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  • You ready to start right now?

    準備好要開始了嗎?

  • Oh yeah, yeah. Yeah, yeah. Thank you.

    喔對,對。謝謝

  • [MUSIC]

    [音樂]

  • There have been a number of shifts in the way we think about computing

    過去數十年來,我們對運算模式的思維

  • over the past few decades.

    已有許多改變

  • The terminology artificial intelligence has come in and out of favor in the scientific community.

    「人工智慧」這個術語在科學界爭議不斷

  • Sometimes it's called machine learning.

    有時我們稱之為「機器學習」

  • We tend to call it machine intelligence these days.

    最近傾向以「機器智能」稱呼

  • I just call it intelligence.

    我都叫它「智能」

  • And sometimes it's just the effort to build machines that are better.

    有時只是傾力打造更好的機器

  • So in the early days

    在早期

  • everything was built on logic.

    一切運算以邏輯式建立

  • Doing mathematical integration problems. Playing chess.

    處理數學整合問題,下棋

  • But we realized that what the real challenges were were the things

    但我們發現它真正的挑戰在於那些

  • that people can do every day.

    人們每天都可以做到的小事情

  • The real world is actually very messy. Hard logical rules are not the way to solve

    真實的世界是非常凌亂的,用複雜的邏輯運算也不能解決

  • really interesting real world problems.

    這個有趣現實世界的問題

  • You have to have a system that will learn to get the knowledge in. You can't just

    你必須有個會學習知識的系統,而不是

  • program it all in.

    將所有問題寫進程式裡

  • Artificial intelligence is an effort to build machines that can learn from their environment,

    人工智慧就是讓機器具有學習的能力,無論從環境、

  • from mistakes and from people.

    錯誤中或從人身上學習皆可。

  • And we're still at the stage where we don't know what is the right path

    我們仍在一個未知的階段,不曉得什麼才是正確的方向,

  • and the right breakthrough.

    或是應有的突破

  • So I mean there's certainly a whole raft of different approaches.

    但我想一定有許多不同的方法

  • One of the subfields we call pattern recognition.

    其中一個分支稱為「模式識別」(用數學方法來研究生命體對環境及事物的自動處理及判讀,主要透過光學及聲學訊息識別)

  • Artificial neural network.

    「人工神經網絡」(模仿生物神經的結構功能產生的計算模組)

  • Reinforcement learning, for example.

    例如「強化學習」(機器透過觀察學習動作)

  • Statistical inference and probabilistic machine learning.

    「統計推斷」和「概率機器學習」(統計中尋找參數最大值來估算未來行為,可說是用機率推算)

  • Supervised learning. Unsupervised learning. And we're not quite sure what technique is

    「監督學習」「非監督學習」(經驗中產生函數,新數據使用所產生函數推算) 我們不確定哪種技術

  • going to lead to better systems. And, in fact, it's probably not one technique for everything,

    能打造更好的系統。而且事實上,我們可能無法單靠一種技術解決所有事

  • it's probably a bunch of different techniques and combinations of those techniques.

    而是同時採用各種技術及技術組合

  • Any progress we make in building truly intelligent systems is going to depend on progress in

    我們在智能系統上的任何進展,大致上必須仰賴

  • technology generally.

    科技的進步

  • And until recently, we didn't have computers that were fast enough or data sets that were

    直到最近,我們才有夠快的電腦和夠大的數據機

  • big enough to do that.

    足以做到這點

  • And so being able to take a particular problem and spread it out over lots and lots of machines

    能夠將一個特定問題交由許多機台同時處理

  • is a very important approach because it makes our research faster.

    是非常重大的進步,因為它縮短了研究時間。

  • So there's applications of artificial intelligence around us all the time.

    我們周圍一直存在人工智慧的相關應用

  • When it begins to work or it does work it's all of a sudden given another name.

    只是在工作初期或工作時被賦予了別的名稱

  • We're all already using it and very comfortable with it.

    我們早就在使用,而且已經相當習慣了

  • Things that now we regard as routine 30 years ago would have been regarded as amazing

    現在我們認為理所當然的事,在30年前被視為

  • examples of artificial intelligence.

    人工智慧的驚人應用

  • Antilock braking.

    防鎖死煞車系統

  • Autopilot systems for planes.

    飛機自動駕駛

  • Search.

    搜尋引擎

  • Recommendations.

    推薦系統

  • Maps.

    地圖

  • To decide whether or not this particular email is spam or not spam.

    判別特定郵件是否為垃圾郵件

  • The ability to translate one language to another with your phone.

    用手機翻譯成另一種語言的能力

  • Ten years ago if you tried to talk to your computer or to your phone, you know, that

    十年前,如果你想和你的電腦或手機對話,你知道

  • would just be hopeless.

    那是不可能的

  • We are seeing a steady torrent of these tricks one after the other getting figured out right now.

    我們見證了一個個謎團,漸漸地水落石出

  • I think a lot of people that are close to the field have this

    我想許多相關領域的人都有這種

  • do have that kind of breathless sense that things are moving quickly.

    類似窒息的感覺,沒想到它發展得如此快速

  • It's a progressive thing. It's about building things that are slightly better

    它是會成長的,就是創造某樣東西,它會自己一點點

  • slightly better, slightly better.

    一點點,變得更好

  • Intelligence is really not going to be something that we ever succeed in defining in a succinct

    智能真的不是像我們過去一樣,可以用簡明且

  • and singular way. It's really this whole constellation of different capabilities

    單一的方式來定義。它涵蓋了千姿百態的能力範疇

  • that all kind of are beautifully orchestrated and working together.

    而且都可以合作地天衣無縫

  • Predicting the long term future is very difficult.

    預測日後的長遠發展是相當困難的

  • Nobody can really do it.

    沒人能真正做到這點

  • And the bad thing to do is take whatever's working best now and assume the future's going to be like that forever.

    但是我們不該預設,現在最先進的科技,在未來將一成不變

  • [MUSIC]

    [音樂]

You ready to start right now?

準備好要開始了嗎?

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B1 中級 中文 美國腔 Google 學習 機器 人工 智能 運算

機器學習。讓一個混亂的世界變得有意義 (Machine Learning: Making Sense of a Messy World)

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    Shrek Nguyen 發佈於 2021 年 01 月 14 日
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