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b\u4e4e\u539f\u6587\u4f20\u9001\u95e8\uff1a zhuanlan.zhihu.com\/p\/650450884<\/a><\/p>\n

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\u5982\u4f55\u5b9a\u4e49AGI<\/h2>\n

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    \u95ee\u9898\uff1a LLM\u5c31\u662fAGI\u5417\uff1f<\/h2>\n

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    LLM\u6700\u6839\u672c\u8fd8\u662f\u4e00\u4e2a\u751f\u6210\u5f0f\u7684\u8bed\u8a00\u6a21\u578b\uff0c\u5176\u57fa\u4e8e\u4e0a\u4e0b\u6587\u7684\u63a8\u7406\u80fd\u529b\u5e76\u6ca1\u6709\u878d\u5408\u4e16\u754c\u77e5\u8bc6\u3002\u5927\u8bed\u8a00\u6a21\u578b\u7684\u672c\u8d28\u662f\u901a\u8fc7\u7edf\u8ba1\u5efa\u6a21\u5728\u5927\u91cf\u6587\u672c\u4e0a\u8fdb\u884c\u8bad\u7ec3\uff0c\u5b66\u4e60\u6587\u672c\u4e4b\u95f4\u7684\u8bed\u8a00\u5b66\u76f8\u5173\u5173\u7cfb\uff0c\u4ece\u800c\u6839\u636e\u4e0a\u4e2a\u8bcd\u6c47\u9884\u6d4b\u4e0b\u4e2a\u8bcd\u6c47\u3002\u7528\u5317\u4eac\u901a\u7528\u4eba\u5de5\u667a\u80fd\u7814\u7a76\u9662\u6731\u677e\u7eaf\u6559\u6388\u56e2\u961f\u7684\u8bdd\u6765\u8bf4\uff0c\u5927\u6a21\u578b\u65e0\u5f02\u4e8e\u7f38\u4e2d\u4e4b\u8111\uff0c\u56e0\u4e3a\u5927\u6a21\u578b\u672c\u8eab\u5e76\u4e0d\u5728\u771f\u5b9e\u4e16\u754c\u4e2d\uff0c\u5b83\u65e0\u6cd5\u50cf\u4eba\u4e00\u6837\u5b9e\u73b0\u4ece\u201d\u8bcd\u8bed\u201c\u5230\u201d\u4e16\u754c\u201c\u7684\u8054\u7ed3\u3002\u7f3a\u4e4f\u7b26\u53f7\u843d\u5730\u4f7f\u5f97\u5927\u6a21\u578b\u5f88\u5bb9\u6613\u9677\u5165\u201c\u7ed5\u5708\u5708\u201d\u7684\u5883\u5730\u3002<\/p>\n

    \u4ece\u591a\u6a21\u6001\u4fe1\u606f\u7684\u5904\u7406\u4e0a\u6765\u770b\uff0c\u5c3d\u7ba1\u57fa\u4e8eCLIP\uff0cBLIP\u7b49\u591a\u6a21\u6001-\u6587\u672cembedding\u7b97\u6cd5\uff0c\u591a\u6a21\u6001\u5927\u6a21\u578b\u4e5f\u6709\u4e86\u5f88\u591a\u6bd4\u8f83\u6210\u529f\u7684\u6848\u4f8b\uff0c\u4f8b\u5982ChatGPT4\uff0c MiniGPT4\u7b49\uff0c\u4ece\u51c6\u786e\u5ea6\u6765\u8bf4\uff0c\u57fa\u4e8e\u5927\u8bed\u8a00\u6a21\u578b\u7684\u591a\u6a21\u6001\u5927\u6a21\u578b\u662f\u5dee\u5f3a\u4eba\u610f\u7684\u3002\u5c31\u62ffCV\u65b9\u5411\u7684\u6a21\u578b\u6765\u8bf4\uff0c\u4f20\u7edf\u7684CV\u4efb\u52a1\uff0c\u5305\u62ec\u56fe\u50cf\u8bc6\u522b\uff0c\u8ffd\u8e2a\uff0c\u6ce8\u91ca\uff0c\u751f\u6210\u7b49\u90fd\u662f\u57fa\u4e8e\u622a\u7136\u4e0d\u540c\u7684\u7f51\u7edc\u67b6\u6784\u5b9e\u73b0\u7684\u3002\u6bd4\u5982\u8bf4\uff0c\u5bf9\u4e8e\u56fe\u50cf\u8bc6\u522b\u548c\u5206\u5272\u4efb\u52a1\uff0c\u6211\u4eec\u901a\u5e38\u4f9d\u8d56\u4e8ehead-modules + backbone-modules\uff0c\u7528\u591a\u6a21\u6001\u5927\u6a21\u578b\uff0c\u65e0\u8bbaprompt\u5982\u4f55\u8bbe\u8ba1\uff0c\u51c6\u786e\u5ea6\u90fd\u96be\u4ee5\u4f01\u53ca\u3002\u5f53\u7136CV\u754c\u786e\u5b9e\u6709\u5f88\u591a\u5927\u4e00\u7edf\u67b6\u6784\u7684\u65b9\u5411\uff0c\u57fa\u4e8eCLIP,BLIP+prompt\u7684\u67b6\u6784\u4e5f\u662f\u5176\u4e2d\u7684\u4e00\u4e2a\u65b9\u5411\uff0c\u9664\u6b64\u4e4b\u5916\u8fd8\u6709\u4e00\u4e9b\u8bf8\u5982open-world visual recognition, segment anything task, generalized visual encoding\u7b49\u7b49\uff0c\u4f46\u5c31\u76ee\u524d\u800c\u8a00\u8fd8\u6ca1\u6709\u4e00\u4e2a\u5b9a\u8bba\u3002\u9664\u4e86\u56fe\u50cf\u4ee5\u5916\uff0c\u58f0\u97f3\uff0c\u611f\u77e5\uff0c\u52a8\u4f5c\u7b49\u90fd\u5c5e\u4e8e\u591a\u6a21\u6001\u7684\u8303\u7574\u3002<\/p>\n

    \u7b80\u5355\u6765\u8bf4\uff0c\u5927\u8bed\u8a00\u6a21\u578b\u7684\u4e0d\u53ef\u89e3\u91ca\u6027\u4ee5\u53ca\u6709\u9650\u7684\u6301\u7eed\u5b66\u4e60\u80fd\u529b\uff08\u77e5\u8bc6\u4fee\u6539\u3001\u77e5\u8bc6\u66f4\u8fed\u3001\u81ea\u4e3b\u5b66\u4e60\uff09\u662f\u5176\u6700\u5927\u7684\u5f0a\u7aef\u3002\u52a0\u4e4b\u5176\u751f\u6210\u5f0f\u8bed\u8a00\u7684\u672c\u8d28\u4ee5\u53ca\u5728\u591a\u6a21\u6001\u4fe1\u606f\u5904\u7406\u80fd\u529b\u4e0a\u7684\u4e0d\u8db3\uff0c\u5c31\u5927\u8bed\u8a00\u6a21\u578b\u672c\u8eab\u800c\u8a00\uff0c\u8fd8\u8fdc\u4e0d\u662fAGI\u7684\u4e00\u4e2a\u6700\u7ec8\u89e3\u3002\u6211\u4eec\u6240\u671f\u5f85\u7684\u662f\u4e00\u4e2a\u4ee5\u8db3\u4ee5\u5ab2\u7f8e\u4eba\u7c7b\u7684\u8ba4\u77e5\u80fd\u529b\u6765\u89e3\u51b3\u4efb\u4f55\u4efb\u52a1\u7684\u81ea\u4e3b\u667a\u80fd\u4f53\u3002<\/p>\n

    AGI\u9700\u8981\u6743\u8861\u7684\u65b9\u9762<\/h2>\n

    OpenAI\u5b98\u7f51\u4e0a\u5bf9\u4e8eAGI\u7684Roadmap\u4e2d\uff0c\u9610\u8ff0\u4e86OpenAI\u5bf9\u4e8eAGI\u7684\u957f\u8fdc\u548c\u77ed\u671f\u76ee\u6807\u3002\u5c0f\u7f16\u518d\u7ed3\u5408\u4e0a\u9762\u7684\u95ee\u9898\u4ee5\u53ca\u4e00\u4e9b\u5176\u4ed6\u7814\u7a76\u8bba\u6587\uff0c\u5f15\u51fa\u5982\u4e0b\u4e00\u7cfb\u5217AGI\u9700\u8981\u6743\u8861\u7684\u65b9\u9762\uff1a<\/p>\n

      \n
    1. \u53ef\u89e3\u91ca\u6027<\/li>\n
    2. \u666e\u9002\u6027<\/li>\n
    3. \u77e5\u8bc6\u8fed\u4ee3<\/li>\n
    4. \u4e0e\u4e16\u754c\u77e5\u8bc6\u7684\u7ed3\u5408<\/li>\n
    5. \u591a\u6a21\u6001<\/li>\n
    6. \u81ea\u4e3b\u5b66\u4e60\u80fd\u529b<\/li>\n
    7. \u7b97\u529b<\/li>\n
    8. \u76d1\u7ba1<\/li>\n<\/ol>\n

      \u4e00\u4e9bAGI\u7684\u6210\u679c\u548c\u613f\u666f<\/h2>\n

      Deepmind<\/h3>\n

      RT2: DeepMind\u516c\u53f8\u7684Robotic Transformer 2\uff08RT-2\uff09\u662f\u4eba\u5de5\u901a\u7528\u667a\u80fd\uff08AGI\uff09\u9886\u57df\u7684\u91cd\u5927\u7a81\u7834\u3002RT-2\u662f\u4e00\u79cd\u521b\u65b0\u7684\u89c6\u89c9-\u8bed\u8a00-\u52a8\u4f5c\uff08VLA\uff09\u6a21\u578b\uff0c\u5b83\u7ed3\u5408\u4e86\u6765\u81ea\u7f51\u7edc\u548c\u673a\u5668\u4eba\u7684\u6570\u636e\uff0c\u4e3a\u673a\u5668\u4eba\u63a7\u5236\u63d0\u4f9b\u4e86\u901a\u7528\u6307\u4ee4\u3002\u5176\u521b\u65b0\u4e4b\u5904\u5728\u4e8e\u80fd\u591f\u5f25\u5408\u5728\u5927\u89c4\u6a21\u7f51\u7edc\u6570\u636e\u96c6\u4e0a\u8bad\u7ec3\u7684\u9ad8\u5bb9\u91cf\u89c6\u89c9-\u8bed\u8a00\u6a21\u578b\uff08VLM\uff09\u4e0e\u673a\u5668\u4eba\u63a7\u5236\u7684\u5b9e\u9645\u9700\u6c42\u4e4b\u95f4\u7684\u5dee\u8ddd\u3002\u901a\u8fc7\u9002\u5e94Pathways Language and Image model\uff08PaLI-X\uff09\u548cPathways Language model Embodied\uff08PaLM-E\uff09\uff0cRT-2\u5c06\u89c6\u89c9\u548c\u8bed\u4e49\u7406\u89e3\u4e0e\u673a\u5668\u4eba\u52a8\u4f5c\u76f8\u7ed3\u5408\u3002RT-2\u7684\u72ec\u7279\u4e4b\u5904\u5728\u4e8e\u5176\u51fa\u8272\u7684\u6cdb\u5316\u80fd\u529b\u3002\u5b83\u8d85\u8d8a\u4e86\u5bf9\u673a\u5668\u4eba\u6570\u636e\u7684\u66b4\u9732\uff0c\u5c55\u793a\u4e86\u8bed\u4e49\u7406\u89e3\u548c\u591a\u9636\u6bb5\u63a8\u7406\u7684\u80fd\u529b\u3002\u5b83\u80fd\u591f\u89e3\u91ca\u65b0\u9896\u7684\u6307\u4ee4\uff0c\u5e76\u5c55\u793a\u4e86\u57fa\u672c\u7684\u63a8\u7406\u80fd\u529b\uff0c\u5982\u8fa8\u522b\u7269\u4f53\u7c7b\u522b\u548c\u9ad8\u5c42\u63cf\u8ff0\u3002\u503c\u5f97\u6ce8\u610f\u7684\u662f\uff0c\u901a\u8fc7\u201c\u601d\u7ef4\u94fe\u201d\u63a8\u7406\uff0cRT-2\u80fd\u591f\u6267\u884c\u590d\u6742\u4efb\u52a1\uff0c\u89c4\u5212\u548c\u6267\u884c\u4e00\u7cfb\u5217\u52a8\u4f5c\uff0c\u5c55\u793a\u4e86\u524d\u6240\u672a\u6709\u7684\u80fd\u529b\u3002\u901a\u8fc7\u5e7f\u6cdb\u7684\u5b9a\u6027\u548c\u5b9a\u91cf\u5b9e\u9a8c\uff0cRT-2\u5c55\u73b0\u4e86\u5176\u6280\u80fd\u7684\u51fa\u73b0\u3002\u5b83\u80fd\u591f\u7406\u89e3\u548c\u6267\u884c\u6d89\u53ca\u672a\u89c1\u8fc7\u7684\u7269\u4f53\u3001\u80cc\u666f\u548c\u73af\u5883\u7684\u4efb\u52a1\uff0c\u76f8\u8f83\u4e8e\u4e4b\u524d\u7684\u6a21\u578b\uff0c\u6cdb\u5316\u6027\u80fd\u63d0\u5347\u4e863\u500d\u3002\u5b83\u5728\u6a21\u62df\u548c\u771f\u5b9e\u4e16\u754c\u60c5\u5883\u4e2d\u90fd\u8868\u73b0\u51fa\u8272\uff0c\u5c55\u793a\u4e86\u5176\u9002\u5e94\u6027\u548c\u7a33\u5065\u6027\u3002\u603b\u4e4b\uff0cRT-2\u5c55\u793a\u4e86\u89c6\u89c9-\u8bed\u8a00\u6a21\u578b\u5728\u5b9e\u73b0AGI\u65b9\u9762\u7684\u8f6c\u53d8\u6f5c\u529b\u3002<\/p>\n

      \n

      \"rt2_0.gif\"<\/figure>\n<\/p>\n

      OpenAI<\/h3>\n

      OpenAI\u7684\u4ea7\u54c1\u5e94\u8be5\u662f\u6211\u4eec\u90fd\u6bd4\u8f83\u719f\u6089\u7684\u3002ChatGPT3.5\u662f\u6700\u521d\u7206\u706b\u7684\u4e00\u6b3e\u57fa\u4e8e\u5bf9\u8bdd\u7684\u901a\u7528\u8bed\u8a00\u6a21\u578b\u3002\u4e4b\u540e\u7684ChatGPT4.0\u66f4\u662f\u4f18\u5316\u4e86\u5176\u5bf9\u4e8e\u56fe\u50cf\u6a21\u6001\u6570\u636e\u7684\u652f\u6301\u3002ChatGPT4.0\u652f\u6301\u7684\u63d2\u4ef6\u6a21\u5f0f\uff0c\u8ba9\u5927\u8bed\u8a00\u6a21\u578b\u5177\u5907\u4e86\u4f7f\u7528\u5de5\u5177\u548c\u4e13\u5bb6\u7cfb\u7edf\u7684\u80fd\u529b\u3002\u7f51\u4f20GPT4.0\u91c7\u7528\u4e86MoE\u6df7\u5408\u4e13\u5bb6\u6a21\u578b\uff0c\u901a\u8fc7\u628a\u4e00\u4e2a\u4efb\u52a1\u62c6\u89e3\u6210\u5b50\u4efb\u52a1\uff0c\u7528Gating Model\u6765\u51b3\u5b9a\u4f7f\u7528\u54ea\u4e00\u4e2a\u4e13\u5bb6\u6a21\u578b\u6765\u5904\u7406\u5b50\u4efb\u52a1\uff0c\u6700\u7ec8\u6c60\u5316\u4ee5\u5f97\u51fa\u6700\u7ec8\u7b54\u6848\u3002\u4e0d\u7ba1\u7f51\u4f20\u662f\u5426\u4e3a\u771f\uff0c\u8fd0\u7528\u5927\u8bed\u8a00\u6a21\u578b\u9a71\u52a8\u4e13\u5bb6\u6a21\u578b\uff08\u6216\u8005\u5de5\u5177\uff09\uff0c\u9274\u4e8e\u4e13\u4e1a\u9886\u57dfnarrow AI model\u6bd4\u901a\u7528\u6a21\u578b\u66f4\u4f18\u7684\u8868\u73b0\u3002\u5728\u5c0f\u7f16\u770b\u6765\u662f\u4e00\u4e2a\u5f88\u4e0d\u9519\u7684AGI\u601d\u8def\u3002<\/p>\n

      \n

      \"openai.PNG\"<\/figure>\n\n\u4eceOpenAI\u5b98\u7f51\u5bf9\u4e8eAGI\u7684Roadmap\u6211\u4eec\u8fd8\u80fd\u83b7\u5f97\u7684\u91cd\u70b9\u662f\u5173\u4e8eAI\u4f26\u7406\u89d2\u5ea6\u7684\u3002\u5728AGI\u6280\u672f\u5b9e\u73b0\u7684\u9053\u8def\u4e0a\uff0c\u540c\u6837\u4e0d\u80fd\u5ffd\u89c6\u5176\u6cbb\u7406\u65b9\u9762\u7684\u95ee\u9898\uff0c\u5305\u62ec\u6ee5\u7528\u98ce\u9669\uff0c\u4e25\u91cd\u4e8b\u6545\uff0c\u53ef\u80fd\u7684\u793e\u4f1a\u52a8\u8361\uff0c\u5229\u76ca\u5206\u914d\u7b49\u3002Anthropic, Google, Microsoft, OpenAI\u90fd\u521b\u7acb\u4e86Frontier Model Forum\uff08\u524d\u6cbf\u6a21\u578b\u8bba\u575b\uff09\u3002\u8be5\u8bba\u575b\u7684\u76ee\u6807\u662f\uff1a\uff08i\uff09\u63a8\u8fdbAI\u5b89\u5168\u7814\u7a76\uff0c\u4fc3\u8fdb\u524d\u6cbf\u6a21\u578b\u7684\u8d1f\u8d23\u4efb\u53d1\u5c55\uff0c\u6700\u5927\u7a0b\u5ea6\u5730\u51cf\u5c11\u6f5c\u5728\u98ce\u9669\uff1b\uff08ii\uff09\u786e\u5b9a\u524d\u6cbf\u6a21\u578b\u7684\u5b89\u5168\u6700\u4f73\u5b9e\u8df5\uff1b\uff08iii\uff09\u4e0e\u653f\u7b56\u5236\u5b9a\u8005\u3001\u5b66\u8005\u3001\u516c\u6c11\u793e\u4f1a\u7b49\u5171\u4eab\u77e5\u8bc6\uff0c\u63a8\u8fdb\u8d1f\u8d23\u4efb\u7684AI\u53d1\u5c55\uff1b\u4ee5\u53ca\uff08iv\uff09\u652f\u6301\u5229\u7528AI\u89e3\u51b3\u793e\u4f1a\u6700\u5927\u6311\u6218\u7684\u52aa\u529b\u3002<\/p>\n

      AutoGPT,AgentGPT,BabyAGI,HuggingGPT,MetaGPT<\/h3>\n

      \u8fd9\u4e9b\u5f00\u6e90\u9879\u76ee\u90fd\u662f\u5229\u7528\u81ea\u6cbb\u7684\u601d\u60f3\uff0c\u901a\u8fc7\u4f7f\u7528\u5927\u6a21\u578b\u62c6\u89e3\u4efb\u52a1\uff0c\u5f62\u6210\u5b50\u4efb\u52a1\uff0c\u901a\u8fc7\u81ea\u6211\u89c4\u5212\u3001\u8bc4\u6d4b\u4e00\u6b65\u4e00\u6b65\u5b8c\u6210\u5b50\u4efb\u52a1\uff0c\u5e76\u6700\u7ec8\u5b8c\u6210\u7528\u6237\u63d0\u51fa\u7684\u9700\u6c42\u3002\u8fd9\u4e9b\u9879\u76ee\u5728\u89e3\u51b3\u666e\u9002\u95ee\u9898\u7684\u80fd\u529b\u4e0a\u662f\u5341\u5206\u63a5\u8fd1AGI\u7684\u3002\u8fd9\u51e0\u4e2a\u9879\u76ee\uff0c\u9664\u4e86MetaGPT\u662f\u6700\u8fd1\uff087\u6708\uff09\u53d1\u5e03\u7684\u4ee5\u5916\uff0c\u90fd\u5df2\u7ecf\u5f00\u6e90\u4e86\u5f88\u4e45\u4e86\uff0c\u5c0f\u7f16\u5927\u6982\u4e09\u6708\u7684\u65f6\u5019\u5c31\u6709\u90e8\u7f72\u8fc7\u8fd9\u4e9b\u9879\u76ee\uff0c\u4e5f\u9605\u8bfb\u4e86\u4ed6\u4eec\u7684\u6e90\u7801\uff0c\u6700\u7ec8\u770b\u5230\u4e86\u4e00\u4e9b***\u8ba4\u77e5\u6a21\u578b\uff08cognitive model\uff09***\u7684\u5f71\u5b50\u3002\u8fd9\u4e9b\u9879\u76ee\u6253\u52a8\u5c0f\u7f16\u7684\u4e0d\u662f\u5176\u843d\u5730\u89e3\u51b3\u95ee\u9898\u7684\u80fd\u529b\uff0c\u771f\u6b63\u6253\u52a8\u6211\u7684\u662f\u5176\u4e2d\u4f53\u73b0\u51fa\u6765\u7684\u601d\u7ef4\u8fc7\u7a0b\u3002\u5c0f\u7f16\u5766\u8a00\uff0c\u4ece\u4f7f\u7528\u4f53\u9a8c\u6765\u8bf4\uff0c\u8fd9\u4e9b\u9879\u76ee\u4e0d\u4ec5\u8fc7\u7a0b\u8017\u65f6\u4e45\uff0c\u6210\u672c\u9ad8\uff0c\u5728\u7a33\u5b9a\u6027\u4e0a\u4e5f\u6709\u5f88\u5927\u7684\u7f3a\u9677\u3002\u4f46\u8fd9\u4e9b\u9879\u76ee\u63d0\u4f9b\u4e86\u4e00\u4e9b\u65b0\u7684\u52aa\u529b\u65b9\u5411\uff0c\u8d77\u7801\u5c0f\u7f16\u770b\u5230\u5f53\u524d\u7684\u6d41\u7a0b\u4e2d\u5c3d\u7ba1\u4f7f\u7528\u4e86Working Memory, \u72b6\u6001\u8bc4\u4f30\u7b49\u601d\u8def\uff0c\u4f46\u4e5f\u4e0d\u662f\u6700\u4f18\u7684\u4f7f\u7528\u65b9\u5f0f\u3002\u8ba4\u77e5\u6a21\u578b\u7684\u7814\u7a76\u53ef\u4ee5\u8ffd\u6eaf\u52301950\u5e74\uff0c\u662f\u6700\u65e9\u7684\u5bf9\u4e8eAGI\u7684\u7814\u7a76\u9886\u57df\uff0c\u73b0\u6709\u7684\u8ba4\u77e5\u6a21\u578b\u6709\u5c06\u8fd1200\u79cd<\/em>\uff0c\u5c0f\u7f16\u8ba4\u4e3a\uff0c\u901a\u8fc7\u9009\u62e9\u548c\u4f18\u5316\u8ba4\u77e5\u6a21\u578b\uff0c\u7ed3\u5408\u5927\u6a21\u578b\uff0c\u6211\u4eec\u80fd\u591f\u5728AGI\u7684\u9053\u8def\u4e0a\u8fc8\u8fdb\u4e00\u5927\u6b65\u3002<\/p>\n

      \u8ba4\u77e5\u67b6\u6784\uff08Cognitive architectures)<\/h2>\n

      \u8ba4\u77e5\u67b6\u6784\uff08Cognitive Architecture\uff09\u662f\u901a\u7528\u4eba\u5de5\u667a\u80fd\uff08General AI\uff09\u7814\u7a76\u7684\u4e00\u90e8\u5206\uff0c\u8d77\u6e90\u4e8e20\u4e16\u7eaa50\u5e74\u4ee3\uff0c\u65e8\u5728\u521b\u5efa\u80fd\u591f\u5728\u4e0d\u540c\u9886\u57df\u8fdb\u884c\u95ee\u9898\u63a8\u7406\u3001\u5f00\u53d1\u6d1e\u5bdf\u529b\u3001\u9002\u5e94\u65b0\u60c5\u5883\u5e76\u8fdb\u884c\u81ea\u6211\u53cd\u601d\u7684\u7a0b\u5e8f\u3002\u7c7b\u4f3c\u5730\uff0c\u8ba4\u77e5\u67b6\u6784\u7814\u7a76\u7684\u6700\u7ec8\u76ee\u6807\u662f\u5efa\u6a21\u4eba\u7c7b\u601d\u7ef4\uff0c\u4ece\u800c\u4f7f\u6211\u4eec\u66f4\u63a5\u8fd1\u6784\u5efa\u4eba\u7c7b\u6c34\u5e73\u7684\u4eba\u5de5\u667a\u80fd\u3002\u8ba4\u77e5\u67b6\u6784\u8bd5\u56fe\u63d0\u4f9b\u8bc1\u636e\uff0c\u8bc1\u660e\u7279\u5b9a\u673a\u5236\u6210\u529f\u4ea7\u751f\u667a\u80fd\u884c\u4e3a\uff0c\u4ece\u800c\u4e3a\u8ba4\u77e5\u79d1\u5b66\u505a\u51fa\u8d21\u732e\u3002\u6b64\u5916\uff0c\u8ba4\u77e5\u67b6\u6784\u7684\u5de5\u4f5c\u4f53\u7cfb\u4ee5\u53ca\u8fd9\u4e2a\u7efc\u8ff0\u8bb0\u5f55\u4e86\u4ee5\u524d\u5c1d\u8bd5\u8fc7\u7684\u65b9\u6cd5\u6216\u7b56\u7565\uff0c\u4ee5\u53ca\u5b83\u4eec\u662f\u5982\u4f55\u4f7f\u7528\u7684\uff0c\u53d6\u5f97\u4e86\u591a\u5927\u7684\u6210\u529f\u6216\u8005\u5f97\u5230\u4e86\u54ea\u4e9b\u6559\u8bad\uff0c\u8fd9\u4e9b\u90fd\u662f\u6307\u5bfc\u672a\u6765\u7814\u7a76\u5de5\u4f5c\u7684\u91cd\u8981\u5143\u7d20\u3002<\/p>\n

      \u8ba4\u77e5\u67b6\u6784\u65e8\u5728\u5b9e\u73b0\u4eba\u7c7b\u601d\u7ef4\u6a21\u578b\uff0c\u4ee5\u4fbf\u66f4\u597d\u5730\u7406\u89e3\u548c\u5b9e\u73b0\u4eba\u7c7b\u7ea7\u522b\u7684\u4eba\u5de5\u667a\u80fd\u3002\u8ba4\u77e5\u67b6\u6784\u5c1d\u8bd5\u901a\u8fc7\u6a21\u62df\u4eba\u7c7b\u667a\u80fd\u884c\u4e3a\u6765\u63d0\u4f9b\u8bc1\u636e\uff0c\u4ee5\u8868\u660e\u7279\u5b9a\u673a\u5236\u80fd\u591f\u4ea7\u751f\u667a\u80fd\u884c\u4e3a\uff0c\u4ece\u800c\u4e3a\u8ba4\u77e5\u79d1\u5b66\u505a\u51fa\u8d21\u732e\u3002\u8ba4\u77e5\u67b6\u6784\u7684\u7814\u7a76\u76ee\u6807\u3001\u7ed3\u6784\u3001\u8fd0\u4f5c\u65b9\u5f0f\u548c\u5e94\u7528\u5b58\u5728\u5206\u6b67\uff0c\u4e0d\u540c\u7684\u67b6\u6784\u8ffd\u6c42\u4e0d\u540c\u7684\u667a\u80fd\u6a21\u578b\u548c\u884c\u4e3a\u3002<\/p>\n

      \u5e38\u89c1\u7684Cognitive architectures<\/h2>\n

      \u4e00\u4e9b\u5e38\u89c1\u7684\u8ba4\u77e5\u67b6\u6784\u5305\u62ecSoar\u3001ACT-R\u3001LIDA\u3001CLARION\u3001CAPS<\/strong>\u7b49\u3002\u4e00\u822c\u6765\u8bf4\uff0c\u8ba4\u77e5\u67b6\u6784\u5206\u4e3a\u4e09\u79cd\uff1a1\uff09Emergent 2\uff09Hybrid 3\uff09Symbolic, \u5176\u5bf9\u5e94\u8fd8\u6709\u7ec6\u5206\u7c7b\u578b\uff0cEmergent \u53ef\u4ee5\u5206\u4e3aConnectionist\u548cneuronal modeling\uff1b Hybrid\u53ef\u5206\u4e3afully-integrated \u548c symbolic sub-processing\u3002<\/p>\n

      \u8ba4\u77e5\u67b6\u6784\u5b9a\u4e49\u548c\u89c4\u5212\u4e86\u5982\u4e0b\u51e0\u4e2a\u65b9\u9762\uff1a<\/p>\n

        \n
      • \u611f\u77e5 perception\n
          \n
        • Vision<\/li>\n
        • Vision using physical sensors<\/li>\n
        • Simulated vision<\/li>\n
        • Audition<\/li>\n
        • Symbolic input<\/li>\n
        • Multi-modal perception<\/li>\n<\/ul>\n<\/li>\n
        • \u6ce8\u610f\u529b\u673a\u5236 Attention<\/li>\n
        • \u884c\u4e3a\u9009\u62e9 Action selection\n
            \n
          • Planning vs reactive action<\/li>\n
          • Dynamic action selection<\/li>\n<\/ul>\n<\/li>\n
          • \u8bb0\u5fc6\u673a\u5236 Memory\n
              \n
            • Sensory memory<\/li>\n
            • Working memory<\/li>\n
            • Long-term memory<\/li>\n
            • Global memory<\/li>\n<\/ul>\n<\/li>\n
            • \u5b66\u4e60\u80fd\u529b Learning\n
                \n
              • Perceptual learning<\/li>\n
              • Declarative learning<\/li>\n
              • Procedural learning<\/li>\n
              • Associative learning<\/li>\n
              • Non-associative learning<\/li>\n
              • Priming<\/li>\n<\/ul>\n<\/li>\n
              • \u7406\u89e3\u80fd\u529b Reasoning<\/li>\n
              • \u5143\u8ba4\u77e5 Metacognition<\/li>\n<\/ul>\n

                \u672c\u7ae0\u5c0f\u7ed3<\/h2>\n

                \u672c\u7ae0\u4ece\u5927\u578b\u8bed\u8a00\u6a21\u578b\uff08LLM\uff09\u4e0eAGI\u7684\u533a\u522b\u5165\u624b\uff0c\u5f3a\u8c03\u4e86LLM\u4e0d\u7b49\u540c\u4e8eAGI\uff0c\u800c\u53ea\u662fAGI\u53d1\u5c55\u4e2d\u7684\u4e00\u90e8\u5206\u3002\u5bf9\u4e8eAGI\uff0c\u5c0f\u7f16\u8ba4\u4e3a\u5e94\u5f53\u5c06LLM\u4e0eCognitive Architecture\u7ed3\u5408\uff0c\u7528LLM\u8d4b\u80fd\u8ba4\u77e5\u67b6\u6784<\/strong>\u3002\u5728\u4e0b\u4e00\u671f\uff0c\u6211\u4eec\u5c06\u8be6\u7ec6\u63a2\u7d22\u8ba4\u77e5\u67b6\u6784\u8fd9\u4e00\u9886\u57df\u3002\u8bfb\u5230\u8fd9\u7684\u8bfb\u8005\uff0c\u5982\u679c\u60a8\u4ece\u7b14\u8005\u7684\u5206\u4eab\u4e2d\u6709\u6240\u6536\u83b7\uff0c\u6216\u8005\u6709\u4e0d\u540c\u7684\u770b\u6cd5\uff0c\u6b22\u8fce\u5728\u8bc4\u8bba\u533a\u4e0e\u6211\u4e92\u52a8\uff01\u70b9\u8d5e\u6536\u85cf\u5173\u6ce8\u4e0d\u8ff7\u8def\uff01\u60a8\u7684\u652f\u6301\u662f\u5c0f\u7f16\u524d\u8fdb\u7684\u52a8\u529b\uff01<\/strong><\/p>","protected":false},"excerpt":{"rendered":"

                \u63a2\u7d22AGI\u7cfb\u5217 | 01. LLM\u4e0d\u7b49\u4e8e\u901a\u7528\u4eba\u5de5\u667a\u80fd\u671b\u5468\u77e5 -> **\u5c06LLM\u4e0eCognitive Architecture\u7ed3\u5408\uff0c\u7528LLM\u8d4b\u80fd\u8ba4\u77e5\u67b6\u6784**<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"rank_math_title":"AGI | \u6a21\u578b | \u8ba4\u77e5 - \u5948\u65af\u79d1\u6280\u793e\u533a","rank_math_description":"\u5948\u65af\u79d1\u6280\u793e\u533a\u4e13\u6ce8\u4e8eAGI\u3001\u6a21\u578b\u3001\u8ba4\u77e5\u3001\u67b6\u6784\u3001\u5c0f\u7f16\uff0c\u63d0\u4f9b\u4e13\u4e1a\u7684\u5206\u4eab\u548c\u8ba8\u8bba\u5e73\u53f0\u3002","rank_math_focus_keyword":"AGI,\u6a21\u578b,\u8ba4\u77e5,\u67b6\u6784,\u5c0f\u7f16","views":"12","footnotes":""},"categories":[3],"tags":[127,128,129,136,126],"collection":[],"class_list":["post-1299","post","type-post","status-publish","format-standard","hentry","category-fenlei2","tag-ai","tag-128","tag-129","tag-136","tag-gpt"],"acf":[],"_links":{"self":[{"href":"https:\/\/www.nicekj.com\/nicekj2024\/wp\/v2\/posts\/1299","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.nicekj.com\/nicekj2024\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.nicekj.com\/nicekj2024\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.nicekj.com\/nicekj2024\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/www.nicekj.com\/nicekj2024\/wp\/v2\/comments?post=1299"}],"version-history":[{"count":0,"href":"https:\/\/www.nicekj.com\/nicekj2024\/wp\/v2\/posts\/1299\/revisions"}],"wp:attachment":[{"href":"https:\/\/www.nicekj.com\/nicekj2024\/wp\/v2\/media?parent=1299"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.nicekj.com\/nicekj2024\/wp\/v2\/categories?post=1299"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.nicekj.com\/nicekj2024\/wp\/v2\/tags?post=1299"},{"taxonomy":"collection","embeddable":true,"href":"https:\/\/www.nicekj.com\/nicekj2024\/wp\/v2\/collection?post=1299"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}