Abundance is our future | Peter Diamandis

497,202 views ・ 2012-03-01

TED


Please double-click on the English subtitles below to play the video.

Translator: Shelley Krishna Tsang Reviewer: Sylvia He
00:15
(Applause)
0
15260
3000
(掌聲)
00:18
(Video) Announcer: Threats, in the wake of Bin Laden's death, have spiked.
1
18260
3000
(賓拉登死後,威脅層出不窮。)
00:21
Announcer Two: Famine in Somalia. Announcer Three: Police pepper spray.
2
21260
2000
(索馬利亞嘅飢荒。) (警方用嘅胡椒噴霧。)
00:23
Announcer Four: Vicious cartels. Announcer Five: Caustic cruise lines.
3
23260
2000
(企業使用惡意壟斷。) (遊船上嘅混亂。)
00:25
Announcer Six: Societal decay. Announcer Seven: 65 dead.
4
25260
3000
(社會嘅腐敗。) (六十五人死亡。)
00:28
Announcer Eight: Tsunami warning. Announcer Nine: Cyberattacks.
5
28260
2000
(海嘯警報。) (網路上嘅駭客攻擊。)
00:30
Multiple Announcers: Drug war. Mass destruction. Tornado.
6
30260
2000
(毒品罪行。) (大規模毀滅。龍捲風。)
00:32
Recession. Default. Doomsday. Egypt. Syria.
7
32260
2000
(經濟衰退。債務拖欠。 世界末日。埃及。敘利亞。)
00:34
Crisis. Death. Disaster.
8
34260
2000
(危機。死亡。災難。)
00:36
Oh, my God.
9
36260
3000
「喔!我嘅天啊!」
00:39
Peter Diamandis: So those are just a few of the clips
10
39260
2000
呢啲新聞頭條只係我喺過去六個月
00:41
I collected over the last six months --
11
41260
2000
收集到嘅一小部分
00:43
could have easily been the last six days
12
43260
2000
同過去六日嘅頭條
00:45
or the last six years.
13
45260
2000
或者過去六年嘅頭條冇咩差別
00:47
The point is that the news media
14
47260
2000
重點係,新聞媒體
00:49
preferentially feeds us negative stories
15
49260
3000
鐘意報導負面新聞
00:52
because that's what our minds pay attention to.
16
52260
3000
因為呢類新聞可以吸引我哋關注
00:55
And there's a very good reason for that.
17
55260
2000
理由係
00:57
Every second of every day,
18
57260
2000
每日
00:59
our senses bring in way too much data
19
59260
2000
我哋接收嘅資訊
01:01
than we can possibly process in our brains.
20
61260
3000
超過我哋大腦負荷
01:04
And because nothing is more important to us
21
64260
2000
對我哋嚟講
01:06
than survival,
22
66260
2000
冇嘢比生存更加重要
01:08
the first stop of all of that data
23
68260
2000
我哋嘅大腦接收信息嘅第一站
01:10
is an ancient sliver of the temporal lobe
24
70260
2000
係一片發現已久嘅
01:12
called the amygdala.
25
72260
2000
叫做「扁桃體」嘅腦葉
01:14
Now the amygdala is our early warning detector,
26
74260
3000
扁桃體係我哋身體最初期嘅
01:17
our danger detector.
27
77260
2000
警告同危險偵測系統
01:19
It sorts and scours through all of the information
28
79260
3000
佢搜尋同整理所有訊息
01:22
looking for anything in the environment that might harm us.
29
82260
3000
揾出喺環境裡邊對我哋有害嘅物質
01:25
So given a dozen news stories,
30
85260
2000
所以喺眾多嘅新聞裡邊
01:27
we will preferentially look
31
87260
2000
我哋會鍾意睇
01:29
at the negative news.
32
89260
2000
負面嘅新聞
01:31
And that old newspaper saying,
33
91260
2000
有一份舊報紙寫話:
01:33
"If it bleeds it leads,"
34
93260
2000
「有血嘅嘢最好賣。」
01:35
is very true.
35
95260
2000
呢句嘢好啱
01:37
So given all of our digital devices
36
97260
3000
宜家各種數碼設備
01:40
that are bringing all the negative news to us
37
100260
2000
一個禮拜七日、一日 24 小時
01:42
seven days a week, 24 hours a day,
38
102260
3000
猛咁對我哋輸啲負面新聞
01:45
it's no wonder that we're pessimistic.
39
105260
2000
唔怪得我哋咁悲觀
01:47
It's no wonder that people think
40
107260
2000
難怪啲人諗
01:49
that the world is getting worse.
41
109260
4000
世界越來越冇希望
01:53
But perhaps that's not the case.
42
113260
3000
事實上可能唔係噉
01:56
Perhaps instead,
43
116260
2000
其實,有可能係
01:58
it's the distortions brought to us
44
118260
2000
媒體帶畀我哋嘅消息
02:00
of what's really going on.
45
120260
3000
係扭曲咗嘅
02:03
Perhaps the tremendous progress we've made
46
123260
2000
可能我哋喺上個世紀
02:05
over the last century
47
125260
2000
藉一連串努力
02:07
by a series of forces
48
127260
2000
而創造嘅進步
02:09
are, in fact, accelerating to a point
49
129260
3000
加速到一個位
02:12
that we have the potential in the next three decades
50
132260
3000
使我哋喺未來三十年有潛力
02:15
to create a world of abundance.
51
135260
3000
去創造一個富足嘅世界
02:18
Now I'm not saying
52
138260
2000
我唔係話
02:20
we don't have our set of problems --
53
140260
2000
我哋冇問題
02:22
climate crisis, species extinction,
54
142260
2000
氣候危機、物種滅絕、水源同能源匱乏
02:24
water and energy shortage -- we surely do.
55
144260
3000
呢哋問題都一直存在
02:27
And as humans, we are far better
56
147260
2000
身為人類,我哋比其他動物
02:29
at seeing the problems way in advance,
57
149260
3000
早睇到問題好多
02:32
but ultimately we knock them down.
58
152260
4000
然後我哋解決呢哋問題
02:36
So let's look
59
156260
2000
我哋睇下
02:38
at what this last century has been
60
158260
2000
上個世紀到底發生咗咩事
02:40
to see where we're going.
61
160260
2000
同我哋下一步會去邊到
02:42
Over the last hundred years,
62
162260
2000
過去百幾年嚟
02:44
the average human lifespan has more than doubled,
63
164260
3000
人類嘅平均壽命係以前嘅兩倍
02:47
average per capita income adjusted for inflation
64
167260
3000
通貨膨脹調整之後 嘅各國平均國民所得
02:50
around the world has tripled.
65
170260
2000
係以前嘅三倍
02:52
Childhood mortality
66
172260
2000
童年嘅死亡率
02:54
has come down a factor of 10.
67
174260
2000
都下降到原來嘅十分之一
02:56
Add to that the cost of food, electricity,
68
176260
2000
仲有,糧食、電能、
02:58
transportation, communication
69
178260
2000
交通、通訊嘅花費
03:00
have dropped 10 to 1,000-fold.
70
180260
4000
變成以前嘅十分之一到千分之一
03:04
Steve Pinker has showed us
71
184260
2000
史迪芬.平克話俾我哋聽
03:06
that, in fact, we're living during the most peaceful time ever
72
186260
3000
宜家係
03:09
in human history.
73
189260
2000
人類史上最和平嘅時代
03:11
And Charles Kenny
74
191260
2000
Charles Kenny 話
03:13
that global literacy has gone from 25 percent to over 80 percent
75
193260
3000
過去 130 年,全球識字率
03:16
in the last 130 years.
76
196260
3000
從 25% 升到 80%
03:19
We truly are living in an extraordinary time.
77
199260
4000
我哋宜家生活喺一個黃金時期
03:23
And many people forget this.
78
203260
2000
但係好多人都唔記得呢樣嘢
03:25
And we keep setting our expectations higher and higher.
79
205260
3000
我哋不斷噉將期望設得越來越高
03:28
In fact, we redefine what poverty means.
80
208260
3000
事實上,我哋重寫咗貧窮嘅定義
03:31
Think of this, in America today,
81
211260
2000
諗下宜家嘅美國
03:33
the majority of people under the poverty line
82
213260
3000
喺貧窮線以下嘅大多數人
03:36
still have electricity, water, toilets, refrigerators,
83
216260
3000
都仲擁有水、電、馬桶、冰箱
03:39
television, mobile phones,
84
219260
2000
電視、手機
03:41
air conditioning and cars.
85
221260
3000
甚至冷氣同車
03:44
The wealthiest robber barons of the last century, the emperors on this planet,
86
224260
3000
上個世紀最富有嘅強盜、貴族、國王
03:47
could have never dreamed of such luxuries.
87
227260
3000
都冇呢啲奢侈品
03:53
Underpinning much of this
88
233260
3000
令呢種現象出現
03:56
is technology,
89
236260
2000
全靠以前嘅科技
03:58
and of late,
90
238260
2000
以至最近嘅
04:00
exponentially growing technologies.
91
240260
2000
幾何級數發展嘅科技
04:02
My good friend Ray Kurzweil
92
242260
2000
我嘅好朋友 Ray Kurzweil 畫咗一幅圖
04:04
showed that any tool that becomes an information technology
93
244260
3000
說明只要物件變成資訊科技工具
04:07
jumps on this curve, on Moore's Law,
94
247260
3000
就會按著摩爾定律嘅曲線
04:10
and experiences price performance doubling
95
250260
2000
每一至兩年
04:12
every 12 to 24 months.
96
252260
3000
性能就會翻倍
04:15
That's why the cellphone in your pocket
97
255260
2000
咁就係點解你哋衫袋裡邊嘅手機
04:17
is literally a million times cheaper and a thousand times faster
98
257260
2000
比起 70 年代嘅超級電腦
04:19
than a supercomputer of the '70s.
99
259260
2000
仲要平咗一百萬倍 同快咗一千倍
04:21
Now look at this curve.
100
261260
2000
睇下呢個曲線
04:23
This is Moore's Law over the last hundred years.
101
263260
2000
呢個係一百幾年前嘅摩爾定律
04:25
I want you to notice two things from this curve.
102
265260
2000
留意下呢條曲線上嘅兩樣嘢
04:27
Number one, how smooth it is --
103
267260
4000
第一,條曲綫好平穩
04:31
through good time and bad time, war time and peace time,
104
271260
3000
唔受時期嘅好壞,或者戰爭同和平
04:34
recession, depression and boom time.
105
274260
3000
經濟衰退、低迷或者繁榮嘅影響
04:37
This is the result of faster computers
106
277260
2000
個結果就係速度快嘅電腦
04:39
being used to build faster computers.
107
279260
3000
用來造更快嘅電腦
04:42
It doesn't slow for any of our grand challenges.
108
282260
4000
電腦冇因爲任何我哋面對嘅問題而慢咗
04:46
And also, even though it's plotted
109
286260
2000
雖然曲線圖採用咗
04:48
on a log curve on the left,
110
288260
2000
對數曲線
04:50
it's curving upwards.
111
290260
2000
佢嘅方向係向上嘅
04:52
The rate at which the technology is getting faster
112
292260
2000
即係,科技進步嘅比率
04:54
is itself getting faster.
113
294260
3000
越來越快
04:57
And on this curve, riding on Moore's Law,
114
297260
3000
喺呢條摩爾定律曲線上邊
05:00
are a set of extraordinarily powerful technologies
115
300260
3000
係一連串可供我哋利用嘅
05:03
available to all of us.
116
303260
2000
強大而先進嘅科技
05:05
Cloud computing,
117
305260
2000
我喺 Autodesk 嘅朋友
05:07
what my friends at Autodesk call infinite computing;
118
307260
2000
叫雲端運算做無限運算
05:09
sensors and networks; robotics;
119
309260
3000
感應器。網路。自動化設備
05:12
3D printing, which is the ability to democratize and distribute
120
312260
3000
3D 印刷有可能喺全球將產品
05:15
personalized production around the planet;
121
315260
2000
大眾化同人性化
05:17
synthetic biology;
122
317260
2000
人造生物學
05:19
fuels, vaccines and foods;
123
319260
3000
燃料、疫苗同食物
05:22
digital medicine; nanomaterials; and A.I.
124
322260
3000
數位醫學。納米材料。人工智慧
05:25
I mean, how many of you saw the winning of Jeopardy
125
325260
3000
在座有幾多人睇到 IBM 沃森
05:28
by IBM's Watson?
126
328260
2000
喺《危險邊緣》贏通街?
05:30
I mean, that was epic.
127
330260
3000
嗰次個節目真係好經典
05:33
In fact, I scoured the headlines
128
333260
2000
我摷勻好多報紙
05:35
looking for the best headline in a newspaper I could.
129
335260
2000
想揾個最好嘅頭條
05:37
And I love this: "Watson Vanquishes Human Opponents."
130
337260
4000
我好鐘意呢個:《沃森擊敗人類對手》
05:42
Jeopardy's not an easy game.
131
342260
2000
《危險邊緣》呢個比賽好難
05:44
It's about the nuance of human language.
132
344260
3000
字眼上差小小,答案就唔一樣
05:47
And imagine if you would
133
347260
2000
想像一下
05:49
A.I.'s like this on the cloud
134
349260
2000
呢種雲端嘅人工智慧
05:51
available to every person with a cellphone.
135
351260
3000
喺人人嘅手機度都有
05:54
Four years ago here at TED,
136
354260
2000
四年前喺 TED
05:56
Ray Kurzweil and I started a new university
137
356260
2000
Ray Kurzweil 同我創辦咗
05:58
called Singularity University.
138
358260
2000
一間叫「奇點大學」嘅新大學
06:00
And we teach our students all of these technologies,
139
360260
3000
我哋向學生傳授呢啲全部嘅科技
尤其係點樣用嚟
06:03
and particularly how they can be used
140
363260
2000
解決人類面對嘅巨大挑戰
06:05
to solve humanity's grand challenges.
141
365260
3000
06:08
And every year we ask them
142
368260
2000
每一年, 我哋要求佢哋
06:10
to start a company or a product or a service
143
370260
3000
開新公司、開發新產品或者新服務
06:13
that can affect positively the lives of a billion people
144
373260
3000
希望喺十年內
06:16
within a decade.
145
376260
2000
幫助到十億人
06:18
Think about that, the fact that, literally, a group of students
146
378260
3000
試諗下,呢班學生真係
06:21
can touch the lives of a billion people today.
147
381260
3000
可以幫到十億人
06:24
30 years ago that would have sounded ludicrous.
148
384260
2000
呢種諗法喺 30 年前係幾荒唐
06:26
Today we can point at dozens of companies
149
386260
3000
宜家我哋可以講出邊間公司
06:29
that have done just that.
150
389260
2000
喺度做緊呢件事
06:31
When I think about creating abundance,
151
391260
6000
對我來講,創造富足嘅社會
06:37
it's not about creating a life of luxury for everybody on this planet;
152
397260
3000
唔係話要世界上 每個人都享受奢華生活
06:40
it's about creating a life of possibility.
153
400260
3000
而係要創造出生活嘅可能性
係要使我哋缺乏嘅嘢
06:43
It is about taking that which was scarce
154
403260
3000
06:46
and making it abundant.
155
406260
2000
變得豐富
06:48
You see, scarcity is contextual,
156
408260
3000
每個人缺乏嘅嘢唔同
06:51
and technology is a resource-liberating force.
157
411260
5000
而科技係一個解放資源嘅力量
06:56
Let me give you an example.
158
416260
3000
我舉個例
06:59
So this is a story of Napoleon III
159
419260
2000
呢個係十八世紀中葉
07:01
in the mid-1800s.
160
421260
2000
關於拿破崙三世嘅故事
07:03
He's the dude on the left.
161
423260
3000
佢就係左邊嗰個人
07:06
He invited over to dinner
162
426260
2000
佢邀請暹羅國王
07:08
the king of Siam.
163
428260
2000
共進晚餐
07:10
All of Napoleon's troops
164
430260
2000
07:12
were fed with silver utensils,
165
432260
3000
拿破崙嘅成個軍隊
用銀製餐具
07:15
Napoleon himself with gold utensils.
166
435260
2000
拿破崙自己就用金製餐具
07:17
But the King of Siam,
167
437260
2000
但係暹羅國王
07:19
he was fed with aluminum utensils.
168
439260
2000
用嘅竟然係鋁製餐具
07:21
You see, aluminum
169
441260
2000
原來
07:23
was the most valuable metal on the planet,
170
443260
3000
鋁曾經係世上最貴嘅金屬
07:26
worth more than gold and platinum.
171
446260
3000
甚至貴過黃金同埋白金
07:29
It's the reason that the tip of the Washington Monument
172
449260
3000
咁就係點解華盛頓紀念碑嘅頂端
07:32
is made of aluminum.
173
452260
2000
係由鋁所製成
07:34
You see, even though aluminum
174
454260
2000
雖然鋁嘅礦量
07:36
is 8.3 percent of the Earth by mass,
175
456260
3000
佔地球質量嘅 8.3%
07:39
it doesn't come as a pure metal.
176
459260
2000
但係金屬鋁好罕見 鋁多數喺礦物裡邊
07:41
It's all bound by oxygen and silicates.
177
461260
3000
佢多數同氧或者矽酸鹽 反應生成化合物
07:44
But then the technology of electrolysis came along
178
464260
3000
有咗電解科技之後
07:47
and literally made aluminum so cheap
179
467260
3000
提煉鋁就變得好平
07:50
that we use it with throw-away mentality.
180
470260
3000
平到用完就掟
07:53
So let's project this analogy going forward.
181
473260
4000
如此類推
07:57
We think about energy scarcity.
182
477260
2000
我哋試諗下能源缺乏
07:59
Ladies and gentlemen,
183
479260
2000
08:01
we are on a planet
184
481260
2000
各位
我哋生活緊嘅地球,係一個
08:03
that is bathed with 5,000 times more energy
185
483260
3000
擁有我哋一年用嘅能源
08:06
than we use in a year.
186
486260
3000
5,000 倍嘅星球
08:09
16 terawatts of energy hits the Earth's surface
187
489260
2000
每 88 分鐘就有 16 兆瓦嘅能源
08:11
every 88 minutes.
188
491260
4000
來到地球嘅表面
08:15
It's not about being scarce,
189
495260
2000
所以問題唔係缺乏
08:17
it's about accessibility.
190
497260
2000
而係獲取能源嘅途徑
08:19
And there's good news here.
191
499260
2000
有個好消息
08:21
For the first time, this year
192
501260
2000
今年係印度有史以來第一次
08:23
the cost of solar-generated electricity
193
503260
3000
太陽能發電嘅花費
08:26
is 50 percent that of diesel-generated electricity in India --
194
506260
4000
係柴油發電嘅一半
08:30
8.8 rupees versus 17 rupees.
195
510260
3000
8.8 盧比對 17 盧比
08:33
The cost of solar dropped 50 percent last year.
196
513260
2000
舊年太陽能發電花費就降咗一半
08:35
Last month, MIT put out a study
197
515260
2000
上個月麻省理工學院發表咗一項研究
08:37
showing that by the end of this decade,
198
517260
2000
話喺 2020 年之前
08:39
in the sunny parts of the United States,
199
519260
2000
美國陽光充足嘅地區
08:41
solar electricity will be six cents a kilowatt hour
200
521260
2000
太陽能電嘅平均價格
08:43
compared to 15 cents
201
523260
2000
會降到 6 仙美金一度
宜家平均一度要 1 毫 5 仙美元
08:45
as a national average.
202
525260
2000
08:47
And if we have abundant energy,
203
527260
3000
我哋有咗充足嘅能源
08:50
we also have abundant water.
204
530260
3000
我哋就會有充足嘅水源
08:53
Now we talk about water wars.
205
533260
5000
我哋來講下水源嘅戰爭
08:58
Do you remember
206
538260
2000
你記唔記得
09:00
when Carl Sagan turned the Voyager spacecraft
207
540260
2000
1990 年 無人探測船航海家 1 號
09:02
back towards the Earth,
208
542260
2000
飛過土星之後
卡爾.薩根叫探測船擰轉身 同太陽系影張全家福
09:04
in 1990 after it just passed Saturn?
209
544260
2000
09:06
He took a famous photo. What was it called?
210
546260
3000
嗰張相好出名,叫咩?
09:09
"A Pale Blue Dot."
211
549260
2000
《蒼藍小點》
09:11
Because we live on a water planet.
212
551260
3000
我哋住喺一個水星球
09:14
We live on a planet 70 percent covered by water.
213
554260
3000
一個 70% 嘅表面俾水覆蓋嘅星球
09:17
Yes, 97.5 percent is saltwater,
214
557260
2000
其中 97.5% 係鹹水
09:19
two percent is ice,
215
559260
2000
09:21
and we fight over a half a percent of the water on this planet,
216
561260
3000
2% 係冰
我哋喺度爭地球上 0.5% 嘅水
09:24
but here too there is hope.
217
564260
2000
天無絕人之路
09:26
And there is technology coming online,
218
566260
3000
有一個新科技出現咗
09:29
not 10, 20 years from now,
219
569260
2000
唔係喺十、二十年後
09:31
right now.
220
571260
2000
係宜家
09:33
There's nanotechnology coming on, nanomaterials.
221
573260
3000
納米科技同納米材料誕生咗
09:36
And the conversation I had with Dean Kamen this morning,
222
576260
3000
我今朝同 Dean Kamen 傾偈
09:39
one of the great DIY innovators,
223
579260
2000
佢係一個偉大嘅 DIY 發明家
09:41
I'd like to share with you -- he gave me permission to do so --
224
581260
3000
佢畀我同你哋分享
09:44
his technology called Slingshot
225
584260
2000
佢嘅發明︰「Slingshot」
09:46
that many of you may have heard of,
226
586260
2000
可能好多人都聽過
09:48
it is the size of a small dorm room refrigerator.
227
588260
2000
佢係一個宿舍用嘅冰箱噉大
09:50
It's able to generate
228
590260
2000
佢可以
09:52
a thousand liters of clean drinking water a day
229
592260
2000
每日淨化一千升嘅水
09:54
out of any source -- saltwater, polluted water, latrine --
230
594260
3000
咩水都可以淨化 鹹水、污染水、廁所污水
09:57
at less than two cents a liter.
231
597260
3000
而且成本低於一升 2 仙美元
10:02
The chairman of Coca-Cola has just agreed
232
602260
2000
可口可樂董事長啱啱批准咗
10:04
to do a major test
233
604260
2000
一個大規模嘅實驗
10:06
of hundreds of units of this in the developing world.
234
606260
3000
喺發展中國家試用幾百套呢種設備
10:09
And if that pans out,
235
609260
2000
如果成功——
10:11
which I have every confidence it will,
236
611260
2000
當然我有信心佢會成功
10:13
Coca-Cola will deploy this globally
237
613260
2000
可口可樂公司就會
10:15
to 206 countries
238
615260
2000
將呢個計畫推廣至
全球 206 個國家
10:17
around the planet.
239
617260
2000
10:19
This is the kind of innovation, empowered by this technology,
240
619260
3000
呢種創新就係
10:22
that exists today.
241
622260
4000
現代科技帶來嘅
10:26
And we've seen this in cellphones.
242
626260
2000
手機就係表表者
10:28
My goodness, we're going to hit 70 percent penetration
243
628260
2000
天啊,喺 2013 之前
10:30
of cellphones in the developing world
244
630260
2000
手機喺開發中國家嘅使用率
10:32
by the end of 2013.
245
632260
2000
就嚟達到 70%
10:34
Think about it,
246
634260
2000
試諗下
10:36
that a Masai warrior on a cellphone in the middle of Kenya
247
636260
3000
一個肯尼亞馬賽族戰士用嘅手機
10:39
has better mobile comm
248
639260
2000
比 25 年前雷根總統在位時
10:41
than President Reagan did 25 years ago.
249
641260
3000
用嘅通訊產品仲要好
10:44
And if they're on a smartphone on Google,
250
644260
2000
10:46
they've got access to more knowledge and information
251
646260
2000
如果佢哋用谷歌嘅智能手機
10:48
than President Clinton did 15 years ago.
252
648260
2000
佢哋就比 15 年前克林頓總統在位時
接收到更多嘅知識同資訊
10:50
They're living in a world of information and communication abundance
253
650260
3000
佢哋生活喺一個 資訊同通訊都充足嘅世界
10:53
that no one could have ever predicted.
254
653260
3000
冇人原本預料到嘅
10:57
Better than that,
255
657260
2000
更好嘅係
10:59
the things that you and I
256
659260
2000
以前花費
好幾十萬美金嘅嘢:
11:01
spent tens and hundreds of thousands of dollars for --
257
661260
2000
11:03
GPS, HD video and still images,
258
663260
3000
GPS、高畫質影片、靜止圖像
11:06
libraries of books and music,
259
666260
3000
書庫、音樂庫
11:09
medical diagnostic technology --
260
669260
2000
醫療診斷科技
11:11
are now literally dematerializing and demonetizing
261
671260
3000
宜家都漸漸融入
11:14
into your cellphone.
262
674260
3000
你哋嘅手機度
而最好嘅係
11:19
Probably the best part of it
263
679260
2000
醫療成本會下降
11:21
is what's coming down the pike in health.
264
681260
3000
11:24
Last month, I had the pleasure of announcing with Qualcomm Foundation
265
684260
4000
上個月我好榮幸同高通基金會一齊宣布
11:28
something called the $10 million Qualcomm Tricorder X Prize.
266
688260
4000
高通 Tricorder X 大獎
11:32
We're challenging teams around the world
267
692260
2000
我哋向全球嘅參賽者提出挑戰
11:34
to basically combine these technologies
268
694260
2000
11:36
into a mobile device
269
696260
2000
要求將呢啲全部功能
融合入一個移動設備度
11:38
that you can speak to, because it's got A.I.,
270
698260
2000
佢要有人工智慧,令你可以同佢講嘢
11:40
you can cough on it, you can do a finger blood prick.
271
700260
3000
你可以對佢咳或者做手指血液採樣
11:43
And to win, it needs to be able to diagnose you better
272
703260
2000
要贏得呢個獎,個儀器嘅診斷技術
11:45
than a team of board-certified doctors.
273
705260
4000
要好過一隊摞牌營業嘅醫師
11:49
So literally, imagine this device
274
709260
3000
想像一下呢個儀器
11:52
in the middle of the developing world where there are no doctors,
275
712260
3000
可以被用喺缺乏醫生嘅發展中國家
11:55
25 percent of the disease burden
276
715260
2000
發展中國家有全世界 25% 嘅病症
11:57
and 1.3 percent of the health care workers.
277
717260
3000
但係只有全世界 1.3% 嘅醫務人員
12:00
When this device sequences an RNA or DNA virus
278
720260
2000
當呢個儀器遇到一個 RNA 或者 DNA 排列
12:02
that it doesn't recognize,
279
722260
2000
係佢未見過嘅
12:04
it calls the CDC
280
724260
2000
佢就會通報美國疾病防治中心
12:06
and prevents the pandemic from happening in the first place.
281
726260
3000
進而防止疾病從一個地區蔓延開去
12:11
But here, here is the biggest force
282
731260
3000
至於為世界帶來富足嘅
12:14
for bringing about a world of abundance.
283
734260
2000
最強大力量
12:16
I call it the rising billion.
284
736260
3000
我叫佢做「上升十億」
12:19
So the white lines here are population.
285
739260
3000
白線代表人口
12:22
We just passed the seven billion mark on Earth.
286
742260
3000
我哋啱啱超過咗七十億大關
12:25
And by the way,
287
745260
2000
順便提一下
12:27
the biggest protection against a population explosion
288
747260
2000
防止人口爆炸嘅最大力量
12:29
is making the world educated
289
749260
2000
就是教育
12:31
and healthy.
290
751260
3000
同埋醫療
12:34
In 2010,
291
754260
2000
喺 2010 年
12:36
we had just short of two billion people
292
756260
2000
全球有唔到 20 億人口
12:38
online, connected.
293
758260
2000
上網
12:40
By 2020,
294
760260
2000
12:42
that's going from two billion to five billion
295
762260
2000
到 2020 年
網民從 20 億
12:44
Internet users.
296
764260
2000
躍進到 50 億
12:46
Three billion new minds
297
766260
2000
新加入嘅 30 億人口
12:48
who have never been heard from before
298
768260
2000
之前從來冇上過網
12:50
are connecting to the global conversation.
299
770260
4000
但佢哋終於能夠同個世界對話
12:54
What will these people want?
300
774260
2000
呢啲人想要咩?
12:56
What will they consume? What will they desire?
301
776260
2000
佢哋會消費咩?佢哋渴望咩?
12:58
And rather than having economic shutdown,
302
778260
2000
經濟唔會再蕭條
13:00
we're about to have the biggest economic injection ever.
303
780260
3000
而係要迎接有史以來最繁榮嘅經濟
13:03
These people represent
304
783260
2000
呢啲人意味著
13:05
tens of trillions of dollars
305
785260
2000
有幾十兆美元
13:07
injected into the global economy.
306
787260
3000
投入全球嘅經濟市場
13:10
And they will get healthier
307
790260
2000
佢哋就會藉由
13:12
by using the Tricorder,
308
792260
2000
Tricorder 診斷儀器變得更健康
13:14
and they'll become better educated by using the Khan Academy,
309
794260
2000
同藉由可漢學院接受好嘅教育
13:16
and by literally being able to use
310
796260
3000
佢哋可以藉使用
13:19
3D printing and infinite computing
311
799260
3000
3D 打印技術同無限運算功能
13:22
[become] more productive than ever before.
312
802260
3000
變得更有生產力
13:25
So what could three billion rising,
313
805260
3000
所以呢 30 億
13:28
healthy, educated, productive members of humanity
314
808260
3000
健康、教育良好同高生産力嘅人口
13:31
bring to us?
315
811260
2000
能帶畀我哋咩?
13:33
How about a set of voices that have never been heard from before.
316
813260
3000
之前講到被忽視嘅聲音
13:36
What about giving the oppressed,
317
816260
2000
世界各地
13:38
wherever they might be,
318
818260
2000
被壓迫嘅聲音
13:40
the voice to be heard and the voice to act
319
820260
2000
宜家終於會被聽到
13:42
for the first time ever?
320
822260
3000
同受重視
13:45
What will these three billion people bring?
321
825260
3000
呢 30 億人能帶來咩?
13:48
What about contributions we can't even predict?
322
828260
3000
佢哋帶來嘅貢獻可能冇人可以估算
13:51
The one thing I've learned at the X Prize
323
831260
2000
我喺 X 大獎學到嘅一件事
13:53
is that small teams
324
833260
2000
就係即使係一個細團隊
13:55
driven by their passion with a clear focus
325
835260
3000
當佢嘅目標明確同俾熱情所驅動
13:58
can do extraordinary things,
326
838260
2000
都可以做一番大事業
14:00
things that large corporations and governments
327
840260
2000
完成喺以前
14:02
could only do in the past.
328
842260
3000
只有大企業同政府先至作到嘅事
14:05
Let me share and close with a story
329
845260
2000
最尾,我想分享一個故事
14:07
that really got me excited.
330
847260
3000
佢真係好激勵我
14:10
There is a program that some of you might have heard of.
331
850260
2000
應該有人聽過呢個項目
14:12
It's a game called Foldit.
332
852260
2000
有個遊戲叫 Foldit
14:14
It came out of the University of Washington in Seattle.
333
854260
4000
佢係由西雅圖華盛頓大學開發嘅
14:18
And this is a game
334
858260
2000
佢係一個遊戲
14:20
where individuals can actually take a sequence of amino acids
335
860260
4000
玩家可以決定胺基酸嘅排列順序
14:24
and figure out how the protein is going to fold.
336
864260
4000
進而計算出蛋白質跟住會點折疊
14:28
And how it folds dictates its structure and its functionality.
337
868260
2000
蛋白質嘅折疊決定咗佢嘅結構同埋功能
14:30
And it's very important for research in medicine.
338
870260
3000
呢樣對藥物嘅研究好重要
14:33
And up until now, it's been a supercomputer problem.
339
873260
3000
目前為止,呢個問題得超級電腦先做到
14:36
And this game has been played
340
876260
2000
呢個遊戲
14:38
by university professors and so forth.
341
878260
2000
最初俾大學教授之類嘅人玩
14:40
And it's literally, hundreds of thousands of people
342
880260
3000
宜家有幾十萬人
14:43
came online and started playing it.
343
883260
2000
上網玩緊
14:45
And it showed that, in fact, today,
344
885260
2000
事實顯示
14:47
the human pattern recognition machinery
345
887260
2000
人類對模式嘅識別
14:49
is better at folding proteins than the best computers.
346
889260
3000
好過宜家最好嘅電腦
14:52
And when these individuals went and looked
347
892260
2000
呢啲玩家都想知道
14:54
at who was the best protein folder in the world,
348
894260
3000
世界上最識折疊蛋白質嘅人係邊個
14:57
it wasn't an MIT professor,
349
897260
2000
佢唔係麻省理工學院嘅一個教授
14:59
it wasn't a CalTech student,
350
899260
2000
唔係加州理工學院嘅一個學生
15:01
it was a person from England, from Manchester,
351
901260
3000
佢係一個住喺英國曼徹斯特嘅女人
15:04
a woman who, during the day,
352
904260
4000
喺日頭
15:08
was an executive assistant at a rehab clinic
353
908260
3000
佢係一個復健診所嘅行政助理
15:11
and, at night, was the world's best protein folder.
354
911260
4000
到咗晚黑,她就係 世界上最會折疊蛋白質嘅人
15:16
Ladies and gentlemen,
355
916260
2000
各位
15:18
what gives me tremendous confidence
356
918260
2000
係咩嘢令我
15:20
in the future
357
920260
4000
對未來非常有信心?
15:24
is the fact that we are now more empowered as individuals
358
924260
4000
係因為我哋宜家活得更自主更有能力
15:28
to take on the grand challenges of this planet.
359
928260
3000
可以應對各種巨大嘅挑戰
15:31
We have the tools with this exponential technology.
360
931260
3000
我哋嘅科技幾何級數發展
15:34
We have the passion of the DIY innovator.
361
934260
3000
我哋有 DIY 發明家嘅熱情
15:37
We have the capital of the techno-philanthropist.
362
937260
3000
我哋亦都有科技慈善家嘅資本
15:40
And we have three billion new minds
363
940260
2000
仲有 30 億第一次接觸互聯網嘅人
15:42
coming online to work with us
364
942260
2000
嚟幫我哋解決艱鉅嘅難題
15:44
to solve the grand challenges,
365
944260
2000
15:46
to do that which we must do.
366
946260
4000
做我哋應該做嘅嘢
15:50
We are living into extraordinary decades ahead.
367
950260
2000
未來嘅黃金年代等緊我哋
15:52
Thank you.
368
952260
2000
多謝
15:54
(Applause)
369
954260
14000
(掌聲)
About this website

This site will introduce you to YouTube videos that are useful for learning English. You will see English lessons taught by top-notch teachers from around the world. Double-click on the English subtitles displayed on each video page to play the video from there. The subtitles scroll in sync with the video playback. If you have any comments or requests, please contact us using this contact form.

https://forms.gle/WvT1wiN1qDtmnspy7