MDDI 演講稿 · 2025-10-02
楊莉明部長在新加坡理工大學與輝達 AI 中心開幕式上的演講
要點
- • 我們追求的不是「為 AI 而 AI」——是 AI 如何改善生活。NAIS2.0 啟動 18 個月以來已建立近 50 個 AI CoE。
- • Razer 的 AI QA Companion 把遊戲 QA 時間砍半；Grab 的 AI 不只為內部生產力，還助力商家做生意。
- • MIT 研究：放射科醫生使用 CheXpert AI 工具時，診斷準確率反而下降——AI 工具單獨使用時效能優於 2/3 的醫生，但與人協作時設計不佳就會拖累專家判斷。
- • 「不要把第一直覺放在『讓 AI 強到取代人』，而要放在『讓 AI 強到能增強每個人』」。AI 應作為「隊友」（teammate），不是替代品。
- • 三條人才路徑：①擴充套件 AI 實務者（SNAIC AI Programme 用 NVIDIA DLI 課程，3 年培訓 200+）；②培育「AI 雙語者」（domain expert 學 AI 這門「第二語言」）——SIT 啟動應用 AI 博士培訓中心；③產業 + 研究 + 政府生態合作。SNAIC 18 個月已與 70 家公司交付 50 個方案。
完整譯文（繁體中文）
MDDI 英文原文譯文 · 翻譯日期: 2026-05-02
新加坡理工大學（SIT）董事會主席 Bill Chang 先生，
SIT 校長 Chua Kee Chaing 教授，
SIT-NVIDIA AI 中心聯合主任 Ng Aik Beng 博士與 Daniel Wang 博士，
產業夥伴與各位同事：
早安。我很高興回到 SIT——出席 SNAIC 的正式啟用儀式。
你們聽 AI 聽得也許已經發膩了。
「一切都關乎 AI 嗎？」
「除了 AI 就沒別的了嗎？」
我先說一句——我們不是「為 AI 而 AI」。
我們更感興趣的是——這項技術如何能改善生活——無論是為個人，還是通過組織。
如果只談「AI 多偉大」，但它不在真實世界裡帶來收益——那就沒有用。
你們應該聽過黃循財總理在最近的「國慶群眾大會」（NDR）上分享的——AI 如何在新加坡被部署來支援公民、把握機會的幾個例子。
在我們啟動更新版《國家 AI 戰略》（NAIS2.0）後的 18 個月裡，已經建立了近 50 個 AI 卓越中心（CoE），還有更多例子。
我一直在走訪這些 AI CoE——理解 AI 在組織里的影響——令我印象深刻的是它如何被用來提升生產力、為我們的人擴充套件機會。
我分享幾個最近的例子。
雷蛇（Razer）的 AI QA Companion 幾乎把新遊戲 QA 測試與缺陷檢測的耗時砍半。其 AI 套件中的其他工具，也將賦能遊戲開發者及更多人——設計個性化的遊戲體驗。
Grab 把 AI 用於程式碼開發——並且超出內部生產力，幫助平臺上的商家做生意——通過智慧助手提供商業洞察與運營支援。
AI 顯然有一個「競爭維度」——公司想用 AI 取得優勢，國家也一樣。
兩者都會驅動 AI 的採用——但 AI 也應有「協作維度」。
比如 AI 治理——問題眾多，沒有人擁有全部答案。
我剛從紐約的聯合國大會（UNGA）回來——「全球 AI 治理對話」（Global Dialogue on AI Governance）正是在那裡啟動的。
新加坡持續在這種對話中做出一致而堅定的貢獻。
今年早些時候——新加坡承辦了「國際表徵學習大會」（ICLR）——這是 ICLR 在美國之外舉辦過的最大一屆——吸引了 11,000 名代表。
在 ICLR 邊線——我們也舉辦了「新加坡 AI 大會—國際科學交流」（SCAI-ISE）——主題是「安全與負責任 AI 的研究優先事項」。它促成了「新加坡共識」（The Singapore Consensus）——至今仍被關心 AI 治理的人時常提起。
明年 1 月——「人工智慧促進協會」（AAAI）也將在新加坡舉辦年會。
所有這些合作都旨在——找出更好的方式，最大化 AI 部署的上行、最小化下行。
另一個重要的協作領域——是發展我們的人。
聚焦發展技術容易；但聚焦於「構建並使用 AI 的人」往往不夠。
認真考慮「AI 與人類的關係」、以及我們希望兩者如何共事，對我們而言至關重要。
我相信我們的第一直覺不應該是「如何讓 AI 好到取代人」——而應該是「如何讓 AI 好到增強每一個人」。兩者只有一線之差。
MIT 的研究者發現了一件支援我這個信念的事情——
當放射科醫生被允許使用一款叫 CheXpert 的 AI 診斷工具時——他們的診斷準確率反而下降了——儘管這款 AI 工具單獨工作時表現優於 2/3 的放射科醫生。
為什麼這款表現更好的 AI 工具——在被專業人士使用時反而拉低了結果？
問題不在於 AI 的技術能力。
問題很可能是——這款工具不是為「與人類專家協作」而設計的。
它對自己的推理幾乎不透明——以致放射科醫生即便對 CheXpert 的診斷有疑慮，也擱置了自己更好的判斷。
更根本的是——它把專家意見簡化為「影像掃描」——沒有納入醫學史、醫患對話、與其他專家的交流等其他必要考量。
換句話說——我們在思考如何使用 AI 時必須謹慎。
AI 在很多工上確實能高效自動化——價值損失不大或為零。這種情況下，自動化是合理的。
但這並非鐵律。某些情形下——AI 不夠好，為了「最後一點點效能提升以替代人」而追求，是沒意義的。
在那些情形下——我們應當自律地把 AI 當作「與人協作」的工具——讓兩者作為一個整體共事。
這就是我最近談到的「AI 作為隊友」（AI as a teammate）的理念——AI 工具增強人的表現、AI 助手執行與人互補的任務。
我們怎樣才能做到這一點？
擴大 AI 實務人才管線
第一——繼續擴大 AI 實務者池子。這些人具備資料科學、機器學習等深厚技術能力。
在生成式 AI 時代——他們也將具備構建或微調大模型（LLMs）、利用「檢索增強生成」（RAG）來編排 AI 智慧體團隊的能力。
更多 AI 實務者——意味著我們部署「AI 作為隊友」的容量更大。
今天——我很高興宣佈——一項新的「SNAIC AI 計劃」將直接為此目標做貢獻。
在 IMDA「TechSkills Accelerator」（TeSA）的支援下——這一計劃將在未來 3 年內培訓 200 多名應屆生與中職轉型者——圍繞先進 AI 系統與 AI 應用構建。
學員將以兩個月的密集升級開始——基於 NVIDIA Deep Learning Institute 的課程。
隨後是 4 個月由導師指導的產業實操專案——結業後能為產業做出有意義的貢獻。
這將進一步壯大我們正通過其他既有專案培育的 AI 實務者池。
培育跨學科的「AI 雙語者」
新加坡的「AI 夢之隊」不會只有 AI 專家——還需要我所謂的「AI 雙語者」（AI bilingualists）。
他們是放射科醫生、會計師、技師、律師、創作者等領域專家——已經精通自身領域，也就是他們的「母語」。
他們能為團隊帶來寶貴知識——幫助團隊把 AI 用得好——他們能提供資料科學家與機器學習工程師還不具備的語境與洞察。
為了讓這支隊伍合作良好——這些領域專家最好學一門「共同語言」——也就是 AI 這門「語言」。
新加坡人憑經驗都知道——學兩門語言並不容易——但並非不可能。
「一起學、彼此練」更有趣。許多人能用「會話級」就過得去——有些人需要能讀、能寫。
再小一群人，會成為兩門語言的「大師」。
我們的 AI 生態需要不同精熟度的「AI 雙語者」——我們也在系統性地尋找發展他們的機會。
所以我很興奮——SIT 率先啟動「應用 AI 博士培訓中心」（Doctoral Training Centre for Applied AI），培育最高精熟度的「AI 雙語者」。
這是計劃中一系列博士培訓中心的第一個——為已是領域或職能專家的新加坡人，提供同時成為 AI 應用專家的機會。
後續中心將聚焦在新加坡的優先行業——如海事與醫療——培育更多能為創新做貢獻的「AI 雙語者」。
圍繞 AI 部署構建生態級合作
最後——讓我談談另一種同樣重要的合作——產業、研究社群與政府之間的合作。
SNAIC 是個好例子。
通過把 SIT 在應用 AI 與「轉化研究」上的強項，與 NVIDIA 的前沿技術與專業能力結合——這個中心幫助大小企業理解、測試、開發並落地 AI 方案——覆蓋廣泛的行業。
在中心成立後的 1.5 年裡——SNAIC 已與 70 家公司合作——在製造、醫療、金融、交通行業交付了 50 個產生真實業務影響的 AI 方案。
比如——這個中心與陳篤生醫院合作開發協同 AI 工具，幫助臨床醫生交付更快、更準確的護理。
AI 工具幫助縮短了病人等待時間——並在每位病人康復的旅程中提供更好支援。
展望未來
在今天 SIT-NVIDIA AI 中心正式啟用之際——我感謝我們的產業夥伴、研究者與教員對新加坡 AI 發展的承諾與貢獻。
我也感到鼓舞——來自世界各地的夥伴願與新加坡一起在 AI 上創新——歡迎各位繼續與我們的 AI 生態合作。
攜手同行——我相信我們能實現「AI 服務公共利益——為新加坡，也為世界」的願景。
再次感謝。
英文原文
MDDI 官網原始記錄 · 抓取日期: 2026-05-02
Singapore Institute of Technology (SIT) Board of Trustees Chairman, Mr Bill Chang
SIT President, Professor Chua Kee Chaing,
SIT-NVIDIA AI Centre Co-Directors, Dr. Ng Aik Beng and Dr. Daniel Wang
Industry partners and Colleagues,
Good morning. I’m happy to be back at SIT to join you for the formal launch of SNAIC.
You may be sick of hearing about AI by now.
Is everything about AI?
Is there nothing else besides AI?
Let me start by saying we aren’t pursuing AI for AI’s sake.
We are far more interested in how this technology can be used to improve lives, whether for individuals or through organisations.
There is no use if we talk about how great AI is, if it isn’t bringing benefits in the real world.
You would have heard Prime Minister Wong share at the recent National Day Rally (NDR), some examples of how AI is being deployed in Singapore to best support our citizens and make the most of opportunities.
There are many more examples supported by the almost 50 AI Centres of Excellence (CoEs) that have been set up in the last 18 months since we launched the refreshed National AI Strategy (NAIS2.0).
I have been visiting these AI CoEs to understand the impact of AI within organisations, and I’m impressed by how it is being used to raise productivity and expand opportunities for our people.
Let me share some recent examples.
Razer’s AI-driven Quality Assurance (QA) Companion almost halves the time required for new games’ QA testing and bug detection. Other tools in its AI suite will also empower game developers and many others to design personalised gaming experiences.
Grab is using AI for code development. It is also going beyond internal productivity to help its platform merchants do business better, with intelligent assistants that provide business insights and operational support.
Clearly, there is a competitive dimension to AI. Companies want to use AI to give themselves an edge, and countries want the same too.
Both will drive the adoption of AI, but there should also be a collaborative dimension to AI.
Take AI governance for instance, where there are a lot of questions and no one has all the answers.
I just returned from the United Nations General Assembly (UNGA) in New York where the Global Dialogue on AI Governance was just launched.
And Singapore continues to be a consistent and steadfast in contributing to conversations like these.
Earlier this year, Singapore hosted the International Conference on Learning Representations (ICLR). It was the biggest that they have ever held outside of the United States and drew 11,000 delegates.
On the sidelines of ICLR, we also hosted the Singapore Conference on AI - International Scientific Exchange (SCAI-ISE) on Research Priorities for Safe and Responsible AI. This led to the formation of “The Singapore Consensus” on what the priorities should be. It is still talked about when I meet members of the community who care a lot about AI governance.
Next January, Association for the Advancement of AI (AAAI) will also have its meeting in Singapore.
All these collaborations seek to promote better ways to maximise the upsides and minimise the downsides of AI deployment.
Another important area for collaboration is in developing our people.
It is easy to focus on developing the technology, and not enough on the people who build and use AI.
It is important for us to carefully consider the relationship between AI and humans and how we want the two to work together.
I believe our first instinct should not be “how to make the AI so good it replaces humans”. Instead, it should be: “how can we make AI so good, that it enhances all humans?” There is just a fine distinction between the two.
Researchers at MIT found something that supports my belief.
When radiologists were allowed to use an AI diagnostic tool called CheXpert, the accuracy of their diagnoses actually declined, even though the AI tool alone performed better in diagnoses than two-thirds of radiologists.
Why did this superior AI tool produce inferior results when used by professionals?
The problem wasn’t with the AI’s technical ability.
The problem was likely because the tool was not designed for collaboration with human experts.
It offered little transparency about its reasoning, leading radiologists to suspend their better judgement even when they had suspicions about CheXpert’s diagnosis.
More fundamentally, it reduced expert opinion to image scanning, failing to incorporate other essential considerations such as medical histories, doctor-patient conversations, and exchanges with other experts.
In other words, we should be careful when we think about how to use AI.
AI will sometimes be very effective in automating tasks performed by humans, with little or no loss of value. Automation makes sense in such cases.
But this is not a given. In some instances, the AI is not good enough and pursuing the last bit of performance improvement just to replace a human makes no sense.
In such cases, we should instead discipline ourselves to think of using AI to collaborate with humans and let the two work together as one.
This is the idea of AI as a teammate which I spoke about recently. Where AI tools enhance human performance, and AI assistants perform tasks that complement humans.
How we can we go about this?
Developing our pipeline of AI practitioners
First , we should continue to grow the pool of AI practitioners. These are people steeped in technical skills like data science and machine learning.
In the era of Generative AI, they will also have the skills to build or fine-tuning large language models (LLMs), or Retrieval-Augmented Generation (RAG) to orchestrate teams of AI Agents.
Having more AI practitioners will expand our capacity for deploying AI as teammates.
Today, I am pleased to announce a new SNAIC AI Programme that will contribute directly to this goal.
Supported by the Infocomm Media Development Authority’s (IMDA) TechSkills Accelerator (TeSA) initiative, the programme will train more than 200 fresh graduates and mid-career professionals over the next three years in advanced AI systems and building AI applications.
Trainees will begin with two months of intensive upskilling based on the NVIDIA Deep Learning Institute’s curriculum.
They will then undertake four months of guided hands-on industry projects, and emerge from the programme ready to contribute meaningfully to industry.
This will add to our growing pool of AI practitioners that we are nurturing from other existing programmes.
Nurturing interdisciplinary “AI Bilingualists”
The AI “dream team” in Singapore will not just have practitioners who are experts in AI. There will also need to be what I have called “AI bilingualists”.
These are domain experts like radiologists, accountants, technicians, lawyers, and creators, who are already knowledgeable in their fields – or their “mother tongues”.
They can thus bring valuable knowledge to help the team make good use of AI. They are able to provide the context and provide insights that the data scientists and machine-learning engineers do not yet possess.
But for this team to work well together, it helps that these domain experts to learn a common language – that is the “language” of AI.
Singaporeans know from experience that learning two languages is not easy, but it is not impossible.
It is more fun to learn together and practise with one another. Many of us will get on well enough with conversational level ability, but some of us will need to read or write.
And a smaller group may become masters of both languages.
Our AI ecosystem will need “AI bilingualists” at different levels of mastery, and we are systematically identifying opportunities to develop them.
So I am excited that SIT is taking the lead to nurture “AI bilingualists” at the highest level of mastery, with a new Doctoral Training Centre for Applied AI.
This is the first of several planned Doctoral Training Centres that will provide Singaporeans who are already domain or function experts, the chance to be experts in applying AI as well.
Subsequent centres will focus on developing more “AI bilingualists” who can contribute to AI innovation in Singapore’s priority sectors, such as maritime and healthcare.
Building ecosystem-wide partnerships for AI deployment
Finally, let me say something about a different but equally important kind of collaboration – between industry, research community, and government.
The SNAIC is a good example of this.
By combining SIT’s strength in applied AI and translational research with NVIDIA’s cutting-edge technology and technical expertise, the centre helps businesses of all sizes understand, testbed, develop, and implement AI solutions across a wide range of sectors.
In the one and a half years since it was established, the SNAIC has worked with 70 companies to deliver 50 AI solutions that create real business impact across the manufacturing, healthcare, finance, and transport sectors.
For instance, the Centre has worked with Tan Tock Seng Hospital to develop collaborative AI tools that support clinicians in delivering faster and accurate care.
AI tools have helped reduce patient waiting times and provide better support for each patient in the journey of recovery.
Looking forward
As we mark the official launch of the SIT-NVIDIA AI Centre today, I want to thank our industry partners, researchers, and faculty members for their commitment and contributions to Singapore’s AI development.
I am encouraged by the partners from around the world who want to work with Singapore on innovating in AI, and invite you to continue collaborating with our AI ecosystem.
Together, I believe that we can realise the vision of AI for the Public Good, for Singapore and the World.
Thank you once again.