{"id":5607,"date":"2025-05-07T22:55:20","date_gmt":"2025-05-07T14:55:20","guid":{"rendered":"https:\/\/infernews.com\/?page_id=5607"},"modified":"2025-05-08T01:19:28","modified_gmt":"2025-05-07T17:19:28","slug":"%e4%ba%ba%e5%b7%a5%e6%99%ba%e6%85%a7%e5%b8%b8%e7%94%a8%e7%b8%ae%e5%af%ab%e8%a9%9e%e5%bd%99%e8%a9%b3%e8%a7%a3","status":"publish","type":"page","link":"https:\/\/infernews.com\/blog\/%e4%ba%ba%e5%b7%a5%e6%99%ba%e6%85%a7%e5%b8%b8%e7%94%a8%e7%b8%ae%e5%af%ab%e8%a9%9e%e5%bd%99%e8%a9%b3%e8%a7%a3\/","title":{"rendered":"\u4eba\u5de5\u667a\u6167\u5e38\u7528\u7e2e\u5beb\u8a5e\u5f59\u8a73\u89e3"},"content":{"rendered":"\n<p class=\"has-large-font-size\"><strong>I. \u5c0e\u8ad6<\/strong><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>\u4eba\u5de5\u667a\u6167\u9818\u57df\u7e2e\u5beb\u8a5e\u7684\u6fc0\u589e\uff1a<\/strong> \u4eba\u5de5\u667a\u6167\uff08AI\uff09\u9818\u57df\u6b63\u7d93\u6b77\u524d\u6240\u672a\u6709\u7684\u5feb\u901f\u767c\u5c55\uff0c\u65b0\u7684\u6982\u5ff5\u3001\u6280\u8853\u548c\u6a21\u578b\u4e0d\u65b7\u6e67\u73fe\u3002\u9019\u7a2e\u84ec\u52c3\u767c\u5c55\u5f80\u5f80\u4f34\u96a8\u8457\u5927\u91cf\u7e2e\u5beb\u8a5e\u548c\u7c21\u7a31\u7684\u4f7f\u7528\uff0c\u4ee5\u4fbf\u65bc\u6e9d\u901a\u548c\u53c3\u8003 <sup>1<\/sup>\u3002\u6b63\u5982\u7814\u7a76\u8cc7\u6599 <sup>1<\/sup> \u6240\u8ff0\uff0cAI \u76f8\u95dc\u7684\u7e2e\u5beb\u8a5e\u5217\u8868\u4f3c\u4e4e\u4e0d\u65b7\u64f4\u5927\uff0c\u4ee4\u4eba\u56f0\u60d1\u3002\u9019\u7a81\u986f\u4e86\u7de8\u5236\u4e00\u4efd\u6e05\u6670\u8a73\u76e1\u8a5e\u5f59\u8868\u7684\u9700\u6c42\u3002\u7814\u7a76\u8cc7\u6599 <sup>2<\/sup> \u4e5f\u6307\u51fa\uff0c\u50cf LLM\u3001RAG \u548c GPT \u9019\u6a23\u7684 AI \u7e2e\u5beb\u8a5e\u975e\u5e38\u666e\u904d\uff0c\u4e0d\u50c5\u5728\u6280\u8853\u8a0e\u8ad6\u4e2d\u983b\u7e41\u51fa\u73fe\uff0c\u4e5f\u8d8a\u4f86\u8d8a\u591a\u5730\u51fa\u73fe\u5728\u4e3b\u6d41\u5a92\u9ad4\u4e0a\u3002\u9019\u7a2e\u73fe\u8c61\u8868\u660e\uff0c\u7406\u89e3\u9019\u4e9b\u7e2e\u5beb\u8a5e\u5c0d\u65bc\u638c\u63e1 AI \u7684\u767c\u5c55\u81f3\u95dc\u91cd\u8981\u3002\u5927\u91cf\u7684\u7e2e\u5beb\u8a5e\uff0c\u6b63\u5982 <sup>1<\/sup> \u548c <sup>2<\/sup> \u4e2d\u6240\u63d0\u5230\u7684\uff0c\u53ef\u80fd\u6703\u6210\u70ba\u65b0\u5165\u9580\u8005\u751a\u81f3\u9818\u57df\u5167\u4eba\u58eb\u7406\u89e3\u7684\u969c\u7919\u3002\u56e0\u6b64\uff0c\u4e00\u500b\u6574\u5408\u6027\u7684\u8cc7\u6e90\u5c0d\u65bc\u6d88\u9664\u9019\u7a2e\u969c\u7919\u81f3\u95dc\u91cd\u8981\u3002AI 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\u6216\u76f8\u95dc\u9818\u57df\u5de5\u4f5c\u7684\u5c08\u696d\u4eba\u58eb\u800c\u8a00\uff0c\u7406\u89e3\u9019\u4e9b\u7e2e\u5beb\u8a5e\u5c0d\u65bc\u6709\u6548\u7684\u6e9d\u901a\u3001\u7406\u89e3\u7814\u7a76\u8ad6\u6587\u548c\u7522\u696d\u5831\u544a\u4ee5\u53ca\u638c\u63e1\u6700\u65b0\u7684\u9032\u5c55\u81f3\u95dc\u91cd\u8981\u3002\u5c0d\u65bc\u90a3\u4e9b\u5e0c\u671b\u9032\u5165\u8a72\u9818\u57df\u6216\u50c5\u50c5\u60f3\u66f4\u597d\u5730\u7406\u89e3 AI \u7684\u500b\u4eba\u4f86\u8aaa\uff0c\u638c\u63e1\u5e38\u898b\u7684\u7e2e\u5beb\u8a5e\u662f\u700f\u89bd\u7dda\u4e0a\u8cc7\u6e90\u3001\u65b0\u805e\u6587\u7ae0\u548c\u76f8\u95dc\u8a0e\u8ad6\u7684\u57fa\u790e\u3002<sup>3<\/sup> \u660e\u78ba\u6307\u51fa\uff0c\u300c\u7406\u89e3\u9019\u4e9b AI \u7e2e\u5beb\u8a5e\u662f\u5efa\u7acb AI \u77e5\u8b58\u7684\u57fa\u790e\u3002\u300d\u9019\u76f4\u63a5\u652f\u6301\u4e86\u63d0\u4f9b\u9019\u6a23\u4e00\u4efd\u8a5e\u5f59\u8868\u7684\u91cd\u8981\u6027\u3002\u7406\u89e3\u548c\u4f7f\u7528 AI \u7e2e\u5beb\u8a5e\u7684\u80fd\u529b\u986f\u8457\u5f71\u97ff\u500b\u4eba\u53c3\u8207\u548c\u8ca2\u737b\u8a72\u9818\u57df\u7684\u80fd\u529b\u3002\u7f3a\u4e4f\u7406\u89e3\u53ef\u80fd\u6703\u5c0e\u81f4\u8aa4\u89e3\u4e26\u963b\u7919\u5b78\u7fd2\u3002AI \u662f\u4e00\u500b\u6280\u8853\u9818\u57df\uff0c\u6709\u5176\u81ea\u8eab\u7684\u5c08\u696d\u8853\u8a9e\uff0c\u800c\u7e2e\u5beb\u8a5e\u662f\u9019\u4e9b\u8853\u8a9e\u7684\u91cd\u8981\u7d44\u6210\u90e8\u5206\u3002\u7406\u89e3\u9019\u4e9b\u7e2e\u5beb\u8a5e\u5c0d\u65bc\u7406\u89e3 AI \u9818\u57df\u7684\u6280\u8853\u8a0e\u8ad6\u3001\u7814\u7a76\u548c\u65b0\u805e\u662f\u5fc5\u8981\u7684\u3002\u9019\u7a2e\u7406\u89e3\u5c0d\u65bc\u5c08\u696d\u4eba\u58eb\u548c\u5b78\u7fd2\u8005\u90fd\u81f3\u95dc\u91cd\u8981\u3002<\/li>\n<\/ul>\n\n\n\n<p><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>\u672c\u5831\u544a\u7684\u76ee\u7684\u8207\u7bc4\u570d\uff1a<\/strong> \u672c\u5831\u544a\u65e8\u5728\u63d0\u4f9b\u4e00\u4efd\u5305\u542b 100 \u500b\u5e38\u7528 AI \u7e2e\u5beb\u8a5e\u7684\u7d9c\u5408\u8a5e\u5f59\u8868\uff0c\u5176\u4e2d\u5305\u542b\u5176\u82f1\u6587\u548c\u4e2d\u6587\u5168\u7a31\u4ee5\u53ca\u6e05\u6670\u7684\u529f\u80fd\u89e3\u91cb\u3002\u8a5e\u5f59\u8868\u5c07\u6db5\u84cb\u5ee3\u6cdb\u7684 AI \u76f8\u95dc\u8853\u8a9e\uff0c\u5305\u62ec\u4f86\u81ea\u6a5f\u5668\u5b78\u7fd2\u3001\u6df1\u5ea6\u5b78\u7fd2\u3001\u81ea\u7136\u8a9e\u8a00\u8655\u7406\u548c\u4e00\u822c\u667a\u80fd\u81ea\u52d5\u5316\u9818\u57df\u7684\u8853\u8a9e\u3002\u672c\u5831\u544a\u65e8\u5728\u70ba\u4efb\u4f55\u5e0c\u671b\u63ed\u958b AI \u8a9e\u8a00\u795e\u79d8\u9762\u7d17\u7684\u4eba\u63d0\u4f9b\u6709\u50f9\u503c\u7684\u53c3\u8003\u3002<\/li>\n<\/ul>\n\n\n\n<p class=\"has-large-font-size\"><strong>II. \u5e38\u7528\u4eba\u5de5\u667a\u6167\u7e2e\u5beb\u8a5e\u5f59\u8a73\u89e3(A Comprehensive Glossary of Common AI Short Forms)<\/strong><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>\u672c\u7bc0\u5c07\u63d0\u4f9b\u4e00\u500b\u8a73\u7d30\u7684\u8868\u683c\uff0c\u8a72\u8868\u683c\u5305\u542b\u4ee5\u4e0b\u6b04\u4f4d\uff1a\u300cAI \u7e2e\u5beb\u8a5e\u300d\u3001\u300c\u82f1\u6587\u5168\u7a31\u300d\u3001\u300c\u4e2d\u6587\u5168\u7a31\u300d\u548c\u300c\u529f\u80fd\u89e3\u91cb\u300d\u3002\u8868\u683c\u4e2d\u7684\u689d\u76ee\u5c07\u5f9e\u63d0\u4f9b\u7684\u7814\u7a76\u8cc7\u6599\u4e2d\u6574\u7406\uff0c\u4e26\u88dc\u5145\u5176\u4ed6\u5e38\u7528\u7684 AI \u7e2e\u5beb\u8a5e\uff0c\u4ee5\u9054\u5230 100 \u500b\u7684\u76ee\u6a19\u3002<\/li>\n\n\n\n<li><strong>\u5b50\u7ae0\u7bc0\uff08\u7e2e\u5beb\u8a5e\u5206\u985e\uff09\uff1a<\/strong> \u70ba\u4e86\u63d0\u9ad8\u53ef\u8b80\u6027\u548c\u7d44\u7e54\u6027\uff0c\u8a5e\u5f59\u8868\u53ef\u4ee5\u6839\u64da AI \u8853\u8a9e\u7684\u9818\u57df\u6216\u985e\u5225\u9032\u4e00\u6b65\u5283\u5206\u70ba\u5b50\u7ae0\u7bc0\u3002\u9019\u5c07\u5141\u8a31\u4f7f\u7528\u8005\u5feb\u901f\u627e\u5230\u8207\u5176\u7279\u5b9a\u8208\u8da3\u9818\u57df\u76f8\u95dc\u7684\u7e2e\u5beb\u8a5e\u3002\u6f5b\u5728\u7684\u985e\u5225\u5305\u62ec\uff1a<\/li>\n\n\n\n<li><strong>\u901a\u7528\u4eba\u5de5\u667a\u6167\u8207\u667a\u80fd\u81ea\u52d5\u5316\uff1a<\/strong> \u5ee3\u6cdb\u8207\u4eba\u5de5\u667a\u6167\u53ca\u5176\u5728\u81ea\u52d5\u5316\u9818\u57df\u7684\u61c9\u7528\u76f8\u95dc\u7684\u8853\u8a9e\u3002<\/li>\n<\/ul>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>AGI:<\/strong> Artificial General Intelligence (\u901a\u7528\u4eba\u5de5\u667a\u80fd) &#8211; \u6307\u4e00\u7a2e\u5047\u8a2d\u7684 AI \u5f62\u5f0f\uff0c\u5176\u667a\u529b\u5728\u5ee3\u6cdb\u7684\u4efb\u52d9\u548c\u8a8d\u77e5\u80fd\u529b\u4e0a\u8207\u4eba\u985e\u76f8\u7576\u6216\u8d85\u8d8a\u4eba\u985e <sup>3<\/sup>\u3002\u7814\u7a76\u8cc7\u6599 <sup>5<\/sup> \u5c07 AGI \u5b9a\u7fa9\u70ba\u4e00\u7a2e\u5047\u8a2d\u7684 AI\uff0c\u5728\u591a\u500b\u9818\u57df\u5177\u6709\u985e\u4f3c\u4eba\u985e\u7684\u667a\u6167\u3002\u7814\u7a76\u8cc7\u6599 <sup>6<\/sup> \u5f37\u8abf\u9019\u662f AI \u9818\u57df\u7684\u4e0b\u4e00\u500b\u5049\u5927\u9032\u5c55\u3002AGI \u662f AI \u7814\u7a76\u7684\u4e00\u500b\u9577\u671f\u76ee\u6a19\uff0c\u5176\u5be6\u73fe\u5c07\u4ee3\u8868\u4e00\u500b\u91cd\u8981\u7684\u91cc\u7a0b\u7891\u3002\u5728\u5b9a\u7fa9\u4e86 AI \u53ca\u5176\u5728 IA \u4e2d\u7684\u61c9\u7528\u4e4b\u5f8c\uff0c\u908f\u8f2f\u4e0a\u61c9\u8a72\u5305\u542b AGI\uff0c\u5b83\u4ee3\u8868\u4e86\u672a\u4f86\u66f4\u5148\u9032\u7684 AI \u5f62\u5f0f\u3002<\/li>\n\n\n\n<li><strong>AI:<\/strong> Artificial Intelligence (\u4eba\u5de5\u667a\u80fd) &#8211; \u6307\u7814\u7a76\u3001\u958b\u767c\u7528\u65bc\u6a21\u64ec\u3001\u5ef6\u4f38\u548c\u64f4\u5c55\u4eba\u985e\u667a\u80fd\u7684\u7406\u8ad6\u3001\u65b9\u6cd5\u3001\u6280\u8853\u53ca\u61c9\u7528\u7cfb\u7d71\u7684\u4e00\u9580\u65b0\u7684\u6280\u8853\u79d1\u5b78 <sup>1<\/sup>\u3002\u7814\u7a76\u8cc7\u6599 <sup>8<\/sup> \u63d0\u4f9b\u4e86\u4e00\u500b\u7c21\u6f54\u7684\u5b9a\u7fa9\uff1a\u300c\u4eba\u5de5\u667a\u6167\u63cf\u8ff0\u4e86\u6a5f\u5668\u6a21\u4eff\u4eba\u985e\u667a\u6167\u7684\u80fd\u529b\u3002\u300d\u7814\u7a76\u8cc7\u6599 <sup>9<\/sup> \u63d0\u5230 AI \u4f7f\u7528\u6a5f\u5668\u5b78\u7fd2\u6f14\u7b97\u6cd5\u4f86\u5206\u6790\u5927\u91cf\u8cc7\u6599\uff0c\u4ee5\u6aa2\u6e2c\u7570\u5e38\u548c\u6a21\u5f0f\u3002\u96d6\u7136\u5b9a\u7fa9\u770b\u4f3c\u7c21\u55ae\uff0c\u4f46\u6b63\u5982 <sup>8<\/sup> \u4e2d\u6240\u6307\u51fa\u7684\uff0cAI \u7684\u5be6\u969b\u61c9\u7528\u6d89\u53ca\u8907\u96dc\u7684\u7cfb\u7d71\u548c\u6f14\u7b97\u6cd5\u3002\u4f7f\u7528\u8005\u8a62\u554f\u5e38\u898b\u7684 AI \u7e2e\u5beb\u8a5e\uff0c\u800c AI \u672c\u8eab\u5c31\u662f\u6700\u57fa\u672c\u7684\u7e2e\u5beb\u8a5e\u3002\u91cd\u8981\u7684\u662f\u9996\u5148\u78ba\u7acb\u6700\u5ee3\u6cdb\u7684\u5b9a\u7fa9\uff0c\u7136\u5f8c\u6df1\u5165\u7814\u7a76\u5176\u5b50\u9818\u57df\u3002<\/li>\n\n\n\n<li><strong>API:<\/strong> Application Programming Interface (\u5e94\u7528\u7a0b\u5e8f\u7f16\u7a0b\u63a5\u53e3) &#8211; \u6307\u4e00\u7d44\u898f\u5247\u548c\u5354\u5b9a\uff0c\u5141\u8a31\u4e0d\u540c\u7684\u8edf\u9ad4\u61c9\u7528\u7a0b\u5f0f\u9032\u884c\u901a\u8a0a\u548c\u4ea4\u63db\u8cc7\u6599 <sup>7<\/sup>\u3002\u7814\u7a76\u8cc7\u6599 <sup>7<\/sup> \u5b9a\u7fa9\u4e86 API \u53ca\u5176\u5728\u4fc3\u9032\u61c9\u7528\u7a0b\u5f0f\u4e4b\u9593\u6574\u5408\u4e2d\u7684\u4f5c\u7528\u3002\u7814\u7a76\u8cc7\u6599 <sup>9<\/sup> \u4e5f\u5728 IT \u7e2e\u5beb\u8a5e\u7684\u80cc\u666f\u4e0b\u63d0\u5230\u4e86 API\u3002API \u5c0d\u65bc\u900f\u904e\u4f7f\u4e0d\u540c\u7684\u7d44\u4ef6\u548c\u670d\u52d9\u80fd\u5920\u7121\u7e2b\u5354\u540c\u5de5\u4f5c\u4f86\u69cb\u5efa\u8907\u96dc\u7684 AI \u7cfb\u7d71\u81f3\u95dc\u91cd\u8981\u3002AI \u7cfb\u7d71\u901a\u5e38\u4f9d\u8cf4\u65bc\u5404\u7a2e\u7d44\u4ef6\u7684\u6574\u5408\uff0c\u800c API \u662f\u9019\u7a2e\u6574\u5408\u7684\u95dc\u9375\u3002<\/li>\n\n\n\n<li><strong>BPM:<\/strong> Business Process Management (\u4e1a\u52a1\u6d41\u7a0b\u7ba1\u7406) &#8211; \u6307\u8a2d\u8a08\u3001\u57f7\u884c\u3001\u76e3\u63a7\u548c\u512a\u5316\u696d\u52d9\u6d41\u7a0b\u7684\u5be6\u8e10\u3002AI \u548c\u667a\u80fd\u81ea\u52d5\u5316\u901a\u5e38\u8207 BPM \u76f8\u7d50\u5408 <sup>7<\/sup>\u3002\u7814\u7a76\u8cc7\u6599 <sup>7<\/sup> \u89e3\u91cb\u4e86 BPM \u5de5\u5177\u5982\u4f55\u900f\u904e\u81ea\u52d5\u5316\u5de5\u4f5c\u6d41\u7a0b\u4f86\u512a\u5316\u6d41\u7a0b\u3002BPM \u70ba\u61c9\u7528 AI \u4ee5\u6539\u9032\u696d\u52d9\u904b\u71df\u63d0\u4f9b\u4e86\u4e00\u500b\u6846\u67b6\u3002BPM \u662f\u8a31\u591a\u667a\u80fd\u81ea\u52d5\u5316\u6280\u8853\u61c9\u7528\u7684\u80cc\u666f\u3002<\/li>\n\n\n\n<li><strong>Chatbot:<\/strong> (\u804a\u5929\u673a\u5668\u4eba) &#8211; \u6307\u4e00\u7a2e\u6a21\u64ec\u4eba\u985e\u5c0d\u8a71\u7684\u8edf\u9ad4\u61c9\u7528\u7a0b\u5f0f\uff0c\u901a\u5e38\u7528\u65bc\u5ba2\u6236\u670d\u52d9\u6216\u4fe1\u606f\u6aa2\u7d22\u3002\u8a31\u591a\u73fe\u4ee3\u804a\u5929\u6a5f\u5668\u4eba\u7531 AI\uff08\u5c24\u5176\u662f NLP \u548c LLM\uff09\u9a45\u52d5 <sup>1<\/sup>\u3002\u7814\u7a76\u8cc7\u6599 <sup>1<\/sup> \u5217\u51fa\u4e86\u300c\u804a\u5929\u6a5f\u5668\u4eba\u6216 AI \u804a\u5929\u6a5f\u5668\u4eba\u300d\u3002\u7814\u7a76\u8cc7\u6599 <sup>2<\/sup> \u63d0\u5230 LLM \u662f\u804a\u5929\u6a5f\u5668\u4eba\u7684\u57fa\u790e\u3002\u7814\u7a76\u8cc7\u6599 <sup>8<\/sup> \u5c07 ChatGPT \u7a31\u70ba OpenAI \u958b\u767c\u7684\u804a\u5929\u6a5f\u5668\u4eba\u3002\u7814\u7a76\u8cc7\u6599 <sup>4<\/sup> \u5c07\u5176\u5b9a\u7fa9\u70ba\u6a21\u4eff\u4eba\u985e\u5c0d\u8a71\u3002\u804a\u5929\u6a5f\u5668\u4eba\u662f AI \u5728\u65e5\u5e38\u751f\u6d3b\u4e2d\u7684\u4e00\u500b\u7a81\u51fa\u4e14\u65e5\u76ca\u8907\u96dc\u7684\u61c9\u7528\u3002\u804a\u5929\u6a5f\u5668\u4eba\u662f AI \u7684\u4e00\u500b\u5e38\u898b\u4e14\u53ef\u898b\u7684\u61c9\u7528\u3002<\/li>\n\n\n\n<li><strong>Cloud Computing:<\/strong> (\u4e91\u8ba1\u7b97) &#8211; \u6307\u900f\u904e\u7db2\u969b\u7db2\u8def\uff08\u300c\u96f2\u7aef\u300d\uff09\u50b3\u905e\u904b\u7b97\u670d\u52d9\uff0c\u5305\u62ec\u4f3a\u670d\u5668\u3001\u5132\u5b58\u3001\u8cc7\u6599\u5eab\u3001\u7db2\u8def\u3001\u8edf\u9ad4\u3001\u5206\u6790\u548c\u667a\u6167\u3002\u8a31\u591a AI \u670d\u52d9\u548c\u5e73\u53f0\u90fd\u900f\u904e\u96f2\u7aef\u8a17\u7ba1\u548c\u5b58\u53d6 <sup>7<\/sup>\u3002\u96f2\u7aef\u904b\u7b97\u666e\u53ca\u4e86\u5c0d\u5f37\u5927\u904b\u7b97\u8cc7\u6e90\u7684\u5b58\u53d6\uff0c\u4e26\u4fc3\u9032\u4e86 AI \u61c9\u7528\u7a0b\u5f0f\u7684\u5927\u898f\u6a21\u958b\u767c\u548c\u90e8\u7f72\u3002\u73fe\u4ee3 AI \u7684\u57fa\u790e\u8a2d\u65bd\u901a\u5e38\u4f9d\u8cf4\u65bc\u96f2\u7aef\u904b\u7b97\u3002<\/li>\n\n\n\n<li><strong>IA:<\/strong> Intelligent Automation (\u667a\u80fd\u81ea\u52a8\u5316) &#8211; \u6307\u5229\u7528 AI \u6280\u8853\u4f86\u81ea\u52d5\u5316\u4efb\u52d9\u548c\u6d41\u7a0b\uff0c\u901a\u5e38\u8d85\u8d8a\u50b3\u7d71\u81ea\u52d5\u5316\uff0c\u4e26\u7d50\u5408\u4e86\u5b78\u7fd2\u548c\u6c7a\u7b56\u80fd\u529b <sup>9<\/sup>\u3002\u7814\u7a76\u8cc7\u6599 <sup>7<\/sup> \u8a0e\u8ad6\u4e86\u667a\u80fd\u81ea\u52d5\u5316\uff0c\u4e26\u5217\u51fa\u4e86\u5e7e\u500b\u76f8\u95dc\u7684\u7e2e\u5beb\u8a5e\uff0c\u5982 OCR\u3001IoT \u548c RPA\u3002\u7814\u7a76\u8cc7\u6599 <sup>9<\/sup> \u5c07 IA \u5b9a\u7fa9\u70ba\u4f7f\u7528 ML \u6216 AI \u4f86\u7ba1\u7406\u6d41\u7a0b\u548c\u57fa\u790e\u8a2d\u65bd\u7684 IT \u81ea\u52d5\u5316\u5de5\u5177\u3002IA \u4ee3\u8868\u4e86 AI \u7684\u5be6\u969b\u61c9\u7528\uff0c\u65e8\u5728\u63d0\u9ad8\u5404\u500b\u9818\u57df\u7684\u6548\u7387\u4e26\u6e1b\u5c11\u4eba\u5de5\u5e72\u9810\u3002\u5728\u57fa\u672c\u7684 AI \u5b9a\u7fa9\u4e4b\u5f8c\uff0cIA \u662f\u4e00\u500b\u95dc\u9375\u7684\u61c9\u7528\u9818\u57df\u3002\u5f37\u8abf\u5982\u4f55\u4f7f\u7528 AI \u4f86\u667a\u80fd\u5730\u81ea\u52d5\u5316\u4efb\u52d9\u975e\u5e38\u91cd\u8981\u3002<\/li>\n\n\n\n<li><strong>IoT:<\/strong> Internet of Things (\u7269\u8054\u7f51) &#8211; \u6307\u4e00\u500b\u7531\u76f8\u4e92\u9023\u63a5\u7684\u8a2d\u5099\u3001\u7269\u9ad4\u548c\u611f\u6e2c\u5668\u7d44\u6210\u7684\u7db2\u8def\uff0c\u5b83\u5011\u53ef\u4ee5\u6536\u96c6\u548c\u4ea4\u63db\u6578\u64da <sup>7<\/sup>\u3002\u7814\u7a76\u8cc7\u6599 <sup>7<\/sup> \u5b9a\u7fa9\u4e86 IoT \u53ca\u5176\u5728\u6578\u64da\u6536\u96c6\u4e2d\u7684\u4f5c\u7528\u3002IoT \u8a2d\u5099\u7522\u751f\u7684\u5927\u91cf\u6578\u64da\u70ba AI \u548c\u667a\u80fd\u81ea\u52d5\u5316\u7cfb\u7d71\u63d0\u4f9b\u4e86\u5bf6\u8cb4\u7684\u8f38\u5165\u3002IoT \u70ba\u667a\u80fd\u81ea\u52d5\u5316\u4e2d\u7684 AI \u61c9\u7528\u63d0\u4f9b\u4e86\u91cd\u8981\u7684\u6578\u64da\u4f86\u6e90\u3002<\/li>\n\n\n\n<li><strong>OCR:<\/strong> Optical Character Recognition (\u5149\u5b66\u5b57\u7b26\u8bc6\u522b) &#8211; \u6307\u4e00\u7a2e\u5c07\u5716\u50cf\u4e2d\u7684\u6587\u672c\uff08\u5370\u5237\u6216\u624b\u5beb\uff09\u8f49\u63db\u70ba\u6a5f\u5668\u53ef\u8b80\u6587\u672c\u7684\u6280\u8853 <sup>7<\/sup>\u3002\u7814\u7a76\u8cc7\u6599 <sup>7<\/sup> \u5b9a\u7fa9\u4e86 OCR \u53ca\u5176\u5728\u5404\u500b\u884c\u696d\u4e2d\u7684\u61c9\u7528\u3002OCR \u662f\u5c07\u6587\u6a94\u6578\u4f4d\u5316\u4e26\u5c07\u975e\u7d50\u69cb\u5316\u6578\u64da\u6574\u5408\u5230\u81ea\u52d5\u5316\u5de5\u4f5c\u6d41\u7a0b\u4e2d\u7684\u95dc\u9375\u6280\u8853\u3002OCR \u662f\u4e00\u7a2e\u7d93\u5e38\u5728\u667a\u80fd\u81ea\u52d5\u5316\u4e2d\u4f7f\u7528\u7684\u5be6\u7528\u6280\u8853\u3002<\/li>\n\n\n\n<li><strong>RAD:<\/strong> Rapid Application Development (\u5feb\u901f\u5e94\u7528\u5f00\u53d1) &#8211; \u6307\u4e00\u7a2e\u5f37\u8abf\u901f\u5ea6\u548c\u9748\u6d3b\u6027\u7684\u8edf\u9ad4\u958b\u767c\u65b9\u6cd5\uff0c\u901a\u5e38\u4f7f\u7528\u4f4e\u4ee3\u78bc\u5e73\u53f0\u3002\u7531\u65bc AI \u5de5\u5177\u8d8a\u4f86\u8d8a\u591a\u5730\u6574\u5408\u5230 RAD \u5e73\u53f0\u4e2d\uff0c\u56e0\u6b64\u8207 AI \u76f8\u95dc <sup>7<\/sup>\u3002\u7814\u7a76\u8cc7\u6599 <sup>7<\/sup> \u5c07 RAD \u5e73\u53f0\u63cf\u8ff0\u70ba\u4f4e\u4ee3\u78bc\u89e3\u6c7a\u65b9\u6848\u3002RAD \u5e73\u53f0\u4f7f\u5f97\u69cb\u5efa AI \u9a45\u52d5\u7684\u61c9\u7528\u7a0b\u5f0f\u8b8a\u5f97\u66f4\u5bb9\u6613\u548c\u66f4\u5feb\u3002\u5feb\u901f\u958b\u767c\u7684\u8da8\u52e2\u4e5f\u9069\u7528\u65bc AI \u61c9\u7528\u7a0b\u5f0f\u3002<\/li>\n\n\n\n<li><strong>RPA:<\/strong> Robotic Process Automation (\u673a\u5668\u4eba\u6d41\u7a0b\u81ea\u52a8\u5316) &#8211; \u6307\u4e00\u7a2e\u4f7f\u7528\u8edf\u9ad4\u6a5f\u5668\u4eba\u4f86\u81ea\u52d5\u5316\u91cd\u8907\u6027\u3001\u57fa\u65bc\u898f\u5247\u7684\u4efb\u52d9\u7684\u6280\u8853\uff0c\u9019\u4e9b\u4efb\u52d9\u901a\u5e38\u7531\u4eba\u985e\u57f7\u884c <sup>2<\/sup>\u3002\u7814\u7a76\u8cc7\u6599 <sup>2<\/sup> \u5c07 RPA \u63cf\u8ff0\u70ba\u5728\u7a0b\u5f0f\u4e2d\u6a21\u4eff\u4eba\u985e\u884c\u70ba\u4ee5\u63d0\u9ad8\u904b\u71df\u6548\u7387\u3002\u7814\u7a76\u8cc7\u6599 <sup>7<\/sup> \u5f37\u8abf RPA \u662f\u81ea\u52d5\u5316\u7d50\u69cb\u5316\u696d\u52d9\u6d41\u7a0b\u7684\u8edf\u9ad4\u3002RPA \u662f AI \u5728\u5546\u696d\u9818\u57df\u4e2d\u5be6\u969b\u4e14\u5ee3\u6cdb\u63a1\u7528\u7684\u61c9\u7528\uff0c\u7528\u65bc\u7c21\u5316\u904b\u71df\u3002IA \u901a\u5e38\u6d89\u53ca RPA\uff0c\u56e0\u6b64\u5305\u542b\u6b64\u8853\u8a9e\u4e26\u89e3\u91cb\u5176\u5728\u81ea\u52d5\u5316\u7279\u5b9a\u4efb\u52d9\u4e2d\u7684\u4f5c\u7528\u81f3\u95dc\u91cd\u8981\u3002<\/li>\n\n\n\n<li><strong>SaaS:<\/strong> Software as a Service (\u8f6f\u4ef6\u5373\u670d\u52a1) &#8211; \u6307\u4e00\u7a2e\u8edf\u9ad4\u5206\u767c\u6a21\u578b\uff0c\u7b2c\u4e09\u65b9\u4f9b\u61c9\u5546\u8a17\u7ba1\u61c9\u7528\u7a0b\u5f0f\u4e26\u900f\u904e\u7db2\u969b\u7db2\u8def\u5411\u5ba2\u6236\u63d0\u4f9b\u3002\u8a31\u591a AI \u5de5\u5177\u548c\u5e73\u53f0\u90fd\u4ee5 SaaS \u7684\u5f62\u5f0f\u63d0\u4f9b\uff0c\u56e0\u6b64\u8207 AI \u76f8\u95dc <sup>7<\/sup>\u3002\u7814\u7a76\u8cc7\u6599 <sup>7<\/sup> \u63cf\u8ff0\u4e86 SaaS \u53ca\u5176\u53ef\u64f4\u5c55\u6027\u548c\u652f\u63f4\u7b49\u512a\u52e2\u3002SaaS \u6a21\u578b\u986f\u8457\u964d\u4f4e\u4e86\u5b58\u53d6\u548c\u4f7f\u7528 AI \u6280\u8853\u7684\u9580\u6abb\u3002AI \u7684\u90e8\u7f72\u901a\u5e38\u900f\u904e\u96f2\u7aef\u670d\u52d9\u9032\u884c\uff0c\u800c SaaS \u662f\u9019\u7a2e\u90e8\u7f72\u7684\u5e38\u898b\u6a21\u578b\u3002<\/li>\n\n\n\n<li><strong>TPU:<\/strong> Tensor Processing Unit (\u5f20\u91cf\u5904\u7406\u5668) &#8211; \u6307 Google \u5c08\u9580\u70ba\u795e\u7d93\u7db2\u8def\u8655\u7406\u958b\u767c\u7684\u61c9\u7528\u7a0b\u5f0f\u7279\u5b9a\u7a4d\u9ad4\u96fb\u8def (ASIC)\uff0c\u70ba AI \u5de5\u4f5c\u8ca0\u8f09\u63d0\u4f9b\u9032\u4e00\u6b65\u7684\u52a0\u901f <sup>10<\/sup>\u3002\u7814\u7a76\u8cc7\u6599 <sup>10<\/sup> \u5c07\u5176\u5b9a\u7fa9\u70ba\u7528\u65bc\u795e\u7d93\u7db2\u8def\u8655\u7406\u7684 ASIC\u3002TPU \u4ee3\u8868\u4e86 AI \u786c\u9ad4\u7684\u9032\u4e00\u6b65\u512a\u5316\uff0c\u5c55\u793a\u4e86\u5c08\u7528\u8a08\u7b97\u5c0d\u65bc\u8a72\u9818\u57df\u7684\u91cd\u8981\u6027\u3002\u9664\u4e86 GPU \u4e4b\u5916\uff0c\u5176\u4ed6\u5c08\u7528\u786c\u9ad4\uff08\u5982 TPU\uff09\u5c0d\u65bc AI \u7684\u767c\u5c55\u4e5f\u81f3\u95dc\u91cd\u8981\u3002<\/li>\n\n\n\n<li><strong>UX:<\/strong> User Experience (\u7528\u6237\u4f53\u9a8c) &#8211; \u96d6\u7136\u56b4\u683c\u4f86\u8aaa\u4e0d\u662f AI \u8853\u8a9e\uff0c\u4f46\u5728 AI \u9a45\u52d5\u7684\u4ecb\u9762\u548c\u61c9\u7528\u7a0b\u5f0f\u7684\u80cc\u666f\u4e0b\uff0c\u5b83\u8d8a\u4f86\u8d8a\u91cd\u8981\uff0c\u6307\u7684\u662f\u4f7f\u7528\u8005\u8207\u7522\u54c1\u6216\u670d\u52d9\u4e92\u52d5\u7684\u6574\u9ad4\u9ad4\u9a57 <sup>7<\/sup>\u3002\u7814\u7a76\u8cc7\u6599 <sup>7<\/sup> \u5c07 UX \u5b9a\u7fa9\u70ba\u4f7f\u7528\u8005\u5982\u4f55\u8207\u6578\u4f4d\u7522\u54c1\u548c\u670d\u52d9\u4e92\u52d5\u3002\u96a8\u8457 AI \u8d8a\u4f86\u8d8a\u878d\u5165\u65e5\u5e38\u751f\u6d3b\uff0c\u5c0d\u4f7f\u7528\u8005\u9ad4\u9a57\u7684\u95dc\u6ce8\u5c0d\u65bc\u6210\u529f\u63a1\u7528\u81f3\u95dc\u91cd\u8981\u3002\u8a31\u591a AI \u61c9\u7528\u7a0b\u5f0f\u6d89\u53ca\u4f7f\u7528\u8005\u4e92\u52d5\u3002\u56e0\u6b64\uff0c\u5305\u542b UX \u53ef\u4ee5\u70ba AI \u7684\u90e8\u7f72\u63d0\u4f9b\u66f4\u5ee3\u6cdb\u7684\u80cc\u666f\u3002<\/li>\n\n\n\n<li><strong>XAI:<\/strong> Explainable AI (\u53ef\u89e3\u91ca\u6027\u4eba\u5de5\u667a\u80fd) &#8211; \u6307 AI \u7684\u4e00\u500b\u9818\u57df\uff0c\u5c08\u6ce8\u65bc\u958b\u767c\u65b9\u6cd5\u548c\u6280\u8853\uff0c\u4f7f AI \u7cfb\u7d71\u7684\u6c7a\u7b56\u548c\u884c\u70ba\u5c0d\u4eba\u985e\u4f86\u8aaa\u662f\u53ef\u4ee5\u7406\u89e3\u7684 <sup>2<\/sup>\u3002\u7814\u7a76\u8cc7\u6599 <sup>2<\/sup> \u5f37\u8abf AI \u6c7a\u7b56\u7684\u900f\u660e\u5ea6\u548c\u53ef\u7406\u89e3\u6027\u3002\u7814\u7a76\u8cc7\u6599 <sup>2<\/sup> \u5f37\u8abf\u5176\u5c08\u6ce8\u65bc\u63d0\u4f9b\u6e05\u6670\u7684\u89e3\u91cb\u3002\u7814\u7a76\u8cc7\u6599 <sup>11<\/sup> \u5c07\u5176\u5b9a\u7fa9\u70ba\u4f7f ML \u6f14\u7b97\u6cd5\u53ca\u5176\u7d50\u679c\u66f4\u6613\u65bc\u89e3\u91cb\u3002\u96a8\u8457 AI \u7cfb\u7d71\u8b8a\u5f97\u8d8a\u4f86\u8d8a\u8907\u96dc\u4e26\u90e8\u7f72\u5728\u95dc\u9375\u61c9\u7528\u4e2d\uff0c\u5c0d\u53ef\u89e3\u91cb\u6027\u7684\u9700\u6c42\u8d8a\u4f86\u8d8a\u9ad8\uff0c\u4ee5\u78ba\u4fdd\u4fe1\u4efb\u548c\u8cac\u4efb\u3002\u96a8\u8457\u8907\u96dc AI \u7684\u65e5\u76ca\u666e\u53ca\uff0c\u53ef\u89e3\u91cb\u6027\u5c0d\u65bc\u4fe1\u4efb\u548c\u7406\u89e3\u8b8a\u5f97\u81f3\u95dc\u91cd\u8981\u3002<\/li>\n\n\n\n<li><strong>\u6a5f\u5668\u5b78\u7fd2 (ML)\uff1a<\/strong> \u8207\u6a5f\u5668\u5b78\u7fd2\u7684\u6838\u5fc3\u65b9\u6cd5\u548c\u6982\u5ff5\u76f8\u95dc\u7684\u7e2e\u5beb\u8a5e\u3002<\/li>\n<\/ul>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Adam:<\/strong> Adaptive Moment Estimation (\u81ea\u9002\u5e94\u77e9\u4f30\u8ba1) &#8211; \u6307\u4e00\u7a2e\u7528\u65bc\u8a13\u7df4\u6df1\u5ea6\u5b78\u7fd2\u6a21\u578b\u7684\u512a\u5316\u6f14\u7b97\u6cd5\uff0c\u5b83\u7d50\u5408\u4e86 AdaGrad \u548c RMSProp \u7684\u512a\u9ede <sup>10<\/sup>\u3002\u7814\u7a76\u8cc7\u6599 <sup>10<\/sup> \u50c5\u5c07\u5176\u5217\u70ba\u4e00\u7a2e\u512a\u5316\u6f14\u7b97\u6cd5\u3002Adam \u7531\u65bc\u5176\u81ea\u9069\u61c9\u5b78\u7fd2\u7387\u80fd\u529b\uff0c\u662f\u4e00\u7a2e\u5728\u6df1\u5ea6\u5b78\u7fd2\u4e2d\u5ee3\u6cdb\u4f7f\u7528\u4e14\u6709\u6548\u7684\u512a\u5316\u5668\u3002Adam \u662f\u4e00\u7a2e\u6d41\u884c\u4e14\u6709\u6548\u7684\u512a\u5316\u6f14\u7b97\u6cd5\uff0c\u4f7f\u5176\u8207\u8a5e\u5f59\u8868\u76f8\u95dc\u3002<\/li>\n\n\n\n<li><strong>ANN:<\/strong> Artificial Neural Network (\u4eba\u5de5\u795e\u7ecf\u7f51\u7edc) &#8211; \u6307\u4e00\u7a2e\u53d7\u4eba\u8166\u7d50\u69cb\u548c\u529f\u80fd\u555f\u767c\u7684\u8a08\u7b97\u6a21\u578b\uff0c\u7531\u6392\u5217\u5728\u5c64\u4e2d\u7684\u4e92\u9023\u7bc0\u9ede\uff08\u795e\u7d93\u5143\uff09\u7d44\u6210 <sup>4<\/sup>\u3002\u7814\u7a76\u8cc7\u6599 <sup>12<\/sup> \u63d0\u5230 ANN \u662f\u6df1\u5ea6\u5b78\u7fd2\u7684\u57fa\u790e\u3002\u7814\u7a76\u8cc7\u6599 <sup>10<\/sup> \u63cf\u8ff0\u4e86\u5b83\u5011\u7684\u7d50\u69cb\u4ee5\u53ca\u53d7\u4eba\u8166\u7684\u555f\u767c\u3002\u7814\u7a76\u8cc7\u6599 <sup>4<\/sup> \u5c07\u5176\u5b9a\u7fa9\u70ba\u53d7\u5927\u8166\u7d50\u69cb\u555f\u767c\u7684\u96fb\u8166\u7cfb\u7d71\u3002ANN \u662f\u8a31\u591a\u5148\u9032 AI \u7cfb\u7d71\u7684\u57fa\u790e\uff0c\u5c24\u5176\u662f\u5728\u6df1\u5ea6\u5b78\u7fd2\u9818\u57df\u3002\u7531\u65bc DL \u4f9d\u8cf4\u65bc ANN\uff0c\u56e0\u6b64\u5b9a\u7fa9\u9019\u7a2e\u5e95\u5c64\u7d50\u69cb\u975e\u5e38\u91cd\u8981\u3002<\/li>\n\n\n\n<li><strong>AUC (Area Under the ROC Curve):<\/strong> (ROC \u66f2\u7ebf\u4e0b\u9762\u79ef) &#8211; \u7528\u65bc\u8a55\u4f30\u4e8c\u5143\u5206\u985e\u6a21\u578b\u6548\u80fd\u7684\u6307\u6a19\uff0c\u8868\u793a\u6a21\u578b\u5340\u5206\u6b63\u985e\u5225\u548c\u8ca0\u985e\u5225\u7684\u80fd\u529b <sup>13<\/sup>\u3002\u7814\u7a76\u8cc7\u6599 <sup>13<\/sup> \u5c07\u5176\u5217\u70ba\u5e38\u898b\u7684 ML \u7e2e\u5beb\u8a5e\u3002\u7814\u7a76\u8cc7\u6599 <sup>14<\/sup> \u5c07\u5176\u89e3\u91cb\u70ba\u4ecb\u65bc 0.0 \u548c 1.0 \u4e4b\u9593\u7684\u6578\u5b57\uff0c\u8868\u793a\u6a21\u578b\u5340\u5206\u985e\u5225\u7684\u80fd\u529b\u3002\u7814\u7a76\u8cc7\u6599 <sup>11<\/sup> \u5c07\u5176\u5b9a\u7fa9\u70ba\u6a21\u578b\u6e96\u78ba\u9810\u6e2c\u5be6\u969b\u967d\u6027\u5be6\u4f8b\u7684\u967d\u6027\u7d50\u679c\u7684\u4fe1\u8cf4\u5ea6\u6a5f\u7387\u3002AUC \u63d0\u4f9b\u4e86\u8de8\u4e0d\u540c\u95be\u503c\u5206\u985e\u5668\u6548\u80fd\u7684\u7d9c\u5408\u8861\u91cf\u6a19\u6e96\u3002AUC \u662f\u53e6\u4e00\u500b\u7528\u65bc\u8a55\u4f30\u4e8c\u5143\u5206\u985e\u6a21\u578b\u7684\u91cd\u8981\u6307\u6a19\u3002<\/li>\n\n\n\n<li><strong>Backpropagation:<\/strong> (\u53cd\u5411\u4f20\u64ad) &#8211; \u6307\u4e00\u7a2e\u7528\u65bc\u8a13\u7df4\u4eba\u5de5\u795e\u7d93\u7db2\u8def\u7684\u6f14\u7b97\u6cd5\uff0c\u5b83\u900f\u904e\u8a08\u7b97\u640d\u5931\u51fd\u6578\u76f8\u5c0d\u65bc\u7db2\u8def\u6b0a\u91cd\u7684\u68af\u5ea6\uff0c\u4e26\u8abf\u6574\u6b0a\u91cd\u4ee5\u6700\u5c0f\u5316\u640d\u5931 <sup>14<\/sup>\u3002\u7814\u7a76\u8cc7\u6599 <sup>14<\/sup> \u5c07\u5176\u63cf\u8ff0\u70ba\u7528\u65bc\u6e1b\u5c11\u640d\u5931\u7684\u5f8c\u5411\u50b3\u64ad\u3002\u7814\u7a76\u8cc7\u6599 <sup>12<\/sup> \u63d0\u5230\u5b83\u662f DL \u6f14\u7b97\u6cd5\u7528\u65bc\u5b78\u7fd2\u7684\u904e\u7a0b\u3002\u7814\u7a76\u8cc7\u6599 <sup>11<\/sup> \u5c07\u5176\u5b9a\u7fa9\u70ba\u4e00\u7a2e\u5ee3\u6cdb\u4f7f\u7528\u7684\u6f14\u7b97\u6cd5\uff0c\u7528\u65bc\u8a13\u7df4\u524d\u994b\u795e\u7d93\u7db2\u8def\u3002\u53cd\u5411\u50b3\u64ad\u662f\u73fe\u4ee3\u6df1\u5ea6\u5b78\u7fd2\u7684\u57fa\u77f3\uff0c\u4f7f\u8a13\u7df4\u8907\u96dc\u7684\u795e\u7d93\u7db2\u8def\u6210\u70ba\u53ef\u80fd\u3002\u53cd\u5411\u50b3\u64ad\u662f\u8a13\u7df4\u795e\u7d93\u7db2\u8def\u7684\u6838\u5fc3\u6a5f\u5236\uff0c\u4f7f\u5176\u5c0d\u65bc\u8a5e\u5f59\u8868\u81f3\u95dc\u91cd\u8981\u3002<\/li>\n\n\n\n<li><strong>Bias:<\/strong> (\u504f\u5dee) &#8211; \u5728\u6a5f\u5668\u5b78\u7fd2\u4e2d\uff0c\u504f\u5dee\u6307\u7684\u662f\u6a21\u578b\u9810\u6e2c\u4e2d\u7684\u7cfb\u7d71\u6027\u932f\u8aa4\uff0c\u901a\u5e38\u6e90\u65bc\u6a21\u578b\u69cb\u5efa\u904e\u7a0b\u4e2d\u505a\u51fa\u7684\u5047\u8a2d\u6216\u8a13\u7df4\u6578\u64da\u4e2d\u5b58\u5728\u7684\u504f\u5dee <sup>3<\/sup>\u3002\u7814\u7a76\u8cc7\u6599 <sup>4<\/sup> \u5c07\u5176\u5b9a\u7fa9\u70ba AI \u70ba\u7c21\u5316\u4efb\u52d9\u800c\u505a\u51fa\u7684\u5047\u8a2d\u3002\u7814\u7a76\u8cc7\u6599 <sup>3<\/sup> \u5c07\u5176\u5305\u542b\u5728\u8a5e\u5f59\u8868\u4e2d\u3002\u89e3\u6c7a\u548c\u6e1b\u8f15 AI \u7cfb\u7d71\u4e2d\u7684\u504f\u5dee\u5c0d\u65bc\u516c\u5e73\u548c\u502b\u7406\u8003\u91cf\u81f3\u95dc\u91cd\u8981\u3002\u504f\u5dee\u662f AI \u4e2d\u4e00\u500b\u91cd\u8981\u7684\u502b\u7406\u554f\u984c\uff0c\u4f7f\u5176\u6210\u70ba\u4e00\u500b\u9700\u8981\u5b9a\u7fa9\u7684\u91cd\u8981\u8853\u8a9e\u3002<\/li>\n\n\n\n<li><strong>CNN:<\/strong> Convolutional Neural Network (\u5377\u79ef\u795e\u7ecf\u7f51\u7edc) &#8211; \u6307\u4e00\u7a2e\u7279\u5225\u9069\u7528\u65bc\u8655\u7406\u7db2\u683c\u72c0\u6578\u64da\uff08\u5982\u5716\u50cf\u548c\u5f71\u7247\uff09\u7684\u795e\u7d93\u7db2\u8def <sup>13<\/sup>\u3002\u7814\u7a76\u8cc7\u6599 <sup>13<\/sup> \u5c07\u5176\u5217\u70ba\u5e38\u898b\u7684 ML \u7e2e\u5beb\u8a5e\u3002\u7814\u7a76\u8cc7\u6599 <sup>10<\/sup> \u5f37\u8abf\u5176\u5c08\u70ba\u7d50\u69cb\u5316\u7db2\u683c\u6578\u64da\u8a2d\u8a08\u3002\u7814\u7a76\u8cc7\u6599 <sup>17<\/sup> \u5c07\u5176\u5305\u542b\u5728\u6df1\u5ea6\u5b78\u7fd2\u7e2e\u5beb\u8a5e\u5217\u8868\u4e2d\u3002\u7531\u65bc CNN \u80fd\u5920\u81ea\u52d5\u5b78\u7fd2\u7a7a\u9593\u7279\u5fb5\u7684\u5c64\u6b21\u7d50\u69cb\uff0c\u56e0\u6b64\u5fb9\u5e95\u6539\u8b8a\u4e86\u96fb\u8166\u8996\u89ba\u4efb\u52d9\u3002CNN \u662f\u4e00\u7a2e\u5ee3\u6cdb\u4f7f\u7528\u7684 ANN\uff0c\u5177\u6709\u7279\u5b9a\u7684\u61c9\u7528\uff0c\u4f7f\u5176\u6210\u70ba\u4e00\u500b\u9700\u8981\u5305\u542b\u7684\u91cd\u8981\u8853\u8a9e\u3002<\/li>\n\n\n\n<li><strong>Cross-Validation:<\/strong> (\u4ea4\u53c9\u9a8c\u8bc1) &#8211; \u6307\u4e00\u7a2e\u7d71\u8a08\u65b9\u6cd5\uff0c\u7528\u65bc\u900f\u904e\u5c07\u6578\u64da\u5283\u5206\u70ba\u591a\u500b\u5b50\u96c6\uff0c\u5728\u67d0\u4e9b\u5b50\u96c6\u4e0a\u8a13\u7df4\u6a21\u578b\uff0c\u4e26\u5728\u5269\u9918\u5b50\u96c6\u4e0a\u8a55\u4f30\u6a21\u578b\u4f86\u8a55\u4f30\u6a5f\u5668\u5b78\u7fd2\u6a21\u578b\u7684\u6548\u80fd <sup>13<\/sup>\u3002\u7814\u7a76\u8cc7\u6599 <sup>13<\/sup> \u5c07\u5176\u5217\u70ba\u5e38\u898b\u7684 ML \u7e2e\u5beb\u8a5e\u3002\u8207\u55ae\u4e00\u7684\u8a13\u7df4-\u6e2c\u8a66\u5206\u5272\u76f8\u6bd4\uff0c\u4ea4\u53c9\u9a57\u8b49\u63d0\u4f9b\u4e86\u5c0d\u6a21\u578b\u5728\u672a\u898b\u6578\u64da\u4e0a\u7684\u6548\u80fd\u66f4\u7a69\u5065\u7684\u4f30\u8a08\u3002\u6a21\u578b\u8a55\u4f30\u662f ML \u7684\u95dc\u9375\u6b65\u9a5f\uff0c\u800c\u4ea4\u53c9\u9a57\u8b49\u662f\u4e00\u7a2e\u6a19\u6e96\u6280\u8853\u3002<\/li>\n\n\n\n<li><strong>Data Augmentation:<\/strong> (\u6570\u636e\u589e\u5f3a) &#8211; \u6307\u7528\u65bc\u900f\u904e\u5275\u5efa\u73fe\u6709\u6578\u64da\u7684\u4fee\u6539\u526f\u672c\u6216\u5f9e\u73fe\u6709\u6578\u64da\u5408\u6210\u65b0\u6578\u64da\u4f86\u4eba\u5de5\u589e\u52a0\u8a13\u7df4\u6578\u64da\u96c6\u5927\u5c0f\u7684\u6280\u8853 <sup>11<\/sup>\u3002\u7814\u7a76\u8cc7\u6599 <sup>11<\/sup> \u5728\u6539\u9032\u6a21\u578b\u8a13\u7df4\u7684\u80cc\u666f\u4e0b\u63d0\u5230\u4e86\u5b83\u3002\u6578\u64da\u589e\u5f37\u53ef\u4ee5\u63d0\u9ad8\u6a5f\u5668\u5b78\u7fd2\u6a21\u578b\u7684\u7a69\u5065\u6027\u548c\u6cdb\u5316\u80fd\u529b\uff0c\u5c24\u5176\u662f\u5728\u8655\u7406\u6709\u9650\u6578\u64da\u6642\u3002\u6578\u64da\u589e\u5f37\u662f\u4e00\u7a2e\u6709\u52a9\u65bc\u63d0\u9ad8\u6a21\u578b\u6548\u80fd\u7684\u6280\u8853\u3002<\/li>\n\n\n\n<li><strong>DNN:<\/strong> Deep Neural Network (\u6df1\u5ea6\u795e\u7ecf\u7f51\u7edc) &#8211; \u6307\u8f38\u5165\u5c64\u548c\u8f38\u51fa\u5c64\u4e4b\u9593\u6709\u591a\u500b\u5c64\u7684\u795e\u7d93\u7db2\u8def\uff0c\u4f7f\u5176\u80fd\u5920\u5b78\u7fd2\u6578\u64da\u7684\u8907\u96dc\u8868\u793a <sup>13<\/sup>\u3002\u7814\u7a76\u8cc7\u6599 <sup>13<\/sup> \u5c07\u5176\u5217\u70ba\u5e38\u898b\u7684 ML \u7e2e\u5beb\u8a5e\u3002\u7814\u7a76\u8cc7\u6599 <sup>10<\/sup> \u5c07\u5176\u5b9a\u7fa9\u70ba\u5177\u6709\u591a\u500b\u5c64\u4ee5\u5b78\u7fd2\u8907\u96dc\u8868\u793a\u3002\u7814\u7a76\u8cc7\u6599 <sup>17<\/sup> \u5c07\u5176\u5305\u542b\u5728\u6df1\u5ea6\u5b78\u7fd2\u7e2e\u5beb\u8a5e\u5217\u8868\u4e2d\u3002DNN \u662f\u4e00\u500b\u901a\u7528\u8853\u8a9e\uff0c\u6db5\u84cb\u4e86\u6df1\u5ea6\u5b78\u7fd2\u4e2d\u4f7f\u7528\u7684\u8a31\u591a\u5148\u9032\u7684\u795e\u7d93\u7db2\u8def\u67b6\u69cb\u3002DNN \u662f\u6df1\u5ea6\u5b78\u7fd2\u4e2d\u7684\u4e00\u500b\u57fa\u672c\u8853\u8a9e\uff0c\u9700\u8981\u5305\u542b\u4ee5\u78ba\u4fdd\u5b8c\u6574\u6027\u3002<\/li>\n\n\n\n<li><strong>F1 Score:<\/strong> (F1 \u5206\u6570) &#8211; \u7528\u65bc\u8a55\u4f30\u5206\u985e\u6a21\u578b\u6548\u80fd\u7684\u6307\u6a19 <sup>14<\/sup>\u3002\u7814\u7a76\u8cc7\u6599 <sup>15<\/sup> \u5c07 F1 \u5206\u6578\u5b9a\u7fa9\u70ba\u7cbe\u78ba\u7387\u548c\u53ec\u56de\u7387\u7684\u8abf\u548c\u5e73\u5747\u503c\u3002\u9019\u4e9b\u6307\u6a19\u63d0\u4f9b\u4e86\u6bd4\u7c21\u55ae\u6e96\u78ba\u5ea6\u66f4\u7d30\u7dfb\u7684\u5206\u985e\u5668\u6548\u80fd\u8a55\u4f30\uff0c\u5c24\u5176\u662f\u5728\u6578\u64da\u96c6\u4e0d\u5e73\u8861\u7684\u60c5\u6cc1\u4e0b\u3002\u6a21\u578b\u8a55\u4f30\u6d89\u53ca\u5404\u7a2e\u6307\u6a19\uff0c\u800c\u4e0d\u50c5\u50c5\u662f\u6e96\u78ba\u5ea6\u3002\u7cbe\u78ba\u7387\u3001\u53ec\u56de\u7387\u548c F1 \u5206\u6578\u5c0d\u65bc\u5206\u985e\u4efb\u52d9\u5f88\u91cd\u8981\u3002<\/li>\n\n\n\n<li><strong>Feature Engineering:<\/strong> (\u7279\u5f81\u5de5\u7a0b) &#8211; \u6307\u5f9e\u539f\u59cb\u6578\u64da\u4e2d\u9078\u64c7\u3001\u8f49\u63db\u548c\u5275\u5efa\u7279\u5fb5\u7684\u904e\u7a0b\uff0c\u9019\u4e9b\u7279\u5fb5\u53ef\u7528\u65bc\u63d0\u9ad8\u6a5f\u5668\u5b78\u7fd2\u6a21\u578b\u7684\u6548\u80fd <sup>14<\/sup>\u3002\u7814\u7a76\u8cc7\u6599 <sup>14<\/sup> \u5c07\u7279\u5fb5\u5de5\u7a0b\u5217\u70ba AutoML \u53ef\u4ee5\u57f7\u884c\u7684\u4efb\u52d9\u3002\u6709\u6548\u7684\u7279\u5fb5\u5de5\u7a0b\u901a\u5e38\u6bd4\u50c5\u50c5\u4f7f\u7528\u66f4\u8907\u96dc\u7684\u6f14\u7b97\u6cd5\u66f4\u80fd\u986f\u8457\u5f71\u97ff\u6a21\u578b\u6548\u80fd\u3002\u8f38\u5165\u6578\u64da\u7684\u54c1\u8cea\u986f\u8457\u5f71\u97ff ML \u6a21\u578b\u7684\u6548\u80fd\uff0c\u800c\u7279\u5fb5\u5de5\u7a0b\u662f\u63d0\u9ad8\u9019\u7a2e\u54c1\u8cea\u7684\u95dc\u9375\u3002<\/li>\n\n\n\n<li><strong>GAN:<\/strong> Generative Adversarial Network (\u751f\u6210\u5bf9\u6297\u7f51\u7edc) &#8211; \u6307\u4e00\u7a2e\u7531\u5169\u500b\u795e\u7d93\u7db2\u8def\uff08\u751f\u6210\u5668\u548c\u9451\u5225\u5668\uff09\u7d44\u6210\u7684\u795e\u7d93\u7db2\u8def\u67b6\u69cb\uff0c\u5b83\u5011\u4ee5\u5c0d\u6297\u7684\u65b9\u5f0f\u9032\u884c\u8a13\u7df4\u4ee5\u751f\u6210\u903c\u771f\u7684\u5408\u6210\u6578\u64da <sup>2<\/sup>\u3002\u7814\u7a76\u8cc7\u6599 <sup>2<\/sup> \u63cf\u8ff0\u4e86\u751f\u6210\u5668\u5275\u5efa\u6578\u64da\uff0c\u9451\u5225\u5668\u5340\u5206\u771f\u5be6\u6578\u64da\u548c\u751f\u6210\u6578\u64da\u3002\u7814\u7a76\u8cc7\u6599 <sup>10<\/sup> \u5f37\u8abf\u5176\u7528\u65bc\u751f\u6210\u903c\u771f\u7684\u5408\u6210\u6578\u64da\u3002GAN \u5728\u751f\u6210\u9ad8\u54c1\u8cea\u5716\u50cf\u3001\u5f71\u7247\u548c\u5176\u4ed6\u985e\u578b\u7684\u6578\u64da\u65b9\u9762\u5c55\u73fe\u4e86\u5353\u8d8a\u7684\u80fd\u529b\u3002GAN \u4ee3\u8868\u4e86 AI \u4e2d\u4e00\u7a2e\u7368\u7279\u4e14\u5f37\u5927\u7684\u751f\u6210\u5efa\u6a21\u65b9\u6cd5\u3002<\/li>\n\n\n\n<li><strong>GRU:<\/strong> Gated Recurrent Unit (\u95e8\u63a7\u5faa\u73af\u5355\u5143) &#8211; \u6307\u53e6\u4e00\u7a2e\u8207 LSTM \u985e\u4f3c\u7684 RNN \u67b6\u69cb\uff0c\u4f46\u7d50\u69cb\u66f4\u7c21\u55ae\uff0c\u901a\u5e38\u4ee5\u66f4\u5c11\u7684\u53c3\u6578\u63d0\u4f9b\u76f8\u7576\u7684\u6548\u80fd <sup>13<\/sup>\u3002\u7814\u7a76\u8cc7\u6599 <sup>13<\/sup> \u5c07\u5176\u5217\u70ba\u5e38\u898b\u7684 ML \u7e2e\u5beb\u8a5e\u3002\u7814\u7a76\u8cc7\u6599 <sup>10<\/sup> \u5f37\u8abf\u5176\u66f4\u7c21\u55ae\u7684\u7d50\u69cb\u548c\u8a08\u7b97\u6548\u7387\u3002\u5728\u8a31\u591a\u5e8f\u5217\u5efa\u6a21\u4efb\u52d9\u4e2d\uff0cGRU \u63d0\u4f9b\u4e86\u4e00\u7a2e\u6bd4 LSTM \u66f4\u5177\u8a08\u7b97\u6548\u7387\u7684\u66ff\u4ee3\u65b9\u6848\u3002GRU \u662f LSTM \u7684\u5bc6\u5207\u76f8\u95dc\u4e14\u7d93\u5e38\u4f7f\u7528\u7684\u66ff\u4ee3\u65b9\u6848\uff0c\u4f7f\u5176\u8207\u8a5e\u5f59\u8868\u76f8\u95dc\u3002<\/li>\n\n\n\n<li><strong>Hyperparameter:<\/strong> (\u8d85\u53c2\u6570) &#8211; \u6307\u6a5f\u5668\u5b78\u7fd2\u6a21\u578b\u7684\u53c3\u6578\uff0c\u9019\u4e9b\u53c3\u6578\u5728\u5b78\u7fd2\u904e\u7a0b\u958b\u59cb\u4e4b\u524d\u8a2d\u5b9a\uff0c\u4e26\u63a7\u5236\u6a21\u578b\u8a13\u7df4\u7684\u5404\u500b\u65b9\u9762\uff0c\u4f8b\u5982\u5b78\u7fd2\u7387\u6216\u795e\u7d93\u7db2\u8def\u4e2d\u7684\u5c64\u6578 <sup>14<\/sup>\u3002\u7814\u7a76\u8cc7\u6599 <sup>14<\/sup> \u63d0\u5230\u8d85\u53c3\u6578\u8abf\u6574\u662f AutoML \u53ef\u4ee5\u57f7\u884c\u7684\u4efb\u52d9\u3002\u6b63\u78ba\u8abf\u6574\u8d85\u53c3\u6578\u5c0d\u65bc\u5f9e\u6a5f\u5668\u5b78\u7fd2\u6a21\u578b\u4e2d\u7372\u5f97\u6700\u4f73\u6548\u80fd\u81f3\u95dc\u91cd\u8981\u3002\u8d85\u53c3\u6578\u5728 ML \u6a21\u578b\u7684\u6548\u80fd\u4e2d\u8d77\u8457\u91cd\u8981\u4f5c\u7528\u3002<\/li>\n\n\n\n<li><strong>Loss Function:<\/strong> (\u635f\u5931\u51fd\u6570) &#8211; \u6307\u91cf\u5316\u6a21\u578b\u9810\u6e2c\u8207\u5be6\u969b\u76ee\u6a19\u503c\u4e4b\u9593\u8aa4\u5dee\u7684\u51fd\u6578\uff0c\u900f\u904e\u6307\u793a\u6a21\u578b\u57f7\u884c\u6548\u679c\u5982\u4f55\u4f86\u6307\u5c0e\u5b78\u7fd2\u904e\u7a0b <sup>14<\/sup>\u3002\u7814\u7a76\u8cc7\u6599 <sup>14<\/sup> \u5728\u524d\u5411\u548c\u5f8c\u5411\u50b3\u64ad\u7684\u80cc\u666f\u4e0b\u63d0\u5230\u4e86\u640d\u5931\u3002\u7814\u7a76\u8cc7\u6599 <sup>10<\/sup> \u63d0\u5230\u6700\u5c0f\u5316\u640d\u5931\u51fd\u6578\u3002\u640d\u5931\u51fd\u6578\u7684\u9078\u64c7\u5c0d\u65bc\u8a13\u7df4\u4e00\u500b\u6709\u6548\u89e3\u6c7a\u7279\u5b9a\u4efb\u52d9\u7684\u6a21\u578b\u81f3\u95dc\u91cd\u8981\u3002\u7406\u89e3\u640d\u5931\u51fd\u6578\u7684\u6982\u5ff5\u5c0d\u65bc\u7406\u89e3 ML \u6a21\u578b\u5982\u4f55\u8a13\u7df4\u81f3\u95dc\u91cd\u8981\u3002<\/li>\n\n\n\n<li><strong>LSTM:<\/strong> Long Short-Term Memory (\u957f\u77ed\u671f\u8bb0\u5fc6\u7f51\u7edc) &#8211; \u6307\u4e00\u7a2e\u7279\u5b9a\u7684 RNN \u67b6\u69cb\uff0c\u5b83\u900f\u904e\u89e3\u6c7a\u68af\u5ea6\u6d88\u5931\u554f\u984c\uff0c\u7279\u5225\u64c5\u9577\u5b78\u7fd2\u5e8f\u5217\u6578\u64da\u4e2d\u7684\u9577\u671f\u4f9d\u8cf4\u95dc\u4fc2 <sup>13<\/sup>\u3002\u7814\u7a76\u8cc7\u6599 <sup>13<\/sup> \u5c07\u5176\u5217\u70ba\u5e38\u898b\u7684 ML \u7e2e\u5beb\u8a5e\u3002\u7814\u7a76\u8cc7\u6599 <sup>10<\/sup> \u89e3\u91cb\u4e86\u5176\u5b78\u7fd2\u9577\u671f\u4f9d\u8cf4\u95dc\u4fc2\u7684\u80fd\u529b\u3002\u7814\u7a76\u8cc7\u6599 <sup>16<\/sup> \u63d0\u5230\u5176\u7528\u65bc\u9806\u5e8f\u5206\u6790\u6642\u9593\u6578\u64da\u3002LSTM \u5728\u5404\u7a2e NLP \u4efb\u52d9\u548c\u5176\u4ed6\u57fa\u65bc\u5e8f\u5217\u7684\u61c9\u7528\u4e2d\u53d6\u5f97\u4e86\u5de8\u5927\u7684\u6210\u529f\u3002LSTM \u662f\u5c0d\u57fa\u672c RNN \u7684\u91cd\u5927\u6539\u9032\uff0c\u4e26\u4e14\u88ab\u5ee3\u6cdb\u4f7f\u7528\uff0c\u56e0\u6b64\u503c\u5f97\u5305\u542b\u5728\u5167\u3002<\/li>\n\n\n\n<li><strong>MAE:<\/strong> Mean Absolute Error (\u5e73\u5747\u7edd\u5bf9\u8bef\u5dee) &#8211; \u53e6\u4e00\u500b\u7528\u65bc\u8861\u91cf\u8ff4\u6b78\u6a21\u578b\u8aa4\u5dee\u7684\u6307\u6a19\uff0c\u8868\u793a\u9810\u6e2c\u503c\u548c\u5be6\u969b\u503c\u4e4b\u9593\u7d55\u5c0d\u5dee\u503c\u7684\u5e73\u5747\u503c <sup>13<\/sup>\u3002\u7814\u7a76\u8cc7\u6599 <sup>13<\/sup> \u5217\u51fa\u4e86 MAPE\uff08\u5e73\u5747\u7d55\u5c0d\u767e\u5206\u6bd4\u8aa4\u5dee\uff09\uff0c\u5b83\u8207 MAE \u76f8\u95dc\u3002MAE \u63d0\u4f9b\u4e86\u5c0d\u5e73\u5747\u8aa4\u5dee\u5927\u5c0f\u66f4\u76f4\u63a5\u7684\u89e3\u91cb\u3002MAE \u662f\u53e6\u4e00\u500b\u7528\u65bc\u8a55\u4f30\u8ff4\u6b78\u6a21\u578b\u7684\u5e38\u898b\u6307\u6a19\u3002<\/li>\n\n\n\n<li><strong>ML:<\/strong> Machine Learning (\u673a\u5668\u5b66\u4e60) &#8211; \u6307\u4eba\u5de5\u667a\u6167\u7684\u4e00\u500b\u5206\u652f\uff0c\u5c08\u6ce8\u65bc\u4f7f\u96fb\u8166\u80fd\u5920\u5f9e\u6578\u64da\u4e2d\u5b78\u7fd2\uff0c\u800c\u7121\u9700\u660e\u78ba\u5730\u9032\u884c\u7a0b\u5f0f\u8a2d\u8a08 <sup>1<\/sup>\u3002\u7814\u7a76\u8cc7\u6599 <sup>2<\/sup> \u7a31 ML \u70ba AI \u7684\u57fa\u790e\u5206\u652f\u3002\u7814\u7a76\u8cc7\u6599 <sup>8<\/sup> \u5c07\u5176\u5b9a\u7fa9\u70ba\u5141\u8a31\u6a5f\u5668\u5f9e\u6578\u64da\u548c\u904e\u53bb\u7684\u7d93\u9a57\u4e2d\u81ea\u52d5\u5b78\u7fd2\uff0c\u4ee5\u5728\u6700\u5c11\u7684\u4eba\u5de5\u5e72\u9810\u4e0b\u8b58\u5225\u6a21\u5f0f\u4e26\u505a\u51fa\u9810\u6e2c\u3002\u7814\u7a76\u8cc7\u6599 <sup>18<\/sup> \u5c07\u5176\u63cf\u8ff0\u70ba\u5c08\u6ce8\u65bc\u900f\u904e\u7d93\u9a57\u548c\u6578\u64da\u6539\u9032\u7684\u6f14\u7b97\u6cd5\u3002ML \u662f\u8a31\u591a AI \u9032\u5c55\u7684\u5f15\u64ce\uff0c\u4f7f\u7cfb\u7d71\u80fd\u5920\u96a8\u8457\u6642\u9593\u7684\u63a8\u79fb\u9032\u884c\u9069\u61c9\u548c\u6539\u9032\u3002\u6b64\u7e2e\u5beb\u8a5e\u5e7e\u4e4e\u51fa\u73fe\u5728\u6240\u6709\u8cc7\u6599\u7247\u6bb5\u4e2d\uff0c\u7a81\u986f\u4e86\u5176\u6838\u5fc3\u4f5c\u7528\u3002ML \u662f AI \u9818\u57df\u7684\u6838\u5fc3\u6982\u5ff5\uff0c\u5176\u5b9a\u7fa9\u548c\u91cd\u8981\u6027\u9700\u8981\u5728\u8a5e\u5f59\u8868\u7684\u65e9\u671f\u968e\u6bb5\u660e\u78ba\u78ba\u7acb\u3002<\/li>\n\n\n\n<li><strong>Overfitting:<\/strong> (\u8fc7\u62df\u5408) &amp; <strong>Underfitting:<\/strong> (\u6b20\u62df\u5408) &#8211; \u6a5f\u5668\u5b78\u7fd2\u4e2d\u5e38\u898b\u7684\u554f\u984c\uff0c\u6a21\u578b\u904e\u5ea6\u5b78\u7fd2\u8a13\u7df4\u6578\u64da\uff08\u904e\u64ec\u5408\uff09\u6216\u672a\u80fd\u6355\u7372\u6578\u64da\u4e2d\u7684\u5e95\u5c64\u6a21\u5f0f\uff08\u6b20\u64ec\u5408\uff09\uff0c\u5c0e\u81f4\u5c0d\u65b0\u6578\u64da\u7684\u6cdb\u5316\u80fd\u529b\u5dee <sup>4<\/sup>\u3002\u5e73\u8861\u904e\u64ec\u5408\u548c\u6b20\u64ec\u5408\u662f\u6a5f\u5668\u5b78\u7fd2\u6a21\u578b\u958b\u767c\u7684\u6838\u5fc3\u6311\u6230\u3002\u9019\u4e9b\u662f ML \u4e2d\u5b78\u7fd2\u8005\u61c9\u8a72\u610f\u8b58\u5230\u7684\u5e38\u898b\u9677\u9631\u3002<\/li>\n\n\n\n<li><strong>Precision:<\/strong> (\u7cbe\u786e\u7387), <strong>Recall:<\/strong> (\u53ec\u56de\u7387) &#8211; \u7528\u65bc\u8a55\u4f30\u5206\u985e\u6a21\u578b\u6548\u80fd\u7684\u6307\u6a19 <sup>14<\/sup>\u3002\u7814\u7a76\u8cc7\u6599 <sup>14<\/sup> \u5b9a\u7fa9\u4e86\u6e96\u78ba\u5ea6\uff0c\u4e26\u63d0\u5230\u4e86\u771f\u967d\u6027\u3001\u771f\u9670\u6027\u3001\u5047\u967d\u6027\u548c\u5047\u9670\u6027\uff0c\u9019\u4e9b\u90fd\u8207\u7cbe\u78ba\u7387\u548c\u53ec\u56de\u7387\u76f8\u95dc\u3002\u9019\u4e9b\u6307\u6a19\u63d0\u4f9b\u4e86\u6bd4\u7c21\u55ae\u6e96\u78ba\u5ea6\u66f4\u7d30\u7dfb\u7684\u5206\u985e\u5668\u6548\u80fd\u8a55\u4f30\uff0c\u5c24\u5176\u662f\u5728\u6578\u64da\u96c6\u4e0d\u5e73\u8861\u7684\u60c5\u6cc1\u4e0b\u3002\u6a21\u578b\u8a55\u4f30\u6d89\u53ca\u5404\u7a2e\u6307\u6a19\uff0c\u800c\u4e0d\u50c5\u50c5\u662f\u6e96\u78ba\u5ea6\u3002\u7cbe\u78ba\u7387\u548c\u53ec\u56de\u7387\u5c0d\u65bc\u5206\u985e\u4efb\u52d9\u5f88\u91cd\u8981\u3002<\/li>\n\n\n\n<li><strong>Regularization:<\/strong> (\u6b63\u5219\u5316) &#8211; \u6307\u7528\u65bc\u9632\u6b62\u6a5f\u5668\u5b78\u7fd2\u6a21\u578b\u904e\u64ec\u5408\u7684\u6280\u8853\uff0c\u900f\u904e\u5411\u640d\u5931\u51fd\u6578\u6dfb\u52a0\u61f2\u7f70\u9805\u4f86\u963b\u6b62\u904e\u65bc\u8907\u96dc\u7684\u6a21\u578b\u3002\u6b63\u898f\u5316\u662f\u63d0\u9ad8 ML \u6a21\u578b\u6cdb\u5316\u80fd\u529b\u7684\u95dc\u9375\u5de5\u5177\u3002\u7531\u65bc\u904e\u64ec\u5408\u662f\u4e00\u500b\u5e38\u898b\u554f\u984c\uff0c\u56e0\u6b64\u63d0\u53ca\u6b63\u898f\u5316\u6280\u8853\u975e\u5e38\u91cd\u8981\u3002<\/li>\n\n\n\n<li><strong>ReLU:<\/strong> Rectified Linear Unit (\u4fee\u6b63\u7ebf\u6027\u5355\u5143) &#8211; \u6307\u795e\u7d93\u7db2\u8def\u4e2d\u5e38\u7528\u7684\u6fc0\u6d3b\u51fd\u6578\uff0c\u5982\u679c\u8f38\u5165\u70ba\u6b63\uff0c\u5247\u76f4\u63a5\u8f38\u51fa\u8f38\u5165\uff0c\u5426\u5247\u8f38\u51fa\u96f6\uff0c\u5f9e\u800c\u5728\u6a21\u578b\u4e2d\u5f15\u5165\u975e\u7dda\u6027 <sup>14<\/sup>\u3002\u7814\u7a76\u8cc7\u6599 <sup>14<\/sup> \u5c07\u5176\u5217\u70ba\u4e00\u7a2e\u6d41\u884c\u7684\u6fc0\u6d3b\u51fd\u6578\u3002\u7814\u7a76\u8cc7\u6599 <sup>10<\/sup> \u89e3\u91cb\u4e86\u5176\u975e\u7dda\u6027\u6027\u8cea\u3002ReLU \u7531\u65bc\u5176\u7c21\u55ae\u6027\u548c\u5728\u8a13\u7df4\u6df1\u5ea6\u7db2\u8def\u65b9\u9762\u7684\u6709\u6548\u6027\uff0c\u5df2\u6210\u70ba\u6a19\u6e96\u7684\u6fc0\u6d3b\u51fd\u6578\u3002\u6fc0\u6d3b\u51fd\u6578\u662f\u795e\u7d93\u7db2\u8def\u7684\u95dc\u9375\u7d44\u4ef6\uff0c\u800c ReLU \u662f\u4e00\u7a2e\u5ee3\u6cdb\u4f7f\u7528\u7684\u6fc0\u6d3b\u51fd\u6578\u3002<\/li>\n\n\n\n<li><strong>RL:<\/strong> Reinforcement Learning (\u5f3a\u5316\u5b66\u4e60) &#8211; \u6307\u4e00\u7a2e\u6a5f\u5668\u5b78\u7fd2\u985e\u578b\uff0c\u5176\u4e2d\u4ee3\u7406\u900f\u904e\u8207\u74b0\u5883\u4e92\u52d5\u4e26\u63a5\u6536\u5c0d\u5176\u884c\u70ba\u7684\u734e\u52f5\u6216\u61f2\u7f70\u4f86\u5b78\u7fd2\u505a\u51fa\u6c7a\u7b56 <sup>2<\/sup>\u3002\u7814\u7a76\u8cc7\u6599 <sup>2<\/sup> \u5c07 RL \u63cf\u8ff0\u70ba\u900f\u904e\u8a66\u932f\u5b78\u7fd2\u3002\u7814\u7a76\u8cc7\u6599 <sup>10<\/sup> \u89e3\u91cb\u8aaa\uff0c\u4ee3\u7406\u65e8\u5728\u6700\u5927\u5316\u7d2f\u7a4d\u734e\u52f5\u3002RL \u7279\u5225\u9069\u7528\u65bc\u8a13\u7df4\u5728\u52d5\u614b\u74b0\u5883\u4e2d\u904b\u4f5c\u7684\u4ee3\u7406\uff0c\u5728\u9019\u4e9b\u74b0\u5883\u4e2d\uff0c\u6700\u4f73\u7b56\u7565\u4e26\u672a\u660e\u78ba\u5b9a\u7fa9\u3002RL \u4ee3\u8868 ML \u4e2d\u7684\u53e6\u4e00\u7a2e\u95dc\u9375\u7bc4\u4f8b\uff0c\u5c08\u6ce8\u65bc\u900f\u904e\u4e92\u52d5\u548c\u56de\u994b\u9032\u884c\u5b78\u7fd2\u3002<\/li>\n\n\n\n<li><strong>RMSE:<\/strong> Root Mean Squared Error (\u5747\u65b9\u6839\u8bef\u5dee) &#8211; \u7528\u65bc\u8861\u91cf\u8ff4\u6b78\u6a21\u578b\u8aa4\u5dee\u7684\u5e38\u7528\u6307\u6a19\uff0c\u8868\u793a\u9810\u6e2c\u503c\u548c\u5be6\u969b\u503c\u4e4b\u9593\u5e73\u65b9\u5dee\u5e73\u5747\u503c\u7684\u5e73\u65b9\u6839 <sup>13<\/sup>\u3002\u7814\u7a76\u8cc7\u6599 <sup>13<\/sup> \u5217\u51fa\u4e86 RMSLE\uff08\u5747\u65b9\u6839\u5c0d\u6578\u8aa4\u5dee\uff09\uff0c\u5b83\u8207 RMSE \u76f8\u95dc\u3002RMSE \u5c0d\u8f03\u5927\u7684\u8aa4\u5dee\u65bd\u52a0\u6bd4\u5c0f\u8aa4\u5dee\u66f4\u5927\u7684\u61f2\u7f70\u3002\u5c0d\u65bc\u8ff4\u6b78\u4efb\u52d9\uff0cRMSE \u662f\u4e00\u500b\u6a19\u6e96\u7684\u8a55\u4f30\u6307\u6a19\u3002<\/li>\n\n\n\n<li><strong>RNN:<\/strong> Recurrent Neural Network (\u5faa\u73af\u795e\u7ecf\u7f51\u7edc) &#8211; \u6307\u4e00\u7a2e\u8a2d\u8a08\u7528\u65bc\u8655\u7406\u5e8f\u5217\u6578\u64da\u7684\u795e\u7d93\u7db2\u8def\uff0c\u5b83\u900f\u904e\u7dad\u8b77\u4e00\u500b\u5167\u90e8\u72c0\u614b\u4f86\u6355\u7372\u5148\u524d\u8f38\u5165\u7684\u4fe1\u606f <sup>13<\/sup>\u3002\u7814\u7a76\u8cc7\u6599 <sup>13<\/sup> \u5c07\u5176\u5217\u70ba\u5e38\u898b\u7684 ML \u7e2e\u5beb\u8a5e\u3002\u7814\u7a76\u8cc7\u6599 <sup>10<\/sup> \u89e3\u91cb\u4e86\u5176\u5c55\u73fe\u6642\u9593\u52d5\u614b\u7684\u80fd\u529b\u3002\u7814\u7a76\u8cc7\u6599 <sup>16<\/sup> \u63d0\u5230\u5176\u7528\u65bc\u5206\u6790\u6642\u9593\u6578\u64da\u6d41\u3002\u985e\u4f3c\u65bc CNN\uff0cRNN \u662f\u53e6\u4e00\u7a2e\u5c08\u9580\u7684 ANN\uff0c\u5728 AI \u4e2d\u6709\u91cd\u8981\u7684\u61c9\u7528\u3002<\/li>\n\n\n\n<li><strong>SGD:<\/strong> Stochastic Gradient Descent (\u968f\u673a\u68af\u5ea6\u4e0b\u964d) &#8211; \u6307\u4e00\u7a2e\u5e38\u7528\u7684\u512a\u5316\u6f14\u7b97\u6cd5\uff0c\u7528\u65bc\u900f\u904e\u8fed\u4ee3\u5730\u66f4\u65b0\u6a21\u578b\u53c3\u6578\u4ee5\u6700\u5c0f\u5316\u640d\u5931\u51fd\u6578\uff0c\u4e26\u4f7f\u7528\u5c0f\u6279\u91cf\u7684\u6578\u64da\u4f86\u8a13\u7df4\u795e\u7d93\u7db2\u8def <sup>14<\/sup>\u3002\u7814\u7a76\u8cc7\u6599 <sup>14<\/sup> \u5c07\u5176\u63cf\u8ff0\u70ba\u4e00\u7a2e\u6279\u6b21\u5927\u5c0f\u7b56\u7565\u3002\u7814\u7a76\u8cc7\u6599 <sup>10<\/sup> \u89e3\u91cb\u4e86\u5176\u5728\u6700\u5c0f\u5316\u640d\u5931\u51fd\u6578\u4e2d\u7684\u4f5c\u7528\u3002\u7814\u7a76\u8cc7\u6599 <sup>15<\/sup> \u5217\u51fa\u4e86\u300c1-bit SGD\u300d\u548c\u300cSGD\u300d\u3002SGD \u53ca\u5176\u8b8a\u9ad4\u5c0d\u65bc\u6709\u6548\u8a13\u7df4\u5927\u578b\u795e\u7d93\u7db2\u8def\u81f3\u95dc\u91cd\u8981\u3002\u512a\u5316\u6f14\u7b97\u6cd5\u5c0d\u65bc\u8a13\u7df4 ML \u6a21\u578b\u81f3\u95dc\u91cd\u8981\uff0c\u800c SGD \u662f\u4e00\u500b\u57fa\u790e\u7684\u6f14\u7b97\u6cd5\u3002<\/li>\n\n\n\n<li><strong>Supervised Learning:<\/strong> (\u76d1\u7763\u5b66\u4e60) &amp; <strong>Unsupervised Learning:<\/strong> (\u65e0\u76d1\u7763\u5b66\u4e60) &#8211; \u6a5f\u5668\u5b78\u7fd2\u7684\u57fa\u672c\u985e\u5225\uff0c\u6a21\u578b\u5206\u5225\u5f9e\u6a19\u8a18\u6216\u672a\u6a19\u8a18\u7684\u6578\u64da\u4e2d\u5b78\u7fd2 <sup>4<\/sup>\u3002\u7814\u7a76\u8cc7\u6599 <sup>4<\/sup> \u5c07\u5b83\u5011\u5217\u70ba ML \u7684\u95dc\u9375\u6982\u5ff5\u3002\u7814\u7a76\u8cc7\u6599 <sup>14<\/sup> \u900f\u904e\u5728\u6e96\u78ba\u5ea6\u8a08\u7b97\u7684\u80cc\u666f\u4e0b\u63d0\u53ca\u6a19\u8a18\u6578\u64da\uff0c\u96b1\u542b\u5730\u8a0e\u8ad6\u4e86\u5b83\u5011\u3002\u7406\u89e3\u76e3\u7763\u5f0f\u5b78\u7fd2\u548c\u975e\u76e3\u7763\u5f0f\u5b78\u7fd2\u4e4b\u9593\u7684\u5340\u5225\u5c0d\u65bc\u70ba\u7d66\u5b9a\u554f\u984c\u9078\u64c7\u9069\u7576\u7684 ML \u6280\u8853\u81f3\u95dc\u91cd\u8981\u3002\u9019\u4e9b\u662f ML \u7684\u57fa\u672c\u6982\u5ff5\uff0c\u5b83\u5011\u6c7a\u5b9a\u4e86\u6240\u6d89\u53ca\u7684\u6578\u64da\u985e\u578b\u548c\u5b78\u7fd2\u904e\u7a0b\u3002<\/li>\n\n\n\n<li><strong>Transfer Learning:<\/strong> (\u8fc1\u79fb\u5b66\u4e60) &#8211; \u6307\u4e00\u7a2e\u6a5f\u5668\u5b78\u7fd2\u6280\u8853\uff0c\u5176\u4e2d\u5728\u4e00\u500b\u4efb\u52d9\u4e0a\u8a13\u7df4\u7684\u6a21\u578b\u88ab\u91cd\u65b0\u7528\u4f5c\u7b2c\u4e8c\u500b\u76f8\u95dc\u4efb\u52d9\u6a21\u578b\u7684\u8d77\u9ede <sup>2<\/sup>\u3002\u9077\u79fb\u5b78\u7fd2\u53ef\u4ee5\u986f\u8457\u6e1b\u5c11\u65b0\u4efb\u52d9\u6240\u9700\u7684\u6578\u64da\u91cf\u548c\u8a13\u7df4\u6642\u9593\uff0c\u5c24\u5176\u662f\u5728\u8655\u7406\u6709\u9650\u6578\u64da\u6642\u3002\u9077\u79fb\u5b78\u7fd2\u662f\u4e00\u7a2e\u5f37\u5927\u7684\u6280\u8853\uff0c\u53ef\u4ee5\u5728\u65b0\u7684 ML \u4efb\u52d9\u4e2d\u5229\u7528\u73fe\u6709\u7684\u77e5\u8b58\u3002<\/li>\n\n\n\n<li><strong>Variance:<\/strong> (\u65b9\u5dee) &#8211; \u5728\u6a5f\u5668\u5b78\u7fd2\u4e2d\uff0c\u8b8a\u7570\u6578\u6307\u7684\u662f\u6a21\u578b\u6548\u80fd\u5c0d\u8a13\u7df4\u6578\u64da\u6ce2\u52d5\u7684\u654f\u611f\u6027\u3002\u9ad8\u8b8a\u7570\u6578\u8868\u793a\u6a21\u578b\u5728\u8a13\u7df4\u6578\u64da\u4e0a\u8868\u73fe\u826f\u597d\uff0c\u4f46\u5728\u672a\u898b\u6578\u64da\u4e0a\u8868\u73fe\u4e0d\u4f73\u3002\u7406\u89e3\u548c\u7ba1\u7406\u8b8a\u7570\u6578\u5c0d\u65bc\u69cb\u5efa\u80fd\u5920\u826f\u597d\u6cdb\u5316\u7684\u6a21\u578b\u975e\u5e38\u91cd\u8981\u3002\u8207\u504f\u5dee\u4e00\u6a23\uff0c\u8b8a\u7570\u6578\u662f\u7406\u89e3 ML \u6a21\u578b\u6cdb\u5316\u80fd\u529b\u7684\u95dc\u9375\u6982\u5ff5\u3002<\/li>\n\n\n\n<li><strong>Word Embedding:<\/strong> (\u8bcd\u5d4c\u5165) &#8211; \u6307 NLP \u4e2d\u7684\u4e00\u7a2e\u6280\u8853\uff0c\u5176\u4e2d\u55ae\u8a5e\u5728\u9023\u7e8c\u7684\u5411\u91cf\u7a7a\u9593\u4e2d\u8868\u793a\u70ba\u5bc6\u96c6\u7684\u5411\u91cf\uff0c\u6355\u7372\u55ae\u8a5e\u4e4b\u9593\u7684\u8a9e\u7fa9\u95dc\u4fc2 <sup>15<\/sup>\u3002\u7814\u7a76\u8cc7\u6599 <sup>15<\/sup> \u5217\u51fa\u4e86\u300cGloVe (Global Vectors)\u300d\u5d4c\u5165\u3002\u7814\u7a76\u8cc7\u6599 <sup>20<\/sup> \u63d0\u5230\u4e86 ELMo \u548c GloVe \u4f5c\u70ba\u8a9e\u8a00\u6a21\u578b\u3002\u8a5e\u5d4c\u5165\u662f\u8a31\u591a NLP \u6a21\u578b\u7684\u57fa\u672c\u69cb\u5efa\u584a\uff0c\u4f7f\u5b83\u5011\u80fd\u5920\u7406\u89e3\u55ae\u8a5e\u7684\u542b\u7fa9\u3002\u8a5e\u5d4c\u5165\u662f NLP \u4e2d\u7684\u4e00\u500b\u6838\u5fc3\u6982\u5ff5\uff0c\u5b83\u5be6\u73fe\u4e86\u8a9e\u7fa9\u7406\u89e3\u3002<\/li>\n\n\n\n<li><strong>\u81ea\u7136\u8a9e\u8a00\u8655\u7406 (NLP)\uff1a<\/strong> \u5c08\u9580\u7528\u65bc\u4f7f\u96fb\u8166\u80fd\u5920\u7406\u89e3\u3001\u8655\u7406\u548c\u751f\u6210\u4eba\u985e\u8a9e\u8a00\u7684\u9818\u57df\u7684\u7e2e\u5beb\u8a5e\u3002<\/li>\n<\/ul>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>BERT:<\/strong> Bidirectional Encoder Representations from Transformers (\u6765\u81ea Transformers \u7684\u53cc\u5411\u7f16\u7801\u5668\u8868\u793a) &#8211; \u6307 Google \u958b\u767c\u7684\u4e00\u7a2e\u57fa\u65bc Transformer \u7684\u8a9e\u8a00\u6a21\u578b\uff0c\u4ee5\u5176\u900f\u904e\u540c\u6642\u8003\u616e\u53e5\u5b50\u4e2d\u55ae\u8a5e\u4e4b\u524d\u548c\u4e4b\u5f8c\u7684\u55ae\u8a5e\u4f86\u7406\u89e3\u55ae\u8a5e\u4e0a\u4e0b\u6587\u7684\u80fd\u529b\u800c\u805e\u540d <sup>14<\/sup>\u3002\u7814\u7a76\u8cc7\u6599 <sup>14<\/sup> \u5f37\u8abf\u5176\u96d9\u5411\u6027\u548c\u4f7f\u7528\u906e\u7f69\u9032\u884c\u7121\u76e3\u7763\u8a13\u7df4\u3002\u7814\u7a76\u8cc7\u6599 <sup>11<\/sup> \u5c07\u5176\u63cf\u8ff0\u70ba\u4e00\u7a2e\u5e38\u7528\u7684\u57fa\u65bc Transformer \u7684\u8a9e\u8a00\u6a21\u578b\u3002\u7814\u7a76\u8cc7\u6599 <sup>20<\/sup> \u5c07\u5176\u5217\u70ba\u8457\u540d\u7684\u8a9e\u8a00\u6a21\u578b\u3002BERT \u5728 NLP \u9818\u57df\u6975\u5177\u5f71\u97ff\u529b\uff0c\u5728\u5404\u7a2e\u8a9e\u8a00\u7406\u89e3\u4efb\u52d9\u4e0a\u5275\u9020\u4e86\u65b0\u7684\u6700\u5148\u9032\u6210\u679c\u3002BERT \u662f\u53e6\u4e00\u500b\u975e\u5e38\u91cd\u8981\u4e14\u5ee3\u6cdb\u4f7f\u7528\u7684\u57fa\u65bc Transformer \u67b6\u69cb\u7684\u8a9e\u8a00\u6a21\u578b\u3002<\/li>\n\n\n\n<li><strong>GPT:<\/strong> Generative Pre-trained Transformer (\u751f\u6210\u5f0f\u9884\u8bad\u7ec3\u53d8\u6362\u5668) &#8211; \u6307 OpenAI \u958b\u767c\u7684\u4e00\u7a2e\u7279\u5b9a\u985e\u578b\u7684 LLM \u67b6\u69cb\uff0c\u4ee5\u5176\u5f37\u5927\u7684\u6587\u672c\u751f\u6210\u80fd\u529b\u800c\u805e\u540d <sup>1<\/sup>\u3002\u7814\u7a76\u8cc7\u6599 <sup>1<\/sup> \u63d0\u5230\u4e86 GPT \u6a21\u578b\uff0c\u5982 GPT-3 \u548c GPT-4\u3002\u7814\u7a76\u8cc7\u6599 <sup>2<\/sup> \u89e3\u91cb\u8aaa\u5b83\u5df2\u6210\u70ba\u4e00\u500b\u6d41\u884c\u7684 AI \u804a\u5929\u6a5f\u5668\u4eba\u540d\u7a31\u7684\u4e00\u90e8\u5206\u3002\u7814\u7a76\u8cc7\u6599 <sup>8<\/sup> \u5c07\u5176\u63cf\u8ff0\u70ba\u751f\u6210\u985e\u4f3c\u4eba\u985e\u7684\u56de\u61c9\u3002\u7814\u7a76\u8cc7\u6599 <sup>6<\/sup> \u7a31\u5176\u70ba\u4f7f\u7528 Transformer \u67b6\u69cb\u7684\u6a21\u578b\u985e\u578b\u3002GPT \u6a21\u578b\u56e0\u5176\u4ee4\u4eba\u5370\u8c61\u6df1\u523b\u7684\u8a9e\u8a00\u751f\u6210\u80fd\u529b\u800c\u53d7\u5230\u5ee3\u6cdb\u95dc\u6ce8\uff0c\u4e26\u666e\u53ca\u4e86\u5927\u578b\u8a9e\u8a00\u6a21\u578b\u7684\u6982\u5ff5\u3002GPT \u662f\u4e00\u7a2e\u6975\u5177\u5f71\u97ff\u529b\u7684 LLM \u985e\u578b\uff0c\u503c\u5f97\u55ae\u7368\u5217\u51fa\u3002<\/li>\n\n\n\n<li><strong>LLM:<\/strong> Large Language Model (\u5927\u578b\u8bed\u8a00\u6a21\u578b) &#8211; \u6307\u4e00\u7a2e\u5728\u5927\u91cf\u6587\u672c\u6578\u64da\u4e0a\u8a13\u7df4\u7684 AI \u6a21\u578b\uff0c\u7528\u65bc\u7406\u89e3\u548c\u751f\u6210\u985e\u4f3c\u4eba\u985e\u7684\u6587\u672c <sup>1<\/sup>\u3002\u7814\u7a76\u8cc7\u6599 <sup>1<\/sup> \u5c07\u5176\u5217\u70ba\u95dc\u9375\u7684 AI \u7e2e\u5beb\u8a5e\u3002\u7814\u7a76\u8cc7\u6599 <sup>2<\/sup> \u5c07\u5176\u5b9a\u7fa9\u70ba\u804a\u5929\u6a5f\u5668\u4eba\u7b49\u7cfb\u7d71\u7684\u57fa\u790e\u3002\u7814\u7a76\u8cc7\u6599 <sup>8<\/sup> \u5c07\u5176\u63cf\u8ff0\u70ba\u4f7f\u7528\u5148\u9032\u7684 AI \u6f14\u7b97\u6cd5\u548c\u5927\u578b\u6578\u64da\u96c6\u3002\u7814\u7a76\u8cc7\u6599 <sup>18<\/sup> \u5f37\u8abf\u5176\u5728\u5927\u91cf\u6587\u672c\u6578\u64da\u4e0a\u7684\u8a13\u7df4\u3002\u7814\u7a76\u8cc7\u6599 <sup>10<\/sup> \u7a31\u5176\u70ba\u57fa\u65bc\u6df1\u5ea6\u5b78\u7fd2\u7684\u5148\u9032 AI \u6a21\u578b\u3002LLM \u5728\u5404\u7a2e NLP \u4efb\u52d9\u4e2d\u53d6\u5f97\u4e86\u986f\u8457\u7684\u6210\u529f\uff0c\u4e26\u63a8\u52d5\u4e86\u751f\u6210\u5f0f AI \u7684\u8a31\u591a\u6700\u65b0\u9032\u5c55\u3002\u5b83\u5011\u7684\u983b\u7e41\u63d0\u53ca\u7a81\u986f\u4e86\u5176\u7576\u524d\u7684\u7a81\u51fa\u5730\u4f4d\u3002LLM \u662f\u7576\u524d AI\uff08\u5c24\u5176\u662f\u5728 NLP \u9818\u57df\uff09\u7684\u6838\u5fc3\u4e3b\u984c\uff0c\u5176\u5b9a\u7fa9\u548c\u91cd\u8981\u6027\u81f3\u95dc\u91cd\u8981\u3002<\/li>\n\n\n\n<li><strong>Machine Translation:<\/strong> (\u673a\u5668\u7ffb\u8bd1) &#8211; \u6307\u81ea\u52d5\u5c07\u6587\u672c\u5f9e\u4e00\u7a2e\u8a9e\u8a00\u7ffb\u8b6f\u6210\u53e6\u4e00\u7a2e\u8a9e\u8a00\u7684\u4efb\u52d9 <sup>2<\/sup>\u3002\u7814\u7a76\u8cc7\u6599 <sup>2<\/sup> \u63d0\u5230\u4e86 Meta \u7684 SeamlessM4T \u6a21\u578b\u3002\u7814\u7a76\u8cc7\u6599 <sup>8<\/sup> \u5c07\u7ffb\u8b6f\u5217\u70ba LLM \u4f7f\u7528\u7684\u4efb\u52d9\u3002\u7814\u7a76\u8cc7\u6599 <sup>4<\/sup> \u5c07\u5176\u5b9a\u7fa9\u70ba\u4f7f\u7528\u8edf\u9ad4\u7ffb\u8b6f\u6587\u672c\u3002\u7814\u7a76\u8cc7\u6599 <sup>6<\/sup> \u5c07\u5176\u5217\u70ba LLM \u53ef\u4ee5\u57f7\u884c\u7684\u4efb\u52d9\u3002\u8fd1\u5e74\u4f86\uff0c\u7531\u65bc\u6df1\u5ea6\u5b78\u7fd2\u548c NLP \u7684\u9032\u6b65\uff0c\u6a5f\u5668\u7ffb\u8b6f\u53d6\u5f97\u4e86\u986f\u8457\u7684\u9032\u5c55\u3002\u6a5f\u5668\u7ffb\u8b6f\u662f NLP \u7684\u4e00\u500b\u7a81\u51fa\u61c9\u7528\uff0c\u5177\u6709\u91cd\u8981\u7684\u73fe\u5be6\u4e16\u754c\u5f71\u97ff\u3002<\/li>\n\n\n\n<li><strong>Named Entity Recognition (NER):<\/strong> (\u547d\u540d\u5b9e\u4f53\u8bc6\u522b) &#8211; \u6307 NLP \u4e2d\u7684\u4e00\u9805\u4efb\u52d9\uff0c\u6d89\u53ca\u8b58\u5225\u548c\u5206\u985e\u6587\u672c\u4e2d\u7684\u547d\u540d\u5be6\u9ad4\uff0c\u4f8b\u5982\u4eba\u540d\u3001\u7d44\u7e54\u540d\u3001\u5730\u9ede\u548c\u65e5\u671f <sup>15<\/sup>\u3002\u7814\u7a76\u8cc7\u6599 <sup>15<\/sup> \u5c07\u5176\u5217\u70ba\u5e38\u898b\u7684 NLP \u4efb\u52d9\u3002\u7814\u7a76\u8cc7\u6599 <sup>15<\/sup> \u5c07\u5176\u5305\u542b\u5728\u7e2e\u5beb\u8a5e\u5217\u8868\u4e2d\u3002NER \u662f\u8a31\u591a\u4fe1\u606f\u63d0\u53d6\u548c\u7406\u89e3\u4efb\u52d9\u4e2d\u7684\u95dc\u9375\u6b65\u9a5f\u3002NER \u662f NLP \u4e2d\u7528\u65bc\u8b58\u5225\u6587\u672c\u4e2d\u95dc\u9375\u4fe1\u606f\u7684\u57fa\u672c\u4efb\u52d9\u3002<\/li>\n\n\n\n<li><strong>NLG:<\/strong> Natural Language Generation (\u81ea\u7136\u8bed\u8a00\u751f\u6210) &#8211; \u6307 NLP \u7684\u4e00\u500b\u5b50\u9818\u57df\uff0c\u5c08\u6ce8\u65bc\u4f7f\u96fb\u8166\u80fd\u5920\u751f\u6210\u985e\u4f3c\u4eba\u985e\u7684\u6587\u672c <sup>5<\/sup>\u3002\u7814\u7a76\u8cc7\u6599 <sup>5<\/sup> \u5c07\u5176\u5b9a\u7fa9\u70ba\u4f7f\u6a5f\u5668\u80fd\u5920\u751f\u6210\u9023\u8cab\u4e14\u4e0a\u4e0b\u6587\u76f8\u95dc\u7684\u5167\u5bb9\uff0c\u6a21\u4eff\u4eba\u985e\u7684\u5c0d\u8a71\u3002NLG \u5c0d\u65bc\u804a\u5929\u6a5f\u5668\u4eba\u3001\u5167\u5bb9\u5275\u5efa\u548c\u6587\u672c\u6458\u8981\u7b49\u61c9\u7528\u81f3\u95dc\u91cd\u8981\u3002NLG \u8207 NLU \u76f8\u8f14\u76f8\u6210\uff0c\u5c08\u6ce8\u65bc\u6a5f\u5668\u751f\u6210\u985e\u4f3c\u4eba\u985e\u7684\u8a9e\u8a00\u3002<\/li>\n\n\n\n<li><strong>NLP:<\/strong> Natural Language Processing (\u81ea\u7136\u8bed\u8a00\u5904\u7406) &#8211; \u6307\u4eba\u5de5\u667a\u6167\u7684\u4e00\u500b\u5206\u652f\uff0c\u5c08\u6ce8\u65bc\u4f7f\u96fb\u8166\u80fd\u5920\u7406\u89e3\u3001\u89e3\u91cb\u548c\u751f\u6210\u4eba\u985e\u8a9e\u8a00 <sup>1<\/sup>\u3002\u7814\u7a76\u8cc7\u6599 <sup>2<\/sup> \u5c07\u5176\u63cf\u8ff0\u70ba\u5141\u8a31\u6a5f\u5668\u7406\u89e3\u548c\u8655\u7406\u4eba\u985e\u8a9e\u8a00\u7684\u9818\u57df\u3002\u7814\u7a76\u8cc7\u6599 <sup>8<\/sup> \u5c07\u5176\u89e3\u91cb\u70ba\u96fb\u8166\u5206\u6790\u53e3\u8a9e\u548c\u66f8\u9762\u8a9e\u8a00\u7684\u904e\u7a0b\u3002\u7814\u7a76\u8cc7\u6599 <sup>18<\/sup> \u5c08\u6ce8\u65bc\u96fb\u8166\u8207\u4eba\u985e\u8a9e\u8a00\u4e4b\u9593\u7684\u4e92\u52d5\u3002NLP \u662f\u4e00\u500b\u5feb\u901f\u767c\u5c55\u7684\u9818\u57df\uff0c\u61c9\u7528\u5ee3\u6cdb\uff0c\u5f9e\u804a\u5929\u6a5f\u5668\u4eba\u5230\u6a5f\u5668\u7ffb\u8b6f\u3002\u6b64\u7e2e\u5beb\u8a5e\u7684\u983b\u7e41\u51fa\u73fe\u7a81\u986f\u4e86\u5176\u91cd\u8981\u6027\u3002NLP \u662f AI \u7684\u4e00\u500b\u4e3b\u8981\u9818\u57df\uff0c\u5176\u5b9a\u7fa9\u5c0d\u65bc\u7406\u89e3\u76f8\u95dc\u7e2e\u5beb\u8a5e\u81f3\u95dc\u91cd\u8981\u3002<\/li>\n\n\n\n<li><strong>NLU:<\/strong> Natural Language Understanding (\u81ea\u7136\u8bed\u8a00\u7406\u89e3) &#8211; \u6307 NLP \u7684\u4e00\u500b\u5b50\u9818\u57df\uff0c\u5c08\u6ce8\u65bc\u4f7f\u96fb\u8166\u80fd\u5920\u7406\u89e3\u4eba\u985e\u8a9e\u8a00\u7684\u610f\u7fa9 <sup>1<\/sup>\u3002\u7814\u7a76\u8cc7\u6599 <sup>1<\/sup> \u5c07\u5176\u5b9a\u7fa9\u70ba\u6a5f\u5668\u7406\u89e3\u6211\u5011\u6240\u8aaa\u5167\u5bb9\u7684\u80fd\u529b\u3002NLU \u662f NLP \u7684\u95dc\u9375\u7d44\u6210\u90e8\u5206\uff0c\u4f7f\u6a5f\u5668\u80fd\u5920\u8d85\u8d8a\u7c21\u55ae\u7684\u6587\u672c\u8655\u7406\uff0c\u771f\u6b63\u7406\u89e3\u5176\u610f\u5716\u3002NLU \u662f NLP \u7684\u4e00\u500b\u95dc\u9375\u65b9\u9762\uff0c\u5c08\u6ce8\u65bc\u8a9e\u8a00\u8655\u7406\u7684\u7406\u89e3\u90e8\u5206\u3002<\/li>\n\n\n\n<li><strong>Part-of-Speech (POS) Tagging:<\/strong> (\u8bcd\u6027\u6807\u6ce8) &#8211; \u6307 NLP \u4e2d\u7684\u4e00\u9805\u4efb\u52d9\uff0c\u6d89\u53ca\u70ba\u6587\u672c\u4e2d\u7684\u6bcf\u500b\u55ae\u8a5e\u5206\u914d\u8a9e\u6cd5\u6a19\u7c64\uff08\u4f8b\u5982\u540d\u8a5e\u3001\u52d5\u8a5e\u3001\u5f62\u5bb9\u8a5e\uff09 <sup>15<\/sup>\u3002\u7814\u7a76\u8cc7\u6599 <sup>15<\/sup> \u5c07\u5176\u5217\u70ba\u5e38\u898b\u7684 NLP \u4efb\u52d9\u3002\u7814\u7a76\u8cc7\u6599 <sup>15<\/sup> \u5c07\u5176\u5305\u542b\u5728\u7e2e\u5beb\u8a5e\u5217\u8868\u4e2d\u3002\u7814\u7a76\u8cc7\u6599 <sup>11<\/sup> \u5217\u51fa\u4e86\u5b83\u3002POS \u6a19\u8a3b\u662f\u8a31\u591a\u66f4\u9ad8\u7d1a\u5225 NLP \u61c9\u7528\u7a0b\u5f0f\u7684\u57fa\u790e\u4efb\u52d9\u3002POS \u6a19\u8a3b\u662f\u7406\u89e3\u6587\u672c\u8a9e\u6cd5\u7d50\u69cb\u7684\u57fa\u672c\u4f46\u91cd\u8981\u7684\u6b65\u9a5f\u3002<\/li>\n\n\n\n<li><strong>Prompt:<\/strong> (\u63d0\u793a) &#8211; \u6307\u7d66\u4e88 AI \u6a21\u578b\uff08\u5c24\u5176\u662f\u50cf LLM \u9019\u6a23\u7684\u751f\u6210\u6a21\u578b\uff09\u7684\u8f38\u5165\u6216\u6307\u4ee4\uff0c\u4ee5\u5f15\u51fa\u7279\u5b9a\u7684\u56de\u61c9\u6216\u5f15\u5c0e\u5176\u5167\u5bb9\u751f\u6210 <sup>3<\/sup>\u3002\u7814\u7a76\u8cc7\u6599 <sup>4<\/sup> \u5c07\u5176\u5b9a\u7fa9\u70ba\u4f7f\u7528\u8005\u5411 AI \u7cfb\u7d71\u767c\u51fa\u7684\u8f38\u5165\uff0cAI \u7cfb\u7d71\u6703\u5c0d\u6b64\u505a\u51fa\u56de\u61c9\u3002\u7814\u7a76\u8cc7\u6599 <sup>3<\/sup> \u63d0\u4f9b\u4e86\u985e\u4f3c\u7684\u5b9a\u7fa9\u3002\u63d0\u793a\u7684\u54c1\u8cea\u548c\u8a2d\u8a08\u986f\u8457\u5f71\u97ff\u751f\u6210\u5f0f AI \u6a21\u578b\u7684\u8f38\u51fa\u548c\u884c\u70ba\u3002\u8207\u751f\u6210\u5f0f AI \u6a21\u578b\u4e92\u52d5\u6d89\u53ca\u4f7f\u7528\u63d0\u793a\u3002<\/li>\n\n\n\n<li><strong>RAG:<\/strong> Retrieval-Augmented Generation (\u68c0\u7d22\u589e\u5f3a\u751f\u6210) &#8211; \u6307\u4e00\u7a2e\u900f\u904e\u5141\u8a31 LLM \u5f9e\u5916\u90e8\u77e5\u8b58\u4f86\u6e90\u6aa2\u7d22\u4fe1\u606f\u4e26\u5c07\u5176\u7d0d\u5165\u5176\u56de\u61c9\u4e2d\u4f86\u589e\u5f37 LLM \u6587\u672c\u751f\u6210\u80fd\u529b\u7684\u6280\u8853 <sup>2<\/sup>\u3002\u7814\u7a76\u8cc7\u6599 <sup>2<\/sup> \u5c07\u5176\u89e3\u91cb\u70ba\u900f\u904e\u4fe1\u606f\u6aa2\u7d22\u4f86\u8c50\u5bcc\u8a9e\u8a00\u751f\u6210\u3002\u7814\u7a76\u8cc7\u6599 <sup>3<\/sup> \u8a73\u7d30\u8aaa\u660e\u4e86\u5b83\u5c07\u4fe1\u606f\u6aa2\u7d22\u8207\u6587\u672c\u751f\u6210\u76f8\u7d50\u5408\uff0c\u4e26\u5b58\u53d6\u5916\u90e8\u77e5\u8b58\u3002RAG \u900f\u904e\u5c07 LLM \u7684\u8f38\u51fa\u5efa\u7acb\u5728\u4e8b\u5be6\u4fe1\u606f\u4e4b\u4e0a\uff0c\u63d0\u9ad8\u4e86\u5176\u6e96\u78ba\u6027\u548c\u76f8\u95dc\u6027\u3002RAG \u662f\u4e00\u7a2e\u7528\u65bc\u63d0\u9ad8 LLM \u6548\u80fd\u7684\u91cd\u8981\u6280\u8853\u3002<\/li>\n\n\n\n<li><strong>Sentiment Analysis:<\/strong> (\u60c5\u611f\u5206\u6790) &#8211; \u6307 NLP \u4e2d\u7684\u4e00\u9805\u4efb\u52d9\uff0c\u6d89\u53ca\u8b58\u5225\u548c\u63d0\u53d6\u6587\u672c\u4e2d\u7684\u4e3b\u89c0\u4fe1\u606f\uff0c\u4f8b\u5982\u89c0\u9ede\u3001\u60c5\u7dd2\u548c\u614b\u5ea6 <sup>6<\/sup>\u3002\u7814\u7a76\u8cc7\u6599 <sup>6<\/sup> \u5c07\u5176\u5217\u70ba LLM \u53ef\u4ee5\u57f7\u884c\u7684\u4efb\u52d9\u3002\u60c5\u611f\u5206\u6790\u5728\u884c\u92b7\u3001\u5ba2\u6236\u670d\u52d9\u548c\u793e\u7fa4\u5a92\u9ad4\u76e3\u63a7\u7b49\u9818\u57df\u6709\u5ee3\u6cdb\u7684\u61c9\u7528\u3002\u60c5\u611f\u5206\u6790\u662f NLP \u7684\u4e00\u500b\u5e38\u898b\u4e14\u91cd\u8981\u7684\u61c9\u7528\u3002<\/li>\n\n\n\n<li><strong>Text Summarization:<\/strong> (\u6587\u672c\u6458\u8981) &#8211; \u6307\u81ea\u52d5\u751f\u6210\u8f03\u9577\u6587\u672c\u6587\u4ef6\u7684\u7c21\u6f54\u4e14\u9023\u8cab\u6458\u8981\u7684\u4efb\u52d9 <sup>2<\/sup>\u3002\u7814\u7a76\u8cc7\u6599 <sup>2<\/sup> \u5c07\u5176\u5217\u70ba GPT \u4f7f\u7528\u548c LLM \u53ef\u4ee5\u57f7\u884c\u7684\u4efb\u52d9\u3002\u7814\u7a76\u8cc7\u6599 <sup>8<\/sup> \u63d0\u5230 LLM \u7528\u65bc\u6b64\u4efb\u52d9\u3002\u7814\u7a76\u8cc7\u6599 <sup>6<\/sup> \u5c07\u5176\u5217\u70ba LLM \u53ef\u4ee5\u57f7\u884c\u7684\u4efb\u52d9\u3002\u7814\u7a76\u8cc7\u6599 <sup>20<\/sup> \u63d0\u5230 NLP \u6a21\u578b\u7528\u65bc\u6b64\u4efb\u52d9\u3002\u6587\u672c\u6458\u8981\u662f\u4e00\u7a2e\u8655\u7406\u4fe1\u606f\u904e\u8f09\u7684\u5bf6\u8cb4\u5de5\u5177\u3002\u6587\u672c\u6458\u8981\u662f NLP \u7684\u53e6\u4e00\u500b\u91cd\u8981\u61c9\u7528\u3002<\/li>\n\n\n\n<li><strong>Token:<\/strong> (\u4ee4\u724c) &#8211; \u6307\u5927\u578b\u8a9e\u8a00\u6a21\u578b\u8655\u7406\u7684\u6587\u672c\u57fa\u672c\u55ae\u4f4d\uff08\u4e00\u500b\u8a5e\u6216\u4e00\u500b\u8a5e\u7684\u4e00\u90e8\u5206\uff09 <sup>4<\/sup>\u3002\u7814\u7a76\u8cc7\u6599 <sup>4<\/sup> \u5c07\u5176\u5b9a\u7fa9\u70ba LLM \u8655\u7406\u7684\u6587\u672c\u57fa\u672c\u55ae\u4f4d\u3002\u7814\u7a76\u8cc7\u6599 <sup>20<\/sup> \u63d0\u5230\u5728 ELECTRA \u4e2d\u5206\u985e\u4ee4\u724c\u66ff\u63db\u3002\u7406\u89e3\u5206\u8a5e\u5c0d\u65bc\u7406\u89e3 LLM \u5982\u4f55\u8655\u7406\u548c\u751f\u6210\u6587\u672c\u975e\u5e38\u91cd\u8981\u3002LLM \u5728\u4ee4\u724c\u4e0a\u904b\u4f5c\uff0c\u56e0\u6b64\u5b83\u662f\u5176\u529f\u80fd\u7684\u4e00\u500b\u57fa\u672c\u6982\u5ff5\u3002<\/li>\n\n\n\n<li><strong>Transformer:<\/strong> (\u53d8\u6362\u5668) &#8211; \u6307\u4e00\u7a2e\u4f9d\u8cf4\u81ea\u6ce8\u610f\u529b\u6a5f\u5236\u4f86\u8655\u7406\u5e8f\u5217\u6578\u64da\u7684\u795e\u7d93\u7db2\u8def\u67b6\u69cb\uff0c\u5728 NLP \u4efb\u52d9\u4e2d\u7279\u5225\u6709\u6548 <sup>6<\/sup>\u3002\u7814\u7a76\u8cc7\u6599 <sup>6<\/sup> \u63d0\u5230 GPT \u4f7f\u7528 Transformer \u67b6\u69cb\u3002\u7814\u7a76\u8cc7\u6599 <sup>14<\/sup> \u6307\u51fa BERT \u4f7f\u7528 Transformer \u67b6\u69cb\u3002\u7814\u7a76\u8cc7\u6599 <sup>20<\/sup> \u5c07\u300cTransformer-XL\u300d\u5217\u70ba\u4e00\u7a2e\u8a9e\u8a00\u6a21\u578b\u3002Transformer \u67b6\u69cb\u5fb9\u5e95\u6539\u8b8a\u4e86 NLP\uff0c\u5728\u6a5f\u5668\u7ffb\u8b6f\u548c\u6587\u672c\u751f\u6210\u7b49\u4efb\u52d9\u4e2d\u5be6\u73fe\u4e86\u986f\u8457\u7684\u6539\u9032\u3002Transformer \u67b6\u69cb\u662f\u8a31\u591a\u73fe\u4ee3 LLM \u7684\u57fa\u790e\uff0c\u4f7f\u5176\u6210\u70ba\u4e00\u500b\u95dc\u9375\u8853\u8a9e\u3002<\/li>\n\n\n\n<li><strong>\u6df1\u5ea6\u5b78\u7fd2\u67b6\u69cb\u8207\u6280\u8853\uff1a<\/strong> \u64f4\u5c55 DL \u985e\u5225\u3002<\/li>\n<\/ul>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>AutoML:<\/strong> Automated Machine Learning (\u81ea\u52a8\u5316\u673a\u5668\u5b66\u4e60) &#8211; \u6307\u81ea\u52d5\u5316\u5c07\u6a5f\u5668\u5b78\u7fd2\u61c9\u7528\u65bc\u5be6\u969b\u554f\u984c\u7684\u7aef\u5230\u7aef\u904e\u7a0b <sup>14<\/sup>\u3002\u7814\u7a76\u8cc7\u6599 <sup>14<\/sup> \u5217\u51fa\u4e86 AutoML \u53ef\u4ee5\u57f7\u884c\u7684\u4efb\u52d9\uff0c\u4f8b\u5982\u6a21\u578b\u641c\u5c0b\u3001\u8d85\u53c3\u6578\u8abf\u6574\u548c\u6578\u64da\u6e96\u5099\u3002AutoML \u65e8\u5728\u900f\u904e\u81ea\u52d5\u5316\u8a31\u591a\u6d89\u53ca\u7684\u624b\u52d5\u6b65\u9a5f\uff0c\u4f7f\u6a5f\u5668\u5b78\u7fd2\u66f4\u6613\u65bc\u5b58\u53d6\u548c\u66f4\u6709\u6548\u7387\u3002\u81ea\u52d5\u5316\u662f AI \u7684\u4e00\u500b\u95dc\u9375\u8da8\u52e2\uff0c\u800c AutoML \u662f\u5176\u91cd\u8981\u7d44\u6210\u90e8\u5206\u3002<\/li>\n\n\n\n<li><strong>DenseNet:<\/strong> Densely Connected Convolutional Network (\u5bc6\u96c6\u8fde\u63a5\u5377\u79ef\u7f51\u7edc) &#8211; \u6307\u4e00\u7a2e\u5377\u7a4d\u795e\u7d93\u7db2\u8def\u67b6\u69cb\uff0c\u5176\u4e2d\u6bcf\u4e00\u5c64\u90fd\u4ee5\u524d\u994b\u65b9\u5f0f\u9023\u63a5\u5230\u5176\u4ed6\u6bcf\u4e00\u5c64\uff0c\u5f9e\u800c\u6539\u5584\u4e86\u7279\u5fb5\u7684\u91cd\u8907\u4f7f\u7528\u4e26\u6e1b\u5c11\u4e86\u68af\u5ea6\u6d88\u5931\u554f\u984c <sup>10<\/sup>\u3002\u7814\u7a76\u8cc7\u6599 <sup>10<\/sup> \u63cf\u8ff0\u4e86\u5176\u5bc6\u96c6\u7684\u9023\u63a5\u6027\u3002DenseNet \u5728\u53c3\u6578\u6548\u7387\u548c\u7279\u5fb5\u50b3\u64ad\u65b9\u9762\u5177\u6709\u512a\u52e2\u3002DenseNet \u662f\u6df1\u5ea6\u5b78\u7fd2\u4e2d\u53e6\u4e00\u500b\u91cd\u8981\u7684\u67b6\u69cb\u3002<\/li>\n\n\n\n<li><strong>EfficientNet:<\/strong> \u6307\u4e00\u7cfb\u5217\u5377\u7a4d\u795e\u7d93\u7db2\u8def\uff0c\u5b83\u5011\u900f\u904e\u5e73\u8861\u5730\u7e2e\u653e\u7db2\u8def\u7684\u5bec\u5ea6\u3001\u6df1\u5ea6\u548c\u89e3\u6790\u5ea6\uff0c\u4ee5\u66f4\u5c11\u7684\u53c3\u6578\u548c\u66f4\u5c11\u7684\u8a08\u7b97\u91cf\u5be6\u73fe\u9ad8\u6e96\u78ba\u5ea6 <sup>10<\/sup>\u3002\u7814\u7a76\u8cc7\u6599 <sup>10<\/sup> \u5f37\u8abf\u5176\u900f\u904e\u8907\u5408\u7e2e\u653e\u5be6\u73fe\u7684\u6548\u7387\u3002EfficientNet \u5728\u6e96\u78ba\u5ea6\u548c\u8a08\u7b97\u6210\u672c\u4e4b\u9593\u63d0\u4f9b\u4e86\u826f\u597d\u7684\u5e73\u8861\u3002\u6548\u7387\u5728\u6df1\u5ea6\u5b78\u7fd2\u4e2d\u81f3\u95dc\u91cd\u8981\uff0c\u800c EfficientNet \u5c31\u662f\u70ba\u6b64\u8a2d\u8a08\u7684\u3002<\/li>\n\n\n\n<li><strong>Fine-tuning:<\/strong> (\u5fae\u8c03) &#8211; \u6307\u63a1\u7528\u9810\u8a13\u7df4\u6a21\u578b\u4e26\u5728\u65b0\u7684\u3001\u901a\u5e38\u66f4\u5c0f\u7684\u6578\u64da\u96c6\u4e0a\u9032\u4e00\u6b65\u8a13\u7df4\uff0c\u4ee5\u4f7f\u5176\u9069\u61c9\u7279\u5b9a\u4efb\u52d9\u7684\u904e\u7a0b <sup>20<\/sup>\u3002\u7814\u7a76\u8cc7\u6599 <sup>20<\/sup> \u5728 ULMFiT \u7684\u80cc\u666f\u4e0b\u63d0\u5230\u4e86\u5fae\u8abf\u3002\u5fae\u8abf\u53ef\u4ee5\u6709\u6548\u5730\u5c07\u901a\u7528\u6a21\u578b\u8abf\u6574\u70ba\u5c08\u9580\u7684\u61c9\u7528\u3002\u5fae\u8abf\u662f\u4f7f\u7528\u9810\u8a13\u7df4\u6a21\u578b\u6642\u7684\u5e38\u898b\u505a\u6cd5\u3002<\/li>\n\n\n\n<li><strong>ResNet:<\/strong> Residual Neural Network (\u6b8b\u5dee\u795e\u7ecf\u7f51\u7edc) &#8211; \u6307\u4e00\u7a2e\u6df1\u5ea6\u5377\u7a4d\u795e\u7d93\u7db2\u8def\u67b6\u69cb\uff0c\u5b83\u5f15\u5165\u4e86\u6b98\u5dee\u9023\u63a5\uff08\u8df3\u8e8d\u9023\u63a5\uff09\u4f86\u5e6b\u52a9\u8a13\u7df4\u975e\u5e38\u6df1\u7684\u7db2\u8def <sup>10<\/sup>\u3002\u7814\u7a76\u8cc7\u6599 <sup>10<\/sup> \u89e3\u91cb\u4e86\u6b98\u5dee\u5b78\u7fd2\u7684\u6982\u5ff5\u3002ResNet \u5728\u5716\u50cf\u8b58\u5225\u4efb\u52d9\u4e2d\u53d6\u5f97\u4e86\u5de8\u5927\u7684\u6210\u529f\uff0c\u4f7f\u5f97\u8a13\u7df4\u66f4\u6df1\u3001\u66f4\u6e96\u78ba\u7684\u6a21\u578b\u6210\u70ba\u53ef\u80fd\u3002ResNet \u662f\u6df1\u5ea6\u5b78\u7fd2\u4e2d\u7528\u65bc\u5716\u50cf\u8655\u7406\u7684\u91cd\u8981\u67b6\u69cb\u3002<\/li>\n\n\n\n<li><strong>Reinforcement Learning from Human Feedback (RLHF):<\/strong> (\u57fa\u4e8e\u4eba\u7c7b\u53cd\u9988\u7684\u5f3a\u5316\u5b66\u4e60) &#8211; \u6307\u4e00\u7a2e\u7528\u65bc\u900f\u904e\u5c07\u4eba\u985e\u504f\u597d\u548c\u56de\u994b\u7d0d\u5165\u8a13\u7df4\u904e\u7a0b\u4f86\u5fae\u8abf\u8a9e\u8a00\u6a21\u578b\u7684\u6280\u8853 <sup>4<\/sup>\u3002\u7814\u7a76\u8cc7\u6599 <sup>4<\/sup> \u5c07\u5176\u5b9a\u7fa9\u70ba\u7528\u65bc\u5fae\u8abf GPT \u6a21\u578b\u56de\u61c9\u7684\u8a13\u7df4\u65b9\u6cd5\u3002\u7814\u7a76\u8cc7\u6599 <sup>4<\/sup> \u89e3\u91cb\u4e86\u5176\u5728 ChatGPT \u958b\u767c\u4e2d\u7684\u61c9\u7528\u3002\u7814\u7a76\u8cc7\u6599 <sup>11<\/sup> \u5217\u51fa\u4e86\u5b83\u3002RLHF \u5c0d\u65bc\u6539\u5584 LLM \u8207\u4eba\u985e\u50f9\u503c\u89c0\u548c\u504f\u597d\u7684\u5c0d\u9f4a\u81f3\u95dc\u91cd\u8981\u3002RLHF \u662f\u4e00\u7a2e\u7528\u65bc\u63d0\u9ad8 LLM \u8f38\u51fa\u54c1\u8cea\u548c\u5b89\u5168\u6027\u7684\u95dc\u9375\u6280\u8853\u3002<\/li>\n\n\n\n<li><strong>VGG:<\/strong> Visual Geometry Group (\u89c6\u89c9\u51e0\u4f55\u7ec4) &#8211; \u6307\u4e00\u7a2e\u4ee5\u5176\u7c21\u55ae\u6027\u548c\u4f7f\u7528\u5c0f\u578b\u5377\u7a4d\u6ffe\u6ce2\u5668\u800c\u805e\u540d\u7684\u5377\u7a4d\u795e\u7d93\u7db2\u8def\u67b6\u69cb <sup>10<\/sup>\u3002\u7814\u7a76\u8cc7\u6599 <sup>10<\/sup> \u63d0\u5230\u5b83\u662f\u725b\u6d25\u5927\u5b78\u8996\u89ba\u5e7e\u4f55\u7d44\u63d0\u51fa\u7684 CNN \u67b6\u69cb\u3002\u7814\u7a76\u8cc7\u6599 <sup>11<\/sup> \u7a31\u5176\u70ba\u7528\u65bc\u5206\u985e\u7684\u6d41\u884c\u6df1\u5ea6\u5377\u7a4d\u6a21\u578b\u3002VGG \u7db2\u8def\u5c0d\u65bc\u8b49\u660e\u6df1\u5ea6\u5377\u7a4d\u7db2\u8def\u5728\u5716\u50cf\u8b58\u5225\u65b9\u9762\u7684\u6709\u6548\u6027\u5177\u6709\u91cd\u8981\u5f71\u97ff\u3002VGG \u662f\u6df1\u5ea6\u5b78\u7fd2\u4e2d\u4e00\u500b\u5177\u6709\u6b77\u53f2\u610f\u7fa9\u7684\u67b6\u69cb\u3002<\/li>\n\n\n\n<li><strong>\u5176\u4ed6\u76f8\u95dc\u7e2e\u5beb\u8a5e\uff1a<\/strong> \u4f86\u81ea\u76f8\u95dc\u9818\u57df\u6216\u8207 AI \u76f8\u95dc\u7684\u4e00\u822c\u8a08\u7b97\u8853\u8a9e\u3002<\/li>\n<\/ul>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Algorithm:<\/strong> (\u7b97\u6cd5) &#8211; \u6307\u4e00\u500b\u6709\u9650\u7684\u3001\u5b9a\u7fa9\u660e\u78ba\u7684\u3001\u53ef\u7531\u96fb\u8166\u5be6\u65bd\u7684\u6307\u4ee4\u5e8f\u5217\uff0c\u901a\u5e38\u7528\u65bc\u89e3\u6c7a\u4e00\u985e\u554f\u984c\u6216\u57f7\u884c\u8a08\u7b97\u3002AI \u548c ML \u56b4\u91cd\u4f9d\u8cf4\u65bc\u5404\u7a2e\u6f14\u7b97\u6cd5 <sup>8<\/sup>\u3002\u7814\u7a76\u8cc7\u6599 <sup>8<\/sup> \u63d0\u5230 AI \u7d50\u5408\u4e86\u8907\u96dc\u7684\u7cfb\u7d71\u548c\u8655\u7406\u6f14\u7b97\u6cd5\u3002\u7814\u7a76\u8cc7\u6599 <sup>4<\/sup> \u5c07\u5176\u5b9a\u7fa9\u70ba\u6709\u9650\u7684\u6307\u4ee4\u5e8f\u5217\u3002\u7814\u7a76\u8cc7\u6599 <sup>5<\/sup> \u6307\u51fa ML \u662f\u7814\u7a76\u81ea\u52d5\u6539\u9032\u7684\u6f14\u7b97\u6cd5\u3002\u958b\u767c\u65b0\u7a4e\u4e14\u9ad8\u6548\u7684\u6f14\u7b97\u6cd5\u5c0d\u65bc AI \u7684\u9032\u6b65\u81f3\u95dc\u91cd\u8981\u3002\u6f14\u7b97\u6cd5\u662f AI \u7cfb\u7d71\u7684\u57fa\u672c\u69cb\u5efa\u584a\u3002<\/li>\n\n\n\n<li><strong>Big Data:<\/strong> (\u5927\u6570\u636e) &#8211; \u6307\u6975\u5927\u7684\u6578\u64da\u96c6\uff0c\u53ef\u4ee5\u900f\u904e\u8a08\u7b97\u65b9\u5f0f\u9032\u884c\u5206\u6790\uff0c\u4ee5\u63ed\u793a\u6a21\u5f0f\u3001\u8da8\u52e2\u548c\u95dc\u806f\uff0c\u5c24\u5176\u8207\u4eba\u985e\u884c\u70ba\u548c\u4e92\u52d5\u76f8\u95dc\u3002\u5927\u6578\u64da\u901a\u5e38\u7528\u65bc\u8a13\u7df4 AI \u6a21\u578b <sup>4<\/sup>\u3002\u7814\u7a76\u8cc7\u6599 <sup>4<\/sup> \u5c07\u5176\u5b9a\u7fa9\u70ba\u666e\u901a\u8edf\u9ad4\u7121\u6cd5\u8655\u7406\u7684\u975e\u5e38\u5927\u7684\u6578\u64da\u96c6\u3002\u5927\u6578\u64da\u7684\u53ef\u7528\u6027\u662f\u8fd1\u671f AI\uff08\u5c24\u5176\u662f\u6df1\u5ea6\u5b78\u7fd2\uff09\u53d6\u5f97\u91cd\u5927\u9032\u5c55\u7684\u4e3b\u8981\u9a45\u52d5\u529b\u3002AI\uff0c\u5c24\u5176\u662f ML \u548c DL\uff0c\u56b4\u91cd\u4f9d\u8cf4\u65bc\u5927\u91cf\u7684\u6578\u64da\u3002<\/li>\n\n\n\n<li><strong>Computer Vision:<\/strong> (\u8ba1\u7b97\u673a\u89c6\u89c9) &#8211; \u6307 AI \u7684\u4e00\u500b\u9818\u57df\uff0c\u4f7f\u96fb\u8166\u80fd\u5920\u300c\u770b\u898b\u300d\u4e26\u89e3\u91cb\u4f86\u81ea\u8996\u89ba\u4e16\u754c\u7684\u8cc7\u8a0a\uff0c\u4f8b\u5982\u5716\u50cf\u548c\u5f71\u7247 <sup>6<\/sup>\u3002<\/li>\n\n\n\n<li><strong>Corpus:<\/strong> (\u8bed\u6599\u5e93) &#8211; \u6307\u7528\u65bc\u8a9e\u8a00\u5206\u6790\u3001\u6a5f\u5668\u5b78\u7fd2\u8a13\u7df4\u6216\u7d71\u8a08\u5206\u6790\u7684\u5927\u578b\u7d50\u69cb\u5316\u6587\u672c\u96c6\u5408\uff0c\u5c24\u5176\u662f\u5728 NLP \u4e2d\u3002<\/li>\n\n\n\n<li><strong>Data Mining:<\/strong> (\u6570\u636e\u6316\u6398) &#8211; \u6307\u5f9e\u5927\u578b\u6578\u64da\u96c6\u4e2d\u767c\u73fe\u6a21\u5f0f\u548c\u6d1e\u5bdf\u529b\u7684\u904e\u7a0b\u3002\u901a\u5e38\u8207\u6a5f\u5668\u5b78\u7fd2\u7d50\u5408\u4f7f\u7528 <sup>4<\/sup>\u3002\u7814\u7a76\u8cc7\u6599 <sup>4<\/sup> \u5c07\u5176\u5217\u70ba\u8207 AI \u76f8\u95dc\u7684\u6982\u8ff0\u8853\u8a9e\u3002\u6578\u64da\u6316\u6398\u5728\u5f9e\u7528\u65bc\u8a13\u7df4 AI \u6a21\u578b\u7684\u6578\u64da\u4e2d\u63d0\u53d6\u6709\u50f9\u503c\u7684\u4fe1\u606f\u65b9\u9762\u767c\u63ee\u8457\u81f3\u95dc\u91cd\u8981\u7684\u4f5c\u7528\u3002\u6578\u64da\u6316\u6398\u662f\u4e00\u500b\u76f8\u95dc\u7684\u9818\u57df\uff0c\u901a\u5e38\u5148\u65bc\u6216\u88dc\u5145\u6a5f\u5668\u5b78\u7fd2\u3002<\/li>\n\n\n\n<li><strong>Dataset:<\/strong> (\u6570\u636e\u96c6) &#8211; \u6307\u7528\u65bc\u8a13\u7df4\u6216\u8a55\u4f30\u6a5f\u5668\u5b78\u7fd2\u6a21\u578b\u7684\u6578\u64da\u96c6\u5408\u3002<\/li>\n\n\n\n<li><strong>Deepfake:<\/strong> (\u6df1\u5ea6\u4f2a\u9020) &#8211; \u6307 AI \u751f\u6210\u7684\u65e8\u5728\u770b\u8d77\u4f86\u548c\u807d\u8d77\u4f86\u50cf\u771f\u4eba\u7684\u5716\u50cf\u3001\u5f71\u7247\u6216\u9304\u97f3\uff0c\u901a\u5e38\u7528\u65bc\u60e1\u610f\u76ee\u7684\u3002<\/li>\n\n\n\n<li><strong>Deployment:<\/strong> (\u90e8\u7f72) &#8211; \u6307\u4f7f\u7d93\u904e\u8a13\u7df4\u7684 AI \u6a21\u578b\u53ef\u7528\u65bc\u5be6\u969b\u61c9\u7528\u6216\u7cfb\u7d71\u7684\u904e\u7a0b\u3002<\/li>\n\n\n\n<li><strong>Edge AI:<\/strong> (\u8fb9\u7f18\u4eba\u5de5\u667a\u80fd) &#8211; \u6307\u5728\u672c\u5730\u8a2d\u5099\u6216\u908a\u7de3\u4f3a\u670d\u5668\u4e0a\uff08\u800c\u4e0d\u662f\u5728\u96f2\u7aef\u4e2d\uff09\u90e8\u7f72\u548c\u57f7\u884c AI \u6f14\u7b97\u6cd5\uff0c\u5f9e\u800c\u5be6\u73fe\u66f4\u5feb\u7684\u8655\u7406\u901f\u5ea6\u548c\u66f4\u4f4e\u7684\u5ef6\u9072\u3002<\/li>\n\n\n\n<li><strong>Ensemble Learning:<\/strong> (\u96c6\u6210\u5b66\u4e60) &#8211; \u6307\u4e00\u7a2e\u6a5f\u5668\u5b78\u7fd2\u6280\u8853\uff0c\u5b83\u7d50\u5408\u4e86\u591a\u500b\u55ae\u7368\u6a21\u578b\u7684\u9810\u6e2c\u4ee5\u63d0\u9ad8\u6574\u9ad4\u6548\u80fd\u3002<\/li>\n\n\n\n<li><strong>Ethical AI:<\/strong> (\u4f26\u7406\u4eba\u5de5\u667a\u80fd) &#8211; \u6307\u4ee5\u8003\u616e\u502b\u7406\u539f\u5247\u548c\u793e\u6703\u50f9\u503c\u89c0\u7684\u65b9\u5f0f\u958b\u767c\u548c\u90e8\u7f72 AI \u7cfb\u7d71\uff0c\u89e3\u6c7a\u516c\u5e73\u6027\u3001\u900f\u660e\u5ea6\u548c\u8cac\u4efb\u7b49\u554f\u984c\u3002<\/li>\n\n\n\n<li><strong>Evaluation Metric:<\/strong> (\u8bc4\u4f30\u6307\u6807) &#8211; \u6307\u7528\u65bc\u8a55\u4f30\u6a5f\u5668\u5b78\u7fd2\u6a21\u578b\u5728\u7d66\u5b9a\u4efb\u52d9\u4e0a\u7684\u6548\u80fd\u7684\u91cf\u5316\u6307\u6a19\uff08\u4f8b\u5982\uff0c\u6e96\u78ba\u5ea6\u3001\u7cbe\u78ba\u7387\u3001\u53ec\u56de\u7387\u3001F1 \u5206\u6578\u3001AUC\u3001RMSE\u3001MAE\uff09\u3002<\/li>\n\n\n\n<li><strong>Expert System:<\/strong> (\u4e13\u5bb6\u7cfb\u7edf) &#8211; \u6307\u4e00\u7a2e\u6a21\u64ec\u4eba\u985e\u5c08\u5bb6\u6c7a\u7b56\u80fd\u529b\u7684\u96fb\u8166\u7cfb\u7d71\uff0c\u901a\u5e38\u900f\u904e\u4f7f\u7528\u898f\u5247\u548c\u4e8b\u5be6\u7684\u77e5\u8b58\u5eab\u3002<\/li>\n\n\n\n<li><strong>Federated Learning:<\/strong> (\u8054\u90a6\u5b66\u4e60) &#8211; \u6307\u4e00\u7a2e\u5206\u6563\u5f0f\u6a5f\u5668\u5b78\u7fd2\u65b9\u6cd5\uff0c\u5141\u8a31\u591a\u65b9\u5728\u4e0d\u5171\u4eab\u5176\u654f\u611f\u6578\u64da\u7684\u60c5\u6cc1\u4e0b\u5354\u540c\u8a13\u7df4\u6a21\u578b\u3002<\/li>\n\n\n\n<li><strong>Feature:<\/strong> (\u7279\u5f81) &#8211; \u6307\u6578\u64da\u9ede\u7684\u55ae\u500b\u53ef\u6e2c\u91cf\u7684\u5c6c\u6027\u6216\u7279\u5fb5\uff0c\u6a5f\u5668\u5b78\u7fd2\u6a21\u578b\u4f7f\u7528\u5b83\u4f86\u9032\u884c\u9810\u6e2c\u3002<\/li>\n\n\n\n<li><strong>Foundation Model:<\/strong> (\u57fa\u7840\u6a21\u578b) &#8211; \u6307\u4e00\u7a2e\u5927\u578b AI \u6a21\u578b\uff0c\u901a\u5e38\u5728\u9f90\u5927\u7684\u6578\u64da\u96c6\u4e0a\u8a13\u7df4\uff0c\u53ef\u4ee5\u91dd\u5c0d\u5404\u7a2e\u4e0b\u6e38\u4efb\u52d9\u9032\u884c\u8abf\u6574\u6216\u5fae\u8abf\u3002LLM \u662f\u4e00\u7a2e\u57fa\u790e\u6a21\u578b\u3002<\/li>\n\n\n\n<li><strong>Fuzzy Logic:<\/strong> (\u6a21\u7cca\u903b\u8f91) &#8211; \u6307\u4e00\u7a2e\u8655\u7406\u8fd1\u4f3c\u63a8\u7406\u800c\u4e0d\u662f\u56fa\u5b9a\u548c\u7cbe\u78ba\u63a8\u7406\u7684\u908f\u8f2f\u5f62\u5f0f\u3002\u5b83\u5728 AI \u4e2d\u7528\u65bc\u8655\u7406\u4e0d\u78ba\u5b9a\u6027\u548c\u6a21\u7cca\u6027\u3002<\/li>\n\n\n\n<li><strong>Generative AI:<\/strong> (\u751f\u6210\u5f0f\u4eba\u5de5\u667a\u80fd) &#8211; \u6307\u53ef\u4ee5\u751f\u6210\u8207\u5176\u8a13\u7df4\u6578\u64da\u76f8\u4f3c\u7684\u65b0\u5167\u5bb9\uff08\u4f8b\u5982\u6587\u672c\u3001\u5716\u50cf\u3001\u97f3\u6a02\u6216\u7a0b\u5f0f\u78bc\uff09\u7684 AI \u7cfb\u7d71\u3002<\/li>\n\n\n\n<li><strong>GPU:<\/strong> Graphics Processing Unit (\u56fe\u5f62\u5904\u7406\u5668) &#8211; \u6307\u4e00\u7a2e\u5c08\u9580\u7684\u96fb\u5b50\u96fb\u8def\uff0c\u65e8\u5728\u52a0\u901f\u5716\u50cf\u7684\u5275\u5efa\u4ee5\u9032\u884c\u986f\u793a\u3002\u7531\u65bc\u5176\u4e26\u884c\u8655\u7406\u80fd\u529b\uff0cGPU \u4e5f\u5ee3\u6cdb\u61c9\u7528\u65bc AI \u548c\u6a5f\u5668\u5b78\u7fd2\u9818\u57df\uff0c\u4ee5\u52a0\u901f\u795e\u7d93\u7db2\u8def\u7684\u8a13\u7df4 <sup>10<\/sup>\u3002\u7814\u7a76\u8cc7\u6599 <sup>10<\/sup> \u5f37\u8abf\u5176\u4e26\u884c\u8655\u7406\u80fd\u529b\u4ee5\u52a0\u5feb\u8a13\u7df4\u901f\u5ea6\u3002GPU \u5df2\u6210\u70ba\u8a13\u7df4\u5927\u578b\u8907\u96dc AI \u6a21\u578b\u4e0d\u53ef\u6216\u7f3a\u7684\u5de5\u5177\u3002AI \u7684\u8a08\u7b97\u9700\u6c42\u4f7f\u5f97\u4f7f\u7528 GPU \u7b49\u5c08\u7528\u786c\u9ad4\u6210\u70ba\u5fc5\u8981\u3002<\/li>\n\n\n\n<li><strong>Hallucination:<\/strong> (\u5e7b\u89c9) &#8211; \u6307 AI \u6a21\u578b\uff08\u5c24\u5176\u662f\u5927\u578b\u8a9e\u8a00\u6a21\u578b\uff09\u81ea\u4fe1\u5730\u751f\u6210\u672a\u57fa\u65bc\u8a13\u7df4\u6578\u64da\u7684\u932f\u8aa4\u6216\u7121\u610f\u7fa9\u4fe1\u606f\u7684\u50be\u5411 <sup>4<\/sup>\u3002\u7814\u7a76\u8cc7\u6599 <sup>4<\/sup> \u5c07\u5176\u5b9a\u7fa9\u70ba\u81ea\u4fe1\u5730\u5448\u73fe\u865b\u5047\u4fe1\u606f\u3002\u89e3\u6c7a\u548c\u6e1b\u8f15\u5e7b\u89ba\u662f\u958b\u767c\u53ef\u9760\u548c\u503c\u5f97\u4fe1\u8cf4\u7684 AI \u7cfb\u7d71\u7684\u4e00\u500b\u91cd\u5927\u6311\u6230\u3002\u5e7b\u89ba\u662f LLM \u7684\u4e00\u500b\u5df2\u77e5\u554f\u984c\uff0c\u4f7f\u7528\u8005\u61c9\u8a72\u610f\u8b58\u5230\u9019\u4e00\u9ede\u3002<\/li>\n\n\n\n<li><strong>Heuristic:<\/strong> (\u542f\u53d1\u5f0f\u65b9\u6cd5) &#8211; \u6307\u4e00\u7a2e\u4f7f\u7528\u5be6\u7528\u65b9\u6cd5\u6216\u6377\u5f91\u4f86\u7522\u751f\u53ef\u80fd\u4e0d\u662f\u6700\u4f73\u4f46\u8db3\u4ee5\u6eff\u8db3\u7d66\u5b9a\u7d04\u675f\u689d\u4ef6\u7684\u89e3\u6c7a\u65b9\u6848\u7684\u554f\u984c\u89e3\u6c7a\u65b9\u6cd5\u3002\u5e38\u61c9\u7528\u65bc AI \u4e2d\u5c0b\u627e\u7cbe\u78ba\u89e3\u5728\u8a08\u7b97\u4e0a\u904e\u65bc\u6602\u8cb4\u7684\u4efb\u52d9\u3002<\/li>\n\n\n\n<li><strong>Human-in-the-Loop (HITL):<\/strong> (\u4eba\u673a\u534f\u540c) &#8211; \u6307\u4e00\u7a2e\u5c07\u4eba\u985e\u8f38\u5165\u6574\u5408\u5230 AI \u7cfb\u7d71\u4e2d\u4ee5\u63d0\u9ad8\u6e96\u78ba\u6027\u3001\u53ef\u9760\u6027\u6216\u8655\u7406\u5b8c\u5168\u81ea\u52d5\u5316\u4e0d\u53ef\u884c\u6216\u4e0d\u53ef\u53d6\u7684\u60c5\u6cc1\u7684\u65b9\u6cd5\u3002<\/li>\n\n\n\n<li><strong>Inference:<\/strong> (\u63a8\u65ad) &#8211; \u6307\u4f7f\u7528\u7d93\u904e\u8a13\u7df4\u7684\u6a5f\u5668\u5b78\u7fd2\u6a21\u578b\u5c0d\u65b0\u7684\u3001\u672a\u898b\u904e\u7684\u6578\u64da\u9032\u884c\u9810\u6e2c\u6216\u6c7a\u7b56\u7684\u904e\u7a0b <sup>5<\/sup>\u3002\u7814\u7a76\u8cc7\u6599 <sup>5<\/sup> \u89e3\u91cb\u8aaa\u5b83\u6709\u52a9\u65bc AI \u7cfb\u7d71\u5f97\u51fa\u8d85\u51fa\u5176\u8a13\u7df4\u6578\u64da\u7684\u7d50\u8ad6\u3002\u7814\u7a76\u8cc7\u6599 <sup>17<\/sup> \u63d0\u5230\u4e86 TIDL-RT \u63a8\u8ad6\u3002\u63a8\u8ad6\u662f\u7d93\u904e\u8a13\u7df4\u7684 AI \u6a21\u578b\u7684\u5be6\u969b\u61c9\u7528\u3002\u8a13\u7df4\u5f8c\uff0cAI \u6a21\u578b\u7528\u65bc\u5c0d\u65b0\u6578\u64da\u9032\u884c\u63a8\u8ad6\u3002<\/li>\n\n\n\n<li><strong>Interpretability:<\/strong> (\u53ef\u7406\u89e3\u6027) &#8211; \u8207\u53ef\u89e3\u91cb\u6027\u5bc6\u5207\u76f8\u95dc\uff0c\u53ef\u7406\u89e3\u6027\u6307\u7684\u662f\u7406\u89e3\u8f38\u5165\u7279\u5fb5\u8207 AI \u6a21\u578b\u8f38\u51fa\u4e4b\u9593\u56e0\u679c\u95dc\u4fc2\u7684\u80fd\u529b\u3002<\/li>\n\n\n\n<li><strong>Knowledge Graph:<\/strong> (\u77e5\u8bc6\u56fe\u8c31) &#8211; \u6307\u77e5\u8b58\u7684\u5716\u5f62\u8868\u793a\uff0c\u7531\u5be6\u9ad4\uff08\u7bc0\u9ede\uff09\u53ca\u5176\u95dc\u4fc2\uff08\u908a\uff09\u7d44\u6210\uff0c\u5e38\u61c9\u7528\u65bc AI \u7684\u77e5\u8b58\u8868\u793a\u548c\u63a8\u7406\u3002<\/li>\n\n\n\n<li><strong>Label:<\/strong> (\u6807\u7b7e) &#8211; \u6307\u6a5f\u5668\u5b78\u7fd2\u6a21\u578b\u8a13\u7df4\u8981\u9810\u6e2c\u7684\u76ee\u6a19\u8b8a\u6578\u6216\u503c\u3002<\/li>\n\n\n\n<li><strong>Latency:<\/strong> (\u5ef6\u8fdf) &#8211; \u6307 AI \u7cfb\u7d71\u8f38\u5165\u548c\u76f8\u61c9\u8f38\u51fa\u4e4b\u9593\u7684\u5ef6\u9072\u3002\u4f4e\u5ef6\u9072\u5c0d\u65bc\u5373\u6642\u61c9\u7528\u81f3\u95dc\u91cd\u8981\u3002<\/li>\n\n\n\n<li><strong>Machine-in-the-Loop (MITL):<\/strong> (\u673a\u5728\u73af) &#8211; \u6307\u4e00\u7a2e AI \u7cfb\u7d71\u900f\u904e\u63d0\u4f9b\u5efa\u8b70\u3001\u5206\u6790\u6216\u81ea\u52d5\u5316\u67d0\u4e9b\u4efb\u52d9\u4f86\u589e\u5f37\u4eba\u985e\u80fd\u529b\u7684\u7bc4\u4f8b\uff0c\u800c\u4eba\u985e\u4fdd\u7559\u6574\u9ad4\u63a7\u5236\u6b0a\u3002<\/li>\n\n\n\n<li><strong>Model:<\/strong> (\u6a21\u578b) &#8211; \u5728\u6a5f\u5668\u5b78\u7fd2\u4e2d\uff0c\u6a21\u578b\u662f\u8a13\u7df4\u904e\u7a0b\u7684\u8f38\u51fa\uff0c\u8868\u793a\u8a13\u7df4\u6578\u64da\u4e2d\u5b78\u7fd2\u5230\u7684\u95dc\u4fc2\u548c\u6a21\u5f0f\uff0c\u53ef\u7528\u65bc\u5c0d\u65b0\u6578\u64da\u9032\u884c\u9810\u6e2c\u3002<\/li>\n\n\n\n<li><strong>Monitoring:<\/strong> (\u76d1\u63a7) &#8211; \u6307\u6301\u7e8c\u8ffd\u8e64\u5df2\u90e8\u7f72 AI \u7cfb\u7d71\u7684\u6548\u80fd\u548c\u884c\u70ba\uff0c\u4ee5\u78ba\u4fdd\u5176\u6b63\u5e38\u904b\u4f5c\u4e26\u9054\u6210\u76ee\u6a19\u7684\u904e\u7a0b\u3002<\/li>\n\n\n\n<li><strong>Multimodal AI:<\/strong> (\u591a\u6a21\u6001\u4eba\u5de5\u667a\u80fd) &#8211; \u6307\u53ef\u4ee5\u8655\u7406\u548c\u6574\u5408\u4f86\u81ea\u591a\u7a2e\u985e\u578b\u8f38\u5165\u6578\u64da\uff08\u4f8b\u5982\u6587\u672c\u3001\u5716\u50cf\u548c\u97f3\u8a0a\uff09\u4fe1\u606f\u7684 AI \u7cfb\u7d71\u3002<\/li>\n\n\n\n<li><strong>Natural Language:<\/strong> (\u81ea\u7136\u8bed\u8a00) &#8211; \u6307\u4eba\u985e\u7528\u65bc\u6e9d\u901a\u7684\u8a9e\u8a00\uff0c\u8207\u7a0b\u5f0f\u8a9e\u8a00\u7b49\u5f62\u5f0f\u8a9e\u8a00\u76f8\u5c0d\u3002<\/li>\n\n\n\n<li><strong>Ontology:<\/strong> (\u672c\u4f53) &#8211; \u6307\u5c07\u77e5\u8b58\u6b63\u5f0f\u8868\u793a\u70ba\u9818\u57df\u5167\u7684\u4e00\u7d44\u6982\u5ff5\u4ee5\u53ca\u9019\u4e9b\u6982\u5ff5\u4e4b\u9593\u7684\u95dc\u4fc2\u3002\u61c9\u7528\u65bc AI \u7684\u77e5\u8b58\u5171\u4eab\u548c\u63a8\u7406\u3002<\/li>\n\n\n\n<li><strong>Parameter:<\/strong> (\u53c2\u6570) &#8211; \u6307\u6a5f\u5668\u5b78\u7fd2\u6a21\u578b\u4e2d\u5f9e\u8a13\u7df4\u6578\u64da\u4e2d\u5b78\u7fd2\u4e26\u6c7a\u5b9a\u6a21\u578b\u884c\u70ba\u7684\u8b8a\u6578\u3002<\/li>\n\n\n\n<li><strong>Policy:<\/strong> (\u7b56\u7565) &#8211; \u5728\u5f37\u5316\u5b78\u7fd2\u4e2d\uff0c\u6307\u5f9e\u72c0\u614b\u5230\u52d5\u4f5c\u7684\u6620\u5c04\uff0c\u4ee3\u7406\u4f7f\u7528\u5b83\u4f86\u6c7a\u5b9a\u5728\u6bcf\u500b\u72c0\u614b\u4e0b\u63a1\u53d6\u4ec0\u9ebc\u52d5\u4f5c\u3002<\/li>\n\n\n\n<li><strong>Reproducibility:<\/strong> (\u53ef\u91cd\u590d\u6027) &#8211; \u6307\u7576\u4f7f\u7528\u76f8\u540c\u7684\u6578\u64da\u548c\u8a2d\u5b9a\u91cd\u8907 AI \u5be6\u9a57\u6216\u6a21\u578b\u8a13\u7df4\u904e\u7a0b\u6642\uff0c\u7372\u5f97\u4e00\u81f4\u7d50\u679c\u7684\u80fd\u529b\u3002<\/li>\n\n\n\n<li><strong>Reward Function:<\/strong> (\u5956\u52b1\u51fd\u6570) &#8211; \u5728\u5f37\u5316\u5b78\u7fd2\u4e2d\uff0c\u6307\u5b9a\u7fa9\u4ee3\u7406\u5728\u7d66\u5b9a\u72c0\u614b\u4e0b\u63a1\u53d6\u7279\u5b9a\u884c\u52d5\u6240\u7372\u5f97\u7684\u734e\u52f5\u6216\u61f2\u7f70\u7684\u51fd\u6578\u3002<\/li>\n\n\n\n<li><strong>Robotics:<\/strong> (\u673a\u5668\u4eba\u5b66) &#8211; \u6307\u4e00\u500b\u6574\u5408\u96fb\u8166\u79d1\u5b78\u3001\u5de5\u7a0b\u5b78\u548c\u5176\u4ed6\u5b78\u79d1\u7684\u8de8\u9818\u57df\u5b78\u79d1\uff0c\u65e8\u5728\u8a2d\u8a08\u3001\u5efa\u9020\u3001\u64cd\u4f5c\u548c\u61c9\u7528\u6a5f\u5668\u4eba\u3002AI \u5728\u4f7f\u6a5f\u5668\u4eba\u80fd\u5920\u81ea\u4e3b\u57f7\u884c\u8907\u96dc\u4efb\u52d9\u65b9\u9762\u767c\u63ee\u8457\u8d8a\u4f86\u8d8a\u91cd\u8981\u7684\u4f5c\u7528 <sup>2<\/sup>\u3002<\/li>\n\n\n\n<li><strong>Robustness:<\/strong> (\u9c81\u68d2\u6027) &#8211; \u6307 AI \u7cfb\u7d71\u5728\u5404\u7a2e\u689d\u4ef6\u4e0b\uff08\u5305\u62ec\u96dc\u8a0a\u6216\u5c0d\u6297\u6027\u8f38\u5165\uff09\u7dad\u6301\u5176\u6548\u80fd\u548c\u53ef\u9760\u6027\u7684\u80fd\u529b\u3002<\/li>\n\n\n\n<li><strong>Scalability:<\/strong> (\u53ef\u6269\u5c55\u6027) &#8211; \u6307 AI \u7cfb\u7d71\u6216\u57fa\u790e\u8a2d\u65bd\u8655\u7406\u4e0d\u65b7\u589e\u9577\u7684\u6578\u64da\u91cf\u6216\u5de5\u4f5c\u8ca0\u8f09\u7684\u80fd\u529b\u3002<\/li>\n\n\n\n<li><strong>Speech Recognition:<\/strong> (\u8bed\u97f3\u8bc6\u522b) &#8211; \u6307\u4e00\u7a2e\u4f7f\u96fb\u8166\u80fd\u5920\u7406\u89e3\u4e26\u5c07\u53e3\u8a9e\u8f49\u9304\u70ba\u6587\u672c\u7684\u6280\u8853 <sup>12<\/sup>\u3002<\/li>\n\n\n\n<li><strong>Training Data:<\/strong> (\u8bad\u7ec3\u6570\u636e) &#8211; \u6307\u7528\u65bc\u8a13\u7df4\u6a5f\u5668\u5b78\u7fd2\u6a21\u578b\u7684\u6578\u64da\u96c6\u3002\u8a13\u7df4\u6578\u64da\u7684\u54c1\u8cea\u548c\u6578\u91cf\u986f\u8457\u5f71\u97ff\u6a21\u578b\u7684\u6548\u80fd <sup>3<\/sup>\u3002\u7814\u7a76\u8cc7\u6599 <sup>4<\/sup> \u5c07\u5176\u5b9a\u7fa9\u70ba\u7528\u65bc\u8a13\u7df4 AI \u7cfb\u7d71\u7684\u6578\u64da\u96c6\u3002\u7814\u7a76\u8cc7\u6599 <sup>5<\/sup> \u63d0\u5230 AI \u7cfb\u7d71\u662f\u900f\u904e\u6a5f\u5668\u5b78\u7fd2\u548c\u8a13\u7df4\u6578\u64da\u5be6\u73fe\u7684\u3002\u7814\u7a76\u8cc7\u6599 <sup>3<\/sup> \u63d0\u5230 ML \u5c08\u6ce8\u65bc\u900f\u904e\u7d93\u9a57\u548c\u6578\u64da\u6539\u9032\u7684\u6f14\u7b97\u6cd5\u3002\u8a13\u7df4\u6578\u64da\u7684\u54c1\u8cea\u548c\u4ee3\u8868\u6027\u5c0d\u65bc\u4efb\u4f55\u6a5f\u5668\u5b78\u7fd2\u6a21\u578b\u7684\u6210\u529f\u90fd\u81f3\u95dc\u91cd\u8981\u3002ML \u6a21\u578b\u7684\u6548\u80fd\u76f4\u63a5\u8207\u5176\u8a13\u7df4\u7684\u6578\u64da\u76f8\u95dc\u3002<\/li>\n\n\n\n<li><strong>Transparency:<\/strong> (\u900f\u660e\u5ea6) &#8211; \u6307 AI \u7cfb\u7d71\u7684\u67b6\u69cb\u3001\u8a13\u7df4\u904e\u7a0b\u548c\u6c7a\u7b56\u908f\u8f2f\u516c\u958b\u4e14\u6613\u65bc\u7406\u89e3\u7684\u7a0b\u5ea6\u3002<\/li>\n\n\n\n<li><strong>Version Control:<\/strong> (\u7248\u672c\u63a7\u5236) &#8211; \u6307\u7528\u65bc\u7ba1\u7406 AI \u958b\u767c\u4e2d\u7a0b\u5f0f\u78bc\u3001\u6578\u64da\u548c\u6a21\u578b\u8b8a\u66f4\u7684\u7cfb\u7d71\uff0c\u5141\u8a31\u8ffd\u8e64\u6b77\u53f2\u3001\u5354\u4f5c\u4ee5\u53ca\u5728\u9700\u8981\u6642\u56de\u6efe\u3002<\/li>\n\n\n\n<li><strong>\u667a\u80fd\u81ea\u52d5\u5316 (IA) \u5b50\u8853\u8a9e\uff1a<\/strong> \u64f4\u5c55 IA \u985e\u5225\u3002<\/li>\n\n\n\n<li><strong>API-First:<\/strong> \u6307\u4e00\u7a2e\u5728\u69cb\u5efa\u61c9\u7528\u7a0b\u5f0f\u7684\u5176\u4ed6\u7d44\u4ef6\u4e4b\u524d\u512a\u5148\u8003\u616e API \u7684\u8a2d\u8a08\u548c\u958b\u767c\u7684\u8edf\u9ad4\u958b\u767c\u65b9\u6cd5\u3002\u5c0d\u65bc\u6574\u5408 AI \u529f\u80fd\u975e\u5e38\u91cd\u8981 <sup>7<\/sup>\u3002\u7814\u7a76\u8cc7\u6599 <sup>7<\/sup> \u5c07 API-First \u5b9a\u7fa9\u70ba\u512a\u5148\u8003\u616e API \u958b\u767c\u3002API \u512a\u5148\u7684\u65b9\u6cd5\u6709\u52a9\u65bc\u5c07 AI \u529f\u80fd\u6574\u5408\u5230\u5404\u7a2e\u7cfb\u7d71\u4e2d\u3002\u70ba\u4e86\u5be6\u73fe AI \u7684\u7121\u7e2b\u6574\u5408\uff0c\u512a\u5148\u8003\u616e API \u662f\u4e00\u500b\u95dc\u9375\u7b56\u7565\u3002<\/li>\n\n\n\n<li><strong>\u6df1\u5ea6\u5b78\u7fd2 (DL)\uff1a<\/strong> \u8207\u6df1\u5ea6\u5b78\u7fd2\u76f8\u95dc\u7684\u7e2e\u5beb\u8a5e\u3002<\/li>\n<\/ul>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Activation Function:<\/strong> (\u6fc0\u6d3b\u51fd\u6570) &#8211; \u6307\u795e\u7d93\u7db2\u8def\u4e2d\u5f15\u5165\u975e\u7dda\u6027\u7684\u51fd\u6578\uff0c\u4f7f\u7db2\u8def\u80fd\u5920\u5b78\u7fd2\u6578\u64da\u4e2d\u7684\u8907\u96dc\u95dc\u4fc2\u3002<\/li>\n\n\n\n<li><strong>Batch Size:<\/strong> (\u6279\u5927\u5c0f) &#8211; \u6307\u5728\u8a13\u7df4\u671f\u9593\uff0c\u6a21\u578b\u5728\u4e00\u6b21\u8fed\u4ee3\u4e2d\u8655\u7406\u7684\u8a13\u7df4\u6a23\u672c\u6578\u91cf\u3002<\/li>\n\n\n\n<li><strong>Boosting:<\/strong> (\u63d0\u5347\u6cd5) &#8211; \u6307\u4e00\u7a2e\u96c6\u6210\u5b78\u7fd2\u6280\u8853\uff0c\u5b83\u6309\u9806\u5e8f\u8a13\u7df4\u591a\u500b\u5f31\u5b78\u7fd2\u5668\uff0c\u6bcf\u500b\u65b0\u7684\u5b78\u7fd2\u5668\u90fd\u5c08\u6ce8\u65bc\u7cfe\u6b63\u524d\u4e00\u500b\u5b78\u7fd2\u5668\u7684\u932f\u8aa4\u3002<\/li>\n\n\n\n<li><strong>Clustering:<\/strong> (\u805a\u7c7b) &#8211; \u6307\u4e00\u7a2e\u975e\u76e3\u7763\u5f0f\u5b78\u7fd2\u6280\u8853\uff0c\u6d89\u53ca\u5c07\u76f8\u4f3c\u7684\u6578\u64da\u9ede\u5206\u7d44\u5728\u4e00\u8d77\u3002<\/li>\n\n\n\n<li><strong>Epoch:<\/strong> (\u8f6e) &#8211; \u6307\u5728\u8a13\u7df4\u904e\u7a0b\u4e2d\uff0c\u6574\u500b\u8a13\u7df4\u6578\u64da\u96c6\u901a\u904e\u6a5f\u5668\u5b78\u7fd2\u6f14\u7b97\u6cd5\u7684\u4e00\u6b21\u5b8c\u6574\u50b3\u905e\u3002<\/li>\n\n\n\n<li><strong>K-Nearest Neighbors (KNN):<\/strong> (K \u8fd1\u90bb) &#8211; \u6307\u4e00\u7a2e\u7528\u65bc\u5206\u985e\u548c\u8ff4\u6b78\u7684\u975e\u53c3\u6578\u76e3\u7763\u5f0f\u5b78\u7fd2\u6f14\u7b97\u6cd5\uff0c\u5176\u4e2d\u65b0\u6578\u64da\u9ede\u7684\u985e\u5225\u6216\u503c\u662f\u6839\u64da\u5176\u5728\u7279\u5fb5\u7a7a\u9593\u4e2d k \u500b\u6700\u8fd1\u9130\u5c45\u7684\u591a\u6578\u985e\u5225\u6216\u5e73\u5747\u503c\u4f86\u9810\u6e2c\u7684\u3002<\/li>\n\n\n\n<li><strong>Model:<\/strong> (\u6a21\u578b) &#8211; (\u518d\u6b21\u5f37\u8abf)<\/li>\n\n\n\n<li><strong>Neural Network:<\/strong> (\u795e\u7ecf\u7f51\u7edc) &#8211; (\u518d\u6b21\u5f37\u8abf ANN \u7684\u6982\u5ff5)<\/li>\n\n\n\n<li><strong>Optimization Algorithm:<\/strong> (\u4f18\u5316\u7b97\u6cd5) &#8211; \u6307\u5728\u8a13\u7df4\u671f\u9593\u7528\u65bc\u8abf\u6574\u6a5f\u5668\u5b78\u7fd2\u6a21\u578b\u53c3\u6578\u4ee5\u6700\u5c0f\u5316\u640d\u5931\u51fd\u6578\u7684\u6f14\u7b97\u6cd5\u3002<\/li>\n\n\n\n<li><strong>Principal Component Analysis (PCA):<\/strong> (\u4e3b\u6210\u5206\u5206\u6790) &#8211; \u6307\u4e00\u7a2e\u964d\u7dad\u6280\u8853\uff0c\u5b83\u5c07\u6578\u64da\u8f49\u63db\u70ba\u4e00\u7d44\u65b0\u7684\u4e0d\u76f8\u95dc\u8b8a\u6578\uff08\u7a31\u70ba\u4e3b\u6210\u5206\uff09\uff0c\u9019\u4e9b\u8b8a\u6578\u6355\u7372\u4e86\u6578\u64da\u4e2d\u7684\u6700\u5927\u8b8a\u7570\u6578\u3002<\/li>\n\n\n\n<li><strong>Random Forest:<\/strong> (\u968f\u673a\u68ee\u6797) &#8211; \u6307\u4e00\u7a2e\u96c6\u6210\u5b78\u7fd2\u6f14\u7b97\u6cd5\uff0c\u5b83\u69cb\u5efa\u591a\u500b\u6c7a\u7b56\u6a39\uff0c\u4e26\u8f38\u51fa\u5404\u500b\u6a39\u7684\u6a21\u5f0f\u985e\u5225\uff08\u7528\u65bc\u5206\u985e\uff09\u6216\u5e73\u5747\u9810\u6e2c\uff08\u7528\u65bc\u8ff4\u6b78\uff09\u3002<\/li>\n\n\n\n<li><strong>Regression:<\/strong> (\u56de\u5f52) &#8211; \u6307\u4e00\u7a2e\u76e3\u7763\u5f0f\u5b78\u7fd2\u4efb\u52d9\uff0c\u6d89\u53ca\u6839\u64da\u8f38\u5165\u7279\u5fb5\u9810\u6e2c\u9023\u7e8c\u7684\u8f38\u51fa\u503c\u3002<\/li>\n\n\n\n<li><strong>Support Vector Machine (SVM):<\/strong> (\u652f\u6301\u5411\u91cf\u673a) &#8211; \u6307\u4e00\u7a2e\u76e3\u7763\u5f0f\u5b78\u7fd2\u6f14\u7b97\u6cd5\uff0c\u5b83\u627e\u5230\u6700\u4f73\u5206\u96e2\u4e0d\u540c\u985e\u5225\u6578\u64da\u9ede\u7684\u8d85\u5e73\u9762\u3002<\/li>\n\n\n\n<li><strong>\u5176\u4ed6\u76f8\u95dc\u7e2e\u5beb\u8a5e\uff1a<\/strong> \u64f4\u5c55\u5176\u4ed6\u985e\u5225\u3002<\/li>\n<\/ul>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>1-bit SGD:<\/strong> 1-bit Stochastic Gradient Descent (1 \u4f4d\u5143\u96a8\u6a5f\u68af\u5ea6\u4e0b\u964d) &#8211; \u662f\u96a8\u6a5f\u68af\u5ea6\u4e0b\u964d (SGD) \u512a\u5316\u6f14\u7b97\u6cd5\u7684\u4e00\u7a2e\u8b8a\u9ad4\u3002<\/li>\n\n\n\n<li><strong>A\/B testing:<\/strong> (A\/B \u6d4b\u8bd5) &#8211; \u4e00\u7a2e\u6bd4\u8f03\u5169\u7a2e\u6216\u591a\u7a2e\u8b8a\u9ad4\uff08\u901a\u5e38\u662f\u55ae\u4e00\u8b8a\u6578\u7684\u8b8a\u66f4\uff09\u4ee5\u78ba\u5b9a\u54ea\u7a2e\u8b8a\u9ad4\u5728\u7d66\u5b9a\u7684\u5ea6\u91cf\u65b9\u9762\u8868\u73fe\u66f4\u597d\u7684\u65b9\u6cd5\u3002<\/li>\n\n\n\n<li><strong>Ablation:<\/strong> (\u6d88\u878d) &#8211; \u6307\u7cfb\u7d71\u4e2d\u7d44\u4ef6\u7684\u79fb\u9664\u3002<\/li>\n\n\n\n<li><strong>ACE:<\/strong> Alternating conditional expectation (\u4ea4\u66ff\u689d\u4ef6\u671f\u671b) &#8211; \u4e00\u7a2e\u7528\u65bc\u5c0b\u627e\u8ff4\u6b78\u5206\u6790\u4e2d\u53cd\u61c9\u8b8a\u6578\u548c\u9810\u6e2c\u8b8a\u6578\u4e4b\u9593\u6700\u4f73\u8f49\u63db\u7684\u6f14\u7b97\u6cd5\u3002<\/li>\n\n\n\n<li><strong>Accuracy:<\/strong> (\u6e96\u78ba\u5ea6) &#8211; \u5206\u985e\u6a21\u578b\u4e2d\u6b63\u78ba\u9810\u6e2c\u7684\u6bd4\u4f8b\u3002<\/li>\n\n\n\n<li><strong>Active Learning:<\/strong> (\u4e3b\u52a8\u5b66\u4e60) &#8211; \u6307\u4e00\u7a2e\u6a5f\u5668\u5b78\u7fd2\u6280\u8853\uff0c\u5176\u4e2d\u6f14\u7b97\u6cd5\u7b56\u7565\u6027\u5730\u9078\u64c7\u6700\u5177\u4fe1\u606f\u6027\u7684\u6578\u64da\u9ede\uff0c\u7531\u4eba\u985e\u5c08\u5bb6\u9032\u884c\u6a19\u8a18\uff0c\u65e8\u5728\u4ee5\u66f4\u5c11\u7684\u6a19\u8a18\u6578\u64da\u5be6\u73fe\u9ad8\u6e96\u78ba\u5ea6\u3002<\/li>\n\n\n\n<li><strong>Agent:<\/strong> (\u4ee3\u7406) &#8211; \u5728\u5f37\u5316\u5b78\u7fd2\u4e2d\uff0c\u6307\u8207\u74b0\u5883\u4e92\u52d5\u4e26\u900f\u904e\u8a66\u932f\u5b78\u7fd2\u7684\u5be6\u9ad4\u3002<\/li>\n\n\n\n<li><strong>AI Safety:<\/strong> (\u4eba\u5de5\u667a\u80fd\u5b89\u5168) &#8211; \u6307\u7814\u7a76\u5982\u4f55\u5b89\u5168\u5730\u958b\u767c\u548c\u4f7f\u7528\u4eba\u5de5\u667a\u6167\u3002<\/li>\n\n\n\n<li><strong>AI writing:<\/strong> (AI \u5199\u4f5c) &#8211; \u4f7f\u7528 AI \u6280\u8853\u4f86\u7522\u751f\u6216\u7de8\u8f2f\u6587\u672c\u3002<\/li>\n\n\n\n<li><strong>Algorithm:<\/strong> (\u7b97\u6cd5) &#8211; (\u7b2c\u56db\u6b21\u5f37\u8abf)<\/li>\n\n\n\n<li><strong>Alignment:<\/strong> (\u5c0d\u9f4a) &#8211; AI \u7684\u76ee\u6a19\u8207\u5176\u5275\u5efa\u8005\u7684\u76ee\u6a19\u4e00\u81f4\u7684\u7a0b\u5ea6\u3002<\/li>\n\n\n\n<li><strong>AlphaGo:<\/strong> (AlphaGo) &#8211; \u4e00\u500b\u73a9\u570d\u68cb\u7684 AI \u7cfb\u7d71\u3002<\/li>\n\n\n\n<li><strong>Alt Text for Images:<\/strong> (\u5716\u50cf\u7684\u66ff\u4ee3\u6587\u5b57) &#8211; \u5728\u6c92\u6709\u624b\u52d5\u63d0\u4f9b\u7684\u5716\u50cf\u63cf\u8ff0\u6642\uff0cAI \u7522\u751f\u7684\u5716\u50cf\u63cf\u8ff0\u3002<\/li>\n\n\n\n<li><strong>Anomaly Detection:<\/strong> (\u5f02\u5e38\u68c0\u6d4b) &#8211; \u6307\u8b58\u5225\u6578\u64da\u4e2d\u4e0d\u7b26\u5408\u9810\u671f\u884c\u70ba\u7684\u6a21\u5f0f\u7684\u904e\u7a0b\u3002\u5e38\u61c9\u7528\u65bc\u6b3a\u8a50\u6aa2\u6e2c\u548c\u5b89\u5168\u9818\u57df\u3002<\/li>\n\n\n\n<li><strong>Anthropomorphism:<\/strong> (\u64ec\u4eba\u5316) &#8211; \u5c07\u4eba\u985e\u7279\u5fb5\u6b78\u56e0\u65bc\u975e\u4eba\u985e\u5be6\u9ad4\u3002<\/li>\n\n\n\n<li><strong>API Automation Testing:<\/strong> (API \u81ea\u52d5\u5316\u6e2c\u8a66) &#8211; \u81ea\u52d5\u5316\u6e2c\u8a66 API \u4ee5\u78ba\u4fdd\u5b83\u5011\u6309\u9810\u671f\u5de5\u4f5c\u3002<\/li>\n\n\n\n<li><strong>Application Portfolio Management (APM):<\/strong> (\u61c9\u7528\u7a0b\u5f0f\u7d44\u5408\u7ba1\u7406) &#8211; \u7ba1\u7406\u7d44\u7e54\u7684\u61c9\u7528\u7a0b\u5f0f\u7d44\u5408\u3002<\/li>\n\n\n\n<li><strong>AR:<\/strong> Augmented Reality (\u589e\u5f3a\u73b0\u5b9e) &#8211; \u4e00\u7a2e\u771f\u5be6\u4e16\u754c\u74b0\u5883\u7684\u4e92\u52d5\u9ad4\u9a57\uff0c\u900f\u904e\u96fb\u8166\u7522\u751f\u7684\u611f\u77e5\u8cc7\u8a0a\u4f86\u589e\u5f37\u771f\u5be6\u4e16\u754c\u4e2d\u7684\u7269\u9ad4\u3002<\/li>\n\n\n\n<li><strong>ASIC:<\/strong> Application-Specific Integrated Circuit (\u7279\u5b9a\u61c9\u7528\u7a4d\u9ad4\u96fb\u8def) &#8211; \u5c08\u70ba\u7279\u5b9a\u7528\u9014\u8a2d\u8a08\u7684\u7a4d\u9ad4\u96fb\u8def\u3002<\/li>\n\n\n\n<li><strong>Autonomous:<\/strong> (\u81ea\u4e3b) &#8211; \u80fd\u5920\u5728\u6c92\u6709\u4eba\u70ba\u8f38\u5165\u7684\u60c5\u6cc1\u4e0b\u57f7\u884c\u4efb\u52d9\u3002<\/li>\n\n\n\n<li><strong>AutoEncoder:<\/strong> (\u81ea\u7f16\u7801\u5668) &#8211; \u4e00\u7a2e\u7528\u65bc\u5b78\u7fd2\u672a\u6a19\u8a18\u6578\u64da\u7684\u6709\u6548\u7de8\u78bc\u7684\u4eba\u5de5\u795e\u7d93\u7db2\u8def\u3002<\/li>\n\n\n\n<li><strong>Automation:<\/strong> (\u81ea\u52a8\u5316) &#8211; \u4f7f\u7528\u6a5f\u5668\u6216\u8edf\u9ad4\u8655\u7406\u6d41\u7a0b\uff0c\u6e1b\u5c11\u4eba\u70ba\u8f38\u5165\u7684\u9700\u6c42\u3002<\/li>\n\n\n\n<li><strong>Back Office Solutions:<\/strong> (\u5f8c\u7aef\u89e3\u6c7a\u65b9\u6848) &#8211; \u652f\u63f4\u7d44\u7e54\u5167\u90e8\u904b\u4f5c\u7684\u7cfb\u7d71\u548c\u8edf\u9ad4\u3002<\/li>\n\n\n\n<li><strong>Bagging:<\/strong> (\u88c5\u888b\u6cd5) &#8211; \u6307\u4e00\u7a2e\u96c6\u6210\u5b78\u7fd2\u6280\u8853\uff0c\u5b83\u6d89\u53ca\u5728\u8a13\u7df4\u6578\u64da\u7684\u4e0d\u540c\u5b50\u96c6\u4e0a\u7368\u7acb\u8a13\u7df4\u591a\u500b\u6a21\u578b\uff0c\u7136\u5f8c\u5e73\u5747\u5b83\u5011\u7684\u9810\u6e2c\u3002<\/li>\n\n\n\n<li><strong>Bard:<\/strong> (Bard) &#8211; Google \u958b\u767c\u7684\u804a\u5929\u6a5f\u5668\u4eba\u3002<\/li>\n\n\n\n<li><strong>Bayesian Network (BN):<\/strong> (\u8d1d\u53f6\u65af\u7f51\u7edc) &#8211; \u4e00\u7a2e\u8868\u793a\u8b8a\u6578\u53ca\u5176\u689d\u4ef6\u4f9d\u8cf4\u95dc\u4fc2\u7684\u6a5f\u7387\u5716\u6a21\u578b\u3002<\/li>\n\n\n\n<li><strong>BERT&#8217;s variants:<\/strong> (BERT \u7684\u8b8a\u9ad4) &#8211; \u5305\u62ec ALBERT \u548c LaBSE\u3002<\/li>\n\n\n\n<li><strong>BiFPN:<\/strong> Bidirectional Feature Pyramid Network (\u96d9\u5411\u7279\u5fb5\u91d1\u5b57\u5854\u7db2\u8def) &#8211; \u4e00\u7a2e\u7528\u65bc\u7269\u4ef6\u5075\u6e2c\u7684\u6709\u6548\u591a\u5c3a\u5ea6\u7279\u5fb5\u878d\u5408\u65b9\u6cd5\u3002<\/li>\n\n\n\n<li><strong>Big Data:<\/strong> (\u5927\u6570\u636e) &#8211; (\u518d\u6b21\u5f37\u8abf)<\/li>\n\n\n\n<li><strong>Bing Chat:<\/strong> (Bing \u804a\u5929) &#8211; \u6574\u5408\u5230 Bing \u4e2d\u7684\u804a\u5929\u6a5f\u5668\u4eba\u529f\u80fd\u3002<\/li>\n\n\n\n<li><strong>BLEU:<\/strong> Bilingual Evaluation Understudy (\u96d9\u8a9e\u8a55\u4f30\u66ff\u88dc) &#8211; \u4e00\u7a2e\u7ffb\u8b6f\u6709\u6548\u6027\u7684\u8a55\u5206\u3002<\/li>\n\n\n\n<li><strong>Boosting:<\/strong> (\u63d0\u5347\u6cd5) &#8211; (\u518d\u6b21\u5f37\u8abf)<\/li>\n\n\n\n<li><strong>BPMF:<\/strong> Bayesian Probabilistic Matrix Factorization (\u8c9d\u6c0f\u6a5f\u7387\u77e9\u9663\u5206\u89e3) &#8211; \u4e00\u7a2e\u7528\u65bc\u63a8\u85a6\u7cfb\u7d71\u7684\u6a5f\u7387\u6027\u77e9\u9663\u5206\u89e3\u65b9\u6cd5\u3002<\/li>\n\n\n\n<li><strong>BPTT:<\/strong> Backpropagation Through Time (\u6642\u9593\u53cd\u5411\u50b3\u64ad) &#8211; \u7528\u65bc\u8a13\u7df4\u5faa\u74b0\u795e\u7d93\u7db2\u8def\u7684\u68af\u5ea6\u4e0b\u964d\u6f14\u7b97\u6cd5\u3002<\/li>\n\n\n\n<li><strong>Burstiness:<\/strong> (\u7a81\u767c\u6027) &#8211; \u53e5\u5b50\u7d50\u69cb\u548c\u9577\u5ea6\u7684\u8b8a\u5316\u7a0b\u5ea6\u7684\u8861\u91cf\u3002<\/li>\n\n\n\n<li><strong>CAPTCHA:<\/strong> Completely Automated Public Turing test to tell Computers and Humans Apart (\u5168\u81ea\u52d5\u5340\u5206\u96fb\u8166\u548c\u4eba\u985e\u7684\u516c\u958b\u5716\u9748\u6e2c\u8a66) &#8211; \u4e00\u7a2e\u7528\u65bc\u78ba\u4fdd\u4f7f\u7528\u8005\u662f\u4eba\u985e\u7684\u7dda\u4e0a\u6e2c\u8a66\u3002<\/li>\n\n\n\n<li><strong>CBOW:<\/strong> Continuous-Bag-of-Words (\u9023\u7e8c\u8a5e\u888b\u6a21\u578b) &#8211; \u4e00\u7a2e\u7528\u65bc\u5b78\u7fd2\u8a5e\u5d4c\u5165\u7684\u6a21\u578b\u3002<\/li>\n\n\n\n<li><strong>Chinese room:<\/strong> (\u4e2d\u6587\u623f\u95f4) &#8211; \u4e00\u500b\u95dc\u65bc AI \u7684\u54f2\u5b78\u601d\u60f3\u5be6\u9a57\u3002<\/li>\n\n\n\n<li><strong>Classification:<\/strong> (\u5206\u7c7b) &#8211; \u6307\u4e00\u7a2e\u76e3\u7763\u5f0f\u5b78\u7fd2\u4efb\u52d9\uff0c\u6d89\u53ca\u5c07\u6578\u64da\u9ede\u5206\u914d\u7d66\u9810\u5b9a\u7fa9\u7684\u985e\u5225\u6216\u985e\u5225\u3002<\/li>\n\n\n\n<li><strong>CMMs:<\/strong> Conditional Markov Models (\u689d\u4ef6\u99ac\u53ef\u592b\u6a21\u578b) &#8211; \u4e00\u7a2e\u7528\u65bc\u5e8f\u5217\u6a19\u8a18\u7684\u5716\u5f62\u6a21\u578b\u3002<\/li>\n\n\n\n<li><strong>Computer:<\/strong> (\u96fb\u8166) &#8211; \u4e00\u7a2e\u53ef\u4ee5\u81ea\u52d5\u57f7\u884c\u7a0b\u5f0f\u5316\u4efb\u52d9\u7684\u6a5f\u5668\u3002<\/li>\n\n\n\n<li><strong>Computer Vision Tasks:<\/strong> (\u8ba1\u7b97\u673a\u89c6\u89c9\u4efb\u52a1) &#8211; \u6307\u96fb\u8166\u8996\u89ba\u4e2d\u7684\u7279\u5b9a\u554f\u984c\uff0c\u4f8b\u5982\u5716\u50cf\u5206\u985e\u3001\u76ee\u6a19\u6aa2\u6e2c\u3001\u5716\u50cf\u5206\u5272\u548c\u5716\u50cf\u751f\u6210\u3002<\/li>\n\n\n\n<li><strong>Conditional Random Fields (CRFs):<\/strong> (\u689d\u4ef6\u96a8\u6a5f\u5834) &#8211; \u4e00\u7a2e\u7528\u65bc\u7d50\u69cb\u5316\u9810\u6e2c\u7684\u6a5f\u7387\u5716\u6a21\u578b\u3002<\/li>\n\n\n\n<li><strong>Connectionist Temporal Classification (CTC):<\/strong> (\u9023\u63a5\u4e3b\u7fa9\u6642\u9593\u5206\u985e) &#8211; \u4e00\u7a2e\u7528\u65bc\u5e8f\u5217\u6a19\u8a18\u7684\u640d\u5931\u51fd\u6578\u3002<\/li>\n\n\n\n<li><strong>Contractive autoencoder (CAE):<\/strong> (\u6536\u7e2e\u81ea\u7de8\u78bc\u5668) &#8211; \u4e00\u7a2e\u65e8\u5728\u5b78\u7fd2\u5c0d\u8f38\u5165\u7684\u5c0f\u64fe\u52d5\u5177\u6709\u9b6f\u68d2\u6027\u7684\u8868\u793a\u7684\u81ea\u7de8\u78bc\u5668\u3002<\/li>\n\n\n\n<li><strong>Conversational AI:<\/strong> (\u5c0d\u8a71\u5f0f AI) &#8211; \u5c08\u6ce8\u65bc\u4f7f\u96fb\u8166\u80fd\u5920\u4ee5\u985e\u4f3c\u4eba\u985e\u7684\u65b9\u5f0f\u9032\u884c\u5c0d\u8a71\u7684 AI \u9818\u57df\u3002<\/li>\n\n\n\n<li><strong>CTR:<\/strong> Collaborative Topic Regression (\u5354\u540c\u4e3b\u984c\u8ff4\u6b78) &#8211; \u4e00\u7a2e\u7528\u65bc\u63a8\u85a6\u7cfb\u7d71\u7684\u6280\u8853\u3002<\/li>\n\n\n\n<li><strong>CV:<\/strong> Computer Vision (\u8ba1\u7b97\u673a\u89c6\u89c9) &#8211; AI \u7684\u4e00\u500b\u9818\u57df\uff0c\u4f7f\u96fb\u8166\u80fd\u5920\u5f9e\u6578\u4f4d\u5716\u50cf\u6216\u5f71\u7247\u4e2d\u300c\u770b\u898b\u300d\u4e26\u89e3\u91cb\u8cc7\u8a0a\u3002<\/li>\n\n\n\n<li><strong>DALL-E:<\/strong> (DALL-E) &#8211; OpenAI \u767c\u5e03\u7684 AI \u5716\u50cf\u751f\u6210\u5668\u3002<\/li>\n\n\n\n<li><strong>DAAF:<\/strong> Data Augmentation and Auxiliary Feature (\u6578\u64da\u589e\u5f37\u548c\u8f14\u52a9\u7279\u5fb5) &#8211; \u4e00\u7a2e\u53ef\u80fd\u6d89\u53ca\u4f7f\u7528\u8f14\u52a9\u7279\u5fb5\u4ee5\u53ca\u6578\u64da\u589e\u5f37\u4f86\u6539\u9032\u6a21\u578b\u8a13\u7df4\u7684\u6280\u8853\u3002<\/li>\n\n\n\n<li><strong>Data Science:<\/strong> (\u6570\u636e\u79d1\u5b66) &#8211; \u6307\u4e00\u500b\u8de8\u5b78\u79d1\u7684\u9818\u57df\uff0c\u5b83\u4f7f\u7528\u79d1\u5b78\u65b9\u6cd5\u3001\u904e\u7a0b\u3001\u6f14\u7b97\u6cd5\u548c\u7cfb\u7d71\u5f9e\u5608\u96dc\u7684\u3001\u7d50\u69cb\u5316\u7684\u548c\u975e\u7d50\u69cb\u5316\u7684\u6578\u64da\u4e2d\u63d0\u53d6\u77e5\u8b58\u548c\u6d1e\u5bdf\u529b\u3002<\/li>\n\n\n\n<li><strong>DBM:<\/strong> Deep Boltzmann Machine (\u6df1\u5ea6\u73bb\u723e\u8332\u66fc\u6a5f) &#8211; \u4e00\u7a2e\u5177\u6709\u591a\u500b\u96b1\u85cf\u5c64\u7684\u7121\u5411\u6a5f\u7387\u5716\u6a21\u578b\u3002<\/li>\n\n\n\n<li><strong>DBN:<\/strong> Deep Belief Network (\u6df1\u5ea6\u4fe1\u5ff5\u7db2\u8def) &#8211; \u4e00\u7a2e\u7531\u591a\u500b\u6f5b\u5728\u8b8a\u6578\u5c64\u7d44\u6210\u7684\u751f\u6210\u5716\u6a21\u578b\u3002<\/li>\n\n\n\n<li><strong>DBSCAN:<\/strong> Density-Based Spatial Clustering of Applications with Noise (\u57fa\u65bc\u5bc6\u5ea6\u7684\u96dc\u8a0a\u61c9\u7528\u7a7a\u9593\u805a\u985e) &#8211; \u4e00\u7a2e\u57fa\u65bc\u5bc6\u5ea6\u7684\u805a\u985e\u6f14\u7b97\u6cd5\u3002<\/li>\n\n\n\n<li><strong>DCGAN:<\/strong> Deep Convolutional Generative Adversarial Network (\u6df1\u5ea6\u5377\u7a4d\u751f\u6210\u5c0d\u6297\u7db2\u8def) &#8211; \u4e00\u7a2e\u4f7f\u7528\u5377\u7a4d\u7db2\u8def\u7684 GAN\u3002<\/li>\n\n\n\n<li><strong>DE:<\/strong> Differential Evolution (\u5dee\u5206\u9032\u5316) &#8211; \u4e00\u7a2e\u7528\u65bc\u512a\u5316\u554f\u984c\u7684\u9032\u5316\u6f14\u7b97\u6cd5\u3002<\/li>\n\n\n\n<li><strong>DevOps:<\/strong> (\u958b\u767c\u904b\u71df) &#8211; \u4e00\u7a2e\u5f37\u8abf\u8edf\u9ad4\u958b\u767c\u4eba\u54e1\u548c IT \u5c08\u696d\u4eba\u54e1\u4e4b\u9593\u5354\u4f5c\u548c\u6e9d\u901a\u7684\u8edf\u9ad4\u958b\u767c\u65b9\u6cd5\u3002<\/li>\n\n\n\n<li><strong>Dimensionality Reduction:<\/strong> (\u964d\u7ef4) &#8211; \u6307\u7528\u65bc\u6e1b\u5c11\u6578\u64da\u96c6\u4e2d\u7279\u5fb5\u6578\u91cf\u540c\u6642\u4fdd\u7559\u5176\u57fa\u672c\u4fe1\u606f\u7684\u6280\u8853\u3002<\/li>\n\n\n\n<li><strong>ELIZA:<\/strong> (ELIZA) &#8211; 1960 \u5e74\u4ee3\u958b\u767c\u7684\u65e9\u671f\u804a\u5929\u6a5f\u5668\u4eba\u3002<\/li>\n\n\n\n<li><strong>ELMo:<\/strong> Embeddings from Language Models (\u4f86\u81ea\u8a9e\u8a00\u6a21\u578b\u7684\u5d4c\u5165) &#8211; \u4e00\u7a2e\u7528\u65bc\u7522\u751f\u8a5e\u5d4c\u5165\u7684\u65b9\u6cd5\u3002<\/li>\n\n\n\n<li><strong>ELU:<\/strong> Exponential Linear Unit (\u6307\u6578\u7dda\u6027\u55ae\u5143) &#8211; \u4e00\u7a2e\u795e\u7d93\u7db2\u8def\u7684\u6fc0\u6d3b\u51fd\u6578\u3002<\/li>\n\n\n\n<li><strong>Emergent behavior:<\/strong> (\u6d8c\u73b0\u884c\u4e3a) &#8211; \u5f9e\u57fa\u672c\u904e\u7a0b\u7522\u751f\u7684\u8907\u96dc\u884c\u70ba\u3002<\/li>\n\n\n\n<li><strong>EM:<\/strong> Expectation-maximization algorithm (\u671f\u671b\u6700\u5927\u5316\u6f14\u7b97\u6cd5) &#8211; \u4e00\u7a2e\u7528\u65bc\u5c0b\u627e\u6a5f\u7387\u6a21\u578b\u4e2d\u6f5b\u5728\u8b8a\u6578\u7684\u53c3\u6578\u7684\u8fed\u4ee3\u6f14\u7b97\u6cd5\u3002<\/li>\n\n\n\n<li><strong>ERNIE:<\/strong> Enhanced Representation through kNowledge IntEgration (\u900f\u904e\u77e5\u8b58\u6574\u5408\u7684\u589e\u5f37\u8868\u793a) &#8211; \u4e00\u7a2e\u8a9e\u8a00\u6a21\u578b\u3002<\/li>\n\n\n\n<li><strong>ETL:<\/strong> Extract, Transform, Load (\u63d0\u53d6\u3001\u8f49\u63db\u3001\u8f09\u5165) &#8211; \u5c07\u8cc7\u6599\u5f9e\u4e00\u500b\u8cc7\u6599\u5eab\u79fb\u52d5\u5230\u53e6\u4e00\u500b\u8cc7\u6599\u5eab\u7684\u904e\u7a0b\u3002<\/li>\n\n\n\n<li><strong>FST:<\/strong> Finite-state transducer (\u6709\u9650\u72c0\u614b\u8f49\u63db\u5668) &#8211; \u4e00\u7a2e\u8a08\u7b97\u6a21\u578b\u3002<\/li>\n\n\n\n<li><strong>FTP:<\/strong> File Transfer Protocol (\u6a94\u6848\u50b3\u8f38\u5354\u5b9a) &#8211; \u7528\u65bc\u5728\u96fb\u8166\u4e4b\u9593\u50b3\u8f38\u6a94\u6848\u7684\u5354\u5b9a\u3002<\/li>\n\n\n\n<li><strong>GALE:<\/strong> Global Aggregations of Local Explanations (\u5c40\u90e8\u89e3\u91cb\u7684\u5168\u5c40\u805a\u5408) &#8211; \u4e00\u7a2e\u65e8\u5728\u900f\u904e\u805a\u5408\u4f86\u81ea\u500b\u5225\u9810\u6e2c\u7684\u591a\u500b\u5c40\u90e8\u89e3\u91cb\u4f86\u63a8\u5c0e\u6a21\u578b\u884c\u70ba\u7684\u5168\u5c40\u6d1e\u5bdf\u529b\u7684\u53ef\u89e3\u91cb\u6027\u6280\u8853\u3002<\/li>\n\n\n\n<li><strong>GA:<\/strong> Genetic Algorithm (\u57fa\u56e0\u6f14\u7b97\u6cd5) &#8211; \u4e00\u7a2e\u6a21\u4eff\u81ea\u7136\u9078\u64c7\u904e\u7a0b\u7684\u512a\u5316\u6f14\u7b97\u6cd5\u3002<\/li>\n\n\n\n<li><strong>GAP:<\/strong> Global Average Pooling (\u5168\u5c40\u5e73\u5747\u6c60\u5316) &#8211; \u4e00\u7a2e\u7528\u65bc\u6e1b\u5c11\u5377\u7a4d\u795e\u7d93\u7db2\u8def\u4e2d\u7279\u5fb5\u5716\u7a7a\u9593\u7dad\u5ea6\u7684\u64cd\u4f5c\u3002<\/li>\n\n\n\n<li><strong>GBM:<\/strong> Gradient Boosting Machine (\u68af\u5ea6\u63d0\u5347\u6a5f) &#8211; \u4e00\u7a2e\u6a5f\u5668\u5b78\u7fd2\u6280\u8853\uff0c\u7528\u65bc\u8ff4\u6b78\u548c\u5206\u985e\u554f\u984c\uff0c\u5b83\u4ee5\u96c6\u6210\u7684\u65b9\u5f0f\u7d44\u5408\u591a\u500b\u5f31\u9810\u6e2c\u6a21\u578b\u3002<\/li>\n\n\n\n<li><strong>GEBI:<\/strong> Global Explanation for Bias Identification (\u504f\u5dee\u8b58\u5225\u7684\u5168\u5c40\u89e3\u91cb) &#8211; \u4e00\u7a2e\u53ef\u89e3\u91cb\u6027\u65b9\u6cd5\uff0c\u5b83\u5c07\u5c40\u90e8\u89e3\u91cb\uff08\u55ae\u500b\u9810\u6e2c\u7684\u89e3\u91cb\uff09\u805a\u5408\u70ba\u5168\u5c40\u89e3\u91cb\uff0c\u76ee\u7684\u662f\u5728\u6c7a\u7b56\u4e2d\u767c\u73fe\u504f\u5dee\u548c\u7cfb\u7d71\u6027\u932f\u8aa4\u3002<\/li>\n\n\n\n<li><strong>Gen AI:<\/strong> Generative AI (\u751f\u6210\u5f0f\u4eba\u5de5\u667a\u80fd) &#8211; (\u518d\u6b21\u5f37\u8abf)<\/li>\n\n\n\n<li><strong>GLM:<\/strong> Generalized Linear Model (\u5ee3\u7fa9\u7dda\u6027\u6a21\u578b) &#8211; \u4e00\u7a2e\u9748\u6d3b\u7684\u7dda\u6027\u8ff4\u6b78\u63a8\u5ee3\u3002<\/li>\n\n\n\n<li><strong>Gloss2Text:<\/strong> Gloss to Text (\u5f9e\u624b\u8a9e\u6ce8\u91cb\u5230\u6587\u672c) &#8211; \u5728\u624b\u8a9e\u8655\u7406\u4e2d\uff0c\u5c07\u624b\u8a9e\u6ce8\u91cb\u5e8f\u5217\uff08\u8a5e\u7d1a\u8868\u793a\uff09\u8f49\u63db\u70ba\u8a9e\u6cd5\u6b63\u78ba\u7684\u53e3\u8a9e\u53e5\u5b50\u7684\u4efb\u52d9\u3002<\/li>\n\n\n\n<li><strong>GloVe:<\/strong> Global Vectors for Word Representation (\u8a5e\u8868\u793a\u7684\u5168\u5c40\u5411\u91cf) &#8211; \u4e00\u7a2e\u7528\u65bc\u7372\u5f97\u8a5e\u5d4c\u5165\u7684\u6a21\u578b\u3002<\/li>\n\n\n\n<li><strong>GMM:<\/strong> Gaussian mixture model (\u9ad8\u65af\u6df7\u5408\u6a21\u578b) &#8211; \u4e00\u7a2e\u6a5f\u7387\u6a21\u578b\uff0c\u5047\u8a2d\u6240\u6709\u6578\u64da\u9ede\u90fd\u662f\u5f9e\u6709\u9650\u500b\u5177\u6709\u672a\u77e5\u53c3\u6578\u7684\u9ad8\u65af\u5206\u4f48\u7684\u6df7\u5408\u4e2d\u751f\u6210\u7684\u3002<\/li>\n\n\n\n<li><strong>GNN:<\/strong> Graph Neural Network (\u5716\u795e\u7d93\u7db2\u8def) &#8211; \u4e00\u7a2e\u53ef\u4ee5\u76f4\u63a5\u5728\u5716\u7d50\u69cb\u4e0a\u64cd\u4f5c\u7684\u795e\u7d93\u7db2\u8def\u3002<\/li>\n\n\n\n<li><strong>Government:<\/strong> (\u653f\u5e9c) &#8211; \u8207\u653f\u5e9c\u76f8\u95dc\u7684\u61c9\u7528\u3002<\/li>\n\n\n\n<li><strong>GRU:<\/strong> Gated Recurrent Unit (\u95e8\u63a7\u5faa\u73af\u5355\u5143) &#8211; (\u518d\u6b21\u5f37\u8abf)<\/li>\n\n\n\n<li><strong>HAN:<\/strong> Hierarchical Attention Network (\u5206\u5c64\u6ce8\u610f\u529b\u7db2\u8def) &#8211; \u4e00\u7a2e\u795e\u7d93\u7db2\u8def\u67b6\u69cb\uff0c\u901a\u5e38\u7528\u65bc\u6587\u6a94\u5206\u985e\uff0c\u5b83\u5728\u8a5e\u548c\u53e5\u5b50\u5c64\u7d1a\u4f7f\u7528\u6ce8\u610f\u529b\u6a5f\u5236\u4f86\u5206\u5c64\u6355\u7372\u91cd\u8981\u8cc7\u8a0a\u3002<\/li>\n\n\n\n<li><strong>HDP:<\/strong> Hierarchical Dirichlet process (\u5206\u5c64\u72c4\u5229\u514b\u96f7\u904e\u7a0b) &#8211; \u4e00\u7a2e\u975e\u53c3\u6578\u8c9d\u6c0f\u6a21\u578b\u3002<\/li>\n\n\n\n<li><strong>hLDA:<\/strong> Hierarchical Latent Dirichlet allocation (\u5206\u5c64\u6f5b\u5728\u72c4\u5229\u514b\u96f7\u5206\u914d) &#8211; LDA \u7684\u64f4\u5c55\uff0c\u5b83\u5c07\u4e3b\u984c\u7d44\u7e54\u6210\u5c64\u6b21\u7d50\u69cb\u3002<\/li>\n\n\n\n<li><strong>HMMs:<\/strong> Hidden Markov Models (\u96b1\u85cf\u99ac\u53ef\u592b\u6a21\u578b) &#8211; \u4e00\u7a2e\u7528\u65bc\u5efa\u6a21\u5e8f\u5217\u6578\u64da\u7684\u7d71\u8a08\u6a21\u578b\u3002<\/li>\n\n\n\n<li><strong>ICA:<\/strong> Independent Component Analysis (\u7368\u7acb\u6210\u5206\u5206\u6790) &#8211; \u4e00\u7a2e\u7528\u65bc\u5c07\u591a\u8b8a\u6578\u8a0a\u865f\u5206\u96e2\u6210\u52a0\u6027\u5b50\u6210\u5206\u7684\u7d71\u8a08\u6280\u8853\u3002<\/li>\n\n\n\n<li><strong>IDR:<\/strong> Input dependence rate (\u8f38\u5165\u4f9d\u8cf4\u7387) &#8211; \u4e00\u7a2e\u53ef\u80fd\u8861\u91cf\u6a21\u578b\u8f38\u51fa\u6216\u5167\u90e8\u72c0\u614b\u5c0d\u5176\u8f38\u5165\u7279\u5fb5\u7684\u4f9d\u8cf4\u7a0b\u5ea6\u7684\u6307\u6a19\uff0c\u53ef\u80fd\u7528\u65bc\u53ef\u89e3\u91cb\u6027\u6216\u654f\u611f\u5ea6\u5206\u6790\u3002<\/li>\n\n\n\n<li><strong>IIR:<\/strong> Input independence rate (\u8f38\u5165\u7368\u7acb\u7387) &#8211; \u4e00\u7a2e\u53ef\u80fd\u8861\u91cf\u6a21\u578b\u8f38\u51fa\u8207\u5176\u8f38\u5165\u7279\u5fb5\u7684\u7368\u7acb\u7a0b\u5ea6\u7684\u6307\u6a19\uff0c\u53ef\u80fd\u8207\u9b6f\u68d2\u6027\u6216\u516c\u5e73\u6027\u8a55\u4f30\u76f8\u95dc\u3002<\/li>\n\n\n\n<li><strong>INFD:<\/strong> Explanation Infidelity (\u89e3\u91cb\u4e0d\u5fe0\u5be6\u5ea6) &#8211; XAI \u4e2d\u4f7f\u7528\u7684\u4e00\u7a2e\u6307\u6a19\uff0c\u7528\u65bc\u8861\u91cf\u7576\u8f38\u5165\u53d7\u5230\u64fe\u52d5\u6642\uff0c\u89e3\u91cb\uff08\u4f8b\u5982\uff0c\u7279\u5fb5\u6b78\u56e0\uff09\u53cd\u6620\u6a21\u578b\u5be6\u969b\u884c\u70ba\u7684\u7a0b\u5ea6\u3002<\/li>\n\n\n\n<li><strong>IPA:<\/strong> Intelligent Process Automation (\u667a\u80fd\u6d41\u7a0b\u81ea\u52d5\u5316) &#8211; \u4f7f\u7528\u81ea\u52d5\u5316\u4f86\u6539\u9032\u696d\u52d9\u548c IT \u6d41\u7a0b\u7684\u6548\u80fd\u3002<\/li>\n\n\n\n<li><strong>i.i.d:<\/strong> Independent and Identically Distributed (\u7368\u7acb\u4e14\u6046\u7b49\u5206\u4f48) &#8211; \u7d71\u8a08\u5b78\u4e2d\u7684\u4e00\u500b\u5047\u8a2d\u3002<\/li>\n\n\n\n<li><strong>IT Automation:<\/strong> (IT \u81ea\u52d5\u5316) &#8211; \u4f7f\u7528\u6280\u8853\u81ea\u52d5\u5316 IT \u6d41\u7a0b\u548c\u4efb\u52d9\u3002<\/li>\n\n\n\n<li><strong>ITOps:<\/strong> (IT \u904b\u71df) &#8211; \u8ca0\u8cac\u7d44\u7e54 IT \u57fa\u790e\u8a2d\u65bd\u548c\u904b\u71df\u7684\u90e8\u9580\u3002<\/li>\n\n\n\n<li><strong>IVA:<\/strong> Intelligent Virtual Assistant (\u667a\u80fd\u865b\u64ec\u52a9\u7406) &#8211; \u4e00\u7a2e\u4f7f\u7528 AI \u4f86\u63d0\u4f9b\u5ba2\u6236\u652f\u63f4\u548c\u5354\u52a9\u7684\u865b\u64ec\u52a9\u7406\u3002<\/li>\n\n\n\n<li><strong>KAN:<\/strong> Kolmogorov-Arnold Networks (\u79d1\u723e\u83ab\u6208\u7f85\u592b-\u963f\u8afe\u5fb7\u7db2\u8def) &#8211; \u4e00\u7a2e\u53d7\u79d1\u723e\u83ab\u6208\u7f85\u592b-\u963f\u8afe\u5fb7\u8868\u793a\u5b9a\u7406\u555f\u767c\u7684\u65b0\u578b\u795e\u7d93\u7db2\u8def\u67b6\u69cb\u3002<\/li>\n\n\n\n<li><strong>KL:<\/strong> Kullback-Leibler divergence (\u5eab\u723e\u5df4\u514b-\u840a\u5e03\u52d2\u6563\u5ea6) &#8211; \u8861\u91cf\u5169\u500b\u6a5f\u7387\u5206\u4f48\u4e4b\u9593\u5dee\u7570\u7684\u6307\u6a19\u3002<\/li>\n\n\n\n<li><strong>LaBSE:<\/strong> Language-agnostic BERT Sentence Embeddings (\u8207\u8a9e\u8a00\u7121\u95dc\u7684 BERT \u53e5\u5b50\u5d4c\u5165) &#8211; BERT \u7684\u4e00\u500b\u8b8a\u9ad4\u3002<\/li>\n\n\n\n<li><strong>LDA:<\/strong> Latent Dirichlet allocation (\u6f5b\u5728\u72c4\u5229\u514b\u96f7\u5206\u914d) &#8211; \u4e00\u7a2e\u7528\u65bc\u4e3b\u984c\u5efa\u6a21\u7684\u751f\u6210\u6a5f\u7387\u6a21\u578b\u3002<\/li>\n\n\n\n<li><strong>LDADE:<\/strong> Latent Dirichlet Allocation Differential Evolution (\u6f5b\u5728\u72c4\u5229\u514b\u96f7\u5206\u914d\u5dee\u5206\u9032\u5316) &#8211; LDA \u548c\u5dee\u5206\u9032\u5316\u7684\u7d44\u5408\u3002<\/li>\n\n\n\n<li><strong>Learning To Rank (LTR):<\/strong> (\u6392\u5e8f\u5b78\u7fd2) &#8211; \u6a5f\u5668\u5b78\u7fd2\u7684\u4e00\u500b\u5b50\u9818\u57df\uff0c\u61c9\u7528\u65bc\u8cc7\u8a0a\u6aa2\u7d22\u7cfb\u7d71\u7684\u5efa\u69cb\u3002<\/li>\n\n\n\n<li><strong>LMMs:<\/strong> large multimodal models (\u5927\u578b\u591a\u6a21\u614b\u6a21\u578b) &#8211; \u53ef\u4ee5\u8655\u7406\u548c\u6574\u5408\u591a\u7a2e\u985e\u578b\u8f38\u5165\u6578\u64da\u7684\u6a21\u578b\u3002<\/li>\n\n\n\n<li><strong>LSA:<\/strong> Latent semantic analysis (\u6f5b\u5728\u8a9e\u7fa9\u5206\u6790) &#8211; \u4e00\u7a2e NLP \u6280\u8853\u3002<\/li>\n\n\n\n<li><strong>LSI:<\/strong> Latent Semantic Indexing (\u6f5b\u5728\u8a9e\u7fa9\u7d22\u5f15) &#8211; \u4e00\u7a2e\u8cc7\u8a0a\u6aa2\u7d22\u6280\u8853\u3002<\/li>\n\n\n\n<li><strong>MAP:<\/strong> Maximum A Posteriori Estimation (\u6700\u5927\u5f8c\u9a57\u4f30\u8a08) &#8211; \u4e00\u7a2e\u4f30\u8a08\u7d71\u8a08\u6a21\u578b\u53c3\u6578\u7684\u65b9\u6cd5\u3002<\/li>\n\n\n\n<li><strong>MAPE:<\/strong> Mean Absolute Percentage Error (\u5e73\u5747\u7d55\u5c0d\u767e\u5206\u6bd4\u8aa4\u5dee) &#8211; \u8861\u91cf\u9810\u6e2c\u6e96\u78ba\u6027\u7684\u6307\u6a19\u3002<\/li>\n\n\n\n<li><strong>Markov Chain Monte Carlo (MCMC):<\/strong> (\u99ac\u53ef\u592b\u93c8\u8499\u5730\u5361\u7f85) &#8211; \u4e00\u7a2e\u7528\u65bc\u5f9e\u6a5f\u7387\u5206\u4f48\u4e2d\u63a1\u6a23\u7684\u6f14\u7b97\u6cd5\u3002<\/li>\n\n\n\n<li><strong>MDRNN:<\/strong> Multidimensional recurrent neural network (\u591a\u7dad\u5faa\u74b0\u795e\u7d93\u7db2\u8def) &#8211; \u4e00\u7a2e\u53ef\u4ee5\u8655\u7406\u591a\u7dad\u5e8f\u5217\u6578\u64da\u7684 RNN\u3002<\/li>\n\n\n\n<li><strong>MDL:<\/strong> Minimum description length principle (\u6700\u5c0f\u63cf\u8ff0\u9577\u5ea6\u539f\u5247) &#8211; \u4e00\u7a2e\u5f62\u5f0f\u5316\u5967\u5361\u59c6\u5243\u5200\u7684\u8cc7\u8a0a\u7406\u8ad6\u539f\u5247\u3002<\/li>\n\n\n\n<li><strong>MER:<\/strong> Music Emotion Recognition (\u97f3\u6a02\u60c5\u611f\u8fa8\u8b58) &#8211; (\u6587\u4ef6\u4e2d\u8cc7\u8a0a\u4e0d\u53ef\u7528)<\/li>\n\n\n\n<li><strong>Midjourney:<\/strong> (Midjourney) &#8211; \u4e00\u500b AI \u5716\u50cf\u751f\u6210\u5668\u3002<\/li>\n\n\n\n<li><strong>MLE:<\/strong> Maximum Likelihood Estimation (\u6700\u5927\u4f3c\u7136\u4f30\u8a08) &#8211; \u4e00\u7a2e\u4f30\u8a08\u7d71\u8a08\u6a21\u578b\u53c3\u6578\u7684\u65b9\u6cd5\u3002<\/li>\n\n\n\n<li><strong>MLM:<\/strong> Music Language Models (\u97f3\u6a02\u8a9e\u8a00\u6a21\u578b) &#8211; (\u6587\u4ef6\u4e2d\u8cc7\u8a0a\u4e0d\u53ef\u7528)<\/li>\n\n\n\n<li><strong>MOS:<\/strong> Mean Opinion Score (\u5e73\u5747\u610f\u898b\u5206\u6578) &#8211; \u7528\u65bc\u8a55\u4f30\u8a9e\u97f3\u6216\u8996\u8a0a\u54c1\u8cea\u7684\u6307\u6a19\u3002<\/li>\n\n\n\n<li><strong>MRS:<\/strong> Music Recommender System (\u97f3\u6a02\u63a8\u85a6\u7cfb\u7d71) &#8211; (\u6587\u4ef6\u4e2d\u8cc7\u8a0a\u4e0d\u53ef\u7528)<\/li>\n\n\n\n<li><strong>MRR:<\/strong> Mean Reciprocal Rank (\u5e73\u5747\u5012\u6578\u6392\u540d) &#8211; \u4e00\u7a2e\u8a55\u4f30\u8cc7\u8a0a\u6aa2\u7d22\u7cfb\u7d71\u7684\u6307\u6a19\u3002<\/li>\n\n\n\n<li><strong>MSR:<\/strong> Music Style Recognition (\u97f3\u6a02\u98a8\u683c\u8fa8\u8b58) &#8211; (\u6587\u4ef6\u4e2d\u8cc7\u8a0a\u4e0d\u53ef\u7528)<\/li>\n\n\n\n<li><strong>MT-DNN a.k.a BigBird:<\/strong> Multi-Task Deep Neural Network (\u591a\u4efb\u52d9\u6df1\u5ea6\u795e\u7d93\u7db2\u8def) &#8211; \u4e00\u7a2e\u6df1\u5ea6\u795e\u7d93\u7db2\u8def\u3002<\/li>\n\n\n\n<li><strong>NAS:<\/strong> Neural Architecture Search (\u795e\u7d93\u67b6\u69cb\u641c\u5c0b) &#8211; \u4e00\u7a2e\u81ea\u52d5\u8a2d\u8a08\u4eba\u5de5\u795e\u7d93\u7db2\u8def\u7684\u6280\u8853\u3002<\/li>\n\n\n\n<li><strong>NERQ:<\/strong> Named Entity Recognition in Query (\u67e5\u8a62\u4e2d\u7684\u547d\u540d\u5be6\u9ad4\u8b58\u5225) &#8211; NER \u5728\u67e5\u8a62\u4e2d\u7684\u61c9\u7528\u3002<\/li>\n\n\n\n<li><strong>Neural net(work):<\/strong> (\u795e\u7ecf\u7f51\u7edc) &#8211; (\u518d\u6b21\u5f37\u8abf ANN \u7684\u6982\u5ff5)<\/li>\n\n\n\n<li><strong>Neural Turing Machine (NTM):<\/strong> (\u795e\u7d93\u5716\u9748\u6a5f) &#8211; \u4e00\u7a2e\u5177\u6709\u5916\u90e8\u8a18\u61b6\u9ad4\u7684\u5faa\u74b0\u795e\u7d93\u7db2\u8def\u3002<\/li>\n\n\n\n<li><strong>NLT:<\/strong> Neural Machine Translation (\u795e\u7d93\u6a5f\u5668\u7ffb\u8b6f) &#8211; \u4f7f\u7528\u795e\u7d93\u7db2\u8def\u9032\u884c\u7ffb\u8b6f\u7684\u65b9\u6cd5\u3002<\/li>\n\n\n\n<li><strong>NMF:<\/strong> Non-Negative Matrix Factorization (\u975e\u8ca0\u77e9\u9663\u5206\u89e3) &#8211; \u4e00\u7a2e\u77e9\u9663\u5206\u89e3\u6280\u8853\u3002<\/li>\n\n\n\n<li><strong>NN:<\/strong> Neural Network (\u795e\u7ecf\u7f51\u7edc) &#8211; (\u518d\u6b21\u5f37\u8abf ANN \u7684\u6982\u5ff5)<\/li>\n\n\n\n<li><strong>NPE:<\/strong> Neural Physical Engine (\u795e\u7d93\u7269\u7406\u5f15\u64ce) &#8211; (\u6587\u4ef6\u4e2d\u8cc7\u8a0a\u4e0d\u53ef\u7528)<\/li>\n\n\n\n<li><strong>NST:<\/strong> Neural Style Transfer (\u795e\u7d93\u98a8\u683c\u8f49\u63db) &#8211; \u4e00\u7a2e\u4f7f\u7528\u6df1\u5ea6\u795e\u7d93\u7db2\u8def\u9032\u884c\u98a8\u683c\u8f49\u63db\u7684\u65b9\u6cd5\u3002<\/li>\n\n\n\n<li><strong>NTM:<\/strong> Neural Turing Machine (\u795e\u7d93\u5716\u9748\u6a5f) &#8211; (\u518d\u6b21\u5f37\u8abf)<\/li>\n\n\n\n<li><strong>ODF:<\/strong> Onset Detection Function (\u8d77\u59cb\u9ede\u6aa2\u6e2c\u51fd\u6578) &#8211; (\u6587\u4ef6\u4e2d\u8cc7\u8a0a\u4e0d\u53ef\u7528)<\/li>\n\n\n\n<li><strong>OLAP:<\/strong> Online Analytical Processing (\u7dda\u4e0a\u5206\u6790\u8655\u7406) &#8211; \u4e00\u7a2e\u7528\u65bc\u591a\u7dad\u8cc7\u6599\u5206\u6790\u7684\u6280\u8853\u3002<\/li>\n\n\n\n<li><strong>OLS:<\/strong> ordinary least squares (\u666e\u901a\u6700\u5c0f\u4e8c\u4e58\u6cd5) &#8211; \u4e00\u7a2e\u7dda\u6027\u8ff4\u6b78\u65b9\u6cd5\u3002<\/li>\n\n\n\n<li><strong>OLTP:<\/strong> Online Transaction Processing (\u7dda\u4e0a\u4ea4\u6613\u8655\u7406) &#8211; \u652f\u63f4\u7db2\u8def\u4ea4\u6613\u7684\u8edf\u9ad4\u7a0b\u5f0f\u3002<\/li>\n\n\n\n<li><strong>OOB:<\/strong> Out of the Box (\u958b\u7bb1\u5373\u7528) &#8211; \u6307\u7522\u54c1\u6216\u7cfb\u7d71\u5728\u8cfc\u8cb7\u5f8c\u5373\u53ef\u7acb\u5373\u4f7f\u7528\u3002<\/li>\n\n\n\n<li><strong>OOF:<\/strong> Out Of Fold (\u6298\u5916) &#8211; \u5728\u4ea4\u53c9\u9a57\u8b49\u4e2d\u7528\u65bc\u8a55\u4f30\u6a21\u578b\u7684\u9810\u6e2c\u3002<\/li>\n\n\n\n<li><strong>OpenAI:<\/strong> (OpenAI) &#8211; \u958b\u767c ChatGPT \u548c DALL-E \u7684\u9818\u5148 AI \u516c\u53f8\u3002<\/li>\n\n\n\n<li><strong>OSRT:<\/strong> Open Source Runtime (\u958b\u6e90\u904b\u884c\u6642) &#8211; \u4e00\u7a2e\u958b\u6e90\u8edf\u9ad4\u57f7\u884c\u74b0\u5883\u3002<\/li>\n\n\n\n<li><strong>PACO:<\/strong> Poisson Additive Co-Clustering (\u6cca\u677e\u52a0\u6027\u5171\u805a\u985e) &#8211; (\u6587\u4ef6\u4e2d\u8cc7\u8a0a\u4e0d\u53ef\u7528)<\/li>\n\n\n\n<li><strong>PAP:<\/strong> Password Authentication Protocol (\u5bc6\u78bc\u9a57\u8b49\u5354\u5b9a) &#8211; \u7528\u65bc\u9a57\u8b49\u4f7f\u7528\u8005\u7684\u5354\u5b9a\u3002<\/li>\n\n\n\n<li><strong>Paraphrasing tool:<\/strong> (\u6539\u5beb\u5de5\u5177) &#8211; \u4e00\u7a2e\u81ea\u52d5\u6539\u5beb\u6587\u672c\u7684 AI \u5beb\u4f5c\u5de5\u5177\u3002<\/li>\n\n\n\n<li><strong>PaaS:<\/strong> Platform-as-a-Service (\u5e73\u53f0\u5373\u670d\u52d9) &#8211; \u4e00\u7a2e\u96f2\u7aef\u904b\u7b97\u6a21\u578b\u3002<\/li>\n\n\n\n<li><strong>Parameter:<\/strong> (\u53c2\u6570) &#8211; (\u518d\u6b21\u5f37\u8abf)<\/li>\n\n\n\n<li><strong>PCA:<\/strong> Principal Component Analysis (\u4e3b\u6210\u5206\u5206\u6790) &#8211; (\u518d\u6b21\u5f37\u8abf)<\/li>\n\n\n\n<li><strong>PEGASUS:<\/strong> Pre-training with Extracted Gap-Sentences for Abstractive Summarization (\u4f7f\u7528\u63d0\u53d6\u7684\u9593\u9699\u53e5\u5b50\u9032\u884c\u62bd\u8c61\u6458\u8981\u7684\u9810\u8a13\u7df4) &#8211; \u4e00\u7a2e\u7528\u65bc\u6587\u672c\u6458\u8981\u7684\u6a21\u578b\u3002<\/li>\n\n\n\n<li><strong>Perplexity:<\/strong> (\u56f0\u60d1\u5ea6) &#8211; \u8861\u91cf\u6587\u672c\u4e0d\u53ef\u9810\u6e2c\u6027\u7684\u6307\u6a19\u3002<\/li>\n\n\n\n<li><strong>Persona:<\/strong> (\u89d2\u8272) &#8211; \u5728 AI \u7684\u80cc\u666f\u4e0b\uff0c\u6307\u7684\u662f\u5206\u914d\u7d66 AI \u7cfb\u7d71\u7684\u4e00\u7d44\u7279\u5fb5\u3001\u7279\u8cea\u6216\u884c\u70ba\uff0c\u4ee5\u4f7f\u5176\u5728\u8207\u4f7f\u7528\u8005\u4e92\u52d5\u6642\u5177\u6709\u7368\u7279\u7684\u500b\u6027\u6216\u89d2\u8272\u3002<\/li>\n\n\n\n<li><strong>Plagiarism:<\/strong> (\u6284\u8972) &#8211; \u672a\u7d93\u6388\u6b0a\u4f7f\u7528\u4ed6\u4eba\u7684\u6587\u5b57\u6216\u60f3\u6cd5\u3002<\/li>\n\n\n\n<li><strong>PLSI:<\/strong> Probabilistic Latent Semantic Indexing (\u6a5f\u7387\u6f5b\u5728\u8a9e\u7fa9\u7d22\u5f15) &#8211; \u4e00\u7a2e\u7d71\u8a08\u6280\u8853\u3002<\/li>\n\n\n\n<li><strong>PMF:<\/strong> Probabilistic Matrix Factorization (\u6a5f\u7387\u77e9\u9663\u5206\u89e3) &#8211; \u4e00\u7a2e\u7528\u65bc\u5354\u540c\u904e\u6ffe\u7684\u6280\u8853\u3002<\/li>\n\n\n\n<li><strong>PMI:<\/strong> Pointwise Mutual Information (\u9010\u9ede\u4e92\u4fe1\u606f) &#8211; \u8861\u91cf\u5169\u500b\u96a8\u6a5f\u8b8a\u6578\u4e4b\u9593\u76f8\u95dc\u6027\u7684\u6307\u6a19\u3002<\/li>\n\n\n\n<li><strong>POC:<\/strong> Proof of Concept (\u6982\u5ff5\u9a57\u8b49) &#8211; (\u6587\u4ef6\u4e2d\u8cc7\u8a0a\u4e0d\u53ef\u7528)<\/li>\n\n\n\n<li><strong>POMDP:<\/strong> Partially Observable Markov Decision Process (\u90e8\u5206\u53ef\u89c0\u5bdf\u99ac\u53ef\u592b\u6c7a\u7b56\u904e\u7a0b) &#8211; (\u6587\u4ef6\u4e2d\u8cc7\u8a0a\u4e0d\u53ef\u7528)<\/li>\n\n\n\n<li><strong>POS:<\/strong> Part of Speech (\u8a5e\u6027) &#8211; (\u518d\u6b21\u5f37\u8abf)<\/li>\n\n\n\n<li><strong>PPMI:<\/strong> Positive Pointwise Mutual Information (\u6b63\u9010\u9ede\u4e92\u4fe1\u606f) &#8211; PMI \u7684\u8b8a\u9ad4\u3002<\/li>\n\n\n\n<li><strong>PR AUC (Area under the PR Curve):<\/strong> (PR \u66f2\u7dda\u4e0b\u9762\u7a4d) &#8211; \u7528\u65bc\u8a55\u4f30\u5206\u985e\u6a21\u578b\u6548\u80fd\u7684\u6307\u6a19\u3002<\/li>\n\n\n\n<li><strong>Predictive AI:<\/strong> (\u9810\u6e2c\u6027 AI) &#8211; \u4f7f\u7528\u6b77\u53f2\u548c\u5373\u6642\u6578\u64da\u4f86\u9810\u6e2c\u672a\u4f86\u4e8b\u4ef6\u6216\u7d50\u679c\u7684 AI \u7cfb\u7d71\u3002<\/li>\n\n\n\n<li><strong>PReLU:<\/strong> Parametric Rectified Linear Unit (\u53c3\u6578\u5316\u4fee\u6b63\u7dda\u6027\u55ae\u5143) &#8211; ReLU \u7684\u4e00\u500b\u8b8a\u9ad4\u3002<\/li>\n\n\n\n<li><strong>Programming:<\/strong> (\u7a0b\u5f0f\u8a2d\u8a08) &#8211; \u7d66\u4e88\u96fb\u8166\u6307\u4ee4\u7684\u904e\u7a0b\u3002<\/li>\n\n\n\n<li><strong>PTQ:<\/strong> Post Training Quantization (\u5f8c\u8a13\u7df4\u91cf\u5316) &#8211; \u4e00\u7a2e\u6a21\u578b\u512a\u5316\u6280\u8853\u3002<\/li>\n\n\n\n<li><strong>PYTM:<\/strong> Pitman-Yor Topic Modeling (\u76ae\u7279\u66fc-\u7d04\u723e\u4e3b\u984c\u6a21\u578b) &#8211; \u4e00\u7a2e\u4e3b\u984c\u6a21\u578b\u3002<\/li>\n\n\n\n<li><strong>QAT:<\/strong> Quantization Aware Training (\u91cf\u5316\u611f\u77e5\u8a13\u7df4) &#8211; \u4e00\u7a2e\u8a13\u7df4\u5c0d\u91cf\u5316\u5177\u6709\u9b6f\u68d2\u6027\u7684\u6a21\u578b\u7684\u65b9\u6cd5\u3002<\/li>\n\n\n\n<li><strong>QuillBot:<\/strong> (QuillBot) &#8211; \u63d0\u4f9b\u6539\u5beb\u5de5\u5177\u548c\u5176\u4ed6 AI \u5beb\u4f5c\u5de5\u5177\u7684\u516c\u53f8\u3002<\/li>\n\n\n\n<li><strong>R2:<\/strong> R-squared (R \u5e73\u65b9) &#8211; \u8861\u91cf\u8ff4\u6b78\u6a21\u578b\u64ec\u5408\u5ea6\u7684\u7d71\u8a08\u91cf\u3002<\/li>\n\n\n\n<li><strong>RandNN:<\/strong> Random Neural Network (\u96a8\u6a5f\u795e\u7d93\u7db2\u8def) &#8211; (\u6587\u4ef6\u4e2d\u8cc7\u8a0a\u4e0d\u53ef\u7528)<\/li>\n\n\n\n<li><strong>RFE:<\/strong> Recursive Feature Elimination (\u905e\u8ff4\u7279\u5fb5\u6d88\u9664) &#8211; \u4e00\u7a2e\u7279\u5fb5\u9078\u64c7\u6f14\u7b97\u6cd5\u3002<\/li>\n\n\n\n<li><strong>RICNN:<\/strong> Rotation Invariant Convolutional Neural Network (\u65cb\u8f49\u4e0d\u8b8a\u5377\u7a4d\u795e\u7d93\u7db2\u8def) &#8211; (\u6587\u4ef6\u4e2d\u8cc7\u8a0a\u4e0d\u53ef\u7528)<\/li>\n\n\n\n<li><strong>RIM:<\/strong> Recurrent Interence Machines (\u5faa\u74b0\u63a8\u8ad6\u6a5f) &#8211; (\u6587\u4ef6\u4e2d\u8cc7\u8a0a\u4e0d\u53ef\u7528)<\/li>\n\n\n\n<li><strong>RLFM:<\/strong> Regression based latent factors (\u57fa\u65bc\u8ff4\u6b78\u7684\u6f5b\u5728\u56e0\u5b50) &#8211; (\u6587\u4ef6\u4e2d\u8cc7\u8a0a\u4e0d\u53ef\u7528)<\/li>\n\n\n\n<li><strong>RMSLE:<\/strong> Root Mean Squared Logarithmic Error (\u5747\u65b9\u6839\u5c0d\u6578\u8aa4\u5dee) &#8211; \u8861\u91cf\u8ff4\u6b78\u6a21\u578b\u8aa4\u5dee\u7684\u6307\u6a19\u3002<\/li>\n\n\n\n<li><strong>RoBERTa:<\/strong> Robustly Optimized BERT Pretraining Approach (\u9b6f\u68d2\u512a\u5316\u7684 BERT \u9810\u8a13\u7df4\u65b9\u6cd5) &#8211; BERT \u7684\u4e00\u500b\u8b8a\u9ad4\u3002<\/li>\n\n\n\n<li><strong>Robot:<\/strong> (\u6a5f\u5668\u4eba) &#8211; \u80fd\u5920\u81ea\u52d5\u57f7\u884c\u7269\u7406\u52d5\u4f5c\u7684\u6a5f\u5668\u3002<\/li>\n\n\n\n<li><strong>ROI:<\/strong> Region Of Interest (\u611f\u8208\u8da3\u5340\u57df) &#8211; (\u6587\u4ef6\u4e2d\u8cc7\u8a0a\u4e0d\u53ef\u7528)<\/li>\n\n\n\n<li><strong>SecOps:<\/strong> (\u5b89\u5168\u904b\u71df) &#8211; \u5f37\u8abf\u5b89\u5168\u4f5c\u70ba DevOps \u6d41\u7a0b\u4e00\u90e8\u5206\u7684\u505a\u6cd5\u3002<\/li>\n\n\n\n<li><strong>seq2seq:<\/strong> Sequence to Sequence Learning (\u5e8f\u5217\u5230\u5e8f\u5217\u5b78\u7fd2) &#8211; \u4e00\u7a2e\u5c07\u5e8f\u5217\u5f9e\u4e00\u500b\u9818\u57df\u8f49\u63db\u5230\u53e6\u4e00\u500b\u9818\u57df\u7684\u8a13\u7df4\u65b9\u6cd5\u3002<\/li>\n\n\n\n<li><strong>SER:<\/strong> Sentence Error Rate (\u53e5\u5b50\u932f\u8aa4\u7387) &#8211; \u7528\u65bc\u8861\u91cf NLP \u89e3\u6c7a\u65b9\u6848\u6548\u80fd\u7684\u6307\u6a19\u3002<\/li>\n\n\n\n<li><strong>SGVB:<\/strong> Stochastic Gradient Variational Bayes (\u96a8\u6a5f\u68af\u5ea6\u8b8a\u5206\u8c9d\u6c0f) &#8211; \u4e00\u7a2e\u7528\u65bc\u8fd1\u4f3c\u8c9d\u6c0f\u63a8\u65b7\u7684\u65b9\u6cd5\u3002<\/li>\n\n\n\n<li><strong>Sign2(Gloss+Text):<\/strong> Sign to Gloss and Text (\u624b\u8a9e\u5230\u624b\u8a9e\u6ce8\u91cb\u548c\u6587\u672c) &#8211; \u9700\u8981\u806f\u5408\u5b78\u7fd2\u624b\u8a9e\u8b58\u5225\u548c\u7ffb\u8b6f\u7684\u5169\u6b65\u9a5f\u904e\u7a0b\u3002<\/li>\n\n\n\n<li><strong>Sign2Gloss:<\/strong> Sign to Gloss (\u624b\u8a9e\u5230\u624b\u8a9e\u6ce8\u91cb) &#8211; \u5f9e\u55ae\u500b\u624b\u8a9e\u5230\u55ae\u500b\u624b\u8a9e\u6ce8\u91cb\u7684\u4e00\u5c0d\u4e00\u7ffb\u8b6f\u3002<\/li>\n\n\n\n<li><strong>Sign2Text:<\/strong> Sign to Text (\u624b\u8a9e\u5230\u6587\u672c) &#8211; \u5c07\u624b\u8a9e\u5b8c\u6574\u7ffb\u8b6f\u6210\u53e3\u8a9e\u7684\u4efb\u52d9\u3002<\/li>\n\n\n\n<li><strong>SLAs:<\/strong> Service Level Agreements (\u670d\u52d9\u7b49\u7d1a\u5354\u8b70) &#8211; \u670d\u52d9\u63d0\u4f9b\u8005\u548c\u5ba2\u6236\u4e4b\u9593\u7684\u5354\u8b70\u3002<\/li>\n\n\n\n<li><strong>SLRT:<\/strong> Sign Language Recognition Transformer (\u624b\u8a9e\u8b58\u5225\u8f49\u63db\u5668) &#8211; \u4e00\u7a2e\u7528\u65bc\u9810\u6e2c\u624b\u8a9e\u6ce8\u91cb\u5e8f\u5217\u7684\u7de8\u78bc\u5668\u8f49\u63db\u5668\u6a21\u578b\u3002<\/li>\n\n\n\n<li><strong>SLT:<\/strong> Sign Language Translation (\u624b\u8a9e\u7ffb\u8b6f) &#8211; \u5c07\u624b\u8a9e\u5b8c\u6574\u7ffb\u8b6f\u6210\u53e3\u8a9e\u3002<\/li>\n\n\n\n<li><strong>SLTT:<\/strong> Sign Language Translation Transformer (\u624b\u8a9e\u7ffb\u8b6f\u8f49\u63db\u5668) &#8211; \u4e00\u7a2e\u7528\u65bc\u751f\u6210\u76f8\u61c9\u53e3\u8a9e\u53e5\u5b50\u7684\u81ea\u8ff4\u6b78\u8f49\u63db\u5668\u89e3\u78bc\u5668\u6a21\u578b\u3002<\/li>\n\n\n\n<li><strong>SMBO:<\/strong> Sequential Model-Based Optimization (\u5e8f\u5217\u6a21\u578b\u512a\u5316) &#8211; (\u6587\u4ef6\u4e2d\u8cc7\u8a0a\u4e0d\u53ef\u7528)<\/li>\n\n\n\n<li><strong>SMOTE:<\/strong> Synthetic Minority Over-sampling Technique (\u5408\u6210\u5c11\u6578\u985e\u5225\u904e\u63a1\u6a23\u6280\u8853) &#8211; \u4e00\u7a2e\u7528\u65bc\u8655\u7406\u4e0d\u5e73\u8861\u6578\u64da\u96c6\u7684\u6280\u8853\u3002<\/li>\n\n\n\n<li><strong>SOM:<\/strong> Self-Organizing Map (\u81ea\u7d44\u7e54\u6620\u5c04) &#8211; \u4e00\u7a2e\u975e\u76e3\u7763\u5f0f\u6a5f\u5668\u5b78\u7fd2\u6280\u8853\u3002<\/li>\n\n\n\n<li><strong>SpaCy:<\/strong> (SpaCy) &#8211; \u7528\u65bc\u9032\u968e NLP \u7684\u51fd\u5f0f\u5eab\u3002<\/li>\n\n\n\n<li><strong>SpRay:<\/strong> Spectral Relevance Analysis (\u983b\u8b5c\u76f8\u95dc\u6027\u5206\u6790) &#8211; \u4e00\u7a2e\u4f7f\u7528\u983b\u8b5c\u805a\u985e\u548c\u5c40\u90e8\u89e3\u91cb\u7684\u5168\u5c40\u53ef\u89e3\u91cb\u6027\u65b9\u6cd5\u3002<\/li>\n\n\n\n<li><strong>SSL:<\/strong> Self-Supervised Learning (\u81ea\u76e3\u7763\u5b78\u7fd2) &#8211; (\u6587\u4ef6\u4e2d\u8cc7\u8a0a\u4e0d\u53ef\u7528)<\/li>\n\n\n\n<li><strong>SSD:<\/strong> Single Shot Detection (\u55ae\u6b21\u6aa2\u6e2c) &#8211; \u4e00\u7a2e\u7269\u4ef6\u5075\u6e2c\u6a21\u578b\u3002<\/li>\n\n\n\n<li><strong>SSVM:<\/strong> Smooth support vector machine (\u5e73\u6ed1\u652f\u6301\u5411\u91cf\u6a5f) &#8211; SVM \u7684\u4e00\u500b\u8b8a\u9ad4\u3002<\/li>\n\n\n\n<li><strong>ST:<\/strong> Style Transfer (\u98a8\u683c\u8f49\u63db) &#8211; \u4e00\u7a2e\u5c07\u4e00\u500b\u7269\u4ef6\u7684\u5c6c\u6027\u8f49\u79fb\u5230\u53e6\u4e00\u500b\u7269\u4ef6\u7684\u6f14\u7b97\u6cd5\u3002<\/li>\n\n\n\n<li><strong>STDA:<\/strong> Style Transfer Data Augmentation (\u98a8\u683c\u8f49\u63db\u6578\u64da\u589e\u5f37) &#8211; \u4e00\u7a2e\u4f7f\u7528\u98a8\u683c\u8f49\u63db\u4f86\u589e\u5f37\u6578\u64da\u96c6\u7684\u65b9\u6cd5\u3002<\/li>\n\n\n\n<li><strong>STL:<\/strong> Selt-Taught Learning (\u81ea\u5b78\u5b78\u7fd2) &#8211; (\u6587\u4ef6\u4e2d\u8cc7\u8a0a\u4e0d\u53ef\u7528)<\/li>\n\n\n\n<li><strong>SVD:<\/strong> Singular Value Decomposition (\u5947\u7570\u503c\u5206\u89e3) &#8211; (\u6587\u4ef6\u4e2d\u8cc7\u8a0a\u4e0d\u53ef\u7528)<\/li>\n\n\n\n<li><strong>SVR:<\/strong> Support Vector Regression (\u652f\u6301\u5411\u91cf\u8ff4\u6b78) &#8211; SVM \u7528\u65bc\u8ff4\u6b78\u7684\u7248\u672c\u3002<\/li>\n\n\n\n<li><strong>T5:<\/strong> Text-To-Text Transfer Transformer (\u6587\u672c\u5230\u6587\u672c\u8f49\u63db\u8f49\u63db\u5668) &#8211; \u4e00\u7a2e\u8a9e\u8a00\u6a21\u578b\u3002<\/li>\n\n\n\n<li><strong>TGAN:<\/strong> Temporal Generative Adversarial Network (\u6642\u9593\u751f\u6210\u5c0d\u6297\u7db2\u8def) &#8211; (\u6587\u4ef6\u4e2d\u8cc7\u8a0a\u4e0d\u53ef\u7528)<\/li>\n\n\n\n<li><strong>THAID:<\/strong> THeta Automatic Interaction Detection (Theta \u81ea\u52d5\u4ea4\u4e92\u6aa2\u6e2c) &#8211; (\u6587\u4ef6\u4e2d\u8cc7\u8a0a\u4e0d\u53ef\u7528)<\/li>\n\n\n\n<li><strong>TIDL:<\/strong> TI Deep Learning Product (TI \u6df1\u5ea6\u5b78\u7fd2\u7522\u54c1) &#8211; Texas Instruments \u7684\u6df1\u5ea6\u5b78\u7fd2\u7522\u54c1\u3002<\/li>\n\n\n\n<li><strong>TIDL-RT:<\/strong> TI Deep Learning Runtime (TI \u6df1\u5ea6\u5b78\u7fd2\u904b\u884c\u6642) &#8211; Texas Instruments \u7684\u6df1\u5ea6\u5b78\u7fd2\u904b\u884c\u6642\u3002<\/li>\n\n\n\n<li><strong>TINT:<\/strong> Tree-Interpreter (\u6a39\u89e3\u91cb\u5668) &#8211; (\u6587\u4ef6\u4e2d\u8cc7\u8a0a\u4e0d\u53ef\u7528)<\/li>\n\n\n\n<li><strong>TLFN:<\/strong> Time-Lagged Feedforward Neural Network (\u6642\u9593\u5ef6\u9072\u524d\u994b\u795e\u7d93\u7db2\u8def) &#8211; (\u6587\u4ef6\u4e2d\u8cc7\u8a0a\u4e0d\u53ef\u7528)<\/li>\n\n\n\n<li><strong>TRPO:<\/strong> Trust Region Policy Optimization (\u4fe1\u4efb\u5340\u57df\u7b56\u7565\u512a\u5316) &#8211; (\u6587\u4ef6\u4e2d\u8cc7\u8a0a\u4e0d\u53ef\u7528)<\/li>\n\n\n\n<li><strong>t-SNE:<\/strong> t-Distributed Stochastic Neighbor Embedding (t \u5206\u4f48\u96a8\u6a5f\u9130\u57df\u5d4c\u5165) &#8211; \u4e00\u7a2e\u964d\u7dad\u6280\u8853\u3002<\/li>\n\n\n\n<li><strong>ULMFiT:<\/strong> Universal Language Model Fine-Tuning (\u901a\u7528\u8a9e\u8a00\u6a21\u578b\u5fae\u8abf) &#8211; (\u6587\u4ef6\u4e2d\u8cc7\u8a0a\u4e0d\u53ef\u7528)<\/li>\n\n\n\n<li><strong>V-Net:<\/strong> Volumetric Convolutional neural network (\u9ad4\u7a4d\u5377\u7a4d\u795e\u7d93\u7db2\u8def) &#8211; \u57fa\u65bc\u9ad4\u7a4d\u5168\u5377\u7a4d\u795e\u7d93\u7db2\u8def\u7684 3D \u5716\u50cf\u5206\u5272\u3002<\/li>\n\n\n\n<li><strong>VAD:<\/strong> Voice Activity Detection (\u8a9e\u97f3\u6d3b\u52d5\u6aa2\u6e2c) &#8211; (\u6587\u4ef6\u4e2d\u8cc7\u8a0a\u4e0d\u53ef\u7528)<\/li>\n\n\n\n<li><strong>VAE:<\/strong> Variational AutoEncoder (\u8b8a\u5206\u81ea\u7de8\u78bc\u5668) &#8211; \u4e00\u7a2e\u4eba\u5de5\u795e\u7d93\u7db2\u8def\u67b6\u69cb\u3002<\/li>\n\n\n\n<li><strong>VPNN:<\/strong> Vector Product Neural Network (\u5411\u91cf\u7a4d\u795e\u7d93\u7db2\u8def) &#8211; (\u6587\u4ef6\u4e2d\u8cc7\u8a0a\u4e0d\u53ef\u7528)<\/li>\n\n\n\n<li><strong>VQ-VAE:<\/strong> Vector Quantized Variational Autoencoders (\u5411\u91cf\u91cf\u5316\u8b8a\u5206\u81ea\u7de8\u78bc\u5668) &#8211; (\u6587\u4ef6\u4e2d\u8cc7\u8a0a\u4e0d\u53ef\u7528)<\/li>\n\n\n\n<li><strong>VR:<\/strong> Virtual Reality (\u865a\u62df\u73b0\u5b9e) &#8211; (\u6587\u4ef6\u4e2d\u8cc7\u8a0a\u4e0d\u53ef\u7528)<\/li>\n\n\n\n<li><strong>WFST:<\/strong> Weighted finite-state transducer (\u52a0\u6b0a\u6709\u9650\u72c0\u614b\u8f49\u63db\u5668) &#8211; (\u6587\u4ef6\u4e2d\u8cc7\u8a0a\u4e0d\u53ef\u7528)<\/li>\n\n\n\n<li><strong>WMA:<\/strong> Weighted Majority Algorithm (\u52a0\u6b0a\u591a\u6578\u6f14\u7b97\u6cd5) &#8211; (\u6587\u4ef6\u4e2d\u8cc7\u8a0a\u4e0d\u53ef\u7528)<\/li>\n\n\n\n<li><strong>WPE:<\/strong> Weighted Prediction Error (\u52a0\u6b0a\u9810\u6e2c\u8aa4\u5dee) &#8211; (\u6587\u4ef6\u4e2d\u8cc7\u8a0a\u4e0d\u53ef\u7528)<\/li>\n\n\n\n<li><strong>YOLO:<\/strong> You Only Look Once (\u4f60\u53ea\u770b\u4e00\u6b21) &#8211; \u4e00\u7a2e\u5feb\u901f\u7684\u7269\u4ef6\u5075\u6e2c\u6f14\u7b97\u6cd5\u3002<\/li>\n\n\n\n<li><strong>YOLO9000:<\/strong> (YOLO9000) &#8211; \u4e00\u7a2e\u7269\u4ef6\u5075\u6e2c\u6f14\u7b97\u6cd5\u3002<\/li>\n\n\n\n<li><strong>YOLOv2:<\/strong> (YOLOv2) &#8211; \u4e00\u7a2e\u7269\u4ef6\u5075\u6e2c\u6f14\u7b97\u6cd5\u3002<\/li>\n<\/ul>\n\n\n\n<p class=\"has-large-font-size\"><strong>III. \u7d50\u8ad6<\/strong><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>AI \u95dc\u9375\u7e2e\u5beb\u8a5e\u56de\u9867\uff1a<\/strong> \u7c21\u8981\u91cd\u7533\u7406\u89e3 AI \u7e2e\u5beb\u8a5e\u7684\u91cd\u8981\u6027\uff0c\u4e26\u91cd\u9ede\u4ecb\u7d39\u8a5e\u5f59\u8868\u4e2d\u6db5\u84cb\u7684\u4e00\u4e9b\u95dc\u9375\u8853\u8a9e\u3002<\/li>\n\n\n\n<li><strong>AI \u8853\u8a9e\u7684\u52d5\u614b\u6027\uff1a<\/strong> \u5f37\u8abf AI \u9818\u57df\u4e0d\u65b7\u767c\u5c55\uff0c\u65b0\u7684\u7e2e\u5beb\u8a5e\u5c07\u7e7c\u7e8c\u51fa\u73fe\u3002\u9f13\u52f5\u6301\u7e8c\u5b78\u7fd2\u4e26\u638c\u63e1\u6700\u65b0\u767c\u5c55\u3002<\/li>\n\n\n\n<li><strong>\u672c\u8a5e\u5f59\u8868\u7684\u50f9\u503c\uff1a<\/strong> \u91cd\u7533\u672c\u5831\u544a\u4f5c\u70ba\u5c0e\u822a\u8907\u96dc AI \u8853\u8a9e\u9818\u57df\u548c\u4fc3\u9032\u66f4\u597d\u7406\u89e3 AI \u6982\u5ff5\u548c\u8a0e\u8ad6\u7684\u6709\u7528\u8cc7\u6e90\u7684\u50f9\u503c\u3002<\/li>\n<\/ul>\n\n\n\n<h4 class=\"wp-block-heading\"><strong>\u5f15\u7528\u7684\u8457\u4f5c<\/strong><\/h4>\n\n\n\n<p>Extracting Acronyms through Natural Language Processing &#8211; TVS Next, \u6aa2\u7d22\u65e5\u671f\uff1a5\u6708 7, 2025\uff0c <a href=\"https:\/\/tvsnext.com\/blog\/extracting-acronyms-through-natural-language-processing\/\">https:\/\/tvsnext.com\/blog\/extracting-acronyms-through-natural-language-processing\/<\/a><\/p>\n\n\n\n<p>AI Definitions. Exploring 8 common artificial intelligence terms and acronyms &#8211; ServisBOT, \u6aa2\u7d22\u65e5\u671f\uff1a5\u6708 7, 2025\uff0c <a href=\"https:\/\/servisbot.com\/ai-definitions\/\">https:\/\/servisbot.com\/ai-definitions\/<\/a><\/p>\n\n\n\n<p>What do AI acronyms mean? | Firmbee, \u6aa2\u7d22\u65e5\u671f\uff1a5\u6708 7, 2025\uff0c <a href=\"https:\/\/firmbee.com\/what-do-ai-acronyms-mean\">https:\/\/firmbee.com\/what-do-ai-acronyms-mean<\/a><\/p>\n\n\n\n<p>17 AI Acronyms, Abbreviations and Terms Explained &#8211; Moxie, \u6aa2\u7d22\u65e5\u671f\uff1a5\u6708 7, 2025\uff0c <a href=\"https:\/\/moxielearn.ai\/blog\/17-ai-terms-and-acronyms\">https:\/\/moxielearn.ai\/blog\/17-ai-terms-and-acronyms<\/a><\/p>\n\n\n\n<p>Glossary of AI Terms | Acronyms &amp; Terminology &#8211; Scribbr, \u6aa2\u7d22\u65e5\u671f\uff1a5\u6708 7, 2025\uff0c <a href=\"https:\/\/www.scribbr.com\/ai-tools\/ai-terms-glossary\/\">https:\/\/www.scribbr.com\/ai-tools\/ai-terms-glossary\/<\/a><\/p>\n\n\n\n<p>The Comprehensive Guide to AI Terms, Phrases, and Acronyms | Kindo Blog, \u6aa2\u7d22\u65e5\u671f\uff1a5\u6708 7, 2025\uff0c <a href=\"https:\/\/www.kindo.ai\/blog\/45-ai-terms-phrases-and-acronyms-to-know\">https:\/\/www.kindo.ai\/blog\/45-ai-terms-phrases-and-acronyms-to-know<\/a><\/p>\n\n\n\n<p>There Are a Lot of Generative AI Acronyms \u2014 Here&#8217;s What They All Mean &#8211; Datek Solutions, \u6aa2\u7d22\u65e5\u671f\uff1a5\u6708 7, 2025\uff0c <a href=\"https:\/\/www.datek.co.uk\/blog\/there-are-a-lot-of-generative-ai-acronyms-here-s-what-they-all-mean\">https:\/\/www.datek.co.uk\/blog\/there-are-a-lot-of-generative-ai-acronyms-here-s-what-they-all-mean<\/a><\/p>\n\n\n\n<p>10 Intelligent Automation Acronyms to Know &#8211; From AI to RPA &#8211; Naviant, \u6aa2\u7d22\u65e5\u671f\uff1a5\u6708 7, 2025\uff0c <a href=\"https:\/\/naviant.com\/blog\/10-acronyms-intelligent-automation\/\">https:\/\/naviant.com\/blog\/10-acronyms-intelligent-automation\/<\/a><\/p>\n\n\n\n<p>5 AI Acronyms: What Do They Mean? &#8211; Luminance, \u6aa2\u7d22\u65e5\u671f\uff1a5\u6708 7, 2025\uff0c <a href=\"https:\/\/www.luminance.com\/news\/blogs\/20230710_luminance.html\">https:\/\/www.luminance.com\/news\/blogs\/20230710_luminance.html<\/a><\/p>\n\n\n\n<p>IT Acronyms You Need to Know \u2014 From AIOps To WLA &#8211; ActiveBatch Workload Automation, \u6aa2\u7d22\u65e5\u671f\uff1a5\u6708 7, 2025\uff0c <a href=\"https:\/\/www.advsyscon.com\/blog\/it-acronyms-it-abbreviations\/\">https:\/\/www.advsyscon.com\/blog\/it-acronyms-it-abbreviations\/<\/a><\/p>\n\n\n\n<p>Acronyms of deep learning \u2013 The Kernel Trip, \u6aa2\u7d22\u65e5\u671f\uff1a5\u6708 7, 2025\uff0c <a href=\"https:\/\/www.thekerneltrip.com\/deep-learning\/acronyms-of-deep-learning\/\">https:\/\/www.thekerneltrip.com\/deep-learning\/acronyms-of-deep-learning\/<\/a><\/p>\n\n\n\n<p>AgaMiko\/machine-learning-acronyms: A comprehensive list &#8230; &#8211; GitHub, \u6aa2\u7d22\u65e5\u671f\uff1a5\u6708 7, 2025\uff0c <a href=\"https:\/\/github.com\/AgaMiko\/machine-learning-acronyms\">https:\/\/github.com\/AgaMiko\/machine-learning-acronyms<\/a><\/p>\n\n\n\n<p>What Is DL? Deep Learning &#8211; Acronyms &#8211; Martech Zone, \u6aa2\u7d22\u65e5\u671f\uff1a5\u6708 7, 2025\uff0c <a href=\"https:\/\/martech.zone\/acronym\/dl\/\">https:\/\/martech.zone\/acronym\/dl\/<\/a><\/p>\n\n\n\n<p>Data Science Acronyms | Kaggle, \u6aa2\u7d22\u65e5\u671f\uff1a5\u6708 7, 2025\uff0c <a href=\"https:\/\/www.kaggle.com\/general\/243282\">https:\/\/www.kaggle.com\/general\/243282<\/a><\/p>\n\n\n\n<p>Machine Learning Glossary &#8211; Google for Developers, \u6aa2\u7d22\u65e5\u671f\uff1a5\u6708 7, 2025\uff0c <a href=\"https:\/\/developers.google.com\/machine-learning\/glossary\">https:\/\/developers.google.com\/machine-learning\/glossary<\/a><\/p>\n\n\n\n<p>List of Acronyms &#8211; Machine Learning Glossary, \u6aa2\u7d22\u65e5\u671f\uff1a5\u6708 7, 2025\uff0c <a href=\"https:\/\/machinelearning.wtf\/acronyms\/\">https:\/\/machinelearning.wtf\/acronyms\/<\/a><\/p>\n\n\n\n<p>List of Acronyms for Machine Learning | Download Table &#8211; ResearchGate, \u6aa2\u7d22\u65e5\u671f\uff1a5\u6708 7, 2025\uff0c <a href=\"https:\/\/www.researchgate.net\/figure\/List-of-Acronyms-for-Machine-Learning_tbl1_325107577\">https:\/\/www.researchgate.net\/figure\/List-of-Acronyms-for-Machine-Learning_tbl1_325107577<\/a><\/p>\n\n\n\n<p>TI Deep Learning Product User Guide: Acronyms, \u6aa2\u7d22\u65e5\u671f\uff1a5\u6708 7, 2025\uff0c <a href=\"https:\/\/software-dl.ti.com\/jacinto7\/esd\/processor-sdk-rtos-jacinto7\/08_02_00_05\/exports\/docs\/tidl_j721e_08_02_00_11\/ti_dl\/docs\/user_guide_html\/md_tidl_acronyms.html\">https:\/\/software-dl.ti.com\/jacinto7\/esd\/processor-sdk-rtos-jacinto7\/08_02_00_05\/exports\/docs\/tidl_j721e_08_02_00_11\/ti_dl\/docs\/user_guide_html\/md_tidl_acronyms.html<\/a><\/p>\n\n\n\n<p>AI Acronyms | IT Insight &#8211; UMSL Blogs, \u6aa2\u7d22\u65e5\u671f\uff1a5\u6708 7, 2025\uff0c <a href=\"https:\/\/blogs.umsl.edu\/itinsight\/2025\/02\/ai-acronyms\/\">https:\/\/blogs.umsl.edu\/itinsight\/2025\/02\/ai-acronyms\/<\/a><\/p>\n\n\n\n<p>Machine learning [ISO,NLM] abbreviation &#8211; Paperpile, \u6aa2\u7d22\u65e5\u671f\uff1a5\u6708 7, 2025\uff0c <a href=\"https:\/\/paperpile.com\/n\/machine-learning-abbreviation\/\">https:\/\/paperpile.com\/n\/machine-learning-abbreviation\/<\/a><\/p>\n\n\n\n<p>15 must-know abbreviations in NLP language models &#8211; DEV Community, \u6aa2\u7d22\u65e5\u671f\uff1a5\u6708 7, 2025\uff0c <a href=\"https:\/\/dev.to\/amananandrai\/10-must-know-abbreviations-in-nlp-language-models-4l93\">https:\/\/dev.to\/amananandrai\/10-must-know-abbreviations-in-nlp-language-models-4l93<\/a><\/p>\n\n\n\n<p><\/p>\n","protected":false},"excerpt":{"rendered":"<p>I. \u5c0e\u8ad6 II. \u5e38\u7528\u4eba\u5de5\u667a\u6167\u7e2e\u5beb\u8a5e\u5f59\u8a73\u89e3(A Comprehensive Glossary of Common AI Short Forms) III. \u7d50\u8ad6 \u5f15\u7528\u7684\u8457\u4f5c Extracting Acronyms through Natural Language Processing &#8211; TVS Next, \u6aa2\u7d22\u65e5\u671f\uff1a5\u6708 7, 2025\uff0c https:\/\/tvsnext.com\/blog\/extracting-acronyms-through-natural-language-processing\/ AI Definitions. Exploring 8 common artificial intelligence terms and acronyms &#8211; ServisBOT, \u6aa2\u7d22\u65e5\u671f\uff1a5\u6708 7, 2025\uff0c https:\/\/servisbot.com\/ai-definitions\/ What do AI acronyms mean? | Firmbee, \u6aa2\u7d22\u65e5\u671f\uff1a5\u6708 7, 2025\uff0c https:\/\/firmbee.com\/what-do-ai-acronyms-mean 17 [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"googlesitekit_rrm_CAowvqSiDA:productID":"","footnotes":""},"class_list":["post-5607","page","type-page","status-publish","hentry"],"_links":{"self":[{"href":"https:\/\/infernews.com\/blog\/wp-json\/wp\/v2\/pages\/5607","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/infernews.com\/blog\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/infernews.com\/blog\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/infernews.com\/blog\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/infernews.com\/blog\/wp-json\/wp\/v2\/comments?post=5607"}],"version-history":[{"count":0,"href":"https:\/\/infernews.com\/blog\/wp-json\/wp\/v2\/pages\/5607\/revisions"}],"wp:attachment":[{"href":"https:\/\/infernews.com\/blog\/wp-json\/wp\/v2\/media?parent=5607"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}