ABOUT THE SPEAKER
Ray Kurzweil - Inventor, futurist
Ray Kurzweil is an engineer who has radically advanced the fields of speech, text and audio technology. He's revered for his dizzying -- yet convincing -- writing on the advance of technology, the limits of biology and the future of the human species.

Why you should listen

Inventor, entrepreneur, visionary, Ray Kurzweil's accomplishments read as a startling series of firsts -- a litany of technological breakthroughs we've come to take for granted. Kurzweil invented the first optical character recognition (OCR) software for transforming the written word into data, the first print-to-speech software for the blind, the first text-to-speech synthesizer, and the first music synthesizer capable of recreating the grand piano and other orchestral instruments, and the first commercially marketed large-vocabulary speech recognition.

Yet his impact as a futurist and philosopher is no less significant. In his best-selling books, which include How to Create a Mind, The Age of Spiritual Machines, The Singularity Is Near: When Humans Transcend Biology, Kurzweil depicts in detail a portrait of the human condition over the next few decades, as accelerating technologies forever blur the line between human and machine.

In 2009, he unveiled Singularity University, an institution that aims to "assemble, educate and inspire leaders who strive to understand and facilitate the development of exponentially advancing technologies." He is a Director of Engineering at Google, where he heads up a team developing machine intelligence and natural language comprehension.

More profile about the speaker
Ray Kurzweil | Speaker | TED.com
TED2014

Ray Kurzweil: Get ready for hybrid thinking

雷库‧日韦尔: 准备好进入混合思维的时代

Filmed:
3,548,296 views

两亿多年前,我们的祖先哺乳动物发展了一种新的大脑功能:大脑皮层。这邮票大小的一块组织(缠绕着一个核桃大小的区块)便是人类之所以成为人类的关键。现在,未来学家雷‧库日韦尔指出,就在崭新的云端计算能力急速开发的同时,我们应为大脑的下一次大飞跃做好准备。
- Inventor, futurist
Ray Kurzweil is an engineer who has radically advanced the fields of speech, text and audio technology. He's revered for his dizzying -- yet convincing -- writing on the advance of technology, the limits of biology and the future of the human species. Full bio

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

让我来给你们讲个故事。
00:12
Let me tell you a story故事.
0
988
2316
00:15
It goes back 200 million百万 years年份.
1
3304
1799
要追溯到两亿年前。
00:17
It's a story故事 of the neocortex新皮层,
2
5103
1984
是个关于新大脑皮层的故事,
00:19
which哪一个 means手段 "new rind果皮."
3
7087
1974
讲的就是大脑的表层。
00:21
So in these early mammals哺乳动物,
4
9061
2431
对于早期的哺乳类动物,
00:23
because only mammals哺乳动物 have a neocortex新皮层,
5
11492
2055
由于只有他们有新大脑皮层,
00:25
rodent-like啮齿动物类 creatures生物.
6
13547
1664
就像啮齿类的生物。
00:27
It was the size尺寸 of a postage邮资 stamp邮票 and just as thin,
7
15211
3579
皮质尺寸像邮票一样而且很薄,
00:30
and was a thin covering覆盖 around
8
18790
1439
这个薄的皮质包裹着他们
00:32
their walnut-sized核桃大小 brain,
9
20229
2264
像核桃大小的头脑。
00:34
but it was capable of a new type类型 of thinking思维.
10
22493
3701
但它可以产生新的思维方式。
00:38
Rather than the fixed固定 behaviors行为
11
26194
1567
不像那些非哺乳类动物,
00:39
that non-mammalian非哺乳动物 animals动物 have,
12
27761
1992
只有固定的行为,
00:41
it could invent发明 new behaviors行为.
13
29753
2692
它可以创造新的行为。
00:44
So a mouse老鼠 is escaping逃逸 a predator捕食者,
14
32445
2553
例如一只老鼠在逃避捕食者,
00:46
its path路径 is blocked受阻,
15
34998
1540
它的路被堵住了,
00:48
it'll它会 try to invent发明 a new solution.
16
36538
2129
就想想出一个新的解决方案。
00:50
That may可能 work, it may可能 not,
17
38667
1266
那方案可能成功也可能失败,
00:51
but if it does, it will remember记得 that
18
39933
1910
但如果成功了,它就会记住,
00:53
and have a new behavior行为,
19
41843
1292
于是就有了一种新的行为,
00:55
and that can actually其实 spread传播 virally病毒
20
43135
1457
同时那个方案会迅速传遍
00:56
through通过 the rest休息 of the community社区.
21
44592
2195
到其余的团体。
00:58
Another另一个 mouse老鼠 watching观看 this could say,
22
46787
1609
比如另一只老鼠看到这会说,
01:00
"Hey, that was pretty漂亮 clever聪明, going around that rock,"
23
48396
2704
“噢,绕过那块岩石,真是高明,”
01:03
and it could adopt采用 a new behavior行为 as well.
24
51100
3725
于是它也会采取那种行为。
01:06
Non-mammalian非哺乳动物 animals动物
25
54825
1717
非哺乳类动物
01:08
couldn't不能 do any of those things.
26
56542
1713
不能做这些事情。
01:10
They had fixed固定 behaviors行为.
27
58255
1215
因为他们有固定的行为方式。
01:11
Now they could learn学习 a new behavior行为
28
59470
1331
现在他们能学习新的行为
01:12
but not in the course课程 of one lifetime一生.
29
60801
2576
但不是在一个生命的过程中。
01:15
In the course课程 of maybe a thousand lifetimes寿命,
30
63377
1767
也许在几千个生命周期内,
01:17
it could evolve发展 a new fixed固定 behavior行为.
31
65144
3330
它可以衍生出一个新的固定的行为。
01:20
That was perfectly完美 okay 200 million百万 years年份 ago.
32
68474
3377
那对两亿年前来讲是好极了。
01:23
The environment环境 changed very slowly慢慢地.
33
71851
1981
那时的环境变化很慢。
01:25
It could take 10,000 years年份 for there to be
34
73832
1554
那时可能要过一万年才会
01:27
a significant重大 environmental环境的 change更改,
35
75386
2092
发生一次巨大的环境变化,
01:29
and during that period of time
36
77478
1382
在那期间,
01:30
it would evolve发展 a new behavior行为.
37
78860
2929
可能进化一种新的行为。
01:33
Now that went along沿 fine,
38
81789
1521
那样发展似乎还不错,
01:35
but then something happened发生.
39
83310
1704
但有些事情发生了。
01:37
Sixty-five六十五 million百万 years年份 ago,
40
85014
2246
六千五百万年前,
01:39
there was a sudden突然, violent暴力
change更改 to the environment环境.
41
87260
2615
发生了一个突然的,剧烈的环境变化。
01:41
We call it the Cretaceous白垩纪 extinction灭绝 event事件.
42
89875
3505
我们称之为白垩纪灭绝事件。
01:45
That's when the dinosaurs恐龙 went extinct绝种,
43
93380
2293
那是恐龙走向灭绝的时候,
01:47
that's when 75 percent百分 of the
44
95673
3449
是百分之七十五的动植物
01:51
animal动物 and plant species种类 went extinct绝种,
45
99122
2746
走向灭绝的时候,
01:53
and that's when mammals哺乳动物
46
101868
1745
也是哺乳类动物
01:55
overtook超越 their ecological生态 niche壁龛,
47
103613
2152
取代生态位,
01:57
and to anthropomorphize人格化, biological生物 evolution演化 said,
48
105765
3654
而达到人格化,生物进化学说道,
02:01
"Hmm, this neocortex新皮层 is pretty漂亮 good stuff东东,"
49
109419
2025
“嗯,这个新大脑皮层是个好东西,”
02:03
and it began开始 to grow增长 it.
50
111444
1793
于是开始发展。
02:05
And mammals哺乳动物 got bigger,
51
113237
1342
哺乳类动物逐渐变大,
02:06
their brains大脑 got bigger at an even faster更快 pace步伐,
52
114579
2915
他们的大脑变大的速度更快,
02:09
and the neocortex新皮层 got bigger even faster更快 than that
53
117494
3807
新大脑皮层同时变大的速度也更快,
02:13
and developed发达 these distinctive独特 ridges and folds褶皱
54
121301
2929
发展出明显的隆起和褶皱
02:16
basically基本上 to increase增加 its surface表面 area.
55
124230
2881
来增加它的表面积。
02:19
If you took the human人的 neocortex新皮层
56
127111
1819
如果你有一个人的新大脑皮层
02:20
and stretched拉伸 it out,
57
128930
1301
然后把它伸展开,
02:22
it's about the size尺寸 of a table napkin餐巾,
58
130231
1713
大概有一方餐巾那么大,
02:23
and it's still a thin structure结构体.
59
131944
1306
它也是一个很薄的构造。
02:25
It's about the thickness厚度 of a table napkin餐巾.
60
133250
1980
就像餐巾那么薄。
02:27
But it has so many许多 convolutions卷积 and ridges
61
135230
2497
但它有很多的隆起和褶皱。
02:29
it's now 80 percent百分 of our brain,
62
137727
3075
现在它占据我们的大脑有百分之八十
02:32
and that's where we do our thinking思维,
63
140802
2461
那也是我们用来思考的地方,
02:35
and it's the great sublimator升华.
64
143263
1761
所以那是个很棒的升华。
02:37
We still have that old brain
65
145024
1114
我们仍旧还是有那个
02:38
that provides提供 our basic基本 drives驱动器 and motivations动机,
66
146138
2764
提供基本动力和动机的大脑,
02:40
but I may可能 have a drive驾驶 for conquest征服,
67
148902
2716
但也许我会有一个要去征服的想法,
02:43
and that'll那会 be sublimated升华 by the neocortex新皮层
68
151618
2715
那就要新大脑皮层
02:46
into writing写作 a poem or inventing发明了 an app应用
69
154333
2909
借由写首诗或发明一个程序
02:49
or giving a TEDTED Talk,
70
157242
1509
或来一个TED演讲而达到升华,
02:50
and it's really the neocortex新皮层 that's where
71
158751
3622
它的确是在新的大脑皮层
有了行动。
02:54
the action行动 is.
72
162373
1968
五十年前,我写了一篇论文,
02:56
Fifty五十 years年份 ago, I wrote a paper
73
164341
1717
02:58
describing说明 how I thought the brain worked工作,
74
166058
1918
描述我对大脑如何运作的想法,
02:59
and I described描述 it as a series系列 of modules模块.
75
167976
3199
我描述说大脑就像一系列模块。
03:03
Each module could do things with a pattern模式.
76
171175
2128
每个模块可以用一种方式做事情。
03:05
It could learn学习 a pattern模式. It could remember记得 a pattern模式.
77
173303
2746
每个模块可以学习和记住一种方式。
03:08
It could implement实行 a pattern模式.
78
176049
1407
也可以执行一种方式。
03:09
And these modules模块 were organized有组织的 in hierarchies等级,
79
177456
2679
然后这些模块被分派到统治集团中,
03:12
and we created创建 that hierarchy等级制度 with our own拥有 thinking思维.
80
180135
2954
我们用我们自己的想法创造了统治集团。
03:15
And there was actually其实 very little to go on
81
183089
3333
后来我的这个想法就没怎么继续了。
那还是50年前。
03:18
50 years年份 ago.
82
186422
1562
它让我去见了约翰逊总统。
03:19
It led me to meet遇到 President主席 Johnson约翰逊.
83
187984
2115
03:22
I've been thinking思维 about this for 50 years年份,
84
190099
2173
我已经思考了五十年,
03:24
and a year and a half ago I came来了 out with the book
85
192272
2828
一年半前我出了本书,
03:27
"How To Create创建 A Mind心神,"
86
195100
1265
”如何创造思想,“
03:28
which哪一个 has the same相同 thesis论文,
87
196365
1613
这本书和那篇论文有着相同的主题,
03:29
but now there's a plethora过多 of evidence证据.
88
197978
2812
但现在有了更多的证据支撑。
03:32
The amount of data数据 we're getting得到 about the brain
89
200790
1814
我们从神经科学得到
03:34
from neuroscience神经科学 is doubling加倍 every一切 year.
90
202604
2203
关于大脑的数据每年都成倍增加。
03:36
Spatial空间的 resolution解析度 of brainscanningbrainscanning of all types类型
91
204807
2654
各类脑扫描的空间分辨率也是。
每年双倍增加。
03:39
is doubling加倍 every一切 year.
92
207461
2285
我们现在可以看到一个活大脑的内部
03:41
We can now see inside a living活的 brain
93
209746
1717
03:43
and see individual个人 interneuralinterneural connections连接
94
211463
2870
看到个别神经元间的连接,
实时连接,实时放电
03:46
connecting in real真实 time, firing射击 in real真实 time.
95
214333
2703
我们可以看到你大脑创造思维。
03:49
We can see your brain create创建 your thoughts思念.
96
217036
2419
03:51
We can see your thoughts思念 create创建 your brain,
97
219455
1575
我们可以看到你的思维也在创造你的大脑,
03:53
which哪一个 is really key to how it works作品.
98
221030
1999
这对了解大脑如何运作很重要。
03:55
So let me describe描述 briefly简要地 how it works作品.
99
223029
2219
让我简单描述一下大脑如何工作的。
03:57
I've actually其实 counted these modules模块.
100
225248
2275
我算过这些单位的数量。
03:59
We have about 300 million百万 of them,
101
227523
2046
我们大约有三亿,
04:01
and we create创建 them in these hierarchies等级.
102
229569
2229
我们在大脑层里创造他们。
04:03
I'll give you a simple简单 example.
103
231798
2082
给你们简单举例。
04:05
I've got a bunch of modules模块
104
233880
2805
我有一堆模块,
04:08
that can recognize认识 the crossbar横梁 to a capital首都 A,
105
236685
3403
它们可以认知A的一横,
04:12
and that's all they care关心 about.
106
240088
1914
那是它们关心的全部。
04:14
A beautiful美丽 song歌曲 can play,
107
242002
1578
一首动人的歌在播放,
04:15
a pretty漂亮 girl女孩 could walk步行 by,
108
243580
1434
一个美丽的姑娘经过,
04:17
they don't care关心, but they see
a crossbar横梁 to a capital首都 A,
109
245014
2846
它们都不在意,但当它们看见A的一横,
04:19
they get very excited兴奋 and they say "crossbar横梁,"
110
247860
3021
它们就会很兴奋的说“横,”
04:22
and they put out a high probability可能性
111
250881
2112
然后他们
从输出轴突输出一个高度的可能性,
04:24
on their output产量 axon轴突.
112
252993
1634
那就到了下一个等级,
04:26
That goes to the next下一个 level水平,
113
254627
1333
04:27
and these layers are organized有组织的 in conceptual概念上的 levels水平.
114
255960
2750
这些层次被分布在概念性等级中。
04:30
Each is more abstract抽象 than the next下一个 one,
115
258710
1856
每一个都比下一个更抽象,
04:32
so the next下一个 one might威力 say "capital首都 A."
116
260566
2418
所以下一个可能说“字母A。”
04:34
That goes up to a higher更高
level水平 that might威力 say "Apple苹果."
117
262984
2891
去到更高一个等级可能说“apple”
04:37
Information信息 flows流动 down also.
118
265875
2167
信息也这样流动。
04:40
If the apple苹果 recognizer识别 has seen看到 A-P-P-LAPPL,
119
268042
2936
如果那个认出apple的看到 a-p-p-l,
04:42
it'll它会 think to itself本身, "Hmm, I
think an E is probably大概 likely容易,"
120
270978
3219
它就会想,“嗯,我觉得接下来是e,”
04:46
and it'll它会 send发送 a signal信号 down to all the E recognizers识别
121
274197
2564
然后它就会把信号传送个所有认知e的
04:48
saying, "Be on the lookout小心 for an E,
122
276761
1619
说,“看住e,
04:50
I think one might威力 be coming未来."
123
278380
1556
我觉得它就要来了。”
04:51
The E recognizers识别 will lower降低 their threshold
124
279936
2843
e的认知这就会降低警觉
04:54
and they see some sloppy稀松
thing, could be an E.
125
282779
1945
它们可能粗心的看到一些东西觉得就是E。
04:56
Ordinarily按说 you wouldn't不会 think so,
126
284724
1490
通常你不会这样想,
04:58
but we're expecting期待 an E, it's good enough足够,
127
286214
2009
但我们在期待一个E, 那就够了,
05:00
and yeah, I've seen看到 an E, and then apple苹果 says,
128
288223
1817
于是我看到了E,然后认知的apple说,
05:02
"Yeah, I've seen看到 an Apple苹果."
129
290040
1728
“太好了,我看到了apple。”
05:03
Go up another另一个 five levels水平,
130
291768
1746
再往上五个等级,
05:05
and you're now at a pretty漂亮 high level水平
131
293514
1353
现在你就在一个很高的水平,
05:06
of this hierarchy等级制度,
132
294867
1569
的这种大脑层,
05:08
and stretch伸展 down into the different不同 senses感官,
133
296436
2353
于是延伸到不同的感官,
05:10
and you may可能 have a module
that sees看到 a certain某些 fabric,
134
298789
2655
你可能有一个模块看到了一个特殊东西,
05:13
hears就听 a certain某些 voice语音 quality质量,
smells气味 a certain某些 perfume香水,
135
301444
2844
听到一个声音,闻到到某个特殊的香水,
05:16
and will say, "My wife妻子 has entered进入 the room房间."
136
304288
2513
它就会说,“我老婆进来房间了。”
05:18
Go up another另一个 10 levels水平, and now you're at
137
306801
1895
往上十个等级,现在你在
一个非常高的等级。
05:20
a very high level水平.
138
308696
1160
05:21
You're probably大概 in the frontal前面的 cortex皮质,
139
309856
1937
你可能在大脑额叶,
05:23
and you'll你会 have modules模块 that say, "That was ironic具有讽刺意味.
140
311793
3767
然后你有模块说,“那很讽刺。
05:27
That's funny滑稽. She's pretty漂亮."
141
315560
2370
那很有趣。她很美。”
05:29
You might威力 think that those are more sophisticated复杂的,
142
317930
2105
你可能觉得那些模块很复杂,
05:32
but actually其实 what's more complicated复杂
143
320035
1506
实际上更复杂的是
05:33
is the hierarchy等级制度 beneath下面 them.
144
321541
2669
在他们之下的大脑层集团。
05:36
There was a 16-year-old-岁 girl女孩, she had brain surgery手术,
145
324210
2620
有一个十六岁的女孩,她做了一个大脑手术,
05:38
and she was conscious意识 because the surgeons外科医生
146
326830
2051
她依然是清醒的,因为外科医生
要和她谈话。
05:40
wanted to talk to her.
147
328881
1537
05:42
You can do that because there's no pain疼痛 receptors受体
148
330418
1822
手术可以做是因为大脑里没有疼痛的感觉器官。
在大脑里,
05:44
in the brain.
149
332240
1038
05:45
And whenever每当 they stimulated刺激 particular特定,
150
333278
1800
当他们刺激到某个部分,
05:47
very small points on her neocortex新皮层,
151
335078
2463
在她大脑皮层的很小的点,
05:49
shown显示 here in red, she would laugh.
152
337541
2665
这里显示红色的,她就会笑。
05:52
So at first they thought they were triggering触发
153
340206
1440
所以一开始他们以为他们触碰到
05:53
some kind of laugh reflex反射,
154
341646
1720
某个笑神经,
05:55
but no, they quickly很快 realized实现 they had found发现
155
343366
2519
但不是,他们很快意识到他们发现
05:57
the points in her neocortex新皮层 that detect检测 humor幽默,
156
345885
3044
那些在新大脑皮层的小点能探测到幽默,
06:00
and she just found发现 everything hilarious欢闹的
157
348929
1969
然后她发现一切都很可笑,
06:02
whenever每当 they stimulated刺激 these points.
158
350898
2437
每当刺激到那些点的时候。
“你们站在这里真是太好笑了,”
06:05
"You guys are so funny滑稽 just standing常设 around,"
159
353335
1925
06:07
was the typical典型 comment评论,
160
355260
1738
这是她典型的言论,
06:08
and they weren't funny滑稽,
161
356998
2302
06:11
not while doing surgery手术.
162
359300
3247
实际上他们在做手术时并不有趣。
06:14
So how are we doing today今天?
163
362547
4830
所以我们现在怎么样?
06:19
Well, computers电脑 are actually其实 beginning开始 to master
164
367377
3054
事实上电脑逐渐开始
通过科技掌握人类语言,
06:22
human人的 language语言 with techniques技术
165
370431
2001
06:24
that are similar类似 to the neocortex新皮层.
166
372432
2867
这和新大脑皮层类似。
06:27
I actually其实 described描述 the algorithm算法,
167
375299
1514
我实际上描述了运算法则,
06:28
which哪一个 is similar类似 to something called
168
376813
2054
这和
06:30
a hierarchical分级 hidden Markov马尔科夫 model模型,
169
378867
2233
脑层隐藏的马尔可夫模型类似,
06:33
something I've worked工作 on since以来 the '90s.
170
381100
3241
这是一些我从90年代就开始研究的事。
06:36
"Jeopardy危险" is a very broad广阔 natural自然 language语言 game游戏,
171
384341
3238
"Jeopardy"是一个很广泛的语言游戏,
06:39
and Watson沃森 got a higher更高 score得分了
172
387579
1892
Watson得了一个
06:41
than the best最好 two players玩家 combined结合.
173
389471
2000
比两人加在一起还高的分数。
06:43
It got this query询问 correct正确:
174
391471
2499
它纠正了这个问题:
06:45
"A long, tiresome烦人的 speech言语
175
393970
2085
"一段很长很无聊的演讲
06:48
delivered交付 by a frothy多泡的 pie馅饼 topping配料,"
176
396055
2152
就像馅饼上的装饰。“
06:50
and it quickly很快 responded回应,
"What is a meringue酥皮 harangue长篇大论?"
177
398207
2796
于是有了很快的回复,”什么是长篇大论?“
06:53
And Jennings詹宁斯 and the other guy didn't get that.
178
401003
2635
没人理解这个问题。
06:55
It's a pretty漂亮 sophisticated复杂的 example of
179
403638
1926
那是个很复杂的例子
06:57
computers电脑 actually其实 understanding理解 human人的 language语言,
180
405564
1914
关于电脑理解人类语言,
06:59
and it actually其实 got its knowledge知识 by reading
181
407478
1652
它实际得到了自己的知识通过
07:01
Wikipedia维基百科 and several一些 other encyclopedias百科全书.
182
409130
3785
维基百科和一些其他百科。
07:04
Five to 10 years年份 from now,
183
412915
2133
从现在起五到十年,
07:07
search搜索 engines引擎 will actually其实 be based基于 on
184
415048
2184
搜索引擎会不仅仅基于
07:09
not just looking for combinations组合 of words and links链接
185
417232
2794
对文字、链接组合的寻找,
07:12
but actually其实 understanding理解,
186
420026
1914
而是真正的理解,
07:13
reading for understanding理解 the billions数十亿 of pages网页
187
421940
2411
通过阅读来理解
07:16
on the web卷筒纸 and in books图书.
188
424351
2733
网络和书中成千上万页。
07:19
So you'll你会 be walking步行 along沿, and Google谷歌 will pop流行的 up
189
427084
2616
那么当你郁郁独行,古狗会跳出来
07:21
and say, "You know, Mary玛丽, you expressed表达 concern关心
190
429700
3081
说,”你知道吗,Mary, 你一个月前
07:24
to me a month ago that your glutathione谷胱甘肽 supplement补充
191
432781
3019
你向我述说的你补充的谷胱甘肽
07:27
wasn't getting得到 past过去 the blood-brain血脑屏障 barrier屏障.
192
435800
2231
没有通过血脑屏障。
07:30
Well, new research研究 just came来了 out 13 seconds ago
193
438031
2593
十三秒前刚出了个新研究,
07:32
that shows节目 a whole整个 new approach途径 to that
194
440624
1711
显示有一个全新方法的来解决这个问题。
07:34
and a new way to take glutathione谷胱甘肽.
195
442335
1993
07:36
Let me summarize总结 it for you."
196
444328
2562
一个服用谷胱甘肽的新方法,
让我来为你总结一下。“
07:38
Twenty二十 years年份 from now, we'll have nanobots纳米机器人,
197
446890
3684
从现在起二十年, 我们会有纳米机器人,
07:42
because another另一个 exponential指数 trend趋势
198
450574
1627
因为另一个指数趋势
07:44
is the shrinking萎缩 of technology技术.
199
452201
1615
显示科技的收缩。
07:45
They'll他们会 go into our brain
200
453816
2370
他们会通过
毛细血管进入我们的大脑
07:48
through通过 the capillaries毛细血管
201
456186
1703
然后把我们的大脑皮层连接
07:49
and basically基本上 connect our neocortex新皮层
202
457889
2477
07:52
to a synthetic合成的 neocortex新皮层 in the cloud
203
460366
3185
到枢纽里的合成大脑皮层。
07:55
providing提供 an extension延期 of our neocortex新皮层.
204
463551
3591
07:59
Now today今天, I mean,
205
467142
1578
而提供一个大脑皮层的延伸
08:00
you have a computer电脑 in your phone电话,
206
468720
1530
现在,我的意思是,
你手机里有一个电脑,
08:02
but if you need 10,000 computers电脑 for a few少数 seconds
207
470250
2754
但如果你需要一万台电脑用几秒钟
08:05
to do a complex复杂 search搜索,
208
473004
1495
来做一个复杂的研究,
08:06
you can access访问 that for a second第二 or two in the cloud.
209
474499
3396
你可以进入那个枢纽一两秒。
08:09
In the 2030s, if you need some extra额外 neocortex新皮层,
210
477895
3095
在2030年,如果你需要一些额外的大脑皮层,
08:12
you'll你会 be able能够 to connect to that in the cloud
211
480990
2273
你可以在枢纽里
直接与你大脑连接。
08:15
directly from your brain.
212
483263
1648
所以当我走过我会说,
08:16
So I'm walking步行 along沿 and I say,
213
484911
1543
08:18
"Oh, there's Chris克里斯 Anderson安德森.
214
486454
1363
“哦,这是Chris Anderson。”
08:19
He's coming未来 my way.
215
487817
1525
他在向我走来。
08:21
I'd better think of something clever聪明 to say.
216
489342
2335
我最好想一个聪明的方式来说。
08:23
I've got three seconds.
217
491677
1524
我有三秒钟来想。
08:25
My 300 million百万 modules模块 in my neocortex新皮层
218
493201
3097
我大脑皮层里的三亿个模块
还不够
08:28
isn't going to cut it.
219
496298
1240
我需要另外一亿个。“
08:29
I need a billion十亿 more."
220
497538
1246
08:30
I'll be able能够 to access访问 that in the cloud.
221
498784
3323
于是我可以在枢纽里实现。
08:34
And our thinking思维, then, will be a hybrid混合动力
222
502107
2812
那时我们的思考,会像一个
08:36
of biological生物 and non-biological非生物 thinking思维,
223
504919
3522
生物和非生物思考的混合,
08:40
but the non-biological非生物 portion一部分
224
508441
1898
但非生物的部分
08:42
is subject学科 to my law of accelerating加速 returns回报.
225
510339
2682
取决于我加速回收的原则。
它会成指数增长。
08:45
It will grow增长 exponentially成倍.
226
513021
2239
08:47
And remember记得 what happens发生
227
515260
2016
还记得
08:49
the last time we expanded扩大 our neocortex新皮层?
228
517276
2645
上次我们伸展我们大脑皮层发生了什么吗?
那是两亿年前
08:51
That was two million百万 years年份 ago
229
519921
1426
08:53
when we became成为 humanoids类人型机器人
230
521347
1236
当我们成为变类人
08:54
and developed发达 these large foreheads额头.
231
522583
1594
进化这些大前额时。
08:56
Other primates灵长类动物 have a slanted倾斜 brow眉头.
232
524177
2583
其他灵长目动物有倾斜的额。
08:58
They don't have the frontal前面的 cortex皮质.
233
526760
1745
他们没有前大脑皮层。
09:00
But the frontal前面的 cortex皮质 is not
really qualitatively定性 different不同.
234
528505
3685
但那不是真正的不同。
09:04
It's a quantitative expansion扩张 of neocortex新皮层,
235
532190
2743
不同在于大脑皮层的伸展,
09:06
but that additional额外 quantity数量 of thinking思维
236
534933
2703
但那额外的思考
是我们能够飞跃的启动因子,
09:09
was the enabling启用 factor因子 for us to take
237
537636
1779
并因此发明了语言、艺术、科学
09:11
a qualitative定性 leap飞跃 and invent发明 language语言
238
539415
3346
艺术,科学和科技
09:14
and art艺术 and science科学 and technology技术
239
542761
1967
还有TED会议。
09:16
and TEDTED conferences会议.
240
544728
1454
09:18
No other species种类 has doneDONE that.
241
546182
2131
没有其他物种可以做到这样。
09:20
And so, over the next下一个 few少数 decades几十年,
242
548313
2075
在接下来的几十年,
09:22
we're going to do it again.
243
550388
1760
我们要再一次。
09:24
We're going to again expand扩大 our neocortex新皮层,
244
552148
2274
我们会再次伸展我们的新大脑皮层,
09:26
only this time we won't惯于 be limited有限
245
554422
1756
只有这样我们不会
09:28
by a fixed固定 architecture建筑 of enclosure附件.
246
556178
4280
被固定的框架结构所限制。
09:32
It'll它会 be expanded扩大 without limit限制.
247
560458
3304
它会无限伸展。
那个额外的量
会再次成为一个启动因子
09:35
That additional额外 quantity数量 will again
248
563762
2243
使我们在文化科技中有一个质的飞跃。
09:38
be the enabling启用 factor因子 for another另一个 qualitative定性 leap飞跃
249
566005
3005
09:41
in culture文化 and technology技术.
250
569010
1635
09:42
Thank you very much.
251
570645
2054
非常感谢!
09:44
(Applause掌声)
252
572699
3086
(鼓掌)
Translated by Mingjing Zhang
Reviewed by Yuanqing Edberg

▲Back to top

ABOUT THE SPEAKER
Ray Kurzweil - Inventor, futurist
Ray Kurzweil is an engineer who has radically advanced the fields of speech, text and audio technology. He's revered for his dizzying -- yet convincing -- writing on the advance of technology, the limits of biology and the future of the human species.

Why you should listen

Inventor, entrepreneur, visionary, Ray Kurzweil's accomplishments read as a startling series of firsts -- a litany of technological breakthroughs we've come to take for granted. Kurzweil invented the first optical character recognition (OCR) software for transforming the written word into data, the first print-to-speech software for the blind, the first text-to-speech synthesizer, and the first music synthesizer capable of recreating the grand piano and other orchestral instruments, and the first commercially marketed large-vocabulary speech recognition.

Yet his impact as a futurist and philosopher is no less significant. In his best-selling books, which include How to Create a Mind, The Age of Spiritual Machines, The Singularity Is Near: When Humans Transcend Biology, Kurzweil depicts in detail a portrait of the human condition over the next few decades, as accelerating technologies forever blur the line between human and machine.

In 2009, he unveiled Singularity University, an institution that aims to "assemble, educate and inspire leaders who strive to understand and facilitate the development of exponentially advancing technologies." He is a Director of Engineering at Google, where he heads up a team developing machine intelligence and natural language comprehension.

More profile about the speaker
Ray Kurzweil | Speaker | TED.com