[{"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":81},{"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。",4,"2026-06-03T16:32:29.415063+00:00","2026-06-03T16:32:29.404+00:00","0c35a120-52fc-41fc-afa3-d404eb934158",{"tags":33,"relatedLang":40,"relatedPosts":44},[34,36,38],{"name":19,"slug":35},"openai",{"name":21,"slug":37},"llm",{"name":17,"slug":39},"ai",{"id":15,"slug":41,"title":42,"language":43},"mathematicians-warn-ai-could-distort-math-en","Mathematicians Warn AI Could Distort Math","en",[45,51,57,63,69,75],{"id":46,"slug":47,"title":48,"cover_image":49,"image_url":49,"created_at":50,"category":13},"a1c5b218-d9ff-4e46-9c58-07d0fe5152fc","vlm-accuracy-visual-cognitive-errors-decade-zh","VLM 描述複雜場景變準了","https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1783926189859-c95z.png","2026-07-13T07:02:36.585294+00:00",{"id":52,"slug":53,"title":54,"cover_image":55,"image_url":55,"created_at":56,"category":13},"2ec5f4bf-f90a-4dc9-98e0-dc8189169e56","visual-pretraining-language-models-zh","視覺預訓練勝過純文字","https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1783924384413-4ob9.png","2026-07-13T06:32:35.520894+00:00",{"id":58,"slug":59,"title":60,"cover_image":61,"image_url":61,"created_at":62,"category":13},"8b8f7b87-7e93-415f-a52d-56613e17b278","phinn-eeg-topology-dream-state-eeg-zh","PHINN-EEG 用拓撲看夢境 EEG","https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1783922588253-kq48.png","2026-07-13T06:02:34.287269+00:00",{"id":64,"slug":65,"title":66,"cover_image":67,"image_url":67,"created_at":68,"category":13},"c4597538-217d-4b81-83d0-9b3cc4153861","google-android-bench-update-gemini-gap-zh","Android Bench 更新，Gemini 掉到第五","https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1783906366388-1v3j.png","2026-07-13T01:32:25.247653+00:00",{"id":70,"slug":71,"title":72,"cover_image":73,"image_url":73,"created_at":74,"category":13},"f25ed4f5-db61-4d8c-bc59-e80c93e27927","llm-benchmarks-not-enough-2026-zh","2026 年挑 LLM，別再把 benchmark 當答案","https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1783818161840-t0n4.png","2026-07-12T01:02:19.419242+00:00",{"id":76,"slug":77,"title":78,"cover_image":79,"image_url":79,"created_at":80,"category":13},"35378c9f-bc39-4cc0-b9e1-1ce4a746ba5b","rust-breaks-into-tiobe-top-10-zh","Rust 進入 TIOBE 前十的判讀筆記","https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1783816365723-zjl1.png","2026-07-12T00:32:23.969578+00:00",[82,87,92,97,102,107,112,117,122,127],{"id":83,"slug":84,"title":85,"created_at":86},"f18dbadb-8c59-4723-84a4-6ad22746c77a","deepmind-bets-on-continuous-learning-ai-2026-zh","DeepMind 押注 2026 連續學習 AI","2026-03-26T08:16:02.367355+00:00",{"id":88,"slug":89,"title":90,"created_at":91},"f4a106cb-02a6-4508-8f39-9720a0a93cee","ml-papers-of-the-week-github-research-desk-zh","每週 ML 論文清單，為何紅到 GitHub","2026-03-27T01:11:39.284175+00:00",{"id":93,"slug":94,"title":95,"created_at":96},"c4f807ca-4e5f-47f1-a48c-961cf3fc44dc","ai-ml-conferences-to-watch-in-2026-zh","2026 AI 研討會投稿時程整理","2026-03-27T01:51:53.874432+00:00",{"id":98,"slug":99,"title":100,"created_at":101},"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":103,"slug":104,"title":105,"created_at":106},"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":108,"slug":109,"title":110,"created_at":111},"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":113,"slug":114,"title":115,"created_at":116},"a9901203-d69b-447b-8854-15d14eab32b4","vision-aided-beam-prediction-cnn-eca-zh","影像輔助波束預測升級 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