fe1\u606f\u8bba\u4e0e\u5927\u6a21\u578b\u3001\u751f\u547d\u3001\u667a\u80fd\u7684\u8054\u7cfb"},"content":{"rendered":"
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\u7b97\u6cd5\u4fe1\u606f\u8bba<\/strong><\/span>\uff08Algorithmic information theory\uff09<\/span>\u662f\u4f7f\u7528\u7406\u8bba\u8ba1\u7b97\u673a\u79d1\u5b66\u7684\u5de5\u5177\uff0c\u7814\u7a76\u590d\u6742\u6027\u6982\u5ff5\u7684\u5b66\u79d1\u9886\u57df\u3002\u5b83\u662f\u4fe1\u606f\u7406\u8bba\u7684\u4e00\u73af\uff0c\u5173\u6ce8\u8ba1\u7b97\u4e0e\u4fe1\u606f\u4e4b\u95f4\u7684\u5173\u7cfb\u3002\u6309\u7167 Gregory Chaitin \u7684\u8bf4\u6cd5\uff0c\u5b83\u662f\u201c\u628a\u9999\u519c\u7684\u4fe1\u606f\u8bba\u548c\u56fe\u7075\u7684\u53ef\u8ba1\u7b97\u8bba\u653e\u5728\u8c03\u9152\u676f\u4f7f\u52b2\u6447\u6643\u7684\u7ed3\u679c\u3002\u201d<\/span><\/section>\n

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\u514b\u52b3\u5fb7\u00b7\u9999\u519c<\/span>\uff08Claude Elwood Shannon\uff0c1916\u5e744\u670830\u65e5\u20142001\u5e742\u670824\u65e5\uff09<\/span>\uff0c\u7f8e\u56fd\u6570\u5b66\u5bb6\u3001\u7535\u5b50\u5de5\u7a0b\u5e08\u548c\u5bc6\u7801\u5b66\u5bb6\uff0c\u88ab\u8a89\u4e3a\u4fe1\u606f\u8bba\u7684\u521b\u59cb\u4eba\u3002\u9999\u519c\u662f\u5bc6\u6b47\u6839\u5927\u5b66\u5b66\u58eb\uff0c\u9ebb\u7701\u7406\u5de5\u5b66\u9662\u535a\u58eb\u30021948\u5e74\uff0c\u9999\u519c\u53d1\u8868\u4e86\u5212\u65f6\u4ee3\u7684\u8bba\u6587\u2014\u2014\u300a\u901a\u4fe1\u7684\u6570\u5b66\u7406\u8bba\u300b\uff0c\u5960\u5b9a\u4e86\u73b0\u4ee3\u4fe1\u606f\u8bba\u7684\u57fa\u7840\u3002\u4e0d\u4ec5\u5982\u6b64\uff0c\u9999\u519c\u8fd8\u88ab\u8ba4\u4e3a\u662f\u6570\u5b57\u8ba1\u7b97\u673a\u7406\u8bba\u548c\u6570\u5b57\u7535\u8def\u8bbe\u8ba1\u7406\u8bba\u7684\u521b\u59cb\u4eba\u3002<\/span><\/section>\n

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\u5230\u4e86\u5341\u4e5d\u4e16\u7eaa60\u5e74\u4ee3\uff0cSolomonoff\u3001Kolmogorov\u3001Chaitin\u76f8\u7ee7\u53d1\u5c55\u51fa\u7b97\u6cd5\u4fe1\u606f\u8bba<\/strong><\/span>\uff08Algorithmic Information Theory\uff09<\/span>\uff0c\u4ed6\u4eec\u5f3a\u8c03\u590d\u6742\u6027<\/strong>\u3001<\/strong>\u4fe1\u606f\u8bba\u4e0e\u7b97\u6cd5\u4e4b\u95f4\u7684\u5173\u7cfb<\/strong>\uff0c\u7b97\u6cd5\u4fe1\u606f\u8bba\u7531\u6b64\u8bde\u751f\u3002<\/span><\/section>\n

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\u9999\u519c\u71b5<\/strong>\u662f\u4fe1\u606f\u8bba\u4e2d\u7684\u6838\u5fc3\u6982\u5ff5\uff0c\u7528\u4e8e\u63cf\u8ff0\u4fe1\u606f\u7684\u6df7\u4e71\u7a0b\u5ea6\uff0c\u8ba1\u7b97\u5982\u4e0b\uff1a<\/span><\/section>\n

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\u5176\u4e2dPi<\/sub>\u4e3a\u7b2ci\u7c7b\u4fe1\u606f\u51fa\u73b0\u7684\u9891\u7387\uff0c\u4f46\u662f\u9999\u519c\u71b5\u7f3a\u4e4f\u5bf9\u4e8e\u4fe1\u606f\u987a\u5e8f\u8868\u5f81\u7684\u63cf\u8ff0\uff0c\u5982\u201c331213221312\u201d\u548c\u201c123123123123\u201d\u8ba1\u7b97\u6240\u5f97\u7684\u9999\u519c\u71b5\u76f8\u540c\u3002<\/span><\/section>\n

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\u67ef<\/strong>\u5c14\u83ab\u54e5\u6d1b\u592b\u590d\u6742\u5ea6<\/strong><\/span>\uff08Kolmogorov Complexity\uff09<\/span>\u5373\u67ef\u5f0f\u590d\u6742\u5ea6\uff0c\u662f\u7b97\u6cd5\u4fe1\u606f\u8bba\u4e2d\u7684\u6838\u5fc3\u6982\u5ff5\uff0c\u7528\u4e8e\u91cf\u5316\u5bf9\u8c61\u7684\u590d\u6742\u6027\uff0c\u662f\u6307\u5728\u901a\u7528\u56fe\u7075\u673a\u4e0a\u6700\u77ed\u7684\u4fe1\u606f\u63cf\u8ff0\u8868\u8fbe\u3002<\/span><\/section>\n

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\u4e00\u4e9b\u67ef\u6c0f\u590d\u6742\u5ea6\u7684\u4f8b\u5b50\uff1a<\/span><\/p>\n

111111111111                 12\u6b21\u91cd\u590d1<\/span><\/p>\n

123123123123             4 \u6b21\u91cd\u590d 123<\/span><\/p>\n

331213221312             331213221312<\/span><\/span><\/p>\n

172579241727             (3n<\/sup>-2) for n = 1 to 6<\/span><\/p>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n


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\u7136\u800c\uff0c\u67ef\u6c0f\u590d\u6742\u5ea6\u901a\u5e38\u60c5\u51b5\u4e0b\u5f88\u96be\u627e\u5230\u666e\u9002\u7684\u63cf\u8ff0\u65b9\u6cd5\u3002<\/span><\/section>\n

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\u71b5\u7f16\u7801\u538b\u7f29<\/span><\/strong><\/span><\/h2>\n

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\u71b5\u7f16\u7801<\/strong><\/span>\uff08Huffman Coding\uff09<\/span>\u662f\u4e00\u79cd\u72ec\u7acb\u4e8e\u4ecb\u8d28\u7684\u5177\u4f53\u7279\u5f81\u8fdb\u884c\u65e0\u635f\u6570\u636e\u538b\u7f29\u7684\u65b9\u6848\u3002\u4e00\u79cd\u4e3b\u8981\u7c7b\u578b\u7684\u71b5\u7f16\u7801\u65b9\u5f0f\u662f\u5bf9\u8f93\u5165\u7684\u6bcf\u4e00\u4e2a\u7b26\u53f7\uff0c\u521b\u5efa\u5e76\u5206\u914d\u4e00\u4e2a\u552f\u4e00\u7684\u524d\u7f00\u7801\uff0c\u7136\u540e\uff0c\u901a\u8fc7\u5c06\u6bcf\u4e2a\u56fa\u5b9a\u957f\u5ea6\u7684\u8f93\u5165\u7b26\u53f7\u66ff\u6362\u6210\u76f8\u5e94\u7684\u53ef\u53d8\u957f\u5ea6\u524d\u7f00\u65e0\u5173<\/span>\uff08prefix-free\uff09<\/span>\u8f93\u51fa\u7801\u5b57\u66ff\u6362\uff0c\u4ece\u800c\u8fbe\u5230\u538b\u7f29\u6570\u636e\u7684\u76ee\u7684\u3002\u6bcf\u4e2a\u7801\u5b57\u7684\u957f\u5ea6\u8fd1\u4f3c\u4e0e\u6982\u7387\u7684\u8d1f\u5bf9\u6570\u6210\u6bd4\u4f8b\u3002\u56e0\u6b64\uff0c\u6700\u5e38\u89c1\u7684\u7b26\u53f7\u4f7f\u7528\u6700\u77ed\u7684\u7801\u3002\u71b5\u7f16\u7801\u66f4\u504f\u5411\u4e8e\u4fe1\u606f\u8bba\uff0c\u5982\u679c\u4e00\u4e2a\u5b57\u7b26\u51fa\u73b0\u7684\u9891\u7387\u8f83\u9ad8\uff0c\u90a3\u4e48\u6211\u4eec\u53ef\u4ee5\u7528\u66f4\u5c11\u7684\u5b57\u7b26\u6570\u91cf\u53bb\u63cf\u8ff0\u3002<\/span><\/section>\n

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\u4e00\u79cd\u8ba1\u7b97\u65b9\u5f0f\u5982\u4e0b\uff1a\u6bd4\u5982\uff0c\u8981\u63cf\u8ff0\u201cthis_is_an_ant\u201d\uff0c\u6211\u4eec\u5148\u5c06\u51fa\u73b0\u7684\u5b57\u7b26\u5217\u51fa\uff0c\u7136\u540e\u7edf\u8ba1\u76f8\u5e94\u7684\u5b57\u7b26\u51fa\u73b0\u9891\u7387\uff0c\u4ece\u5c0f\u5230\u5927\u6392\u5e8f\uff0c\u7136\u540e\u5c06\u5b57\u7b26\u6309\u987a\u5e8f\u4e24\u4e24\u53e0\u52a0\u5728\u4e00\u8d77\u3002<\/span><\/section>\n

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\u6309\u7167\u5b57\u7b26\u4ece\u7ec4\u5408\u5173\u7cfb\u751f\u6210\u4e00\u4e2a\u5305\u542b\u5173\u7cfb\u7684\u6811\u72b6\u56fe\uff0c\u79f0\u4e4b\u4e3a\u54c8\u592b\u66fc\u6811<\/strong><\/span>\uff08Huffman Tree\uff09<\/span>\u3002<\/span><\/section>\n

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\u6709\u4e86\u54c8\u592b\u66fc\u6811\u4e4b\u540e\uff0c\u6211\u4eec\u5c06\u6811\u4e00\u4fa7\u7684\u5206\u652f\u547d\u540d\u4e3a0\uff0c\u53e6\u4e00\u4fa7\u7684\u5206\u652f\u547d\u540d\u4e3a1\uff1a<\/span><\/section>\n

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\u7136\u540e\u4f9d\u636e\u5206\u652f\u7684\u6570\u5b57\uff0c\u5bf9\u6bcf\u4e2a\u5b57\u7b26\u8fdb\u884c\u76f8\u5e94\u7684\u7f16\u7801\uff1a<\/span><\/section>\n

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\u53ef\u4ee5\u53d1\u73b0\u51fa\u73b0\u9891\u7387\u6700\u5927\u7684\u5b57\u7b26\uff0c\u6211\u4eec\u7528\u6700\u77ed\u7684\u7f16\u7801\u7b26\u53f7\u6765\u8868\u793a\uff0c\u5982\u201c-\u201d\u91c7\u7528\u201c00\u201d\u6765\u7f16\u7801\u3002<\/span><\/section>\n

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\u6700\u540e\uff0c\u751f\u6210\u54c8\u592b\u66fc\u7f16\u7801\u5e8f\u5217\u4e3a\uff1a<\/span><\/section>\n

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010011100101001001010011011100110111010<\/span><\/section>\n

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\u8fd9\u91cc\u6211\u4eec\u5f15\u5165\u4e00\u4e2a\u91cd\u8981\u7684\u91cf\u2014\u2014\u6bcf\u5b57\u7b26\u6bd4\u7279<\/strong><\/span>\uff08bit per character, bpc\uff09<\/span>\uff0c\u5373\u6bcf\u4e2a\u5b57\u7b26\u9700\u8981\u7528\u591a\u5c11\u6bd4\u7279\u6765\u8868\u793a\uff0c\u5728\u8fd9\u4e2a\u4f8b\u5b50\u4e2d\uff0c\u201cthis_is_an_ant\u201d \u8fd914\u4e2a\u5b57\u7b26\u53ef\u4ee5\u752839\u4e2a01\u4ee3\u7801\u6765\u8868\u793a\uff0c\u537339\u4e2a\u6bd4\u7279<\/span>\uff08bit\uff09<\/span>\u5355\u4f4d\uff0c\u5b57\u7b26\u6bcf\u6bd4\u7279\u5927\u5c0f\u4e3a\uff1a<\/span><\/section>\n

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39\/14=2.78471<\/span><\/section>\n

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\u6211\u4eec\u8ba1\u7b97\u8fd9\u4e2a\u5b57\u7b26\u4e32\u7684\u9999\u519c\u71b5\uff1a<\/span><\/section>\n

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\u4fe1\u6e90\u7f16\u7801\u5b9a\u7406<\/strong><\/span>\uff08Source coding theorem\uff09<\/span>\u8868\u660e\uff1a\u65e0\u8bba\u7528\u4efb\u4f55\u65b9\u6cd5\u53bb\u7f16\u7801\u5b57\u7b26\u4e32\uff0c\u76f8\u5e94\u7f16\u7801\u7684\u6bcf\u5b57\u7b26\u6bd4\u7279<\/span>\uff08\u6b64\u4f8b\u5b50\u4e2d\u662f2.78471\uff09<\/span>\u662f\u4e0d\u53ef\u80fd\u5c0f\u4e8e\u5176\u9999\u519c\u71b5\u7684<\/span>\uff08\u6b64\u4f8b\u5b50\u4e2d\u662f2.7534\uff09<\/span>\u3002<\/span>\uff08\u540e\u9762\u6211\u4eec\u4f1a\u518d\u770b\u5230\u8fd9\u4e2a\u91cd\u8981\u7684\u4fe1\u6e90\u7f16\u7801\u5b9a\u7406\uff09<\/span><\/section>\n

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LZ77\u538b\u7f29\u7b97\u6cd5<\/span><\/strong><\/span><\/h2>\n

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LZ77\u538b\u7f29\u7b97\u6cd5<\/strong><\/span>\uff08\u57281977\u5e74\u7531Lempel\u548cZiv\u63d0\u51fa\uff09<\/span>\u901a\u8fc7\u4f7f\u7528\u7f16\u7801\u5668\u6216\u8005\u89e3\u7801\u5668\u4e2d\u5df2\u7ecf\u51fa\u73b0\u8fc7\u7684\u76f8\u5e94\u5339\u914d\u6570\u636e\u4fe1\u606f\uff0c\u66ff\u6362\u5f53\u524d\u6570\u636e\u4ece\u800c\u5b9e\u73b0\u538b\u7f29\u529f\u80fd\u3002\u8fd9\u4e2a\u5339\u914d\u4fe1\u606f\u4f7f\u7528\u79f0\u4e3a\u957f\u5ea6-\u8ddd\u79bb\u5bf9\u7684\u4e00\u5bf9\u6570\u636e\u8fdb\u884c\u7f16\u7801\uff0c\u5b83\u7b49\u540c\u4e8e\u201c\u6bcf\u4e2a\u7ed9\u5b9a\u957f\u5ea6\u5b57\u7b26\u90fd\u7b49\u4e8e\u540e\u9762\u7279\u5b9a\u8ddd\u79bb\u5b57\u7b26\u4f4d\u7f6e\u4e0a\u7684\u672a\u538b\u7f29\u6570\u636e\u6d41\u201d\u3002\u7f16\u7801\u5668\u548c\u89e3\u7801\u5668\u90fd\u5fc5\u987b\u4fdd\u5b58\u4e00\u5b9a\u6570\u91cf\u7684\u6700\u8fd1\u7684\u6570\u636e\uff0c\u5982\u6700\u8fd12 KB\u30014 KB\u6216\u800532 KB\u7684\u6570\u636e\u3002\u4fdd\u5b58\u8fd9\u4e9b\u6570\u636e\u7684\u7ed3\u6784\u53eb\u4f5c\u6ed1\u52a8\u7a97\u53e3<\/strong>\uff0c\u56e0\u4e3a\u8fd9\u6837\u6240\u4ee5LZ77\u6709\u65f6\u4e5f\u79f0\u4f5c\u6ed1\u52a8\u7a97\u53e3\u538b\u7f29<\/strong>\u3002<\/span><\/section>\n

<\/section>\n
LZ77\u7f16\u7801\u662f\u7b97\u6cd5\u4fe1\u606f\u8bba\u4e2d\u7684\u8868\u5f81\uff0c\u8fd9\u79cd\u7f16\u7801\u6709\u4e24\u4e2a\u91cd\u8981\u53c2\u6570\uff1aL_window\u4ee5\u53caL_buffer\uff0c\u6211\u4eec\u8fd8\u4ee5\u5b57\u7b26\u4e32\u201cthis_is_an_ant \u201d\u4e3a\u4f8b\uff0c\u5728\u8fd9\u4e2a\u4f8b\u5b50\u4e2d\u5c06 L_window \u548c L_buffer \u4e24\u4e2a\u53c2\u6570\u5206\u522b\u8bbe\u4e3a6\u548c5<\/span>\uff08L_window = 6, L_buffer = 5\uff09<\/span>\uff0c\u4ee3\u8868\u5b57\u7b26\u524d\u8bbe\u7f6e6\u4e2a\u7a7a\u683c\uff0c\u7f13\u51b2\u533a\u957f\u5ea6\u4e3a5\uff0c\u76f8\u5e94\u7684\uff0c\u4e24\u4e2a\u53c2\u6570\u8bbe\u7f6e\u8d8a\u5927\uff0c\u538b\u7f29\u6548\u7387\u8d8a\u9ad8\u3002<\/span><\/section>\n

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\u5c06\u8272\u5757\u4e0d\u65ad\u5411\u524d\u79fb\u52a8\uff0c\u5e76\u4e14\u68c0\u67e5\u7f13\u51b2\u533a\u5185\u662f\u5426\u6709\u4e0e\u7a7a\u683c\u533a\u5185\u5b57\u7b26\u7247\u6bb5\u91cd\u590d\u7684\u90e8\u5206\uff0c\u672c\u4f8b\u4e2d\uff0c\u968f\u7740\u7f13\u51b2\u533a\u5411\u524d\u79fb\u52a8\uff0c\u201ct\u201d\uff0c\u201cth\u201d\uff0c\u201cthi\u201d\uff0c\u201cthis\u201d\uff0c\u201cthis_\u201d\u5747\u6ca1\u6709\u4e0e\u4e4b\u524d\u91cd\u590d\uff0c\u800c\u518d\u5411\u524d\u79fb\u52a8\u65f6\uff0c\u201cis_\u201d\u5728\u7a7a\u683c\u533a\u5185\u6709\u91cd\u590d\u90e8\u5206\uff0c\u6211\u4eec\u91c7\u7528\u4e00\u79cd\u4e09\u5143\u7ec4\uff083\uff0c3\uff0ca\uff09\u6765\u63cf\u8ff0\u91cd\u590d\u90e8\u5206\uff0c\u5728\u7a7a\u683c\u533a\u57df\u5012\u6570\u7b2c3\u4e2a\u4f4d\u7f6e\u5f00\u59cb\uff0c\u4e14\u5b57\u6bb5\u957f\u5ea6\u4e3a3\uff0c\u91cd\u590d\u90e8\u5206\u7684\u540e\u7eed\u5b57\u7b26\u4e3a\u201ca\u201d\uff0c\u8fd9\u6837\u5b8c\u6210\u91cd\u590d\u90e8\u5206\u7684\u538b\u7f29\u3002\u663e\u7136\uff0c\u5f53\u5b57\u7b26\u4e32\u7684\u91cd\u590d\u7387\u8d8a\u9ad8\u65f6\uff0c\u538b\u7f29\u6548\u7387\u4e5f\u8d8a\u9ad8\u3002<\/span><\/section>\n

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\u540c\u7406\uff0c\u540e\u7eed\u90e8\u5206\u7ee7\u7eed\u6309\u7167\u8fd9\u65b9\u5f0f\u538b\u7f29\uff0c\u4e14\u5c06\u6bcf\u4e2a\u5b57\u7b26\u90fd\u5f53\u6210\u4e00\u4e2a\u4e09\u5143\u7ec4\uff0c\u6700\u540e\u7684\u5b57\u7b26\u4e32\u53ef\u4ee5\u8868\u8fbe\u4e3a\uff1a<\/span><\/section>\n

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(0,0,t)(0,0,h)(0,0,i)(0,0,s)(0,0,_)(3,3,a)(0,0,n)(3,3,t)<\/span><\/section>\n

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\u8fd9\u662f\u66f4\u503e\u5411\u4e8e\u67ef\u6c0f\u590d\u6742\u5ea6\u7684\u4e00\u79cd\u63cf\u8ff0\u3002<\/span><\/section>\n

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\u538b\u7f29\u5b8c\u6210\u7684\u4e09\u5143\u7ec4\u53ef\u4ee5\u7ee7\u7eed\u7528\u54c8\u592b\u66fc\u7f16\u7801\u7ee7\u7eed\u538b\u7f29\uff0c\u518d\u52a0\u4e0a\u4e00\u4e9b\u6280\u5de7\uff0c\u6700\u7ec8\u4e00\u8d77\u6574\u5408\u6210\u4e86\u8457\u540d\u7684Deflate\u7b97\u6cd5<\/strong>\uff08Phil Katz 1989s\uff09\u3002<\/strong>\u5728Deflate\u7b97\u6cd5\u7684\u57fa\u7840\u4e0a\u6539\u8fdb\u7684gzip\u7b97\u6cd5\u662f\u5e73\u65f6\u66f4\u52a0\u5e38\u7528\u7684<\/span>\uff08gzip\uff0cJean-loup Gailly & Mark Adler 1993\uff09<\/span>\u3002<\/strong><\/span><\/section>\n

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\u4fe1\u6e90\u7f16\u7801\u5b9a\u7406\uff08Source coding theorem)<\/span><\/strong><\/span><\/h2>\n

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\u4fe1\u6e90\u7f16\u7801\u5b9a\u7406\u8868\u660e\uff0c\u5728\u6781\u9650\u60c5\u51b5\u4e0b\uff0c\u968f\u7740\u72ec\u7acb\u540c\u5206\u5e03\u968f\u673a\u53d8\u91cf\u6570\u636e\u6d41\u7684\u957f\u5ea6\u8d8b\u4e8e\u65e0\u7a77\uff0c\u4e0d\u53ef\u80fd\u628a\u6570\u636e\u538b\u7f29\u5f97\u7801\u7387<\/span>\uff08\u6bcf\u4e2a\u7b26\u53f7\u7684\u6bd4\u7279\u7684\u5e73\u5747\u6570\uff09<\/span>\u6bd4\u4fe1\u6e90\u7684\u9999\u519c\u71b5\u8fd8\u5c0f\uff0c\u53c8\u4e0d\u4e22\u5931\u4fe1\u606f\uff0c\u9999\u519c\u71b5\u7684\u538b\u7f29\u7387\u662f\u8fbe\u5230\u4fe1\u606f\u538b\u7f29\u7684\u6781\u9650<\/span>\uff08Source coding theorem, Shannon 1948\uff09<\/span>\u3002<\/span><\/section>\n

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\u7136\u800c\uff0c\u5728\u4e0b\u9762\u7684\u4f8b\u5b50\u4e2d\uff0c\u5927\u5bb6\u53ef\u4ee5\u770b\u5230\uff0c<\/span>\u7528gzip\u538b\u7f29<\/span><\/section>\n
\u201c<\/span>this_is_an_antthis_is_an_ant……this_is_an_ant<\/span>\u201d<\/span>\uff08\u91cd\u590d100\u6b21\uff09<\/span>\uff0c<\/span><\/section>\n
\u5176\u6bcf\u5b57\u7b26\u6bd4\u7279\u8fbe\u5230\u4e860.25\uff0c\u8fdc\u8fdc\u5c0f\u4e8e\u8fd9\u4e2a\u5e8f\u5217\u7684\u9999\u519c\u71b52.75\u3002\u96be\u9053\u4fe1\u6e90\u7f16\u7801\u5b9a\u7406\u88ab\u6253\u7834\u4e86\u5417\uff1f\u5927\u5bb6\u77e5\u9053\u95ee\u9898\u51fa\u5728\u4e86\u54ea\u91cc\uff1f<\/span><\/section>\n
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\u4ee5\u4e0a\uff0c\u6211\u4eec\u5c06\u4fe1\u606f\u8bba\u548c\u7b97\u6cd5\u4fe1\u606f\u8bba\u7684\u80cc\u666f\u3001\u57fa\u7840\u77e5\u8bc6\u548c\u76f8\u5e94\u7684\u4e00\u4e9b\u538b\u7f29\u65b9\u6cd5\u505a\u4e86\u7b80\u5355\u4ecb\u7ecd\uff0c\u4e0b\u9762\u6211\u4eec\u770b\u5982\u4f55\u7528\u538b\u7f29\u5b9e\u73b0\u6587\u672c\u5206\u7c7b\u3002<\/span><\/section>\n

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2. \u5229\u7528\u538b\u7f29\u505a\u6587\u672c\u5206\u7c7b<\/strong><\/span><\/strong><\/strong><\/p>\n<\/section>\n

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\u6587\u672c\u5206\u7c7b\u7684\u65b9\u6cd5<\/span><\/strong><\/span><\/h2>\n

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\u6700\u8fd1\u7684\u4e00\u7bc7\u6587\u7ae0[1]\u53d1\u73b0 gzip + k-nearest neighbors<\/span> (kNN) <\/span>\u65b9\u6cd5\u4e0e\u5927\u8bed\u8a00\u6a21\u578b\u662f\u5dee\u4e0d\u591a\u7684\uff0cgzip \u6211\u4eec\u5df2\u7ecf\u63d0\u5230\uff0c\u662f\u57fa\u4e8eLZ77\u7684\u4e00\u79cd\u538b\u7f29\u7b97\u6cd5\uff0ckNN\u662f\u673a\u5668\u5b66\u4e60\u4e2d\u7684\u6700\u90bb\u8fd1\u6cd5<\/span>\uff08k-nearst neighbors\uff09<\/span>\uff0c\u65b0\u6587\u672c\u4f9d\u636e\u5176\u548c\u65e2\u6709\u6587\u672c\u7684\u8ddd\u79bb\u6765\u5224\u65ad\u5176\u7c7b\u578b\u3002<\/span><\/section>\n

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[1] \u201cLow-Resource\u201d Text Classification: A Parameter-Free Classification Method with Compressors<\/span><\/section>\n
https:\/\/aclanthology.org\/2023.findings-acl.426\/<\/span><\/section>\n<\/blockquote>\n

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\u90a3\u4e48\u5982\u4f55\u901a\u8fc7\u538b\u7f29\u6765\u5b9a\u4e49\u6587\u672c\u4e4b\u95f4\u7684\u8ddd\u79bb\u5462\uff1f\u2014\u2014\u901a\u8fc7\u538b\u7f29\uff01Gzip\u672c\u8d28\u4e0a\u662f\u4e00\u79cd\u538b\u7f29\u673a\uff0c\u53ef\u4ee5\u5b9e\u73b0\u5bf9\u6587\u672c\u7684\u538b\u7f29\uff0c\u8fd9\u91cc\u6211\u4eec\u6709\u8fd9\u6837\u4e00\u79cd\u76f4\u89c9\uff0c\u4e24\u7bc7\u6587\u7ae0\u8fdb\u884c\u538b\u7f29\u65f6\uff0c\u5982\u679c\u6587\u7ae0\u662f\u76f8\u540c\u7c7b\u578b\u7684\uff0c\u90a3\u4e48\u53ef\u538b\u7f29\u7684\u90e8\u5206\u662f\u76f8\u5f53\u591a\u7684\uff0c\u53ef\u4ee5\u83b7\u53d6\u8f83\u9ad8\u7684\u538b\u7f29\u7387\uff0c\u800c\u5f53\u4e24\u7bc7\u6587\u7ae0\u5185\u5bb9\u76f8\u5dee\u5f88\u5927\uff0c\u5c5e\u4e8e\u4e0d\u540c\u7c7b\u578b\u65f6\uff0c\u76f8\u5e94\u7684\u538b\u7f29\u7387\u5c31\u4f1a\u5f88\u4f4e\uff0c\u8fd9\u6837\u901a\u8fc7\u538b\u7f29\u7387\u7684\u9ad8\u4f4e\uff0c\u6211\u4eec\u53ef\u4ee5\u5224\u65ad\u6587\u672c\u4e4b\u95f4\u7684\u8ddd\u79bb<\/strong>\u3002<\/span><\/section>\n

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\u5982\u4f55\u7528\u538b\u7f29\u5b9a\u4e49\u8ddd\u79bb\uff1f<\/span><\/strong><\/p>\n


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\u65e9\u57281998\u5e74\uff0cBennett\u7b49\u4eba[2]\u5c31\u4ecb\u7ecd\u4e86\u5982\u4f55\u7528\u67ef\u5f0f\u590d\u6742\u5ea6\u5b9a\u4e49\u8ddd\u79bb\uff1a<\/span><\/p>\n

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<\/p>\n

\u5176\u4e2d\uff0cK(x|y)\u4e3a\u7528\u6587\u672cy\u538b\u7f29\u540e\u7684\u4fe1\u606f\u533a\u538b\u7f29x\u7684\u67ef\u6c0f\u590d\u6742\u5ea6\uff0cK(y|x)\u4e3a\u7528\u6587\u672cx\u538b\u7f29\u540e\u7684\u4fe1\u606f\u533a\u538b\u7f29y\u7684\u67ef\u6c0f\u590d\u6742\u5ea6\uff0c\u8fd9\u4e24\u8005\u7684\u6700\u5927\u503c\u5373\u4e3a\u6587\u672cx\u548cy\u4e4b\u95f4\u7684\u8ddd\u79bb\uff0c\u7136\u800c\uff0c\u67ef\u6c0f\u590d\u6742\u5ea6\u662f\u65e0\u6cd5\u8ba1\u7b97\u7684\u3002<\/span><\/p>\n


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[2] 1998, Charles Bennett, Peter Gacs, Ming Li, Paul Vitanyi, Wojciech Zurek , Information Distance<\/span><\/p>\n

https:\/\/arxiv.org\/abs\/1006.3520<\/span><\/p>\n


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

2004\u5e74\uff0cLi Ming\u7b49\u63d0\u51fa\u4e86\u66f4\u5b9e\u7528\u7684NCD\u65b9\u6cd5\uff08Normalized Compression Diatance\uff09[3]<\/span>\uff1a<\/span><\/p>\n


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\u7528\u538b\u7f29\u673a\u7684\u538b\u7f29\u8ddd\u79bb\u533a\u903c\u8fd1\u67ef\u6c0f\u590d\u6742\u5ea6\u3002<\/span><\/section>\n

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[3] 2004, Ming Li, Xin Chen, Xin Li, Bin Ma, Paul Vitanyi , The similarity metric<\/span><\/section>\n
https:\/\/ieeexplore.ieee.org\/document\/1362909<\/span><\/section>\n

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\u4efb\u610f\u4e00\u4e2a\u538b\u7f29\u673a\u90fd\u884c\u5417\uff1f\u4e0d\u662f\u7684\u3002\u8981\u80fd\u6bd4\u8f83\u597d\u5730\u903c\u8fd1\u67ef\u6c0f\u590d\u6742\u5ea6\uff0c\u8fd9\u4e2a\u538b\u7f29\u673a\u5fc5\u987b\u6709\u4e00\u4e9b\u4f18\u826f\u7684\u6027\u8d28\u3002\u7b80\u5355\u8d77\u89c1\uff0c\u8fd9\u91cc\u53ea\u63d0\u5176\u4e2d\u4e00\u4e2a\u6027\u8d28\uff0c\u5373\u5f3a\u5206\u914d\u5f8b(Distributivity)\uff1a<\/strong><\/span><\/section>\n

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C(xyz)+C(z) \u2264 C(xz)+C(yz) <\/span><\/section>\n

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\u8fd9\u4e2a\u5f3a\u5206\u914d\u7387\u53ef\u5f31\u5316\u4e3a\u5f31\u5206\u914d\u5f8b\uff1a<\/span><\/section>\n

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C(xy)+C(z) \u2264<\/span> C(xz)+C(yz)<\/span><\/section>\n

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\u8fdb\u4e00\u6b65\u5f31\u5316\uff0c\u5373\u4e3a\u6b21\u53ef\u52a0\u6027(Subadditivity)\uff08\u5c06\u5f31\u5206\u914d\u5f8b\u4e2dz\u8bbe\u4e3a\u7a7a\u5b57\u7b26\uff0c\u5373\u5f97\u5230\u6b21\u53ef\u52a0\u6027\uff09<\/span><\/section>\n

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C(xy) \u2264<\/span> C(x)+C(y)<\/span><\/section>\n

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\u6211\u4eec\u9a8c\u8bc1\u8fc7\uff0cgzip\u4e00\u822c\u5f88\u96be\u6ee1\u8db3\u5f3a\u5206\u914d\u5f8b\u7684\uff0c\u5f88\u591a\u60c5\u51b5\u4e0b\u6ee1\u8db3\u5f31\u5206\u914d\u5f8b\u4f46\u5076\u5c14\u4e5f\u4e0d\u6ee1\u8db3\uff0c\u6b21\u53ef\u52a0\u6027\u4e00\u822c\u90fd\u662f\u53ef\u4ee5\u6ee1\u8db3\u7684\uff08\u4e25\u683c\u610f\u4e49\u4e0a\u7684\u201c\u67ef\u6c0f\u538b\u7f29\u673a\u201d\u9700\u8981\u6ee1\u8db3\u5f3a\u5206\u914d\u5f8b\uff09\u3002\u4f46\u662fgzip\u4f5c\u4e3a\u4e00\u79cd\u903c\u8fd1\u65b9\u6cd5\uff0c\u4e5f\u8fd8\u7b97\u662f\u4e00\u4e2a\u6548\u679c\u8fd8\u4e0d\u9519\u7684\u538b\u7f29\u673a\u3002\u5f53\u7136\uff0c\u6ee1\u8db3\u66f4\u5f3a\u6761\u4ef6\u7684\u538b\u7f29\u673a\u4f1a\u66f4\u597d\u7684\u3002<\/span><\/section>\n

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\u4f7f\u7528\u538b\u7f29\u673a\u5b9a\u4e49\u5b8c\u8ddd\u79bb\u4e4b\u540e\uff0c\u5206\u7c7b\u95ee\u9898\u5c31\u5bb9\u6613\u4e86\uff08\u5927\u6a21\u578b\u7684\u8bed\u4e49\u5176\u5b9e\u672c\u8d28\u4e0a\u5c31\u662f\u8bcd\u5411\u91cf\u4e4b\u95f4\u7684\u8ddd\u79bb\uff09\u30022005\u5e74\u538b\u7f29\u88ab\u7528\u4e8e\u5206\u7c7b[4]\uff0c\u8fd9\u65f6\u5019\u5e76\u6ca1\u6709\u5927\u6a21\u578b\u51fa\u73b0\uff0c\u6587\u7ae0\u505a\u4e86\u4e00\u4e9b\u5c0f\u7684\u6570\u636e\u96c6\uff0c\u5982\u8fdb\u884c\u8bed\u8a00\u7684\u5206\u7c7b\uff0c\u97f3\u4e50\u7684\u5206\u7c7b\u3001\u86cb\u767d\u8d28\u7684\u5206\u7c7b\u3001\u7269\u79cd\u7684\u5206\u7c7b\u3002<\/span><\/section>\n

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[4] 2005, Rudi Cilibrasi, Paul Vitanyi\uff0cClustering by compression<\/span><\/section>\n
https:\/\/arxiv.org\/abs\/cs\/0312044<\/span><\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n

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\u5728\u6b64\u80cc\u666f\u4e0b\uff0c\u6587\u7ae0[5]\u91c7\u7528\u4e0d\u540c\u6a21\u578b\u5728\u4e0d\u540c\u7684\u6570\u636e\u96c6\u4e0a\u8fdb\u884c\u8bad\u7ec3\u6765\u8fdb\u884c\u6587\u672c\u5206\u7c7b\uff0cgzip+k<\/span>NN \u8fbe\u5230\u4e86\u76f8\u5f53\u597d\u7684\u6548\u679c\uff0c\u57fa\u672c\u4e0a\u53ef\u4ee5\u4e0e\u5927\u6a21\u578b\u76f8\u5f53\u3002<\/span><\/section>\n

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[5] Low-Resouree Text Classification: A Parameter-Free ClassificationMethod with compressors<\/span><\/section>\n
https:\/\/aclanthology.org\/2023.findings-acl.426\/<\/span><\/section>\n<\/blockquote>\n

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PS\uff1a\u7136\u800c\u5e76\u4e0d\u662f\u538b\u7f29\u7387\u8d8a\u9ad8\uff0c\u5206\u7c7b\u6548\u679c\u8d8a\u597d\uff0cbz2 \u4e5f\u662f\u4e00\u7c7b\u5e38\u7528\u7684\u538b\u7f29\u673a\uff0c\u538b\u7f29\u7387\u8981\u9ad8\u4e8egzip\uff0c\u4f46\u662fbz2\u5df2\u7ecf\u5c06\u6587\u672c\u8bed\u4e49\u6253\u4e71\u4e86\uff0c\u5206\u7c7b\u6548\u679c\u53cd\u800c\u5f88\u5dee\u3002<\/span><\/section>\n

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\u68af\u5f84\uff08Ladderpath\uff09\u65b9\u6cd5<\/span><\/strong><\/span><\/h2>\n

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\u7b97\u6cd5\u4fe1\u606f\u8bba\u6846\u67b6\u4e0b\u7684\u68af\u5f84<\/span>\uff08Ladderpath\uff09<\/span>\u65b9\u6cd5\u4e5f\u662f\u53ef\u4ee5\u5b9e\u73b0\u538b\u7f29\u7684\uff0c\u800c\u4e14\u6211\u4eec\u53d1\u73b0\u6548\u679c\u6bd4\u4e0a\u8ff0\u6587\u7ae0\u597d\u3002\u8fd9\u662f\u6211\u4eec\u8bfe\u9898\u7ec4\u81ea\u5df1\u7684\u7814\u7a76\u5de5\u4f5c<\/span>\uff08\u8fd9\u91cc\u4e0d\u505a\u8be6\u7ec6\u4ecb\u7ecd\uff0c\u5982\u6709\u9700\u8981\u53ef\u4ee5\u8054\u7cfb\u7b14\u8005\uff09<\/span>\u3002<\/span><\/p>\n


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3. \u8bed\u8a00\u5efa\u6a21\u4e0e\u538b\u7f29\u662f\u7b49\u4ef7\u7684<\/strong><\/span><\/strong><\/strong><\/p>\n<\/section>\n

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\u8fd1\u5e74\u6765\uff0c\u673a\u5668\u5b66\u4e60\u793e\u533a\u4e00\u76f4\u4e13\u6ce8\u4e8e\u8bad\u7ec3\u65e5\u76ca\u5e9e\u5927\u548c\u5f3a\u5927\u7684\u8bed\u8a00\u6a21\u578b\uff0c\u8fd9\u4e9b\u6a21\u578b\u8868\u73b0\u51fa\u4ee4\u4eba\u5370\u8c61\u6df1\u523b\u7684\u9884\u6d4b\u80fd\u529b\u30022024\u5e745\u6708\u7684\u6587\u7ae0 Language Modeling Is Compression \u4ecb\u7ecd\u4e86\u5982\u4f55\u5c06\u8bed\u8a00\u6a21\u578b\u7528\u4e8e\u65e0\u635f\u538b\u7f29\uff0c\u53cd\u4e4b\u4ea6\u53ef\u3002\u4f5c\u8005\u4e3b\u5f20\u901a\u8fc7\u538b\u7f29\u7684\u89d2\u5ea6\u6765\u89c2\u5bdf\u9884\u6d4b\u95ee\u9898\uff0c\u5e76\u8bc4\u4f30\u8bed\u8a00\u6a21\u578b\u7684\u538b\u7f29\u80fd\u529b\u3002\u7ed3\u679c\u8868\u660e\uff0c\u5927\u578b\u8bed\u8a00\u6a21\u578b\u662f\u5f3a\u5927\u7684\u901a\u7528\u9884\u6d4b\u5668<\/strong>\u3002\u538b\u7f29\u89c2\u70b9\u63d0\u4f9b\u4e86\u5bf9\u667a\u80fd\u3001\u6807\u5ea6\u5f8b\u3001\u6807\u8bb0\u5316<\/span>\uff08tokenization\uff09<\/span>\u548c\u4e0a\u4e0b\u6587\u5b66\u4e60\u7684\u65b0\u89c1\u89e3\u3002<\/span><\/section>\n

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\u6587\u7ae0 Language Modeling Is Compression \u4f5c\u8005\u4e4b\u4e00\u201c\u5927\u795e\u201dMarcus Hutter\u57282006\u5e74\u53d1\u8d77\u4e86\u8457\u540d\u7684\u4fe1\u606f\u538b\u7f29\u7ade\u8d5b\u2014\u2014\u201c500 000\u20ac Prize for Compressing Human Knowledge\u201d\u3002\u4ed6\u575a\u5b9a\u5730\u8ba4\u4e3a\u201c\u538b\u7f29\u5373\u667a\u80fd\u201d\u3002<\/span><\/section>\n

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\u201c\u867d\u7136\u667a\u80fd\u662f\u4e00\u4e2a\u6a21\u7cca\u7684\u6982\u5ff5\uff0c\u4f46\u6587\u4ef6\u5927\u5c0f\u662f\u786c\u6570\u5b57\u3002\u7ef4\u57fa\u767e\u79d1\u662f\u4eba\u7c7b\u77e5\u8bc6\u7684\u5e7f\u6cdb\u5feb\u7167\u3002\u5982\u679c\u4f60\u80fd\u6bd4\u4f60\u7684\u524d\u8f88\u66f4\u597d\u5730\u538b\u7f29\u7ef4\u57fa\u767e\u79d1\u7684\u524d1GB\uff0c\u4f60\u7684\u538b\u7f29\uff08\u89e3\u538b\u7f29\uff09\u5668\u5f88\u53ef\u80fd\u662f\u66f4\u52a0\u667a\u80fd\u7684\u3002\u8be5\u5956\u9879\u7684\u76ee\u7684\u662f\u9f13\u52b1\u5f00\u53d1\u667a\u80fd\u538b\u7f29\u673a\/\u7a0b\u5e8f\uff0c\u4ee5\u6b64\u4f5c\u4e3a\u901a\u5f80AGI\u7684\u9053\u8def\u3002\u201d<\/span><\/section>\n<\/blockquote>\n

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\u4e8b\u5b9e\u4e0a\uff0c\u57282023\u5e744\u6708 DeepMind \u7684\u521b\u59cb\u4eba\u4e4b\u4e00 Ilya Sutskever \u4ee5\u53ca Jack Rae \u4e5f\u8ba8\u8bba\u8fc7\u67ef\u6c0f\u590d\u6742\u5ea6\u3001\u538b\u7f29\u673a\u4ee5\u53ca\u5927\u8bed\u8a00\u6a21\u578b\u4e4b\u95f4\u7684\u5173\u7cfb\u3002<\/strong><\/span><\/section>\n

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\u4ee5\u4e0a\u7684\u7814\u7a76\u8868\u660e\uff0c\u8bed\u8a00\u5efa\u6a21\u548c\u538b\u7f29\u53ef\u80fd\u662f\u7b49\u4ef7\u7684\uff0c\u5927\u8bed\u8a00\u6a21\u578b\u662f\u53ef\u4ee5\u7528\u6765\u8fdb\u884c\u65e0\u635f\u538b\u7f29\u7684\u3002\u5982\u679c\u4e00\u4e2a\u538b\u7f29\u673a\u53ef\u4ee5\u5b8c\u7f8e\u5730\u8fbe\u5230\u67ef\u6c0f\u590d\u6742\u5ea6\u7684\u6781\u9650\uff0c\u90a3\u4e48\u5b83\u5f88\u53ef\u80fd\u5c31\u662fAGI<\/strong>\u3002\u867d\u7136\u6570\u5b66\u8bc1\u660e\u8868\u660e\uff0c\u67ef\u6c0f\u590d\u6742\u5ea6\u4e0d\u53ef\u8fbe\u5230\u7684\uff0c\u4f46\u662f\u53ef\u4ee5\u65e0\u9650\u8d8b\u8fd1\uff0cgzip\u672c\u8eab\u662f\u4e00\u7c7b\u5f88\u597d\u7684\u538b\u7f29\u673a\uff0c\u4f46\u662f\u7528\u8bed\u8a00\u6a21\u578b\u538b\u7f29\u662f\u4e00\u7c7b\u66f4\u52a0\u6709\u6548\u7684\u65b9\u6cd5\u3002<\/span><\/section>\n

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\u7b97\u672f\u7f16\u7801<\/span><\/strong><\/span><\/h2>\n

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\u4e0b\u9762\u6211\u4eec\u4ece\u5177\u4f53\u6848\u4f8b\u51fa\u53d1\uff0c\u770b\u4e00\u4e0b\u5927\u6a21\u578b\u7a76\u7adf\u662f\u5982\u4f55\u5b9e\u73b0\u538b\u7f29\u7684\uff0c\u8fd9\u90e8\u5206\u5185\u5bb9\u4ecd\u7136\u6765\u81ea\u6587\u7ae0 Language Modeling Is Compression\u3002\u9996\u5148\uff0c\u7b97\u672f\u7f16\u7801<\/span>\uff08Arithmetic Coding\uff09<\/span>\u4e5f\u662f\u4e00\u79cd\u71b5\u7f16\u7801\uff0c\u5b83\u4f9d\u636e\u5b57\u7b26\u51fa\u73b0\u6b21\u6570\u7684\u9891\u7387\u6765\u8fdb\u884c\u7f16\u7801\uff0c\u8fd9\u4e2a\u8fc7\u7a0b\u53ef\u4ee5\u7528\u8bed\u8a00\u6a21\u578b\u6765\u5b9e\u73b0\u3002<\/span><\/section>\n

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\u6bd4\u5982\uff0c\u5bf9\u5b57\u7b26\u4e32 \u201cAIXI\u201d \u8fdb\u884c\u538b\u7f29\uff0c\u9996\u5148\u5bf9\u7b2c\u4e00\u4e2a\u5b57\u7b26\u201cA\u201d\u8fdb\u884c\u6982\u7387\u8ba1\u7b97\uff0c\u7136\u540e\u5728\u7b2c\u4e00\u4e2a\u5b57\u7b26\u4e3a\u201cA\u201d\u7684\u60c5\u51b5\u4e0b\uff0c\u8ba1\u7b97\u7b2c\u4e8c\u4e2a\u5b57\u7b26\u4e3a\u201cI\u201d\u7684\u6761\u4ef6\u6982\u7387\uff0c\u540e\u7eed\u4f9d\u6b21\u7ee7\u7eed\u8ba1\u7b97\u201cX\u201d\u548c\u201cI\u201d\uff0c\u6700\u7ec8\u5f97\u5230\u6574\u4f53\u5206\u5e03\u7684\u6982\u7387\u533a\u95f4\u3002<\/span>\uff08\u8fd9\u91cc\u662f\u4e00\u4e2a\u975e\u5e38\u7c97\u7cd9\u7684\u6982\u8ff0\uff0c\u8be6\u7ec6\u4e86\u89e3\u53ef\u89c2\u770b\u8bfb\u4e66\u4f1a\u5206\u4eab\uff09<\/span><\/section>\n

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\u76f8\u5e94\u5730\uff0c\u5b57\u7b26\u4e32\u8d8a\u957f\uff0c\u5f97\u5230\u7684\u6982\u7387\u533a\u95f4\u5c31\u8d8a\u7cbe\u7ec6\uff0c\u8d8a\u9760\u8fd1\u4e0a\u9762\u5706\u76d8\u7684\u8fb9\u7f18\u4f4d\u7f6e\uff0c\u538b\u7f29\u5b8c\u6210\u540e\u5f97\u5230\u7684\u662f\u4e00\u4e2a\u4f4d\u4e8e0-1\u4e4b\u95f4\u7684\u6982\u7387\u533a\u95f4\uff0c\u5982\u201cAIXI\u201d\u5728\u672c\u4f8b\u4e2d\u538b\u7f29\u4e3a\u533a\u95f4 [0.322, 0.341)\u3002<\/span><\/section>\n

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\u5f97\u5230\u7684\u6982\u7387\u533a\u95f4\u53ef\u4ee5\u8fdb\u4e00\u6b65\u8fdb\u884c\u538b\u7f29\uff0c\u5f97\u523001\u5b57\u7b26\u4e32\uff0c\u672c\u4f8b\u4e2d\uff0c\u201cAIXI\u201d\u6700\u7ec8\u538b\u7f29\u4e3a0101010\u3002<\/span><\/section>\n

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\u90a3\u4e48\u5927\u6a21\u578b\u4e0e\u538b\u7f29\u4e4b\u95f4\u7684\u5173\u7cfb\u662f\u4ec0\u4e48\u5462\uff1f\u2014\u2014\u662f\u6761\u4ef6\u6982\u7387\uff01<\/strong>\u6211\u4eec\u53ef\u4ee5\u901a\u8fc7\u5927\u6a21\u578b\u5f88\u597d\u5730\u9884\u6d4btoken\u7684\u6761\u4ef6\u6982\u7387\uff0c\u7136\u540e\u57fa\u4e8e\u8fd9\u4e9b\u6761\u4ef6\u6982\u7387\u6765\u5b9e\u73b0\u540e\u7eed\u538b\u7f29\u3002\u901a\u8fc7\u5927\u6a21\u578b\uff0c\u53ef\u4ee5\u5b9e\u73b0\u8fdc\u9ad8\u4e8e\u4f20\u7edf\u538b\u7f29\u6280\u672f\u7684\u538b\u7f29\u7387\u3002<\/span><\/section>\n

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\u6807\u5ea6\u5f8b\uff08Scaling law\uff09<\/span><\/strong><\/span><\/h2>\n

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\u4ece\u7ed3\u679c\u6765\u770b\uff0c\u5229\u7528\u8bed\u8a00\u6a21\u578b\u8fdb\u884c\u7b97\u672f\u7f16\u7801<\/span>\uff08Arithmetic Encoding\uff09<\/span>\u7684\u538b\u7f29\u7387\u6bd4\u4f20\u7edf\u538b\u7f29\u6280\u672f\u9ad8\u5f88\u591a<\/span>\uff08\u4f46\u662f\u8981\u5254\u9664\u6a21\u578b\u89c4\u6a21\u7684\u5f71\u54cd\uff09<\/span>\u3002\u6587\u4e2d\u5bf9\u4f20\u7edf\u65b9\u6cd5<\/span>\uff08gzip\u3001LZMA2\u3001PNG\u3001FLAC\uff09<\/span>\u548c\u8bed\u8a00\u6a21\u578b\u65b9\u6cd5\u5728\u4e0d\u540c\u6570\u636e\u96c6\u4e0a\u8fdb\u884c\u4e86\u5b9e\u9a8c\uff0c\u7ed3\u679c\u5982\u4e0b<\/span>\uff08\u6ce8\u610f\uff0c\u4e00\u822c\u6240\u8bf4\u7684\u538b\u7f29\u7387\u662f\u6307<\/span><\/span>(1-\u538b\u7f29\u540e\u6587\u672c\u957f\u5ea6 \/ \u539f\u957f\u5ea6)\uff0c\u800c\u6587\u4e2d\u7684compression rate\u5176\u5b9e\u662f\u6307\uff081-\u538b\u7f29\u7387\uff09\uff1b\u4e3a\u4e86\u907f\u514d\u6b67\u4e49\uff0c\u6211\u4eec\u79f0\u5176\u4e3a\u538b\u7f29\u540e\u6bd4\u4f8b\uff09<\/span>\uff1a<\/span><\/section>\n

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\u4e8b\u5b9e\u4e0a\uff0c\u6211\u4eec\u53ef\u80fd\u9700\u8981\u8003\u8651\u6a21\u578b\u672c\u8eab\u7684\u5927\u5c0f\uff0c\u5373\u77eb\u6b63\u540e\u7684\u538b\u7f29\u540e\u6bd4\u4f8b<\/span>\uff08Adjusted Compression Rate\uff09<\/span>\u3002\u5bf9\u4e8e\u4f20\u7edf\u65b9\u6cd5\u6765\u8bb2\uff0c\u538b\u7f29\u7387\u51e0\u4e4e\u662f\u4e0d\u53d8\u7684\uff0c\u5927\u6a21\u578b\u5de8\u91cf\u7684\u53c2\u6570\u4fe1\u606f\u4f7f\u5f97\u603b\u7684\u538b\u7f29\u7387\u5927\u5927\u63d0\u5347\uff0c\u751a\u81f3\u8fbe\u5230\u4e86\u539f\u59cb\u538b\u7f29\u6570\u636e\u4f53\u79ef\u7684\u6570\u500d\u4e43\u81f3\u6570\u767e\u500d\u3002<\/span><\/section>\n
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\u8fdb\u4e00\u6b65\u7684\u7814\u7a76\u53d1\u73b0\uff0c\u8f83\u5927\u89c4\u6a21\u7684\u6a21\u578b\uff0c\u5176\u538b\u7f29\u6548\u679c\u4f1a\u66f4\u597d\uff0c\u8fd9\u4e00\u73b0\u8c61\u4e00\u822c\u88ab\u79f0\u4e3a\u6807\u5ea6\u5f8b<\/span>\uff08Scaling law\uff09<\/span>\u3002\u7136\u800c\u5f53\u6a21\u578b\u7684\u89c4\u6a21\u8d85\u8fc7\u4e00\u5b9a\u9650\u5ea6\u7ee7\u7eed\u589e\u5927\u65f6\uff0c\u538b\u7f29\u6548\u679c\u4f1a\u53d8\u5dee\uff0c\u538b\u7f29\u540e\u6bd4\u4f8b\u4f1a\u589e\u52a0\u3002\u53e6\u4e00\u65b9\u9762\uff0c\u5927\u89c4\u6a21\u6a21\u578b\u5728\u8f83\u5c0f\u7684\u6570\u636e\u96c6\u4e0a\u5b9e\u73b0\u7684\u538b\u7f29\u6548\u679c\u8f83\u5dee\uff0c\u6240\u4ee5\u6a21\u578b\u89c4\u6a21\u5e76\u4e0d\u662f\u8d8a\u5927\u8d8a\u597d\u3002\u6587\u7ae0\u6700\u540e\u603b\u7ed3\u8bf4\uff0c\u6570\u636e\u96c6\u5927\u5c0f\u76f4\u63a5\u9650\u5236\u4e86\u6a21\u578b\u89c4\u6a21\uff0c\u6807\u5ea6\u5f8b\u5b9e\u9645\u4e0a\u4e5f\u53d6\u51b3\u4e8e\u6d4b\u8bd5\u96c6\u7684\u5927\u5c0f\uff1b\u5bf9\u4e8e\u4e00\u5b9a\u89c4\u6a21\u7684\u6570\u636e\u96c6\uff0c\u6a21\u578b\u4f1a\u6709\u4e00\u4e2a\u6548\u679c\u6700\u597d\u7684\u6781\u503c\u3002<\/span><\/section>\n

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\u6700\u540e\u503c\u5f97\u63d0\u4e00\u70b9\uff0c\u6587\u7ae0\u4e2d\u4e5f\u540c\u6837\u7528\u538b\u7f29\u673a<\/span>\uff08\u6bd4\u5982gzip\uff09<\/span>\u505a\u4e86\u751f\u6210\u4efb\u52a1\u3002\u867d\u7136\u6587\u4e2d\u4e5f\u627f\u8ba4\u5176\u6548\u679c\u4e00\u822c\uff0c\u4f46\u662f\u53ef\u4ee5\u8fdb\u4e00\u6b65\u63a2\u7d22\u3002\u6b63\u662f\u7531\u4e8e\u53ef\u4ee5\u7528\u5927\u6a21\u578b\u505a\u65e0\u635f\u538b\u7f29<\/strong>\uff0c\u53cd\u8fc7\u6765\uff0c\u4e5f\u53ef\u4ee5\u7528\u538b\u7f29\u673a\u6765\u505a\u751f\u6210<\/strong>\uff0c\u6240\u4ee5\u6587\u7ae0\u8bf4\u8bed\u8a00\u5efa\u6a21\u548c\u538b\u7f29\u662f\u7b49\u4ef7\u7684<\/strong>\uff0c\u4e5f\u5c31\u662f\u201c\u538b\u7f29\u5373\u667a\u80fd\u201d\u3002<\/strong><\/span><\/section>\n

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\u8fd9\u91cc\u7b80\u8981\u4ecb\u7ecd\u51e0\u7bc7\u6587\u7ae0\u3002\u5728\u6587\u7ae0 Symmetry and simplicity spontaneously emerge from the algorithmic nature of evolution<\/em> \u4e2d\u8bf4\u5230\uff1a\u5bf9\u79f0\u6027\u4e0e\u7b80\u5355\u6027\u662f\u53ef\u4ee5\u4ece\u81ea\u7136\u8fdb\u5316\u4e2d\u81ea\u53d1\u5730\u6f14\u5316\u51fa\u6765\u7684\uff0c\u4e0d\u9700\u8981\u4e00\u4e2a\u9009\u62e9\u8fc7\u7a0b\u3002\u5de5\u7a0b\u8bbe\u8ba1\u4e2d\u4e5f\u603b\u662f\u91c7\u7528\u6a21\u5757\u5316\u4ee5\u53ca\u5bf9\u79f0\u6027\uff0c\u751f\u547d\u7cfb\u7edf\u4e2d\u86cb\u767d\u7684\u67ef\u6c0f\u590d\u6742\u5ea6\u8f83\u9ad8\u7684\u86cb\u767d\u662f\u5f88\u5c11\u7684\u3002<\/span><\/section>\n

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[6] Symmetry and simplicity spontaneously emerge from the algorithmic nature of evolution<\/span><\/section>\n
https:\/\/www.pnas.org\/doi\/full\/10.1073\/pnas.2113883119<\/span><\/section>\n<\/blockquote>\n

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2022\u5e74\u7684\u6587\u7ae0 Hallucinating symmetric protein assemblies \u7528\u5927\u6a21\u578b\u7684\u5e7b\u89c9\u53bb\u8bbe\u8ba1\u5927\u86cb\u767d\u3002\u53ea\u9700\u8981\u8bbe\u5b9a\u4e9a\u57fa\u6570\u76ee\u548c\u86cb\u767d\u957f\u5ea6\uff0c\u5c31\u53ef\u4ee5\u751f\u6210\u5f88\u591a\u5177\u6709\u5bf9\u79f0\u7ed3\u6784\u7684\u86cb\u767d\u8d28\uff0c\u800c\u4e14\u6709\u4e9b\u5df2\u7ecf\u88ab\u771f\u5b9e\u5730\u5236\u9020\u51fa\u6765\u3002<\/span><\/section>\n

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[7] Hallucinating symmetric protein assemblies<\/span><\/section>\n
https:\/\/www.science.org\/doi\/10.1126\/science.add1964<\/span><\/section>\n<\/blockquote>\n

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4. \u603b\u7ed3<\/strong><\/span><\/strong><\/strong><\/p>\n<\/section>\n

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