ABOUT THE SPEAKER
Jun Wang - Genomics researcher
At iCarbonX, Jun Wang aims to establish a big data platform for health management.

Why you should listen

In 1999, Jun Wang founded the Bioinformatics Department of Beijing Genomics Institute (BGI, now known as BGI Shenzhen), one of China’s premier research facilities. Until July 2015, Wang led the institution of 5,000+ people engaged in studies of genomics and its informatics, including genome assembly, annotation, expression, comparative genomics, molecular evolution, transcriptional regulation, genome variation analysis, database construction as well as methodology development such as the sequence assembler and alignment tools. He also focuses on interpretation of the definition of "gene" by expression and conservation study. In 2003, Wang was also involved in the SARS genome analysis and the silkworm genome assembly and analysis in cooperation with Chinese Southeast Agricultural University. The Pig Genome Project was completed at BGI under his leadership, as well as the chicken genome variation map and the TreeFam in collaboration with the Sanger Institute. In 2007, he and his group finished the first Asian diploid genome, the 1000 genome project, and many more projects. He initiated the "million genomes project" which seeks to better understand health based on human, plant, animal and micro-ecosystem genomes.

In late 2015, Wang founded a new institute/company, iCarbonX, aiming to develop an artificial intelligence engine to interpret and mine multiple health-related data and help people better manage their health and defeat disease.

More profile about the speaker
Jun Wang | Speaker | TED.com
TED2017

Jun Wang: How digital DNA could help you make better health choices

王俊: 数字DNA如何助你实现更好的健康选择

Filmed:
1,303,361 views

你是否知道食物和药物如何影响你的健康——在你食用它们之前?基因学研究员王俊致力于开发真人数字版;从基因代码开始,考虑各种因素,从食物摄入,睡眠数据到智能马桶数据的收集。运用这些宝贵的数据,王俊希望重构我们对健康的思考方式,无论是在个人还是集体层面。
- Genomics researcher
At iCarbonX, Jun Wang aims to establish a big data platform for health management. Full bio

Double-click the English transcript below to play the video.

00:12
Today今天 I'm here, actually其实,
to pose提出 you a question.
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今天我在这里给大家抛出一个问题
00:16
What is life?
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什么是生命
00:17
It has been really puzzling令人费解 me
for more than 25 years年份,
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这个问题困扰了我至少25年
可能在未来的25年内依然如此
00:21
and will probably大概 continue继续 doing so
for the next下一个 25 years年份.
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00:25
This is the thesis论文 I did
when I was still in undergraduate大学本科 school学校.
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这是我在念本科时做的论文
00:31
While my colleagues同事 still treated治疗
computers电脑 as big calculators计算器,
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当我的同学们依然拿
电脑作为大型计算器使用
00:38
I started开始 to teach computers电脑 to learn学习.
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我已经开始思考如何教电脑学习
00:41
I built内置 digital数字 lady淑女 beetles甲虫
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我构建了一个数字化瓢虫
00:44
and tried试着 to learn学习 from real真实 lady淑女 beetles甲虫,
just to do one thing:
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试图从自然界的瓢虫那里学习一件事
00:49
search搜索 for food餐饮.
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觅食
00:51
And after very simple简单 neural神经 network网络 --
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通过一个简单的神经网络
00:54
genetic遗传 algorithms算法 and so on --
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比如遗传算法等
00:56
look at the pattern模式.
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来观察这个模型
00:57
They're almost几乎 identical相同 to real真实 life.
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它几乎和活生生的瓢虫没有区别
01:01
A very striking引人注目 learning学习 experience经验
for a twenty-year-old二十多岁.
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对20岁的我来说这是非常震撼的体验
01:07
Life is a learning学习 program程序.
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生命本身就是一套学习程序
01:12
When you look
at all of this wonderful精彩 world世界,
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纵观大千世界
每个物种都有自己的学习程序
01:15
every一切 species种类 has
its own拥有 learning学习 program程序.
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01:19
The learning学习 program程序 is genome基因组,
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这个程序就是 基因组
01:22
and the code of that program程序 is DNA脱氧核糖核酸.
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程序的代码就是 DNA
01:27
The different不同 genomes基因组 of each species种类
represent代表 different不同 survival生存 strategies策略.
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每个物种的不同基因
代表了不同的生存策略
01:33
They represent代表 hundreds数以百计 of millions百万
of years年份 of evolution演化.
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也代表了数亿年的进化演变
01:38
The interaction相互作用 between之间
every一切 species'种类' ancestor祖先
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以及物种的祖先和环境的
相互作用
01:42
and the environment环境.
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01:46
I was really fascinated入迷 about the world世界,
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我十分着迷于这个世界
着迷于DNA
01:48
about the DNA脱氧核糖核酸,
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生命的语言
01:49
about, you know, the language语言 of life,
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还有学习程序
01:52
the program程序 of learning学习.
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01:54
So I decided决定 to co-found共发现
the institute研究所 to read them.
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所以我决定跟人合作建立
一个机构来解读它们
我做了很多相关研究
01:59
I read many许多 of them.
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02:01
We probably大概 read more than half
of the prior animal动物 genomes基因组 in the world世界.
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我们可能已经解读了世界上
超过一半动物的基因
02:06
I mean, up to date日期.
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到目前为止
02:09
We did learn学习 a lot.
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我们收获了很多
02:11
We did sequence序列, also,
one species种类 many许多, many许多 times ...
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我们对一个物种进行了多次基因测序
包括人类基因组
02:15
human人的 genome基因组.
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我们完成了第一个
亚洲人基因组的测序
02:16
We sequenced测序 the first Asian亚洲.
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我对自己的基因也进行了多次测序
02:18
I sequenced测序 it myself many许多, many许多 times,
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02:21
just to take advantage优点 of that platform平台.
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为了充分利用这个平台
02:24
Look at all those repeating重复 base基础 pairs:
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看看所有这些重复出现的碱基对
02:27
ATCGATCG.
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ATCG
你几乎无法从中读懂任何含义
02:29
You don't understand理解 anything there.
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但是再看一下这组字母
02:31
But look at that one base基础 pair.
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AGGAA 这五个字母
02:32
Those five letters, the AGGAAAGGAA.
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02:35
These five SNPs单核苷酸多态性 represent代表
a very specific具体 haplotype单倍型
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这五个SNP(单核苷酸多态性)
代表了一种非常特别的单形体
02:39
in the Tibetan population人口
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它们是位于藏族人身体中
一种叫做EPAS1的基因
02:41
around the gene基因 called EPASEPAS1.
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这种基因已被证明是
02:43
That gene基因 has been proved证实 --
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高度选择性的结果
02:45
it's highly高度 selective可选择的 --
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是藏族人对高海拔
适应性进行积极选择的
02:46
it's the most significant重大 signature签名
of positive selection选择 of Tibetans西藏人
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02:50
for the higher更高 altitude高度 adaptation适应.
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重要标志
02:53
You know what?
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这说明什么呢
这五个SNP极有可能是
灭绝的丹尼索瓦人 或与丹尼索瓦人
02:54
These five SNPs单核苷酸多态性 were the result结果
of integration积分 of Denisovans丹尼索瓦人,
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03:00
or Denisovan-like丹尼索瓦人样 individuals个人 into humans人类.
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有亲缘关系的人种的DNA
与人类DNA结合的结果
03:04
This is the reason原因
why we need to read those genomes基因组.
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这就是我们需要解读这些基因的原因
它可以让你了解历史
03:06
To understand理解 history历史,
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了解基因这套学习程序
03:08
to understand理解 what kind
of learning学习 process处理
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03:12
the genome基因组 has been through通过
for the millions百万 of years年份.
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在数百万年中经历了什么样的演变
03:17
By reading a genome基因组,
it can give you a lot of information信息 --
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通过解读基因可以得到很多信息
它能告诉你基因中的一些错误
03:20
tells告诉 you the bugs虫子 in the genome基因组 --
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例如出生缺陷 单基因遗传病
03:22
I mean, birth分娩 defects缺陷,
monogenetic单成 disorders障碍.
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而仅仅需要一滴血
03:25
Reading a drop下降 of blood血液
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它就能解释你为什么会发烧
03:26
could tell you why you got a fever发热,
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或者当你生病 特别是罹患癌症时
03:28
or it tells告诉 you which哪一个 medicine医学
and dosage剂量 needs需求 to be used
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告诉你需要使用哪种药物和多少剂量
03:31
when you're sick生病, especially特别 for cancer癌症.
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03:35
A lot of things could be studied研究,
but look at that:
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通过基因检测可以获得很多信息
不过看看这个数据
03:38
30 years年份 ago, we were still poor较差的 in China中国.
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30年前 中国还很穷
03:43
Only .67 percent百分 of the Chinese中文
adult成人 population人口 had diabetes糖尿病.
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当时只有0.67%的成年人患有糖尿病
03:47
Look at now: 11 percent百分.
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而现在 这个数字是11%
03:49
Genetics遗传学 cannot不能 change更改 over 30 years年份 --
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我们的基因不会在
30年内发生这么大的改变
在只有一代人的时间跨度内
03:53
only one generation.
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03:54
It must必须 be something different不同.
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肯定有别的影响因素
03:56
Diet饮食?
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是饮食
03:57
The environment环境?
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环境
03:59
Lifestyle生活方式?
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或者生活方式吗
04:01
Even identical相同 twins双胞胎
could develop发展 totally完全 differently不同.
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即使是同卵双胞胎
都可以变得很不一样
04:07
It could be one becomes
very obese肥胖, the other is not.
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一个很胖 一个很瘦
04:11
One develops发展 a cancer癌症
and the other does not.
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一个人得了癌症 另一个则没有
更不用说我们每天都生活在
一个充满压力的环境中
04:13
Not mentioning living活的
in a very stressed强调 environment环境.
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04:19
I moved移动 to Shenzhen深圳 10 years年份 ago ...
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10年前我搬到了深圳
04:22
for some reason原因, people may可能 know.
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由于某些原因 大家可能都知道
04:25
If the gene's基因的 under stress强调,
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一个人的基因在感受到压力时的
表现会完全不一样
04:27
it behaves的行为 totally完全 differently不同.
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04:30
Life is a journey旅程.
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人生是一场旅程
04:32
A gene基因 is just a starting开始 point,
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基因只是一个起点
而不是终点
04:35
not the end结束.
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04:37
You have this statistical统计 risk风险
of certain某些 diseases疾病 when you are born天生.
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你出生时就拥有某种疾病的患病风险
04:42
But every一切 day you make different不同 choices选择,
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然而每天你都会做出不同的决策
这些决策会增加或
减少某种疾病的风险
04:45
and those choices选择 will increase增加
or decrease减少 the risk风险 of certain某些 diseases疾病.
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04:51
But do you know
where you are on the curve曲线?
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但你知道自己在这个曲线上的位置吗
04:54
What's the past过去 curve曲线 look like?
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过去的曲线是什么样子的
04:56
What kind of decisions决定
are you facing面对 every一切 day?
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你每天又面对着什么样的选择
04:59
And what kind of decision决定 is the right one
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在人生旅程的这张曲线图上
05:02
to make your own拥有 right curve曲线
over your life journey旅程?
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能让你处在正确位置上的
选择究竟是什么
05:07
What's that?
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那是什么
05:09
The only thing you cannot不能 change更改,
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唯一你无法改变
无法逆转的事情
05:11
you cannot不能 reverse相反 back,
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05:13
is time.
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就是时间
目前是这样
不过这件事儿也说不好
05:14
Probably大概 not yet然而; maybe in the future未来.
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05:16
(Laughter笑声)
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(笑声)
既然不能改变已经做出的决定
05:17
Well, you cannot不能 change更改
the decision决定 you've made制作,
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那我们可以做些什么呢
05:20
but can we do something there?
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05:22
Can we actually其实 try to run
multiple options选项 on me,
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我们能够同时运行不同的选择
05:27
and try to predict预测 right
on the consequence后果,
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对所期待的结果进行预测
再做出正确的选择吗
05:31
and be able能够 to make the right choice选择?
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05:34
After all,
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毕竟
每个人都是由自己的选择决定的
05:35
we are our choices选择.
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05:38
These lady淑女 beetles甲虫 came来了 to me afterwards之后.
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这些瓢虫后来启发了我
05:41
25 years年份 ago, I made制作
the digital数字 lady淑女 beetles甲虫
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25年前 我构建了数字化的瓢虫
试图模拟自然界中的瓢虫
05:45
to try to simulate模拟 real真实 lady淑女 beetles甲虫.
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05:47
Can I make a digital数字 me ...
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我是否可以同样
构建一个数字化的我
05:49
to simulate模拟 me?
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来模拟真实的我自己呢
05:51
I understand理解 the neural神经
network网络 could become成为
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我当然明白其中的神经网络可能会
05:54
much more sophisticated复杂的
and complicated复杂 there.
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变得更加复杂
那么我是否可以做一个
05:57
Can I make that one,
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05:59
and try to run multiple options选项
on that digital数字 me --
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并尝试运行这个数字化的我
06:03
to compute计算 that?
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来计算出不同的选择结果呢
06:05
Then I could live生活 in different不同 universes宇宙,
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这样我就可以同时平行的生活在
06:08
in parallel平行, at the same相同 time.
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不同的宇宙中
06:11
Then I would choose选择
whatever随你 is good for me.
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然后选择对我来说最合适的方案
06:14
I probably大概 have the most comprehensive全面
digital数字 me on the planet行星.
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我的生命数据可能是
这个星球上最全面的
我在自己身上可是做了不少投资
06:18
I've spent花费 a lot of dollars美元
on me, on myself.
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06:21
And the digital数字 me told me
I have a genetic遗传 risk风险 of gout痛风
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这个数字化的我告诉我
种种信息表明我有痛风方面的
遗传风险
06:27
by all of those things there.
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06:29
You need different不同 technology技术 to do that.
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需要不同的技术才能打造
这个数字化的我
你需要蛋白质 基因
06:31
You need the proteins蛋白质, genes基因,
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代谢 抗体的数据
06:32
you need metabolized代谢 antibodies抗体,
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06:35
you need to screen屏幕 all your body身体
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你需要筛查全身
06:38
about the bacterias and viruses病毒
covering覆盖 you, or in you.
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搜集体内外所有细菌和病毒的数据
你需要各种智能设备
06:41
You need to have
all the smart聪明 devices设备 there --
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智能车 智能家居 智能桌子
06:44
smart聪明 cars汽车, smart聪明 house, smart聪明 tables,
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06:47
smart聪明 watch, smart聪明 phone电话
to track跟踪 all of your activities活动 there.
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智能手表 智能手机等等
来跟踪你所有的活动
06:51
The environment环境 is important重要 --
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要知道环境数据很重要
一切都很重要
06:52
everything's一切的 important重要 --
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另外别忘了智能马桶
06:54
and don't forget忘记 the smart聪明 toilet厕所.
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(笑)
06:55
(Laughter笑声)
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这简直就是一种浪费 是吧
06:56
It's such这样 a waste浪费, right?
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每天这么多宝贵的信息
就这么被水冲掉了
06:58
Every一切 day, so much invaluable无价 information信息
just has been flushed into the water.
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07:04
And you need them.
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你需要它们
需要测量这些数据
07:06
You need to measure测量 all of them.
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你应该测量并计算周围的
07:07
You need to be able能够 to measure测量
everything around you
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所有这些东西
07:10
and compute计算 them.
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07:12
And the digital数字 me told me
I have a genetic遗传 defect缺陷.
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而这个数字化的我告诉我
我有遗传缺陷
07:16
I have a very high risk风险 of gout痛风.
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我患痛风的概率很大
07:19
I don't feel anything now,
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我暂时还没什么感觉
看着挺健康的
07:21
I'm still healthy健康.
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但看看我的尿酸水平
07:22
But look at my uric尿酸 acid level水平.
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07:24
It's double the normal正常 range范围.
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是正常范围的两倍
07:26
And the digital数字 me searched搜索
all the medicine医学 books图书,
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而数字化的我搜索了
所有的医药典籍
07:29
and it tells告诉 me, "OK, you could
drink burdock tea" --
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告诉我 你可以喝牛蒡茶
07:33
I cannot不能 even pronounce发音 it right --
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这个词我甚至都不太会读
07:35
(Laughter笑声)
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(笑)
这种茶来自中国的古老智慧
07:36
That is from old Chinese中文 wisdom智慧.
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07:39
And I drank that tea for three months个月.
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不过我喝了三个月的牛蒡茶
尿酸值就恢复正常了
07:41
My uric尿酸 acid has now gone走了 back to normal正常.
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07:45
I mean, it worked工作 for me.
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这招儿对我还挺管用的
07:46
All those thousands数千 of years年份
of wisdom智慧 worked工作 for me.
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所有这些千年智慧
对我而言都是有用的
我很幸运
07:49
I was lucky幸运.
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07:50
But I'm probably大概 not lucky幸运 for you.
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但可能对于你们来说就不一定了
07:55
All of this existing现有
knowledge知识 in the world世界
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世界上一切现有的知识
不可能对每个人都有效
不可能都是对症下药
07:57
cannot不能 possibly或者 be efficient高效 enough足够
or personalized个性化 enough足够 for yourself你自己.
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08:03
The only way to make
that digital数字 me work ...
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能让这个数字化的我有效的唯一方法
08:07
is to learn学习 from yourself你自己.
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就是从自己的身体中学习
08:11
You have to ask a lot
of questions问题 about yourself你自己:
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你必须问自己很多问题
08:13
"What if?" --
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如果这么做会怎样
我现在有时差反应
08:15
I'm being存在 jet-lagged喷射滞后 now here.
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你们可能看不出来 但我确实有
08:17
You don't probably大概 see it, but I do.
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08:20
What if I eat less?
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如果我少吃点呢
08:21
When I took metformin二甲双胍,
supposedly按说 to live生活 longer?
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如果我服用二甲双胍
是否就可以活得更长呢
08:25
What if I climb Mt公吨. Everest珠峰?
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如果我去爬珠穆朗玛峰呢
08:26
It's not that easy简单.
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可能没那么容易
或者去跑马拉松呢
08:28
Or run a marathon马拉松?
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08:30
What if I drink a bottle瓶子 of mao-tai茅台酒,
171
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如果我喝一瓶茅台酒
一种中国的烈性酒
08:32
which哪一个 is a Chinese中文 liquor,
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08:33
and I get really drunk?
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真的喝醉了会怎么样
08:35
I was doing a video视频 rehearsal排演 last time
with the folks乡亲 here,
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上次在这里跟工作人员
进行了一次录像彩排
我那会儿喝醉了
08:39
when I was drunk,
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结果我的演讲内容完全跑偏了
08:40
and I totally完全 delivered交付
a different不同 speech言语.
176
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(笑)
08:42
(Laughter笑声)
177
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08:45
What if I work less, right?
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如果我的工作量少一点呢
我的压力是否就减轻了
08:48
I have been less stressed强调, right?
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这种情况从来没有发生在我身上
08:50
So that probably大概 never happened发生 to me,
180
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我每天压力都很大
08:51
I was really stressed强调 every一切 day,
181
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但我希望我的压力能够小一点
08:53
but I hope希望 I could be less stressed强调.
182
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08:56
These early studies学习 told us,
183
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这些早期研究告诉我们
即使吃同样的一根香蕉
08:58
even with the same相同 banana香蕉,
184
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不同个体的血糖水平反应
09:00
we have totally完全 different不同
glucose-level葡萄糖水平 reactions反应
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都可能完全不同
09:03
over different不同 individuals个人.
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那我呢
09:04
How about me?
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09:06
What is the right breakfast早餐 for me?
188
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1998
一顿适合我的早餐应该吃些什么
我需要做两个星期的对照实验
09:08
I need to do two weeks
of controlled受控 experiments实验,
189
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测试各种不同的食物成分
09:11
of testing测试 all kinds of different不同
food餐饮 ingredients配料 on me,
190
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3745
并检查我身体的反应
09:15
and check my body's身体的 reaction反应.
191
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我不知道对我来说精确的营养
09:17
And I don't know
the precise精确 nutrition营养 for me,
192
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3117
到底应该包含什么
09:20
for myself.
193
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1150
09:23
Then I wanted to search搜索
all the Chinese中文 old wisdom智慧
194
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我想搜遍所有中国的古老智慧
找到让我活得更久 更健康的秘诀
09:27
about how I can live生活 longer,
and healthier健康.
195
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2992
09:30
I did it.
196
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1228
我确实付诸行动了
不过其中有一些并不现实
09:32
Some of them are really unachievable无法实现.
197
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2251
09:34
I did this once一旦 last October十月,
198
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2865
我在去年十月份尝试了一次
七天不吃饭
09:37
by not eating for seven days.
199
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09:40
I did a fast快速 for seven days
with six partners伙伴 of mine.
200
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我与我的六个伙伴一起进行了
为期七天的绝食体验
09:44
Look at those people.
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看看他们
有一个人笑了
09:46
One smile微笑.
202
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1151
知道为什么吗
09:47
You know why he smiled笑笑?
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1151
他作弊了
09:48
He cheated被骗.
204
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1167
(笑)
09:49
(Laughter笑声)
205
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1000
晚上他偷偷喝了一杯咖啡
09:50
He drank one cup杯子 of coffee咖啡 at night,
206
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我们直接从他的数据中发现了
09:53
and we caught抓住 it from the data数据.
207
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09:55
(Laughter笑声)
208
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1045
(笑)
我们差不多解读出了
数据中的所有内容
09:56
We measured测量 everything from the data数据.
209
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我们能够追踪这些数据
09:58
We were able能够 to track跟踪 them,
210
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2214
而且切切实实的看到了
10:01
and we could really see --
211
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1558
10:02
for example, my immune免疫的 system系统,
212
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2001
我的免疫系统的变化 举个例子
给大家一点直观的信息
10:04
just to give you a little hint暗示 there.
213
592693
1762
我的免疫系统在24小时内
发生了巨大的变化
10:06
My immune免疫的 system系统 changed
dramatically显着 over 24 hours小时 there.
214
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4304
10:11
And my antibody抗体 regulates调整对象 my proteins蛋白质
215
599918
3133
而基于这个巨大变化
我的抗体开始对我体内的
10:15
for that dramatic戏剧性 change更改.
216
603075
1536
蛋白质进行调节
所有参与者都是如此
10:16
And everybody每个人 was doing that.
217
604635
1381
尽管每个人的免疫系统
天生各不相同
10:18
Even if we're essentially实质上
totally完全 different不同 at the very beginning开始.
218
606040
3332
这很可能是将来治疗
癌症或类似疾病的
10:21
And that probably大概 will be
an interesting有趣 treatment治疗 in the future未来
219
609396
3045
10:24
for cancer癌症 and things like that.
220
612465
1643
一个有意思的方法
而且正在变得越来越有趣
10:26
It becomes very, very interesting有趣.
221
614132
1630
10:28
But something you probably大概
don't want to try,
222
616286
2701
但有些方法你可能未必想尝试
比如饮用健康人的粪水
10:31
like drinking fecal粪便 water
from a healthier健康 individual个人,
223
619011
3676
10:34
which哪一个 will make you feel healthier健康.
224
622711
1667
虽然这会让你感觉更健康
这也是来自古老中国的智慧
10:36
This is from old Chinese中文 wisdom智慧.
225
624402
1715
不妨看一下
10:38
Look at that, right?
226
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1436
10:39
Like 1,700 years年份 ago,
227
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2166
1700年前
10:41
it's already已经 there, in the book.
228
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2280
这种方法就已经被记录在册了
10:44
But I still hate讨厌 the smell.
229
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1327
但我还是没法接受那个味道
10:46
(Laughter笑声)
230
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1150
(笑)
10:47
I want to find out the true真正 way to do it,
231
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2406
我想找出另一种方式
10:49
maybe find a combination组合 of cocktails鸡尾酒
of bacterias and drink it,
232
637841
4354
或许我们可以用混合了
益生菌的鸡尾酒来替代
会让我感觉好一点
10:54
it probably大概 will make me better.
233
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1524
所以我打算试试
10:55
So I'm trying to do that.
234
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1191
尽管我依然在努力尝试
10:56
Even though虽然 I'm trying this hard,
235
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3002
但是要测试出所有可能的
方法依然是非常困难的
11:00
it's so difficult to test测试 out
all possible可能 conditions条件.
236
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5026
11:05
It's not possible可能 to do
all kinds of experiments实验 at all ...
237
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5237
针对每个个体做
各种实验也并不现实
但是我们在这个星球上
有70亿个学习程序
11:11
but we do have seven billion十亿
learning学习 programs程式 on this planet行星.
238
659341
3813
11:15
Seven billion十亿.
239
663178
1266
70亿
11:16
And every一切 program程序
is running赛跑 in different不同 conditions条件
240
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3651
每个学习程序都在
不同的条件下运行
并进行不同的实验
11:20
and doing different不同 experiments实验.
241
668143
1781
11:21
Can we all measure测量 them?
242
669948
1851
我们可以测量所有这些个体吗
11:24
Seven years年份 ago,
I wrote an essay文章 in "Science科学"
243
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3215
七年前 我在《科学》杂志上
发表了一篇文章
11:28
to celebrate庆祝 the human人的 genome's基因组的
10-year-年 anniversary周年.
244
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3292
为了庆祝人类基因组计划10周年
11:32
I said, "Sequence序列 yourself你自己,
245
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1654
我当时说过 测序你自己
11:33
for one and for all."
246
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1623
为了个体 也为了全人类
11:35
But now I'm going to say,
247
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1868
但是现在我要说的是
11:37
"Digitalize数字化 yourself你自己 for one and for all."
248
685690
3746
数字化你自己
为了个人 也为了全人类
11:42
When we make this digital数字 me
into a digital数字 we,
249
690275
5600
当我们把这个 数字化的我
变成了 数字化的我们
11:47
when we try to form形成 an internet互联网 of life,
250
695899
3752
当我们试着构建
一个数字化生命网络
11:51
when people can learn学习 from each other,
251
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2861
当人们可以互相学习
学习彼此的经验
11:54
when people can learn学习
from their experience经验,
252
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2707
11:57
their data数据,
253
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1731
彼此的数据
11:59
when people can really form形成
a digital数字 me by themselves他们自己
254
707046
3601
当人们真的可以自主
打造一个数字化的我
12:02
and we learn学习 from it,
255
710671
1611
让我们得以中进行学习
12:05
the digital数字 we will be
totally完全 different不同 with a digital数字 me.
256
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5732
这个 数字化的我们 将与
数字化的我 完全不同
12:11
But it can only come from the digital数字 me.
257
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3420
但它只能来自 数字化的我
12:16
And this is what I try to propose提出 here.
258
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2979
这就是我在这里提出的建议
12:20
Join加入 me --
259
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1150
加入 我
12:21
become成为 we,
260
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1150
成为 我们
12:23
and everybody每个人 should build建立 up
their own拥有 digital数字 me,
261
731792
4938
每个人都应该建立数字化的自我
12:28
because only by that
will you learn学习 more about you,
262
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4519
因为只有这样
你才能更加了解你自己
12:33
about me,
263
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1424
了解 我
12:34
about us ...
264
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1165
了解 我们
12:36
about the question I just posed构成
at the very beginning开始:
265
744678
3680
了解我最开始提出的那个问题
12:40
"What is life?"
266
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1150
什么是生命
12:42
Thank you.
267
750066
1169
谢谢
(鼓掌)
12:43
(Applause掌声)
268
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5950
12:49
Chris克里斯 Anderson安德森:
One quick question for you.
269
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2761
克里斯·安德森(CA):
我想快速的问一个问题
12:52
I mean, the work is amazing惊人.
270
760818
1974
这项工作无疑相当出色
我猜大家可能还有一个问题
12:54
I suspect疑似 one question people have is,
271
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3198
因为我们都期待着这些神奇的
12:58
as we look forward前锋 to these amazing惊人
technical技术 possibilities可能性
272
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3281
13:01
of personalized个性化 medicine医学,
273
769343
1361
个性化医疗技术成为可能
而在短期内 感觉还只有
少数人才能负担得起
13:02
in the near-term短期 it feels感觉 like
they're only going to be affordable实惠
274
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3303
是吗
13:06
for a few少数 people, right?
275
774055
1276
仅仅是做基因测序就需要花很多钱
13:07
It costs成本 many许多 dollars美元 to do
all the sequencing测序 and so forth向前.
276
775355
2991
13:10
Is this going to lead to a kind of,
277
778889
2912
这是否会导致在某种程度上
13:13
you know, increasing增加 inequality不等式?
278
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2317
增加了不平等
13:16
Or do you have this vision视力
that the knowledge知识 that you get
279
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3911
或者您是否有这样的构想
从这些早期的志愿者
13:20
from the pioneers开拓者
280
788101
1352
身上获取的知识
可以被快速的复制推广
13:21
can actually其实 be
pretty漂亮 quickly很快 disseminated传播
281
789477
2096
13:23
to help a broader更广泛 set of recipients收件人?
282
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4124
从而帮助更广泛的群体呢
王俊:很好的问题
13:27
Jun Wang: Well, good question.
283
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1534
十七年前 当我与人合伙创立华大基因
13:29
I'll tell you that seven years年份 ago,
when I co-founded共同创立 BGIBGI,
284
797303
3551
并担任这家公司CEO的时候
13:32
and served提供服务 as the CEOCEO
of the company公司 there,
285
800878
3405
我唯一的目标就是
13:36
the only goal目标 there for me to do
286
804307
2381
推动基因测序成本的下降
13:38
was to drive驾驶 the sequencing测序 cost成本 down.
287
806712
1983
13:41
It started开始 from 100 million百万 dollars美元
per human人的 genome基因组.
288
809044
2775
从最开始要花1亿美元完成
对一个人全基因组的测序
13:43
Now, it's a couple一对 hundred dollars美元
for a human人的 genome基因组.
289
811843
2591
到现在只需要几百美元
13:46
The only reason原因 to do it
is to get more people to benefit效益 from it.
290
814458
3614
我这么做的唯一原因
就是希望让更多的人从中受益
13:50
So for the digital数字 me,
it's the same相同 thing.
291
818378
2157
而 数字化的我 也是由此诞生的
13:52
Now, you probably大概 need,
292
820559
1489
目前你可能需要
一百万美元去数字化一个生命个体
13:54
you know, one million百万 dollars美元
to digitize数字化 a person.
293
822072
3229
13:57
I think it has to be 100 dollars美元.
294
825801
1675
但我认为在未来
必须要降到100美元
13:59
It has to be free自由 for many许多 of those people
that urgently迫切 need that.
295
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4049
甚至是免费 尤其对于那些迫切需要
这项技术的人来说 这是必须的
14:04
So this is our goal目标.
296
832372
1267
所以这就是我们的目标
14:05
And it seems似乎 that with all
this merging合并 of the technology技术,
297
833993
3423
当这一切技术都能够融合之后
14:09
I'm thinking思维 that in the very near future未来,
298
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2592
我认为在不远的将来
或许三到五年
14:12
let's say three to five years年份,
299
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2365
一切就会成为现实
14:14
it will come to reality现实.
300
842445
1482
这就是为什么我创立了碳云智能
14:15
And this is the whole整个 idea理念
of why I founded成立 iCarbonXiCarbonX,
301
843951
3979
我的第二家公司
14:19
my second第二 company公司.
302
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1219
其目的就是要把成本降低到
14:21
It's really trying to get the cost成本 down
303
849197
2868
14:24
to a level水平 where every一切 individual个人
could have the benefit效益.
304
852089
3420
让每个人都可以从中受益的水平
CA:好的 所以您的梦想
不是让它为少数的精英服务
14:27
CACA: All right, so the dream梦想 is not
elite原种 health健康 services服务 for few少数,
305
855533
3048
而是真正要使
14:30
it's to really try
306
858605
1234
14:31
and actually其实 make overall总体 health健康 care关心
much more cost成本 effective有效 --
307
859863
3111
医疗健康服务更具普世价值
王俊:的确如此 但我们需要从
一些早期的先行者开始
14:34
JWJW: But we started开始
from some early adopters采纳者,
308
862998
2430
14:37
people believing相信 ideas思路 and so on,
309
865452
2506
从更加相信这个想法的一些人开始
14:39
but eventually终于, it will become成为
everybody's每个人的 benefit效益.
310
867982
3642
但最终它将能够让每个人受益
14:44
CACA: Well, Jun, I think
it's got to be true真正 to say
311
872303
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CA:王俊 我不得不说
你是这个星球上
最令人叹服的科学家之一
14:46
you're one of the most amazing惊人
scientific科学 minds头脑 on the planet行星,
312
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2958
真的很荣幸能邀请到你
14:49
and it's an honor荣誉 to have you.
313
877642
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王俊:谢谢
14:51
JWJW: Thank you.
314
879095
1158
(鼓掌)
14:52
(Applause掌声)
315
880277
1150
Translated by xiao gu

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ABOUT THE SPEAKER
Jun Wang - Genomics researcher
At iCarbonX, Jun Wang aims to establish a big data platform for health management.

Why you should listen

In 1999, Jun Wang founded the Bioinformatics Department of Beijing Genomics Institute (BGI, now known as BGI Shenzhen), one of China’s premier research facilities. Until July 2015, Wang led the institution of 5,000+ people engaged in studies of genomics and its informatics, including genome assembly, annotation, expression, comparative genomics, molecular evolution, transcriptional regulation, genome variation analysis, database construction as well as methodology development such as the sequence assembler and alignment tools. He also focuses on interpretation of the definition of "gene" by expression and conservation study. In 2003, Wang was also involved in the SARS genome analysis and the silkworm genome assembly and analysis in cooperation with Chinese Southeast Agricultural University. The Pig Genome Project was completed at BGI under his leadership, as well as the chicken genome variation map and the TreeFam in collaboration with the Sanger Institute. In 2007, he and his group finished the first Asian diploid genome, the 1000 genome project, and many more projects. He initiated the "million genomes project" which seeks to better understand health based on human, plant, animal and micro-ecosystem genomes.

In late 2015, Wang founded a new institute/company, iCarbonX, aiming to develop an artificial intelligence engine to interpret and mine multiple health-related data and help people better manage their health and defeat disease.

More profile about the speaker
Jun Wang | Speaker | TED.com

Data provided by TED.

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