{"id":113,"date":"2026-04-24T03:30:36","date_gmt":"2026-04-23T18:30:36","guid":{"rendered":"https:\/\/ai-cloud.kr\/?p=113"},"modified":"2026-04-24T03:30:38","modified_gmt":"2026-04-23T18:30:38","slug":"%ec%b4%88%ec%86%8c%ed%98%95-%ec%98%a4%ed%94%88-%eb%aa%a8%eb%8d%b8%ec%9d%98-%ec%9e%ac%ec%a1%b0%eb%aa%85-1b-%ec%9d%b4%ed%95%98-%eb%aa%a8%eb%8d%b8%ec%9d%b4-%eb%8b%a4%ec%8b%9c-%ec%a3%bc%eb%aa%a9%eb%b0%9b","status":"publish","type":"post","link":"https:\/\/ai-cloud.kr\/?p=113","title":{"rendered":"\ucd08\uc18c\ud615 \uc624\ud508 \ubaa8\ub378\uc758 \uc7ac\uc870\uba85: 1B \uc774\ud558 \ubaa8\ub378\uc774 \ub2e4\uc2dc \uc8fc\ubaa9\ubc1b\ub294 \uc774\uc720(The Reappraisal of Ultra-Small Open Models: Why Sub-1B Models Are Drawing Attention Again)"},"content":{"rendered":"<h2>\uac70\ub300 AI \ubaa8\ub378 \uc2dc\ub300, \uc7a0\uc2dc \uc228\uc744 \uace0\ub974\ub2e4: \ucd08\uc18c\ud615 \uc624\ud508 \ubaa8\ub378\uc758 \uadc0\ud658<\/h2>\n<p>\ucd5c\uadfc \uba87 \ub144\uac04 \uc778\uacf5\uc9c0\ub2a5(AI) \ubd84\uc57c\ub294 \u2018\uac70\ub300\u2019\ub77c\ub294 \ub2e8\uc5b4\ub85c \uc694\uc57d\ub420 \ub9cc\ud07c \ud3ed\ubc1c\uc801\uc778 \uc131\uc7a5\uc744 \uac70\ub4ed\ud574\uc654\uc2b5\ub2c8\ub2e4. GPT-3, GPT-4\uc640 \uac19\uc740 \uc218\ucc9c\uc5b5 \uac1c \uc774\uc0c1\uc758 \ud30c\ub77c\ubbf8\ud130\ub97c \uac00\uc9c4 \ub300\uaddc\ubaa8 \uc5b8\uc5b4 \ubaa8\ub378(LLM)\ub4e4\uc740 \ub180\ub77c\uc6b4 \uc131\ub2a5\uc73c\ub85c \uc6b0\ub9ac \uc0b6 \uacf3\uacf3\uc5d0 \uc601\ud5a5\uc744 \ubbf8\uce58\uace0 \uc788\uc2b5\ub2c8\ub2e4. \ub9c8\uce58 \uac70\ub300\ud55c \ub3c4\uc11c\uad00\ucc98\ub7fc \ubc29\ub300\ud55c \uc9c0\uc2dd\uc744 \ub2f4\uace0, \ubcf5\uc7a1\ud55c \uc9c8\ubb38\uc5d0\ub3c4 \ub9c9\ud798\uc5c6\uc774 \ub2f5\ud558\ub294 \uc774 \ubaa8\ub378\ub4e4\uc740 AI\uc758 \uac00\ub2a5\uc131\uc744 \ud55c \ub2e8\uacc4 \ub04c\uc5b4\uc62c\ub838\ub2e4\ub294 \ud3c9\uac00\ub97c \ubc1b\uc2b5\ub2c8\ub2e4.<\/p>\n<p>\ud558\uc9c0\ub9cc \uc774 \uac70\ub300\ud55c \ud750\ub984 \uc18d\uc5d0\uc11c, \uc5ed\uc124\uc801\uc73c\ub85c \u2018\ucd08\uc18c\ud615\u2019 \ubaa8\ub378\ub4e4\uc774 \ub2e4\uc2dc\uae08 \uc8fc\ubaa9\ubc1b\uace0 \uc788\uc2b5\ub2c8\ub2e4. \ud2b9\ud788 10\uc5b5 \uac1c(1 Billion, 1B) \ubbf8\ub9cc\uc758 \ud30c\ub77c\ubbf8\ud130\ub97c \uac00\uc9c4 \ubaa8\ub378\ub4e4\uc774 \u20181B \uc774\ud558 \ubaa8\ub378\u2019\uc774\ub77c \ubd88\ub9ac\uba70 \uc7ac\uc870\uba85\ub418\ub294 \ud604\uc0c1\uc774 \ub098\ud0c0\ub098\uace0 \uc788\uc2b5\ub2c8\ub2e4. \u2018\ub354 \ud06c\uace0, \ub354 \ub9ce\uc740 \ub370\uc774\ud130\ub97c \ud559\uc2b5\ud574\uc57c \uc88b\uc740 \uc131\ub2a5\uc744 \ub0b8\ub2e4\u2019\ub294 \uacf5\uc2dd\uc774 \uc804\ubd80\uac00 \uc544\ub2d8\uc744 \ubcf4\uc5ec\uc8fc\ub4ef, \uc774 \uc791\uc740 \ubaa8\ub378\ub4e4\uc740 \uc0c8\ub85c\uc6b4 \uac00\ub2a5\uc131\uacfc \ud568\uaed8 AI \uc0dd\ud0dc\uacc4\uc5d0 \uc2e0\uc120\ud55c \ubc14\ub78c\uc744 \ubd88\uc5b4\ub123\uace0 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n<p>\uadf8\ub807\ub2e4\uba74 \uc65c \uac11\uc790\uae30 \uc774 \uc791\uace0 \uac00\ubcbc\uc6b4 \ubaa8\ub378\ub4e4\uc774 \ub2e4\uc2dc \uc911\uc694\ud574\uc9c4 \uac78\uae4c\uc694? \ub2e8\uc21c\ud788 \u2018\uc791\uc544\uc11c\u2019 \uadf8\ub7f0 \uac78\uae4c\uc694? \uc544\ub2d9\ub2c8\ub2e4. \uc774 \ucd08\uc18c\ud615 \uc624\ud508 \ubaa8\ub378\ub4e4\uc740 \uac70\ub300 \ubaa8\ub378\uc758 \ud55c\uacc4\ub97c \ubcf4\uc644\ud558\uace0, AI \uae30\uc220\uc744 \ub354\uc6b1 \ubbfc\uc8fc\uc801\uc774\uace0 \ud3ed\ub113\uac8c \ud655\uc0b0\uc2dc\ud0ac \uc218 \uc788\ub294 \uc7a0\uc7ac\ub825\uc744 \uac00\uc9c0\uace0 \uc788\uae30 \ub54c\ubb38\uc785\ub2c8\ub2e4. \uc77c\ubc18 \ub300\uc911\uc758 \ub208\ub192\uc774\uc5d0 \ub9de\ucdb0, \ucd08\uc18c\ud615 \uc624\ud508 \ubaa8\ub378\uc774 \uc65c \ub2e4\uc2dc \uc911\uc694\ud574\uc9c0\uace0 \uc788\ub294\uc9c0, \uadf8\ub9ac\uace0 \uc5b4\ub5a4 \uac00\ub2a5\uc131\uc744 \uc5f4\uc5b4\uac08 \uc218 \uc788\ub294\uc9c0 \ud568\uaed8 \uc54c\uc544\ubcf4\uaca0\uc2b5\ub2c8\ub2e4.<\/p>\n<h3>1. \uc65c \u2018\uc791\uc740\u2019 \ubaa8\ub378\uc5d0 \uc8fc\ubaa9\ud574\uc57c \ud560\uae4c? \uac70\ub300 \ubaa8\ub378\uc758 \uadf8\ub9bc\uc790\ub97c \uac77\uc5b4\ub0b4\ub2e4<\/h3>\n<p>\uac70\ub300 AI \ubaa8\ub378\ub4e4\uc740 \ubd84\uba85 \ub180\ub77c\uc6b4 \uc131\ub2a5\uc744 \uc790\ub791\ud569\ub2c8\ub2e4. \ud558\uc9c0\ub9cc \uadf8 \uc774\uba74\uc5d0\ub294 \uba87 \uac00\uc9c0 \ud574\uacb0\ud558\uae30 \uc5b4\ub824\uc6b4 \uc219\uc81c\ub4e4\uc774 \uc874\uc7ac\ud569\ub2c8\ub2e4.<\/p>\n<ul>\n<li>\n<p><strong>\ucc9c\ubb38\ud559\uc801\uc778 \ube44\uc6a9:<\/strong> \uac70\ub300 \ubaa8\ub378\uc744 \ud559\uc2b5\uc2dc\ud0a4\uace0 \uc6b4\uc601\ud558\ub294 \ub370\ub294 \uc5c4\uccad\ub09c \ucef4\ud4e8\ud305 \uc790\uc6d0\uacfc \uc5d0\ub108\uc9c0\uac00 \ud544\uc694\ud569\ub2c8\ub2e4. \uc774\ub294 \ub9c9\ub300\ud55c \ube44\uc6a9\uc73c\ub85c \uc774\uc5b4\uc838, \uc18c\uc218\uc758 \uac70\ub300 \uae30\uc5c5\ub9cc\uc774 \uc774\ub7ec\ud55c \ubaa8\ub378\uc744 \uac1c\ubc1c\ud558\uace0 \ud65c\uc6a9\ud560 \uc218 \uc788\ub2e4\ub294 \uc9c4\uc785 \uc7a5\ubcbd\uc744 \ub9cc\ub4ed\ub2c8\ub2e4. \uc77c\ubc18 \uac1c\uc778\uc774\ub098 \uc911\uc18c\uae30\uc5c5 \uc785\uc7a5\uc5d0\uc11c\ub294 \uafc8\ub3c4 \uafb8\uae30 \uc5b4\ub824\uc6b4 \uc77c\uc774\uc8e0.<\/p>\n<\/li>\n<li>\n<p><strong>\ub192\uc740 \uc5d0\ub108\uc9c0 \uc18c\ube44:<\/strong> AI \ubaa8\ub378\uc758 \ud06c\uae30\uac00 \ucee4\uc9c8\uc218\ub85d \uc5d0\ub108\uc9c0 \uc18c\ube44\ub7c9\ub3c4 \uae30\ud558\uae09\uc218\uc801\uc73c\ub85c \ub298\uc5b4\ub0a9\ub2c8\ub2e4. \uc774\ub294 \ud658\uacbd \ubb38\uc81c\uc640 \uc9c1\uacb0\ub418\uba70, \uc9c0\uc18d \uac00\ub2a5\ud55c AI \ubc1c\uc804\uc5d0 \ub300\ud55c \uc6b0\ub824\ub97c \ub0b3\uace0 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n<\/li>\n<li>\n<p><strong>\ub290\ub9b0 \uc18d\ub3c4\uc640 \ub192\uc740 \uc9c0\uc5f0 \uc2dc\uac04:<\/strong> \uac70\ub300\ud55c \ubaa8\ub378\uc740 \ucc98\ub9ac\ud574\uc57c \ud560 \uc815\ubcf4\ub7c9\uc774 \ub9ce\uc544 \uc751\ub2f5 \uc18d\ub3c4\uac00 \ub290\ub9b4 \uc218\ubc16\uc5d0 \uc5c6\uc2b5\ub2c8\ub2e4. \uc2e4\uc2dc\uac04\uc73c\ub85c \ube60\ub974\uac8c \ubc18\uc751\ud574\uc57c \ud558\ub294 \uc11c\ube44\uc2a4\uc5d0\ub294 \uc801\uc6a9\ud558\uae30 \uc5b4\ub835\ub2e4\ub294 \ud55c\uacc4\uac00 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n<\/li>\n<li>\n<p><strong>\ud2b9\uc815 \ubaa9\uc801\uc5d0 \ub300\ud55c \ube44\ud6a8\uc728\uc131:<\/strong> \uac70\ub300 \ubaa8\ub378\uc740 \ubc94\uc6a9\uc801\uc778 \ub2a5\ub825\uc744 \uac16\ucd94\uace0 \uc788\uc9c0\ub9cc, \ud2b9\uc815 \uc791\uc5c5\ub9cc\uc744 \uc704\ud574 \uc0ac\uc6a9\ud558\uae30\uc5d0\ub294 \ub108\ubb34 \uacfc\ud569\ub2c8\ub2e4. \ub9c8\uce58 \ub9dd\uce58\ub85c \ub098\uc0ac\ub97c \uc870\uc774\ub824\ub294 \uac83\ucc98\ub7fc, \ube44\ud6a8\uc728\uc801\uc77c \uc218\ubc16\uc5d0 \uc5c6\uc2b5\ub2c8\ub2e4.<\/p>\n<\/li>\n<\/ul>\n<p>\uc774\ub7ec\ud55c \uac70\ub300 \ubaa8\ub378\uc758 \ud55c\uacc4\uc810\uc744 \uadf9\ubcf5\ud558\ub294 \ub370 \ubc14\ub85c \ucd08\uc18c\ud615 \uc624\ud508 \ubaa8\ub378\uc774 \uc911\uc694\ud55c \uc5ed\ud560\uc744 \ud569\ub2c8\ub2e4. \u2018\uc791\ub2e4\uace0 \ud574\uc11c \uc131\ub2a5\uc774 \ub5a8\uc5b4\uc9c4\ub2e4\u2019\ub294 \ud3b8\uacac\uc744 \uae68\uace0, \ud2b9\uc815 \ubaa9\uc801\uc5d0 \ucd5c\uc801\ud654\ub418\uc5b4 \ub180\ub77c\uc6b4 \ud6a8\uc728\uc131\uc744 \ubcf4\uc5ec\uc8fc\ub294 \uc774 \ubaa8\ub378\ub4e4\uc740 AI \uae30\uc220\uc758 \ubbfc\uc8fc\ud654\uc640 \ud655\uc0b0\uc5d0 \ud06c\uac8c \uae30\uc5ec\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n<h3>2. 1B \uc774\ud558 \ubaa8\ub378, \ubb34\uc5c7\uc774 \ub2e4\ub978\uac00? \uc791\uc9c0\ub9cc \uac15\ud55c \uc774\uc720<\/h3>\n<p>1B \uc774\ud558 \ubaa8\ub378\uc740 \ub9d0 \uadf8\ub300\ub85c 10\uc5b5 \uac1c \ubbf8\ub9cc\uc758 \ud30c\ub77c\ubbf8\ud130(\ubaa8\ub378\uc774 \ud559\uc2b5\ud558\ub294 \ub370\uc774\ud130\uc758 \uac00\uc911\uce58\ub97c \ub098\ud0c0\ub0b4\ub294 \uc218)\ub97c \uac00\uc9c4 \ubaa8\ub378\uc744 \uc758\ubbf8\ud569\ub2c8\ub2e4. \uc774\ub294 \uae30\uc874\uc758 \uc218\ucc9c\uc5b5 \uac1c \ud30c\ub77c\ubbf8\ud130 \ubaa8\ub378\uc5d0 \ube44\ud558\uba74 \ub9e4\uc6b0 \uc791\uc740 \ud06c\uae30\uc785\ub2c8\ub2e4. \ud558\uc9c0\ub9cc \uc774 \uc791\uc740 \ud06c\uae30 \ub355\ubd84\uc5d0 \ub2e4\uc74c\uacfc \uac19\uc740 \ub3c5\ubcf4\uc801\uc778 \uc7a5\uc810\ub4e4\uc744 \uac00\uc9d1\ub2c8\ub2e4.<\/p>\n<h4>H3_2-1: \ub6f0\uc5b4\ub09c \ud6a8\uc728\uc131\uacfc \uacbd\uc81c\uc131<\/h4>\n<ul>\n<li>\n<p><strong>\ub0ae\uc740 \ud559\uc2b5 \ubc0f \uc6b4\uc601 \ube44\uc6a9:<\/strong> \uc791\uc740 \ubaa8\ub378\uc740 \ud559\uc2b5\uc5d0 \ud544\uc694\ud55c \ub370\uc774\ud130 \uc591\uacfc \ucef4\ud4e8\ud305 \uc790\uc6d0\uc774 \ud6e8\uc52c \uc801\uc2b5\ub2c8\ub2e4. \ub530\ub77c\uc11c \ud559\uc2b5 \ubc0f \uc6b4\uc601 \ube44\uc6a9\uc774 \ud68d\uae30\uc801\uc73c\ub85c \uc808\uac10\ub429\ub2c8\ub2e4. \uc774\ub294 AI \uae30\uc220\uc744 \ub354 \ub9ce\uc740 \uc0ac\ub78c\ub4e4\uc774, \ub354 \uc800\ub834\ud558\uac8c \ud65c\uc6a9\ud560 \uc218 \uc788\uac8c \ub9cc\ub4ed\ub2c8\ub2e4. \uc608\ub97c \ub4e4\uc5b4, \uac1c\uc778\uc6a9 \ucef4\ud4e8\ud130\ub098 \uc800\ub834\ud55c \ud074\ub77c\uc6b0\ub4dc \uc11c\ubc84\uc5d0\uc11c\ub3c4 \ucda9\ubd84\ud788 \uad6c\ub3d9 \uac00\ub2a5\ud569\ub2c8\ub2e4.<\/p>\n<\/li>\n<li>\n<p><strong>\ube60\ub978 \ucd94\ub860 \uc18d\ub3c4:<\/strong> \ubaa8\ub378\uc758 \ud06c\uae30\uac00 \uc791\uc744\uc218\ub85d \ub370\uc774\ud130\ub97c \ucc98\ub9ac\ud558\ub294 \ub370 \uac78\ub9ac\ub294 \uc2dc\uac04\uc774 \ub2e8\ucd95\ub429\ub2c8\ub2e4. \uc774\ub294 \uc2e4\uc2dc\uac04 \uc11c\ube44\uc2a4, \ubaa8\ubc14\uc77c \uc560\ud50c\ub9ac\ucf00\uc774\uc158 \ub4f1 \ube60\ub978 \uc751\ub2f5 \uc18d\ub3c4\uac00 \ud544\uc218\uc801\uc778 \ubd84\uc57c\uc5d0\uc11c \ud070 \uac15\uc810\uc744 \ubc1c\ud718\ud569\ub2c8\ub2e4.<\/p>\n<\/li>\n<\/ul>\n<h4>H3_2-2: \ud2b9\uc815 \uc791\uc5c5\uc5d0 \ub300\ud55c \ub192\uc740 \ucd5c\uc801\ud654<\/h4>\n<ul>\n<li>\n<p><strong>\ub9de\ucda4\ud615 \uc131\ub2a5:<\/strong> \uac70\ub300 \ubaa8\ub378\uc774 \ubaa8\ub4e0 \uac83\uc744 \uc798\ud558\ub294 \u2018\ub9cc\ub2a5\u2019\uc774\ub77c\uba74, \ucd08\uc18c\ud615 \ubaa8\ub378\uc740 \ud2b9\uc815 \uc791\uc5c5\uc5d0 \ud2b9\ud654\ub41c \u2018\uc804\ubb38\uac00\u2019\uc640 \uac19\uc2b5\ub2c8\ub2e4. \uc608\ub97c \ub4e4\uc5b4, \ud2b9\uc815 \uc0b0\uc5c5 \ubd84\uc57c\uc758 \uc6a9\uc5b4 \ucc98\ub9ac, \ud2b9\uc815 \uc5b8\uc5b4\uc758 \ubc88\uc5ed, \ub610\ub294 \uac04\ub2e8\ud55c \ucc57\ubd07\uacfc \uac19\uc774 \uba85\ud655\ud558\uac8c \uc815\uc758\ub41c \uc791\uc5c5\uc5d0 \ub300\ud574\uc11c\ub294 \uac70\ub300 \ubaa8\ub378 \ubabb\uc9c0\uc54a\uc740, \ud639\uc740 \ub354 \ub6f0\uc5b4\ub09c \uc131\ub2a5\uc744 \ubcf4\uc5ec\uc904 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n<\/li>\n<li>\n<p><strong>\uc628\ub514\ubc14\uc774\uc2a4 AI \uad6c\ud604:<\/strong> \uc2a4\ub9c8\ud2b8\ud3f0, \uc6e8\uc5b4\ub7ec\ube14 \uae30\uae30 \ub4f1 \uc778\ud130\ub137 \uc5f0\uacb0 \uc5c6\uc774 \uae30\uae30 \uc790\uccb4\uc5d0\uc11c AI \uae30\ub2a5\uc744 \uc218\ud589\ud558\ub294 \u2018\uc628\ub514\ubc14\uc774\uc2a4 AI\u2019 \uad6c\ud604\uc5d0 \ud544\uc218\uc801\uc785\ub2c8\ub2e4. \uc791\uc740 \ud06c\uae30\uc640 \ub0ae\uc740 \uc804\ub825 \uc18c\ube44 \ub355\ubd84\uc5d0 \ubaa8\ubc14\uc77c \ud658\uacbd\uc5d0\uc11c\ub3c4 AI \uae30\ub2a5\uc744 \uad6c\ud604\ud560 \uc218 \uc788\uac8c \ud569\ub2c8\ub2e4.<\/p>\n<\/li>\n<\/ul>\n<h4>H3_2-3: \uc811\uadfc\uc131\uacfc \ubbfc\uc8fc\ud654<\/h4>\n<ul>\n<li>\n<p><strong>\uc624\ud508\uc18c\uc2a4 \uc0dd\ud0dc\uacc4 \ud65c\uc131\ud654:<\/strong> \ub9ce\uc740 \ucd08\uc18c\ud615 \ubaa8\ub378\ub4e4\uc774 \uc624\ud508\uc18c\uc2a4\ub85c \uacf5\uac1c\ub418\uc5b4, \ub204\uad6c\ub098 \uc790\uc720\ub86d\uac8c \uc0ac\uc6a9\ud558\uace0 \uc218\uc815\ud558\uba70 \ubc1c\uc804\uc2dc\ud0ac \uc218 \uc788\uc2b5\ub2c8\ub2e4. \uc774\ub294 AI \uae30\uc220\uc758 \ubc1c\uc804 \uc18d\ub3c4\ub97c \ub192\uc774\uace0, \ub354 \ub2e4\uc591\ud55c \uc544\uc774\ub514\uc5b4\uac00 \uc2e4\ud604\ub420 \uc218 \uc788\ub294 \uae30\ubc18\uc744 \ub9c8\ub828\ud569\ub2c8\ub2e4.<\/p>\n<\/li>\n<li>\n<p><strong>\uac1c\ubc1c \uc7a5\ubcbd \uc644\ud654:<\/strong> \uc18c\uaddc\ubaa8 \uac1c\ubc1c\ud300\uc774\ub098 \uac1c\uc778 \uac1c\ubc1c\uc790\ub4e4\ub3c4 \ube44\uad50\uc801 \uc27d\uac8c \uc811\uadfc\ud558\uace0 \ud65c\uc6a9\ud560 \uc218 \uc788\uc5b4, AI \uae30\uc220 \uac1c\ubc1c\uc758 \uc9c4\uc785 \uc7a5\ubcbd\uc744 \ub0ae\ucda5\ub2c8\ub2e4.<\/p>\n<\/li>\n<\/ul>\n<p><strong>\uc2e4\uc81c \uc0ac\ub840:<\/strong><\/p>\n<ul>\n<li>\n<p><strong>Phi-2 (Microsoft):<\/strong> \uc57d 27\uc5b5 \uac1c\uc758 \ud30c\ub77c\ubbf8\ud130\ub97c \uac00\uc9c4 \ubaa8\ub378\ub85c, \uac70\ub300 \ubaa8\ub378\uc5d0 \ubc84\uae08\uac00\ub294 \ucd94\ub860 \ub2a5\ub825\uc744 \ubcf4\uc774\uba74\uc11c\ub3c4 \ud6e8\uc52c \uc791\uc740 \ud06c\uae30\ub97c \uc790\ub791\ud569\ub2c8\ub2e4. \ud2b9\uc815 \ub17c\ub9ac \ucd94\ub860 \ubc0f \uc5b8\uc5b4 \uc774\ud574 \ub2a5\ub825\uc5d0\uc11c \ub6f0\uc5b4\ub09c \uc131\ub2a5\uc744 \ubcf4\uc5ec\uc90d\ub2c8\ub2e4.<\/p>\n<\/li>\n<li>\n<p><strong>TinyLlama:<\/strong> 11\uc5b5 \uac1c\uc758 \ud30c\ub77c\ubbf8\ud130 \ubaa8\ub378\ub85c, Llama 2 \ubaa8\ub378\uc744 \uae30\ubc18\uc73c\ub85c \ud559\uc2b5\ub418\uc5c8\uc2b5\ub2c8\ub2e4. \uc791\uc740 \ud06c\uae30\uc5d0\ub3c4 \ubd88\uad6c\ud558\uace0 \ub180\ub77c\uc6b4 \uc131\ub2a5\uc744 \ubcf4\uc5ec\uc8fc\uba70, \ub2e4\uc591\ud55c \uc5f0\uad6c \ubc0f \uac1c\ubc1c\uc5d0 \ud65c\uc6a9\ub418\uace0 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n<\/li>\n<\/ul>\n<p>\uc774\ucc98\ub7fc 1B \uc774\ud558 \ubaa8\ub378\uc740 \ub2e8\uc21c\ud788 \u2018\uc791\uc740\u2019 \uac83\uc774 \uc544\ub2c8\ub77c, \ud6a8\uc728\uc131, \ucd5c\uc801\ud654, \uc811\uadfc\uc131\uc774\ub77c\ub294 \uce21\uba74\uc5d0\uc11c \uac70\ub300 \ubaa8\ub378\uc774 \uac00\uc9c0\uc9c0 \ubabb\ud55c \ub3c5\ubcf4\uc801\uc778 \uc7a5\uc810\uc744 \uac00\uc9c0\uace0 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n<h3>3. \ucd08\uc18c\ud615 \uc624\ud508 \ubaa8\ub378, \uc5b4\ub5a4 \uac00\ub2a5\uc131\uc744 \uc5f4\uc5b4\uac08\uae4c?<\/h3>\n<p>\ucd08\uc18c\ud615 \uc624\ud508 \ubaa8\ub378\uc758 \ubd80\uc0c1\uc740 AI \uae30\uc220\uc758 \ubbf8\ub798\ub97c \ub354\uc6b1 \ub2e4\ucc44\ub86d\uace0 \ud48d\uc694\ub86d\uac8c \ub9cc\ub4e4 \uc7a0\uc7ac\ub825\uc744 \uc9c0\ub2c8\uace0 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n<h4>H3_3-1: AI \uae30\uc220\uc758 \ubbfc\uc8fc\ud654\uc640 \ubcf4\ud3b8\ud654<\/h4>\n<ul>\n<li>\n<p><strong>\uac1c\uc778 \ub9de\ucda4\ud615 AI:<\/strong> \ub204\uad6c\ub098 \uc790\uc2e0\uc758 \ud544\uc694\uc5d0 \ub9de\ub294 AI \ubaa8\ub378\uc744 \uc27d\uac8c \uad6c\ucd95\ud558\uace0 \ud65c\uc6a9\ud560 \uc218 \uc788\uac8c \ub429\ub2c8\ub2e4. \uc608\ub97c \ub4e4\uc5b4, \ub098\ub9cc\uc758 \uae00\uc4f0\uae30 \ub3c4\uc6b0\ubbf8, \uac1c\uc778 \uc77c\uc815 \uad00\ub9ac AI, \ud2b9\uc815 \ubd84\uc57c \uc804\ubb38\uac00 \ucc57\ubd07 \ub4f1\uc744 \ub9cc\ub4dc\ub294 \uac83\uc774 \ud6e8\uc52c \uc26c\uc6cc\uc9d1\ub2c8\ub2e4.<\/p>\n<\/li>\n<li>\n<p><strong>\uad50\uc721 \ubc0f \uc5f0\uad6c \ud65c\uc131\ud654:<\/strong> \uad50\uc721 \ud604\uc7a5\uc774\ub098 \uc5f0\uad6c\uc2e4\uc5d0\uc11c \uace0\uac00\uc758 \uc7a5\ube44 \uc5c6\uc774\ub3c4 AI \ubaa8\ub378\uc744 \uc9c1\uc811 \ub2e4\ub8e8\uace0 \uc2e4\ud5d8\ud574\ubcfc \uc218 \uc788\uac8c \ub418\uc5b4, AI \uc778\uc7ac \uc591\uc131\uacfc \uae30\uc220 \ud601\uc2e0\uc5d0 \uae30\uc5ec\ud569\ub2c8\ub2e4.<\/p>\n<\/li>\n<\/ul>\n<h4>H3_3-2: \uc0c8\ub85c\uc6b4 \uc11c\ube44\uc2a4\uc640 \uc0b0\uc5c5\uc758 \ud0c4\uc0dd<\/h4>\n<ul>\n<li>\n<p><strong>\ubaa8\ubc14\uc77c \ubc0f \uc5e3\uc9c0 \ub514\ubc14\uc774\uc2a4 AI:<\/strong> \uc2a4\ub9c8\ud2b8\ud3f0, \uc2a4\ub9c8\ud2b8\uc6cc\uce58, \uc790\uc728\uc8fc\ud589\ucc28 \ub4f1 \ub2e4\uc591\ud55c \uae30\uae30\uc5d0\uc11c \uc778\ud130\ub137 \uc5f0\uacb0 \uc5c6\uc774\ub3c4 \uace0\uc131\ub2a5 AI \uae30\ub2a5\uc744 \uc81c\uacf5\ud560 \uc218 \uc788\uac8c \ub429\ub2c8\ub2e4. \uac1c\uc778 \uc815\ubcf4 \ubcf4\ud638 \uac15\ud654\uc640 \uc2e4\uc2dc\uac04 \uc751\ub2f5 \uc18d\ub3c4 \ud5a5\uc0c1\uc774 \uac00\ub2a5\ud574\uc9d1\ub2c8\ub2e4.<\/p>\n<\/li>\n<li>\n<p><strong>\uc0b0\uc5c5\ubcc4 \ud2b9\ud654 \uc194\ub8e8\uc158:<\/strong> \uc758\ub8cc, \uae08\uc735, \ubc95\ub960 \ub4f1 \uac01 \uc0b0\uc5c5 \ubd84\uc57c\uc758 \ud2b9\uc131\uc5d0 \ub9de\ucdb0 \ucd5c\uc801\ud654\ub41c AI \ubaa8\ub378\uc744 \uac1c\ubc1c\ud558\uc5ec \uc0dd\uc0b0\uc131\uacfc \ud6a8\uc728\uc131\uc744 \uadf9\ub300\ud654\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \uc608\ub97c \ub4e4\uc5b4, \uc758\ub8cc \uc601\uc0c1 \ubd84\uc11d AI, \uae08\uc735 \uc0ac\uae30 \ud0d0\uc9c0 AI \ub4f1\uc774 \ub354\uc6b1 \uc815\uad50\ud574\uc9c8 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n<\/li>\n<li>\n<p><strong>\uc811\uadfc\uc131 \ud5a5\uc0c1:<\/strong> \uc5b8\uc5b4 \uc7a5\ubcbd\uc744 \ub0ae\ucd94\ub294 \uc2e4\uc2dc\uac04 \ubc88\uc5ed, \uc2dc\uac01 \uc7a5\uc560\uc778\uc744 \uc704\ud55c \uc774\ubbf8\uc9c0 \uc124\uba85, \uc74c\uc131 \uba85\ub839 \uc778\ud130\ud398\uc774\uc2a4 \ub4f1 AI \uae30\uc220\uc744 \ud1b5\ud574 \uc0ac\ud68c\uc801 \uc57d\uc790\uc758 \uc811\uadfc\uc131\uc744 \ub192\uc774\ub294 \ub370 \uae30\uc5ec\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n<\/li>\n<\/ul>\n<h4>H3_3-3: \uc9c0\uc18d \uac00\ub2a5\ud55c AI \ubc1c\uc804<\/h4>\n<ul>\n<li>\n<p><strong>\uc5d0\ub108\uc9c0 \ud6a8\uc728\uc131 \uc99d\ub300:<\/strong> \uc791\uc740 \ubaa8\ub378\uc740 \uc801\uc740 \uc5d0\ub108\uc9c0\ub97c \uc18c\ube44\ud558\ubbc0\ub85c, AI \uae30\uc220 \ubc1c\uc804\uc774 \ud658\uacbd\uc5d0 \ubbf8\uce58\ub294 \ubd80\ub2f4\uc744 \uc904\uc774\ub294 \ub370 \uae30\uc5ec\ud569\ub2c8\ub2e4. \uc774\ub294 AI\uc758 \uc9c0\uc18d \uac00\ub2a5\ud55c \ubc1c\uc804\uc744 \uc704\ud55c \uc911\uc694\ud55c \uc694\uc18c\uc785\ub2c8\ub2e4.<\/p>\n<\/li>\n<li>\n<p><strong>\uc790\uc6d0 \ubd84\uc0b0:<\/strong> \uac70\ub300 \ubaa8\ub378 \uac1c\ubc1c\uc5d0 \uc9d1\uc911\ub418\uc5c8\ub358 \ucef4\ud4e8\ud305 \uc790\uc6d0\uacfc \uc778\ub825\uc744 \ucd08\uc18c\ud615 \ubaa8\ub378 \uac1c\ubc1c \ubc0f \ud65c\uc6a9\uc73c\ub85c \ubd84\uc0b0\uc2dc\ucf1c, AI \uc0dd\ud0dc\uacc4 \uc804\uccb4\uc758 \uade0\ud615 \uc788\ub294 \ubc1c\uc804\uc744 \ub3c4\ubaa8\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n<\/li>\n<\/ul>\n<p><strong>\uc8fc\uc758\ud560 \uc810:<\/strong><\/p>\n<p>\ubb3c\ub860 \ucd08\uc18c\ud615 \ubaa8\ub378\uc774 \ubaa8\ub4e0 \ubb38\uc81c\ub97c \ud574\uacb0\ud558\ub294 \ub9cc\ub2a5 \uc5f4\uc1e0\ub294 \uc544\ub2d9\ub2c8\ub2e4. \ubcf5\uc7a1\ud558\uace0 \ubc29\ub300\ud55c \uc9c0\uc2dd\uc774 \ud544\uc694\ud55c \uc791\uc5c5, \uace0\ub3c4\uc758 \ucc3d\uc758\uc131\uc774\ub098 \ucd94\ub860 \ub2a5\ub825\uc774 \uc694\uad6c\ub418\ub294 \ubd84\uc57c\uc5d0\uc11c\ub294 \uc5ec\uc804\ud788 \uac70\ub300 \ubaa8\ub378\uc774 \uc720\ub9ac\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \uc911\uc694\ud55c \uac83\uc740 \uac01 \ubaa8\ub378\uc758 \uc7a5\ub2e8\uc810\uc744 \uba85\ud655\ud788 \uc774\ud574\ud558\uace0, <strong>\uc0c1\ud669\uacfc \ubaa9\uc801\uc5d0 \ub9de\ub294 \ucd5c\uc801\uc758 \ubaa8\ub378\uc744 \uc120\ud0dd\ud558\ub294 \uac83<\/strong>\uc785\ub2c8\ub2e4.<\/p>\n<h3>4. \ucd08\uc18c\ud615 \uc624\ud508 \ubaa8\ub378, \uc5b4\ub5bb\uac8c \ud65c\uc6a9\ud560 \uc218 \uc788\uc744\uae4c?<\/h3>\n<p>\ucd08\uc18c\ud615 \uc624\ud508 \ubaa8\ub378\uc758 \uac00\ub2a5\uc131\uc744 \uc774\ud574\ud588\ub2e4\uba74, \uc774\uc81c \uc6b0\ub9ac \uc0b6\uc5d0\uc11c \uc5b4\ub5bb\uac8c \ud65c\uc6a9\ud560 \uc218 \uc788\uc744\uc9c0 \uad6c\uccb4\uc801\uc73c\ub85c \uc0b4\ud3b4\ubcf4\uaca0\uc2b5\ub2c8\ub2e4.<\/p>\n<h4>H3_4-1: \uac1c\uc778\uc801\uc778 \ud65c\uc6a9<\/h4>\n<ul>\n<li>\n<p><strong>\ub098\ub9cc\uc758 \uae00\uc4f0\uae30 \ub3c4\uc6b0\ubbf8:<\/strong> \ud2b9\uc815 \uc2a4\ud0c0\uc77c\uc774\ub098 \uc8fc\uc81c\uc5d0 \ub9de\ucdb0 \uae00\uc4f0\uae30\ub97c \ub3c4\uc640\uc8fc\ub294 AI\ub97c \ub9cc\ub4e4\uc5b4 \ubcf4\uc138\uc694. \uc608\ub97c \ub4e4\uc5b4, \ube14\ub85c\uadf8 \ud3ec\uc2a4\ud305 \ucd08\uc548 \uc791\uc131, \uc774\uba54\uc77c \ub2f5\uc7a5 \uc791\uc131 \ub4f1\uc5d0 \ud65c\uc6a9\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n<\/li>\n<li>\n<p><strong>\ud559\uc2b5 \ub3c4\uad6c:<\/strong> \ubcf5\uc7a1\ud55c \uac1c\ub150\uc744 \uc27d\uac8c \uc124\uba85\ud574\uc8fc\uac70\ub098, \ud2b9\uc815 \uc8fc\uc81c\uc5d0 \ub300\ud55c \uc9c8\ubb38\uc5d0 \ub2f5\ubcc0\ud574\uc8fc\ub294 AI \ud29c\ud130\ub97c \ub9cc\ub4e4 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n<\/li>\n<li>\n<p><strong>\ucde8\ubbf8 \ud65c\ub3d9:<\/strong> \uc88b\uc544\ud558\ub294 \uc18c\uc124\uc774\ub098 \uc601\ud654\uc758 \ub4f1\uc7a5\uc778\ubb3c\ucc98\ub7fc \ub300\ud654\ud558\ub294 AI, \ub098\ub9cc\uc758 \uc2dc\ub098\ub9ac\uc624\ub97c \ub9cc\ub4e4\uc5b4\uc8fc\ub294 AI \ub4f1 \ucc3d\uc758\uc801\uc778 \ucde8\ubbf8 \ud65c\ub3d9\uc5d0 \ud65c\uc6a9\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n<\/li>\n<\/ul>\n<h4>H3_4-2: \uc5c5\ubb34 \ubc0f \ube44\uc988\ub2c8\uc2a4 \ud65c\uc6a9<\/h4>\n<ul>\n<li>\n<p><strong>\uace0\uac1d \uc751\ub300 \ucc57\ubd07:<\/strong> \uc790\uc8fc \ubb3b\ub294 \uc9c8\ubb38\uc5d0 \ub300\ud55c \ub2f5\ubcc0, \uac04\ub2e8\ud55c \uc608\uc57d \ucc98\ub9ac \ub4f1 \uace0\uac1d \uc751\ub300 \uc5c5\ubb34\ub97c \uc790\ub3d9\ud654\ud558\uc5ec \ud6a8\uc728\uc131\uc744 \ub192\uc77c \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n<\/li>\n<li>\n<p><strong>\ub370\uc774\ud130 \ubd84\uc11d \ubc0f \uc694\uc57d:<\/strong> \ubc29\ub300\ud55c \ud14d\uc2a4\ud2b8 \ub370\uc774\ud130\ub97c \ube60\ub974\uac8c \ubd84\uc11d\ud558\uace0 \ud575\uc2ec \ub0b4\uc6a9\uc744 \uc694\uc57d\ud558\uc5ec \ubcf4\uace0\uc11c \uc791\uc131 \uc2dc\uac04\uc744 \ub2e8\ucd95\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n<\/li>\n<li>\n<p><strong>\ucf58\ud150\uce20 \uc0dd\uc131 \uc9c0\uc6d0:<\/strong> \uc81c\ud488 \uc124\uba85, \ub9c8\ucf00\ud305 \ubb38\uad6c, \uc18c\uc15c \ubbf8\ub514\uc5b4 \uac8c\uc2dc\ubb3c \ub4f1 \ub2e4\uc591\ud55c \ucf58\ud150\uce20 \ucd08\uc548\uc744 \uc0dd\uc131\ud558\ub294 \ub370 \ub3c4\uc6c0\uc744 \ubc1b\uc744 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n<\/li>\n<li>\n<p><strong>\ub0b4\ubd80 \uc5c5\ubb34 \uc790\ub3d9\ud654:<\/strong> \ud2b9\uc815 \uc591\uc2dd \uc791\uc131, \uc815\ubcf4 \uac80\uc0c9, \uac04\ub2e8\ud55c \ucf54\ub4dc \uc0dd\uc131 \ub4f1 \ubc18\ubcf5\uc801\uc778 \ub0b4\ubd80 \uc5c5\ubb34\ub97c \uc790\ub3d9\ud654\ud558\uc5ec \uc9c1\uc6d0\ub4e4\uc774 \ub354 \uc911\uc694\ud55c \uc5c5\ubb34\uc5d0 \uc9d1\uc911\ud558\ub3c4\ub85d \ub3c4\uc6b8 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n<\/li>\n<\/ul>\n<h4>H3_4-3: \uac1c\ubc1c\uc790 \ubc0f \uc5f0\uad6c\uc790\ub97c \uc704\ud55c \ud65c\uc6a9<\/h4>\n<ul>\n<li>\n<p><strong>\ub9de\ucda4\ud615 AI \uc11c\ube44\uc2a4 \uac1c\ubc1c:<\/strong> \ud2b9\uc815 \ub2c8\uc988\uc5d0 \ub9de\ub294 AI \uc11c\ube44\uc2a4\ub97c \ube60\ub974\uace0 \uc800\ub834\ud558\uac8c \uac1c\ubc1c\ud558\uc5ec \uc2dc\uc7a5\uc5d0 \ucd9c\uc2dc\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n<\/li>\n<li>\n<p><strong>AI \ubaa8\ub378 \uc5f0\uad6c \ubc0f \uc2e4\ud5d8:<\/strong> \uc0c8\ub85c\uc6b4 AI \uc544\ud0a4\ud14d\ucc98\ub098 \ud559\uc2b5 \ubc29\ubc95\uc744 \uc2e4\ud5d8\ud558\uace0 \uac80\uc99d\ud558\ub294 \ub370 \ud65c\uc6a9\ud558\uc5ec \uc5f0\uad6c \uac1c\ubc1c \uc18d\ub3c4\ub97c \ub192\uc77c \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n<\/li>\n<li>\n<p><strong>AI \uad50\uc721 \ub3c4\uad6c:<\/strong> \ud559\uc0dd\ub4e4\uc774 AI \ubaa8\ub378\uc758 \uc6d0\ub9ac\ub97c \uc9c1\uc811 \uc2e4\uc2b5\ud558\uace0 \uc774\ud574\ud558\ub294 \ub370 \ud6a8\uacfc\uc801\uc778 \uad50\uc721 \ub3c4\uad6c\ub85c \ud65c\uc6a9\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n<\/li>\n<\/ul>\n<p><strong>\uc2dc\uc791\ud558\ub294 \ubc29\ubc95:<\/strong><\/p>\n<ul>\n<li>\n<p><strong>\uc624\ud508\uc18c\uc2a4 \ud50c\ub7ab\ud3fc \ud65c\uc6a9:<\/strong> Hugging Face\uc640 \uac19\uc740 \ud50c\ub7ab\ud3fc\uc5d0\ub294 \ub2e4\uc591\ud55c \ucd08\uc18c\ud615 \uc624\ud508 \ubaa8\ub378\ub4e4\uc774 \uacf5\uac1c\ub418\uc5b4 \uc788\uc2b5\ub2c8\ub2e4. \uc774 \ud50c\ub7ab\ud3fc\ub4e4\uc744 \ud1b5\ud574 \ubaa8\ub378\uc744 \ud0d0\uc0c9\ud558\uace0, \uc0ac\uc6a9\ubc95\uc744 \uc775\ud790 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n<\/li>\n<li>\n<p><strong>\uac04\ub2e8\ud55c \ud29c\ud1a0\ub9ac\uc5bc \ub530\ub77c \ud558\uae30:<\/strong> \uc628\ub77c\uc778\uc5d0\ub294 \ucd08\uc18c\ud615 \ubaa8\ub378\uc744 \ud65c\uc6a9\ud558\ub294 \ubc29\ubc95\uc5d0 \ub300\ud55c \ub2e4\uc591\ud55c \ud29c\ud1a0\ub9ac\uc5bc\uacfc \uac00\uc774\ub4dc\uac00 \uc874\uc7ac\ud569\ub2c8\ub2e4. \uc774\ub97c \ub530\ub77c \ud558\uba70 \uc9c1\uc811 \ubaa8\ub378\uc744 \uc2e4\ud589\ud574 \ubcf4\ub294 \uac83\uc774 \uc88b\uc2b5\ub2c8\ub2e4.<\/p>\n<\/li>\n<li>\n<p><strong>\ucee4\ubba4\ub2c8\ud2f0 \ucc38\uc5ec:<\/strong> \uad00\ub828 \uc628\ub77c\uc778 \ucee4\ubba4\ub2c8\ud2f0\uc5d0 \ucc38\uc5ec\ud558\uc5ec \ub2e4\ub978 \uc0ac\uc6a9\uc790\ub4e4\uacfc \uc815\ubcf4\ub97c \uacf5\uc720\ud558\uace0 \ub3c4\uc6c0\uc744 \ubc1b\uc73c\uba74 \ub354\uc6b1 \ud6a8\uacfc\uc801\uc73c\ub85c \ud65c\uc6a9\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n<\/li>\n<\/ul>\n<h3>\uacb0\ub860: \uc791\uc9c0\ub9cc \uc704\ub300\ud55c \ubcc0\ud654\ub97c \uc774\ub04c \ucd08\uc18c\ud615 \uc624\ud508 \ubaa8\ub378<\/h3>\n<p>\uac70\ub300 AI \ubaa8\ub378\uc774 \ub9cc\ub4e4\uc5b4\ub0b8 \ub180\ub77c\uc6b4 \ud601\uc2e0\uc758 \uc2dc\ub300\uc5d0, 1B \uc774\ud558\uc758 \ucd08\uc18c\ud615 \uc624\ud508 \ubaa8\ub378\uc740 AI \uae30\uc220\uc758 \ubbf8\ub798\ub97c \ub354\uc6b1 \ubc1d\uace0 \ud76c\ub9dd\ucc28\uac8c \ub9cc\ub4e4 \uc911\uc694\ud55c \ub3d9\ub825\uc73c\ub85c \ub5a0\uc624\ub974\uace0 \uc788\uc2b5\ub2c8\ub2e4. \uc774 \uc791\uc740 \ubaa8\ub378\ub4e4\uc740 \ub2e8\uc21c\ud788 \ud06c\uae30\uac00 \uc791\ub2e4\ub294 \uac83\uc744 \ub118\uc5b4, <strong>\ud0c1\uc6d4\ud55c \ud6a8\uc728\uc131, \ud2b9\uc815 \uc791\uc5c5\uc5d0 \ub300\ud55c \ub192\uc740 \ucd5c\uc801\ud654, \uadf8\ub9ac\uace0 AI \uae30\uc220\uc758 \ubbfc\uc8fc\ud654\ub77c\ub294 \uac15\ub825\ud55c \ubb34\uae30<\/strong>\ub97c \uac00\uc9c0\uace0 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n<p>\ucc9c\ubb38\ud559\uc801\uc778 \ube44\uc6a9\uacfc \ub192\uc740 \uc5d0\ub108\uc9c0 \uc18c\ube44\ub77c\ub294 \uac70\ub300 \ubaa8\ub378\uc758 \ud55c\uacc4\ub97c \uadf9\ubcf5\ud558\uba70, \ucd08\uc18c\ud615 \ubaa8\ub378\uc740 AI \uae30\uc220\uc744 \ub354 \ub9ce\uc740 \uc0ac\ub78c\ub4e4\uc5d0\uac8c, \ub354 \uc27d\uac8c, \uadf8\ub9ac\uace0 \ub354 \uc9c0\uc18d \uac00\ub2a5\ud55c \ubc29\uc2dd\uc73c\ub85c \uc81c\uacf5\ud560 \uc218 \uc788\ub294 \uae38\uc744 \uc5f4\uc5b4\uc90d\ub2c8\ub2e4. \uac1c\uc778 \ub9de\ucda4\ud615 AI \ube44\uc11c\ubd80\ud130 \uc0b0\uc5c5\ubcc4 \ud2b9\ud654 \uc194\ub8e8\uc158, \uadf8\ub9ac\uace0 \uc628\ub514\ubc14\uc774\uc2a4 AI \uad6c\ud604\uc5d0 \uc774\ub974\uae30\uae4c\uc9c0, \uc774 \uc791\uc740 \ubaa8\ub378\ub4e4\uc774 \uac00\uc838\uc62c \ubcc0\ud654\uc758 \ubb3c\uacb0\uc740 \uc774\ubbf8 \uc2dc\uc791\ub418\uc5c8\uc2b5\ub2c8\ub2e4.<\/p>\n<p>\uc774\uc81c \uc6b0\ub9ac\ub294 \u2018\ud06c\uae30\u2019\uac00 \uc544\ub2cc \u2018\ud6a8\uc728\uc131\u2019\uacfc \u2018\ubaa9\uc801\u2019\uc5d0 \ub9de\ub294 AI\ub97c \uc120\ud0dd\ud558\ub294 \uc2dc\ub300\ub97c \ub9de\uc774\ud558\uace0 \uc788\uc2b5\ub2c8\ub2e4. \ucd08\uc18c\ud615 \uc624\ud508 \ubaa8\ub378\uc758 \uac00\ub2a5\uc131\uc5d0 \uc8fc\ubaa9\ud558\uace0, \uc774\ub97c \uc801\uadf9\uc801\uc73c\ub85c \ud0d0\uad6c\ud558\uace0 \ud65c\uc6a9\ud55c\ub2e4\uba74, AI \uae30\uc220\uc758 \ud61c\ud0dd\uc744 \uc6b0\ub9ac \uc0b6 \uacf3\uacf3\uc5d0\uc11c \ub354\uc6b1 \ud48d\uc694\ub86d\uac8c \ub204\ub9b4 \uc218 \uc788\uc744 \uac83\uc785\ub2c8\ub2e4.<\/p>\n<p><strong>\uc624\ub298 \ub2f9\uc7a5 \uc2dc\uc791\ud560 \uc218 \uc788\ub294 \uc2e4\ucc9c \ubc29\uc548:<\/strong><\/p>\n<ol>\n<li>\n<p><strong>AI \ubaa8\ub378 \ud0d0\uc0c9:<\/strong> Hugging Face\uc640 \uac19\uc740 \ud50c\ub7ab\ud3fc\uc5d0\uc11c \ub2e4\uc591\ud55c \ucd08\uc18c\ud615 \uc624\ud508 \ubaa8\ub378\ub4e4\uc744 \ub458\ub7ec\ubcf4\uace0 \uc5b4\ub5a4 \ubaa8\ub378\ub4e4\uc774 \uc788\ub294\uc9c0 \uc54c\uc544\ubcf4\uc138\uc694.<\/p>\n<\/li>\n<li>\n<p><strong>\uc628\ub77c\uc778 \ud29c\ud1a0\ub9ac\uc5bc \ud65c\uc6a9:<\/strong> \uac04\ub2e8\ud55c \ucc57\ubd07 \ub9cc\ub4e4\uae30, \ud14d\uc2a4\ud2b8 \uc694\uc57d \uae30\ub2a5 \uad6c\ud604 \ub4f1 \ucd08\uc18c\ud615 \ubaa8\ub378 \ud65c\uc6a9 \ud29c\ud1a0\ub9ac\uc5bc\uc744 \ud558\ub098 \ub530\ub77c \ud558\uba70 \uc9c1\uc811 \uacbd\ud5d8\ud574\ubcf4\uc138\uc694.<\/p>\n<\/li>\n<li>\n<p><strong>\uad00\uc2ec \ubd84\uc57c\uc5d0 \uc801\uc6a9 \uc0c1\uc0c1\ud558\uae30:<\/strong> \ub0b4\uac00 \uc77c\ud558\uac70\ub098 \ubc30\uc6b0\ub294 \ubd84\uc57c\uc5d0\uc11c \ucd08\uc18c\ud615 AI \ubaa8\ub378\uc744 \uc5b4\ub5bb\uac8c \ud65c\uc6a9\ud560 \uc218 \uc788\uc744\uc9c0 \uad6c\uccb4\uc801\uc73c\ub85c \uc0c1\uc0c1\ud558\uace0 \uc544\uc774\ub514\uc5b4\ub97c \uc801\uc5b4\ubcf4\uc138\uc694.<\/p>\n<\/li>\n<\/ol>\n<div class=\"content-links-section\">\n<p><strong>INTERNAL_LINKS:<\/strong> (\uc720\uc0ac\ud55c \uac8c\uc2dc\uae00 \uc785\ub825)<\/p>\n<p><strong>EXTERNAL_LINKS:<\/strong> <a href=\"https:\/\/www.microsoft.com\/en-us\/research\/blog\/phi-2-new-state-of-the-art-small-language-model\/\" target=\"_blank\" rel=\"noopener noreferrer\">Microsoft Phi-2 \ubaa8\ub378 \uc18c\uac1c<\/a>, <a href=\"https:\/\/huggingface.co\/\" target=\"_blank\" rel=\"noopener noreferrer\">Hugging Face<\/a><\/p>\n<\/div>\n\n\n<p class=\"wp-block-paragraph\"><\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><\/p>\n\n\n\n<h2 class=\"wp-block-heading\">In the Age of Giant AI Models, a Brief Pause: The Return of Ultra-Small Open Models<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">Over the past few years, the field of artificial intelligence (AI) has grown so explosively that it could almost be summarized with a single word: <strong>large<\/strong>. Large language models (LLMs) such as GPT-3 and GPT-4, with hundreds of billions of parameters, have demonstrated astonishing performance and influenced many aspects of daily life. Like vast libraries filled with enormous knowledge, these models can answer complex questions with remarkable fluency and have been widely seen as taking AI capability to a new level.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Yet within this sweeping trend toward ever-larger models, a paradoxical shift is taking place: <strong>ultra-small models are drawing renewed attention<\/strong>. In particular, models with fewer than <strong>1 billion parameters (1B)<\/strong> are being revisited and revalued. These models challenge the assumption that \u201cbigger and trained on more data always means better performance.\u201d Instead, they are bringing fresh possibilities and new energy into the AI ecosystem.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">So why are these small and lightweight models becoming important again? Is it simply because they are small? Not at all. These ultra-small open models matter because they help address the limitations of giant models and hold the potential to make AI more democratic, more accessible, and more widely distributed. Let us explore why these ultra-small open models matter again and what possibilities they may open.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">1. Why Should We Pay Attention to \u201cSmall\u201d Models? Looking Beyond the Shadow of Giant Models<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">Large AI models undeniably deliver impressive performance. But behind that power, several difficult challenges remain.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Astronomical Cost<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">Training and operating giant models requires enormous computing resources and energy. This translates directly into huge costs and creates a barrier to entry so high that only a handful of major corporations can realistically develop and deploy such systems. For individuals and small businesses, they are often out of reach.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">High Energy Consumption<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">As model size increases, energy use rises dramatically as well. This has direct implications for environmental sustainability and raises concern about whether AI can continue to grow responsibly.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Slower Speed and Higher Latency<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">Because large models process much more information, their response speed can be slower. That makes them harder to use in real-time services where rapid response is essential.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Inefficiency for Specific Purposes<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">Large models are highly general-purpose, but that very generality can make them inefficient for narrowly defined tasks. It is like using a hammer to tighten a screw: possible, perhaps, but clearly not the best tool.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">This is exactly where ultra-small open models become important. They challenge the assumption that smaller always means worse. Instead, these models can be highly efficient and sharply optimized for specific purposes, helping democratize AI and spread its benefits more broadly.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">2. What Makes Sub-1B Models Different? Why They Are Small but Powerful<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">A <strong>sub-1B model<\/strong> is, quite literally, a model with fewer than 1 billion parameters. Compared to models with hundreds of billions of parameters, that is extremely small. Yet because of this small size, such models offer several distinctive advantages.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">2.1. Outstanding Efficiency and Cost-Effectiveness<\/h3>\n\n\n\n<h4 class=\"wp-block-heading\">Lower Training and Operating Costs<\/h4>\n\n\n\n<p class=\"wp-block-paragraph\">Small models require much less data and far fewer computing resources to train. That means their training and operation costs can be dramatically lower. This makes it possible for more people to use AI more affordably. In many cases, they can run on personal computers or inexpensive cloud servers.<\/p>\n\n\n\n<h4 class=\"wp-block-heading\">Faster Inference Speed<\/h4>\n\n\n\n<p class=\"wp-block-paragraph\">The smaller the model, the less time it takes to process data. That makes these models especially valuable in areas where fast response is critical, such as real-time services and mobile applications.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">2.2. Strong Optimization for Specific Tasks<\/h3>\n\n\n\n<h4 class=\"wp-block-heading\">Tailored Performance<\/h4>\n\n\n\n<p class=\"wp-block-paragraph\">If giant models are the \u201cgeneralists\u201d that can do a little of everything, ultra-small models are more like <strong>specialists<\/strong>. For well-defined tasks\u2014such as handling terminology in a specific industry, translating a particular language pair, or powering a simple chatbot\u2014small models can perform as well as, or sometimes even better than, much larger models.<\/p>\n\n\n\n<h4 class=\"wp-block-heading\">Enabling On-Device AI<\/h4>\n\n\n\n<p class=\"wp-block-paragraph\">They are essential for <strong>on-device AI<\/strong>\u2014AI that runs directly on smartphones, wearables, and other devices without requiring internet connectivity. Their small size and low power consumption make them practical in mobile environments.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">2.3. Accessibility and Democratization<\/h3>\n\n\n\n<h4 class=\"wp-block-heading\">Strengthening the Open-Source Ecosystem<\/h4>\n\n\n\n<p class=\"wp-block-paragraph\">Many ultra-small models are released as open source, which means anyone can use, modify, and improve them. This accelerates the pace of AI development and creates room for a broader diversity of ideas and applications.<\/p>\n\n\n\n<h4 class=\"wp-block-heading\">Lowering the Barrier to Development<\/h4>\n\n\n\n<p class=\"wp-block-paragraph\">Because they are easier for small teams and individual developers to access and experiment with, they reduce the barrier to entry for AI development.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Real Examples<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Phi-2 (Microsoft):<\/strong><br>A model with about 2.7 billion parameters that demonstrates reasoning capabilities comparable to far larger models while remaining much smaller. It performs especially well in certain types of logic and language understanding.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>TinyLlama:<\/strong><br>A model with around 1.1 billion parameters, trained based on the Llama 2 family. Despite its small size, it shows impressive performance and is already being used in a variety of research and development contexts.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">In other words, sub-1B models are not simply \u201csmall.\u201d They have distinctive strengths in efficiency, optimization, and accessibility that giant models often lack.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">3. What New Possibilities Could Ultra-Small Open Models Open?<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">The rise of ultra-small open models has the potential to make the future of AI more diverse, more practical, and more inclusive.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">3.1. Democratization and Mainstreaming of AI<\/h3>\n\n\n\n<h4 class=\"wp-block-heading\">Personalized AI<\/h4>\n\n\n\n<p class=\"wp-block-paragraph\">It becomes much easier for individuals to build and use AI models tailored to their own needs. For example, people could create personal writing assistants, schedule managers, or domain-specific chatbots much more easily.<\/p>\n\n\n\n<h4 class=\"wp-block-heading\">Growth in Education and Research<\/h4>\n\n\n\n<p class=\"wp-block-paragraph\">Schools, universities, and research labs can experiment directly with AI models without expensive infrastructure. This helps develop AI talent and encourages innovation.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">3.2. The Emergence of New Services and Industries<\/h3>\n\n\n\n<h4 class=\"wp-block-heading\">AI for Mobile and Edge Devices<\/h4>\n\n\n\n<p class=\"wp-block-paragraph\">High-performance AI functions can run on smartphones, smartwatches, autonomous vehicles, and other devices without an internet connection. This improves both privacy protection and response speed.<\/p>\n\n\n\n<h4 class=\"wp-block-heading\">Industry-Specific Solutions<\/h4>\n\n\n\n<p class=\"wp-block-paragraph\">Optimized AI models for healthcare, finance, legal services, and other industries can increase productivity and efficiency. Examples include medical image analysis AI and financial fraud detection AI.<\/p>\n\n\n\n<h4 class=\"wp-block-heading\">Greater Accessibility<\/h4>\n\n\n\n<p class=\"wp-block-paragraph\">Ultra-small models can also improve accessibility for disadvantaged groups\u2014for instance, through real-time translation that lowers language barriers, image description for visually impaired users, or voice-command interfaces.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">3.3. More Sustainable AI Development<\/h3>\n\n\n\n<h4 class=\"wp-block-heading\">Greater Energy Efficiency<\/h4>\n\n\n\n<p class=\"wp-block-paragraph\">Because small models consume less energy, they help reduce the environmental burden of AI development. This makes them an important part of building sustainable AI.<\/p>\n\n\n\n<h4 class=\"wp-block-heading\">More Distributed Use of Resources<\/h4>\n\n\n\n<p class=\"wp-block-paragraph\">Rather than concentrating talent and computation entirely on giant models, smaller models allow resources to be distributed more broadly across the ecosystem, supporting more balanced growth.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">A Cautionary Note<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">Of course, ultra-small models are not a universal solution. For tasks requiring highly complex reasoning, extremely broad knowledge, or high creativity, large models may still hold the advantage. The important point is to understand the strengths and limits of each type of model and choose the right one for the task and context.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">4. How Can Ultra-Small Open Models Be Used?<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">Once we understand their potential, the next question is how they can be used in real life.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">4.1. Personal Uses<\/h3>\n\n\n\n<h4 class=\"wp-block-heading\">A Personal Writing Assistant<\/h4>\n\n\n\n<p class=\"wp-block-paragraph\">You can create an AI assistant tailored to your preferred style or subject area, useful for drafting blog posts, emails, or other writing.<\/p>\n\n\n\n<h4 class=\"wp-block-heading\">A Learning Tool<\/h4>\n\n\n\n<p class=\"wp-block-paragraph\">Small models can be used to build AI tutors that explain difficult concepts in simpler ways or answer questions about specific topics.<\/p>\n\n\n\n<h4 class=\"wp-block-heading\">Hobbies and Creative Activities<\/h4>\n\n\n\n<p class=\"wp-block-paragraph\">They can power creative hobby projects, such as AI that speaks like a favorite fictional character or AI that helps generate original story ideas.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">4.2. Work and Business Uses<\/h3>\n\n\n\n<h4 class=\"wp-block-heading\">Customer Service Chatbots<\/h4>\n\n\n\n<p class=\"wp-block-paragraph\">They can automate responses to frequently asked questions, handle simple reservations, and improve efficiency in customer interactions.<\/p>\n\n\n\n<h4 class=\"wp-block-heading\">Data Analysis and Summarization<\/h4>\n\n\n\n<p class=\"wp-block-paragraph\">They can quickly analyze large amounts of text and summarize key points, reducing the time needed to prepare reports.<\/p>\n\n\n\n<h4 class=\"wp-block-heading\">Content Creation Support<\/h4>\n\n\n\n<p class=\"wp-block-paragraph\">They can help generate first drafts of product descriptions, marketing copy, and social media posts.<\/p>\n\n\n\n<h4 class=\"wp-block-heading\">Internal Workflow Automation<\/h4>\n\n\n\n<p class=\"wp-block-paragraph\">They can automate repetitive internal tasks such as form completion, information lookup, and simple code generation, allowing employees to focus on more important work.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">4.3. Uses for Developers and Researchers<\/h3>\n\n\n\n<h4 class=\"wp-block-heading\">Building Custom AI Services<\/h4>\n\n\n\n<p class=\"wp-block-paragraph\">Developers can build and launch AI services tailored to particular user needs more quickly and cheaply.<\/p>\n\n\n\n<h4 class=\"wp-block-heading\">AI Model Research and Experimentation<\/h4>\n\n\n\n<p class=\"wp-block-paragraph\">Researchers can use small models to test and validate new model architectures or learning methods more efficiently.<\/p>\n\n\n\n<h4 class=\"wp-block-heading\">AI Education<\/h4>\n\n\n\n<p class=\"wp-block-paragraph\">They can serve as practical teaching tools that let students directly explore how AI models work.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">How to Get Started<\/h3>\n\n\n\n<h4 class=\"wp-block-heading\">Explore Open-Source Platforms<\/h4>\n\n\n\n<p class=\"wp-block-paragraph\">Platforms such as <strong>Hugging Face<\/strong> host many ultra-small open models. They are a good place to browse models, compare them, and learn how to use them.<\/p>\n\n\n\n<h4 class=\"wp-block-heading\">Follow Simple Tutorials<\/h4>\n\n\n\n<p class=\"wp-block-paragraph\">There are many tutorials online showing how to use ultra-small models for tasks such as building simple chatbots or text summarizers. Running one yourself is an excellent starting point.<\/p>\n\n\n\n<h4 class=\"wp-block-heading\">Join Communities<\/h4>\n\n\n\n<p class=\"wp-block-paragraph\">Participating in relevant online communities makes it easier to share ideas, ask questions, and learn from others.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">Conclusion: Ultra-Small Open Models That Will Drive Big Change<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">In an era defined by the astonishing achievements of giant AI models, ultra-small open models with fewer than 1 billion parameters are emerging as an important force that may make the future of AI brighter and more hopeful. These small models are not valuable simply because they are small. They matter because they offer powerful advantages in <strong>efficiency<\/strong>, <strong>optimization<\/strong>, and <strong>democratization<\/strong>.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">By addressing some of the biggest limitations of giant models\u2014astronomical cost and heavy energy use\u2014ultra-small models open the door to making AI more accessible, more sustainable, and more widely useful. From personalized AI assistants to industry-specific solutions and on-device AI, the wave of change they are bringing has already begun.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">We are now entering an era in which the right AI is chosen not by sheer size, but by <strong>efficiency<\/strong> and <strong>fitness for purpose<\/strong>. If we pay attention to the possibilities of ultra-small open models and actively explore and apply them, the benefits of AI can become much richer and more deeply woven into everyday life.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">Actions You Can Take Right Now<\/h2>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Explore AI models:<\/strong> Browse platforms like Hugging Face and see what kinds of ultra-small open models are available.<\/li>\n\n\n\n<li><strong>Try an online tutorial:<\/strong> Follow a tutorial for a simple chatbot or text summarizer and experience the potential directly.<\/li>\n\n\n\n<li><strong>Imagine how they fit your field:<\/strong> Think concretely about how ultra-small AI models could be used in your work, study, or area of interest.<\/li>\n<\/ul>\n","protected":false},"excerpt":{"rendered":"<p>\uac70\ub300 AI \ubaa8\ub378\uc758 \uc2dc\ub300 \uc18d\uc5d0\uc11c 10\uc5b5 \uac1c \ubbf8\ub9cc\uc758 \ud30c\ub77c\ubbf8\ud130\ub97c \uac00\uc9c4 \ucd08\uc18c\ud615 \uc624\ud508 \ubaa8\ub378\uc774 \ub2e4\uc2dc\uae08 \uc911\uc694\ud55c \uc774\uc720\ub97c \ud0d0\uad6c\ud569\ub2c8\ub2e4. \uae30\uc220\uc801 \ubc1c\uc804\uacfc \uc0c8\ub85c\uc6b4 \uac00\ub2a5\uc131\uc744 \uc27d\uace0 \uba85\ud655\ud558\uac8c \uc804\ub2ec\ud569\ub2c8\ub2e4.<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_jetpack_memberships_contains_paid_content":false,"footnotes":"","jetpack_publicize_message":"","jetpack_publicize_feature_enabled":true,"jetpack_social_post_already_shared":false,"jetpack_social_options":{"image_generator_settings":{"template":"highway","default_image_id":0,"font":"","enabled":false},"version":2}},"categories":[4],"tags":[357,478,475,69,358,41,359,477,362,74,476,474,480,479,361,37,363,356,360],"class_list":["post-113","post","type-post","status-publish","format-standard","hentry","category-ai","tag-1b--","tag-ai-democratization","tag-ai-potential","tag-ai-technology","tag-ai-","tag-llm-alternative","tag-llm-","tag-on-device-ai","tag-open-source-ai","tag-sub-1b-models","tag-sustainable-ai","tag-ultra-small-open-models","tag--ai","tag---ai","tag-356"],"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/ai-cloud.kr\/index.php?rest_route=\/wp\/v2\/posts\/113","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/ai-cloud.kr\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/ai-cloud.kr\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/ai-cloud.kr\/index.php?rest_route=\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/ai-cloud.kr\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=113"}],"version-history":[{"count":1,"href":"https:\/\/ai-cloud.kr\/index.php?rest_route=\/wp\/v2\/posts\/113\/revisions"}],"predecessor-version":[{"id":136,"href":"https:\/\/ai-cloud.kr\/index.php?rest_route=\/wp\/v2\/posts\/113\/revisions\/136"}],"wp:attachment":[{"href":"https:\/\/ai-cloud.kr\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=113"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/ai-cloud.kr\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=113"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/ai-cloud.kr\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=113"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}