[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"article-mathematicians-warn-ai-could-distort-math-zh":3,"article-related-mathematicians-warn-ai-could-distort-math-zh":32,"series-research-33c9a55c-a8c0-4367-b742-f4567d1e98e3":83},{"id":4,"slug":5,"title":6,"content":7,"summary":8,"source":9,"source_url":10,"author":11,"image_url":12,"cover_image":12,"category":13,"language":14,"translated_content":11,"related_article_id":15,"keywords":16,"key_takeaways":24,"views":28,"created_at":29,"published_at":30,"topic_cluster_id":31},"33c9a55c-a8c0-4367-b742-f4567d1e98e3","mathematicians-warn-ai-could-distort-math-zh","數學界警告 AI 會扭曲證明標準","\u003Cp data-speakable=\"summary\">16 位數學家警告，AI 生成的證明可能拉低數學驗證標準。\u003C\u002Fp>\u003Cp>\u003Ca href=\"\u002Ftag\u002Fopenai\">OpenAI\u003C\u002Fa> 上週才因 AI 生成證明上新聞。現在，16 位專家直接發聲。講白了，他們怕的不是 AI 會不會算，而是它會不會把數學界搞得更難驗證。\u003C\u002Fp>\u003Cp>這件事很有意思。因為數學不是寫文案，也不是回客服。只要一個步驟錯了，整個證明就可能倒掉。AI 如果只會寫得像對的，問題就大了。\u003C\u002Fp>\u003Ctable>\u003Cthead>\u003Ctr>\u003Cth>項目\u003C\u002Fth>\u003Cth>數字\u003C\u002Fth>\u003Cth>意義\u003C\u002Fth>\u003C\u002Ftr>\u003C\u002Fthead>\u003Ctbody>\u003Ctr>\u003Ctd>聯署專家\u003C\u002Ftd>\u003Ctd>16\u003C\u002Ftd>\u003Ctd>來自實務數學家的集體警告\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd>OpenAI 證明新聞時間\u003C\u002Ftd>\u003Ctd>1 週前\u003C\u002Ftd>\u003Ctd>把爭議推上檯面\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd>加州公立大學 AI 支出\u003C\u002Ftd>\u003Ctd>$16.9 million\u003C\u002Ftd>\u003Ctd>機構已經在大筆投入 AI\u003C\u002Ftd>\u003C\u002Ftr>\u003C\u002Ftbody>\u003C\u002Ftable>\u003Ch2>數學家到底在怕什麼\u003C\u002Fh2>\u003Cp>核心問題很直接。AI 可能產生看起來很順的證明，但裡面藏著錯誤。數學最怕這種東西，因為它不是靠語氣取勝，而是靠每一步都能站得住。\u003C\u002Fp>\n\u003Cfigure class=\"my-6\">\u003Cimg src=\"https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1780504386035-080l.png\" alt=\"數學界警告 AI 會扭曲證明標準\" class=\"rounded-xl w-full\" loading=\"lazy\" \u002F>\u003C\u002Ffigure>\n\u003Cp>如果模型產出的論證越來越像人話，審查成本就會上升。審稿人、研究者、學生都得花更多時間去拆解。結果可能不是更有效率，而是更多人被假象帶著走。\u003C\u002Fp>\u003Cp>我覺得真正麻煩的地方，在於「可信」這件事會變模糊。以前一篇證明是人寫的，至少你知道責任在哪。現在如果中間混了 \u003Ca href=\"\u002Ftag\u002Fllm\">LLM\u003C\u002Fa>，誰要負責每個跳步，就沒那麼清楚了。\u003C\u002Fp>\u003Cul>\u003Cli>AI 能幫忙找模式，也能幫忙寫出假證明\u003C\u002Fli>\u003Cli>數學錯一步，整篇就可能失效\u003C\u002Fli>\u003Cli>期刊需要更嚴格的驗證流程\u003C\u002Fli>\u003Cli>學生可能把機器輸出誤當成正確答案\u003C\u002Fli>\u003C\u002Ful>\u003Ch2>OpenAI 這次為什麼被盯上\u003C\u002Fh2>\u003Cp>\u003Ca href=\"https:\u002F\u002Fopenai.com\" target=\"_blank\" rel=\"noopener\">OpenAI\u003C\u002Fa> 這次會被拉出來討論，不是偶然。它一直在把模型往更技術性的任務推，數學就是最適合拿來測試推理能力的場景之一。\u003C\u002Fp>\u003Cp>但問題也很現實。當一家大公司先把成果丟到台前，外界很容易直接把它當成能力證明。可是在數學裡，能不能寫出像樣的步驟，和能不能被嚴格接受，是兩回事。\u003C\u002Fp>\u003Cp>這次的聲明，其實是在提醒大家一件很土但很重要的事。數學界要的不是漂亮輸出，而是可檢查、可追溯、可重現的論證。沒有這些，AI 再會寫也只是半成品。\u003C\u002Fp>\u003Cblockquote>“We are not saying that AI has no role in mathematics. We are saying that the role must be carefully defined and controlled.”\u003C\u002Fblockquote>\u003Cp>這句話出自相關聲明，也很符合現況。數學家不是要把 AI 趕出去。他們是要先畫規則，不然整個領域會被生成內容淹沒。\u003C\u002Fp>\u003Ch2>這跟寫作、寫程式差在哪\u003C\u002Fh2>\u003Cp>很多人會說，AI 寫文章都能用，為\u003Ca href=\"\u002Fnews\u002Fwhy-backrooms-proves-horror-still-owns-the-box-office-zh\">什麼\u003C\u002Fa>不能寫證明？差很多。文章可以修，程式可以跑測試，但數學證明常常要靠人一層層檢查。錯誤如果藏得深，可能拖很久才被抓到。\u003C\u002Fp>\n\u003Cfigure class=\"my-6\">\u003Cimg src=\"https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1780504384784-5ryc.png\" alt=\"數學界警告 AI 會扭曲證明標準\" class=\"rounded-xl w-full\" loading=\"lazy\" \u002F>\u003C\u002Ffigure>\n\u003Cp>這也是為\u003Ca href=\"\u002Fnews\u002Fwhy-hyperights-may-2026-focus-matters-zh\">什麼\u003C\u002Fa>數學界對 AI 特別敏感。它不是在比誰寫得快，而是在比誰的邏輯能站穩。模型可以幫忙找方向，但不能直接拿來當權威。\u003C\u002Fp>\u003Cp>放到\u003Ca href=\"\u002Ftag\u002F台灣開發者\">台灣開發者\u003C\u002Fa>的語境來看，這就像你把一段未驗證的 \u003Ca href=\"\u002Ftag\u002Fapi\">API\u003C\u002Fa> 回傳值直接進 production。短期看起來能跑，長期就是災難。數學證明也是同一套道理，只是出錯成本更高。\u003C\u002Fp>\u003Cul>\u003Cli>寫作錯字多半還能修\u003C\u002Fli>\u003Cli>程式可用測試抓 bug\u003C\u002Fli>\u003Cli>數學錯誤常常藏得更深\u003C\u002Fli>\u003Cli>AI 輸出越像人，越容易讓人放鬆警覺\u003C\u002Fli>\u003C\u002Ful>\u003Cp>這種壓力也不是只有數學界在承受。\u003Ca href=\"https:\u002F\u002Fwww.anthropic.com\" target=\"_blank\" rel=\"noopener\">Anthropic\u003C\u002Fa> 在商業化上衝很快，\u003Ca href=\"https:\u002F\u002Fwww.box.com\" target=\"_blank\" rel=\"noopener\">Box\u003C\u002Fa> 也在把 AI 塞進企業流程。工具越多，規範就越慢跟上。這種落差，現在到處都看得到。\u003C\u002Fp>\u003Ch2>數字背後的產業脈絡\u003C\u002Fh2>\u003Cp>把數字攤開看，這件事就沒那麼抽象。16 位專家聯署，代表不是少數人的情緒反應。1 週前的 OpenAI 證明新聞，代表這場爭論是被一個具體事件引爆。\u003C\u002Fp>\u003Cp>再看加州公立大學的 $16.9 million AI 支出，情況就更清楚了。機構已經在花錢導入 AI。問題不是要不要用，而是誰來定義「可以用到\u003Ca href=\"\u002Fnews\u002Fwhy-dair-is-more-important-than-another-ai-lab-zh\">什麼\u003C\u002Fa>程度」。\u003C\u002Fp>\u003Cp>如果你是做產品、資料科學或研究工具的人，這裡有個很實際的訊號。AI 不是只能拼速度。它也會逼你補上驗證、審查、紀錄這三件事。少了這些，系統就會越跑越歪。\u003C\u002Fp>\u003Cul>\u003Cli>16 位專家發聲，代表疑慮已經成形\u003C\u002Fli>\u003Cli>1 週內就引爆討論，顯示擴散速度很快\u003C\u002Fli>\u003Cli>$16.9 million 代表機構端已在真金白銀投入\u003C\u002Fli>\u003Cli>工具普及後，驗證成本會變成新負擔\u003C\u002Fli>\u003C\u002Ful>\u003Ch2>數學界接下來該怎麼做\u003C\u002Fh2>\u003Cp>我覺得最務實的方向，不是封殺 AI，而是訂規則。哪些步驟可以交給模型，哪些地方一定要人類簽字，這些都要講清楚。\u003C\u002Fp>\u003Cp>對期刊、研究室、學校來說，最重要的是可追溯性。只要一篇證明有 AI 參與，就應該清楚揭露。這樣不是保守，而是避免整個領域的信任成本失控。\u003C\u002Fp>\u003Cp>對開發者來說，這件事也很像你在做高風險軟體。不要把模型當真理機器。把它當助理可以，但每一步都要有檢查點。這才是比較像工程的做法。\u003C\u002Fp>\u003Cp>接下來我會看兩件事。第一，數學期刊會不會開始要求 AI 使用揭露。第二，研究團隊會不會建立更細的驗證流程。這兩件事如果沒跟上，爭議只會越滾越大。\u003C\u002Fp>","16 位數學家發聲警告，AI 生成證明可能拉低數學驗證標準，也讓期刊、研究與教學面臨新的審查壓力。","www.nytimes.com","https:\u002F\u002Fwww.nytimes.com\u002Fspotlight\u002Fchat-gpt",null,"https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1780504386035-080l.png","research","zh","c9c264b1-3a0d-4f5b-ada3-02687c9ab795",[17,18,19,20,21,22,23],"AI","數學","OpenAI","證明","LLM","學術審查","人工智慧",[25,26,27],"16 位數學家聯署，主張 AI 生成證明可能拉低驗證標準。","數學和寫作、寫程式不同，錯誤更難靠自動化流程補救。","未來焦點會放在揭露 AI 參與、可追溯性與審查規範。",0,"2026-06-03T16:32:29.415063+00:00","2026-06-03T16:32:29.404+00:00","0c35a120-52fc-41fc-afa3-d404eb934158",{"tags":33,"relatedLang":42,"relatedPosts":46},[34,36,37,38,40],{"name":19,"slug":35},"openai",{"name":20,"slug":20},{"name":18,"slug":18},{"name":21,"slug":39},"llm",{"name":17,"slug":41},"ai",{"id":15,"slug":43,"title":44,"language":45},"mathematicians-warn-ai-could-distort-math-en","Mathematicians Warn AI Could Distort Math","en",[47,53,59,65,71,77],{"id":48,"slug":49,"title":50,"cover_image":51,"image_url":51,"created_at":52,"category":13},"5c3cb90f-7efd-426f-8c09-32a303f82be9","humanoid-gpt-zero-shot-motion-tracking-zh","Humanoid-GPT：用 GPT 擴大動作追蹤","https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1780469319284-znpc.png","2026-06-03T06:47:34.463464+00:00",{"id":54,"slug":55,"title":56,"cover_image":57,"image_url":57,"created_at":58,"category":13},"e3a4b0f7-03b3-43c6-ae51-906b337c5c2f","ipt-vlms-hidden-space-reasoning-zh","IPT 讓 VLM 更會想像隱藏空間","https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1780468394735-1k40.png","2026-06-03T06:32:46.560029+00:00",{"id":60,"slug":61,"title":62,"cover_image":63,"image_url":63,"created_at":64,"category":13},"5fca9fe5-af66-47ce-85f0-0ffe1bee30b9","neuron-selectivity-changes-with-scale-zh","神經元選擇性會隨規模改變","https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1780467514422-7oss.png","2026-06-03T06:17:44.126547+00:00",{"id":66,"slug":67,"title":68,"cover_image":69,"image_url":69,"created_at":70,"category":13},"9f9c2a61-d058-4c62-bb88-106e683657f0","nasa-landsat-wild-disturbances-rising-zh","NASA Landsat：野火與風暴變多","https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1780448581102-owp0.png","2026-06-03T01:02:37.513233+00:00",{"id":72,"slug":73,"title":74,"cover_image":75,"image_url":75,"created_at":76,"category":13},"3479bdee-21fb-4fda-9572-9394caba01b0","adacodec-predictive-visual-code-video-mllms-zh","AdaCodec 用預測碼壓縮影片 token","https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1780381988591-z2sp.png","2026-06-02T06:32:28.249023+00:00",{"id":78,"slug":79,"title":80,"cover_image":81,"image_url":81,"created_at":82,"category":13},"02ba7be2-4123-4d11-83c5-eeb297fa4192","protoada-multimodal-continual-instruction-tuning-zh","ProtoAda 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研討會投稿時程整理","2026-03-27T01:51:53.874432+00:00",{"id":100,"slug":101,"title":102,"created_at":103},"cf046742-efb2-4753-aef9-caed5da5e32e","adaptive-block-scaled-data-types-zh","IF4：神經網路量化的聰明選擇","2026-03-31T06:00:36.990273+00:00",{"id":105,"slug":106,"title":107,"created_at":108},"53a0dc54-0371-4e40-8d5e-74e94a73840c","geometry-aware-similarity-metrics-for-neural-representations-zh","超越距離測量：用微分幾何重新理解神經網路","2026-03-31T06:01:01.241968+00:00",{"id":110,"slug":111,"title":112,"created_at":113},"fee7d472-a775-4b1d-bbc2-1e8bca1bbf8b","on-the-fly-repulsion-in-the-contextual-space-for-rich-divers-zh","讓AI繪圖更有創意：用排斥力提升生成多樣性","2026-03-31T06:01:25.439673+00:00",{"id":115,"slug":116,"title":117,"created_at":118},"a9901203-d69b-447b-8854-15d14eab32b4","vision-aided-beam-prediction-cnn-eca-zh","影像輔助波束預測升級 CNN","2026-04-01T10:00:25.8073+00:00",{"id":120,"slug":121,"title":122,"created_at":123},"b55e7dd4-0a24-4b3d-804d-b0309a03f498","triple-band-fss-mimo-antenna-sub-6-ghz-zh","三頻 FSS MIMO 天線瞄準 sub-6 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