TED@IBM
Raphael Arar: How we can teach computers to make sense of our emotions
拉斐尔 · 阿拉尔: 我们怎样才能教会电脑理解我们的情感？
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怎样才能让人类真正地与人工智能进行互动？拉斐尔 · 阿拉尔建议我们从创造艺术开始。他分享了能让人工智能探究复杂想法的交互项目，例如怀旧、直觉、对话——所有的工作都在朝着将来人工智能尽可能和人类一样的目标而努力。
Raphael Arar - Designer, researcher
IBM's Raphael Arar creates art and designs experiences that examine the complexities of our increasingly technocentric lives. Full bio
IBM's Raphael Arar creates art and designs experiences that examine the complexities of our increasingly technocentric lives. Full bio
Double-click the English transcript below to play the video.
00:13
I consider myself one part artist
and one part designer.
and one part designer.
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我自认为既是一位艺术家，
又是一位设计师。
又是一位设计师。
我在一个研究
人工智能的实验室工作。
人工智能的实验室工作。
00:18
And I work at an artificial
intelligence research lab.
intelligence research lab.
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00:22
We're trying to create technology
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我们正在尝试创造一项
在遥远的将来能和人类互动的科技。
00:24
that you'll want to interact with
in the far future.
in the far future.
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不是在六个月之后，
而是几年甚至几十年之后。
而是几年甚至几十年之后。
00:27
Not just six months from now,
but try years and decades from now.
but try years and decades from now.
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00:33
And we're taking a moonshot
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我们正在实施一个“登月计划”，
00:34
that we'll want to be
interacting with computers
interacting with computers
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我们希望能与电脑
进行深层次的情感方面的互动。
00:37
in deeply emotional ways.
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00:40
So in order to do that,
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为了做到这些，
技术不仅要智能，还要人性。
00:41
the technology has to be
just as much human as it is artificial.
just as much human as it is artificial.
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00:46
It has to get you.
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它必须懂你。
00:49
You know, like that inside joke
that'll have you and your best friend
that'll have you and your best friend
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就像一个朋友间的笑话，
能让你和你的朋友
能让你和你的朋友
在地板上笑得前仰后翻；
00:52
on the floor, cracking up.
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00:54
Or that look of disappointment
that you can just smell from miles away.
that you can just smell from miles away.
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或者是，你远远就能
嗅到的失望气息。
嗅到的失望气息。
01:00
I view art as the gateway to help us
bridge this gap between human and machine:
bridge this gap between human and machine:
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我把艺术看作是帮助我们填补
人类与机器之间空白的途径：
人类与机器之间空白的途径：
01:07
to figure out what it means
to get each other
to get each other
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使每个人都能相互了解对方，
使我们能训练
人工智能来“懂我们”。
人工智能来“懂我们”。
01:10
so that we can train AI to get us.
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01:13
See, to me, art is a way
to put tangible experiences
to put tangible experiences
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对我来说，艺术是把有形的经历，
转化为无形的想法、
感受、情感的方式。
感受、情感的方式。
01:17
to intangible ideas,
feelings and emotions.
feelings and emotions.
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01:21
And I think it's one
of the most human things about us.
of the most human things about us.
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我认为这是人性的一个重要特征。
01:25
See, we're a complicated
and complex bunch.
and complex bunch.
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我们是难懂的、复杂的群体。
我们拥有无限的情感，
01:28
We have what feels like
an infinite range of emotions,
an infinite range of emotions,
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而且，我们都是不同的。
01:31
and to top it off, we're all different.
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我们拥有不同的家庭背景，
01:34
We have different family backgrounds,
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不同的经历，不同的心理活动。
01:36
different experiences
and different psychologies.
and different psychologies.
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01:40
And this is what makes life
really interesting.
really interesting.
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这是为什么生活那么有趣的原因，
01:43
But this is also what makes
working on intelligent technology
working on intelligent technology
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但这同时也是研究智能技术
最难的地方。
01:46
extremely difficult.
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01:49
And right now, AI research, well,
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如今，对人工智能的研究
过于偏重技术，
01:53
it's a bit lopsided on the tech side.
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这也很好理解。
01:55
And that makes a lot of sense.
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关于我们的每一个定性的特征，
01:57
See, for every
qualitative thing about us --
qualitative thing about us --
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比如属于我们情感的、
动态的、主观的部分——
动态的、主观的部分——
01:59
you know, those parts of us that are
emotional, dynamic and subjective --
emotional, dynamic and subjective --
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我们要把它转化为一个量化指标：
02:04
we have to convert it
to a quantitative metric:
to a quantitative metric:
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能通过一些事实、图形和
电脑代码表现出来。
电脑代码表现出来。
02:07
something that can be represented
with facts, figures and computer code.
with facts, figures and computer code.
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02:13
The issue is, there are
many qualitative things
many qualitative things
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问题是，有很多定性的东西
是很难量化的。
02:16
that we just can't put our finger on.
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02:20
So, think about hearing
your favorite song for the first time.
your favorite song for the first time.
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想一想你第一次听到
你最喜欢的歌的时候，
你最喜欢的歌的时候，
02:25
What were you doing?
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你在做什么？
02:28
How did you feel?
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你有什么感受？
02:30
Did you get goosebumps?
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你起鸡皮疙瘩了吗？
02:33
Or did you get fired up?
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你有没有感到热血沸腾？
02:36
Hard to describe, right?
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很难描述，对吗？
02:38
See, parts of us feel so simple,
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我们一些看似很简单的感受
背后其实是很复杂的。
02:40
but under the surface,
there's really a ton of complexity.
there's really a ton of complexity.
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而将这些复杂的东西
翻译成机器语言，
翻译成机器语言，
02:44
And translating
that complexity to machines
that complexity to machines
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这就是我们需要实现的
现代“登月计划”。
现代“登月计划”。
02:47
is what makes them modern-day moonshots.
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我不相信我们可以仅仅
用0和1这两个数字
用0和1这两个数字
02:50
And I'm not convinced that we can
answer these deeper questions
answer these deeper questions
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来解决这些难题。
02:54
with just ones and zeros alone.
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02:57
So, in the lab, I've been creating art
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所以，在实验室，我通过创造艺术
来帮助我设计更好的
02:59
as a way to help me
design better experiences
design better experiences
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对尖端科技的体验。
03:01
for bleeding-edge technology.
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艺术作为一种催化剂，
03:03
And it's been serving as a catalyst
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03:05
to beef up the more human ways
that computers can relate to us.
that computers can relate to us.
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让电脑更加人类化，
更理解我们。
更理解我们。
03:10
Through art, we're tacking
some of the hardest questions,
some of the hardest questions,
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通过艺术，我们在解决一些
非常困难的问题，
非常困难的问题，
就像，感受到底是什么意思？
03:12
like what does it really mean to feel?
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03:16
Or how do we engage and know
how to be present with each other?
how to be present with each other?
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我们如何真正参与或投入其中？
03:20
And how does intuition
affect the way that we interact?
affect the way that we interact?
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我们的直觉怎样影响
我们互动的方式？
我们互动的方式？
03:26
So, take for example human emotion.
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以人类的情感为例，
如今，电脑能够明白
我们的基本情感，
我们的基本情感，
03:28
Right now, computers can make sense
of our most basic ones,
of our most basic ones,
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比如开心、伤心、
生气、恐惧、厌恶，
生气、恐惧、厌恶，
03:31
like joy, sadness,
anger, fear and disgust,
anger, fear and disgust,
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把这些特征转化为数学。
03:35
by converting those
characteristics to math.
characteristics to math.
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03:39
But what about the more complex emotions?
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那较复杂的情感呢？
比如那些很难
03:41
You know, those emotions
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03:43
that we have a hard time
describing to each other?
describing to each other?
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用文字向对方描述的情感，
比如，怀旧。
03:45
Like nostalgia.
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03:47
So, to explore this, I created
a piece of art, an experience,
a piece of art, an experience,
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所以，为了探索这个问题，
我创造了一件艺术品，一种体验，
我创造了一件艺术品，一种体验，
03:51
that asked people to share a memory,
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要求人们分享他们的记忆，
我和一些数据科学家组成一个团队，
03:53
and I teamed up with some data scientists
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去研究高度主观的情感是怎样的，
03:55
to figure out how to take
an emotion that's so highly subjective
an emotion that's so highly subjective
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如何将它们精确地转化为数学。
03:59
and convert it into something
mathematically precise.
mathematically precise.
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04:03
So, we created what we call
a nostalgia score
a nostalgia score
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我们创造了一个叫怀旧分数的东西，
这是这个装置的核心。
04:06
and it's the heart of this installation.
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这个装置会要求你分享一则故事，
04:08
To do that, the installation
asks you to share a story,
asks you to share a story,
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电脑会分析它的一些简单的情感，
04:11
the computer then analyzes it
for its simpler emotions,
for its simpler emotions,
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它会检测你使用
过去时态的词语的偏好，
过去时态的词语的偏好，
04:14
it checks for your tendency
to use past-tense wording
to use past-tense wording
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还会寻找与怀旧有关的词语，
04:17
and also looks for words
that we tend to associate with nostalgia,
that we tend to associate with nostalgia,
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比如“家”、“童年”和“过去”。
04:20
like "home," "childhood" and "the past."
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04:24
It then creates a nostalgia score
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它最后会给出一个怀旧分数，
04:26
to indicate how nostalgic your story is.
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代表着你的故事的怀旧程度，
04:29
And that score is the driving force
behind these light-based sculptures
behind these light-based sculptures
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这个分数会
让这个灯箱的颜色发生变化，
让这个灯箱的颜色发生变化，
代表着你的贡献。
04:33
that serve as physical embodiments
of your contribution.
of your contribution.
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分数越高，色调越偏向玫瑰红色，
04:37
And the higher the score,
the rosier the hue.
the rosier the hue.
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就像是通过玫红色的眼镜看世界。
04:40
You know, like looking at the world
through rose-colored glasses.
through rose-colored glasses.
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当你看到你的分数，
04:44
So, when you see your score
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以及它的外部反映时，
04:47
and the physical representation of it,
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有时你会赞同，有时不赞同。
04:50
sometimes you'd agree
and sometimes you wouldn't.
and sometimes you wouldn't.
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有的时候就好像它真的明白
故事里的你当时的感受，
故事里的你当时的感受，
04:53
It's as if it really understood
how that experience made you feel.
how that experience made you feel.
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04:57
But other times it gets tripped up
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但有的时候它也会出错，
会让你觉得它一点也不懂你。
04:59
and has you thinking
it doesn't understand you at all.
it doesn't understand you at all.
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05:02
But the piece really serves to show
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但这个装置能够说明，
如果连我们都很难表述清楚的情感，
05:04
that if we have a hard time explaining
the emotions that we have to each other,
the emotions that we have to each other,
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我们该如何教电脑明白呢？
05:08
how can we teach a computer
to make sense of them?
to make sense of them?
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05:12
So, even the more objective parts
about being human are hard to describe.
about being human are hard to describe.
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甚至很多关于
人性的客观方面也很难描述。
人性的客观方面也很难描述。
比如，对话。
05:15
Like, conversation.
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05:17
Have you ever really tried
to break down the steps?
to break down the steps?
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你曾经尝试过分解谈话的步骤吗？
试着想象一下，
你和你的朋友坐在咖啡馆，
你和你的朋友坐在咖啡馆，
05:20
So think about sitting
with your friend at a coffee shop
with your friend at a coffee shop
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进行简单的交谈，
05:23
and just having small talk.
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05:25
How do you know when to take a turn?
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你怎么知道轮到你说话了？
05:27
How do you know when to shift topics?
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你怎么知道什么时候该转换话题？
05:29
And how do you even know
what topics to discuss?
what topics to discuss?
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你怎么知道要讨论些什么？
05:33
See, most of us
don't really think about it,
don't really think about it,
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大多数人都不会想这些问题，
因为这对我们来说是很自然的。
05:35
because it's almost second nature.
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当我们认识一个人的时候，
我们会对他们越来越了解，
我们会对他们越来越了解，
05:37
And when we get to know someone,
we learn more about what makes them tick,
we learn more about what makes them tick,
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然后我们会知道能聊些什么话题。
05:40
and then we learn
what topics we can discuss.
what topics we can discuss.
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但是，当你教人工智能
怎样与人类互动时，
怎样与人类互动时，
05:43
But when it comes to teaching
AI systems how to interact with people,
AI systems how to interact with people,
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我们需要一步一步教它们该如何做。
05:46
we have to teach them
step by step what to do.
step by step what to do.
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而现在，这个过程还感觉很笨拙。
05:49
And right now, it feels clunky.
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如果你曾尝试和 Alexa, Siri
或谷歌助手聊天，
或谷歌助手聊天，
05:53
If you've ever tried to talk
with Alexa, Siri or Google Assistant,
with Alexa, Siri or Google Assistant,
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你可以感觉得到，
它们仍听上去冷冰冰的。
它们仍听上去冷冰冰的。
05:57
you can tell that it or they
can still sound cold.
can still sound cold.
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06:02
And have you ever gotten annoyed
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你是否曾经因为它们
不明白你说什么而变得恼怒，
06:04
when they didn't understand
what you were saying
what you were saying
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比如为了让它们放一首歌，
你得说上20次？
你得说上20次？
06:06
and you had to rephrase what you wanted
20 times just to play a song?
20 times just to play a song?
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06:11
Alright, to the credit of the designers,
realistic communication is really hard.
realistic communication is really hard.
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不过我们也要理解设计师，毕竟
让机器学会真实的沟通是非常难的。
让机器学会真实的沟通是非常难的。
有一个社会学的分支，
06:16
And there's a whole branch of sociology,
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叫做会话分析，
06:18
called conversation analysis,
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它尝试做不同对话类型的蓝图，
06:20
that tries to make blueprints
for different types of conversation.
for different types of conversation.
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例如像客户服务、心理咨询、
教授课程等等的会话类型。
教授课程等等的会话类型。
06:23
Types like customer service
or counseling, teaching and others.
or counseling, teaching and others.
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06:28
I've been collaborating
with a conversation analyst at the lab
with a conversation analyst at the lab
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我已经在和会话分析学家
在实验室展开合作，
在实验室展开合作，
尝试帮助我们的人工智能系统
进行更多的人性化的对话。
进行更多的人性化的对话。
06:31
to try to help our AI systems
hold more human-sounding conversations.
hold more human-sounding conversations.
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这样，当你和手机
聊天机器人进行互动时，
聊天机器人进行互动时，
06:36
This way, when you have an interaction
with a chatbot on your phone
with a chatbot on your phone
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或者和车载语音系统互动时，
06:39
or a voice-based system in the car,
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这种声音就听上去更人性，
不那么冷淡和缺乏逻辑。
不那么冷淡和缺乏逻辑。
06:41
it sounds a little more human
and less cold and disjointed.
and less cold and disjointed.
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06:46
So I created a piece of art
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我创造的这个艺术品，
重点突显了机械化的、
笨拙的互动方式，
笨拙的互动方式，
06:47
that tries to highlight
the robotic, clunky interaction
the robotic, clunky interaction
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以帮助我们这些设计师明白，
06:50
to help us understand, as designers,
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为什么它听上去不像人类，
我们该如何解决这个问题。
我们该如何解决这个问题。
06:52
why it doesn't sound human yet
and, well, what we can do about it.
and, well, what we can do about it.
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这个艺术品叫 Bot to Bot，
06:57
The piece is called Bot to Bot
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它将一个会话系统
搭建在另一个会话系统之上，
搭建在另一个会话系统之上，
06:58
and it puts one conversational
system against another
system against another
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然后展示给公众。
07:01
and then exposes it to the general public.
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最终会发生的就是，
07:04
And what ends up happening
is that you get something
is that you get something
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它尝试模仿人类的对话，
07:06
that tries to mimic human conversation,
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但是却明显有不足之处。
07:08
but falls short.
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有的时候它还可以，而有的时候
07:10
Sometimes it works and sometimes
it gets into these, well,
it gets into these, well,
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会陷入误解的循环。
07:13
loops of misunderstanding.
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虽然机器与机器的对话
从语法、用意上
从语法、用意上
07:14
So even though the machine-to-machine
conversation can make sense,
conversation can make sense,
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能让人明白，
07:17
grammatically and colloquially,
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但是你还是能感觉到
这个对话的冰冷和机械化。
这个对话的冰冷和机械化。
07:20
it can still end up
feeling cold and robotic.
feeling cold and robotic.
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尽管对话的其他要素都具备，
但却缺少了灵魂，
但却缺少了灵魂，
07:23
And despite checking all the boxes,
the dialogue lacks soul
the dialogue lacks soul
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缺少了那些使我们
之所以为人类的特质。
之所以为人类的特质。
07:27
and those one-off quirks
that make each of us who we are.
that make each of us who we are.
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尽管它的语法也许正确，
07:30
So while it might be grammatically correct
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用对了所有的话题标签和符号表情，
07:32
and uses all the right
hashtags and emojis,
hashtags and emojis,
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但最终听起来还是有些呆板，
还有点儿吓人。
还有点儿吓人。
07:35
it can end up sounding mechanical
and, well, a little creepy.
and, well, a little creepy.
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我们把这称为恐怖谷，
07:39
And we call this the uncanny valley.
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这种科技的恐怖之处在于，
07:41
You know, that creepiness factor of tech
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它无比接近人类，却又缺了点什么。
07:43
where it's close to human
but just slightly off.
but just slightly off.
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这件艺术品能开始用于
07:46
And the piece will start being
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测试交流的人性化，
07:48
one way that we test
for the humanness of a conversation
for the humanness of a conversation
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以及被误解的部分。
07:51
and the parts that get
lost in translation.
lost in translation.
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07:54
So there are other things
that get lost in translation, too,
that get lost in translation, too,
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还有其他一些事情
也容易被电脑误解，
也容易被电脑误解，
比如，人类的直觉。
07:57
like human intuition.
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如今，电脑拥有更多的自主权，
07:59
Right now, computers
are gaining more autonomy.
are gaining more autonomy.
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能为我们管理一些东西，
08:01
They can take care of things for us,
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比如根据我们的偏好
调整房子的温度，
调整房子的温度，
08:03
like change the temperature
of our houses based on our preferences
of our houses based on our preferences
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甚至帮助我们在高速公路驾驶。
08:06
and even help us drive on the freeway.
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08:09
But there are things
that you and I do in person
that you and I do in person
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但是一些我和你会做的事，
是非常难翻译给人工智能的。
08:12
that are really difficult
to translate to AI.
to translate to AI.
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08:15
So think about the last time
that you saw an old classmate or coworker.
that you saw an old classmate or coworker.
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想一想你上一次
见到一位老同学或老同事时，
见到一位老同学或老同事时，
08:21
Did you give them a hug
or go in for a handshake?
or go in for a handshake?
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你跟他们拥抱还是握手了呢？
08:24
You probably didn't think twice
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你可能想都没想，
因为你有过许多这样的经历，
08:26
because you've had so many
built up experiences
built up experiences
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要么拥抱要么握手。
08:28
that had you do one or the other.
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08:31
And as an artist, I feel
that access to one's intuition,
that access to one's intuition,
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作为一名艺术家，我认为
了解一个人的直觉，
了解一个人的直觉，
你的潜意识的知觉，
08:34
your unconscious knowing,
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能够帮助我们创造
令人惊叹的东西。
令人惊叹的东西。
08:36
is what helps us create amazing things.
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08:39
Big ideas, from that abstract,
nonlinear place in our consciousness
nonlinear place in our consciousness
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大的点子，我们潜意识中
抽象的，非线性的东西
抽象的，非线性的东西
是我们所有经历的总和。
08:43
that is the culmination
of all of our experiences.
of all of our experiences.
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08:47
And if we want computers to relate to us
and help amplify our creative abilities,
and help amplify our creative abilities,
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如果我们想让电脑
帮我们提升创造力，
帮我们提升创造力，
我认为我们需要思考
如何才能让电脑有直觉，
如何才能让电脑有直觉，
08:52
I feel that we'll need to start thinking
about how to make computers be intuitive.
about how to make computers be intuitive.
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所以，我想探究
如何将像人类直觉的东西
如何将像人类直觉的东西
08:56
So I wanted to explore
how something like human intuition
how something like human intuition
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直接地翻译给人工智能。
08:59
could be directly translated
to artificial intelligence.
to artificial intelligence.
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于是我创造了一台通过现实空间
09:03
And I created a piece
that explores computer-based intuition
that explores computer-based intuition
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探究电脑直觉的机器。
09:06
in a physical space.
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09:08
The piece is called Wayfinding,
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它叫 Wayfinding，
它有4个动态装置，像一个指南针。
09:10
and it's set up as a symbolic compass
that has four kinetic sculptures.
that has four kinetic sculptures.
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每一个装置代表着一个方向，
09:14
Each one represents a direction,
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北、东、南、西。
09:16
north, east, south and west.
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09:19
And there are sensors set up
on the top of each sculpture
on the top of each sculpture
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装在每个装置顶端的传感器，
能够捕获你离它们的距离有多远。
09:21
that capture how far away
you are from them.
you are from them.
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接着数据会被采集，
09:24
And the data that gets collected
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最终装置就会移动，
09:25
ends up changing the way
that sculptures move
that sculptures move
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从而改变指南针的方向。
09:28
and the direction of the compass.
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09:31
The thing is, the piece doesn't work
like the automatic door sensor
like the automatic door sensor
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不过不像自动门的传感器那样——
你走到它前面的时候，门就会打开，
09:35
that just opens
when you walk in front of it.
when you walk in front of it.
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你的行为只是它
搜集的体验的一部分，
搜集的体验的一部分，
09:37
See, your contribution is only a part
of its collection of lived experiences.
of its collection of lived experiences.
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所有的体验都会影响它的移动。
09:42
And all of those experiences
affect the way that it moves.
affect the way that it moves.
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所以当你在它前面走动时，
09:46
So when you walk in front of it,
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它开始用所有之前
09:48
it starts to use all of the data
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捕获的数据——
09:50
that it's captured
throughout its exhibition history --
throughout its exhibition history --
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或它的直觉——
09:53
or its intuition --
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基于它从其他人那里学习到的，
对你做出机械的响应。
对你做出机械的响应。
09:55
to mechanically respond to you
based on what it's learned from others.
based on what it's learned from others.
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09:59
And what ends up happening
is that as participants
is that as participants
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最终，作为参与者，
我们意识到我们需要怎样的细节
10:02
we start to learn the level
of detail that we need
of detail that we need
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才能同时管理
10:04
in order to manage expectations
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人类和机器的预期。
10:06
from both humans and machines.
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我们几乎可以看到我们的直觉
在电脑中被展示出来，
在电脑中被展示出来，
10:09
We can almost see our intuition
being played out on the computer,
being played out on the computer,
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想象所有的数据
被我们的心灵之眼所处理。
被我们的心灵之眼所处理。
10:13
picturing all of that data
being processed in our mind's eye.
being processed in our mind's eye.
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10:17
My hope is that this type of art
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我希望这种艺术方式，
能帮助我们从不同角度思考直觉，
10:19
will help us think differently
about intuition
about intuition
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以及将来如何
将它运用到人工智能中去。
将它运用到人工智能中去。
10:21
and how to apply that to AI in the future.
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10:24
So these are just a few examples
of how I'm using art to feed into my work
of how I'm using art to feed into my work
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这些都是我在自己的
人工智能设计和研究的工作中
人工智能设计和研究的工作中
10:28
as a designer and researcher
of artificial intelligence.
of artificial intelligence.
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如何利用艺术的例子。
我觉得这是一个
推动创新的重要方式。
推动创新的重要方式。
10:31
And I see it as a crucial way
to move innovation forward.
to move innovation forward.
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因为现在说到人工智能，
两极分化的态度很严重。
两极分化的态度很严重。
10:35
Because right now, there are
a lot of extremes when it comes to AI.
a lot of extremes when it comes to AI.
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比如一些流行电影
将其描绘成毁灭性的力量，
将其描绘成毁灭性的力量，
10:39
Popular movies show it
as this destructive force
as this destructive force
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而一些广告则
把它们描绘为救世主——
把它们描绘为救世主——
10:42
while commercials
are showing it as a savior
are showing it as a savior
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能解决一些世界上
极端复杂的问题。
极端复杂的问题。
10:45
to solve some of the world's
most complex problems.
most complex problems.
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但是不管你站在哪一边，
10:48
But regardless of where you stand,
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我们都无法否认，我们正生活在一个
10:50
it's hard to deny
that we're living in a world
that we're living in a world
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越来越数字化的世界中。
10:53
that's becoming more
and more digital by the second.
and more digital by the second.
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我们的生活被设备、智能家居等充斥，
10:55
Our lives revolve around our devices,
smart appliances and more.
smart appliances and more.
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11:01
And I don't think
this will let up any time soon.
this will let up any time soon.
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而我不觉得这种状况会停止。
11:04
So, I'm trying to embed
more humanness from the start.
more humanness from the start.
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我想在一开始就植入更多的人性，
而我有预感，将艺术
带入人工智能研究
带入人工智能研究
11:08
And I have a hunch that bringing art
into an AI research process
into an AI research process
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就是其中一个方法。
11:13
is a way to do just that.
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谢谢。
11:15
Thank you.
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（掌声）
11:16
(Applause)
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ABOUT THE SPEAKER
Raphael Arar - Designer, researcherIBM's Raphael Arar creates art and designs experiences that examine the complexities of our increasingly technocentric lives.
Why you should listen
While his artwork raises questions about our relationship with modernity and technology, Raphael Arar’s design work revolves around answering those questions in human-centered ways.
Currently a designer and researcher at IBM Research, Arar has been exploring ways to translate aspects of emotional intelligence into machine-readable format. Through this, he is passionately tackling ethical platforms of artificial intelligence. Inc. Magazine says he "epitomizes the style and multi-disciplinary skill set of the modern designer," and in 2017, he was listed as one of Forbes's "30 under 30 in Enterprise Technology."
Raphael Arar | Speaker | TED.com