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Google Is Now Watermarking Its AI-Generated Text
Google 正在為其 AI 生成的文字添加浮水印

But the DeepMind technology isn’t yet a practical solution for everyone
但 DeepMind 的技術尚未成為人人皆宜的實用解決方案

4 min read

閱讀時間:4 分鐘

Eliza Strickland is a Senior Editor at IEEE Spectrum covering AI and biomedical engineering.
Eliza Strickland 是 IEEE Spectrum 的資深編輯,負責報導人工智慧和生物醫學工程相關新聞。

Illustration of a robotic hand holding up a laptop with a speech bubble, book and envelope bursting out from the monitor.
Moor Studio/Getty Images

The chatbot revolution has left our world awash in AI-generated text: It has infiltrated our news feeds, term papers, and inboxes. It’s so absurdly abundant that industries have sprung up to provide moves and countermoves. Some companies offer services to identify AI-generated text by analyzing the material, while others say their tools will “humanize“ your AI-generated text and make it undetectable. Both types of tools have questionable performance, and as chatbots get better and better, it will only get more difficult to tell whether words were strung together by a human or an algorithm.
聊天機器人革命席捲全球,AI 生成的文字充斥著我們的新聞資訊、學術論文和電子郵件收件匣。其數量之龐大已令人匪夷所思,甚至催生出許多相關產業,形成攻防對抗的局面。有些公司提供分析文本以識別 AI 生成文字的服務,另一些公司則宣稱其工具能將您的 AI 生成文字「人性化」,使其難以辨識。這兩種工具的效能都令人質疑,而且隨著聊天機器人技術日益精進,要分辨文字是由人類撰寫還是演算法生成將變得越來越困難。

Here’s another approach: Adding some sort of watermark or content credential to text from the start, which lets people easily check whether the text was AI-generated. New research from Google DeepMind, described today in the journal Nature, offers a way to do just that. The system, called SynthID-Text, doesn’t compromise “the quality, accuracy, creativity, or speed of the text generation,” says Pushmeet Kohli, vice president of research at Google DeepMind and a coauthor of the paper. But the researchers acknowledge that their system is far from foolproof, and isn’t yet available to everyone—it’s more of a demonstration than a scalable solution.
另一種方法是:從一開始就在文字中加入某種浮水印或內容憑證,讓使用者能輕易地檢查文字是否為 AI 生成。來自Google DeepMind的最新研究,今日發表於Nature期刊,提供了一種實現此目標的方法。該系統名為 SynthID-Text,Google DeepMind 研究副總裁兼論文共同作者Pushmeet Kohli表示,它不會影響「文字生成的品質、準確性、創意或速度」。但研究人員也承認,他們的系統遠非萬無一失,目前也尚未普及——它更像是一個示範,而非一個可擴展的解決方案。

Google has already integrated this new watermarking system into its Gemini chatbot, the company announced today. It has also open-sourced the tool and made it available to developers and businesses, allowing them to use the tool to determine whether text outputs have come from their own large language models (LLMs), the AI systems that power chatbots. However, only Google and those developers currently have access to the detector that checks for the watermark. As Kohli says: “While SynthID isn’t a silver bullet for identifying AI-generated content, it is an important building block for developing more reliable AI identification tools.”
Google 今日宣布,已將這套新的浮水印系統整合到其Gemini聊天機器人中。他們也將該工具開源並提供給開發人員和企業,讓他們可以使用該工具來判斷文字輸出是否來自他們自己的大型語言模型(LLMs),也就是驅動聊天機器人的 AI 系統。然而,目前只有 Google 和這些開發人員才能使用用於檢查浮水印的偵測器。正如 Kohli 所言:「雖然 SynthID 並非識別 AI 生成內容的靈丹妙藥,但它是開發更可靠的 AI 識別工具的重要基石。」

The Rise of Content Credentials
內容憑證的興起

Content credentials have been a hot topic for images and video, and have been viewed as one way to combat the rise of deepfakes. Tech companies and major media outlets have joined together in an initiative called C2PA, which has worked out a system for attaching encrypted metadata to image and video files indicating if they’re real or AI-generated. But text is a much harder problem, since text can so easily be altered to obscure or eliminate a watermark. While SynthID-Text isn’t the first attempt at creating a watermarking system for text, it is the first one to be tested on 20 million prompts.
內容憑證一直是圖像和影片的熱門話題,並被視為應對深度偽造興起的一種方法。科技公司和主要媒體機構共同參與了一個名為C2PA的倡議,該倡議制定了一套系統,用於將加密的元數據附加到圖像和影片檔案中,以指示它們是真實的還是 AI 生成的。但文字是一個更棘手的問題,因為文字很容易被修改以隱藏或消除浮水印。雖然 SynthID-Text 並不是第一個嘗試為文字創建浮水印系統的嘗試,但它是第一個在 2000 萬個提示上進行測試的系統。

Outside experts working on content credentials see the DeepMind research as a good step. It “holds promise for improving the use of durable content credentials from C2PA for documents and raw text,” says Andrew Jenks, Microsoft’s director of media provenance and executive chair of the C2PA. “This is a tough problem to solve, and it is nice to see some progress being made,” says Bruce MacCormack, a member of the C2PA steering committee.
從事內容憑證研究的外部專家認為 DeepMind 的研究是一個良好的開端。微軟媒體溯源總監兼 C2PA 執行主席Andrew Jenks表示,它「有望改善 C2PA 持久性內容憑證在文件和原始文字中的應用」。C2PA 指導委員會成員Bruce MacCormack表示:「這是一個很難解決的問題,很高興看到取得了一些進展。」

How Google’s Text Watermarks Work
Google 文字浮水印的工作原理

SynthID-Text works by discreetly interfering in the generation process: It alters some of the words that a chatbot outputs to the user in a way that’s invisible to humans but clear to a SynthID detector. “Such modifications introduce a statistical signature into the generated text,” the researchers write in the paper. “During the watermark detection phase, the signature can be measured to determine whether the text was indeed generated by the watermarked LLM.”
SynthID-Text 通過巧妙地干預生成過程來運作:它以對人類不可見但對 SynthID 偵測器清晰可見的方式,更改聊天機器人輸出給使用者的一些詞語。「這種修改會在生成的文字中引入統計學上的特徵」,研究人員在論文中寫道。「在浮水印檢測階段,可以測量該特徵以確定文字是否確實是由帶有浮水印的LLM生成的。」

The LLMs that power chatbots work by generating sentences word by word, looking at the context of what has come before to choose a likely next word. Essentially, SynthID-Text interferes by randomly assigning number scores to candidate words and having the LLM output words with higher scores. Later, a detector can take in a piece of text and calculate its overall score; watermarked text will have a higher score than non-watermarked text. The DeepMind team checked their system’s performance against other text watermarking tools that alter the generation process, and found that it did a better job of detecting watermarked text.
驅動聊天機器人的LLMs通過逐字生成句子來運作,它會查看之前出現的內容的上下文,以選擇一個可能的下一個詞。基本上,SynthID-Text 通過隨機為候選詞分配數字分數,並讓LLM輸出分數較高的詞語來進行干預。稍後,偵測器可以接收一段文字並計算其總分;帶有浮水印的文字的分數將高於未帶有浮水印的文字。DeepMind 團隊將其系統的效能與其他改變生成過程的文字浮水印工具進行了比較,發現它在檢測帶有浮水印的文字方面做得更好。

However, the researchers acknowledge in their paper that it’s still easy to alter a Gemini-generated text and fool the detector. Even though users wouldn’t know which words to change, if they edit the text significantly or even ask another chatbot to summarize the text, the watermark would likely be obscured.
然而,研究人員在論文中承認,修改 Gemini 生成的文字並愚弄偵測器仍然很容易。即使使用者不知道該更改哪些字詞,如果他們大幅修改文字,甚至請另一個聊天機器人摘要文字,水印很可能會被模糊掉。

Testing Text Watermarks at Scale
大規模測試文字水印

To be sure that SynthID-Text truly didn’t make chatbots produce worse responses, the team tested it on 20 million prompts given to Gemini. Half of those prompts were routed to the SynthID-Text system and got a watermarked response, while the other half got the standard Gemini response. Judging by the “thumbs up” and “thumbs down” feedback from users, the watermarked responses were just as satisfactory to users as the standard ones.
為確保 SynthID-Text 確實沒有讓聊天機器人產生更差的回應,研究團隊在提供給Gemini的 2000 萬個提示上測試了它。其中一半的提示被路由到 SynthID-Text 系統並獲得帶有水印的回應,而另一半則獲得標準的 Gemini 回應。根據使用者的「讚」和「踩」回饋,帶有水印的回應與標準回應一樣令人滿意。

Which is great for Google and the developers building on Gemini. But tackling the full problem of identifying AI-generated text (which some call AI slop) will require many more AI companies to implement watermarking technologies—ideally, in an interoperable manner so that one detector could identify text from many different LLMs. And even in the unlikely event that all the major AI companies signed on to some agreement, there would still be the problem of open-source LLMs, which can easily be altered to remove any watermarking functionality.
這對 Google 和在 Gemini 上開發的開發人員來說是很棒的。但要解決識別 AI 生成的文字(有些人稱之為AI 廢料)的完整問題,需要更多 AI 公司實施水印技術——理想情況下,以互操作的方式,以便一個偵測器可以識別來自許多不同LLMs的文字。即使所有主要 AI 公司都不太可能簽署任何協議,仍然存在開源LLMs的問題,這些開源模型很容易被修改以移除任何水印功能。

MacCormack of C2PA notes that detection is a particular problem when you start to think practically about implementation. “There are challenges with the review of text in the wild,” he says, “where you would have to know which watermarking model has been applied to know how and where to look for the signal.” Overall, he says, the researchers still have their work cut out for them. This effort “is not a dead end,” says MacCormack, “but it’s the first step on a long road.”
C2PA 的 MacCormack 指出,當你開始實際考慮實施時,偵測是一個特殊的問題。「在現實環境中審查文字存在挑戰,」他說,「你必須知道已應用哪個水印模型,才能知道如何以及在哪裡尋找信號。」總體而言,他說,研究人員的工作仍然任重道遠。MacCormack 說,這項努力「並非死胡同」,「但這是漫漫長路上的第一步」。

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Shipt’s Algorithm Squeezed Gig Workers. They Fought Back
Shipt 的演算法壓榨零工經濟的勞工。他們奮起反抗

When their pay suddenly dropped, delivery drivers audited their employer
當他們的薪資突然下降時,外送員們對他們的雇主展開了審計

11 min read

閱讀時間:11 分鐘
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Shipt’s Algorithm Squeezed Gig Workers. They Fought Back
Mike McQuade
DarkBlue1

In early 2020, gig workers for the app-based delivery company Shipt noticed something strange about their paychecks. The company, which had been acquired by Target in 2017 for US $550 million, offered same-day delivery from local stores. Those deliveries were made by Shipt workers, who shopped for the items and drove them to customers’ doorsteps. Business was booming at the start of the pandemic, as the COVID-19 lockdowns kept people in their homes, and yet workers found that their paychecks had become…unpredictable. They were doing the same work they’d always done, yet their paychecks were often less than they expected. And they didn’t know why.
2020 年初,為基於應用程式的送貨公司Shipt工作的零工族注意到他們的薪資單出現了一些奇怪的現象。這家公司於 2017 年以 5.5 億美元的價格被 Target 收購,提供來自當地商店的當天送達服務。這些送貨是由 Shipt 的員工完成的,他們負責採購商品並將其送到顧客家門口。疫情爆發初期,業務蓬勃發展,因為 COVID-19 的封鎖措施讓民眾待在家中,然而,員工卻發現他們的薪資單變得……不可預測。他們做的工作和以往一樣,但薪資卻常常低於預期。他們不知道為什麼。

On Facebook and Reddit, workers compared notes. Previously, they’d known what to expect from their pay because Shipt had a formula: It gave workers a base pay of $5 per delivery plus 7.5 percent of the total amount of the customer’s order through the app. That formula allowed workers to look at order amounts and choose jobs that were worth their time. But Shipt had changed the payment rules without alerting workers. When the company finally issued a press release about the change, it revealed only that the new pay algorithm paid workers based on “effort,” which included factors like the order amount, the estimated amount of time required for shopping, and the mileage driven.
Facebook和 Reddit 上,員工們互相交流資訊。之前,他們知道自己的薪資是多少,因為 Shipt 有一個公式:它給員工每筆送貨 5 美元的底薪,加上客戶通過應用程式訂購總額的 7.5%。這個公式讓員工可以查看訂單金額,並選擇值得他們花時間的工作。但 Shipt 在沒有通知員工的情況下更改了支付規則。當公司最終發布新聞稿說明更改時,只透露新的薪資演算法根據「努力程度」支付員工薪資,其中包括訂單金額、預估購物所需時間和行駛里程等因素。

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U.S. Chip Revival Plan Chooses Sites
美國晶片復興計劃選址

Albany and Silicon Valley land two of three National Semiconductor Technology Center locations
阿爾巴尼和矽谷獲得三個國家半導體技術中心中的兩個據點

4 min read

2024 年 11 月 5 日 閱讀時間:4 分鐘
Several individuals in full-body cleanroom suits work in a brightly-lit white environment with computers and machines

The NSTC's EUV center will be at the Albany Nanotech Complex, where IBM already does lithography research.

IBM

國家半導體技術中心 (NSTC) 的極紫外光 (EUV) 中心將設於奧爾巴尼奈米科技園區,IBM 已在此進行光刻研究。

IBM

Last week the organization tasked with running the the biggest chunk of U.S. CHIPS Act’s US $13 billion R&D program made some significant strides: The National Semiconductor Technology Center (NSTC) released a strategic plan and selected the sites of two of three planned facilities and released a new strategic plan. The locations of the two sites—a “design and collaboration” center in Sunnyvale, Calif., and a lab devoted to advancing the leading edge of chipmaking, in Albany, N.Y.—build on an existing ecosystem at each location, experts say. The location of the third planned center—a chip prototyping and packaging site that could be especially critical for speeding semiconductor startups—is still a matter of speculation.
上週,負責執行美國晶片法案 130 億美元研發計畫中最大一部分的組織取得重大進展:國家半導體技術中心 (NSTC)發布了一項戰略計畫,並選定了三個規劃中的設施中的兩個設施的所在地,並發布了一項新的戰略計畫。這兩個地點——位於加州森尼維爾的「設計與合作」中心,以及位於紐約奧爾巴尼的一個致力於推進晶片製造尖端技術的實驗室——都依託於每個地點現有的生態系統,專家表示。第三個規劃中的中心——一個晶片原型設計和封裝中心,對於加速半導體新創公司發展可能尤其重要——其地點仍在推測之中。

“The NSTC represents a once-in-a-generation opportunity for the U.S. to accelerate the pace of innovation in semiconductor technology,” Deirdre Hanford, CEO of Natcast, the nonprofit that runs the NSTC centers, said in a statement. According to the strategic plan, which covers 2025 to 2027, the NSTC is meant to accomplish three goals: extend U.S. technology leadership, reduce the time and cost to prototype, and build and sustain a semiconductor workforce development ecosystem. The three centers are meant to do a mix of all three.
Natcast 首席執行長、負責管理 NSTC 中心之非營利組織的德麗德·漢福德在一份聲明中表示:「NSTC 代表著美國加速半導體技術創新速度千載難逢的機會。」根據涵蓋 2025 年至 2027 年的戰略規劃,NSTC 的目標是實現三個目標:保持美國技術領先地位、縮短原型設計時間和成本,以及建立和維持半導體人才發展生態系統。這三個中心旨在兼顧這三個目標。

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NYU Researchers Develop New Real-Time Deepfake Detection Method
紐約大學研究人員開發出新的即時深度偽造檢測方法

Chinmay Hegde is exploring challenge-response systems for detecting audio and video deepfakes
Chinmay Hegde 正在探索用於檢測音訊和視訊深度偽造的挑戰-響應系統

5 min read

2024 年 10 月 28 日 閱讀時間:5 分鐘
A photo of a face on a computer monitor with a series of lines on the face.

Deepfake video and audio is powerful in the hands of bad actors. NYU Tandon researchers are developing new techniques to combat deepfake threats.

NYU Tandon

惡意人士掌握深度偽造影音技術後,其威力不容小覷。紐約大學坦登工程學院的研究人員正研發新的技術來應對深度偽造的威脅。

紐約大學坦登工程學院

This sponsored article is brought to you by NYU Tandon School of Engineering.
本贊助文章由紐約大學坦登工程學院提供。

Deepfakes, hyper-realistic videos and audio created using artificial intelligence, present a growing threat in today’s digital world. By manipulating or fabricating content to make it appear authentic, deepfakes can be used to deceive viewers, spread disinformation, and tarnish reputations. Their misuse extends to political propaganda, social manipulation, identity theft, and cybercrime.
深度偽造技術利用人工智慧創造出逼真的影音內容,對當今數位世界構成日益嚴重的威脅。透過操縱或捏造內容使其看似真實,深度偽造技術可用於欺騙觀眾、散播不實訊息以及損害名譽。其濫用範圍涵蓋政治宣傳、社會操控、身份竊盜和網路犯罪。

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Ansys SimAI Software Predicts Fully Transient Vehicle Crash Outcomes
Ansys SimAI 軟體預測全瞬態車輛碰撞結果

Crash Test Prediction at the Speed of AI
AI 速度的碰撞測試預測

1 min read

2024 年 9 月 27 日 閱讀時間:1 分鐘

The Ansys SimAI™ cloud-enabled generative artificial intelligence (AI) platform combines the predictive accuracy of Ansys simulation with the speed of generative AI. Because of the software’s versatile underlying neural networks, it can extend to many types of simulation, including structural applications.
Ansys SimAI™ 雲端啟用生成式人工智慧 (AI) 平台,結合了 Ansys 模擬的預測準確度與生成式 AI 的速度。由於該軟體具有多功能的底層神經網絡,因此它可以應用於許多類型的模擬,包括結構應用。

This white paper shows how the SimAI cloud-based software applies to highly nonlinear, transient structural simulations, such as automobile crashes, and includes:
本白皮書展示了基於雲端的 SimAI 軟體如何應用於高度非線性、瞬態結構模擬,例如汽車碰撞,並包含:

  • Vehicle kinematics and deformation
    車輛運動學和變形
  • Forces acting upon the vehicle
    作用於車輛的力
  • How it interacts with its environment
    車輛與其環境的交互作用
  • How understanding the changing and rapid sequence of events helps predict outcomes
    如何理解事件變化的快速序列有助於預測結果

These simulations can reduce the potential for occupant injuries and the severity of vehicle damage and help understand the crash’s overall dynamics. Ultimately, this leads to safer automotive design.
這些模擬可以降低乘員受傷的可能性和車輛損壞的嚴重程度,並有助於了解碰撞的整體動力學。最終,這將導致更安全的汽車設計。

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Space-Based Sensor Captures Lightning Data in a Bottle
太空感測器捕捉瓶中閃電數據

The Falcon Neuro imager is plugging gaps in lightning research
鷹眼神經影像儀填補了閃電研究的空白

4 min read

2024 年 11 月 5 日 閱讀時間:4 分鐘
A squarish event-based optical sensor attached to the bottom of a payload in outer space, with Earth visible at the bottom.

Falcon Neuro (located on the left side of this instrument payload on the International Space Station) can spot individual lightning strikes with high precision.


鷹眼神經影像儀(位於國際太空站此儀器酬載的左側)能夠以高精度偵測單個閃電擊。

Lightning is one of the most common natural hazards on Earth, and our warming planet is just beginning to feel the effects of a future with more severe thunderstorms and increased lightning strikes. But there’s a lot that atmospheric scientists don’t understand about how lightning works. Better lightning data could improve severe weather forecasts and warnings, and could help researchers to understand where hazards will increase in the future—and the associated impacts such as wildfires and need for lightning-proofed infrastructure.
閃電是地球上最常見的自然災害之一,而我們日益暖化的地球才正開始感受到未來更多嚴重雷暴和閃電擊次數增加的影響。但大氣科學家對閃電的運作方式仍有很多不了解之處。更完善的閃電數據可以改善惡劣天氣預報和警報,並能幫助研究人員了解未來災害將在哪裡加劇——以及相關的影響,例如野火和對防雷基礎設施的需求。

A unique optical sensor that just spent two years on the International Space Station could help fill those gaps. Researchers at Western Sydney University, supported by the U.S. Air Force Research Lab, demonstrated the use of an event-based vision sensor (EBVS) to record lightning strike details from above—at lower cost, higher resolution, and lower data rates than before. “The technology is based on how biology works and can see things that a normal camera can’t,” says Gregory Cohen, the deputy director of Western Sydney University’s International Centre for Neuromorphic Systems.
一種獨特的感光感測器,剛在國際太空站度過了兩年時間,或許有助於填補這些空白。西悉尼大學的研究人員在美國空軍研究實驗室的支持下,展示了使用基於事件的視覺感測器 (EBVS)從上方記錄閃電擊細節的方法——成本更低、解析度更高、數據速率更低。「這項技術基於生物運作方式,可以看到普通相機看不到的東西,」西悉尼大學神經形態系統國際中心的副主任葛瑞格里·柯恩說道。

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將最新的科技新聞郵寄到您的收件匣

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請從列表中選擇訂閱 IEEE Spectrum 電子報。

Do We Dare Use Generative AI for Mental Health?
我們敢將生成式 AI 用於心理健康嗎?

Woebot, a mental-health chatbot, is testing it out
Woebot,一款心理健康聊天機器人,正在測試它

11 min read

閱讀時間:11 分鐘
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Eddie Guy
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The mental-health app Woebot launched in 2017, back when “chatbot” wasn’t a familiar term and someone seeking a therapist could only imagine talking to a human being. Woebot was something exciting and new: a way for people to get on-demand mental-health support in the form of a responsive, empathic, AI-powered chatbot. Users found that the friendly robot avatar checked in on them every day, kept track of their progress, and was always available to talk something through.
心理健康應用程式Woebot於 2017 年推出,當時「聊天機器人」一詞並不常見,尋求治療師幫助的人只能想像與真人交談。Woebot 當時令人興奮且新穎:它提供一種以回應式、感同身受的 AI 驅動聊天機器人的形式,讓使用者隨時獲得心理健康支援。使用者發現,友善的機器人化身每天都會關心他們,追蹤他們的進度,並且隨時準備好與他們一起解決問題。

Today, the situation is vastly different. Demand for mental-health services has surged while the supply of clinicians has stagnated. There are thousands of apps that offer automated support for mental health and wellness. And ChatGPT has helped millions of people experiment with conversational AI.
如今,情況大不相同。對心理健康服務的需求激增,而臨床醫生的人數卻停滯不前。數千個應用程式提供自動化的心理健康和身心健康支援。而ChatGPT已幫助數百萬人體驗對話式 AI。

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Oceans Lock Away Carbon Slower Than Previously Thought
海洋封存碳的速度比先前預想的慢

A low-budget rotating gravity machine reveals potential kink in climate models
一台低成本的旋轉重力機揭示了氣候模型中潛在的缺陷

5 min read

2024 年 11 月 4 日 閱讀時間:5 分鐘
Overhead view of strong sea waves.
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Research expeditions conducted at sea using a rotating gravity machine and microscope found that the Earth’s oceans may not be absorbing as much carbon as researchers have long thought.
利用旋轉重力機和顯微鏡進行的海上研究考察發現,地球的海洋可能吸收的碳量不如研究人員長期以來所認為的那麼多。

Oceans are believed to absorb roughly 26 percent of global carbon dioxide emissions by drawing down CO2 from the atmosphere and locking it away. In this system, CO2 enters the ocean, where phytoplankton and other organisms consume about 70 percent of it. When these organisms eventually die, their soft, small structures sink to the bottom of the ocean in what looks like an underwater snowfall.
海洋據信吸收了全球二氧化碳排放量的約26%,方法是將大氣中的 CO2吸收並封存。在此系統中,CO2進入海洋,浮游植物和其他生物消耗了其中的約70%。當這些生物最終死亡時,它們柔軟細小的結構會像水下降雪般沉入海底。

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Sydney’s Tech Super-Cluster Propels Australia’s AI Industry Forward
雪梨科技超級集群推動澳洲人工智慧產業向前發展

With significant AI research and commercialization, Sydney emerges as a leader in the global AI landscape
雪梨憑藉其顯著的人工智慧研究和商業化成果,在全球人工智慧領域中脫穎而出,成為領導者。

4 min read

2024 年 8 月 24 日 閱讀時間:4 分鐘
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The AI Institute at UNSW Sydney is “a front door to industry and government, to help translate the technology out of the laboratory and into practice,” says Toby Walsh, Scientia Professor of Artificial Intelligence at the University of New South Wales (UNSW Sydney).

UNSW

新南威爾斯大學雪梨分校(UNSW Sydney)人工智慧研究所的科學家托比·沃爾什教授表示:「該研究所是產業和政府的『前沿門戶』,有助於將技術從實驗室轉化為實際應用。」

UNSW

This is a sponsored article brought to you by BESydney.

Australia has experienced a remarkable surge in AI enterprise during the past decade. Significant AI research and commercialization concentrated in Sydney drives the sector’s development nationwide and influences AI trends globally. The city’s cutting-edge AI sector sees academia, business and government converge to foster groundbreaking advancements, positioning Australia as a key player on the international stage.

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Transformative Power of GenAI in Securing Autonomous Systems and Edge Robotics

Unlocking the future: Enhancing security and resilience in edge robotics with generative AI

1 min read

Rapid advances in autonomous systems and edge robotics have unlocked unprecedented opportunities in industries from manufacturing and transportation to healthcare and exploration.

Increasing complexity and connectivity have also introduced new security, resilience, and safety challenges. As edge robots integrate into our daily lives and critical infrastructures, developing innovative approaches to improve these systems' trustworthiness and reliability is mandatory.

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Wireless Signals That Predict Flash Floods

Hagit Messer-Yaron’s algorithm uses cellular networks to collect weather data

6 min read
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IEEE Life Fellow Hagit Messer-Yaron received the 2024 IEEE Medal for Environmental and Safety Technologies.

Hagit Messer-Yaron

Like many innovators, Hagit Messer-Yaron had a life-changing idea while doing something mundane: Talking with a colleague over a cup of coffee. The IEEE Life Fellow, who in 2006 was head of Tel Aviv University’s Porter School of Environmental Studies, was at the school’s cafeteria with a meteorological researcher. He shared his struggles with finding high-resolution weather data for his climate models, which are used to forecast and track flash floods.

Predicting floods is crucial for quickly evacuating residents in affected areas and protecting homes and businesses against damage.

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