How we can teach computers to make sense of our emotions | Raphael Arar

64,579 views ・ 2018-04-24

TED


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00:00
Translator: Ivana Korom Reviewer: Joanna Pietrulewicz
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譯者: Lilian Chiu 審譯者: Yanyan Hong
00:13
I consider myself one part artist and one part designer.
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我自認一部分是藝術家, 一部分是設計師。
00:18
And I work at an artificial intelligence research lab.
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我在一間人工智能研究實驗室工作。
00:22
We're trying to create technology
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我們在試圖創造的技術,
00:24
that you'll want to interact with in the far future.
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是在遙遠的未來 你會想要和它互動的技術。
00:27
Not just six months from now, but try years and decades from now.
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不只是現在算起六個月之後, 而是數年、數十年之後。
00:33
And we're taking a moonshot
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我們做了個很大膽的猜測,
00:34
that we'll want to be interacting with computers
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猜想我們將來會想要和電腦
00:37
in deeply emotional ways.
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用帶有深度感情的方式來互動。
00:40
So in order to do that,
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為了做到這一點,
00:41
the technology has to be just as much human as it is artificial.
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這項技術不能只是很人工, 也要很人性。
00:46
It has to get you.
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它必須要能懂你。
00:49
You know, like that inside joke that'll have you and your best friend
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就像只有你和你最好的朋友之間 才懂的「圈內笑話」
00:52
on the floor, cracking up.
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能讓你們笑到在地上打滾。
00:54
Or that look of disappointment that you can just smell from miles away.
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或是你遠遠就能嗅到的 那種失望表情。
01:00
I view art as the gateway to help us bridge this gap between human and machine:
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我把藝術視為是一種途徑, 能協助我們在人、機之間搭起橋樑:
01:07
to figure out what it means to get each other
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找出「了解彼此」是什麼意思,
01:10
so that we can train AI to get us.
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這樣才能訓練人工智能來了解我們。
01:13
See, to me, art is a way to put tangible experiences
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對我而言,藝術是一種 可以把有形的經驗
01:17
to intangible ideas, feelings and emotions.
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放到無形的想法、感受, 及情緒中的方式。
01:21
And I think it's one of the most human things about us.
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我認為它是我們 最有人性的事物之一。
01:25
See, we're a complicated and complex bunch.
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我們是很複雜又難懂的物種。
01:28
We have what feels like an infinite range of emotions,
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我們的情緒範圍感覺 起來似乎是無限的,
01:31
and to top it off, we're all different.
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更麻煩的是,我們每個人都不同。
01:34
We have different family backgrounds,
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我們有不同的家庭背景、
01:36
different experiences and different psychologies.
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不同的經驗,以及不同的心理。
01:40
And this is what makes life really interesting.
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正是因為如此,人生才有意思。
01:43
But this is also what makes working on intelligent technology
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但也是因為如此,
發展人工智能才極度困難。
01:46
extremely difficult.
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01:49
And right now, AI research, well,
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而現在,人工智能研究,嗯,
01:53
it's a bit lopsided on the tech side.
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它有點不平衡,傾向技術面。
01:55
And that makes a lot of sense.
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那是非常合理的。
01:57
See, for every qualitative thing about us --
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對於人類的質性成分──
01:59
you know, those parts of us that are emotional, dynamic and subjective --
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我們有很多情緒、 動態、主觀的部分──
02:04
we have to convert it to a quantitative metric:
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我們得將之轉換為量性的度量:
02:07
something that can be represented with facts, figures and computer code.
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可以用事實、數字, 和電腦程式來呈現的東西。
02:13
The issue is, there are many qualitative things
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問題在於,有許多質性的東西
02:16
that we just can't put our finger on.
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是我們實在無法認出來的。
02:20
So, think about hearing your favorite song for the first time.
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試想看看,當你第一次 聽見你最喜歡的歌曲時,
02:25
What were you doing?
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你在做什麼?
02:28
How did you feel?
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你的感受是什麼?
02:30
Did you get goosebumps?
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你有起雞皮疙瘩嗎?
02:33
Or did you get fired up?
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你有充滿激情嗎?
02:36
Hard to describe, right?
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很難形容,對吧?
02:38
See, parts of us feel so simple,
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我們的一些部分感覺起來很簡單,
02:40
but under the surface, there's really a ton of complexity.
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但在表面底下, 是相當可觀的複雜度。
02:44
And translating that complexity to machines
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要把那種複雜翻譯給機器了解,
02:47
is what makes them modern-day moonshots.
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讓這個目標變得非常高難度。
02:50
And I'm not convinced that we can answer these deeper questions
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我不相信我們能夠只用零和一來回答
02:54
with just ones and zeros alone.
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這些較深的問題,
02:57
So, in the lab, I've been creating art
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所以,在實驗室中, 我一直在創造藝術,
02:59
as a way to help me design better experiences
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藝術協助我為最先進的技術
03:01
for bleeding-edge technology.
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設計出更佳的體驗。
03:03
And it's been serving as a catalyst
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它一直是一種催化劑,
03:05
to beef up the more human ways that computers can relate to us.
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用來讓電腦用更人性的 方式和我們相處。
03:10
Through art, we're tacking some of the hardest questions,
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我們透過藝術來處理 一些最困難的問題,
03:12
like what does it really mean to feel?
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比如「去感覺」究竟是什麼意思?
03:16
Or how do we engage and know how to be present with each other?
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或者我們要如何與人互動, 如何在彼此相處時不是心不在焉?
03:20
And how does intuition affect the way that we interact?
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直覺又如何影響我們互動的方式?
03:26
So, take for example human emotion.
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以人類情緒為例。
03:28
Right now, computers can make sense of our most basic ones,
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現在,電腦能夠理解 我們最基本的情緒,
03:31
like joy, sadness, anger, fear and disgust,
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比如喜悅、悲傷、 憤怒、恐懼,及作噁,
03:35
by converting those characteristics to math.
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做法是將那些特徵轉換為數學。
03:39
But what about the more complex emotions?
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但遇到更複雜的情緒怎麼辦?
03:41
You know, those emotions
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你們知道的,
03:43
that we have a hard time describing to each other?
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有些情緒我們都很難對彼此形容?
03:45
Like nostalgia.
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像是鄉愁。
03:47
So, to explore this, I created a piece of art, an experience,
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為了探究這一點,我創作出了 一件藝術作品,一項體驗,
03:51
that asked people to share a memory,
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我請大家分享一段記憶,
03:53
and I teamed up with some data scientists
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我和一些資料科學家合作,
03:55
to figure out how to take an emotion that's so highly subjective
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想辦法把一種高度主觀性的情緒
03:59
and convert it into something mathematically precise.
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轉換成很精確的數學。
04:03
So, we created what we call a nostalgia score
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於是我們創造出了鄉愁分數,
04:06
and it's the heart of this installation.
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它是這項裝置的核心。
04:08
To do that, the installation asks you to share a story,
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做法是這樣的:這項裝置 會先請你分享一個故事,
04:11
the computer then analyzes it for its simpler emotions,
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接著,電腦會分析 這個故事中比較簡單的情緒,
04:14
it checks for your tendency to use past-tense wording
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它會檢查你是否傾向 用比較多過去式修辭,
04:17
and also looks for words that we tend to associate with nostalgia,
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也會尋找我們談鄉愁時 比較會用到的字眼,
04:20
like "home," "childhood" and "the past."
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比如「家」、「童年」,和「過去」。
04:24
It then creates a nostalgia score
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接著,它就會算出鄉愁分數,
04:26
to indicate how nostalgic your story is.
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表示你的故事有多麼具有鄉愁。
04:29
And that score is the driving force behind these light-based sculptures
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那個分數,就是這些以光線為 基礎之雕塑背後的驅動力,
04:33
that serve as physical embodiments of your contribution.
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這些雕塑就是將你的貢獻 給具體化的結果。
04:37
And the higher the score, the rosier the hue.
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分數越高,色調就會越偏玫瑰色。
04:40
You know, like looking at the world through rose-colored glasses.
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就像是戴上玫瑰色的 眼鏡來看世界。
04:44
So, when you see your score
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所以,當你看到你的分數
04:47
and the physical representation of it,
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以及它的實體代表呈現之後,
04:50
sometimes you'd agree and sometimes you wouldn't.
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有時你可能可以認同,有時不能。
04:53
It's as if it really understood how that experience made you feel.
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它就像是真正去了解 那體驗帶給你什麼感覺。
04:57
But other times it gets tripped up
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但其他時候,這裝置會犯錯,
04:59
and has you thinking it doesn't understand you at all.
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讓你認為它完全不了解你。
05:02
But the piece really serves to show
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但這件作品實際上呈現出
05:04
that if we have a hard time explaining the emotions that we have to each other,
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如果我們都很難向彼此 解釋我們的某些情緒,
05:08
how can we teach a computer to make sense of them?
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我們要如何教電腦理解那些情緒?
05:12
So, even the more objective parts about being human are hard to describe.
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即使是人類比較客觀的 部分,也很難形容。
05:15
Like, conversation.
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比如,對談。
05:17
Have you ever really tried to break down the steps?
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你可曾真正試過把它拆解成步驟?
05:20
So think about sitting with your friend at a coffee shop
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試想一下,你和朋友 坐在咖啡廳裡,
05:23
and just having small talk.
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只是隨意閒聊。
05:25
How do you know when to take a turn?
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怎麼知道何時要換人說話?
05:27
How do you know when to shift topics?
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怎麼知道何時要換主題?
05:29
And how do you even know what topics to discuss?
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甚至,怎麼知道要聊什麼主題?
05:33
See, most of us don't really think about it,
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我們大部分人並不會去思考這些,
05:35
because it's almost second nature.
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因為我們早就習慣成自然。
05:37
And when we get to know someone, we learn more about what makes them tick,
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當我們漸漸了解一個人, 就會更清楚什麼會打動他,
05:40
and then we learn what topics we can discuss.
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然後就會知道我們能討論什麼話題。
05:43
But when it comes to teaching AI systems how to interact with people,
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但若要教導人工智能系統 怎麼和人互動,
05:46
we have to teach them step by step what to do.
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我們得要教它們每一個步驟。
05:49
And right now, it feels clunky.
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現在,感覺還很笨拙。
05:53
If you've ever tried to talk with Alexa, Siri or Google Assistant,
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如果你曾經試過和 Alexa、 Siri,或 Google 助理說話,
05:57
you can tell that it or they can still sound cold.
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你就知道它們聽起來還是很冰冷。
06:02
And have you ever gotten annoyed
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你是否曾被它們惹惱,
06:04
when they didn't understand what you were saying
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因為它們聽不懂你在說什麼,
06:06
and you had to rephrase what you wanted 20 times just to play a song?
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你得要換二十種說法, 只為播放一首歌?
06:11
Alright, to the credit of the designers, realistic communication is really hard.
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好吧,設計師是很辛苦的, 真實的溝通真的很難。
06:16
And there's a whole branch of sociology,
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而社會學還有一整個分支,
06:18
called conversation analysis,
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就叫做談話分析,
06:20
that tries to make blueprints for different types of conversation.
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試圖為不同類型的對談繪製出藍圖。
06:23
Types like customer service or counseling, teaching and others.
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像客服、諮詢、教學和其他類型。
06:28
I've been collaborating with a conversation analyst at the lab
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我在實驗室和一位對談分析師合作,
06:31
to try to help our AI systems hold more human-sounding conversations.
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試圖協助我們的人工智慧系統 在進行對談時聽起來更像人類。
06:36
This way, when you have an interaction with a chatbot on your phone
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這麼一來,當你用手機 和聊天機器人互動,
06:39
or a voice-based system in the car,
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或和車上的語音系統互動時,
06:41
it sounds a little more human and less cold and disjointed.
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它聽起來會更像人一點, 不那麼冰冷,不那麼沒條理。
06:46
So I created a piece of art
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我創作了一件藝術作品,
06:47
that tries to highlight the robotic, clunky interaction
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試圖強調出機器式、笨拙的互動,
06:50
to help us understand, as designers,
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來協助我們設計師了解
06:52
why it doesn't sound human yet and, well, what we can do about it.
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為什麼它聽起來還不像人, 以及對此我們能怎麼辦。
06:57
The piece is called Bot to Bot
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這件作品叫機器人對機器人,
06:58
and it puts one conversational system against another
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它讓一個交談系統 和另一個交談系統互動,
07:01
and then exposes it to the general public.
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接著讓系統接觸一般民眾。
07:04
And what ends up happening is that you get something
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最後發生的狀況就是, 你得到一種產物,
07:06
that tries to mimic human conversation,
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它會試圖模仿人類交談,
07:08
but falls short.
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但達不到標準。
07:10
Sometimes it works and sometimes it gets into these, well,
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有時它行得通,
有時它會陷入誤解的迴圈當中。
07:13
loops of misunderstanding.
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07:14
So even though the machine-to-machine conversation can make sense,
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雖然機器對機器的交談是有意義的,
07:17
grammatically and colloquially,
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在文法上和口語上皆是如此,
07:20
it can still end up feeling cold and robotic.
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但它最後的感覺 可能還是很冰冷、很機械化。
07:23
And despite checking all the boxes, the dialogue lacks soul
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儘管所有的條件都做到了, 對話還是沒有靈魂,
07:27
and those one-off quirks that make each of us who we are.
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缺乏讓我們每個人 獨一無二的個人特色。
07:30
So while it might be grammatically correct
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所以,即使文法是正確的,
07:32
and uses all the right hashtags and emojis,
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所有「#」和表情符號的 應用也都沒錯,
07:35
it can end up sounding mechanical and, well, a little creepy.
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聽起來還是很機械, 且還有一點詭異。
07:39
And we call this the uncanny valley.
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我們稱之為恐怖谷。
07:41
You know, that creepiness factor of tech
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技術中的怪異因子,
07:43
where it's close to human but just slightly off.
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很接近人類,但又還差那麼一點。
07:46
And the piece will start being
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這件作品將開始成為一種方式,
07:48
one way that we test for the humanness of a conversation
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讓我們能夠測試對話的人性
07:51
and the parts that get lost in translation.
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和翻譯中迷失的部分。
07:54
So there are other things that get lost in translation, too,
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在翻譯中還有其他的東西會遺失,
07:57
like human intuition.
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比如人類直覺。
07:59
Right now, computers are gaining more autonomy.
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目前,電腦越來越自動化。
08:01
They can take care of things for us,
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它們能為我們處理事情,
08:03
like change the temperature of our houses based on our preferences
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比如根據我們的偏好 幫我們改變房子裡的室溫,
08:06
and even help us drive on the freeway.
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甚至協助我們在高速公路上開車。
08:09
But there are things that you and I do in person
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但有些我們個人會做的事情,
08:12
that are really difficult to translate to AI.
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很難翻譯讓人工智能理解。
08:15
So think about the last time that you saw an old classmate or coworker.
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想想看你上回碰見一位 老同學或同事的情況。
08:21
Did you give them a hug or go in for a handshake?
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你是給他一個擁抱,還是握個手?
08:24
You probably didn't think twice
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你可能根本不用思考,
08:26
because you've had so many built up experiences
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因為你有許多既有經驗,
08:28
that had you do one or the other.
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會影響你決定做前者或後者。
08:31
And as an artist, I feel that access to one's intuition,
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身為藝術家,我覺得 正是因為會使用直覺,
08:34
your unconscious knowing,
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潛意識中所知道的事,
08:36
is what helps us create amazing things.
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才讓我們能創造出了不起的事物。
08:39
Big ideas, from that abstract, nonlinear place in our consciousness
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大點子是來自於我們意識中 那塊抽象、非線性的地方,
08:43
that is the culmination of all of our experiences.
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我們所有經驗的最高點。
08:47
And if we want computers to relate to us and help amplify our creative abilities,
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若我們想讓電腦能和我們相處, 並協助強化我們的創意能力,
08:52
I feel that we'll need to start thinking about how to make computers be intuitive.
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我覺得我們得要開始思考 如何讓電腦能有直覺。
08:56
So I wanted to explore how something like human intuition
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所以,我想要探究的是, 像人類直覺這類東西
08:59
could be directly translated to artificial intelligence.
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如何能被直接翻譯成 人工智慧能懂的語言。
09:03
And I created a piece that explores computer-based intuition
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我創作了一件作品, 來探究實體空間中
09:06
in a physical space.
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以電腦為基礎的直覺。
09:08
The piece is called Wayfinding,
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這件作品叫做「找路」,
09:10
and it's set up as a symbolic compass that has four kinetic sculptures.
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它的設計是個象徵性的羅盤, 具有四個動態雕塑。
09:14
Each one represents a direction,
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每一個都代表一個方向,
09:16
north, east, south and west.
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北、東、南、西。
09:19
And there are sensors set up on the top of each sculpture
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每個雕塑上都裝有感測器,
09:21
that capture how far away you are from them.
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能知道你離它們有多遠。
09:24
And the data that gets collected
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資料會被收集起來,
09:25
ends up changing the way that sculptures move
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最後會改變雕塑移動的方式,
09:28
and the direction of the compass.
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以及羅盤的方向。
09:31
The thing is, the piece doesn't work like the automatic door sensor
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重點是,這件作品並不是像 自動門的感測器那樣運作,
09:35
that just opens when you walk in front of it.
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當你走到自動門前時它就會打。
09:37
See, your contribution is only a part of its collection of lived experiences.
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它在收集活體的經驗, 而你的貢獻只是其中一部分。
09:42
And all of those experiences affect the way that it moves.
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所有經驗都會影響它移動的方式。
09:46
So when you walk in front of it,
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所以,當你走到它前方時,
09:48
it starts to use all of the data
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它會開始用所有資料,
09:50
that it's captured throughout its exhibition history --
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所有它在展示期間 所捕捉到的資料──
09:53
or its intuition --
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可以說是它的直覺──
09:55
to mechanically respond to you based on what it's learned from others.
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根據它從其他人身上學到的, 來對你做出機械式的反應。
09:59
And what ends up happening is that as participants
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最後的結果是,我們參與者開始
10:02
we start to learn the level of detail that we need
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學到許多細節, 我們需要這些細節,
10:04
in order to manage expectations
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才能管理來自人類
10:06
from both humans and machines.
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以及機器的期望。
10:09
We can almost see our intuition being played out on the computer,
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我們幾乎可以看見 我們的直覺在電腦上呈現出來,
10:13
picturing all of that data being processed in our mind's eye.
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描繪出我們心靈之眼所看見的 所有被處理的資料。
10:17
My hope is that this type of art
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我希望這類藝術
10:19
will help us think differently about intuition
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能夠協助我們對直覺 有不同的想法,
10:21
and how to apply that to AI in the future.
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及未來要如何 把它用在人工智能上。
10:24
So these are just a few examples of how I'm using art to feed into my work
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這些只是幾個例子,
用來說明我這個 人工智能設計師兼研究者,
10:28
as a designer and researcher of artificial intelligence.
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如何把藝術注入工作中。
10:31
And I see it as a crucial way to move innovation forward.
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我認為,要讓創新再向前邁進, 這種方式十分關鍵。
10:35
Because right now, there are a lot of extremes when it comes to AI.
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因為,目前談到人工智能時, 會有許多極端想法。
10:39
Popular movies show it as this destructive force
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熱門電影把人工智能 呈現成毀滅性的力量,
10:42
while commercials are showing it as a savior
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廣告又把它呈現成救星,
10:45
to solve some of the world's most complex problems.
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用來解決世界上最困難的一些問題。
10:48
But regardless of where you stand,
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但,不論你的立場為何,
10:50
it's hard to deny that we're living in a world
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很難否認,我們所居住的世界
10:53
that's becoming more and more digital by the second.
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每一秒鐘都在變得越來越數位。
10:55
Our lives revolve around our devices, smart appliances and more.
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我們的生活圍繞著我們的裝置、 智慧設備等等在轉動。
11:01
And I don't think this will let up any time soon.
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我不認為近期內這會減緩下來。
11:04
So, I'm trying to embed more humanness from the start.
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所以,我試著打從一開始 就嵌入更多的人性。
11:08
And I have a hunch that bringing art into an AI research process
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我有預感,將藝術帶入
人工智能的研究過程是一種方法。
11:13
is a way to do just that.
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11:15
Thank you.
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謝謝。
11:16
(Applause)
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(掌聲)
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