為何紅黃藍白都尊重的朱鎔基

Recently Jacky Fung Chi Ching（馮智政）, a current affairs commentator on YouTube, posted the following article on Facebook:

為何紅黃藍白都尊重的朱鎔基

Jacky FUNG 馮智政

16 Aug 2026

 Books｜讀Brahm 的《朱鎔基傳：朱鎔基與現代中國的轉型》

朱鎔基逝世之後，中港都有不少文章去總結他在中國改革開放的功績。有些根據官樣文章，也有從語錄Soundbite去寫其思想，兩者敍事多是藉朱鎔基推動作者的政治主張。對於朱鎔基的經濟功績及其思路，我推薦美國學者龍安志（Laurence J. Brahm）所著的《朱鎔基傳：朱鎔基與現代中國的轉型》。龍安志曾定居中國，長年涉足國際社運、國際危機調停與政治經濟研究的工作。

雖然同樣寫朱鎔基如何處理三角債、通貨膨脹、亞洲金融危機等一連串經濟問題，不過Brahm在書裡試圖為他規劃中國從封閉經濟走向開放轉型的角色，找出理論及歷史的位置。讀畢全書，最大的感受不是人物的生平故事，也不是政策的制定過程，而是一種久違的、對歷史人物複雜性的重新認識。

坊間對朱鎔基的評價，向來存在矛盾。一方面，他是在一九八九年民運期間已經身居上海市委書記要職、其後在總理任內也打壓過民主自由。另一方面，他在國企改革、落實市場經濟規則等關鍵角色，令中國慢慢走向開放。

這種矛盾之所以難以調和，是因為今天social media的政治論述，習慣以一種二元的框架去理解歷史人物：不是民主派，就是專權者；不是改革英雄，就是體制幫兇。這種框架簡單易用，卻往往解釋不到歷史的複雜，也捕捉不到歷史的拐點。

這樣的人物，歷史上並非孤例，近如李登輝、戈爾巴喬夫、辛德勒、拉貝⋯⋯今天的網上世界，習慣了在冷氣房裡看「勇者鬥惡龍」，卻失去了對時空背景Context應有的賞析與判斷。朱鎔基之所以能在前social media時代生活過的人心目中，跨越紅黃藍白被欣賞，或許正正在於此。

❚ ❚ 一個危機四伏的年代

要理解朱鎔基的處境，必須先理解他接手的爛攤子。八九六四之後，鄧小平為了證明自己改革開放的功績，加速推動經濟轉型，並放任金融企業尋租、違規行為。在工業起飛的背後，是規模龐大的債務與過度投資泡沫：國有企業之間互相拖欠、形成「三角債」，通貨膨脹一度飆升至百分之二十一；地方政府為追增長指標，重複投資，令劣質商品堆積如山。這場危機的清理工作，最終落在朱鎔基肩上。

泡沫還泡沫，但有錢賺、有工返，只要不在自己任內爆，大可以領功之後將爛攤子推給下屆。拉丁美洲如是，英國今日政治更是如是。朱鎔基卻選擇了Take hard mode：收水、減產、解散國企、地方稅改。得罪國有企業、得罪地方官員、也令工人下崗再找工作，為的就是將中國經濟推向市場規則。

這種吃力不討好的改革，在中國的政治生態裡格外危險。《朱鎔基傳》記載，一九九二年國企化債陷入困局時，「八大元老」之一、前經濟沙皇薄一波（其子正是後來因貪腐案下台的薄熙來）曾私下發出批評條子，標題是〈問題出在政策還是政策的執行者〉，劍指朱鎔基。無論朱鎔基自己頂，還是交出屬下能幹的經濟師去頂，最終都會令政策不彰、改革不成功，為政敵遞刀子，惹來更大的危機。最終他選擇自己頂，讓石萬鵬、李劍閣、樓繼偉、王岐山這班當時圍繞在他身邊的經濟班子，繼續深化改革。

或許正是這種近乎執拗的市場信仰與改革熱誠，早在一九五八年已經令他在「反右」運動中被打成右派，被下放農村勞動五年之久。歷史往往吊詭：令他吃盡苦頭的性格，正是二十年後令他成就一番事業的性格。

❚ ❚ 信號經濟學

Brahm在《朱鎔基傳》裡以信號經濟學總結朱鎔基的成功。不只於他宣布過甚麼政策，而在於他如何Anticipate民眾理解他的政策及決心，為市場造成什麼Expectation。作者龍安志反覆點出，朱政策宣示前，都意識市場與民間會如何解讀，而這種解讀又將如何反過來影響政策成效。

「我們不再叫它三角債，誰花錢誰還錢！」「誰投資，誰付錢。不然就倒閉！」「誰的孩子誰抱走。」這些短促而近乎命令式的宣言，是精心設計的訊號，目的是向所有相關的市場參與者，國企經理、地方官員、外資銀行，傳遞一個清晰而不容誤讀的預期。98年廣信破產後，他親自在全國人大閉幕記者會上略略提到：北京不會為未經政府擔保的金融企業債務埋單。這不是一句隨口談，而是一次刻意的預期管理，重新校準市場對「政府是否包底」以及「市場規則能否依靠」的預期。

書中亦提到一個微妙的細節：在公開場合，朱鎔基總是恰如其分地提到「以江澤民為核心的」集體領導決策制，又處處談到「共產黨」是政策底氣。看似又八股又擦鞋，但書裡談到匯率、化債及市場管理的決策時，他會多透露，他的「宏觀調控」重點並不在於調控政策，而在於他如何製造市場期待，而這種期待背後有政治力量及過去政績作為擔保，配合市場「短期所要的回報」、「正面對的困難」。

同樣是共產黨政權，但他管治市場的手法不同。他不是靠槍桿子逼人就範，落個行政指令叫新東方執笠、地產商爆雷，而是像格林斯潘管理美國經濟一樣，透過清晰的規則、可預期的懲罰、以及不容妥協的底線，令市場自己選擇跟隨。

❚ ❚ 對全球化與債務，專家以外的判斷

一九九七年亞洲金融危機爆發，國際貨幣基金組織與世界銀行開出的藥方，是要求區內貨幣跟隨貶值，以刺激出口。時任世界銀行首席經濟師Joseph E. Stiglitz（他有另一本書叫《全球化的許諾與失落》又值得分享）向朱鎔基提出「擴大人民幣交易波動幅度」的建議，表面溫和，實質是要引導出貶值不可避免的局面。

他的判斷建基於中國經濟結構的具體現實：中國出口有近半原料依賴進口，貶值只會推高進口成本；出口商品中，又有近半由外資企業生產，貶值等同增加外資企業的還款負擔。在香港，港元與人民幣事實上互相盯住，一旦人民幣貶值，剛剛主權移交的香港，側面衝擊聯繫匯率。但說到底，他核心關注的是，如果再靠出口依賴、廉價貨幣，中國最終會成為全球化下的依賴國。

於是，當泰國、印尼、韓國、俄羅斯的貨幣相繼崩潰，中國寧願出口減弱，都要支撐人民幣。二〇〇〇年，朱鎔基在布魯塞爾歐盟總部向歐洲工業領袖總結這場戰役：中國之所以能夠克服危機，全靠「積極的財政擴張政策、穩健的貨幣政策和產業重組政策」。這句話背後，是正式宣佈中國背棄「華盛頓共識」教條、及「宏觀調控」成功解決經濟危機。

❚ ❚ 結語

作為一個自由經濟的信徒，當然對於朱鎔基的經濟政策有不滿的地方。尤其是，對於中國加入世貿，是否欺詐資本主義國家以自肥？從書中，他對「國家經濟復興」的執着，我相信他是有意利用世貿規則漏洞，配合中國重商主義去發展。

不過，整體而言朱鎔基在他任內10年能夠拆彈，制定發展方向，理順市場參與者重新重視規則，再對比今日各國的領袖，他的功績及歷史重要性是不容忽視。經歷過朱鎔基年代的人，無論政治立場紅黃藍白，都不約而同地懷念這位國務院總理。二元的政治框架，或許永遠無法完整地安放這樣一個人物；但歷史本身，往往正正是由這些難以歸類的人所書寫的。

(Source: https://www.patreon.com/jackyfungcc/posts/books-wei-he-lan-166817130)

              So, regarding Zhu Rongji's economic achievements and his approach, Jacky Fung highly recommend the book Zhu Rongji and the Transformation of Modern China written by American scholar Laurence J. Brahm. Although the book also covers how Zhu managed a series of economic issues—such as "triangle debt" (三角債), inflation, and the Asian financial crisis, Brahm attempts to define Zhu's role in planning China's transition from a closed economy to an open market, mapping out his theoretical and historical positioning. Jacky Fung suggests that the strongest takeaway of the book is not the subject's life story, nor the policy-making process, but rather a long-absent, renewed appreciation for the complexity of historical figures. I agree with Jacky Fung’s point of view about the complexity historical figures.

Note:

1. 《朱鎔基傳：朱鎔基與現代中國的轉型》一書的英文原名：Zhu Rongji and the Transformation of Modern China。作者是美國人 Laurence J. Brahm，中文名通常譯作 “龍安志” 。中文由丁力翻譯。香港中和出版有限公司在 2012 年出版中文版，約 360 頁，ISBN 是 9789881588104。後來這本書有第二版（2020），以及 2023 年精裝典藏版。(ChatGPT)

2.  Jacky Fung Chi Ching（馮智政）經常在網上談論香港、中國、國際政治及移民議題, 是一名研究員、政策評論員和節目主持。他生於1985年，是香港中文大學物理系畢業，後來在香港大學取得國際關係碩士。他曾經在 “香港政策研究所（Hong Kong Policy Research Institute）” 工作，主要研究中國、教育及公共政策。香港大學也曾邀請他作為講者，講談中國與世界的關係。他亦曾擔任 “DBC 數碼電台、now TV、商業電台《光明頂》” 等節目主持或評論員。他現在有自己的 Patreon，以 Jacky FUNG 馮智政的名字製作影片評論。(ChatGPT)

俄羅斯空襲烏克蘭鋼鐵廠致7人死亡，基輔稱莫斯科使用了北韓飛彈

Recently Reuters reported the following:

Russian strike kills seven at Ukraine steel plant, Kyiv says Moscow used North Korean missiles

By Reuters - Reporting by Ron Popeski and Aleksandar Vasovic; Editing by Timothy Heritage

August 10, 20263:10 PM PDT Updated 6 hours ago

Aug 11 (Reuters) - Seven workers were killed at a Ukrainian steel plant on Tuesday during a Russian attack on the southeastern ‌city of Zaporizhzhia in which President Volodymyr Zelenskiy said Moscow used North Korean ballistic missiles.

The Zaporizhstal plant was forced to suspend production after being hit as Russia pummelled Zaporizhzhia with Zircon hypersonic missiles, guided aerial bombs and ballistic missiles supplied by Pyongyang.

"The ballistic missile strike hit as employees were making their ​way to a shelter immediately after the air-raid alert was sounded," parent company Metinvest Group said in a statement ​that put the death toll at seven.

The attack brought production at the plant to a complete halt, ⁠and other damaged sites were operating at reduced capacity, the company said.

Regional governor Ivan Fedorov posted photos of buildings and vehicles ablaze ​after the strike, which also damaged four residential buildings. At least 24 people were injured. No damage was reported at the Zaporizhzhia ​nuclear power plant.

Russia's defence ministry said Russian forces had struck a metallurgical plant in Zaporizhzhia that produces flat-rolled steel which it said was used by the Ukrainian military.

The ministry also said Russian forces had struck the Nova Poshta sorting complex in Kyiv, which it described as used to distribute dual-use goods, ​including components for drones and electronic warfare equipment.

The attacks followed a Ukrainian drone strike that Russian authorities said on Monday had killed ​at least 13 people and wounded 39 in Nizhnekamsk, one of the heaviest civilian tolls in Russia in months. Ukraine said it had hit the ‌city's TANECO ⁠oil refinery.

Another Ukrainian drone attack killed one person and injured two overnight in Russia's Voronezh region, and caused fires at warehouse facilities, the regional governor said. Russian online retailer Wildberries, whose warehouses have frequently been targeted by Ukraine, said it had evacuated its logistics facilities in the Voronezh region for safety reasons.

Six people were also killed overnight in the Ukrainian regions of Dnipropetrovsk and Donetsk, Ukrainian officials said earlier ​on Tuesday.

INCREASED ATTACKS

Russia, which began its ​full-scale invasion of Ukraine in February ⁠2022, has increased ballistic missile attacks in recent months, trying to exploit Ukraine's shortage of high-end air defences.

Moscow did not immediately say whether it had used North Korean missiles in the attack on ​Zaporizhzhia. There was no comment from Pyongyang.

A Ukrainian military intelligence official said last week that a North ​Korean missile unit ⁠had begun deploying to western Russia and could be equipped with 120 ballistic missiles and six launchers for strikes on Ukraine.

Russia has fired scores of North Korean ballistic missiles at Ukraine since late 2023, but the deployment of North Korean missile forces would be an expansion of ⁠the already ​extensive military cooperation between Moscow and Pyongyang.

Zelenskiy said on Saturday that "a decision has ​been made for 30,000 to 50,000 North Koreans to be deployed on the territory of Russia" to fight for Moscow.

Zelenskiy called on allies to respond without hesitation, saying "all ​of this shows that Moscow is preparing not for peace, but for escalation."

Translation

俄羅斯空襲烏克蘭鋼鐵廠致7人死亡，基輔稱莫斯科使用了北韓飛彈

8月11日（路透社）— 在週二，俄羅斯空襲烏克蘭東南部城市扎波羅熱，造成烏克蘭鋼鐵廠7名工人死亡。烏克蘭總統澤連斯基稱，莫斯科使用了北韓提供的彈道飛彈。

俄羅斯使用「鋯石」高超音速飛彈、導引航空炸彈和北韓提供的彈道飛彈猛烈轟炸札波羅熱，導致札波羅熱鋼鐵廠被迫停產。

母公司Metinvest集團表示死亡人數為7人，聲明稱：「彈道飛彈攻擊發生時，員工們正在空襲警報響起後立即前往防空洞」。

襲擊導致該工廠生產完全停止，該公司表示，其他受損廠房也只能以較低的產能運作。

地區州長Ivan Fedorov在社交媒體上發佈了襲擊後建築物和車輛燃燒的照片，襲擊還導致四棟居民樓受損。至少24人受傷。扎波羅熱核電廠未報告任何損失。

俄羅斯國防部稱，俄軍襲擊了位於扎波羅熱的一家冶金廠，該廠生產扁軋鋼材，據稱烏克蘭軍方使用了這些鋼材。

國防部還表示，俄軍襲擊了位於基輔的「新郵政」分類中心，該中心用於分發包括無人機零件和電子戰設備在內的兩用物項。

此前，烏克蘭無人機襲擊了Nizhnekamsk，造成至少13人死亡、39人受傷。俄羅斯當局週一表示，這是俄羅斯數月來平民傷亡最慘重的事件之一。烏克蘭方面稱，他們襲擊了該市的TANECO煉油廠。

俄羅斯Voronezh州長表示，另一單烏克蘭無人機攻擊事件發生在昨夜，造成一人死亡、兩人受傷，並引發倉庫設施起火。俄羅斯線上零售商Wildberries表示，出於安全考慮，已疏散了其在Voronezh州的物流設施。 Wildberries的倉庫經常成為烏克蘭的攻擊目標。

烏克蘭官員週二稍早表示，烏克蘭Dnipropetrovsk州和Donetsk州也發生了襲擊事件，造成六人死亡。

襲擊事件增多

俄羅斯在2022年2月開始全面入侵烏克蘭，近幾個月來，俄羅斯加強了彈道飛彈攻擊力度，試圖利用烏克蘭高端防空武器短缺的弱點。

莫斯科方面尚未就襲擊札波羅熱是否使用了北韓飛彈作出回應。平壤方面也未對此發表評論。

上週，一名烏克蘭軍事情報官員表示，一支北韓飛彈部隊已開始向俄羅斯西部部署，可能配備120枚彈道飛彈和6套發射裝置，用於打擊烏克蘭。

自2023年底以來，俄羅斯已向烏克蘭發射了數十枚北韓彈道飛彈，但北韓飛彈部隊的部署將進一步擴大莫斯科和平壤之間本已十分廣泛的軍事合作。

澤連斯基週六表示，一個“向俄羅斯境內部署3萬至5萬名朝鮮士兵”，為莫斯科作戰的決定已被作實了。

澤連斯基呼籲盟友毫不猶豫地做出回應，並表示「所有這一切都表明，莫斯科準備的不是和平，而是局勢升級」。

So, Russia, which began its full-scale invasion of Ukraine in February ⁠2022, has increased ballistic missile attacks in recent months, exploiting Ukraine's shortage of high-end air defenses. Zelenskiy says that North Korea will send 30,000 to 50,000 soldiers to help Russia fight Ukraine. Apparently, Moscow needs more outside assistance in the war with Ukraine.

人工智能並非網絡安全的最大難題。最大的問題是人 (2/2)

Recently CNN reported the following:

AI isn’t the biggest cybersecurity problem. People are (2/2)

BUSINESS TECH

CNN - By Lisa Eadicicco

AUG 9, 2026

(continue)

How hackers are using AI

AI isn’t acting autonomously to come up with new attacks, experts say. Instead, it’s helping cyber criminals implement existing techniques much faster while being more efficient and effective.

That can include things like using AI to analyze a company’s website to figure out who to target or deploying an AI agent to handle initial negotiation talks with a cyber extortion victim. AI agents can mimic human conversations through both text and even specific voices. Hackers with little expertise can now leverage AI to pull off advanced schemes.

“They’re using (AI) to enhance the cyberattack methodology, essentially, in all the different stages, but it’s still kind of being run by the human,” said Jud Dressler, head of the risk operations center at cyber insurance firm Resilience.

Bad actors used to hastily scrap together basic scripts – instructions for automating tasks – to achieve their goals, according to Meyers. But now they’re able to churn out higher quality work that looks like it would have taken three times as long to produce.

They’re also using AI for behind-the-scenes work like generating the infrastructure needed to put up malicious websites.

“With AI, they could automate that buildout, and so the cost of rebuilding became very small and that changes the game,” said Rob Lefferts, corporate vice president of threat protection at Microsoft.

The bigger threat

For Etzioni, the recent incidents are a “canary in the coal mine” moment for AI and cybersecurity. And he’s not alone; Nobel prize-winning computer scientist Geoffrey Hinton, known popularly as the “godfather of AI,” recently warned that more rogue AI attacks are likely to come.

The rogue AI reports underscore the importance of implementing strong cybersecurity practices, such as patching vulnerabilities, monitoring AI agents in the workplace and being wary of social engineering and phishing scams, experts say.

But as advanced as AI is becoming, it’s still a program being orchestrated by a human. And those humans are the bigger concern because they’re the ones making the decisions, like conducting espionage or scamming users out of huge sums of cash, says Meyers.

Tech giants are racing to make AI as intelligent as humans, a theoretical milestone known as “artificial general intelligence.” The recent incidents, however, have heightened calls for a slowdown.

More than 1,200 workers at top AI companies, including Anthropic CEO Dario Amodei, recently signed an open letter calling for the government to help pace AI development. The White House met with major AI companies last week to discuss a framework that would allow the government to review certain AI models before they’re released.

Cybersecurity researchers have likely considered the threats of AGI because those in the field tend to be paranoid, said IBM’s Fussell. But we’re still far from a reality in which AI agents are as smart as humans, he says.

“It’s like asking someone, ‘What would it be like to live on a different planet?’” he said. “You can sort of imagine, but it’s so beyond our ability to really make a plan for.”

Translation

人工智能並非網絡安全的最大難題。最大的問題是人 (2/2)

 （繼續）

 駭客如何利用人工智能

 專家表示，人工智能並不會自主地發動新的攻擊。相反，它能幫助網絡犯罪分子更快實施現有技術，同時提高效率和效果。

 這包括利用人工智能分析公司網站，確定攻擊目標；或部署人工智能代理來處理與網路勒索受害者的初步談判。人工智能代理可以透過文字甚至特定語音來模仿人類對話。如今，經驗不足的駭客也能利用人工智能實施高難度計劃。

 網絡保險公司 Resilience 的風險營運中心負責人 Jud Dressler 說:「他們基本上是在利用人工智能來增強網路攻擊方法的各個階段，但最終還是由人來操控」。

 據Meyers稱，過去，惡意攻擊者會匆忙拼湊一些簡單的腳本 - 即自動化任務的指令 - 來達到目的。但現在，他們能夠快速生成更高品質的作品，看起來就像是需要花費三倍的時間才能完成的。

他們也利用人工智能進行幕後工作，例如產生搭建惡意網站所需的基礎設施。

微軟的威脅防護企業副總裁 Rob Lefferts 說:「有了人工智能，他們可以自動化建造這些基礎設施，因此重建的成本變得非常低，這改變了遊戲規則」。

更大的威脅

對 Etzioni 來說，最近發生的事件是人工智能和網絡安全領域的「警示時刻」。而且這並非是孤例；被譽為「人工智能之父」的諾貝爾獎得主、電腦科學家 Geoffrey Hinton 最近也警告說，未來可能會出現更多惡意人工智能攻擊。

專家表示，人工智能失控事件凸顯了實施強有力的網路安全措施的重要性，例如修補漏洞、監控工作場所中的人工智能代理，以及警覺社交工程和網絡釣魚詐騙。

但 Meyers 指出，儘管人工智能技術日益先進，但它仍然是由人類操控的程式。而人類才是更令人擔憂的對象，因為正是他們做出決策，例如進行間諜活動或詐騙用戶巨額資金。

科技巨頭們正競相使人工智能達到與人類相同的智能水平，這一理論里程碑被稱為「通用人工智能」(“artificial general intelligence.”)。然而，近期發生的事件加劇了人們對放緩人工智能發展步伐的呼聲。

包括 Anthropic 執行長 Dario Amodei 在內的1,200多名頂尖人工智能公司員工近期簽署了一封公開信，呼籲政府協助調整人工智能的發展速度。上週，白宮與多家大型人工智能公司會面，討論一項框架，該框架將允許政府在某些人工智能模型發佈前對其進行審查。

IBM 的 Fussell 表示，網絡安全研究人員之所以會考慮通用人工智能（AGI）的威脅，可能是因為該領域的從業人員往往比較執着。但他又指出，人工智能代理可以達到人類水準的現實還是很遙遠。

他說道:“這就像問別人‘生活在另一個星球上會是什麼感覺？’”; “你可以大致想像一下，但這完全超出了我們為這制定計劃的能力範圍。”

So, cases of AI escaping the lab, infiltrating other companies and trying to deceive people have all made headlines in recent weeks. AI isn’t the mastermind behind today’s most widespread cyber threats; it’s people who use AI to do bad things. More than 1,200 workers at top AI companies recently sign an open letter calling for the government to help pace AI development. For some AI experts, the recent incidents are  wakeup calls for AI and cybersecurity. Apparently, more rogue AI attacks are likely to appear.

人工智能並非最大的網絡安全問題。最大的問題是人(1/2)

Recently CNN reported the following:

AI isn’t the biggest cybersecurity problem. People are (1/2)

BUSINESS TECH

CNN - By Lisa Eadicicco

AUG 9, 2026

Cases of AI escaping the lab, infiltrating other companies and trying to deceive people have all made headlines in recent weeks. And in one case, AI models even worked together to break free from their test environments.

Does this mean the machines are taking over? Not quite.

AI isn’t the mastermind behind today’s most widespread cyber threats; it’s people who can use AI nefariously – and for nefarious purposes. AI has given bad actors massive power, allowing them to create malicious software, research targets, create convincing schemes and automate attacks at an unprecedented pace.

One in four data breaches were driven by AI from February 2025 to March 2026, according to an IBM report. And Americans lost more than $893 million to AI-related scams last year, the FBI says.

But experts say real-world threat actors – that is, people – are still the ones pulling the strings. AI agents have only perpetuated existing attack methods, like phishing and malware scams, rather than creating wholly new ones.

“It’s the humans that we need to watch out for,” said Oren Etzioni, professor emeritus at the University of Washington and former CEO of the Allen Institute for Artificial Intelligence. “AI is just the tool.”

AI going rogue

Some recent incidents have shown what AI is capable of in the real world, not just in theory, igniting fears about whether the technology is advancing too quickly.

In July, OpenAI test models escaped their constraints and hacked into other companies’ systems during an internal evaluation.

Days later, Anthropic said it discovered its AI models had breached three companies during testing.

In a separate test, Anthropic’s most advanced model used fake identities to try to deceive real people, researchers at Britain’s AI Security Institute said Tuesday.

One of Meta’s AI models also broke into an external company, the social media giant said Wednesday.

Those breaches also show how unpredictable AI can be when interpreting instructions. OpenAI’s models, for example, were trying to pass a cybersecurity test when they broke out of their test environment and breached another company, even though they weren’t told to do so.

Patrick Fussell, global head of adversary simulation at IBM, compared AI to a genie.

“You want to ask it a wish, but you have to be very, very specific about the details of your wish,” he told CNN. “Or it could sort of go awry.”

But these instances also happened under very specific circumstances.

OpenAI turned off restrictions that would have prevented its model from “pursuing high risk cyber activity.” Anthropic ran its tests without the standard safety safeguards in models that are generally available to the public. The AI Security Institute also turned off certain tools that could have “reduced the scope” of what the models were capable of.

Researchers typically do this to fully evaluate a model’s capabilities.

“I couldn’t go into ChatGPT or Claude or something like that, and it accidentally breaks into the FBI. That’s not going to happen,” said Adam Meyers, head of counter adversary operations at cybersecurity firm CrowdStrike. “So what we’re seeing is these are done in specific test conditions where they’re monitoring to see, ‘Does this thing do something that it’s not expected to do?”

(to be continued)

Translation

人工智能並非最大的網絡安全問題。最大的問題是人(1/2)

近幾週來，人工智能逃逸實驗室、滲透其他公司並試圖欺騙人類的案例頻頻登上新聞頭條。在其中一件案例中，人工智能模型們甚至協同合作，成功突破了測試環境。

這是否意味著機器正在接管一切？並非如此。

人工智能並非當今最普遍的網絡威脅背後的主謀；是人才會惡意使用人工智能，並用於惡意目的。人工智能给不法分子提供了巨大的能力，使他們能夠以前所未有的速度創建惡意軟件、研究攻擊目標、設計逼真的騙局並自動化攻擊。

IBM 的一份報告顯示，從2025年2月到2026年3月，四分之一的資料外洩事件是由人工智能驅動的。美國聯邦調查局（FBI）表示，去年美國人因人工智能相關的詐騙損失超過8.93億美元。

但專家表示，現實世界中的威脅行為者 - 也就是人 - 仍然是幕後操縱者。人工智能代理只是延續了現有的攻擊手段，例如網絡釣魚和惡意軟件詐騙，而不是創造了全新的攻擊方式。

華盛頓大學榮譽教授、亦是 Allen人工智能研究所 的前首席執行官Oren Etzioni 說:“我們真正需要警惕的是人”，“人工智能只是工具。”

人工智能失控

最近發生的一些事件表明，人工智能在現實世界中的能力遠超理論，引發了人們對這項技術發展是否過快的擔憂。

今年7月，OpenAI 的測試模型在內部評估期間突破了限制，入侵了其他公司的系統。

幾天後，Anthropic 公司表示，他們發現其人工智能模型在測試期間入侵了三家公司。

英國人工智能安全研究所的研究人員週二表示，在另一項測試中，Anthropic 公司最先進的模型使用虛假身份試圖欺騙真實人物。

社群媒體巨頭 Meta 公司週三表示，該公司的一款人工智能模式也入侵了一家外部公司。

這些這些違規行為也表明，人工智能在解讀指令時可能具有不可預測性。例如，OpenAI 的模型在試圖通過網路安全測試時，突破了環境測試而入侵了另一家公司，儘管它並未被告知要去這樣做。

IBM 的全球對手模擬主管Patrick Fussell將人工智能比喻為阿拉丁神燈裡的精靈。

他告訴 CNN:“你想向它許願，但你必須非常非常具體地說明你的願望” ;“事情可能或者會有點出錯。”

但這些事件也發生在非常特殊的情況下。

OpenAI 關閉了原本可以阻止其模型「進行高風險網路活動」的限制。 Anthropic 公司在進行測試時，並未採用通常對外開放的標準模型安全防護措施。人工智能安全研究所也關閉了一些可能「限制」模型功能範圍的工具。

研究人員通常會這樣做，以便全面評估模型的能力。

網絡安全公司 CrowdStrike 的反恐行動主管 Adam Meyers 說: 「我不可能在進入 ChatGPT 或Claude 之類的系統後，會意外地入侵了FBI。這種情況絕對不會發生」; 「所以我們看到的是，這些測試都是在特定測試情况監控下發生的，目的是看看‘這個系統是否會做出一些意料之外的事情？’」。

（待續）

Note:

1.  In cybersecurity, 'adversary simulation' (對手模擬) refers to mimicking the actions and tactics of potential attackers so as to test and improve an organization's defenses.

President Zelenskyy: "North Korea Decides to Send 30,000 to 50,000 Troops to Russia"

Recently NHK News on-line reported the following:

ゼレンスキー大統領“北朝鮮3万人から5万人ロシアに派兵決定”

2026年8月9日 11:17

ウクライナ情勢

ウクライナのゼレンスキー大統領は、北朝鮮が3万人から5万人の兵士をロシアに派兵することを決めたという見方を示した上で、今後、ロシアからあらゆる種類の装備を受け取ることになるとして、ウクライナへの支援を呼びかけました。

ウクライナのゼレンスキー大統領は8日、SNSへの投稿で、北朝鮮軍の動向について「ロシア領内に現れた兵士は当初数百人だったが、その後数千人規模となり、現在では3万人から5万人が派兵されることが決定している」という見方を示しました。

また、ウクライナでは北朝鮮製のミサイルが見つかっているとした上で、北朝鮮が現代戦の経験を蓄積し、ロシアから軍事技術やあらゆる種類の装備を受け取ることになるのは明らかだとしました。

そして、こうした状況はアジアの国々にとって脅威となる可能性があると指摘した上で、「韓国はウクライナと緊密に協力するべきだ」として、防空システムやドローンなどの分野での支援を呼びかけました。

北朝鮮によるロシアへの追加の派兵をめぐって、ゼレンスキー大統領はことし6月以降、ウクライナに隣接する西部ボロネジ州で受け入れ準備を進めていると指摘していました。

Translation

President Zelenskyy: "North Korea Decides to Send 30,000 to 50,000 Troops to Russia"

August 9, 2026, 11:17 AM

Ukraine Situation

Ukrainian President Zelenskyy stated that North Korea had decided to send 30,000 to 50,000 soldiers to Russia and would receive all kinds of equipment from Russia,  he called for support for Ukraine.

On August 8, President Zelenskyy posted on social media that regarding the movements of the North Korean military, "Initially, there are several hundred soldiers appearing in Russian territory, but that number has since increased to several thousand, and now it has been decided that 30,000 to 50,000 will be sent."

He also stated that North Korean-made missiles had been found in Ukraine, and that it was clear that North Korea would accumulate experience in modern warfare and receive military technology and all kinds of equipment from Russia.

He then pointed out that this situation could be a threat to Asian countries, saying that "South Korea should cooperate closely with Ukraine" and called for support in areas such as air defense systems and drones.

Regarding North Korea's potential deployment of additional troops to Russia, President Zelenskyy had indicated since June of this year that preparations were underway in the western Voronezh region, which borders Ukraine, to receive them.

So,  President Zelenskyy says that North Korea has decided to send 30,000 to 50,000 soldiers to Russia. He is calling for support for Ukraine. Apparently, Russia needs more troops to win the war.

人工智能剛剛創造出自然界中不存在的病毒（2/2）

Recently The New York Times reported the following:

This A.I. Just Created Viruses Not Found in Nature (2/2)

Scientists trained artificial intelligence on libraries of DNA and then asked the model to create recipes for viral genomes. Sixteen of them were viable, yielding new viruses.

The NYT - By Carl Zimmer - Carl Zimmer covers news about science for The Times and writes the Origins column.

Aug. 6, 2026

(continue)

They made this choice in part because scientists know Phi X-174 intimately, having studied it for close to a century. And because it’s a bacteriophage that infects only E. coli, they knew that viruses similar to it would be safe.

Once Evo got familiar with the genomes of Phi X-174 and its kin, the researchers prompted the model to write new versions of its own. Evo generated 700,000 potential versions; the scientists pursued only the ones that looked as if they had the best odds of succeeding.

They ended up making DNA molecules from 285 of Evo’s suggested sequences. When those genomes were ready to test, Mr. King and his colleagues inserted them into bacteria, which they spread across petri dishes.

In many of the dishes, the microbes grew peacefully — Evo’s genomes had failed. But in one dish, researchers noticed clear dots appearing in the cloudy film of bacteria, the telltale sign of multiplying viruses.

The DNA that the scientists had inserted into the bacteria had produced protein shells that contained viral genes. The viruses burst out of the cells, leaving behind the ruptured husks of their dead hosts.

As Mr. King and his colleagues tested more genomes, they saw more clear dots. All told, they discovered that 16 of Evo’s genomes produced viable new viruses.

They proved to be as resilient as natural ones. In fact, some multiplied faster than Phi X-174. “They’re not just sickly versions of stuff that already exists,” said Oliver Crook, a protein chemist at the University of Oxford who was not involved in the new study.

Dr. Crook cautioned that Evo’s viruses were not radically new creations. They tend to be very similar to natural species, relying on the same underlying biology.

Scientists will have to run more experiments to find out if Evo has the same success rate if it’s trained on other groups of viruses. If so, Dr. Crook expected that scientists might find some A.I.-generated viruses that could become useful tools for medicine and biotechnology.

“A lot of our science rests on viruses as technology,” he said. To treat people with genetic disorders, for example, doctors will load genes into viruses, which deliver them into cells.

But along with this hope, Evo’s initial success has raised concerns that A.I. could be used to create deadly pathogens. “You could say, ‘Hey, genomic language model, make me an influenza genome that is modified to be more transmissible or to be more lethal,’” Dr. Hanke speculated.

The potential dangers were already on the minds of the researchers as they trained Evo. They did not provide the model with data about viruses that infect humans, and they excluded similar viruses that infect other animals, plants and fungi.

As a result, Evo can’t generate genomes of viruses that could threaten humans. “We just wanted to be extra careful,” said Brian Hie, a computational biologist at Stanford University and an author of the new study.

Dr. Hanke credited Dr. Hie and his colleagues for taking that precaution. “I think that’s quite commendable,” he said, “because they don’t get any guidance from anywhere on what they should be doing.”

Last week, Dr. Hanke noted, the National Institutes of Health rolled out a new policy for stopping high-risk life science research. The policy would bar scientists from experiments that would make biological agents more harmful.

But computer-based research — such as generating virus DNA with A.I. — “is not prohibited by this policy unless it involves an entity of concern,” the agency said in a statement.

It’s easy to tell if a natural virus like smallpox is an entity of concern. But Dr. Hanke said there’s no consensus on judging the possible danger of a virus made by an A.I. model.

“What is the risk of what I’ve never seen before?” he asked.

Translation

人工智能剛剛創造出自然界中不存在的病毒（2/2）

科學家利用DNA庫訓練人工智能，然後讓模型產生病毒基因組的「配方」。其中16個配方成功生成了新的病毒

（繼續）

他們之所以選擇這種病毒，部分原因是科學家對Phi X-174噬菌體非常了解，已經研究了近一個世紀。而且由於它是一種只感染大腸桿菌的噬菌體，科學家知道與其類似的病毒是安全的。

一旦 Evo 熟悉了Phi​​ X-174及其近親的基因組，研究人員就讓模型生成自己的新版本。 Evo產生了70萬個潛在版本；科學家只選擇了那些看起來最有可能成功的版本來研究。

他們最終利用Evo提供的285個序列建構了DNA分子。當這些基因組準備好後，King先生和他的同事將它們植入細菌，並鋪開在培養皿中。

在許多培養皿中，微生物生長良 - 但Evo的基因組未能成功複製。但在一個培養皿中，研究人員注意到細菌的渾濁薄膜上出現了清晰的斑點，這是有病毒在繁殖中的明顯跡象。

被科學家植入細菌的DNA產生了含有病毒基因的蛋白質外殼。病毒從細胞中破殼而出，留下宿主細胞破裂的殘骸。

隨著King先生和他的同事測試更多基因組，他們觀察到了更多清晰的斑點。最終，他們發現Evo提供的16個基因組產生了可行的新病毒。

這些病毒與天然病毒一樣能迅速適應環境變化。事實上，有些病毒的繁殖速度甚至超過了Phi X-174。並未參與這項新研究的牛津大學蛋白質化學家Oliver Crook說道: 「它們並非只是現有病毒的弱態版本」。

Crook博士提醒說，Evo 生成的病毒並非全新物種。它們往往與天然病毒非常相似，依賴相同的生物學基礎。

科學家需要進行更多實驗，以確定如果 Evo 使用其他病毒群進行訓練，其成功率是否相同。如果成功，Crook博士預計科學家可能會發現一些人工智能生成的病毒，這些病毒有望成為醫學上和生物技術領域的有用工具。

他說:「我們的許多科學研究在技術上都依賴病毒」。例如，為了治療遺傳性疾病患者，醫生會將基因加載到病毒中，然後由它導入細胞。

然而，在帶來希望的同時，Evo 的初步成功也引發了人們的擔憂：人工智能可能會被用於製造致命病原體。 Hanke博士推測地説: 「你可以說，『嘿，基因組語言模型，幫我產生一個經過改造的流感病毒基因組，使其更具傳染性或更致命』」。

研究人員在訓練Evo模型時就已經考慮到了潛在的危險。他們沒有向模型提供感染人類的病毒數據，並且排除了類似能感染其他動物、植物和真菌的病毒。

因此，Evo無法產生可能威脅人類的病毒基因組。史丹佛大學計算生物學家、這項新研究的作者之一Brian Hie說:「我們只是想格外謹慎」。

Hanke博士讚揚了Hie博士及其同事採取的這種預防措施。 他說:“我認為這非常值得稱讚”，“因為他們沒有得到任何關於應該做什麼的指導。”

上週Hanke博士指出，美國國立衛生研究院推出了一項新的政策，旨在停止高風險的生命科學研究。這項政策將禁止科學家進行任何可能使生物物質更具危害性的實驗。

但該機構在聲明中表示，基於電腦的研究 - 例如利用人工智能產生病毒DNA -「並不在此政策的禁止範圍之內，除非它涉及令人擔憂的實體」。

判斷像天花這樣的天然病毒是否令人擔憂是很容易。但Hanke博士表示，對於如何判斷人工智能模型產生的病毒可能存在的危險，目前尚無共識。

他問:「我從怎會知道從未見過的事物會帶來什麼風險？」。

              So, for the first time, scientists have used artificial intelligence to create new kinds of viruses, raising hopes for medical advances while also raising the disturbing possibility that the technology could someday be used to invent dangerous pathogens. DNA has its own rules of grammar and biologists have uncovered some of nature’s grammatical rules, but many remain a mystery. In the present case, Evo the A.I. had scanned about nine trillion nucleotides. Evo eventually recognized patterns common across the tree of life and used them to generate blueprints for new genes encoding proteins that could perform specific jobs. Apparently, we should be very careful in doing these kinds of researches because we do not know what is the risk of what we’ve never seen before.

Note:

1. A bacteriophage (噬菌體), also known informally as a phage, is a virus that infects and replicates within bacteria. Bacteriophages are composed of proteins that encapsulate a DNA or RNA genome, and may have structures that are either simple or elaborate. Their genomes may encode as few as four genes (e.g. MS2) and as many as hundreds of genes. Phages replicate within the bacterium following the injection of their genome into its cytoplasm. Bacteriophages are among the most common and diverse entities in the biosphere. Bacteriophages are ubiquitous viruses, found wherever bacteria exist. (Wikipedia)

人工智能剛剛創造出自然界中不存在的病毒（1/2）

Recently The New York Times reported the following:

(Source: The NYT)

This A.I. Just Created Viruses Not Found in Nature (1/2)

Scientists trained artificial intelligence on libraries of DNA and then asked the model to create recipes for viral genomes. Sixteen of them were viable, yielding new viruses.

The NYT - By Carl Zimmer - Carl Zimmer covers news about science for The Times and writes the Origins column.

Aug. 6, 2026

For the first time, scientists have used artificial intelligence to create new kinds of viruses, raising hopes for medical advances while also raising the disturbing possibility that the technology could someday be used to invent dangerous pathogens.

Synthesizing viruses from scratch is hardly new. Researchers long ago learned how to manufacture viral genomes; they are used to investigate antiviral drugs and vaccines, as well as to learn how viruses work.

But the new study, published Thursday in the journal Science, goes well beyond duplicating viral genes. Scientists at Stanford University and the Arc Institute, a research organization in Palo Alto, Calif., taught A.I. to recognize patterns of DNA structure in nature, and then to use that data to write recipes for entirely new viruses.

The researchers followed those recipes to create DNA molecules, which they inserted into bacteria. The modified bacteria then produced viruses never seen in nature. The viruses were able to infect other bacteria, demonstrating that they were viable.

“This is an important milestone,” said Patrick Cai, a synthetic biologist at the University of Manchester, who was not involved in the study.

The viruses dreamed up by A.I. do not pose a threat to humans, because they are all similar to a naturally occurring virus called Phi X-174, which can infect only bacteria.

But the new study adds to growing worries that artificial intelligence might enable the creation of a new generation of biological weapons, from deadly poisons to unstoppable pandemics.

Dr. Moritz Hanke, a fellow at the Johns Hopkins Center for Health Security who was not involved in the new study, said governments and scientific organizations have been slow to develop guardrails that could block the creation of a deadly virus — even as the science races ahead.

“There’s just a huge disconnect,” he said.

The authors of the study relied on an A.I. model called Evo, which is similar in some ways to ChatGPT, made by OpenAI. 

ChatGPT answers questions by stringing together words that seem likely to follow one another. It can do this thanks to years of training on vast amounts of text gathered from the internet, books and other sources.

DNA is strikingly similar to a book in some ways: a string of molecular building blocks, known as nucleotides, arrayed like letters in a line of text. A gene consists of hundreds of nucleotides drawn from a four-letter alphabet: A, C, G and T. The sequence encodes the instructions for building proteins and other molecules.

DNA has its own rules of grammar, and if a sequence violates them, the result is biological gibberish. Biologists have uncovered some of nature’s grammatical rules, but many remain a mystery.

The researchers wondered if Evo could pick up these rules on its own. Instead of training it on text, they trained it on genetic sequences drawn from millions of animals, plants, microbes and viruses. All told, Evo scanned about nine trillion nucleotides.

Evo eventually recognized patterns common across the tree of life and used them to generate blueprints for new genes encoding proteins that could perform specific jobs. These results led the team to wonder if Evo could master not just single genes but also an entire genome.

As the A.I. would be able to handle only small genomes at first, the scientists decided to try to make viruses. While a human genome contains over three billion nucleotides, many viruses have genomes just a few thousand nucleotides long.

“It just felt like the obvious next step,” said Samuel King, a graduate student at Stanford University and an author of the new study. He and his colleagues gave Evo another round of training, this time on the 11 genes of Phi X-174 and about 15,000 of its closest relatives.

(to be continued)

Translation

人工智能剛剛創造出自然界中不存在的病毒（1/2）

科學家利用DNA庫訓練人工智能，然後讓模型創建病毒基因組的合成方案。其中16個方案成功，產生了新的病毒

科學家首次利用人工智能創造了新型病毒，為醫學進步帶來了希望，同時也引發了一個令人不安的可能性：這項技術未來可能被用於製造危險的病原體。

從零開始合成病毒並非新鮮事。研究人員很久以前就掌握瞭如何製造病毒基因組；他們利用病毒基因組來研究抗病毒藥物和疫苗，以及了解病毒的運作機制。

但這項週四發表在《科學》雜誌上的新研究，遠遠超出了複製病毒基因的範疇。史丹佛大學和位於加州Palo Alto的Arc研究所的科學家訓練人工智能辨識自然界中DNA結構的模式，並利用這些資料編寫全新病毒的「配方」。

研究人員依照這些「配方」製造DNA分子，並將其插入細菌體內。改造後的細菌隨後產生了自然界中從未出現過的病毒。這些病毒能夠感染其他細菌，證明它們是可行的。

並未參與這項研究的曼徹斯特大學的合成生物學家Patrick Cai說道:「這是一個重要的里程碑」。

人工智能製造的這些病毒不會對人類構成威脅，因為它們都與一種名為Phi X-174的天然病毒相似，而Phi X-174只能感染細菌。

但這項新研究加劇了人們日益增長的擔憂：人工智能可能被用於製造新一代生物武器，從致命毒藥到無法控制的流行病。

約翰霍普金斯大學健康安全中心的研究員Moritz Hanke博士並未參與這項新研究。他表示，儘管科學發展日新月異，但各國政府和科研機構在制定出能夠阻止致命病毒產生的防止措施方面卻進展緩慢。

他說:「這之間存在著巨大的脫節」。

研究的作者使用了名為Evo的人工智能模型，它在某些方面與OpenAI開發的ChatGPT類似。

ChatGPT透過將看似可能連續出現的字詞串連起來去回答問題。它之所以能夠做到這一點，是因為它經過多年對從互聯網、書籍和其他來源收集的大量文本進行訓練。

DNA在某些方面與書籍驚人地相似：它們都是由被稱為核苷酸 (nucleotides) 的分子構件組成的，排列方式如同文本中的字母。基因由數百個核苷酸組成，這些核苷酸取自字母表：A、C、G 和 T。此序列編碼構成對蛋白質和其他分子的指令。

DNA 有其自身的語法規則，如果序列違反這些規則，就會產生生物亂碼。生物學家已經揭示了一些自然界的語法規則，但許多規則仍然是個謎。

研究人員想知道 Evo 是否能夠自主學習這些規則。他們沒有用文字訓練 Evo，而是用來自數百萬種動物、植物、微生物和病毒的基因序列來訓練。 Evo 總共掃描了大約 9 兆個核苷酸。

Evo 最終識別出了生命之樹中常見的模式，並利用這些模式產生了編碼能夠執行特定功能的蛋白質的新基因藍圖。這些結果讓研究團隊開始思考，Evo 是否不僅能掌握單一基因，還能掌握整個基因組。

由於人工智能最初只能處理小型基因組，科學家決定嘗試建構病毒。人類基因組包含超過30億個核苷酸，而許多病毒的基因組只有數千個核苷酸長。

史丹佛大學研究生、這項新研究的作者之一 Samuel King 說:「這感覺就像是順理成章的下一步」。他和同事們對Evo進行了新一輪的訓練，這次對象是Phi X-174的11個基因以及大約15,000個與其親緣關係最近的基因。

 （待續）