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  • [Dylan Ng Terntzer] With our deep learning,

    [Dylan Ng Terntzer] 隨著我們的深度學習。

  • we'll see an object in front of us.

    我們會看到一個物體在我們面前。

  • We need to tell whether she is a human,

    我們需要判斷她是否是人。

  • a pile of bricks, or a chair.

    一堆磚頭,或者一把椅子。

  • And even if you tell it is a human,

    而且就算你告訴它是人。

  • we must think, 'What is a human going to do next?'

    我們必須思考,'人類接下來要做什麼?

  • Are you going to turn left?

    你要向左轉嗎?

  • Are you gonna turn right?

    你要向右轉嗎?

  • Going to jump in front of us?

    要跳到我們前面去嗎?

  • [Robot] Hi, so sorry, but you're in my way.

    嗨,對不起,你擋住我的路了

  • Could you please move?

    你能不能讓開?

  • LionsBot, we make professional cleaning robots

    LionsBot,我們做專業的清潔機器人

  • so the cleaners don't have to work so hard.

    這樣清潔工就不用那麼辛苦了。

  • Singapore is the Lion City,

    新加坡是獅城。

  • so the lion is the emblem of Singapore.

    所以獅子是新加坡的標誌。

  • Hence, our robots are LionsBot,

    是以,我們的機器人是LionsBot。

  • and at the heart of every robot,

    並且是每個機器人的核心。

  • there is one grain of sand from Singapore,

    有一粒沙子來自新加坡。

  • and it brings the love and the technology of Singapore

    它帶來了新加坡的愛和技術。

  • to the rest of the world.

    向世界其他地方。

  • We have multiple sensors, each feeding in information

    我們有多個傳感器,每個傳感器都能提供資訊

  • multiple times a second.

    一秒鐘多次。

  • In robots, anyone can put in a lot of sonars,

    在機器人中,任何人都可以放很多聲納。

  • a lot of sensors, but it is how we use them,

    很多傳感器,但關鍵在於我們如何使用它們。

  • how we make intelligent decisions

    我們如何做出明智的決定

  • with that information that counts.

    有了這些資訊,才是最重要的。

  • [Laurence Liew] Where you are is our AI Singapore office.

    你所在的地方是我們的人工智能新加坡辦公室。

  • Singapore has a long history of willing to spend money

    新加坡歷史悠久,肯花錢的人很多

  • to get its citizens to re-skill, deep-skill,

    以使其公民重新掌握技能,深。

  • or upgrade their skills.

    或提升自己的技能。

  • Our mission, really, is to promote the use of AI,

    我們的使命,其實就是促進人工智能的應用。

  • get more researchers to embark on a career in AI,

    讓更多的研究人員開始從事人工智能事業。

  • to do AI research.

    來做人工智能研究。

  • We have one very popular program.

    我們有一個非常受歡迎的項目。

  • We call the AI for everyone,

    我們叫大家的人工智能。

  • and the intent is to demystify AI for the man in the street,

    並意圖為大街上的人解開人工智能的神祕面紗。

  • for everyone in that sense.

    從這個意義上講,對大家來說。

  • When the audience walked out of the auditorium,

    當觀眾走出禮堂。

  • they say, 'Ah, OK, AI is not so scary.'

    他們說:"啊,好吧,人工智能沒那麼可怕。

  • AI is actually nothing more than just another piece of code,

    人工智能其實不過是另一段代碼而已。

  • obviously very sophisticated code,

    顯然是非常複雜的代碼。

  • but it is just another IT system or infrastructure.

    但它只是另一個IT系統或基礎設施。

  • [Annabelle Kwok] Hi, I'm Annabelle.

    嗨,我是Annabelle。

  • I founded NeuralBay, which is a software AI company

    我創立了NeuralBay,這是一家軟件AI公司。

  • that looks into image and video processing.

    研究影像和視頻處理的。

  • So I was very lucky to be in Singapore

    所以我很幸運地來到了新加坡

  • where the hackathon scene was slowly starting,

    在那裡,黑客馬拉松的場景正在慢慢開始。

  • and it was still kind of ahead of its time.

    而且它還是一種超前的時代。

  • So when this whole field of image processing came up,

    所以當整個圖像處理這個領域出現的時候。

  • I think that opened a lot of doors for opportunities

    我想這為我們打開了很多機會之門。

  • to not just analyze still photos

    不僅僅是分析靜態照片

  • but also to look at real-life events.

    但也要看看現實生活中的事件。

  • So for example, in traffic flow management in crowded areas,

    所以,比如在人流密集區的交通流管理中。

  • you can help to better direct human traffic.

    你可以幫助更好地引導人流。

  • So we're in our office, and we have a lot

    所以,我們在我們的辦公室,我們有很多。

  • of people walking around.

    的人走來走去。

  • So what we can do with this software is that we can count

    所以我們可以用這個軟件做的是,我們可以算出

  • the number of people in this area,

    這方面的人數。

  • as well as to track their movements.

    以及跟蹤他們的行動。

  • So in recognizing people, it's a very tough problem

    所以在認識人的時候,這是一個非常棘手的問題

  • because when they look away,

    因為當他們看走眼時,

  • can you still recognize that it's the same person?

    你還能認出是同一個人嗎?

  • So Zeldon, if you can just turn around very gracefully.

    所以Zeldon 如果你能很優雅地轉過身來的話

  • So you can see that in this software,

    所以你可以在這個軟件中看到。

  • it still tracks that Zeldon is the same person.

    它仍然跟蹤Zeldon是同一個人。

  • [Laurence] I think when we design AI systems

    我認為當我們設計人工智能系統時

  • or any smart city technology,

    或任何智慧城市技術。

  • ultimately the question to ask is

    歸根結底,要問的是

  • how will it affect the citizen in the country?

    它將如何影響國內的公民?

  • We do have several healthcare related AI projects

    我們確實有幾個醫療相關的人工智能項目

  • that are undergoing, and I think there's lots

    正在進行的,我認為有很多的。

  • of interesting areas where AI could be used in education.

    的有趣領域,人工智能在教育中的應用。

  • When we launched AI for Everyone,

    當我們推出AI for Everyone時。

  • the original target was 10,000 Singaporeans

    原定目標是1萬名新加坡人

  • to be trained in three years.

    將在三年內接受培訓。

  • 1-1/2 years down the road, we are already at 7,000.

    1年半下來,我們已經達到了7000人。

  • I told my team can we do 100,000?

    我跟我的團隊說我們能不能做到10萬?

  • Let's go from 10 to 100, all right?

    讓我們從10到100,好嗎?

  • Training the people, the apprentice,

    培養人,徒弟。

  • they again, at eight or nine months,

    他們又在八九個月時。

  • they will go out to the industry.

    他們將走出去的行業。

  • There is an economic implication in that.

    這裡面有經濟上的含義。

  • [Dylan] Singapore has a wide pool of talented engineers.

    [Dylan]新加坡有大量的天才工程師。

  • The government has spent a lot of money developing

    政府花了很多錢開發

  • and training these engineers,

    並培訓這些工程師。

  • so with such a big latency pool of people

    所以,在這麼大的延遲池的人。

  • that we can tap on, why not build in Singapore?

    我們可以利用的,為什麼不在新加坡建設?

  • [Annabelle] In terms of the software,

    [安娜貝爾]在軟件方面:

  • I think the next step would be accessibility to more data

    我認為下一步將是獲取更多數據的能力

  • and also the diversity of data available.

    以及現有數據的多樣性。

  • Most of the open-source data is from the West

    大部分的開源數據都來自於西

  • and not necessarily from Southeast Asia

    不一定是東南亞人

  • because Southeast Asian countries may not necessarily

    因為東南亞國家不一定

  • have the infrastructure to capture that data.

    具備獲取這些數據的基礎設施;

  • So recognizing a woman of color within Southeast Asia,

    所以在東南亞範圍內認識到一個有色人種的女性。

  • the confidence interval might not be as high

    信心區間可能沒有那麼高

  • as recognizing one from the West.

    作為承認一個來自西方的。

  • By doing it and making it available

    通過做和提供

  • for the small enterprises, hopefully that might help correct

    為小企業服務,希望這可能有助於糾正錯誤。

  • some of the cultural bias in technology.

    技術中的一些文化偏見。

  • We don't always have to give back

    我們不一定要回報

  • in terms of time and money,

    在時間和金錢方面。

  • but we can also give back in terms of knowledge and skills.

    但我們也可以用知識和技能來回饋。

  • As for myself, I'm good at building things,

    至於我自己,我很擅長造東西。

  • so why not build things to help people?

    那為什麼不造東西來幫助人們呢?

[Dylan Ng Terntzer] With our deep learning,

[Dylan Ng Terntzer] 隨著我們的深度學習。

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B1 中級 中文 人工 新加坡 智能 機器人 軟件 ai

新加坡如何為人工智能注入新的活力? (How Singapore Is Putting A New Face on AI)

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    林宜悉 發佈於 2020 年 11 月 09 日
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