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: 數位 DNA 如何能協助你做出更好的健康選擇

Filmed:
1,303,361 views

如果你能明確知道食物或藥物對你的健康有什麼影響──在吃下去之前就知道,那會如何?基因組學研究者王俊在試圖開發出真人的數位幽靈;他們從基因碼開始,但他們也會納入其他類的資料,從攝取食物到睡眠到「智慧馬桶」收集的資料。有這些珍貴資訊,王俊希望能建立一個引擎,來改變我們對健康的看法,不論是在個人層級上或集體層級上。
- 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其實,
to pose提出 you a question.
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今天我來這裡其實
是要問各位一個問題。
00:16
What is life?
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生命是什麼?
00:17
It has been really puzzling令人費解 me
for more than 25 years年份,
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這個問題困惑了我 25 年,
00:21
and will probably大概 continue繼續 doing so
for the next下一個 25 years年份.
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可能在接下來的 25 年
繼續困惑著我。
00:25
This is the thesis論文 I did
when I was still in undergraduate大學本科 school學校.
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這是我在大學時做的論文。
00:31
While my colleagues同事 still treated治療
computers電腦 as big calculators計算器,
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當我的同學仍然把電腦
視為是大型計算機時,
00:38
I started開始 to teach computers電腦 to learn學習.
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我就開始教電腦學習了。
00:41
I built內置 digital數字 lady淑女 beetles甲蟲
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我打造了數位瓢蟲,
00:44
and tried試著 to learn學習 from real真實 lady淑女 beetles甲蟲,
just to do one thing:
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試圖向真實的瓢蟲學習,
讓它們只做一件事:
00:49
search搜索 for food餐飲.
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尋找食物。
00:51
And after very simple簡單 neural神經 network網絡 --
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經過了非常簡單的神經網路──
00:54
genetic遺傳 algorithms算法 and so on --
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基因演算法等等──
00:56
look at the pattern模式.
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看這個模型,
00:57
They're almost幾乎 identical相同 to real真實 life.
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它們幾乎和真實的生命一模一樣。
01:01
A very striking引人注目 learning學習 experience經驗
for a twenty-year-old二十多歲.
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對一個 20 歲的小伙子來說
這是很驚人的學習經驗。
01:07
Life is a learning學習 program程序.
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生命本身就是一個學習程式。
01:12
When you look
at all of this wonderful精彩 world世界,
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這個大千世界,
01:15
every一切 species種類 has
its own擁有 learning學習 program程序.
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每個物種都有自己的學習程式。
01:19
The learning學習 program程序 is genome基因組,
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這學習程式就是基因組,
01:22
and the code of that program程序 is DNA脫氧核糖核酸.
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程式碼就是 DNA。
01:27
The different不同 genomes基因組 of each species種類
represent代表 different不同 survival生存 strategies策略.
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每個物種的不同基因組
代表著不同的生存策略。
01:33
They represent代表 hundreds數以百計 of millions百萬
of years年份 of evolution演化.
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代表著數億年的進化演變,
01:38
The interaction相互作用 between之間
every一切 species'種類' ancestor祖先
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記錄了每個物種的祖先
01:42
and the environment環境.
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與環境之間的互動。
01:46
I was really fascinated入迷 about the world世界,
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我完全迷上了這個世界,
01:48
about the DNA脫氧核糖核酸,
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迷上了 DNA,
01:49
about, you know, the language語言 of life,
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迷上了,你知道的,生命的語言,
01:52
the program程序 of learning學習.
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學習的程式。
01:54
So I decided決定 to co-found共發現
the institute研究所 to read them.
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所以我決定找人共同創辦一個
讀取基因組的機構。
01:59
I read many許多 of them.
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我讀了很多基因組。
02:01
We probably大概 read more than half
of the prior animal動物 genomes基因組 in the world世界.
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我們可能解讀了
世界上超過一半的動物基因組。
02:06
I mean, up to date日期.
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我是指,與時俱進。
02:09
We did learn學習 a lot.
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我們學到了很多。
02:11
We did sequence序列, also,
one species種類 many許多, many許多 times ...
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我們對一個物種進行了
多次基因定序,做了許多次……
02:15
human人的 genome基因組.
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即人類基因組。
02:16
We sequenced測序 the first Asian亞洲.
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我們完成了第一個
亞洲人基因組的定序。
02:18
I sequenced測序 it myself many許多, many許多 times,
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剛好利用平台的優勢,
02:21
just to take advantage優點 of that platform平台.
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我對自己的基因也進行了多次定序。
02:24
Look at all those repeating重複 base基礎 pairs:
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看著所有那些重覆的鹼基對:
02:27
ATCGATCG.
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ATCG。
(註:四種 DNA 鹼基)
02:29
You don't understand理解 anything there.
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你幾乎無法從中讀懂任何含義。
02:31
But look at that one base基礎 pair.
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但看看這一組鹼基對。
02:32
Those five letters, the AGGAAAGGAA.
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AGGAA,這五個字母。
02:35
These five SNPs單核苷酸多態性 represent代表
a very specific具體 haplotype單倍型
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這五個 SNP(單核苷酸多態性)
代表了一種非常特別的單倍型,
02:39
in the Tibetan population人口
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它們是藏族人的身體中
02:41
around the gene基因 called EPASEPAS1.
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一種叫做 EPAS1 的基因。
02:43
That gene基因 has been proved證實 --
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這個基因已被證明是──
02:45
it's highly高度 selective可選擇的 --
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高度選擇性的結果──
02:46
it's the most significant重大 signature簽名
of positive selection選擇 of Tibetans西藏人
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這是藏族人對高海拔適應性
進行正向選擇的重要指標。
02:50
for the higher更高 altitude高度 adaptation適應.
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02:53
You know what?
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你知道嗎?
02:54
These five SNPs單核苷酸多態性 were the result結果
of integration積分 of Denisovans丹尼索瓦人,
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這五個 SNP 是來自
滅絕的丹尼索瓦人,
03:00
or Denisovan-like丹尼索瓦人樣 individuals個人 into humans人類.
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或與丹尼索瓦人有親緣關係的
個體 DNA 與人類雜交的結果。
03:04
This is the reason原因
why we need to read those genomes基因組.
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這就是為什麽我們需要
讀這些基因組的原因。
03:06
To understand理解 history歷史,
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它可以讓你了解歷史,
03:08
to understand理解 what kind
of learning學習 process處理
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了解基因組這套學習程序
03:12
the genome基因組 has been through通過
for the millions百萬 of years年份.
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在數百萬年中經歷了什麽樣的演變。
03:17
By reading a genome基因組,
it can give you a lot of information信息 --
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透過閱讀基因組,
你能得到許多資訊──
03:20
tells告訴 you the bugs蟲子 in the genome基因組 --
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它能告訴你基因組中的一些錯誤──
03:22
I mean, birth分娩 defects缺陷,
monogenetic單成 disorders障礙.
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我指的是像天生缺陷、
單基因遺傳病。
03:25
Reading a drop下降 of blood血液
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而僅僅需要一滴血的判讀,
03:26
could tell you why you got a fever發熱,
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就能告訴你為什麼會發燒,
03:28
or it tells告訴 you which哪一個 medicine醫學
and dosage劑量 needs需求 to be used
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或告訴你在你生病,
特別是得了癌症時,
需要服用什麼藥、多少劑量。
03:31
when you're sick生病, especially特別 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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可以研究的東西很多,但看看這個:
03:38
30 years年份 ago, we were still poor較差的 in China中國.
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三十年前,我們中國還很窮。
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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只有 0.67% 的
中國成年人有糖尿病。
03:47
Look at now: 11 percent百分.
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看看現在:11%。
03:49
Genetics遺傳學 cannot不能 change更改 over 30 years年份 --
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遺傳學不會在三十年間改變──
03:53
only one generation.
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才一個世代而已。
03:54
It must必須 be something different不同.
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一定有什麼其他原因。
03:56
Diet飲食?
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飲食嗎?
03:57
The environment環境?
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環境嗎?
03:59
Lifestyle生活方式?
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生活方式嗎?
04:01
Even identical相同 twins雙胞胎
could develop發展 totally完全 differently不同.
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即使是同卵雙生的雙胞胎
也可能有完全不同的發展。
04:07
It could be one becomes
very obese肥胖, the other is not.
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可能其中一個極肥胖,
另一個不會。
04:11
One develops發展 a cancer癌症
and the other does not.
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其中一個得了癌症,另一個沒有。
04:13
Not mentioning living活的
in a very stressed強調 environment環境.
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更不用提處在壓力很大的環境中了。
04:19
I moved移動 to Shenzhen深圳 10 years年份 ago ...
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我十年前搬到深圳…...
04:22
for some reason原因, people may可能 know.
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有些人可能知道理由。
04:25
If the gene's基因的 under stress強調,
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如果基因在壓力下,
04:27
it behaves的行為 totally完全 differently不同.
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它的行為會全然不同。
04:30
Life is a journey旅程.
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人生是一趟旅程。
04:32
A gene基因 is just a starting開始 point,
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基因只是個起始點,
04:35
not the end結束.
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不是終點。
04:37
You have this statistical統計 risk風險
of certain某些 diseases疾病 when you are born天生.
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在你出生時,就註定會有
某些疾病的風險。
04:42
But every一切 day you make different不同 choices選擇,
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但,你每天做出不同的選擇,
04:45
and those choices選擇 will increase增加
or decrease減少 the risk風險 of certain某些 diseases疾病.
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那些選擇會增加或滅少
某些疾病的風險。
04:51
But do you know
where you are on the curve曲線?
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但你知道你在曲線上的哪個點嗎?
04:54
What's the past過去 curve曲線 look like?
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過去的曲線是什麼樣子的?
04:56
What kind of decisions決定
are you facing面對 every一切 day?
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你每天在面對的是什麼樣的決策?
04:59
And what kind of decision決定 is the right one
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什麼樣的決策才是對的,
05:02
to make your own擁有 right curve曲線
over your life journey旅程?
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才能為你人生旅程產生出對的曲線?
05:07
What's that?
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那是什麼?
05:09
The only thing you cannot不能 change更改,
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你唯一無法改變的,
05:11
you cannot不能 reverse相反 back,
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你無法逆轉的,
05:13
is time.
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就是時間。
05:14
Probably大概 not yet然而; maybe in the future未來.
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目前還不能,但將來不一定。
05:16
(Laughter笑聲)
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(笑聲)
05:17
Well, you cannot不能 change更改
the decision決定 you've made製作,
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你無法改變你已做的決定,
05:20
but can we do something there?
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但我們能不能做點什麼?
05:22
Can we actually其實 try to run
multiple options選項 on me,
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我們能否對自己測試多個選項,
05:27
and try to predict預測 right
on the consequence後果,
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試著預測正確的結果,
05:31
and be able能夠 to make the right choice選擇?
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以做出正確的選擇?
05:34
After all,
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畢竟,
05:35
we are our choices選擇.
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我們就是我們的選擇所決定的。
05:38
These lady淑女 beetles甲蟲 came來了 to me afterwards之後.
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這些瓢蟲後來啟發了我。
05:41
25 years年份 ago, I made製作
the digital數字 lady淑女 beetles甲蟲
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25 年前,我做了數位瓢蟲,
05:45
to try to simulate模擬 real真實 lady淑女 beetles甲蟲.
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試圖模擬自然界的真實瓢蟲。
05:47
Can I make a digital數字 me ...
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我是否可以同樣做出數位化的我……
05:49
to simulate模擬 me?
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來模擬真實的我?
05:51
I understand理解 the neural神經
network網絡 could become成為
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我當然明白其中的神經網絡可能會
05:54
much more sophisticated複雜的
and complicated複雜 there.
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更精密且複雜許多。
05:57
Can I make that one,
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但我能否做到,
05:59
and try to run multiple options選項
on that digital數字 me --
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然後試著用那個數位化的我
來測試多重選項……
來計算出不同的選擇結果?
06:03
to compute計算 that?
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06:05
Then I could live生活 in different不同 universes宇宙,
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那樣一來,我就可以
同時活在不同的平行宇宙中。
06:08
in parallel平行, at the same相同 time.
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06:11
Then I would choose選擇
whatever隨你 is good for me.
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我就可以選擇對我最好的選項。
06:14
I probably大概 have the most comprehensive全面
digital數字 me on the planet行星.
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我的生命數據可能是
這個星球最全面的,
06:18
I've spent花費 a lot of dollars美元
on me, on myself.
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我花了很多錢在我自己身上。
06:21
And the digital數字 me told me
I have a genetic遺傳 risk風險 of gout痛風
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這個數位化的我,
透過這些東西告訴我,
06:27
by all of those things there.
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我有痛風的基因風險。
06:29
You need different不同 technology技術 to do that.
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你需要不同的技術才能做到那樣。
06:31
You need the proteins蛋白質, genes基因,
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你需要蛋白質、基因,
06:32
you need metabolized代謝 antibodies抗體,
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你需要心陳代謝抗體,
06:35
you need to screen屏幕 all your body身體
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你需要掃瞄你的整個身體,
06:38
about the bacterias and viruses病毒
covering覆蓋 you, or in you.
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來找出你身上或體內的細菌及病毒。
06:41
You need to have
all the smart聰明 devices設備 there --
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你需要各種智慧的儀器──
06:44
smart聰明 cars汽車, smart聰明 house, smart聰明 tables,
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智慧車、智慧房屋、智慧桌子、
06:47
smart聰明 watch, smart聰明 phone電話
to track跟踪 all of your activities活動 there.
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智慧手表、智慧手機,
才能追縱你所有的活動。
06:51
The environment環境 is important重要 --
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環境很重要──
06:52
everything's一切的 important重要 --
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一切都很重要──
06:54
and don't forget忘記 the smart聰明 toilet廁所.
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別忘了智慧馬桶。
06:55
(Laughter笑聲)
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(笑聲)
06:56
It's such這樣 a waste浪費, right?
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這真的很浪費,對吧?
06:58
Every一切 day, so much invaluable無價 information信息
just has been flushed into the water.
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每天有那麼多個人資訊
就這樣被水沖掉了。
07:04
And you need them.
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你需要這些資訊。
07:06
You need to measure測量 all of them.
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你需要測量這些資訊。
07:07
You need to be able能夠 to measure測量
everything around you
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你需要能夠測量你周遭的一切,
07:10
and compute計算 them.
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並計算它們。
07:12
And the digital數字 me told me
I have a genetic遺傳 defect缺陷.
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數位化的我告訴我,我有基因缺陷。
07:16
I have a very high risk風險 of gout痛風.
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我的痛風風險很高。
07:19
I don't feel anything now,
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我現在感覺不出來,
07:21
I'm still healthy健康.
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我仍然很健康。
07:22
But look at my uric尿酸 acid level水平.
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但看看我的尿酸濃度。
07:24
It's double the normal正常 range範圍.
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是正常範圍的兩倍。
07:26
And the digital數字 me searched搜索
all the medicine醫學 books圖書,
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數位的我搜尋了所有的醫學書籍,
07:29
and it tells告訴 me, "OK, you could
drink burdock tea" --
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它告訴我:「好,
你可以喝牛蒡茶」──
07:33
I cannot不能 even pronounce發音 it right --
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我甚至不會唸這個字──
07:35
(Laughter笑聲)
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(笑聲)
07:36
That is from old Chinese中文 wisdom智慧.
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那是來自中國的古老智慧。
07:39
And I drank that tea for three months個月.
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我喝了那種茶三個月。
07:41
My uric尿酸 acid has now gone走了 back to normal正常.
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我的尿酸現在回到正常了。
07:45
I mean, it worked工作 for me.
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我是說,它對我有效耶。
07:46
All those thousands數千 of years年份
of wisdom智慧 worked工作 for me.
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數千年的智慧對我有用。
07:49
I was lucky幸運.
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我很幸運。
07:50
But I'm probably大概 not lucky幸運 for you.
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但可能對於你們來說就不一定了。
07:55
All of this existing現有
knowledge知識 in the world世界
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所有世界上既有的知識
07:57
cannot不能 possibly或者 be efficient高效 enough足夠
or personalized個性化 enough足夠 for yourself你自己.
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對你自己而言都不夠有效或個人化。
08:03
The only way to make
that digital數字 me work ...
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要讓這個數位化的我
有效的唯一方法,
08:07
is to learn學習 from yourself你自己.
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就是要向你自己學習。
08:11
You have to ask a lot
of questions問題 about yourself你自己:
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你得要問很多關於你自己的問題:
08:13
"What if?" --
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「假如……?」
08:15
I'm being存在 jet-lagged噴射滯後 now here.
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我現在在這裡有時差。
08:17
You don't probably大概 see it, but I do.
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你們可能看不出來,但確實有。
08:20
What if I eat less?
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如果我吃少一點呢?
08:21
When I took metformin二甲雙胍,
supposedly按說 to live生活 longer?
167
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如果我吃抗糖尿病藥,
會不會比較長壽?
08:25
What if I climb Mt公噸. Everest珠峰?
168
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如果我去爬聖母峰呢?
08:26
It's not that easy簡單.
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那並不容易。
08:28
Or run a marathon馬拉松?
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或跑馬拉松呢?
08:30
What if I drink a bottle瓶子 of mao-tai茅台酒,
171
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如果我喝一瓶茅臺酒呢?
08:32
which哪一個 is a Chinese中文 liquor,
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那是種中國酒,
08:33
and I get really drunk?
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且喝得非常醉呢?
08:35
I was doing a video視頻 rehearsal排演 last time
with the folks鄉親 here,
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上次我和這裡的人在做視訊排演,
08:39
when I was drunk,
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當時我醉了,
08:40
and I totally完全 delivered交付
a different不同 speech言語.
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我的演說完全不一樣。
08:42
(Laughter笑聲)
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(笑聲)
08:45
What if I work less, right?
178
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如果我工作少一點呢?
08:48
I have been less stressed強調, right?
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我就會少點壓力,對吧?
08:50
So that probably大概 never happened發生 to me,
180
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1810
那可能永遠不會發生在我身上,
08:51
I was really stressed強調 every一切 day,
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我每天都非常有壓力,
08:53
but I hope希望 I could be less stressed強調.
182
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1966
但我希望我能少點壓力。
08:56
These early studies學習 told us,
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早先的研究告訴我們,
08:58
even with the same相同 banana香蕉,
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1983
即使是吃同樣的香蕉,
09:00
we have totally完全 different不同
glucose-level葡萄糖水平 reactions反應
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不同的個體也會有全然不同的
09:03
over different不同 individuals個人.
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葡萄糖濃度反應。
09:04
How about me?
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那我呢?
09:06
What is the right breakfast早餐 for me?
188
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1998
一頓正確的早餐應該吃什麽?
09:08
I need to do two weeks
of controlled受控 experiments實驗,
189
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我需要做兩週的對照實驗,
09:11
of testing測試 all kinds of different不同
food餐飲 ingredients配料 on me,
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測試各種不同食材的反應,
09:15
and check my body's身體的 reaction反應.
191
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並檢查我的身體反應。
09:17
And I don't know
the precise精確 nutrition營養 for me,
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我不知道對我來說,精確的營養
09:20
for myself.
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到底應該包含什麽。
09:23
Then I wanted to search搜索
all the Chinese中文 old wisdom智慧
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接著我想要搜尋所有的中國古智慧,
09:27
about how I can live生活 longer,
and healthier健康.
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了解我要如何活得更久、更健康。
09:30
I did it.
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我去做了。
09:32
Some of them are really unachievable無法實現.
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有些真的無法達成。
09:34
I did this once一旦 last October十月,
198
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2865
我是在去年十月做的,
09:37
by not eating for seven days.
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1718
七天沒有吃東西。
09:40
I did a fast快速 for seven days
with six partners夥伴 of mine.
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我和我的六個伙伴一同禁食七天。
09:44
Look at those people.
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看看那些人。
09:46
One smile微笑.
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有一個人在笑。
09:47
You know why he smiled笑笑?
203
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猜猜為何他會笑?
09:48
He cheated被騙.
204
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他作弊。
09:49
(Laughter笑聲)
205
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1000
(笑聲)
09:50
He drank one cup杯子 of coffee咖啡 at night,
206
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3136
晚上他喝了一杯咖啡,
我們從資料中抓到的。
09:53
and we caught抓住 it from the data數據.
207
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1485
09:55
(Laughter笑聲)
208
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1045
(笑聲)
09:56
We measured測量 everything from the data數據.
209
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我們從資料中測量一切。
09:58
We were able能夠 to track跟踪 them,
210
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2214
我們能夠追縱它們,
10:01
and we could really see --
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我們真的能看到──
10:02
for example, my immune免疫的 system系統,
212
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2001
比如,我的免疫系統,
10:04
just to give you a little hint暗示 there.
213
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1762
給各位一點小暗示。
10:06
My immune免疫的 system系統 changed
dramatically顯著 over 24 hours小時 there.
214
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我的免疫系統在 24 小時
發生了巨大改變。
10:11
And my antibody抗體 regulates調整對象 my proteins蛋白質
215
599918
3133
而我的抗體為了適應這樣的變化,
開始對我體內的蛋白質進行調節,
10:15
for that dramatic戲劇性 change更改.
216
603075
1536
10:16
And everybody每個人 was doing that.
217
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1381
所有參與體驗的人都是如此,
10:18
Even if we're essentially實質上
totally完全 different不同 at the very beginning開始.
218
606040
3332
儘管每個人的免疫系統各不相同。
10:21
And that probably大概 will be
an interesting有趣 treatment治療 in the future未來
219
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3045
這很可能是將來治療癌症
10:24
for cancer癌症 and things like that.
220
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或類似疾病的一個有趣方法。
10:26
It becomes very, very interesting有趣.
221
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這件事變得越來越有趣。
10:28
But something you probably大概
don't want to try,
222
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2701
但有些方法你可能未必想嘗試,
10:31
like drinking fecal糞便 water
from a healthier健康 individual個人,
223
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3676
比如去喝健康人的尿
10:34
which哪一個 will make you feel healthier健康.
224
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1667
會讓你更健康。
10:36
This is from old Chinese中文 wisdom智慧.
225
624402
1715
這是來自古老中國的智慧。
10:38
Look at that, right?
226
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你看,是吧?
10:39
Like 1,700 years年份 ago,
227
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大約 1700 年前,
10:41
it's already已經 there, in the book.
228
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書籍上就有這樣的記載了。
10:44
But I still hate討厭 the smell.
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但我仍然很討厭那味道。
10:46
(Laughter笑聲)
230
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(笑聲)
10:47
I want to find out the true真正 way to do it,
231
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我想要找一種真正的方式來做它,
10:49
maybe find a combination組合 of cocktails雞尾酒
of bacterias and drink it,
232
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也許用雞尾酒和細菌
來合成,再喝下去,
10:54
it probably大概 will make me better.
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也許會讓我感覺好些。
10:55
So I'm trying to do that.
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我在試著這麼做。
10:56
Even though雖然 I'm trying this hard,
235
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雖然我非常努力在試,
11:00
it's so difficult to test測試 out
all possible可能 conditions條件.
236
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5026
但是要測試出所有可能的方法
是非常困難的,
11:05
It's not possible可能 to do
all kinds of experiments實驗 at all ...
237
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完全不可能去做所有各種實驗……
11:11
but we do have seven billion十億
learning學習 programs程式 on this planet行星.
238
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3813
但在地球上我們仍然有
七十億個學習程式。
11:15
Seven billion十億.
239
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1266
七十億。
11:16
And every一切 program程序
is running賽跑 in different不同 conditions條件
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3651
每個程式都以不同的條件在執行,
11:20
and doing different不同 experiments實驗.
241
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做著不同的實驗。
11:21
Can we all measure測量 them?
242
669948
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我們能不能把它們全部都量測出來?
11:24
Seven years年份 ago,
I wrote an essay文章 in "Science科學"
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七年前我在《科學》期刊中
寫了一篇短文,
11:28
to celebrate慶祝 the human人的 genome's基因組的
10-year-年 anniversary週年.
244
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3292
來讚頌人類基因組的十週年紀念。
11:32
I said, "Sequence序列 yourself你自己,
245
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1654
我說:「定序你自己,
11:33
for one and for all."
246
681853
1623
為自己,也為所有人。」
11:35
But now I'm going to say,
247
683798
1868
但現在我只打算說:
11:37
"Digitalize數字化 yourself你自己 for one and for all."
248
685690
3746
「把你自己數位化,
為自己,也為所有人。」
11:42
When we make this digital數字 me
into a digital數字 we,
249
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5600
當我們把「數位化的我」
變成「數位化的我們」,
11:47
when we try to form形成 an internet互聯網 of life,
250
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3752
當我們嘗試建構數位化生命網路、
11:51
when people can learn學習 from each other,
251
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2861
當人們可以從中彼此學習、
11:54
when people can learn學習
from their experience經驗,
252
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2707
當人們可以從他們的經驗、
11:57
their data數據,
253
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1731
他們的資料來學習,
11:59
when people can really form形成
a digital數字 me by themselves他們自己
254
707046
3601
當人們能真正做出
「數位化的自己」,
12:02
and we learn學習 from it,
255
710671
1611
我們可以從中進行學習,
12:05
the digital數字 we will be
totally完全 different不同 with a digital數字 me.
256
713416
5732
那麼「數位化的我們」就會和
「數位化的我」截然不同了。
12:11
But it can only come from the digital數字 me.
257
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3420
但它必須要由
「數位化的我」開始建立起。
12:16
And this is what I try to propose提出 here.
258
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2979
這是我在這裡想要提議的事。
12:20
Join加入 me --
259
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1150
加入我──
12:21
become成為 we,
260
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1150
變成「我們」,
12:23
and everybody每個人 should build建立 up
their own擁有 digital數字 me,
261
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4938
每個人都應該要建立
自己的「數位化的我」,
12:28
because only by that
will you learn學習 more about you,
262
736754
4519
因為只有這樣做,
你才能學到更多關於你自己、
12:33
about me,
263
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1424
關於我、
12:34
about us ...
264
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1165
關於我們…...
12:36
about the question I just posed構成
at the very beginning開始:
265
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3680
關於我在一開頭提出的問題:
12:40
"What is life?"
266
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1150
「生命是什麼?」
12:42
Thank you.
267
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1169
謝謝。
12:43
(Applause掌聲)
268
751259
5950
(掌聲)
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:很快請教一個問題。
12:52
I mean, the work is amazing驚人.
270
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1974
這項研究很令人驚奇。
12:54
I suspect疑似 one question people have is,
271
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3198
我想人們可能會有一個疑問,
12:58
as we look forward前鋒 to these amazing驚人
technical技術 possibilities可能性
272
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3281
當我們在期待著這些
個人化醫學的
13:01
of personalized個性化 medicine醫學,
273
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1361
非凡技術可能性時,
13:02
in the near-term短期 it feels感覺 like
they're only going to be affordable實惠
274
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3303
在短期來看,
似乎只有少數的人能負擔,
13:06
for a few少數 people, right?
275
774055
1276
對吧?
13:07
It costs成本 many許多 dollars美元 to do
all the sequencing測序 and so forth向前.
276
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2991
需要很多錢才能做這些定序等等。
13:10
Is this going to lead to a kind of,
277
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2912
這是否會導致某種……
13:13
you know, increasing增加 inequality不等式?
278
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2317
不平等的增加?
13:16
Or do you have this vision視力
that the knowledge知識 that you get
279
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3911
或者您是否有這樣的願景:
從這些早期的志願者
身上獲取的知識,
13:20
from the pioneers開拓者
280
788101
1352
13:21
can actually其實 be
pretty漂亮 quickly很快 disseminated傳播
281
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2096
快速地複製推廣,
13:23
to help a broader更廣泛 set of recipients收件人?
282
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4124
從而幫助更廣泛的群體?
13:27
Jun Wang: Well, good question.
283
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1534
王俊:嗯,好問題。
13:29
I'll tell you that seven years年份 ago,
when I co-founded共同創立 BGIBGI,
284
797303
3551
我可以告訴你,
當我七年前共同創立了華大基因,
13:32
and served提供服務 as the CEOCEO
of the company公司 there,
285
800878
3405
並擔任公司執行長時,
13:36
the only goal目標 there for me to do
286
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2381
我唯一想做到的目標
13:38
was to drive駕駛 the sequencing測序 cost成本 down.
287
806712
1983
是要把做定序的成本降低。
13:41
It started開始 from 100 million百萬 dollars美元
per human人的 genome基因組.
288
809044
2775
早先的人類基因組定序要一億元。
13:43
Now, it's a couple一對 hundred dollars美元
for a human人的 genome基因組.
289
811843
2591
現在每個人類基因組只要幾百塊錢。
13:46
The only reason原因 to do it
is to get more people to benefit效益 from it.
290
814458
3614
這麼做的唯一理由,
就是想讓更多人從中受益。
13:50
So for the digital數字 me,
it's the same相同 thing.
291
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2157
所以,對「數位化的我」也一樣。
13:52
Now, you probably大概 need,
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現在,你可能會需要…...
13:54
you know, one million百萬 dollars美元
to digitize數字化 a person.
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一百萬元才能把一個人數位化。
13:57
I think it has to be 100 dollars美元.
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我想價格得要降到一百元。
13:59
It has to be free自由 for many許多 of those people
that urgently迫切 need that.
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有緊急需求的人要是可以免費的。
14:04
So this is our goal目標.
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這是我們的目標。
14:05
And it seems似乎 that with all
this merging合併 of the technology技術,
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似乎,把所有這些科技結合,
14:09
I'm thinking思維 that in the very near future未來,
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我想,在不遠的將來,
14:12
let's say three to five years年份,
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也許三到五年,
14:14
it will come to reality現實.
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它就會實現。
14:15
And this is the whole整個 idea理念
of why I founded成立 iCarbonXiCarbonX,
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這就是為什麼
我會成立第二間公司 :
14:19
my second第二 company公司.
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iCarbonX(碳雲智能)。
14:21
It's really trying to get the cost成本 down
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我們是真的想把成本下降,
14:24
to a level水平 where every一切 individual個人
could have the benefit效益.
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下降到人人都可受惠的程度。
14:27
CACA: All right, so the dream夢想 is not
elite原種 health健康 services服務 for few少數,
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克里斯:好,所以這個夢想
並不是給少數人的菁英健康服務,
14:30
it's to really try
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是真的要試著去
14:31
and actually其實 make overall總體 health健康 care關心
much more cost成本 effective有效 --
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且實際上去讓整體的
健康照護更有成本效益──
14:34
JWJW: But we started開始
from some early adopters採納者,
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王俊:我們需要從一些
早期的先行者開始,
14:37
people believing相信 ideas思路 and so on,
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從更加相信這個想法的一些人開始,
14:39
but eventually終於, it will become成為
everybody's每個人的 benefit效益.
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但最終它將能夠讓每個人都受益。
14:44
CACA: Well, Jun, I think
it's got to be true真正 to say
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克里斯:王俊,我想這麼說不為過,
14:46
you're one of the most amazing驚人
scientific科學 minds頭腦 on the planet行星,
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你是地球上很有
慈善心腸的科學家之一,
14:49
and it's an honor榮譽 to have you.
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非常榮幸能邀請到你。
14:51
JWJW: Thank you.
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王俊:謝謝。
14:52
(Applause掌聲)
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(掌聲)
Translated by Lilian Chiu
Reviewed by Yi-Fan Yu

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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

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