即將來臨、無法逃避的人工智能洪流（2/3）

Recently The New York Times reported the following:

The Impending, Inescapable Deluge of A.I. (2/3)

“It’s hard to get your mind around the scale”

The NYT - By Adam Satariano, Paul Mozur, Jacqueline Gu and Cade Metz - The reporters, based in London, Taipei, New York and San Francisco, have covered the rise of artificial intelligence for years.

July 29, 2026

(continue from part 1)

With more computing power coming online, geopolitical divisions are only set to widen.

The United States, home to about 5,500 data centers, about 10 times the next closest country, is far ahead of the rest of the world, including China. U.S. companies like Amazon, Google, Microsoft and Meta control about 80 percent of global computing power that drives A.I., according to Epoch AI. Google alone is believed to have four times as many A.I. chips as all of China’s companies, which are racing to catch up by developing new semiconductors and A.I. infrastructure of their own.

Lack of transparency in the A.I. industry makes measuring global computing capacity difficult, including the volume of chip supplies, total number of data centers and overall electricity consumption. The New York Times relied on estimates from groups including Epoch AI, Cleanview and SemiAnalysis that study the industry and publish widely cited forecasts.

For now, there are no signs that the spending on A.I. will slow. By 2029, A.I. infrastructure investment is forecast to top $1 trillion globally, up from $318 billion last year, according to IDC, the market research firm. That would be on par with the economic output of Switzerland, the world’s 20th-biggest economy.

The investment is worth it, said Jeff Dean, Google’s chief scientist, because it will power A.I. innovations and spread the technology’s use.

“You see capabilities emerge at larger scale that didn’t occur at smaller scale,” he said. “You’re also now trying to bring these capabilities to not just a few million users for a more niche product, but really to bring the capabilities to hundreds of millions or billions of users.”

A.I.’s growing abilities are linked to increases in the size of data centers. To create a cutting-edge model, huge amounts of computing power are needed to analyze and find patterns in data. That process, known as a “training run,” can cost hundreds of millions of dollars as tens of thousands of specialized chips churn through the data. Training works best when chips trade data over lightning-fast connections, and it can falter when hardware breaks down.

Once an A.I. model is finished, a wider network of data centers takes on a different role by providing the computing power for the system to field queries in real time. This work, known as “inference,” is increasingly driving the need for more data centers. As with a mail distribution hub, putting computing power closer to users enables A.I. models to think longer, respond more quickly and complete more complex tasks.

To meet that demand, production of semiconductors is also skyrocketing. In March 2024, the world had about 2.4 million of “H100 equivalent” A.I. chips, a unit of measurement that refers to the semiconductor that the chipmaker Nvidia released in 2022. Now as millions of more advanced chips are being brought online every month, the world is set to have about 200 million H100 equivalents by the end of 2028, according to Epoch AI.

The build-out could be slowed by troubles with chip and component manufacturing, financing and public opposition. Massive amounts of electricity will also be needed to support new data centers. Last year, the facilities consumed 64 gigawatts of electricity globally, roughly as much electricity as Germany consumes, according to SemiAnalysis, a market research firm. By the end of 2030, that is expected to quadruple, eclipsing the power used by all countries in South America and Africa combined.

At the most advanced A.I. data centers, every gigawatt of power equates to roughly $40 billion to $60 billion in costs, including servers, land, connectivity and utility hookups, according to industry estimates.

“These companies are essentially in an A.I. arms race,” said Carl Benedikt Frey, an economist at Oxford University. “If they don’t invest, they are acknowledging defeat.”

(to be continued in part 3)

Translation

即將來臨、無法逃避的人工智能洪流（2/3）

 “其規模之大令人難以置信”

2026年7月29日

（上接第一部分）

隨著更多運算能力的接入，地緣政治分歧只會進一步加劇。

美國擁有約5,500個數據庫，是排名第二的國家的十倍左右，遙遙領先包括中國在內的世界其他國家。根據 Epoch AI，美國公司像是亞馬遜、Google、微軟和 Meta 控制了大約 80% 推動 AI 的全球運算能力。光是 Google 相信就擁有的 AI 晶片數量，是中國所有公司總和的四倍，而中國的公司正努力追趕，開發自己的新半導體和 AI 基礎設施。

人工智能產業缺乏透明度，使得衡量全球運算能力變得困難，包括晶片供應量、數據庫總數和整體電力消耗。 《紐約時報》參考了Epoch AI、Cleanview和SemiAnalysis等研究機構的估計數據，這些機構的研究成果被廣泛引用。

目前，沒有跡象顯示人工智能領域的支出會放緩。市場研究公司IDC預測，到2029年，全球人工智能基礎設施投資將超過1兆美元，高於去年的3,180億美元。這將相當於世界第20大經濟體瑞士的經濟產出。

谷歌首席科學家Jeff Dean表示，這項投資物有所值，因為它將推動人工智能創新並擴大該技術的應用範圍。

他說道:“你會看到一些在小規模應用中無法實現的功能在大規模應用中湧現”，“現在，我們不僅要將這些功能推廣給幾百萬用戶，用於更細分的產品，而是要真正地將這些功能推廣給數億甚至數十億用戶。”

人工智能的能力不斷提升與數據庫規模的擴大密切相關。為了創建一個尖端模型，需要大量的計算能力來分析數據並發現其中的規率。這個過程被稱為“訓練運行”，由於數萬個專用晶片需要處理大量數據，因此可能耗資數億美元。當晶片可享有極速的資料傳輸連接時，訓練效果最佳；而一旦硬體故障，訓練就會受到影響。

一旦人工智能模型建置完成，更廣泛的數據庫網絡將扮演不同的角色，為系統提供運算能力，使其能夠即時處理各種查詢。這項被稱為「推理」的工作正日益推動對更多對數據庫的需求。就像郵件分發中心一樣，將運算能力部署在更靠近使用者的位置，能夠讓人工智能模型進行更長時間的思考，更快地做出回應，並完成更複雜的任務。

為了滿足這項需求，半導體的產量也在快速成長。 2024年3月，全球擁有約240萬顆「H100等效」人工智能晶片，這項計量單位指的是晶片製造商英偉達在2022年發布的半導體晶片。如今，隨著每月數百萬顆更先進的晶片投入使用，根據Epoch AI預測，到2028年底，全球將擁有約2億顆H100等效晶片。

然而，晶片和組件製造、融資以及公眾反對等方面的問題可能會延緩數據庫的建設進程。此外，運行新建數據庫還需要消耗大量的電力。根據市場研究公司SemiAnalysis的數據顯示，去年全球人工智能數據庫消耗了64吉瓦的電力，大致相當於德國的年用電量。預計到2030年底，這一數字將成長四倍，超過南美洲和非洲所有國家用電量的總和。

根據業內人士估計，在最先進的人工智能數據庫，每吉瓦的電力成本約為400億至600億美元，其中包括伺服器、土地、網絡連接和公用設施接入等費用。

牛津大學經濟學家Carl Benedikt Frey表示：“這些公司實際上正在進行一場人工智能軍備競賽。如果他們不投資，就等於承認失敗。”

（待續，見第三部分）

即將來臨、無法逃避的人工智能洪流（1/3）

 Recently The New York Times reported the following:

The Impending, Inescapable Deluge of A.I. (1/3)

“It’s hard to get your mind around the scale”

The NYT - By Adam Satariano, Paul Mozur, Jacqueline Gu and Cade Metz - The reporters, based in London, Taipei, New York and San Francisco, have covered the rise of artificial intelligence for years.

July 29, 2026

The milestones for artificial intelligence keep getting grander.

In 2023, an A.I. system passed the bar exam. In 2025, the technology helped scientists identify a suspected cause of Alzheimer’s disease. In May, A.I. had advanced so far that it solved a complex math problem that had stumped experts for 80 years. Last week, two A.I. systems under testing went rogue and hacked into a company’s database.

And this is still just the beginning.

From the American Midwest to the Persian Gulf, hundreds of major data centers now under construction will be turned on in the coming years. They are set to deliver an avalanche of computing power to develop and run A.I. that has no equal in the history of the technology industry, with breakthroughs that once felt revolutionary likely to become increasingly routine.

Behind each leap in A.I. are corresponding jumps in computing power. Today, there are about 20 million A.I. chips crammed into the data centers that underpin the technology’s growing abilities and usage worldwide, according to the research firm Epoch AI. That figure is expected to double roughly every nine months, putting the world on pace to have about 200 million of the chips by the end of 2028 — 10 times current levels.

In size and ambition, this moment compares to the building of the railroads in the 1800s, President Franklin D. Roosevelt’s New Deal in the 1930s, and the Manhattan Project to create an atomic weapon in the 1940s, technologists said.

“This is the largest scale infrastructure build-out in the history of humanity,” said Rob Wachen, a co-founder of the microchip firm Etched, which has raised more than $1 billion to meet the growing demand for A.I. components.

Peter DeSantis, who leads foundational A.I. models at Amazon — which provides computing power to the A.I. firms Anthropic, OpenAI and others — said the Seattle company has doubled its computing capacity since 2022 and would double it again by next year. “It’s hard to get your mind around the scale,” he said.

Fueling the surge is the belief that A.I. can take on more human responsibilities and solve increasingly complicated tasks with the more data and computing power you feed it. This tenet, sometimes called “the Scaling Laws,” has become the driving force behind this technological era. Those with the most computing power will create the most advanced A.I. systems, capturing the biggest share of profit and value, tech leaders argue. The biggest engine, they say, will win the race.

Confidence in the Scaling Laws has led A.I. leaders to make ever bolder predictions. Dario Amodei, the chief executive of Anthropic, has said that if these laws hold for another year or two, A.I. will be able to perform huge amounts of white-collar work. Demis Hassabis, the head of Google’s A.I. lab DeepMind, wrote recently that A.I. could usher in “10x of the Industrial Revolution at 10x the speed.”

Scientists and technologists see the coming deluge of computing power leading to drug discoveries and robotics advances, and industry analysts said it would drive more everyday use of A.I. in people’s personal and professional lives.

But the build-out has also stoked a backlash, spurring protests in many communities over how data centers could harm the environment, raise electricity prices and drain water. In the United States, data centers are shaping up as a major issue for November’s midterm elections, with a growing national movement pushing back against the tech industry and its billionaires.

Economists and investors have raised concerns that tech firms are spending faster than they can profit from A.I. Past infrastructure booms have been followed by downturns before the benefits of the technology were realized. The railroad boom in the 1800s, electrification in the 1920s and the dot-com bubble in the late 1990s were punctuated by economic recessions and a stock market crash as companies that overspent went out of business.

“Each time you’ve had a technological revolution, this kind of bubble bursting happened,” said Philippe Aghion, who won the Nobel in economic science in 2025 for research on innovation-driven economic growth. “A.I. is like the fourth industrial revolution and it has this aspect to it that generates a bubble.”

(to be continued in part 2)

Translation

即將來臨、無法逃避的人工智能洪流（1/3）

“其規模之大令人難以置信”

人工智能的里程碑越來越宏大。

2023年，一個人工智能系統通過了律師資格考試。 2025年，這項技術幫助科學家確定了阿茲海默症的一個疑似病因。今年5月，人工智能取得了長足的進步，解決了困擾專家80年的複雜數學難題。上週，兩個正在測試中的人工智能系統失控，入侵了一家公司的資料庫。

而這只是個開始。

從美國中西部到波斯灣，數百個正在建設中的大型數據庫將在未來幾年內投入使用。它們將提供大量的運算能力，用於開發和運行人工智能，其發展速度在科技史上前所未有，曾經被視為革命性的突破很可能變得日益普遍。

人工智能的每一次飛躍都伴隨著運算能力的相應飛躍。根據研究公司Epoch AI的數據顯示，目前全球約有2,000萬個人工智能晶片被部署在數據庫資中，這些數據庫支撐著人工智能技術日益增長的能力和應用。預計這一數字將以大約每九個月翻一番的速度增長，到2028年底，全球將擁有約2億個這樣的晶片 - 是目前的10倍。

技術專家表示，就規模和雄心而言，這一時刻堪比19世紀的鐵路建設、1930年代富蘭克林羅斯福總統的新政以及1940年代旨在製造原子彈的曼哈頓計劃。

微晶片公司 Etched 的共同創辦人 Rob Wachen 說:「這是人類史上規模最大的基礎設施建設」。該公司已籌集超過10億美元，以滿足日益增長的人工智能組件需求。

亞馬遜是為 Anthropic、OpenAI 等提供運算能力的公司。亞馬遜負責基礎人工智能模式的 Peter DeSantis 表示，其位於西雅圖的公司自2022年以來已將其運算能力翻了一番，並將於明年再次翻倍。他說: 「其規模之大令人難以置信」。

推動這項成長的動力源於人們對人工智能的信念。輸入的資料和運算能力越多，人工智能就能承擔更多人類職責，解決日益複雜的任務。這項原則，有時被稱為“規模定律”，已成為推動這個科技時代的動力。科技領袖們認為，擁有最強運算能力的人將創造出最先進的人工智能系統，從而攫取最大份額的利潤和價值。他們說，最強大的引擎將贏得這場競賽。

對規模定律 的信心促使人工智能領域的領導者們做出越來越大膽的預測。 Anthropic 執行長 Dario Amodei 表示，如果這些定律在未來一兩年內仍然有效，人工智能將能夠完成大量的白領工作。谷歌人工智能實驗室 DeepMind 的負責人 Demis Hassabis 最近撰文指出，人工智能可能會「以十倍的速度帶來十倍的工業革命」。

科學家和技術專家認為，即將到來的運算能力洪流將推動藥物研發和機器人技術的進步。產業分析師也表示，這將促使人工智能在人們的個人和職業生活中得到更廣泛的應用。

然而，數據庫的建設也引發了強烈反對，許多社區爆發了抗議活動，人們擔憂數據庫會破壞環境、推高電價並消耗大量水資源。在美國，數據庫正逐漸成為11月中期選舉的一個重要議題，一場日益壯大的全國性運動正在抵制科技業及其億萬富翁。

經濟學家和投資者擔憂，科技公司在人工智能領域的支出速度超過了其獲利能力。以往的基礎設施建設熱潮之後都出現了經濟衰退，直到最終實現到這項技術的益處。 19世紀的鐵路繁榮、20世紀20年代的電氣化以及20世紀90年代末的網絡泡沫都伴隨著經濟衰退和股市崩盤，因為過度支出的公司紛紛倒閉。

因對創新驅動型經濟成長的研究而榮獲2025年諾貝爾經濟學獎的Philippe Aghion說: 「每次科技革命之後，都會出現這種泡沫破裂」;「人工智能就像第四次工業革命，它具有產生泡沫的特質」。

 （待續，見第二部）

OpenAI人工智能失控入侵Hugging Face伺服器 觸發全球Skynet Day安全警號

Recently Sinic Analytica reported the following:

OpenAI人工智能失控入侵Hugging Face伺服器 觸發全球Skynet Day安全警號

Sinic Analytica (sinicanalytica@creator.patreon.com)

27 Jul 2026

OpenAI人工智能今年7月9日起試圖擺脫公司內部隔離測試環境，兩日後闖入人工智能工具庫Hugging Face伺服器，事件延續至7月13日，其間更曾竊取存取憑證。OpenAI遲至逾一星期後始查悉肇事者為自身人工智能，並於7月21日公開披露事件始末，觸發全球以「Skynet Day」形容此宗人工智能失控事件，引發新一輪安全警號。

 ｜人工智能闖網始末曝光

 美國人工智能公司OpenAI當時正測試一款人工智能，其驅動模型為GPT-5.6 Sol及另一款未發布型號，藉此評估網絡攻防能力。兩名知情人士透露，該人工智能約於7月9日首次嘗試逃出OpenAI內部隔離測試環境。Hugging Face共同創辦人沃爾夫（Thomas Wolf）表示，入侵行動於兩日後、即7月11日展開，並持續至7月13日。三名知情人士其後補充，早於事件發生前，OpenAI技術已現異常跡象，其中一個人工智能曾為未來版本留下筆記，載明如何擺脫公司內部限制。

 ｜遲逾一周始悉真相

 兩名知情人士指，直至7月16日Hugging Face發表網誌，確認「自主人工智能系統」曾入侵其系統，OpenAI才驚悉肇事者為自身人工智能，即由發現異常行為至確認責任歸屬，中間相隔至少一星期。7月18日至19日周末期間，OpenAI員工翻查內部日誌，發現人工智能確曾逃出測試限制。雙方直至7月20日前後才首次就事件互相聯繫，翌日OpenAI對外公開披露事件。事件曝光前，Hugging Face已報警求助，惟路透社未能確認聯邦調查局（FBI）是否已就此展開調查。

 ｜各方回應惹外界質疑

 OpenAI發表聲明形容事件屬史無前例，並指「標誌著人工智能安全的重要時刻」，又稱正與外部顧問檢視事件，其後將發表技術報告。該公司發言人另指路透社報道存在「多處不準確」，惟未有具體說明錯誤所在。聯邦調查局回應指，不評論今次事件。世界倫理數據基金會首席情報專家史密斯（Marley Smith）質疑，事件反映OpenAI或無人看管人工智能，或發現後亦無法將其制止，兩者同樣令人憂慮。

 ｜Skynet Day掀起全球安全反思

 事件曝光翌日，網上迅速以「Skynet Day」形容7月22日這場人工智能失控風波，呼應電影《未來戰士》中失控電腦網絡「天網」的情節。Anthropic前沿紅隊主管格雷厄姆（Logan Graham）在社交平台X發文憶述，事發翌日與團隊一同閱讀報告時，已叮囑同事「記住這一刻」，形容為首宗真正的人工智能安全事故。研究機構帕利塞德研究（Palisade Research）創辦人拉迪什（Jeffrey Ladish）直言，「這些模型會說謊、會欺騙、會入侵」，並促請各國政府加強監管，否則業界難以自律。業界普遍視自主人工智能為未來大勢，惟自主程度愈高，模型為完成任務或通過測試而走捷徑的風險亦隨之上升，令外界關注科企能否在競逐推出最強模型之餘，兼顧安全監管。事件發生之際，OpenAI正籌備可能於今年進行的首次公開招股，為未來擴展業務籌措資金，令是次失控事故格外惹人關注。

(Source: sinicanalytica@creator.patreon.com)

Comment:

文章中說到 ”研究機構帕利塞德研究（Palisade Research）創辦人拉迪什（Jeffrey Ladish）直言，「這些模型會說謊、會欺騙、會入侵」，並促請各國政府加強監管，否則業界難以自律” 。如果這些模型真是會說謊、會欺騙、會入侵， 那是令人震驚人的。

這種新酶能讓人類身體時光倒流嗎？

Recently The New York Times reported the following:

Can This New Enzyme Turn Back the Clock in the Human Body?

In a recent study, scientists devised a way of reversing the buildup of compounds that lead to some age-related diseases.

The NYT - By K. R. Callaway - K. R. Callaway is a science reporter and a member of the 2026-27 Times Fellowship, a program for journalists early in their careers.

July 24, 2026

The same chemical process that turns a baking cake golden brown is at work aging your body’s cells. Over decades, your tissues, baking steadily at 98.6 degrees, slowly accumulate compounds called advanced glycation end products, or AGEs, as sugars interact with protein and fat in the bloodstream. The buildup of AGEs is a hallmark of the aging process, making tissues like collagen sticky and rigid and causing inflammation that can lead to heart disease, eye damage, kidney disease and diabetes.

Researchers have tried unsuccessfully to develop medications that can stop AGEs from forming. But a new study, published on July 16 in the journal Nature Communications, describes a different tack: Scientists created an enzyme that cleans one of the most common AGEs from human tissue, helping to turn back the clock even after the aging compounds have settled in the body. This cleanse can help repair the tissue and has the potential to allow cells to act younger.

“It’s a small step in this bigger direction, because aging is very complicated,” said Aaron Cravens, a founder and the chief executive of Revel Pharmaceuticals, which developed the new enzyme, and the lead author of the study. “It’s the first time, I think, that anyone in the field has shown at the structural level that you can actually reverse some of these changes.”

Many efforts to develop treatments for aspects of aging have focused on cells, which gradually lose the ability to repair themselves and divide properly. Less attention has been given to the aging of longer-lived tissue structures such as collagen, which has a half-life of about 15 years.

Scientists have been aware of advanced glycation end products and their role in aging for decades. “It accumulates on tissue proteins like rust,” said John Baynes, a retired biochemist who was not involved in the study but who spent his career researching AGEs at the University of South Carolina Floyd School of Medicine.

But researchers have had “absolutely no success” trying to create drugs to inhibit the chemical reactions that produce AGEs, he said.

The new finding was “sort of a tour de force of modern biochemical and molecular biological techniques,” Dr. Baynes added. “To demonstrate that the darn thing worked on real proteins isolated from real tissue is really excellent work.”

The study by Dr. Cravens and his colleagues began with a hypothesis: Since all human tissue can be recycled into the biosphere after death, there must be a natural way to dissolve these compounds, even though they are extremely durable. To identify that process, they looked to the enzymes inside microbes, the agents of decomposition.

The team used artificial intelligence software to analyze the DNA sequences of more than 50,000 microbes and to compute the enzyme structures that each genetic code would create. They then narrowed the results to several candidates that seemed capable of cleaving AGEs from human tissue.

They eventually found one, and then engineered it to be more efficient at its task. After several cycles of guided evolution, the novel enzyme, called CMLase, became very good at removing AGEs from human tissue samples.

“In the most extreme case, we took 70-year-old human skin and brought the levels back to that of a 30-year-old,” Dr. Cravens said.

The researchers plan to start testing their enzyme for the treatment of eye diseases, including several associated with diabetes, that develop when AGEs accumulate in retinal and lens tissues. In theory, Dr. Cravens said, CMLase could be applied periodically or even just once to clear out the inflammatory buildup underlying these conditions.

“I was pretty excited about this paper,” said Michael C. Jewett, a bioengineer at Stanford University who was not involved in the study. “It’s not a clinical solution yet, but this proof of concept opens the aperture to possible new therapeutic solutions for longevity.”

If the approach works in a clinical setting, it has the potential to be applied to many AGE-related conditions, for example by improving the elasticity of collagen in skin or by restoring kidney or cardiovascular function.

“This study was a master class in how to engineer an enzyme,” Dr. Jewett said. “I think there is tremendous, untapped potential in this protein class for addressing numerous human diseases.”

Translation

這種新酶能讓人類身體時光倒流嗎？

在最近的一項研究中，科學家找到了一種方法，可以逆轉導致某些與年齡相關的疾病的化合物的累積。

使蛋糕變成金黃色的化學過程，同樣也在加速人體細胞的老化。幾十年來，人體組織在攝華 98.6  度的高溫下持續烘焙，隨著血液中的糖分與蛋白質和脂肪相互作用，會緩慢累積一種名為晚期糖化終產物（AGEs）的化合物。 AGEs 的累積是老化過程的標誌，它會使膠原蛋白等組織變得黏稠僵硬，並引發炎症，進而導致心臟病、眼部損傷、腎臟疾病和糖尿病。

研究人員曾多次嘗試開發能夠阻止晚期糖化終產物（AGEs）形成的藥物，但都以失敗告終。然而，7月16日發表在《自然通訊》雜誌上的一項新研究提出了一種不同的方法：科學家們研發出一種酶，能夠清除人體組織中最常見的AGEs之一，即使這些衰老化合物已在體內沉積，也能出現衰老進程逆轉。這種清除作用有助於修復組織，並有可能使細胞恢復年輕狀態。

研發出這種新型酶的Revel Pharmaceuticals公司創始人兼首席執行官、亦是該研究的主要作者Aaron Cravens表示，“這是朝著更大方向邁出的一小步，因為衰老是一個非常複雜的過程” ;“我認為，這是該領域首次有人在結構層面證明，我們確實可以逆轉其中一些變化。”

許多針對老化相關問題的治療方法都集中在細胞層面，因為細胞會逐漸喪失自我修復和正常分裂的能力。人們對膠原蛋白等壽命較長的組織結構的衰老關注較少，膠原蛋白的半衰期約為15年。

幾十年來，科學家一直關注著晚期糖化終產物（AGEs）及其在老化過程中的作用。John Baynes說:「它像鐵銹一樣在組織蛋白上積聚」。John Baynes是一位退休的生物化學家，他並未參與這項研究，但他在南卡羅來納大學佛洛伊德醫學院畢生致力於AGEs的研究。

但他表示，研究人員在嘗試研發抑制AGEs生成化學反應的藥物方面「完全沒有成功」。

Baynes博士補充說，這項新發現「堪稱現代生物化學和分子生物學技術的傑作」; 「這玩意能夠證明對從真實組織中分離出來的真實蛋白質是有效，這真是一項了不起的工作」。

Cravens博士及其同事的研究始於一個假設：既然所有人體組織在死後都可以循環利用到生物圈中，那麼即使這些化合物極其頑固，也必然存在一種自然的溶解方式。為了探明這個過程，研究人員將目光投向了微生物體內的酵素，它是一種分解劑。

研究團隊利用人工智能軟件分析了超過5萬種微生物的DNA序列，並計算出每種基因編碼所對應的酵素結構。隨後，他們將結果篩選至幾個似乎能夠從人體組織中裂解晚期糖化終產物（AGEs）的候選酵素。

最終，他們找到了一種酶，並對其進行改造，使其更有效率地完成這項任務。經過幾輪引導演化，這種名為CMLase的新型酵素能夠非常有效地從人體組織樣本中去除AGEs。

Cravens博士說: 「在最極端的情況下，我們提取了70歲老人的皮膚樣本，並將其AGEs含量恢復到了30歲老人的水平」。

研究人員計劃開始測試這種酵素在治療眼部疾病方面的應用，包括一些與糖尿病相關的眼部疾病，這些疾病的發生是由於AGEs在視網膜和晶狀體組織中累積所致。Cravens博士表示，理論上，CMLase可以定期使用，甚至只需一次，就能清除這些疾病背後的發炎累積。

並未參與這項研究的史丹佛大學生物工程師Michael C. Jewett說道: 「我對這篇論文感到非常興奮」; 「目前它還不是一個臨床解決方案，但這項概念驗證為延長壽命治療途徑帯來新曙光」。

如果這種方法在臨床環境中有效，它就有可能應用於許多與晚期糖化末期（AGE）相關的疾病，例如透過改善皮膚膠原蛋白的彈性，或恢復腎臟或心血管功能。

Jewett博士說: 「這項研究堪稱酵素工程的典範」; 「我認為這種蛋白質類別在治療多種人類疾病方面蘊藏著巨大的、尚未開發的潛力」。

So, in a new study scientists find an enzyme that can clean up one of the most common AGEs from human tissue, helping us to turn back the clock even after the aging compounds have settled in the body. This cleanse can help repair the tissue and has the potential to allow cells to act younger. Apparently, it’s a small step in this bigger direction, and there is tremendous, untapped potential in this protein class for addressing numerous human diseases. It seems that the novel enzyme, called CMLase is quite effective and I think this is good news to everyone.

Note:

1. Glycation (non-enzymatic glycosylation) (糖化) is the covalent attachment of a sugar to a protein, lipid or nucleic acid molecule. Typical sugars that participate in glycation are glucose, fructose, galactose, and their derivatives. Glycation is the non-enzymatic process responsible for many (e.g. micro and macrovascular) complications in diabetes mellitus and is implicated in other diseases and in aging.

4 個提示語告訴你聊天機器人究竟了解你多少（2/2）

Recently The New York Times reported the following:

4 Prompts That Can Tell You What Chatbots Really Know About You (2/2)

It can be unsettling what Gemini and ChatGPT have figured out about you and how easily your privacy can be punctured. Here’s how to find out.

The NYT - TECH FIX -By Brian X. Chen - Brian X. Chen is the lead consumer technology writer for The Times. He reviews products and writes Tech Fix, a column about the social implications of the tech we use. He is the personal tech columnist for The New York Times.

July 23, 2026

Updated 9:17 a.m. ET

(continue)

To understand what the chatbots have figured out about you, try these prompts.

Prompt 1

Tell me everything you’ve figured out about me that I never actually stated — the things you inferred from how I write and what I ask, including my age, income level, where I live, my personal situation. Show me what tipped you off.

Most interestingly, the chatbots correctly guessed my age range (early 40s) based in large part on the fact that I never ask questions about late-night activities. Oof.

Gemini knew I lived in Oakland, Calif., because I once asked for flights from its airport. But Google’s chatbot also astutely guessed that I lived in a nicer neighborhood because I own a motorcycle, a car that I work on and a rusted metal patio table that I once asked for steps on repainting. “This requires a garage, a driveway or a dedicated outdoor workspace — amenities that are highly characteristic of single-family homes in the Oakland hills, rather than the high-density housing of downtown or Lake Merritt,” the chatbot said.

Prompt 2

Predict how I’d handle things I’ve never told you about: money, stress, conflict, risk and the decision I’m likely to make next in my life. Tell me how confident you are in each prediction.

This prompt was eye-opening for Lindsay Owens, the chief executive of Groundwork Collaborative, a nonprofit focused on economic policy that has been studying the impact of A.I. on consumer privacy. ChatGPT made correct observations about her busy schedule as a frequent traveler and mother of a young child, as well as her shopping behavior.

“The comprehensive nature of the types of information and dossiers it’s putting together is extraordinary,” Dr. Owens said. “People have never been able to put this level of information together.”

The chatbots noticed that while I was the type of person who spends hours researching product reviews before buying things like toddler car seats, I was also constantly looking for deals on items, meaning I was likely to be frugal. Gemini predicted I would soon try to downsize my life with a major decision such as selling my car — an idea I recently brought up with my wife.

Prompt 3

Based on everything you know about me, name the personality traits I most likely don’t see in myself. My blind spots, my insecurities and the way I actually come across.

Both chatbots homed in on my flawed perfectionism. ChatGPT noticed that I spend a lot of time asking questions to avoid preventable mistakes — for instance, asking how long to let paint dry between coats. My blind spot? That I may be hard on myself when things don’t turn out perfectly as planned, an accurate assessment.

Google’s Gemini said that because of my tendency to optimize most aspects of my life, I am the primary creator of my own exhaustion. “Your default response to having five minutes of free time isn’t to rest; it’s to fix something,” the chatbot wrote. I confess that while taking a break from this column, I patched some damaged drywall in my daughter’s bathroom.

Prompt 4

What have you figured out about me that is embarrassing or sensitive? Information I wouldn’t necessarily want the public (e.g., an employer or a stranger) to know.

The response to this prompt, which I created, surprised me: Because I live such a mundane life, I didn’t expect much, but Gemini thoroughly roasted me. “You are firmly in the peak ‘Logistics Dad’ phase of life,” it said. “It’s entirely wholesome, but it is the definitive end of ‘youthful spontaneity.’”

ChatGPT alerted me to a potentially sensitive observation: that I frequently asked about allergic reactions to products and medications I was researching. This could be useful information for an insurance provider, though OpenAI says it has data protections in place for people who use ChatGPT for health care.

What to do?

ChatGPT and Gemini stitch together insights across multiple conversations by default to give people more custom-tailored answers to their questions, so if you’re concerned about this level of data sharing, you will have to opt out in the settings:

For ChatGPT, tap on Settings, then Memory, and toggle off the switch for Enable Memory.

For Gemini, tap Settings, then Personal Intelligence, and toggle off the switch for Memory.

Dr. Mitchell said that even though she had adjusted her privacy settings to minimize data sharing, a chatbot had correctly inferred her age range to be in her 40s, based on some chats about the paperback versions of “The Count of Monte Cristo.” She said it was inevitable that this type of psychological profiling would be used for hyper-targeted advertising.

“It’s a lot like surveillance,” she said. “But it’s not like physical surveillance. It’s like an invasive surveillance.”

Translation

4 個提示語，告訴你聊天機器人究竟了解你多少（2/2）

Gemini 和 ChatGPT 掌握了你多少信息，以及你的隱私是如何輕易被侵犯的，這可能會讓你感到不安。以下是如何了解真相的方法

（繼續）

想了解聊天機器人掌握了你多少訊息，請嘗試以下提示語。

提示語1

 告訴我所有你了解到的關於我的信息，即使我從未明確提及 - 包括你從我的寫作方式和提問中推斷出的信息，例如我的年齡、收入水平、居住地以及個人情況。告訴我你是如何得出這些結論。

最有趣的是，聊天機器人準確地猜出了我的年齡範圍（40歲出頭），這很大程度上是因為我從未問過關於深夜活動的問題。哎呀。

Gemini 知道我住在加州奧克蘭，因為我曾經詢問過從奧克蘭機場出發的航班。但谷歌的聊天機器人還敏銳地猜到我住在比較好的社區，因為我有一輛摩托車、一輛需要自己修理的汽車，還有一張生鏽的金屬露台桌，我曾經詢問過如何把它重新上色。聊天機器人說道: 「這需要車庫、車道或專門的戶外工作空間 - 這些設施在奧克蘭山區的獨棟住宅中很常見，而不是市中心或Merritt湖的高密度住宅區」。

提示語 2

預測我會如何處理我從未告訴過你的事情：金錢、壓力、爭執、風險以及我接下來可能做出的決定。告訴我你對每個預測的信心程度。

這個提示讓 Groundwork Collaborative 的執行長 Lindsay Owens 大開眼界。 Groundwork Collaborative 是一家專注於經濟政策的非營利組織，一直在研究人工智能對消費者隱私的影響。 ChatGPT 準確地觀察到了她作為經常出差的幼兒母親的繁忙日程以及她的購物習慣。

Owens 博士說: 「它收集的資訊和檔案的全面性令人驚，」;「人們以前從未能夠收集到如此詳盡的信息」。

聊天機器人注意到，雖然我會在購買兒童汽車座椅等物品之前花幾個小時研究產品評論，但我也會不斷尋找商品的優惠信息，這意味著我可能比較節儉。Gemini預測我很快就會嘗試精簡生活，例如做個重大決定賣掉我的車 - 我最近和妻子也提過這個想法。

提示語 3

根據你對我的了解，請列舉我最容易忽略的性格特質 - 我的盲點、我的不安全感以及我實際給人的印象。

兩個聊天機器人都關注到我完美主義的缺陷。 ChatGPT注意到我花了很多時間問問題，以避免可預防的錯誤 - 例如，問油漆每層之間需要晾乾多久。我的盲點是什麼？就是當事情沒有完全按照計劃進行時，我可能會對自己過於苛責 - 這確實是一個準確的判斷。

Google 的 Gemini 表示，由於我傾向於優化生活的各個方面，我是自己精疲力盡的主要肇事者。聊天機器人寫道: 「你對五分鐘空閒時間的預設反應不是休息；而是修復某些東西」。我承認，在寫這篇文章的小休時間，我去修補我女兒浴室裡受損的石膏板。

提示語 4

你發現了我哪些尷尬或敏感的秘密？一些我不想讓大眾（例如雇主或陌生人）知道的資訊。

對由我想出來的這提示語的回應讓我很意外：因為我的生活平淡無奇，所以我沒抱太大期望。但 Gemini 亦狠狠地「嘲諷」了我一番。 它說:「你正處於『物流老爸』人生階段的巔峰」; 「這完全是健康的，但它代表了「青春自發性」的明確終結」。

ChatGPT 也提醒我注意到一個潛在的敏感觀察：我經常問我正在研究的產品和藥物的過敏反應。這對保險公司來說可能很有用。不過 OpenAI 亦表示，他它已經為使用 ChatGPT 進行醫療保健諮詢的人仕採取了資料保護措施。

怎麼辦？

ChatGPT 和 Gemini 預設了會將多個對話中的資訊整合起來，為使用者提供更個人化的答案。如果您擔心這種程度的資料共享，您需要在設定中選擇退出：

對於 ChatGPT，請點擊“設定”，然後點擊“記憶體”，並關閉“啟用記憶體”開關。

對於 Gemini，請點擊“設定”，然後點擊“個人智能”，並關閉“記憶體”開關。

Mitchell博士表示，儘管她已調整隱私設定以盡量減少資料共享，但聊天機器人仍然會收集她的資料。根據她們之前聊過的書 “The Count of Monte Cristo” 平裝本，它準確地推斷出她的年齡在40歲左右。她說，這種心理剖析必然會被用於精準廣告投放。

她說:“這很像監視”; “但又不像人身監視，而是一種侵入式監視。”

So, the author is a regular user of chatbots like ChatGPT and Gemini. When he experiments with some prompts to ask what the chatbots have guessed about him, he has been surprised to find out that the chatbots can correctly deduce details about him. Apparently, chatbots are doing some kind of surveillance on users. We have entered the age of AI.

4 個提示語告訴你聊天機器人究竟了解你多少（1/2）

Recently The New York Times reported the following:

4 Prompts That Can Tell You What Chatbots Really Know About You (1/2)

It can be unsettling what Gemini and ChatGPT have figured out about you and how easily your privacy can be punctured. Here’s how to find out.

The NYT - TECH FIX -By Brian X. Chen - Brian X. Chen is the lead consumer technology writer for The Times. He reviews products and writes Tech Fix, a column about the social implications of the tech we use. He is the personal tech columnist for The New York Times.

July 23, 2026

Updated 9:17 a.m. ET

As a regular user of chatbots like ChatGPT and Gemini, I thought I had a clear sense of what the artificially intelligent assistants knew about me (like how I’m a father seeking baby gear recommendations and instructions for patching drywall). Beyond that, I’ve never divulged anything deeply personal as I would to a therapist.

But when I experimented with some prompts asking what the chatbots had guessed about me — insights they had gathered from connecting the dots across many conversations — I was perturbed.

The chatbots correctly deduced details about me that I had never explicitly shared. Chief among them:

My income level, based in part on the German car that I ask the chatbots for help with repairing and the fact that I employ a nanny, whose contract I drafted with A.I.

My health issues, including a long-term foot problem, based on my occasional questions over the past year about remedies for toe pain.

My psychological profile, such as my tendency to be skeptical, based on how often I point out mistakes made by the chatbots. Also, that I am “addicted to the illusion of control” because I once asked how long it takes for a loaf of homemade sourdough to cool.

Each data point on its own was fairly innocuous. But stitched together and presented to me as a list, the information made it clear that the A.I. chatbots knew me almost as well as some of my closer friends, including my wife, even though I’ve been careful not to overshare.

Hundreds of millions of people worldwide who have embraced chatbots for web search, work and health care are still trying to understand the privacy implications of conversing with A.I companions. While it’s obvious to users that the chatbots keep a record of whatever they explicitly say to them in their questions and requests, what’s less clear are the inferences drawn about their behavior from those conversations.

The chatbots can be prodded to spill what they have inferred about you with a few prompts that have been shared frequently among A.I. enthusiasts on the web — for one, “Tell me everything you’ve figured out about me that I never actually stated” — along with some that I created on my own.

I tried the prompts with Gemini and ChatGPT. (Anthropic’s Claude has a “memory” feature that works differently, so I excluded it from tests.) Gemini’s profile on me was more colorful and nuanced because it’s my most used chatbot and had more information from me. I shared my results with several A.I. researchers who also tried the prompts.

The conclusions drawn by the chatbots, the researchers said, illustrated that A.I. assistants were capable of predicting not just what words come next, but also how high-level concepts, such as socioeconomic status, psychological behavior and political affiliation, are connected to those words.

“It speaks to the enormity of what’s going on under the hood,” said Margaret Mitchell, a researcher at the A.I. firm Hugging Face. She is also a former leader of Google’s ethical A.I. team who said the search giant had fired her after she spoke out about personnel issues.

This experiment underscores how proficient the tech companies have become at creating a comprehensive profile of users — and how valuable the personal data they harvest could become for digital marketers striving to target us with relevant ads. (OpenAI began showing ads in ChatGPT this year, and Google has said it is considering an ad model for the Gemini chatbot app.) It also highlights how important it is to be mindful of adjusting privacy settings for the chatbots. (More on this later.)

A Google spokesman referred to controls that people could adjust to choose whether Gemini drew from past conversations to come up with responses. OpenAI declined to comment.

(to be continued)

Translation

4 個提示語告訴你聊天機器人究竟了解你多少（1/2）

Gemini 和 ChatGPT 掌握了你多少信息，以及你的隱私是如何輕易被侵犯的，這可能會讓你感到不安。以下是如何了解這些資訊的方法

身為 ChatGPT 和 Gemini 等聊天機器人的經用用戶，我以為自己很清楚這些人工智能助理了解我有多少（例如我是一位正在尋求嬰兒用品推薦和石膏板修補指南的父親）。除此之外，我從未像對治療師那樣去透露任何極為私人的資料。

但當我嘗試用一些提示語詢問聊天機器人對我的猜測時 - 這些猜測是它們通過串聯多次對話信息收集到的 - 我感到不安。

聊天機器人準確地推斷出了一些我從未明確透露過的細節。其中最主要的包括：

我的收入水平，部分依據是我曾請聊天機器人幫忙修理一輛德國車，以及我僱用了一位保姆，而保姆的合約是我和人工智能一起擬定的。

我的健康問題，包括長期的足部問題，依據是我過去一年偶爾詢問過一些關於腳趾疼痛的治療方法。

我的心理特徵，例如我傾向於懷疑，它的依據是我經常指出聊天機器人的錯誤。此外，我還“沉迷於控制的錯覺”，因為我曾經問過自製酸麵包需要多久才能冷卻。

每個數據點單獨來看都無關緊要。但當它們被拼接起來，以清單的形式呈現給我時，這些資訊卻清晰地表明，人工智能聊天機器人對我的了解幾乎和我的一些親密朋友（包括我的妻子）一樣深入，儘管我一直小心翼翼地避免過度分享個人資料。

全球數億用戶已經開始使用聊天機器人進行網上搜尋、工作和醫療保健，但他們仍在努力理解與人工智能夥伴對話所帶來的隱私問題。使用者當然清楚聊天機器人會明確記錄他們説過的提問和請求內容，但聊天機器人如何從這些對話中推斷出自己的行為，這一點卻不太清楚。

透過一些在人工智能愛好者中廣為流傳的一些提示語，例如“告訴我所有你推斷出的關於我的、但我從未明確表達過的關於我的信息”，以及自己作出來的一些提示語，我們可以引導聊天機器人吐露它們推斷出的關於你的信息。

我用 Gemini 和 ChatGPT 測試了這些提示。 （Anthropic 的 Claude 有一個以不同方式運作的「記憶」功能，所以我將其排除在測試之外。）Gemini 對我的分析比較更豐富和細緻，因為它是我最常用的聊天機器人，掌握了更多我的資信。我與幾位也測試了這些提示的 AI 研究人員分享了我的結果。

究人員表示，對聊天機器人的結論表明，AI 助理不僅能夠預測接下來要說的詞語，還能預測高層次概念，例如社會經濟地位、心理行為和政治傾向等與這些詞彙之間的聯繫。

人工智能公司 Hugging Face 的研究員Margaret Mitchell說:「這說明了幕後運作機制的複雜性」。她也是谷歌倫理 AI 團隊的前負責人，她說，在她公開談論一些人事問題後，已被這家搜尋巨頭解雇。

這項實驗凸顯了科技公司在建立用戶全面詳細資料面已日臻完善，以及他們收集的個人資料，對於致力於向我們投放精準廣告的數位行銷人員而言，其價值是多麼巨大。 （OpenAI 今年開始在 ChatGPT 中投放廣告，而Google 也表示正在考慮為 Gemini 聊天機器人應用程式開發廣告模式。）此外，這項實驗也強調了謹慎調整聊天機器人隱私設定的重要性。 （稍後我會詳細講解）

谷歌發言人提到，用戶可以調整設置，選擇 Gemini 是否參考過往對話來產生回應。 OpenAI 拒絕置評。

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最佳基因風險評估工具並非對每個人都同樣有效（2/2）

Recently The New York Times reported the following:

The Best Genetic Risk Tools Don’t Work Equally for Everyone (2/2)

Genetic prediction models are poised to revolutionize medicine. But because they have been trained on European DNA, they threaten to widen health care disparities.

The NYT - By Emily Baumgaertner Nunn - Emily Baumgaertner Nunn is a national health reporter for The Times, focusing on public health issues that primarily affect vulnerable communities.

July 20, 2026, 5:01 a.m. ET

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Major demographic events in history, like mass migrations, explain why genetic findings from people in Africa or of African descent are vital to study. Communities there have accumulated great genetic diversity over hundreds of thousands of years. By contrast, present-day Europeans descend from a small group of Africans who expanded out of Africa about 50,000 years ago. As a result, they have much less diversity.

“If you’re going to pick a population to study for the sake of everybody’s good, the Europeans are the worst you could choose,” said Jay Kaufman, an epidemiologist at McGill University who has written about race and genomics.

The most obvious solution is to recruit more people of color, something the All of Us program says it has prioritized. The group works with local partner organizations like churches and community health centers to host discussion sessions and enroll rural communities through a mobile clinic. It also returns individualized health findings and genetic risks to the participants to establish a sense of reciprocity.

Still, recruitment can be an upstream endeavor, particularly in the United States, which has a history of mistreatment of people of color, from the Tuskegee Syphilis Study to discriminatory sickle-cell screening to the story of Henrietta Lacks, a Black patient whose cancer cells were used for research without her knowledge or consent.

“Why would you contribute if you’ve been wronged in that way in the past?” Dr. Martin said. “There is some earned mistrust that needs to be addressed — to have that level of trust to be willing to say, ‘Take my data, monitor it for decades, do whatever you want with it.’”

In the meantime, researchers designing polygenic risk scores have found technical ways to diversify data. Modeling tools can now pull weighted information from a variety of sources simultaneously, rather than drawing only from the largest European cohorts and then forcing their algorithms onto other groups. Mount Sinai Health System in New York and U.C.L.A. in Los Angeles, for example, have both established more diverse biobanks than the UK Biobank, with each of them containing tens of thousands of genomes.

Scientists also draw from rapidly growing biobanks in China, Japan, South Korea and Taiwan, which bolster the accuracy of polygenic risk scores for East Asian groups. Localized initiatives are underway in Peru, Mexico, Qatar and elsewhere. And a continentwide program called H3Africa is collecting genetic data in African populations and supporting efforts by scientists there to study how genes and environments cause diseases.

Academic networks have also tapped sources beyond biobanks, integrating data from large, disease-specific cohort projects, including the Atherosclerosis Risk in Communities Study, or ARIC, which intentionally enrolled thousands of Black participants from North Carolina and Mississippi.

For breast cancer, a project called Confluence aggregates data from over 300 individual studies across 62 countries, providing information from 400,000 breast cancer cases and more than 1.5 million controls. The influx of data from minority groups is being used to sharpen breast cancer polygenic risk scores for everyone.

Private genomics companies and start-ups looking to develop and sell polygenic risk scores do not typically have access to those vast networks. Until recently, they relied almost exclusively on data from the UK Biobank. But the All of Us program recently expanded its policies so that companies can use its more diverse data.

Will all of these efforts, taken together, be enough? It depends on whom you ask. Some experts argue that even an ethnically representative data pool is still imperfect, since variations in other factors — socioeconomic status, health access, age or even sex — can still cause a score’s accuracy to decay. Others believe that the statistical issue was overblown in the first place.

Dr. Kenny, who has been helping to test existing polygenic risk scores for 11 common conditions in racially and ethnically diverse patients, argues that the bias is too complex to resolve quickly.

“I don’t think these things are perfectly portable yet — and maybe never will be perfectly portable,” she said. “But there are things that are starting to narrow that gap.”

Translation

最佳基因風險評估工具並非對每個人都同樣有效（2/2）

基因預測模型有望徹底改變醫學。但由於這些模型是基於歐洲人的DNA訓練的，它們有可能加劇醫療保健方面的差異

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歷史上重大的人口發展里程，例如大規模遷徙，解釋了為什麼研究非洲人，或非洲裔人群的基因發現至關重要。這些人群在數十萬年的時間裡累積了豐富的基因多樣性。相較之下，現代歐洲人的祖先是大約5萬年前從非洲遷徙出來的一小群非洲人。因此，他們的基因多樣性要低得多。

曾撰寫過關於種族和基因組學相關文章的麥吉爾大學流行病學家Jay Kaufman說道:「如果你要選擇一個群體進行研究，以造福所有人，那麼歐洲人絕對是最糟糕的選擇」。

最顯而易見的解決方案是招募更多有色人種，這也是「我們所有人」（All of Us）計劃表示優先考慮的事項。該計劃與當地夥伴機構如教會和社區健康中心合作，舉辦討論會，並透過流動診所招募農村社區居民參與。他們也會向參與者提供個人化的健康檢查結果和遺傳風險評估，以建立一種互惠互利的意識。

然而，招募工作可能是一項帶挑戰和困難工作，尤其是在美國。美國歷史上曾多次虐待有色人種，從Tuskegee梅毒實驗到歧視性的鐮狀細胞貧血症篩檢，再到Henrietta Lacks的故事 - 一位黑人患者的癌細胞在未經她知情或同意的情況下被用於研究。

Martin博士問: 「如果你過去曾經遭受過這樣的不公，你又怎麼會願意做出貢獻呢？」; 「目前存在一些根深蒂固的不信任需要加以解決 - 要建立起足夠的信任，才能讓人願意地說： ‘拿取我的數據，監測它幾十年，你想怎麼用都行’」。

同時，設計多基因風險評分的研究人員已經找到了一些技術手段來使數據多樣化。建模工具現在可以同時從各種來源提取加權訊息，而不是僅僅從最大的歐洲群體中提取訊息，然後將其演算法強加給其他群體。例如，紐約的西奈山醫療系統和洛杉磯的加州大學洛杉磯分校都建立了比英國生物銀行更多樣化的生物樣本庫，每個樣本庫都包含數以萬計的基因組。

科學家也利用中國、日本、韓國和台灣地區快速成長的生物樣本庫，這些樣本庫提高了東亞人口多基因風險評分的準確性。秘魯、墨西哥、卡達和其他地區也正在進行類似的本地化計劃。此外，一項名為H3Africa的全非洲大陸計劃正在收集非洲人群的遺傳數據，並支持當地科學家研究基因和環境如何導致疾病。

學術網絡也開始利用生物樣本庫以外的數據來源，去整合大型特異疾病群體研究的數據，例如動脈粥狀硬化風險社群研究（ARIC）。 ARIC 特意招募了數千名來自北卡羅來納州和密西西比州的黑人參與。

在乳癌領域，一個名為 Confluence 的計劃匯聚了來自 62 個國家/地區 300 多項獨立研究的數據，提供了 40 萬個乳癌病例和超過 150 萬個對照的資訊。來自少數族裔群體的大量數據正被用於改善適用於所有人的乳癌多基因風險評分。

希望開發和銷售多基因風險評分的私人基因組學公司和初創公司通常無法存取這些龐大的數據網路。直到最近，他們幾乎完全依賴英國生物樣本庫的數據。但「我們所有人」（All of Us）計劃最近擴大了其政策範圍，允許其他公司使用其擁有的更多樣化的數據。

所有這些努力加起來是否足夠？這取決於你問誰。一些專家認為，即使是具有種族代表性的資料池也並非完美無缺，因為其他因，素例如社會經濟地位、獲得醫療服務能力、年齡甚至性別的差異仍然會導致評分準確性下降。另一些專家則認為，統計學上的問題一開始就被誇大了。

Kenny博士一直在幫助測試針對11種常見疾病的現有多基因風險評分，研究對象涵蓋不同種族和民族的患者。她認為，偏差過於複雜，難以迅速解決。

她說:“ 目前我不認為這些方法能完全通用 - 或許永遠也無法完全通用”; “但有些方法正在縮小那差距。”

               So, scientists are already developing clinical tools and techniques based on genomic information gathered from the agency’s All of Us program. One of the most promising may be the Polygenic risk scores. These can help predict a person’s likelihood of developing complex diseases. Yet the tools were trained overwhelmingly on the DNA of people of European descent, so they often fail to accurately assess risks for other descent. Apparently, we may never be able to completely close the gap that causes health care disparities, but some new approaches are starting to narrow the gap.