{"id":44765,"date":"2025-04-09T14:54:38","date_gmt":"2025-04-09T06:54:38","guid":{"rendered":"https:\/\/www.cla.ntnu.edu.tw\/?p=44765"},"modified":"2025-04-09T15:02:59","modified_gmt":"2025-04-09T07:02:59","slug":"2025-04-09","status":"publish","type":"post","link":"https:\/\/www.cla.ntnu.edu.tw\/index.php\/2025\/04\/09\/2025-04-09\/","title":{"rendered":"\u3010\u4ee3\u516c\u544a\u3011~\u81fa\u5927\u9ad8\u7814\u9662_4\/23\uff08\u4e09\uff09\u3010\u5c16\u7aef\u8b1b\u5ea7\u7cfb\u5217\u3011\u7b2c\u56db\u5341\u56db\u5834\u300cSphinx\u7684\u771f\u5fc3\uff1f\u751f\u6210\u5f0f\u4eba\u5de5\u667a\u6167\u7684\u8a8d\u77e5\u4e2d\u5fc3\u5316\u8207\u6578\u4f4d\u52a0\u901f\u4e3b\u7fa9\u98a8\u96aa\u300d"},"content":{"rendered":"\t\t
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Sphinx<\/strong>\u7684\u771f\u5fc3\uff1f\u751f\u6210\u5f0f\u4eba\u5de5\u667a\u6167\u7684\u8a8d\u77e5\u4e2d\u5fc3\u5316\u8207\u6578\u4f4d\u52a0\u901f\u4e3b\u7fa9\u98a8\u96aa<\/strong>
<\/strong>Cognitive Centrality and Digital Accelerationism Risks in Generative AI<\/p>

\ufe0f\u65e5\u671f\uff1a<\/strong>2025\u5e744\u670823\u65e5\uff08\u4e09\uff09<\/p>

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The technological foundation of Artificial General Intelligence (AGI) is built on a mechanism that pursues the generation of universal knowledge. Current mainstream large language models (LLMs) attempt to simplify humanity\u2019s language-based knowledge systems into the learning and imitation of massive datasets, ultimately creating a new knowledge architecture centered on digital algorithms.<\/p>

However, these models exhibit inherent contradictions: while capable of handling multiple tasks (e.g., writing\u00a0and translation), they frequently suffer from issues like\u00a0\u201cquick learning but faster forgetting\u201d\u00a0(over-reliance on training data),\u00a0\u201cgeneralizing from limited perspectives\u201d\u00a0(data bias), and\u00a0\u201cmechanical regurgitation\u201d\u00a0(lack of genuine understanding). More critically, their practical performance degrades over time\u2014showing significant decline in complex scenarios requiring comprehensive judgment (e.g., medical diagnosis, legal consultation)\u2014resulting in unsustainable commercial applications and reinforcing market monopolies by tech giants.<\/p>

The profound implications of this technological approach demand vigilance: as humans delegate linguistic and experiential complexity to algorithms, we risk both excessive dependence on technical systems and the homogenization of knowledge production. Behind the fa\u00e7ade of algorithmic neutrality lies the replication of capital expansion logic\u2014replacing diverse local knowledge with standardized technical solutions.<\/p>

Despite LLMs\u2019 rapid progress being hailed as a milestone for the information age, their development faces unresolved challenges. Current models reveal practical bottlenecks like excessive energy consumption and unsustainable costs. Rather than blindly pursuing technological breakthroughs, we should establish a multi-stakeholder governance framework. By ensuring algorithmic transparency, we can redirect these technologies to serve the diverse development of human civilization.<\/p>

\u4e3b\u8fa6\u55ae\u4f4d\uff1a<\/strong>\u570b\u7acb\u81fa\u7063\u5927\u5b78\u4eba\u6587\u793e\u6703\u9ad8\u7b49\u7814\u7a76\u9662\u00a0\uf09f<\/strong><\/p>

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__________________________________________________<\/p>

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Email\uff1antuihs@ntu.edu.tw<\/a><\/p>

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