Danny Hillis: Back to the future (of 1994)

80,742 views ・ 2012-02-03

TED


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

00:15
Because I usually take the role
0
15260
3000
00:18
of trying to explain to people
1
18260
2000
00:20
how wonderful the new technologies
2
20260
3000
00:23
that are coming along are going to be,
3
23260
2000
00:25
and I thought that, since I was among friends here,
4
25260
3000
00:28
I would tell you what I really think
5
28260
4000
00:32
and try to look back and try to understand
6
32260
2000
00:34
what is really going on here
7
34260
3000
00:37
with these amazing jumps in technology
8
37260
5000
00:42
that seem so fast that we can barely keep on top of it.
9
42260
3000
00:45
So I'm going to start out
10
45260
2000
00:47
by showing just one very boring technology slide.
11
47260
3000
00:50
And then, so if you can just turn on the slide that's on.
12
50260
3000
00:56
This is just a random slide
13
56260
2000
00:58
that I picked out of my file.
14
58260
2000
01:00
What I want to show you is not so much the details of the slide,
15
60260
3000
01:03
but the general form of it.
16
63260
2000
01:05
This happens to be a slide of some analysis that we were doing
17
65260
3000
01:08
about the power of RISC microprocessors
18
68260
3000
01:11
versus the power of local area networks.
19
71260
3000
01:14
And the interesting thing about it
20
74260
2000
01:16
is that this slide,
21
76260
2000
01:18
like so many technology slides that we're used to,
22
78260
3000
01:21
is a sort of a straight line
23
81260
2000
01:23
on a semi-log curve.
24
83260
2000
01:25
In other words, every step here
25
85260
2000
01:27
represents an order of magnitude
26
87260
2000
01:29
in performance scale.
27
89260
2000
01:31
And this is a new thing
28
91260
2000
01:33
that we talk about technology
29
93260
2000
01:35
on semi-log curves.
30
95260
2000
01:37
Something really weird is going on here.
31
97260
2000
01:39
And that's basically what I'm going to be talking about.
32
99260
3000
01:42
So, if you could bring up the lights.
33
102260
3000
01:47
If you could bring up the lights higher,
34
107260
2000
01:49
because I'm just going to use a piece of paper here.
35
109260
3000
01:52
Now why do we draw technology curves
36
112260
2000
01:54
in semi-log curves?
37
114260
2000
01:56
Well the answer is, if I drew it on a normal curve
38
116260
3000
01:59
where, let's say, this is years,
39
119260
2000
02:01
this is time of some sort,
40
121260
2000
02:03
and this is whatever measure of the technology
41
123260
3000
02:06
that I'm trying to graph,
42
126260
3000
02:09
the graphs look sort of silly.
43
129260
3000
02:12
They sort of go like this.
44
132260
3000
02:15
And they don't tell us much.
45
135260
3000
02:18
Now if I graph, for instance,
46
138260
3000
02:21
some other technology, say transportation technology,
47
141260
2000
02:23
on a semi-log curve,
48
143260
2000
02:25
it would look very stupid, it would look like a flat line.
49
145260
3000
02:28
But when something like this happens,
50
148260
2000
02:30
things are qualitatively changing.
51
150260
2000
02:32
So if transportation technology
52
152260
2000
02:34
was moving along as fast as microprocessor technology,
53
154260
3000
02:37
then the day after tomorrow,
54
157260
2000
02:39
I would be able to get in a taxi cab
55
159260
2000
02:41
and be in Tokyo in 30 seconds.
56
161260
2000
02:43
It's not moving like that.
57
163260
2000
02:45
And there's nothing precedented
58
165260
2000
02:47
in the history of technology development
59
167260
2000
02:49
of this kind of self-feeding growth
60
169260
2000
02:51
where you go by orders of magnitude every few years.
61
171260
3000
02:54
Now the question that I'd like to ask is,
62
174260
3000
02:57
if you look at these exponential curves,
63
177260
3000
03:00
they don't go on forever.
64
180260
3000
03:03
Things just can't possibly keep changing
65
183260
3000
03:06
as fast as they are.
66
186260
2000
03:08
One of two things is going to happen.
67
188260
3000
03:11
Either it's going to turn into a sort of classical S-curve like this,
68
191260
4000
03:15
until something totally different comes along,
69
195260
4000
03:19
or maybe it's going to do this.
70
199260
2000
03:21
That's about all it can do.
71
201260
2000
03:23
Now I'm an optimist,
72
203260
2000
03:25
so I sort of think it's probably going to do something like that.
73
205260
3000
03:28
If so, that means that what we're in the middle of right now
74
208260
3000
03:31
is a transition.
75
211260
2000
03:33
We're sort of on this line
76
213260
2000
03:35
in a transition from the way the world used to be
77
215260
2000
03:37
to some new way that the world is.
78
217260
3000
03:40
And so what I'm trying to ask, what I've been asking myself,
79
220260
3000
03:43
is what's this new way that the world is?
80
223260
3000
03:46
What's that new state that the world is heading toward?
81
226260
3000
03:49
Because the transition seems very, very confusing
82
229260
3000
03:52
when we're right in the middle of it.
83
232260
2000
03:54
Now when I was a kid growing up,
84
234260
3000
03:57
the future was kind of the year 2000,
85
237260
3000
04:00
and people used to talk about what would happen in the year 2000.
86
240260
4000
04:04
Now here's a conference
87
244260
2000
04:06
in which people talk about the future,
88
246260
2000
04:08
and you notice that the future is still at about the year 2000.
89
248260
3000
04:11
It's about as far as we go out.
90
251260
2000
04:13
So in other words, the future has kind of been shrinking
91
253260
3000
04:16
one year per year
92
256260
3000
04:19
for my whole lifetime.
93
259260
3000
04:22
Now I think that the reason
94
262260
2000
04:24
is because we all feel
95
264260
2000
04:26
that something's happening there.
96
266260
2000
04:28
That transition is happening. We can all sense it.
97
268260
2000
04:30
And we know that it just doesn't make too much sense
98
270260
2000
04:32
to think out 30, 50 years
99
272260
2000
04:34
because everything's going to be so different
100
274260
3000
04:37
that a simple extrapolation of what we're doing
101
277260
2000
04:39
just doesn't make any sense at all.
102
279260
3000
04:42
So what I would like to talk about
103
282260
2000
04:44
is what that could be,
104
284260
2000
04:46
what that transition could be that we're going through.
105
286260
3000
04:49
Now in order to do that
106
289260
3000
04:52
I'm going to have to talk about a bunch of stuff
107
292260
2000
04:54
that really has nothing to do
108
294260
2000
04:56
with technology and computers.
109
296260
2000
04:58
Because I think the only way to understand this
110
298260
2000
05:00
is to really step back
111
300260
2000
05:02
and take a long time scale look at things.
112
302260
2000
05:04
So the time scale that I would like to look at this on
113
304260
3000
05:07
is the time scale of life on Earth.
114
307260
3000
05:13
So I think this picture makes sense
115
313260
2000
05:15
if you look at it a few billion years at a time.
116
315260
4000
05:19
So if you go back
117
319260
2000
05:21
about two and a half billion years,
118
321260
2000
05:23
the Earth was this big, sterile hunk of rock
119
323260
3000
05:26
with a lot of chemicals floating around on it.
120
326260
3000
05:29
And if you look at the way
121
329260
2000
05:31
that the chemicals got organized,
122
331260
2000
05:33
we begin to get a pretty good idea of how they do it.
123
333260
3000
05:36
And I think that there's theories that are beginning to understand
124
336260
3000
05:39
about how it started with RNA,
125
339260
2000
05:41
but I'm going to tell a sort of simple story of it,
126
341260
3000
05:44
which is that, at that time,
127
344260
2000
05:46
there were little drops of oil floating around
128
346260
3000
05:49
with all kinds of different recipes of chemicals in them.
129
349260
3000
05:52
And some of those drops of oil
130
352260
2000
05:54
had a particular combination of chemicals in them
131
354260
2000
05:56
which caused them to incorporate chemicals from the outside
132
356260
3000
05:59
and grow the drops of oil.
133
359260
3000
06:02
And those that were like that
134
362260
2000
06:04
started to split and divide.
135
364260
2000
06:06
And those were the most primitive forms of cells in a sense,
136
366260
3000
06:09
those little drops of oil.
137
369260
2000
06:11
But now those drops of oil weren't really alive, as we say it now,
138
371260
3000
06:14
because every one of them
139
374260
2000
06:16
was a little random recipe of chemicals.
140
376260
2000
06:18
And every time it divided,
141
378260
2000
06:20
they got sort of unequal division
142
380260
3000
06:23
of the chemicals within them.
143
383260
2000
06:25
And so every drop was a little bit different.
144
385260
3000
06:28
In fact, the drops that were different in a way
145
388260
2000
06:30
that caused them to be better
146
390260
2000
06:32
at incorporating chemicals around them,
147
392260
2000
06:34
grew more and incorporated more chemicals and divided more.
148
394260
3000
06:37
So those tended to live longer,
149
397260
2000
06:39
get expressed more.
150
399260
3000
06:42
Now that's sort of just a very simple
151
402260
3000
06:45
chemical form of life,
152
405260
2000
06:47
but when things got interesting
153
407260
3000
06:50
was when these drops
154
410260
2000
06:52
learned a trick about abstraction.
155
412260
3000
06:55
Somehow by ways that we don't quite understand,
156
415260
3000
06:58
these little drops learned to write down information.
157
418260
3000
07:01
They learned to record the information
158
421260
2000
07:03
that was the recipe of the cell
159
423260
2000
07:05
onto a particular kind of chemical
160
425260
2000
07:07
called DNA.
161
427260
2000
07:09
So in other words, they worked out,
162
429260
2000
07:11
in this mindless sort of evolutionary way,
163
431260
3000
07:14
a form of writing that let them write down what they were,
164
434260
3000
07:17
so that that way of writing it down could get copied.
165
437260
3000
07:20
The amazing thing is that that way of writing
166
440260
3000
07:23
seems to have stayed steady
167
443260
2000
07:25
since it evolved two and a half billion years ago.
168
445260
2000
07:27
In fact the recipe for us, our genes,
169
447260
3000
07:30
is exactly that same code and that same way of writing.
170
450260
3000
07:33
In fact, every living creature is written
171
453260
3000
07:36
in exactly the same set of letters and the same code.
172
456260
2000
07:38
In fact, one of the things that I did
173
458260
2000
07:40
just for amusement purposes
174
460260
2000
07:42
is we can now write things in this code.
175
462260
2000
07:44
And I've got here a little 100 micrograms of white powder,
176
464260
6000
07:50
which I try not to let the security people see at airports.
177
470260
4000
07:54
(Laughter)
178
474260
2000
07:56
But this has in it --
179
476260
2000
07:58
what I did is I took this code --
180
478260
2000
08:00
the code has standard letters that we use for symbolizing it --
181
480260
3000
08:03
and I wrote my business card onto a piece of DNA
182
483260
3000
08:06
and amplified it 10 to the 22 times.
183
486260
3000
08:09
So if anyone would like a hundred million copies of my business card,
184
489260
3000
08:12
I have plenty for everyone in the room,
185
492260
2000
08:14
and, in fact, everyone in the world,
186
494260
2000
08:16
and it's right here.
187
496260
3000
08:19
(Laughter)
188
499260
5000
08:26
If I had really been a egotist,
189
506260
2000
08:28
I would have put it into a virus and released it in the room.
190
508260
3000
08:31
(Laughter)
191
511260
5000
08:39
So what was the next step?
192
519260
2000
08:41
Writing down the DNA was an interesting step.
193
521260
2000
08:43
And that caused these cells --
194
523260
2000
08:45
that kept them happy for another billion years.
195
525260
2000
08:47
But then there was another really interesting step
196
527260
2000
08:49
where things became completely different,
197
529260
3000
08:52
which is these cells started exchanging and communicating information,
198
532260
3000
08:55
so that they began to get communities of cells.
199
535260
2000
08:57
I don't know if you know this,
200
537260
2000
08:59
but bacteria can actually exchange DNA.
201
539260
2000
09:01
Now that's why, for instance,
202
541260
2000
09:03
antibiotic resistance has evolved.
203
543260
2000
09:05
Some bacteria figured out how to stay away from penicillin,
204
545260
3000
09:08
and it went around sort of creating its little DNA information
205
548260
3000
09:11
with other bacteria,
206
551260
2000
09:13
and now we have a lot of bacteria that are resistant to penicillin,
207
553260
3000
09:16
because bacteria communicate.
208
556260
2000
09:18
Now what this communication allowed
209
558260
2000
09:20
was communities to form
210
560260
2000
09:22
that, in some sense, were in the same boat together;
211
562260
2000
09:24
they were synergistic.
212
564260
2000
09:26
So they survived
213
566260
2000
09:28
or they failed together,
214
568260
2000
09:30
which means that if a community was very successful,
215
570260
2000
09:32
all the individuals in that community
216
572260
2000
09:34
were repeated more
217
574260
2000
09:36
and they were favored by evolution.
218
576260
3000
09:39
Now the transition point happened
219
579260
2000
09:41
when these communities got so close
220
581260
2000
09:43
that, in fact, they got together
221
583260
2000
09:45
and decided to write down the whole recipe for the community
222
585260
3000
09:48
together on one string of DNA.
223
588260
3000
09:51
And so the next stage that's interesting in life
224
591260
2000
09:53
took about another billion years.
225
593260
2000
09:55
And at that stage,
226
595260
2000
09:57
we have multi-cellular communities,
227
597260
2000
09:59
communities of lots of different types of cells,
228
599260
2000
10:01
working together as a single organism.
229
601260
2000
10:03
And in fact, we're such a multi-cellular community.
230
603260
3000
10:06
We have lots of cells
231
606260
2000
10:08
that are not out for themselves anymore.
232
608260
2000
10:10
Your skin cell is really useless
233
610260
3000
10:13
without a heart cell, muscle cell,
234
613260
2000
10:15
a brain cell and so on.
235
615260
2000
10:17
So these communities began to evolve
236
617260
2000
10:19
so that the interesting level on which evolution was taking place
237
619260
3000
10:22
was no longer a cell,
238
622260
2000
10:24
but a community which we call an organism.
239
624260
3000
10:28
Now the next step that happened
240
628260
2000
10:30
is within these communities.
241
630260
2000
10:32
These communities of cells,
242
632260
2000
10:34
again, began to abstract information.
243
634260
2000
10:36
And they began building very special structures
244
636260
3000
10:39
that did nothing but process information within the community.
245
639260
3000
10:42
And those are the neural structures.
246
642260
2000
10:44
So neurons are the information processing apparatus
247
644260
3000
10:47
that those communities of cells built up.
248
647260
3000
10:50
And in fact, they began to get specialists in the community
249
650260
2000
10:52
and special structures
250
652260
2000
10:54
that were responsible for recording,
251
654260
2000
10:56
understanding, learning information.
252
656260
3000
10:59
And that was the brains and the nervous system
253
659260
2000
11:01
of those communities.
254
661260
2000
11:03
And that gave them an evolutionary advantage.
255
663260
2000
11:05
Because at that point,
256
665260
3000
11:08
an individual --
257
668260
3000
11:11
learning could happen
258
671260
2000
11:13
within the time span of a single organism,
259
673260
2000
11:15
instead of over this evolutionary time span.
260
675260
3000
11:18
So an organism could, for instance,
261
678260
2000
11:20
learn not to eat a certain kind of fruit
262
680260
2000
11:22
because it tasted bad and it got sick last time it ate it.
263
682260
4000
11:26
That could happen within the lifetime of a single organism,
264
686260
3000
11:29
whereas before they'd built these special information processing structures,
265
689260
4000
11:33
that would have had to be learned evolutionarily
266
693260
2000
11:35
over hundreds of thousands of years
267
695260
3000
11:38
by the individuals dying off that ate that kind of fruit.
268
698260
3000
11:41
So that nervous system,
269
701260
2000
11:43
the fact that they built these special information structures,
270
703260
3000
11:46
tremendously sped up the whole process of evolution.
271
706260
3000
11:49
Because evolution could now happen within an individual.
272
709260
3000
11:52
It could happen in learning time scales.
273
712260
3000
11:55
But then what happened
274
715260
2000
11:57
was the individuals worked out,
275
717260
2000
11:59
of course, tricks of communicating.
276
719260
2000
12:01
And for example,
277
721260
2000
12:03
the most sophisticated version that we're aware of is human language.
278
723260
3000
12:06
It's really a pretty amazing invention if you think about it.
279
726260
3000
12:09
Here I have a very complicated, messy,
280
729260
2000
12:11
confused idea in my head.
281
731260
3000
12:14
I'm sitting here making grunting sounds basically,
282
734260
3000
12:17
and hopefully constructing a similar messy, confused idea in your head
283
737260
3000
12:20
that bears some analogy to it.
284
740260
2000
12:22
But we're taking something very complicated,
285
742260
2000
12:24
turning it into sound, sequences of sounds,
286
744260
3000
12:27
and producing something very complicated in your brain.
287
747260
4000
12:31
So this allows us now
288
751260
2000
12:33
to begin to start functioning
289
753260
2000
12:35
as a single organism.
290
755260
3000
12:38
And so, in fact, what we've done
291
758260
3000
12:41
is we, humanity,
292
761260
2000
12:43
have started abstracting out.
293
763260
2000
12:45
We're going through the same levels
294
765260
2000
12:47
that multi-cellular organisms have gone through --
295
767260
2000
12:49
abstracting out our methods of recording,
296
769260
3000
12:52
presenting, processing information.
297
772260
2000
12:54
So for example, the invention of language
298
774260
2000
12:56
was a tiny step in that direction.
299
776260
3000
12:59
Telephony, computers,
300
779260
2000
13:01
videotapes, CD-ROMs and so on
301
781260
3000
13:04
are all our specialized mechanisms
302
784260
2000
13:06
that we've now built within our society
303
786260
2000
13:08
for handling that information.
304
788260
2000
13:10
And it all connects us together
305
790260
3000
13:13
into something
306
793260
2000
13:15
that is much bigger
307
795260
2000
13:17
and much faster
308
797260
2000
13:19
and able to evolve
309
799260
2000
13:21
than what we were before.
310
801260
2000
13:23
So now, evolution can take place
311
803260
2000
13:25
on a scale of microseconds.
312
805260
2000
13:27
And you saw Ty's little evolutionary example
313
807260
2000
13:29
where he sort of did a little bit of evolution
314
809260
2000
13:31
on the Convolution program right before your eyes.
315
811260
3000
13:34
So now we've speeded up the time scales once again.
316
814260
3000
13:37
So the first steps of the story that I told you about
317
817260
2000
13:39
took a billion years a piece.
318
819260
2000
13:41
And the next steps,
319
821260
2000
13:43
like nervous systems and brains,
320
823260
2000
13:45
took a few hundred million years.
321
825260
2000
13:47
Then the next steps, like language and so on,
322
827260
3000
13:50
took less than a million years.
323
830260
2000
13:52
And these next steps, like electronics,
324
832260
2000
13:54
seem to be taking only a few decades.
325
834260
2000
13:56
The process is feeding on itself
326
836260
2000
13:58
and becoming, I guess, autocatalytic is the word for it --
327
838260
3000
14:01
when something reinforces its rate of change.
328
841260
3000
14:04
The more it changes, the faster it changes.
329
844260
3000
14:07
And I think that that's what we're seeing here in this explosion of curve.
330
847260
3000
14:10
We're seeing this process feeding back on itself.
331
850260
3000
14:13
Now I design computers for a living,
332
853260
3000
14:16
and I know that the mechanisms
333
856260
2000
14:18
that I use to design computers
334
858260
3000
14:21
would be impossible
335
861260
2000
14:23
without recent advances in computers.
336
863260
2000
14:25
So right now, what I do
337
865260
2000
14:27
is I design objects at such complexity
338
867260
3000
14:30
that it's really impossible for me to design them in the traditional sense.
339
870260
3000
14:33
I don't know what every transistor in the connection machine does.
340
873260
4000
14:37
There are billions of them.
341
877260
2000
14:39
Instead, what I do
342
879260
2000
14:41
and what the designers at Thinking Machines do
343
881260
3000
14:44
is we think at some level of abstraction
344
884260
2000
14:46
and then we hand it to the machine
345
886260
2000
14:48
and the machine takes it beyond what we could ever do,
346
888260
3000
14:51
much farther and faster than we could ever do.
347
891260
3000
14:54
And in fact, sometimes it takes it by methods
348
894260
2000
14:56
that we don't quite even understand.
349
896260
3000
14:59
One method that's particularly interesting
350
899260
2000
15:01
that I've been using a lot lately
351
901260
3000
15:04
is evolution itself.
352
904260
2000
15:06
So what we do
353
906260
2000
15:08
is we put inside the machine
354
908260
2000
15:10
a process of evolution
355
910260
2000
15:12
that takes place on the microsecond time scale.
356
912260
2000
15:14
So for example,
357
914260
2000
15:16
in the most extreme cases,
358
916260
2000
15:18
we can actually evolve a program
359
918260
2000
15:20
by starting out with random sequences of instructions.
360
920260
4000
15:24
Say, "Computer, would you please make
361
924260
2000
15:26
a hundred million random sequences of instructions.
362
926260
3000
15:29
Now would you please run all of those random sequences of instructions,
363
929260
3000
15:32
run all of those programs,
364
932260
2000
15:34
and pick out the ones that came closest to doing what I wanted."
365
934260
3000
15:37
So in other words, I define what I wanted.
366
937260
2000
15:39
Let's say I want to sort numbers,
367
939260
2000
15:41
as a simple example I've done it with.
368
941260
2000
15:43
So find the programs that come closest to sorting numbers.
369
943260
3000
15:46
So of course, random sequences of instructions
370
946260
3000
15:49
are very unlikely to sort numbers,
371
949260
2000
15:51
so none of them will really do it.
372
951260
2000
15:53
But one of them, by luck,
373
953260
2000
15:55
may put two numbers in the right order.
374
955260
2000
15:57
And I say, "Computer,
375
957260
2000
15:59
would you please now take the 10 percent
376
959260
3000
16:02
of those random sequences that did the best job.
377
962260
2000
16:04
Save those. Kill off the rest.
378
964260
2000
16:06
And now let's reproduce
379
966260
2000
16:08
the ones that sorted numbers the best.
380
968260
2000
16:10
And let's reproduce them by a process of recombination
381
970260
3000
16:13
analogous to sex."
382
973260
2000
16:15
Take two programs and they produce children
383
975260
3000
16:18
by exchanging their subroutines,
384
978260
2000
16:20
and the children inherit the traits of the subroutines of the two programs.
385
980260
3000
16:23
So I've got now a new generation of programs
386
983260
3000
16:26
that are produced by combinations
387
986260
2000
16:28
of the programs that did a little bit better job.
388
988260
2000
16:30
Say, "Please repeat that process."
389
990260
2000
16:32
Score them again.
390
992260
2000
16:34
Introduce some mutations perhaps.
391
994260
2000
16:36
And try that again and do that for another generation.
392
996260
3000
16:39
Well every one of those generations just takes a few milliseconds.
393
999260
3000
16:42
So I can do the equivalent
394
1002260
2000
16:44
of millions of years of evolution on that
395
1004260
2000
16:46
within the computer in a few minutes,
396
1006260
3000
16:49
or in the complicated cases, in a few hours.
397
1009260
2000
16:51
At the end of that, I end up with programs
398
1011260
3000
16:54
that are absolutely perfect at sorting numbers.
399
1014260
2000
16:56
In fact, they are programs that are much more efficient
400
1016260
3000
16:59
than programs I could have ever written by hand.
401
1019260
2000
17:01
Now if I look at those programs,
402
1021260
2000
17:03
I can't tell you how they work.
403
1023260
2000
17:05
I've tried looking at them and telling you how they work.
404
1025260
2000
17:07
They're obscure, weird programs.
405
1027260
2000
17:09
But they do the job.
406
1029260
2000
17:11
And in fact, I know, I'm very confident that they do the job
407
1031260
3000
17:14
because they come from a line
408
1034260
2000
17:16
of hundreds of thousands of programs that did the job.
409
1036260
2000
17:18
In fact, their life depended on doing the job.
410
1038260
3000
17:21
(Laughter)
411
1041260
4000
17:26
I was riding in a 747
412
1046260
2000
17:28
with Marvin Minsky once,
413
1048260
2000
17:30
and he pulls out this card and says, "Oh look. Look at this.
414
1050260
3000
17:33
It says, 'This plane has hundreds of thousands of tiny parts
415
1053260
4000
17:37
working together to make you a safe flight.'
416
1057260
4000
17:41
Doesn't that make you feel confident?"
417
1061260
2000
17:43
(Laughter)
418
1063260
2000
17:45
In fact, we know that the engineering process doesn't work very well
419
1065260
3000
17:48
when it gets complicated.
420
1068260
2000
17:50
So we're beginning to depend on computers
421
1070260
2000
17:52
to do a process that's very different than engineering.
422
1072260
4000
17:56
And it lets us produce things of much more complexity
423
1076260
3000
17:59
than normal engineering lets us produce.
424
1079260
2000
18:01
And yet, we don't quite understand the options of it.
425
1081260
3000
18:04
So in a sense, it's getting ahead of us.
426
1084260
2000
18:06
We're now using those programs
427
1086260
2000
18:08
to make much faster computers
428
1088260
2000
18:10
so that we'll be able to run this process much faster.
429
1090260
3000
18:13
So it's feeding back on itself.
430
1093260
3000
18:16
The thing is becoming faster
431
1096260
2000
18:18
and that's why I think it seems so confusing.
432
1098260
2000
18:20
Because all of these technologies are feeding back on themselves.
433
1100260
3000
18:23
We're taking off.
434
1103260
2000
18:25
And what we are is we're at a point in time
435
1105260
3000
18:28
which is analogous to when single-celled organisms
436
1108260
2000
18:30
were turning into multi-celled organisms.
437
1110260
3000
18:33
So we're the amoebas
438
1113260
2000
18:35
and we can't quite figure out what the hell this thing is we're creating.
439
1115260
3000
18:38
We're right at that point of transition.
440
1118260
2000
18:40
But I think that there really is something coming along after us.
441
1120260
3000
18:43
I think it's very haughty of us
442
1123260
2000
18:45
to think that we're the end product of evolution.
443
1125260
3000
18:48
And I think all of us here
444
1128260
2000
18:50
are a part of producing
445
1130260
2000
18:52
whatever that next thing is.
446
1132260
2000
18:54
So lunch is coming along,
447
1134260
2000
18:56
and I think I will stop at that point,
448
1136260
2000
18:58
before I get selected out.
449
1138260
2000
19:00
(Applause)
450
1140260
3000
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