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）

中國的人工智能正在非洲迅速發展，這應該引起矽谷的擔憂（3/3）

Recently The New York Times reported the following:

(Source: The NYT)

China’s A.I. Is Surging Across Africa. That Should Worry Silicon Valley (3/3)

In African tech hubs, developers are picking China’s cheap, freely available artificial intelligence models over more powerful U.S. ones.

The NYT - By Paul MozurAdam Satariano and Aaron Krolik (Paul Mozur and Adam Satariano reported from Nairobi, Kenya, and Aaron Krolik from New York.)

Aug. 5, 2026

(continued from part 2)

Wake Up Call

In a Nairobi high-rise in June, a top Kenyan civil servant for technology said he was surprised when Anthropic pulled access to Fable, its powerful model, at the Trump administration’s behest.

It was a “wake up call” to the world, said John Tanui, the principal secretary in Kenya’s Ministry of Information, Communications and the Digital Economy. The abrupt move was a warning that relying on U.S. models could pose risks, he said.

A former Huawei employee who once led the Konza Technopolis project, Mr. Tanui said China and the United States had pressed Kenya to pick a side. He intends to take from both.

“We don’t lean on West or East,” he said.

Being in the middle is tricky. Developers are building systems controlled elsewhere, which means the technology can be yanked, altered or re-priced without warning. Some worry about dependence on the United States. Others do not trust China.

Mr. Budhabhatti of Craft Silicon said he was considering Huawei’s offer of generous subsidies, but was also cautious. Many of his customers are international banks that could demand their software not use Chinese hardware.

Chinese models, too, have not always been reliable. Shikoh Gitau, the founder of the Nairobi start-up Qhala, recalled when DeepSeek went dark in China before the country’s college entrance exams in 2024. It was to deter cheating, but it also disrupted African businesses like hers. Now American models could vanish because of a White House decision.

“At least with China’s models, I can download it on my computer and run it,” she said.

Others have been burned by A.I. politics. Mr. Mwebaze’s Ugandan chatbot service, Sunflower, ran into trouble when a researcher at China Media Project, an independent media research group, asked it about China. Sunflower described China as a democracy and sidestepped questions about the country’s economic and environmental impacts in Uganda.

Chinese state media praised Sunflower, and Huawei contacted Mr. Mwebaze about using its cloud services.

Mr. Mwebaze, whose nonprofit organization, Sunbird AI, is dedicated to A.I. for social good, was unnerved by the experience. He is now using Google’s open-source model, Gemma, to create a product aimed at smartphone users.

“We need to make sure we’re not on the wrong side of geopolitics,” he said.

America Strikes Back

For all of China’s momentum, much of the A.I. money in Kenya still flows to U.S. companies.

Everyday users are on U.S. systems like ChatGPT and Claude. One 2025 survey found that 42 percent of Kenya’s 23 million internet users had used ChatGPT in the previous month, among the highest rate in the world.

Even Chinese models earn Americans money. Kenyan companies often run Chinese open-source models on cloud services from Amazon and Microsoft, so the U.S. giants make money from their data centers that deliver the technology to customers.

The U.S. advantage was clear in a concrete building in Nairobi’s industrial district, where binders of paper purchase orders lined dark offices for Chandaria Industries, one of Kenya’s largest makers of toilet paper, soap and home goods.

For decades, orders arrived from supermarkets by email or on paper, and clerks keyed them in line by line. An order could take hours to process.

Chandaria recently hired Sanifu, a Kenyan start-up that uses A.I. to streamline business processes, for help. Sanifu uses OpenAI’s models for precision and reliability.

Sanifu’s digital system based on those U.S. models has transformed Chandaria. “Where an order took about two hours to process, that now takes less than 10 minutes,” said Neer Chandaria, who helps run the family firm. Four clerks who once typed in orders now audit the A.I.’s work.

But Sanifu is being courted too. On LinkedIn this year, a Singaporean employee of the Chinese internet firm ByteDance offered Sanifu’s founders access to its A.I. model.

ByteDance’s model was not accurate enough, but the courtship made an impression, said Bernard Momanyi Nyagaka, a co-founder of Sanifu. Nothing like it comes from the American labs.

And Chinese models keep improving, Mr. Nyagaka said.

“If you look at how fast they’re catching up with the American models, it’s really, really fast,” he said.

Translation

中國的人工智能正在非洲迅速發展，這應該引起矽谷的擔憂（3/3）

在非洲的科技中心，開發者們正在選擇中國廉價且免費的人工智能模型，而不是功能更強大的美國模型

（接續第二部分）

警醒

今年6月，在內羅比的一棟高層建築裡，一位肯亞高級技術公務員表示，他對Anthropic公司應朗特普政府的要求撤回其強大的人工智能模式Fable的存取權限感到驚訝。

肯亞資訊、通訊和數位經濟部常務秘書John Tanui表示，這給世界一個「警醒」。他說，這項突然舉動是在警告人們，依賴美國模型可能會帶來風險。

曾任華為員工、並領導Konza Technopoli科技城計劃的Tanui先生表示，中國和美國都曾施壓肯亞選邊站隊。他打算兼顧雙方。

他說「我們不偏袒西方，也不偏袒東方」。

居於中立位置並非易事。開發者正在建立由其他國家控制的系統，這意味著技術可能在毫無預警的情況下被撤回、修改或重新定價。有些人擔心過度依賴美國，而有些人則不信任中國。

Craft Silicon公司的Budhabhatti先生表示，他正在考慮華為提供的豐厚補貼，但也保持謹慎。他的許多客戶都是國際銀行，這些銀行可能會要求其軟件不得使用在中國的硬件。

此外，中國模式也並非總是可靠。內羅比初創公司Qhala的創辦人Shikoh Gitau回憶起，在2024年中國高考前夕，DeepSeek在中國停止營運。此舉旨在防止作弊，但也擾亂了像她這樣的非洲企業。如今，由於白宮的一項決定，美國的人工智能模型可能就會消失。

她說:「至少我可以下載中國的型號到電腦上運行」。

其他人則因人工智能政治而蒙受損失。Mwebaze先生的叫做「向日葵」（Sunflower）的烏干達 聊天機器人服務就遇到了麻煩。當時，獨立媒體研究機構「中國傳媒計劃」（China Media Project）的一位研究員向「向日葵」詢問了有關中國的問題。 「向日葵」將中國描述為一個民主國家，並迴避了有關中國對烏干達經濟和環境影響的問題。

中國官方媒體對「向日葵」大加讚揚，華為也聯繫了Mwebaze先生，希望他使用其雲端服務。

Mwebaze先生的非營利組織「太陽鳥人工智能」（Sunbird AI）致力於利用人工智能造福社會，這次經歷讓他感到不安。他現在正在使用Google的開源模型Gemma開發一款面向智能型手機用戶的產品。

他說:「我們需要確保自己不會站在地緣政治的錯誤一邊」。

美國反擊

儘管中國在人工智領域勢頭強勁，但肯亞的大部分人工智能資金仍流向美國公司。

肯亞的一般用戶都在使用ChatGPT和Claude等美國系統。一項2025年的調查發現，肯亞2,300萬網路用戶中有42%的人在上個月使用過ChatGPT，這比例位居世界前列。

即使是中國的人工智能模型也賺美國錢。肯亞公司經常在亞馬遜和微軟的雲端服務上運行中國的開源模型，這些美國巨頭可以從其數據中心負責向客戶提供技術服務中獲利。

在內羅比工業區的一棟混凝土建築裡，美國的優勢顯而易見。肯亞最大的衛生紙、肥皂和家居用品生產商之一 - 錢德里亞工業公司（Chandaria Industries）昏暗的辦公室裡，一疊疊紙質採購訂單整齊地擺放著。

幾十年來，超市的訂單都是透過電子郵件或紙本形式送達，店員需要逐行輸入。處理一份訂單可能需要幾個小時。

Chandaria 最近聘請了肯亞新創公司 Sanifu 來協助其業務流程優化。 Sanifu 利用人工智能技術簡化業務流程，並使用 OpenAI 的模型來確保精準性和可靠性。

Sanifu 基於這些美國模型的數位化系統徹底改變了 Chandaria 的營運模式。協助管理家族企業的 Neer Chandaria 說: 「以前處理訂單需要大約兩個小時，現在不到 10 分鐘就能完成」。過去負責輸入訂單的四名職員現在負責審核人工智能的處理結果。

Sanifu 也受到了其他公司的青睞。今年，一位來自中國網絡公司字節跳動的新加坡員工在 LinkedIn 上向 Sanifu 的創辦人提供了其人工智能模式的使用權限。

Sanifu 的共同創辦人 Bernard Momanyi Nyagaka 表示，字節跳動的模型雖然不夠精確，但這次青睞卻給他們留下了深刻的印象。美國實驗室從未有類似的產品。

Nyagaka 先生也表示，中國的模型也在不斷改進。

他說:「如果你看看他們追趕美國模型的速度，你會發現是真的非常非常快」。

              So, the A.I. model from the Chinese internet giant Alibaba handles Uganda’s dozens of languages better than anything from Meta or Google. It is inexpensive, and could be customized. In Kenya, entrepreneurs are using the models to streamline legal and business services. China and the United States have pressed Kenya to pick a side.  Some users worry about dependence on the United States. Others do not trust China. Apparently, Africa is a place where China and the US will continue fighting for the AI market.