{"id":39111,"date":"2018-09-24T00:00:26","date_gmt":"2018-09-23T16:00:26","guid":{"rendered":"https:\/\/case.ntu.edu.tw\/blog\/?p=39111"},"modified":"2022-01-05T00:56:17","modified_gmt":"2022-01-04T16:56:17","slug":"%e8%b3%a6%e4%ba%88ai%e4%ba%ba%e9%a1%9e%e7%9b%b4%e8%a6%ba","status":"publish","type":"post","link":"https:\/\/case.ntu.edu.tw\/blog\/?p=39111","title":{"rendered":"\u8ce6\u4e88AI\u4eba\u985e\u76f4\u89ba"},"content":{"rendered":"
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\"\u8ce6\u4e88AI\u4eba\u985e\u76f4\u89ba\"<\/div>\n<\/div>\n
\n

\u7de8\u8b6f\uff0f\u8449\u59dd\u6db5<\/strong><\/span><\/p>\n

\u5169\u7a2eAI<\/strong><\/p>\n

\u73fe\u4eca\u5e7e\u4e4e\u6240\u6709\u7684\u96fb\u8166\u79d1\u5b78\u5bb6\uff0c\u90fd\u5df2\u62cb\u68c4\u96fb\u8166\u79d1\u5b78\u4e4b\u7236\u5716\u9748\uff08Allan Turing\uff09\u7684\u69cb\u60f3\u2500\u4e00\u7a2e\u80fd\u5be6\u969b\u7406\u89e3\u4eba\u985e\u8a9e\u8a00\u8207\u908f\u8f2f\uff0c\u4e26\u4f9d\u64da\u5df2\u77e5\u80cc\u666f\u77e5\u8b58\u4f5c\u51fa\u63a8\u8ad6\u7684\u4eba\u5de5\u667a\u80fd\u3002\u9664\u4e86\u65e9\u671fIBM\u6240\u958b\u767c\u7684DeepBlue\uff0c\u65bc1997\u5e74\u6210\u529f\u64ca\u6557\u6642\u4efb\u4e16\u754c\u897f\u6d0b\u68cb\u51a0\u8ecdGarry Kasparov\u7684\u7576\u4e0b\uff0c\u66fe\u7d93\u98a8\u5149\u4e00\u6642\u5916\uff0c\u9019\u4e9b\u300c\u820a\u6b3e\u4eba\u5de5\u667a\u80fd\u300d\uff08Good-old fashioned AI\uff0cGOFAI\uff09\uff0c\u5728\u5f80\u5f8c\u767c\u5c55\u4e2d\uff0c\u5df2\u9010\u6f38\u70ba\u4ee5\u6a5f\u5668\u5b78\u7fd2\u70ba\u57fa\u790e\u7684AI\uff08Machine Learning AI\uff0cMLAI\uff09\u6240\u53d6\u4ee3\u3002<\/p>\n

\u85c9\u7531\u63a5\u89f8\u5927\u91cf\u6578\u64da\u8207\u8cc7\u6599\uff0c\u4e0d\u65b7\u5617\u8a66\u932f\u8aa4\uff0c\u4ee5\u6478\u7d22\u51fa\u5176\u4e2d\u898f\u5f8b\u8207\u5dee\u7570\u7684MLAI\uff0c\u5728\u8fd1\u5e7e\u5e74\u5df2\u6709\u9577\u8db3\u9032\u5c55\uff1a\u8209\u51e1\u8a9e\u97f3\u8fa8\u8b58\u3001\u5716\u50cf\u8fa8\u8b58\u3001\u764c\u75c7\u8a3a\u65b7\uff0c\u751a\u81f3\u5728\u68cb\u5c40\u8207\u7d19\u724c\u904a\u6232\u4e2d\u64ca\u6557\u4eba\u985e\u73a9\u5bb6\u30022017 \u5e74\uff0c\u5728AlphaGo\u64ca\u6557\u5357\u97d3\u570d\u68cb\u51a0\u8ecd\u674e\u4e16\u77f3\u5f8c\u50c5\u4e00\u5e74\uff0cGoogle\u65d7\u4e0bDeepMind\u5718\u968a\u4fbf\u96a8\u5373\u63a8\u51fa\u540c\u7cfb\u5217\uff0c\u4f46\u4eba\u985e\u9810\u5148\u5beb\u5165\u7684\u898f\u5247\u66f4\u5c11\uff0c\u537b\u8868\u73fe\u66f4\u4f73\u7684 AlphaGo Zero\uff0c\u4ee5\u53ca\u6708\u9918\u5f8c\u7684AlphaZero\u3002\u6a5f\u5668\u5b78\u7fd2\u4e00\u6642\u851a\u70ba\u986f\u5b78\uff0c\u513c\u7136\u6210\u70ba\u4eba\u5de5\u667a\u80fd\u7684\u4ee3\u540d\u8a5e\u3002<\/p>\n

\u95dc\u9375\u5e73\u8861<\/strong><\/p>\n

\u7136\u800c\uff0cMLAI\u4e5f\u4e26\u975e\u7121\u5f80\u4e0d\u5229\u3002\u4ee5\u5716\u50cf\u8fa8\u8b58\u70ba\u4f8b\uff0c\u96fb\u8166\u6216\u8a31\u53ef\u4ee5\u6bd4\u4eba\u985e\u66f4\u5feb\u3001\u66f4\u7cbe\u78ba\u5730\u7531\u4e00\u7cfb\u5217\u72ac\u96bb\u76f8\u7247\u4e2d\u5206\u8fa8\u51fa\u5409\u5a03\u5a03\uff0c\u4f46\u4e5f\u6642\u5e38\u5c07\u5409\u5a03\u5a03\u8aa4\u8a8d\u70ba\u85cd\u8393\u99ac\u82ac\u3002Alpha\u7cfb\u5217\u7a0b\u5f0f\u96d6\u865f\u7a31\u7a31\u9738\u570d\u68cb\u754c\uff0c\u4f46\u82e5\u5c07\u6230\u5834\u8f49\u79fb\u81f321\u00d721\u7684\u68cb\u76e4\uff0c\u800c\u975e\u6a19\u6e96\u8cfd\u5236\u4e2d\u6240\u4f7f\u7528\u768419\u00d719\u68cb\u76e4\uff0c\u5247\u7562\u751f\u529f\u529b\u76e1\u5931\uff0c\u9700\u5f9e\u982d\u4f86\u904e\u3002\u5176\u4e2d\u6700\u5927\u7684\u554f\u984c\uff0c\u5728\u65bcMLAI\u7f3a\u4e4f\u5c07\u6240\u7fd2\u5f97\u7684\u898f\u5247\uff0c\u64f4\u5c55\u5957\u7528\u81f3\u8a13\u7df4\u96c6\u4ee5\u5916\u7684\u8cc7\u6599\u96c6\u6216\u771f\u5be6\u4e16\u754c\u7684\u80fd\u529b\u3002\u5373\u4f7f\u5982\u6b64\uff0c\u8a31\u591a\u96fb\u8166\u79d1\u5b78\u5bb6\u4ecd\u7136\u4e0d\u8a8d\u70ba\u56de\u6b78\u5230\u4ee5\u898f\u5247\u4e3b\u5c0e\u7684AI\uff08Rule-based AI\uff09\u6703\u662f\u554f\u984c\u7684\u7b54\u6848\u3002<\/p>\n

\"\"<\/p>\n

AI\u6709\u6642\u5019\u6703\u5c07\u5409\u5a03\u5a03\u8aa4\u8a8d\u70ba\u85cd\u8393\u99ac\u82ac\u3002( \u4f86\u6e90\uff1aM. Yao, 2018. )<\/p>\n<\/div>\n

\u539f\u56e0\u5176\u5be6\u5f88\u7c21\u55ae\uff1a\u56e0\u70ba\u4ee5\u4eba\u529b\u5f9e\u982d\u5efa\u7acb\u8d77\u4e00\u5957\u898f\u5247\u4e26\u975e\u6613\u4e8b\u3002\u5c24\u5176\u5f88\u591a\u6642\u5019\uff0c\u5176\u5be6\u9023\u6211\u5011\u4eba\u985e\u81ea\u5df1\uff0c\u90fd\u5c0d\u7576\u4e2d\u7684\u898f\u5247\u4e0d\u751a\u4e86\u89e3\uff0c\u66f4\u4e0d\u7528\u63d0\u7d93\u7cfb\u7d71\u5be6\u969b\u904b\u884c\u5f8c\uff0c\u6703\u9020\u6210\u600e\u6a23\u7684\u932f\u8aa4\u8207\u5f8c\u679c\u3002\u7d10\u7d04\u5927\u5b78\u96fb\u8166\u79d1\u5b78\u5bb6\u53caFacebook\u9996\u5e2dAI\u7814\u7a76\u54e1Yann LeCun\uff0c\u5982\u6b64\u8aaa\u9053\u3002<\/p>\n

\u6211\u5011\u7576\u7136\u53ef\u4ee5\u5b9a\u7fa9\u300c\u72d7\u300d\u662f\u4e00\u7a2e\uff1a<\/p>\n

    \n
  1. \u6709\u56db\u96bb\u8173\uff1b<\/li>\n
  2. \u6709\u5c3e\u5df4\uff1b<\/li>\n
  3. \u6bd4\u8c93\u5927\u7684\u52d5\u7269\u3002<\/li>\n<\/ol>\n

    \u7136\u800c\uff0c\u5373\u4f7f\u662f\u4e09\u6b72\u5b30\u5152\uff0c\u4e5f\u7d55\u4e0d\u6703\u8aa4\u628a\u4e00\u96bb\u80a2\u9ad4\u6b98\u7f3a\u7684\u6d41\u6d6a\u72d7\u6216\u5409\u5a03\u5a03\u6392\u9664\u5728\u300c\u72d7\u300d\u7684\u7bc4\u7587\u4e4b\u5916\u3002\u6240\u4ee5\u554f\u984c\u7684\u7665\u7d50\uff0c\u4e0d\u5728\u65bc\u6211\u5011\u662f\u5426\u61c9\u8a72\u56de\u6b78GOFAI\uff0c\u6216\u7e7c\u7e8c\u7576\u524dMLAI\u7684\u9053\u8def\uff0c\u800c\u662f\u5982\u4f55\u5728\u5169\u8005\u9593\u53d6\u5f97\u95dc\u9375\u5e73\u8861\u3002<\/p>\n

    \u5019\u9078\u76f4\u89ba<\/strong><\/p>\n

    \u7d10\u7d04\u5927\u5b78\u8a8d\u77e5\u767c\u5c55\u5b78\u5bb6Garry Marcus\u5247\u8f49\u5411\u4f60\u6211\u8eab\u4e0a\u5c0b\u6c42\u7b54\u6848\u3002\u8207\u4e00\u822c\u5927\u773e\u8a8d\u77e5\u76f8\u5de6\uff0c\u300c\u5fc3\u9748\u767d\u677f\u300d\uff08The blank slate\uff09\u7684\u6559\u80b2\u7406\u8ad6\uff0c\u65e9\u5df2\u70ba\u7576\u4eca\u5fc3\u7406\u5b78\u754c\u6240\u6452\u68c4\uff0c\u4e26\u8a8d\u70ba\uff1a\u4eba\u985e\u5b30\u5152\u5176\u5be6\u8207\u751f\u4ff1\u4f86\u4e00\u5957\u8a8d\u77e5\u67b6\u69cb\u8207\u76f4\u89ba\uff0c\u4e26\u4ee5\u6b64\u4f86\u7406\u89e3\u9019\u500b\u4e16\u754c\uff0c\u4e26\u5f9e\u4e2d\u5b78\u7fd2\u77e5\u8b58\u8207\u6280\u80fd\u3002\u9019\u4e9b\u8a8d\u77e5\u67b6\u69cb\uff0c\u5305\u542b\u80fd\u5efa\u7acb\u8d77\u6642\u7a7a\u9023\u7e8c\u95dc\u4fc2\uff08spatiotemporal contiguity\uff09\u3001\u56e0\u679c\u95dc\u4fc2\uff08causality\uff09\u3001\u5340\u5225\u300c\u7a2e\u985e\u300d\u8207\u300c\u500b\u9ad4\u300d\u5dee\u7570\uff08type-token distinction\uff0c\u4f8b\u5982\u300c\u72d7\u300d\u8207\u300c\u5fe0\u72ac\u5c0f\u516b\u300d\uff09\u4ee5\u53ca\u6210\u672c\u6548\u76ca\u5206\u6790\uff08cost-benefit analysis\uff09\u7b49\u80fd\u529b\uff0c\u6b63\u662f\u7576\u4e0b\u96fb\u8166\u79d1\u5b78\u5bb6\u61c9\u8a72\u8ce6\u4e88AI\u7684\u300c\u57fa\u672c\u76f4\u89ba\u300d\u3002<\/p>\n

    \u4e0d\u53ef\u5426\u8a8d\uff0c\u9019\u4e26\u4e0d\u5bb9\u6613\uff0c\u4f46\u5df2\u6709\u8a31\u591a\u7814\u7a76\u5718\u968a\u5617\u8a66\u4f5c\u51fa\u6539\u8b8a\u3002\u4f4d\u65bc\u820a\u91d1\u5c71\u7684\u6a5f\u68b0\u4eba\u8edf\u9ad4\u516c\u53f8Vicarious\uff0c\u4fbf\u958b\u767c\u51fa\u300c\u57fa\u6a21\u7db2\u8def\u300d\uff08schema network\uff09\uff0c\u9664\u4e86\u53ef\u4ee5\u8a8d\u77e5\u5230\u300c\u7269\u9ad4\u300d\uff08object\uff09\u8207\u5f7c\u6b64\u9593\u300c\u4e92\u52d5\u300d\uff08interaction\uff09\u7684\u5b58\u5728\uff0c\u751a\u81f3\u53ef\u4ee5\u63a8\u65b7\u9023\u7d50\u5169\u8005\u7684\u300c\u56e0\u679c\u95dc\u4fc2\u300d\uff08causation\uff09\u3002\u9019\u4e9b\u80fd\u529b\uff0c\u4f7f\u5f97AI\u4e5f\u53ef\u4ee5\u50cf\u4eba\u985e\u4e00\u822c\uff0c\u91dd\u5c0d\u7279\u5b9a\u76ee\u7684\uff0c\u4f86\u898f\u5283\u81ea\u5df1\u5f8c\u7e8c\u7684\u884c\u52d5\u3002\u4ee5\u7d93\u5178\u8857\u6a5f\u904a\u6232\u300c\u6253\u78da\u584a\u300d\uff08Breakout<\/em>\uff09\u70ba\u4f8b\uff0c\u5373\u4f7f\u904a\u6232\u7684\u80cc\u666f\u8a2d\u5b9a\u6709\u6240\u66f4\u52d5\uff0c\u4f8b\u5982\u7531\u539f\u5148\u55ae\u9846\u7403\u6539\u70ba\u591a\u9846\uff0c\u57fa\u6a21\u7db2\u8def\u4f7f\u5f97AI\u7121\u9700\u591a\u9918\u8a13\u7df4\uff0c\u4fbf\u53ef\u5feb\u901f\u9032\u5165\u72c0\u6cc1\uff0c\u6e38\u5203\u6709\u9918\u3002\u9019\u662f\u7d14\u7cb9\u4ef0\u8cf4\u6a5f\u5668\u5b78\u7fd2\u7684AI\u6240\u7121\u6cd5\u9054\u6210\u7684\u3002<\/p>\n

    \"\"<\/p>\n

    Google\u7684\u773e\u591a\u96b1\u85cf\u904a\u6232\u4e2d\uff0c\u4e5f\u5305\u542b\u7d93\u5178\u8857\u6a5f\u904a\u6232\u300c\u6253\u78da\u584a\u300d\u3002( \u4f86\u6e90\uff1a\u904a\u6232\u622a\u5716 )<\/p>\n<\/div>\n

    \u4e0d\u78ba\u5b9a\u6027<\/strong><\/p>\n

    \u800c\u5728\u9019\u4e9b\u76f4\u89ba\u4e4b\u5916\uff0c\u5982\u4f55\u8b93AI\u80fd\u59a5\u5584\u8655\u7406\u771f\u5be6\u751f\u6d3b\u4e2d\u7684\u300c\u4e0d\u78ba\u5b9a\u6027\u300d\uff0c\u4e5f\u662f\u4e00\u5927\u91cd\u9ede\u3002\u9019\u5c0d\u81ea\u99d5\u8eca\u7cfb\u7d71\u5c24\u70ba\u91cd\u8981\uff1a\u6211\u5011\u4e0d\u53ef\u80fd\u5962\u671b\u6240\u6709\u99d5\u99db\u7121\u6642\u7121\u523b\u606a\u5b88\u4ea4\u901a\u898f\u5247\u3002\u53f2\u4e39\u4f5b\u5927\u5b78\u5fc3\u7406\u5b78\u8207\u96fb\u8166\u79d1\u5b78\u5bb6Noah Goodman\u6240\u958b\u767c\u7684\u6a5f\u7387\u7a0b\u5f0f\u8a9e\u8a00\uff08Probabilistic Programming Languages\uff0cPPLs\uff09\uff0c\u4fbf\u662f\u5c07\u56b4\u8b39\u7684\u96fb\u8166\u7de8\u7a0b\u8207\u6a5f\u7387\u6578\u5b78\u7d50\u5408\u3002\u914d\u5408\u6df1\u5ea6\u5b78\u7fd2\uff0c\u6df1\u5ea6PPL\uff0c\u6216\u7a31Pyro\uff0c\u53ef\u4ee5\u6839\u64da\u8def\u6cc1\u898f\u5283\u6700\u4f73\u884c\u99db\u8def\u7dda\u4e26\u6d3e\u8eca\u63a5\u9001\u4e58\u5ba2\uff0c\u9019\u5c0d\u5982Uber\u7b49\u7db2\u8def\u4ea4\u901a\u670d\u52d9\u5546\u800c\u8a00\uff0c\u6709\u7121\u7aae\u5438\u5f15\u529b\u3002<\/p>\n

    \u800cPyro\u7684\u6f5b\u529b\u4e0d\u6b62\u65bc\u6b64\u3002Goodman\u751a\u81f3\u8a8d\u70baPyro\u80fd\u5c0d\u8a9e\u97f3\u8fa8\u8b58\u6280\u8853\u7684\u767c\u5c55\u6709\u6240\u52a9\u76ca\uff0c\u9032\u4e00\u6b65\u7406\u89e3\u5728\u4eba\u985e\u5c0d\u8a71\u4e2d\u5e38\u898b\u5982\u8a87\u98fe\uff08hyperbole\uff09\u3001\u53cd\u8a9e\uff08sarcasm\uff09\u7b49\u4fee\u8fad\u3002\u6216\u8a31\u6709\u671d\u4e00\u65e5\uff0c\u6a5f\u5668\u4eba\u4e0d\u50c5\u80fd\u5b57\u9762\u4e0a\u7406\u89e3\u4eba\u985e\u7684\u8a71\u8a9e\uff0c\u751a\u81f3\u662f\u90a3\u4e9b\u6c92\u8aaa\u51fa\u53e3\u7684\u5f26\u5916\u4e4b\u97f3\u3002<\/p>\n

     <\/p>\n

    \u7de8\u8b6f\u4f86\u6e90<\/strong><\/p>\n

    M. Hutson, \u201cHow researchers are teaching AI to learn like a child<\/a>,\u201d\u00a0Science|AAAS<\/em>, 2018.<\/p>\n

    \u53c3\u8003\u8cc7\u6599<\/strong><\/p>\n

      \n
    1. M. Yao, \u201cChihuahua or muffin? My search for the best computer vision API<\/a>,\u201d\u00a0freeCodeCamp.org,\u00a0<\/em>2018.<\/li>\n
    2. W. Knight, \u201cCan this man make AI more human?<\/a>\u201c,\u00a0MIT Technology Review<\/em>, 2018.<\/li>\n
    3. T. Simonite, \u201cTeaching machine to understand us<\/a>,\u201d\u00a0MIT Technology Review<\/em>, 2018.<\/li>\n
    4. Debate: \u201cDoes AI Need More Innate Machinery?<\/a>\u201d (Yann LeCun, Gary Marcus).<\/em>\u00a0NYU Center for Mind, Brain and Consciousness, 2017.<\/li>\n<\/ol>\n
      \n

      (\u672c\u6587\u7531\u6559\u80b2\u90e8\u88dc\u52a9\u300cAI\u5831\u5831\u2500AI\u79d1\u666e\u63a8\u5ee3\u8a08\u756b\u300d\u57f7\u884c\u5718\u968a\u7de8\u8b6f)<\/p>\n<\/div>\n<\/div>\n","protected":false},"excerpt":{"rendered":"

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