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  • 1

    我是 柯林・安格爾

  • 00:00:17,043 --> 00:00:19,756

    我在23年前創辦了 iRobot 這家公司

  • My name is Colin Angle and

    純粹是為了想讓機器人更實用

  • 2

    1962年的時候 有一個美國卡通

  • 00:00:19,756 --> 00:00:23,709

    叫做「傑森一家」 裡面有個機器女傭蘿西

  • I founded my company 'iRobot' 23 years ago

    當全世界認識蘿西之後

  • 3

    許多人都很看好接下來的大趨勢

  • 00:00:23,709 --> 00:00:28,793

    但不幸的是 40多年過去了

  • with a single focus to make robots practical.

    大趨勢卻沒發生

  • 4

    那今天我想花幾分鐘跟各位聊聊的

  • 00:00:28,793 --> 00:00:34,840

    是接下來幾年 即將逐漸加速的智慧型機器人產業

  • In 1962, there was an American cartoon,

    我們逐漸看到

  • 5

    智慧型機器人的應用是越來越普遍

  • 00:00:34,840 --> 00:00:38,626

    而事實上 我們也從中發現許多新奇、有趣的商機

  • called The Jetsons with Rosie the robot.

    這些機會從此改變了我們對世界的看法

  • 6

    也改變對機器的看法

  • 00:00:38,626 --> 00:00:41,681

    最重要的頓悟是 機器人不是長這樣子

  • And when the world saw Rosie,

    相反地 如果你想要藉此創業、讓機器人變成生活的一部分

  • 7

    我們必須思考

  • 00:00:41,681 --> 00:00:46,517

    每天生活中最困擾的地方是什麼

  • we all knew what the next big thing was going to be.

    然後你才能開始想

  • 8

    要怎麼樣去創造這些

  • 00:00:46,517 --> 00:00:50,792

    高產值的智慧型機器

  • Unfortunately 40 years passed,

    如果能這樣的話

  • 9

    那我們就能開創一個事業

  • 00:00:50,792 --> 00:00:53,542

    不過如果我們做了一次有趣、很新奇的示範

  • and it didn't happen.

    那也只是一次性的示範

  • 10

    不能對社會產生太大的影響

  • 00:00:53,542 --> 00:00:56,542

    我相信所有成功的機器人商品

  • What I'd like to spend a few minutes with you today talking about,

    背後有好幾千次的示範、實驗

  • 11

    我們來看一個成功的案例

  • 00:00:56,542 --> 00:01:03,543

    這個案例先放掉對機器人的刻板印象

  • is the acceleration and coming of age of the robot industry.

    才能創造出新的應用模式

  • 12

    我們來看看吸塵機器人

  • 00:01:03,543 --> 00:01:05,668

    這個是 Roomba 是我們的機器人

  • We are starting to see around the world

    這種吸塵機器人已賣掉一千多萬台了

  • 13

    如果我說

  • 00:01:05,668 --> 00:01:11,442

    在全球的吸塵器市場 吸塵機器人的市佔率已高達15%

  • applications where robots are actually making a difference.

    我想你們一定會覺得很驚訝

  • 14

    事實上呢

  • 00:01:11,442 --> 00:01:15,347

    在某些國家

  • And we are actually seeing exciting new businesses

    賣最好的吸塵器

  • 15

    就是吸塵機器人

  • 00:01:15,347 --> 00:01:20,619

    以上就是一個

  • spring up and change how we think about the world and how

    找到正確策略的好案例

  • 16

    因為消費者的確不想自己吸地板

  • 00:01:20,619 --> 00:01:25,042

    也沒有時間吸地板

  • we think about machines.

    那我們怎麼解決這問題?

  • 17

    現在分享另一個例子

  • 00:01:25,042 --> 00:01:33,377

    告訴你智慧型機器人

  • The important realization was that robots do not look like this.

    在特別危險的環境中 怎麼創造價值

  • 18

    2002年的時候

  • 00:01:33,377 --> 00:01:37,767

    那年在阿富汗有個很棘手的案子

  • Instead, if you want to make a business, if you want robots in our world

    美國派遣許多軍人到阿富汗

  • 19

    當時的我們看到電視報導

  • 00:01:37,767 --> 00:01:41,292

    得知軍人得在腰上繫一條繩子

  • we need to think about what are the challenges

    才會深入阿富汗的地穴

  • 20

    如果這些軍人受傷、或被射時

  • 00:01:41,292 --> 00:01:45,776

    其他人會用那條繩子

  • that face people on a daily basis,

    把傷兵拖出來

  • 21

    這其實是很不可思議的事情

  • 00:01:45,776 --> 00:01:49,279

    為什麼不用技術解決這問題呢?

  • and to think about how

    為什麼是真人進去探看 而不是機器呢?

  • 22

    所以為此 我們另闢一個智慧型機器人專案

  • 00:01:49,279 --> 00:01:52,182

    派遣這些客製機器人到阿富汗的洞穴裡

  • we can create intelligent machines

    我們透過這些機器人 發現了許多致命的武器

  • 23

    洞穴內的自動化的武器、槍炮彈藥 還有炸彈

  • 00:01:52,182 --> 00:01:57,792

    都是機器人所拍到的

  • that can deliver more value then they cost to produce.

    因此 我們的機器人創造了新的價值

  • 24

    或許

  • 00:01:57,792 --> 00:01:59,376

    最容易讓大家一看就懂這個價值

  • If we can do that,

    就是透過接下來的兩部影片

  • 25

    這些都是在伊拉克的街道發生的

  • 00:01:59,376 --> 00:02:02,662

    我們現在看到的第一部影片 這裡有一台悍馬

  • we have created a great business.

    (可以放大聲一點嗎)

  • 26

    你可以看到 悍馬把炸彈推到街旁

  • 00:02:02,662 --> 00:02:07,958

    相形之下

  • If we make something that is an amazing and exciting demonstration,

    這部影片

  • 27

    也是一台智慧型機器人在做一樣的事

  • 00:02:07,958 --> 00:02:11,459

    (再調一下音量)

  • we have made an exciting demonstration that had

    在橘色桶子旁的就是智慧型機器人

  • 28

    .

  • 00:02:11,459 --> 00:02:15,876

    第一部影片有尖叫聲

  • very little impact on the world.

    還好悍馬裡的司機沒有死

  • 29

    但第二部影片 我們聽到的是笑聲 而且旁白是

  • 00:02:15,876 --> 00:02:20,011

    “太棒了!被我們拍到了!”

  • I believe that for every successful robot product

    除了機器人被損毀之外 一切毫髮無損

  • 30

    最重要的是 我們解決了困境

  • 00:02:20,011 --> 00:02:25,295

    智慧型機器人代替人類 深入非常危險的環境

  • there are many thousands of demonstration of robots.

    它們改變了局面

  • 31

    保護那些站在前線保家衛國的軍人們

  • 00:02:25,295 --> 00:02:28,961

    所以這個科技、這項想法

  • To give you an example of a success,

    如同各位剛剛看到的那些機器人一樣

  • 32

    是非常振奮人心!

  • 00:02:28,961 --> 00:02:35,047

    智慧型機器人可以當成人類的代表

  • where by abandoning traditional notions of what a robot should be

    當我們的分身

  • 33

    改變「本尊出席」的概念

  • 00:02:35,047 --> 00:02:38,548

    所以下一個即將被智慧型機器人

  • we have created something of real impact,

    影響的市場是醫療保健市場

  • 34

    我們可以看到全世界的醫療保健技術

  • 00:02:38,548 --> 00:02:41,766

    漸漸地越來越精湛

  • we can look at vacuuming robots.

    治療方式也變得更多元化

  • 35

    而這一切都需要

  • 00:02:41,766 --> 00:02:43,797

    有專業、廣博知識的醫療團隊

  • So this is the 'Roomba'.

    並精專特定領域 才能確保

  • 36

    病人能得到最準確的治療

  • 00:02:43,797 --> 00:02:48,572

    如何協助病患找到

  • This is my robot. We've sold over 10 million of these robots.

    能醫治他們的專科醫生

  • 37

    是個全新的挑戰

  • 00:02:48,572 --> 00:02:52,341

    所以智慧型機器人在此可提供另外一種用途

  • And I think people would find it surprising if I told you

    它們除了拆除炸彈之外 還能遠端替病人做檢查

  • 38

    於是 我身後的這台智慧型機器人

  • 00:02:52,341 --> 00:02:56,714

    已經部署在醫院內 像這類型的智慧型機器人

  • that 15 percent of all of the money spent in the world

    已經有50個都在

  • 39

    美國和墨西哥的醫院裡

  • 00:02:56,714 --> 00:03:01,547

    漸漸地 這些醫療機器人的數量會逐漸增加

  • on vacuuming cleaners today is now spent on robots.

    它們的運作方式如下

  • 40

    主要是由大城市裡的專科醫生

  • 00:03:01,547 --> 00:03:03,048

    派遣這些智慧型機器人到偏遠地區的醫院

  • And in fact,

    現在這些偏鄉醫院就能協助診斷患者

  • 41

    舉例來說 它們目前最普遍的用途是在腦中風患者身上

  • 00:03:03,048 --> 00:03:07,048

    患者直接去偏鄉醫院

  • in a few countries

    專科醫生不一定要在醫院看門診

  • 42

    而是讓醫療機器人

  • 00:03:07,048 --> 00:03:10,994

    診斷患者的病情 由機器人開出正確的藥方和治療方法

  • the number one selling vacuuming the entire country is now

    協助病人穩定病情

  • 43

    接著 病患可以繼續待在偏鄉醫院裡休養

  • 00:03:10,994 --> 00:03:12,846

    假設病患需要接受更特殊的治療

  • a robot.

    也可以安全地被轉至大醫院

  • 44

    自從這些偏鄉醫院採用了醫療機器人

  • 00:03:12,846 --> 00:03:17,680

    很多患者的狀況明顯地改善

  • This is an example of what can happen

    而我們應該期待對於智慧型機器人在醫學上

  • 45

    扮演更重要的角色

  • 00:03:17,680 --> 00:03:20,679

    因為這個方法可以完全地影響

  • when you actually get it right.

    全世界的醫生們

  • 46

    使其被安置在更需要的醫療環境內

  • 00:03:20,679 --> 00:03:22,680

    現在來看一個居家環境的例子吧

  • You actually say, well, people don't want to vacuum,

    如果只是想在某個地方出現的話

  • 47

    一定要出遠門嗎?

  • 00:03:22,680 --> 00:03:25,512

    現在有個新的概念

  • people don't have time to vacuum.

    假設我今天的演講

  • 48

    是用一個機器人分身來演說

  • 00:03:25,512 --> 00:03:27,846

    然後明天我也可以用同樣的方式

  • So how do we solve that?

    在世界的另一端演講

  • 49

    機器人的多種用途 可協助縮短世界的距離

  • 00:03:27,846 --> 00:03:29,982

    不只是用在醫學 而是用在每一個人的身上

  • Another example

    這是一個振奮人心的想法

  • 50

    所以 我們已經創造一個機器代理人

  • 00:03:29,982 --> 00:03:34,288

    它已經幫我出席過多次演講

  • of where robots are creating value

    機器代理人真的很炫 它可以站著

  • 51

    可以演說 可以在會後回答問題

  • 00:03:34,288 --> 00:03:38,723

    當演講時間結束時 代理人可以直接離開演講廳

  • is in very dangerous situations.

    然後跟大家見面 回答他們的問題

  • 52

    然後表現就像是我本人在場一樣

  • 00:03:38,723 --> 00:03:41,292

    而這樣的結果是 讓世界的距離變得更小

  • In 2002,

    並且讓我們與人的溝通變得更親近

  • 53

    現階段的智慧型機器人產業是不斷地在改變

  • 00:03:41,292 --> 00:03:45,496

    這個產業也正在改變其他產業

  • wethere was a great challenge in Afghanistan. The United States

    包誇機器人產業本身

  • 54

    這樣的連動效應真的很棒

  • 00:03:45,496 --> 00:03:47,465

    1991年我創立了iRobot

  • sent soldiers to Afghanistan.

    那時 如果機器人要透過無線網路連上另一個裝置時

  • 55

    那我必須幫它建立無線電設備

  • 00:03:47,465 --> 00:03:49,802

    也得建立人工智慧

  • And we were watching television and they told us how

    還有 也一定要一套視覺系統

  • 56

    導航系統、機械操作系統

  • 00:03:49,802 --> 00:03:52,262

    甚至是觸控界面、語音辨識系統等等

  • the soldiers would have ropes

    而事實上 過去我能跟其他產業結合的技術

  • 57

    單單就只有電池及馬達而已

  • 00:03:52,262 --> 00:03:56,374

    但現在已經不同了

  • tied around their waists to go into the caves in Afghanistan.

    電玩遊戲產業以及手機產業

  • 58

    雙雙解決許多智慧型機器人產業的問題

  • 00:03:56,374 --> 00:03:58,512

    我現在可以用 Siri 或是 Dragon Naturally

  • So if they were hurt or shot,

    做我演說時的語言辨認 我還可以用無線通訊系統

  • 59

    以及X-Box的體感辨識系統

  • 00:03:58,512 --> 00:04:01,430

    這些高科技技術都已經存在

  • you could drag the solider out

    我只需要好好專心在機器人產業 研發導航、感知能力

  • 60

    以及操作機制

  • 00:04:01,430 --> 00:04:03,380

    我想再提供一個範例

  • using that rope.

    讓大家了解 目前最尖端的科技是什麼

  • 61

    為了開發醫療機器人

  • 00:04:03,380 --> 00:04:06,720

    我們必須讓機器人熟悉醫院的佈局

  • And we said that was a crazy, crazy thing.

    因為醫生並不會親自去操作這些醫療機器人

  • 62

    醫生有更重要的事要做

  • 00:04:06,720 --> 00:04:09,346

    而事實上

  • Why has not technology solved this problem?

    醫生也有可能沒去過那家偏鄉醫院

  • 63

    他其實只要下個命令

  • 00:04:09,346 --> 00:04:12,122

    「我想要看602號房的病人」

  • Why are people doing that instead of a robot?

    然後智慧型機器人就應該會自行前往病房看診

  • 64

    這代表了機器人必須要有認路的能力

  • 00:04:12,122 --> 00:04:14,826

    於是我們也開發了這種機器人

  • So, we had a project to make a robot,

    我們先引導它在大樓裡走動

  • 65

    讓機器人自行作出地圖

  • 00:04:14,826 --> 00:04:16,793

    它會依照大樓內的樓層 記錄每一層樓的佈局

  • we sent it over to Afghanistan,

    然後它就清楚602號房的位置了

  • 66

    我們同時也希望智慧型機器人能理解

  • 00:04:16,793 --> 00:04:22,099

    並辨識房間裡的物品

  • we sent it into caves. We found some very, very scary things.

    我們必須作出一項技術

  • 67

    這技術可以讓機器人回應

  • 00:04:22,099 --> 00:04:26,034

    「噢,我知道這是什麼」

  • This is all automatic weapon, ammunition, and explosives

    於是我們研發了一個叫做ViPR的技術

  • 68

    能夠使機器人辨識物品

  • 00:04:26,034 --> 00:04:29,394

    這種辨識技術

  • founded in a cave by the robot.

    無論光線

  • 69

    或是物品放置的方向

  • 00:04:29,394 --> 00:04:31,441

    都能很精準地辨識其他相同的物品

  • And we made a difference.

    我們能作出這種技術

  • 70

    因為採用了非常小的微處理器

  • 00:04:31,441 --> 00:04:32,876

    你可以看到這邊有隻手機

  • Perhaps,

    這個系統可以讓機器人辨識

  • 71

    分辨出紙鈔、不同的物品

  • 00:04:32,876 --> 00:04:38,977

    前提是 同一個物品沒有多種款項

  • the most easy to understand and graphical way of the difference

    這很酷吧

  • 72

    那物品分類呢?

  • 00:04:38,977 --> 00:04:42,227

    當你走進這間演講廳

  • I will demonstrate with two short videos.

    你或許從來沒看過面前這張椅子

  • 73

    我是說是跟這款一模一樣的

  • 00:04:42,227 --> 00:04:46,309

    但你還是知道它是一張椅子

  • Both happened in the streets of Iraq.

    而且你會拿來坐

  • 74

    所以機器人也必須要有一樣的能力

  • 00:04:46,309 --> 00:04:52,096

    去分辨出物品的分類

  • The first demonstrated here where you have a humvee,

    這樣它們才能清掃物品的底部

  • 75

    或是判斷它在什麼房間

  • 00:04:52,096 --> 00:04:53,478

    才會對自己的定位有意識

  • (can we have the sound up?)

    現在看到的是剛剛說明的實驗

  • 76

    叫做iSpot

  • 00:04:53,478 --> 00:04:58,133

    影片上看到的就是一個智慧型機器人

  • Um, you can see pushing a bomb off the street.

    穿梭在大樓內 裡面充滿各種不同款式的

  • 77

    廚具與廚房空間

  • 00:04:58,133 --> 00:05:02,726

    機器人能及時辨識

  • 78

    在廚房內不同的設備

  • 00:05:02,726 --> 00:05:05,776

    以及設備的種類

  • Now, contrast that video,

    所以假設你是一個機器人 你要作類似的連結

  • 79

    你就必須要查看所有不同的器具

  • 00:05:05,776 --> 00:05:07,180

    以及房間裡所有的固定裝置

  • with this video,

    這樣你才能像人類 用眼睛分辨

  • 80

    這間房間是廚房、餐廳、或是客廳

  • 00:05:07,180 --> 00:05:10,310

    所以當我們的科技不斷在進步

  • of a robot doing the same sort of thing.

    使機器人有更多能力

  • 81

    在瞭解環境

  • 00:05:10,310 --> 00:05:13,017

    甚至是可以撿起或舉起東西

  • (again the volume there)

    搬移物品

  • 82

    這當中我們會創造更多、更有趣的產業

  • 00:05:13,017 --> 00:05:17,287

    這是一件非常好的事情

  • So you can see by that orange can is the robot,

    因為在1962年

  • 83

    我們看到卡通裡的機器女傭蘿西時

  • 00:05:17,287 --> 00:05:35,006

    我們問 什麼時候才會有智慧型機器人呢?

  • 84

    然後50年過去了

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    現在我們終於開始嘗試

  • So, in the first video you heard screams

    把應用程式或是智慧型機器人

  • 85

    融入在我們生活中

  • 00:05:38,009 --> 00:05:41,545

    但如果你去比較

  • the driver of that vehicle was not killed

    目前現有的智慧型機器人總數

  • 86

    再去比對目前智慧型機器人的需求

  • 00:05:41,545 --> 00:05:45,084

    欠缺的數字是反映這產業的商機

  • um survived. But here we have laughter and guys think

    也象徵未來還需要多少智慧型機器人

  • 87

    我相信你會同意

  • 00:05:45,084 --> 00:05:48,786

    以下我要說的:

  • 'Dude, Dude! I caught that on video!'

    我們還未開始啓動

  • 88

    數十年之後能實現的未來

  • 00:05:48,786 --> 00:05:53,894

    謝謝大家

  • So the robot was in fact destroyed but other than that

    .

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    字幕:胡致莉

  • 00:05:53,894 --> 00:05:57,127

  • we had resolved the situation.

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  • 00:05:57,127 --> 00:06:00,859

  • So again, robots for going into very scary and

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  • dangerous situations enable to change the equation

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  • and protect the lives of people trying to protect us.

  • 93

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  • So this technology, this idea

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  • that was represented by those robots that you just saw

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  • is the beginning of a very exciting idea.

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  • We can create robots that can act as surrogates

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  • for our physical self

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  • and change the meaning of what being someplace means.

  • 99

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  • So the next industry which is

  • 100

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  • going to be changed by robots is health care.

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  • We're seeing the world where the sophistication

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  • of health care is increasing every single day.

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  • Treatments are more and more sophisticated.

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  • But it requires doctors

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  • with an increasingly sophisticated understanding

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  • of a narrow area of medicine to make sure that

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  • the patient gets the right treatment.

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  • This is causing a new challenge

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  • in medicine, trying to match up

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  • the specialist with the patient that needs his or her expertise.

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  • So what a great opportunity to create a robot, not to defuse

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  • bombs, but to diagnose patients at a distance.

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  • So this robot that you see

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  • behind me, is deployed. There's about 50 of these robots

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  • in action today in many different hospitals and

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  • the United States and Mexico

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  • and growing into an increasing number of hospitals.

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  • And the way they work

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  • is a big city hospital which has these specialists

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  • actually sends the robots to rural hospitals.

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  • Now that rural hospital can

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  • see a patient. Say right now, the most common uses is for stroke.

  • 123

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  • A patient goes to that small hospital.

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  • Instead of the doctor walking in to see the patient,

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  • a robot drives in,

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  • performs the diagnosis. The right drug, or the right treatment

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  • is applied to help that patient be stable.

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  • And then if the patient can stay in that rural hospital,

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  • he or she does. If he needs additional treatment,

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  • he can now be safely moved to the larger hospital.

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  • We have seen dramatic increases in the outcomes

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  • of patients in these rural hospitals using this robot technology.

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  • And we should expect to see robots play a larger role

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  • in providing health care,

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  • because it will allow us to leverage fully

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  • the education of the doctors

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  • around the world to be put to use in the best possible way.

  • 138

  • 00:09:21,405 --> 00:09:24,367

  • How about something a little closer to home?

  • 139

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  • Why do we all have to travel

  • 140

  • 00:09:28,655 --> 00:09:31,655

  • if we want to go somewhere?

  • 141

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  • This is a new idea where

  • 142

  • 00:09:35,739 --> 00:09:39,549

  • I could be giving this talk to you today

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  • as a robot

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  • and able to do a different talk

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  • on the other side of the world tomorrow

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  • using a robot. This idea of making the world smaller

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  • not just for doctors, but for anyone,

  • 148

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  • is a very exciting idea.

  • 149

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  • So we've created a robot. And I have given

  • 150

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  • a number of talks using this robot.

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  • 00:10:06,842 --> 00:10:08,488

  • And it's very exciting. I can stand up,

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  • 00:10:08,488 --> 00:10:12,048

  • I can give my talk, I can go answer questions afterwards.

  • 153

  • 00:10:12,048 --> 00:10:15,219

  • When my time is up, I can drive out of this hall

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  • 00:10:15,219 --> 00:10:19,057

  • and meet other people and answer their questions

  • 155

  • 00:10:19,057 --> 00:10:24,162

  • and behave exactly like I would if I was here in person.

  • 156

  • 00:10:24,162 --> 00:10:28,030

  • And as a result, I make the world a little smaller,

  • 157

  • 00:10:28,030 --> 00:10:32,903

  • and I increase our ability to communicate with each other.

  • 158

  • 00:10:32,903 --> 00:10:36,957

  • So the robot industry is changing.

  • 159

  • 00:10:36,957 --> 00:10:40,745

  • The robot industry is changing other industries

  • 160

  • 00:10:40,745 --> 00:10:43,791

  • but it's also being changed itself,

  • 161

  • 00:10:43,791 --> 00:10:44,792

  • which is very exciting.

  • 162

  • 00:10:44,792 --> 00:10:47,625

  • In 1991 I founded iRobot

  • 163

  • 00:10:47,625 --> 00:10:50,706

  • if I wanted my robot to be wirelessly connected

  • 164

  • 00:10:50,706 --> 00:10:54,458

  • to something else, I had to build to radio,

  • 165

  • 00:10:54,458 --> 00:10:56,375

  • I had to build the artificial intelligence.

  • 166

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  • Not surprisingly, I had to build the vision system,

  • 167

  • 00:11:00,330 --> 00:11:02,541

  • the navigation system, the manipulators,

  • 168

  • 00:11:02,541 --> 00:11:04,124

  • the touch screen interfaces, the voice recognition. In fact,

  • 169

  • 00:11:04,124 --> 00:11:10,457

  • the only thing I could depend on and borrow from other industries

  • 170

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  • were batteries and motors.

  • 171

  • 00:11:12,958 --> 00:11:17,292

  • Today, that is no longer the case.

  • 172

  • 00:11:17,292 --> 00:11:21,485

  • The video game industry and the mobile industry

  • 173

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  • have solved many, many challenging robot problems.

  • 174

  • 00:11:26,122 --> 00:11:29,542

  • I can use Siri or Dragon Naturally Dictate

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  • 00:11:29,542 --> 00:11:33,863

  • for my speech recognition. I can use wireless communication.

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  • 00:11:33,863 --> 00:11:39,670

  • I can use gesture recognition from my kinetic X-Box.

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  • All of these technologies I can assume exist,

  • 178

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  • and focus the robot industry on navigation, perception

  • 179

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  • and manipulation.

  • 180

  • 00:11:50,747 --> 00:11:53,517

  • So, I'd like to give you a little example of

  • 181

  • 00:11:53,517 --> 00:11:56,286

  • what the state of the art looks like.

  • 182

  • 00:11:56,286 --> 00:12:00,156

  • In order to make that medical robot work,

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  • the robot has to understand the hospital,

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  • 00:12:04,729 --> 00:12:08,203

  • because the doctor isn't going to drive the robot to the patient

  • 185

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  • he has other things to do.

  • 186

  • 00:12:09,399 --> 00:12:10,287

  • And in fact,

  • 187

  • 00:12:10,287 --> 00:12:14,504

  • he may never have been to the hospital before.

  • 188

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  • He would like to say,

  • 189

  • 00:12:15,673 --> 00:12:18,871

  • "I want to see patient in room 602"

  • 190

  • 00:12:18,871 --> 00:12:22,704

  • and the robot should get there all by itself.

  • 191

  • 00:12:22,704 --> 00:12:28,536

  • so that means that robots have to able to map automatically.

  • 192

  • 00:12:28,536 --> 00:12:31,422

  • So we've created a robot that does just that.

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  • 00:12:31,422 --> 00:12:33,870

  • We drive the robot around the building.

  • 194

  • 00:12:33,870 --> 00:12:36,926

  • The robot creates a map.

  • 195

  • 00:12:36,926 --> 00:12:42,036

  • It is registered against a floor plan of the building.

  • 196

  • 00:12:42,036 --> 00:12:47,906

  • And the robot knows where the room 602 is.

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  • 00:12:47,906 --> 00:12:50,007

  • We want the robot to understand and

  • 198

  • 00:12:50,007 --> 00:12:53,704

  • recognize objects in the room.

  • 199

  • 00:12:53,704 --> 00:12:56,703

  • So we needed to create a technology

  • 200

  • 00:12:56,703 --> 00:12:58,615

  • that would allow the robot to say,

  • 201

  • 00:12:58,615 --> 00:13:01,451

  • "I know what that is".

  • 202

  • 00:13:01,451 --> 00:13:05,924

  • So we created a system called ViPR,

  • 203

  • 00:13:05,924 --> 00:13:09,598

  • which recognized exact objects

  • 204

  • 00:13:09,598 --> 00:13:12,829

  • and copy the exact copies of those objects

  • 205

  • 00:13:12,829 --> 00:13:14,014

  • regardless of the lighting,

  • 206

  • 00:13:14,014 --> 00:13:17,667

  • regardless of the orientation of that object,

  • 207

  • 00:13:17,667 --> 00:13:19,671

  • and do that reliably.

  • 208

  • 00:13:19,671 --> 00:13:21,931

  • And we do that, and we are able do that

  • 209

  • 00:13:21,931 --> 00:13:25,431

  • in a very very tiny microprocessor.

  • 210

  • 00:13:25,431 --> 00:13:28,515

  • So here we have a cell-phone.

  • 211

  • 00:13:28,515 --> 00:13:40,266

  • So this system will allow a robot to recognize

  • 212

  • 00:13:40,266 --> 00:13:43,326

  • dollar bills, different objects, as long as they are

  • 213

  • 00:13:43,326 --> 00:13:46,515

  • exactly like other versions of it.

  • 214

  • 00:13:46,515 --> 00:13:48,466

  • So that's cool.

  • 215

  • 00:13:48,466 --> 00:13:53,849

  • But what about generic classes of objects?

  • 216

  • 00:13:53,849 --> 00:13:55,931

  • When you walked into this auditorium,

  • 217

  • 00:13:55,931 --> 00:13:57,766

  • you may have never seen a chair that looks

  • 218

  • 00:13:57,766 --> 00:14:00,042

  • exactly like the one you're sitting in.

  • 219

  • 00:14:00,042 --> 00:14:02,847

  • And yet, you recognized that's a chair,

  • 220

  • 00:14:02,847 --> 00:14:04,597

  • and you used it to sit down. Well,

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  • 00:14:04,597 --> 00:14:09,153

  • a robot should be able to have that same understanding

  • 222

  • 00:14:09,153 --> 00:14:11,054

  • of categories of objects

  • 223

  • 00:14:11,054 --> 00:14:13,182

  • so that it can clean under them.

  • 224

  • 00:14:13,182 --> 00:14:15,091

  • It can recognize what type of the room it's in,

  • 225

  • 00:14:15,091 --> 00:14:18,896

  • and have a better awareness of where it is.

  • 226

  • 00:14:18,896 --> 00:14:21,264

  • So this is a demonstration of a technology

  • 227

  • 00:14:21,264 --> 00:14:23,432

  • called iSpot that does exactly that.

  • 228

  • 00:14:23,432 --> 00:14:28,439

  • So what you'll see here is a robot driving through

  • 229

  • 00:14:28,439 --> 00:14:30,508

  • a building which has a number of different types of

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  • 00:14:30,508 --> 00:14:33,079

  • kitchens and different appliances,

  • 231

  • 00:14:33,079 --> 00:14:36,201

  • and dynamically in real-time, recognizing

  • 232

  • 00:14:36,201 --> 00:14:40,449

  • the category of the appliance in

  • 233

  • 00:14:40,449 --> 00:14:44,857

  • that kitchen area automatically.

  • 234

  • 00:14:44,857 --> 00:14:46,623

  • So if you are a robot and you are doing that mapping

  • 235

  • 00:14:46,623 --> 00:14:51,987

  • you could also be looking at all the different appliances

  • 236

  • 00:14:51,987 --> 00:14:55,737

  • and fixtures in that room and be able to tell,

  • 237

  • 00:14:55,737 --> 00:14:57,320

  • just like your eye could tell,

  • 238

  • 00:14:57,320 --> 00:15:02,373

  • whether that room is a kitchen or the den or the living room.

  • 239

  • 00:15:02,373 --> 00:15:06,153

  • so as we get better and better

  • 240

  • 00:15:06,153 --> 00:15:10,447

  • at making our robots more capable

  • 241

  • 00:15:10,447 --> 00:15:13,321

  • of understanding their environment and

  • 242

  • 00:15:13,321 --> 00:15:18,737

  • ultimately able to pick up and lift

  • 243

  • 00:15:18,737 --> 00:15:19,957

  • and move objects,

  • 244

  • 00:15:19,957 --> 00:15:24,528

  • we create more and more exciting industries.

  • 245

  • 00:15:24,528 --> 00:15:27,320

  • And that's very good.

  • 246

  • 00:15:27,320 --> 00:15:30,935

  • Because 1962,

  • 247

  • 00:15:30,935 --> 00:15:34,820

  • we saw Rosie the robot and we said

  • 248

  • 00:15:34,820 --> 00:15:39,042

  • "when are we going to have robots in our lives?"

  • 249

  • 00:15:39,042 --> 00:15:41,478

  • And 50 years have passed.

  • 250

  • 00:15:41,478 --> 00:15:43,737

  • And now we're starting

  • 251

  • 00:15:43,737 --> 00:15:46,183

  • to see some application and some robots that actually

  • 252

  • 00:15:46,183 --> 00:15:49,052

  • we can afford and bring into our lives.

  • 253

  • 00:15:49,052 --> 00:15:51,988

  • But if you can compare the number

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  • 00:15:51,988 --> 00:15:56,090

  • of robots that are out there today,

  • 255

  • 00:15:56,090 --> 00:15:58,987

  • and match that up against the need,

  • 256

  • 00:15:58,987 --> 00:16:02,465

  • match that against the opportunity,

  • 257

  • 00:16:02,465 --> 00:16:05,654

  • that robots will play in the future,

  • 258

  • 00:16:05,654 --> 00:16:07,820

  • I believe you will agree with me

  • 259

  • 00:16:07,820 --> 00:16:09,040

  • when I say:

  • 260

  • 00:16:09,040 --> 00:16:12,375

  • "we are just about none of the way

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  • 00:16:12,375 --> 00:16:16,713

  • in accomplishing what we will accomplish over the next decades."

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  • 00:16:16,713 --> 00:16:18,849

  • Thank you very much.

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  • Subtitled by Samantha Hu

1

我是 柯林・安格爾

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