ABOUT THE SPEAKER
Stephen Wolfram - Scientist, inventor
Stephen Wolfram is the creator of Mathematica and Wolfram|Alpha, the author of A New Kind of Science, and the founder and CEO of Wolfram Research.

Why you should listen

Stephen Wolfram published his first scientific paper at the age of 15, and received his PhD in theoretical physics from Caltech by the age of 20. Having started to use computers in 1973, Wolfram rapidly became a leader in the emerging field of scientific computing.

In 1981 Wolfram became the youngest recipient of a MacArthur Prize Fellowship. He then set out on an ambitious new direction in science aimed at understanding the origins of complexity in nature. Wolfram's first key idea was to use computer experiments to study the behavior of simple computer programs known as cellular automata. This allowed him to make a series of startling discoveries about the origins of complexity.

Wolfram founded the first research center and the first journal in the field, Complex Systems, and began the development of Mathematica. Wolfram Research soon became a world leader in the software industry -- widely recognized for excellence in both technology and business.

Following the release of Mathematica Version 2 in 1991, Wolfram began to divide his time between Mathematica development and scientific research. Building on his work from the mid-1980s, and now with Mathematica as a tool, Wolfram made a rapid succession of major new discoveries, which he described in his book, A New Kind of Science.

Building on Mathematica, A New Kind of Science, and the success of Wolfram Research, Wolfram recently launched Wolfram|Alpha -- an ambitious, long-term project to make as much of the world's knowledge as possible computable, and accessible to everyone.

More profile about the speaker
Stephen Wolfram | Speaker | TED.com
TED2010

Stephen Wolfram: Computing a theory of all knowledge

Filmed:
1,811,819 views

Stephen Wolfram, creator of Mathematica, talks about his quest to make all knowledge computational -- able to be searched, processed and manipulated. His new search engine, Wolfram Alpha, has no lesser goal than to model and explain the physics underlying the universe.
- Scientist, inventor
Stephen Wolfram is the creator of Mathematica and Wolfram|Alpha, the author of A New Kind of Science, and the founder and CEO of Wolfram Research. Full bio

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

00:16
So I want to talk today about an idea. It's a big idea.
0
1000
3000
00:19
Actually, I think it'll eventually
1
4000
2000
00:21
be seen as probably the single biggest idea
2
6000
2000
00:23
that's emerged in the past century.
3
8000
2000
00:25
It's the idea of computation.
4
10000
2000
00:27
Now, of course, that idea has brought us
5
12000
2000
00:29
all of the computer technology we have today and so on.
6
14000
3000
00:32
But there's actually a lot more to computation than that.
7
17000
3000
00:35
It's really a very deep, very powerful, very fundamental idea,
8
20000
3000
00:38
whose effects we've only just begun to see.
9
23000
3000
00:41
Well, I myself have spent the past 30 years of my life
10
26000
3000
00:44
working on three large projects
11
29000
2000
00:46
that really try to take the idea of computation seriously.
12
31000
3000
00:50
So I started off at a young age as a physicist
13
35000
3000
00:53
using computers as tools.
14
38000
2000
00:55
Then, I started drilling down,
15
40000
2000
00:57
thinking about the computations I might want to do,
16
42000
2000
00:59
trying to figure out what primitives they could be built up from
17
44000
3000
01:02
and how they could be automated as much as possible.
18
47000
3000
01:05
Eventually, I created a whole structure
19
50000
2000
01:07
based on symbolic programming and so on
20
52000
2000
01:09
that let me build Mathematica.
21
54000
2000
01:11
And for the past 23 years, at an increasing rate,
22
56000
2000
01:13
we've been pouring more and more ideas
23
58000
2000
01:15
and capabilities and so on into Mathematica,
24
60000
2000
01:17
and I'm happy to say that that's led to many good things
25
62000
3000
01:20
in R & D and education,
26
65000
2000
01:22
lots of other areas.
27
67000
2000
01:24
Well, I have to admit, actually,
28
69000
2000
01:26
that I also had a very selfish reason for building Mathematica:
29
71000
3000
01:29
I wanted to use it myself,
30
74000
2000
01:31
a bit like Galileo got to use his telescope
31
76000
2000
01:33
400 years ago.
32
78000
2000
01:35
But I wanted to look not at the astronomical universe,
33
80000
3000
01:38
but at the computational universe.
34
83000
3000
01:41
So we normally think of programs as being
35
86000
2000
01:43
complicated things that we build
36
88000
2000
01:45
for very specific purposes.
37
90000
2000
01:47
But what about the space of all possible programs?
38
92000
3000
01:50
Here's a representation of a really simple program.
39
95000
3000
01:53
So, if we run this program,
40
98000
2000
01:55
this is what we get.
41
100000
2000
01:57
Very simple.
42
102000
2000
01:59
So let's try changing the rule
43
104000
2000
02:01
for this program a little bit.
44
106000
2000
02:03
Now we get another result,
45
108000
2000
02:05
still very simple.
46
110000
2000
02:07
Try changing it again.
47
112000
3000
02:10
You get something a little bit more complicated.
48
115000
2000
02:12
But if we keep running this for a while,
49
117000
2000
02:14
we find out that although the pattern we get is very intricate,
50
119000
3000
02:17
it has a very regular structure.
51
122000
3000
02:20
So the question is: Can anything else happen?
52
125000
3000
02:23
Well, we can do a little experiment.
53
128000
2000
02:25
Let's just do a little mathematical experiment, try and find out.
54
130000
3000
02:29
Let's just run all possible programs
55
134000
3000
02:32
of the particular type that we're looking at.
56
137000
2000
02:34
They're called cellular automata.
57
139000
2000
02:36
You can see a lot of diversity in the behavior here.
58
141000
2000
02:38
Most of them do very simple things,
59
143000
2000
02:40
but if you look along all these different pictures,
60
145000
2000
02:42
at rule number 30,
61
147000
2000
02:44
you start to see something interesting going on.
62
149000
2000
02:46
So let's take a closer look
63
151000
2000
02:48
at rule number 30 here.
64
153000
2000
02:50
So here it is.
65
155000
2000
02:52
We're just following this very simple rule at the bottom here,
66
157000
3000
02:55
but we're getting all this amazing stuff.
67
160000
2000
02:57
It's not at all what we're used to,
68
162000
2000
02:59
and I must say that, when I first saw this,
69
164000
2000
03:01
it came as a huge shock to my intuition.
70
166000
3000
03:04
And, in fact, to understand it,
71
169000
2000
03:06
I eventually had to create
72
171000
2000
03:08
a whole new kind of science.
73
173000
2000
03:11
(Laughter)
74
176000
2000
03:13
This science is different, more general,
75
178000
3000
03:16
than the mathematics-based science that we've had
76
181000
2000
03:18
for the past 300 or so years.
77
183000
3000
03:21
You know, it's always seemed like a big mystery:
78
186000
2000
03:23
how nature, seemingly so effortlessly,
79
188000
3000
03:26
manages to produce so much
80
191000
2000
03:28
that seems to us so complex.
81
193000
3000
03:31
Well, I think we've found its secret:
82
196000
3000
03:34
It's just sampling what's out there in the computational universe
83
199000
3000
03:37
and quite often getting things like Rule 30
84
202000
3000
03:40
or like this.
85
205000
3000
03:44
And knowing that starts to explain
86
209000
2000
03:46
a lot of long-standing mysteries in science.
87
211000
3000
03:49
It also brings up new issues, though,
88
214000
2000
03:51
like computational irreducibility.
89
216000
3000
03:54
I mean, we're used to having science let us predict things,
90
219000
3000
03:57
but something like this
91
222000
2000
03:59
is fundamentally irreducible.
92
224000
2000
04:01
The only way to find its outcome
93
226000
2000
04:03
is, effectively, just to watch it evolve.
94
228000
3000
04:06
It's connected to, what I call,
95
231000
2000
04:08
the principle of computational equivalence,
96
233000
2000
04:10
which tells us that even incredibly simple systems
97
235000
3000
04:13
can do computations as sophisticated as anything.
98
238000
3000
04:16
It doesn't take lots of technology or biological evolution
99
241000
3000
04:19
to be able to do arbitrary computation;
100
244000
2000
04:21
just something that happens, naturally,
101
246000
2000
04:23
all over the place.
102
248000
2000
04:25
Things with rules as simple as these can do it.
103
250000
3000
04:29
Well, this has deep implications
104
254000
2000
04:31
about the limits of science,
105
256000
2000
04:33
about predictability and controllability
106
258000
2000
04:35
of things like biological processes or economies,
107
260000
3000
04:38
about intelligence in the universe,
108
263000
2000
04:40
about questions like free will
109
265000
2000
04:42
and about creating technology.
110
267000
3000
04:45
You know, in working on this science for many years,
111
270000
2000
04:47
I kept wondering,
112
272000
2000
04:49
"What will be its first killer app?"
113
274000
2000
04:51
Well, ever since I was a kid,
114
276000
2000
04:53
I'd been thinking about systematizing knowledge
115
278000
2000
04:55
and somehow making it computable.
116
280000
2000
04:57
People like Leibniz had wondered about that too
117
282000
2000
04:59
300 years earlier.
118
284000
2000
05:01
But I'd always assumed that to make progress,
119
286000
2000
05:03
I'd essentially have to replicate a whole brain.
120
288000
3000
05:06
Well, then I got to thinking:
121
291000
2000
05:08
This scientific paradigm of mine suggests something different --
122
293000
3000
05:11
and, by the way, I've now got
123
296000
2000
05:13
huge computation capabilities in Mathematica,
124
298000
3000
05:16
and I'm a CEO with some worldly resources
125
301000
3000
05:19
to do large, seemingly crazy, projects --
126
304000
3000
05:22
So I decided to just try to see
127
307000
2000
05:24
how much of the systematic knowledge that's out there in the world
128
309000
3000
05:27
we could make computable.
129
312000
2000
05:29
So, it's been a big, very complex project,
130
314000
2000
05:31
which I was not sure was going to work at all.
131
316000
3000
05:34
But I'm happy to say it's actually going really well.
132
319000
3000
05:37
And last year we were able
133
322000
2000
05:39
to release the first website version
134
324000
2000
05:41
of Wolfram Alpha.
135
326000
2000
05:43
Its purpose is to be a serious knowledge engine
136
328000
3000
05:46
that computes answers to questions.
137
331000
3000
05:49
So let's give it a try.
138
334000
2000
05:51
Let's start off with something really easy.
139
336000
2000
05:53
Hope for the best.
140
338000
2000
05:55
Very good. Okay.
141
340000
2000
05:57
So far so good.
142
342000
2000
05:59
(Laughter)
143
344000
3000
06:02
Let's try something a little bit harder.
144
347000
3000
06:05
Let's do
145
350000
2000
06:07
some mathy thing,
146
352000
3000
06:10
and with luck it'll work out the answer
147
355000
3000
06:13
and try and tell us some interesting things
148
358000
2000
06:15
things about related math.
149
360000
2000
06:17
We could ask it something about the real world.
150
362000
3000
06:20
Let's say -- I don't know --
151
365000
2000
06:22
what's the GDP of Spain?
152
367000
3000
06:25
And it should be able to tell us that.
153
370000
2000
06:27
Now we could compute something related to this,
154
372000
2000
06:29
let's say ... the GDP of Spain
155
374000
2000
06:31
divided by, I don't know,
156
376000
2000
06:33
the -- hmmm ...
157
378000
2000
06:35
let's say the revenue of Microsoft.
158
380000
2000
06:37
(Laughter)
159
382000
2000
06:39
The idea is that we can just type this in,
160
384000
2000
06:41
this kind of question in, however we think of it.
161
386000
3000
06:44
So let's try asking a question,
162
389000
2000
06:46
like a health related question.
163
391000
2000
06:48
So let's say we have a lab finding that ...
164
393000
3000
06:51
you know, we have an LDL level of 140
165
396000
2000
06:53
for a male aged 50.
166
398000
3000
06:56
So let's type that in, and now Wolfram Alpha
167
401000
2000
06:58
will go and use available public health data
168
403000
2000
07:00
and try and figure out
169
405000
2000
07:02
what part of the population that corresponds to and so on.
170
407000
3000
07:05
Or let's try asking about, I don't know,
171
410000
3000
07:08
the International Space Station.
172
413000
2000
07:10
And what's happening here is that
173
415000
2000
07:12
Wolfram Alpha is not just looking up something;
174
417000
2000
07:14
it's computing, in real time,
175
419000
3000
07:17
where the International Space Station is right now at this moment,
176
422000
3000
07:20
how fast it's going, and so on.
177
425000
3000
07:24
So Wolfram Alpha knows about lots and lots of kinds of things.
178
429000
3000
07:27
It's got, by now,
179
432000
2000
07:29
pretty good coverage of everything you might find
180
434000
2000
07:31
in a standard reference library.
181
436000
3000
07:34
But the goal is to go much further
182
439000
2000
07:36
and, very broadly, to democratize
183
441000
3000
07:39
all of this knowledge,
184
444000
3000
07:42
and to try and be an authoritative
185
447000
2000
07:44
source in all areas.
186
449000
2000
07:46
To be able to compute answers to specific questions that people have,
187
451000
3000
07:49
not by searching what other people
188
454000
2000
07:51
may have written down before,
189
456000
2000
07:53
but by using built in knowledge
190
458000
2000
07:55
to compute fresh new answers to specific questions.
191
460000
3000
07:58
Now, of course, Wolfram Alpha
192
463000
2000
08:00
is a monumentally huge, long-term project
193
465000
2000
08:02
with lots and lots of challenges.
194
467000
2000
08:04
For a start, one has to curate a zillion
195
469000
3000
08:07
different sources of facts and data,
196
472000
3000
08:10
and we built quite a pipeline of Mathematica automation
197
475000
3000
08:13
and human domain experts for doing this.
198
478000
3000
08:16
But that's just the beginning.
199
481000
2000
08:18
Given raw facts or data
200
483000
2000
08:20
to actually answer questions,
201
485000
2000
08:22
one has to compute:
202
487000
2000
08:24
one has to implement all those methods and models
203
489000
2000
08:26
and algorithms and so on
204
491000
2000
08:28
that science and other areas have built up over the centuries.
205
493000
3000
08:31
Well, even starting from Mathematica,
206
496000
3000
08:34
this is still a huge amount of work.
207
499000
2000
08:36
So far, there are about 8 million lines
208
501000
2000
08:38
of Mathematica code in Wolfram Alpha
209
503000
2000
08:40
built by experts from many, many different fields.
210
505000
3000
08:43
Well, a crucial idea of Wolfram Alpha
211
508000
3000
08:46
is that you can just ask it questions
212
511000
2000
08:48
using ordinary human language,
213
513000
3000
08:51
which means that we've got to be able to take
214
516000
2000
08:53
all those strange utterances that people type into the input field
215
518000
3000
08:56
and understand them.
216
521000
2000
08:58
And I must say that I thought that step
217
523000
2000
09:00
might just be plain impossible.
218
525000
3000
09:04
Two big things happened:
219
529000
2000
09:06
First, a bunch of new ideas about linguistics
220
531000
3000
09:09
that came from studying the computational universe;
221
534000
3000
09:12
and second, the realization that having actual computable knowledge
222
537000
3000
09:15
completely changes how one can
223
540000
2000
09:17
set about understanding language.
224
542000
3000
09:20
And, of course, now
225
545000
2000
09:22
with Wolfram Alpha actually out in the wild,
226
547000
2000
09:24
we can learn from its actual usage.
227
549000
2000
09:26
And, in fact, there's been
228
551000
2000
09:28
an interesting coevolution that's been going on
229
553000
2000
09:30
between Wolfram Alpha
230
555000
2000
09:32
and its human users,
231
557000
2000
09:34
and it's really encouraging.
232
559000
2000
09:36
Right now, if we look at web queries,
233
561000
2000
09:38
more than 80 percent of them get handled successfully the first time.
234
563000
3000
09:41
And if you look at things like the iPhone app,
235
566000
2000
09:43
the fraction is considerably larger.
236
568000
2000
09:45
So, I'm pretty pleased with it all.
237
570000
2000
09:47
But, in many ways,
238
572000
2000
09:49
we're still at the very beginning with Wolfram Alpha.
239
574000
3000
09:52
I mean, everything is scaling up very nicely
240
577000
2000
09:54
and we're getting more confident.
241
579000
2000
09:56
You can expect to see Wolfram Alpha technology
242
581000
2000
09:58
showing up in more and more places,
243
583000
2000
10:00
working both with this kind of public data, like on the website,
244
585000
3000
10:03
and with private knowledge
245
588000
2000
10:05
for people and companies and so on.
246
590000
3000
10:08
You know, I've realized that Wolfram Alpha actually gives one
247
593000
3000
10:11
a whole new kind of computing
248
596000
2000
10:13
that one can call knowledge-based computing,
249
598000
2000
10:15
in which one's starting not just from raw computation,
250
600000
3000
10:18
but from a vast amount of built-in knowledge.
251
603000
3000
10:21
And when one does that, one really changes
252
606000
2000
10:23
the economics of delivering computational things,
253
608000
3000
10:26
whether it's on the web or elsewhere.
254
611000
2000
10:28
You know, we have a fairly interesting situation right now.
255
613000
3000
10:31
On the one hand, we have Mathematica,
256
616000
2000
10:33
with its sort of precise, formal language
257
618000
3000
10:36
and a huge network
258
621000
2000
10:38
of carefully designed capabilities
259
623000
2000
10:40
able to get a lot done in just a few lines.
260
625000
3000
10:43
Let me show you a couple of examples here.
261
628000
3000
10:47
So here's a trivial piece of Mathematica programming.
262
632000
3000
10:51
Here's something where we're sort of
263
636000
2000
10:53
integrating a bunch of different capabilities here.
264
638000
3000
10:56
Here we'll just create, in this line,
265
641000
3000
10:59
a little user interface that allows us to
266
644000
3000
11:02
do something fun there.
267
647000
2000
11:05
If you go on, that's a slightly more complicated program
268
650000
2000
11:07
that's now doing all sorts of algorithmic things
269
652000
3000
11:10
and creating user interface and so on.
270
655000
2000
11:12
But it's something that is very precise stuff.
271
657000
3000
11:15
It's a precise specification with a precise formal language
272
660000
3000
11:18
that causes Mathematica to know what to do here.
273
663000
3000
11:21
Then on the other hand, we have Wolfram Alpha,
274
666000
3000
11:24
with all the messiness of the world
275
669000
2000
11:26
and human language and so on built into it.
276
671000
2000
11:28
So what happens when you put these things together?
277
673000
3000
11:31
I think it's actually rather wonderful.
278
676000
2000
11:33
With Wolfram Alpha inside Mathematica,
279
678000
2000
11:35
you can, for example, make precise programs
280
680000
2000
11:37
that call on real world data.
281
682000
2000
11:39
Here's a real simple example.
282
684000
2000
11:44
You can also just sort of give vague input
283
689000
3000
11:47
and then try and have Wolfram Alpha
284
692000
2000
11:49
figure out what you're talking about.
285
694000
2000
11:51
Let's try this here.
286
696000
2000
11:53
But actually I think the most exciting thing about this
287
698000
3000
11:56
is that it really gives one the chance
288
701000
2000
11:58
to democratize programming.
289
703000
3000
12:01
I mean, anyone will be able to say what they want in plain language.
290
706000
3000
12:04
Then, the idea is that Wolfram Alpha will be able to figure out
291
709000
3000
12:07
what precise pieces of code
292
712000
2000
12:09
can do what they're asking for
293
714000
2000
12:11
and then show them examples that will let them pick what they need
294
716000
3000
12:14
to build up bigger and bigger, precise programs.
295
719000
3000
12:17
So, sometimes, Wolfram Alpha
296
722000
2000
12:19
will be able to do the whole thing immediately
297
724000
2000
12:21
and just give back a whole big program that you can then compute with.
298
726000
3000
12:24
Here's a big website
299
729000
2000
12:26
where we've been collecting lots of educational
300
731000
3000
12:29
and other demonstrations about lots of kinds of things.
301
734000
3000
12:32
I'll show you one example here.
302
737000
3000
12:36
This is just an example of one of these computable documents.
303
741000
3000
12:39
This is probably a fairly small
304
744000
2000
12:41
piece of Mathematica code
305
746000
2000
12:43
that's able to be run here.
306
748000
2000
12:47
Okay. Let's zoom out again.
307
752000
3000
12:50
So, given our new kind of science,
308
755000
2000
12:52
is there a general way to use it to make technology?
309
757000
3000
12:55
So, with physical materials,
310
760000
2000
12:57
we're used to going around the world
311
762000
2000
12:59
and discovering that particular materials
312
764000
2000
13:01
are useful for particular
313
766000
2000
13:03
technological purposes.
314
768000
2000
13:05
Well, it turns out we can do very much the same kind of thing
315
770000
2000
13:07
in the computational universe.
316
772000
2000
13:09
There's an inexhaustible supply of programs out there.
317
774000
3000
13:12
The challenge is to see how to
318
777000
2000
13:14
harness them for human purposes.
319
779000
2000
13:16
Something like Rule 30, for example,
320
781000
2000
13:18
turns out to be a really good randomness generator.
321
783000
2000
13:20
Other simple programs are good models
322
785000
2000
13:22
for processes in the natural or social world.
323
787000
3000
13:25
And, for example, Wolfram Alpha and Mathematica
324
790000
2000
13:27
are actually now full of algorithms
325
792000
2000
13:29
that we discovered by searching the computational universe.
326
794000
3000
13:33
And, for example, this -- if we go back here --
327
798000
3000
13:37
this has become surprisingly popular
328
802000
2000
13:39
among composers
329
804000
2000
13:41
finding musical forms by searching the computational universe.
330
806000
3000
13:45
In a sense, we can use the computational universe
331
810000
2000
13:47
to get mass customized creativity.
332
812000
3000
13:50
I'm hoping we can, for example,
333
815000
2000
13:52
use that even to get Wolfram Alpha
334
817000
2000
13:54
to routinely do invention and discovery on the fly,
335
819000
3000
13:57
and to find all sorts of wonderful stuff
336
822000
2000
13:59
that no engineer
337
824000
2000
14:01
and no process of incremental evolution would ever come up with.
338
826000
3000
14:05
Well, so, that leads to kind of an ultimate question:
339
830000
3000
14:08
Could it be that someplace out there in the computational universe
340
833000
3000
14:11
we might find our physical universe?
341
836000
3000
14:14
Perhaps there's even some quite simple rule,
342
839000
2000
14:16
some simple program for our universe.
343
841000
3000
14:19
Well, the history of physics would have us believe
344
844000
2000
14:21
that the rule for the universe must be pretty complicated.
345
846000
3000
14:24
But in the computational universe,
346
849000
2000
14:26
we've now seen how rules that are incredibly simple
347
851000
3000
14:29
can produce incredibly rich and complex behavior.
348
854000
3000
14:32
So could that be what's going on with our whole universe?
349
857000
3000
14:36
If the rules for the universe are simple,
350
861000
2000
14:38
it's kind of inevitable that they have to be
351
863000
2000
14:40
very abstract and very low level;
352
865000
2000
14:42
operating, for example, far below
353
867000
2000
14:44
the level of space or time,
354
869000
2000
14:46
which makes it hard to represent things.
355
871000
2000
14:48
But in at least a large class of cases,
356
873000
2000
14:50
one can think of the universe as being
357
875000
2000
14:52
like some kind of network,
358
877000
2000
14:54
which, when it gets big enough,
359
879000
2000
14:56
behaves like continuous space
360
881000
2000
14:58
in much the same way as having lots of molecules
361
883000
2000
15:00
can behave like a continuous fluid.
362
885000
2000
15:02
Well, then the universe has to evolve by applying
363
887000
3000
15:05
little rules that progressively update this network.
364
890000
3000
15:08
And each possible rule, in a sense,
365
893000
2000
15:10
corresponds to a candidate universe.
366
895000
2000
15:12
Actually, I haven't shown these before,
367
897000
3000
15:16
but here are a few of the candidate universes
368
901000
3000
15:19
that I've looked at.
369
904000
2000
15:21
Some of these are hopeless universes,
370
906000
2000
15:23
completely sterile,
371
908000
2000
15:25
with other kinds of pathologies like no notion of space,
372
910000
2000
15:27
no notion of time, no matter,
373
912000
3000
15:30
other problems like that.
374
915000
2000
15:32
But the exciting thing that I've found in the last few years
375
917000
3000
15:35
is that you actually don't have to go very far
376
920000
2000
15:37
in the computational universe
377
922000
2000
15:39
before you start finding candidate universes
378
924000
2000
15:41
that aren't obviously not our universe.
379
926000
3000
15:44
Here's the problem:
380
929000
2000
15:46
Any serious candidate for our universe
381
931000
3000
15:49
is inevitably full of computational irreducibility.
382
934000
3000
15:52
Which means that it is irreducibly difficult
383
937000
3000
15:55
to find out how it will really behave,
384
940000
2000
15:57
and whether it matches our physical universe.
385
942000
3000
16:01
A few years ago, I was pretty excited to discover
386
946000
3000
16:04
that there are candidate universes with incredibly simple rules
387
949000
3000
16:07
that successfully reproduce special relativity,
388
952000
2000
16:09
and even general relativity and gravitation,
389
954000
3000
16:12
and at least give hints of quantum mechanics.
390
957000
3000
16:15
So, will we find the whole of physics?
391
960000
2000
16:17
I don't know for sure,
392
962000
2000
16:19
but I think at this point it's sort of
393
964000
2000
16:21
almost embarrassing not to at least try.
394
966000
2000
16:23
Not an easy project.
395
968000
2000
16:25
One's got to build a lot of technology.
396
970000
2000
16:27
One's got to build a structure that's probably
397
972000
2000
16:29
at least as deep as existing physics.
398
974000
2000
16:31
And I'm not sure what the best way to organize the whole thing is.
399
976000
3000
16:34
Build a team, open it up, offer prizes and so on.
400
979000
3000
16:37
But I'll tell you, here today,
401
982000
2000
16:39
that I'm committed to seeing this project done,
402
984000
2000
16:41
to see if, within this decade,
403
986000
3000
16:44
we can finally hold in our hands
404
989000
2000
16:46
the rule for our universe
405
991000
2000
16:48
and know where our universe lies
406
993000
2000
16:50
in the space of all possible universes ...
407
995000
2000
16:52
and be able to type into Wolfram Alpha, "the theory of the universe,"
408
997000
3000
16:55
and have it tell us.
409
1000000
2000
16:57
(Laughter)
410
1002000
2000
17:00
So I've been working on the idea of computation
411
1005000
2000
17:02
now for more than 30 years,
412
1007000
2000
17:04
building tools and methods and turning intellectual ideas
413
1009000
3000
17:07
into millions of lines of code
414
1012000
2000
17:09
and grist for server farms and so on.
415
1014000
2000
17:11
With every passing year,
416
1016000
2000
17:13
I realize how much more powerful
417
1018000
2000
17:15
the idea of computation really is.
418
1020000
2000
17:17
It's taken us a long way already,
419
1022000
2000
17:19
but there's so much more to come.
420
1024000
2000
17:21
From the foundations of science
421
1026000
2000
17:23
to the limits of technology
422
1028000
2000
17:25
to the very definition of the human condition,
423
1030000
2000
17:27
I think computation is destined to be
424
1032000
2000
17:29
the defining idea of our future.
425
1034000
2000
17:31
Thank you.
426
1036000
2000
17:33
(Applause)
427
1038000
14000
17:47
Chris Anderson: That was astonishing.
428
1052000
2000
17:49
Stay here. I've got a question.
429
1054000
2000
17:51
(Applause)
430
1056000
4000
17:57
So, that was, fair to say, an astonishing talk.
431
1062000
3000
18:01
Are you able to say in a sentence or two
432
1066000
3000
18:04
how this type of thinking
433
1069000
3000
18:07
could integrate at some point
434
1072000
2000
18:09
to things like string theory or the kind of things that people think of
435
1074000
2000
18:11
as the fundamental explanations of the universe?
436
1076000
3000
18:14
Stephen Wolfram: Well, the parts of physics
437
1079000
2000
18:16
that we kind of know to be true,
438
1081000
2000
18:18
things like the standard model of physics:
439
1083000
2000
18:20
what I'm trying to do better reproduce the standard model of physics
440
1085000
3000
18:23
or it's simply wrong.
441
1088000
2000
18:25
The things that people have tried to do in the last 25 years or so
442
1090000
2000
18:27
with string theory and so on
443
1092000
2000
18:29
have been an interesting exploration
444
1094000
2000
18:31
that has tried to get back to the standard model,
445
1096000
3000
18:34
but hasn't quite gotten there.
446
1099000
2000
18:36
My guess is that some great simplifications of what I'm doing
447
1101000
3000
18:39
may actually have considerable resonance
448
1104000
3000
18:42
with what's been done in string theory,
449
1107000
2000
18:44
but that's a complicated math thing
450
1109000
3000
18:47
that I don't yet know how it's going to work out.
451
1112000
3000
18:50
CA: Benoit Mandelbrot is in the audience.
452
1115000
2000
18:52
He also has shown how complexity
453
1117000
2000
18:54
can arise out of a simple start.
454
1119000
2000
18:56
Does your work relate to his?
455
1121000
2000
18:58
SW: I think so.
456
1123000
2000
19:00
I view Benoit Mandelbrot's work
457
1125000
2000
19:02
as one of the founding contributions
458
1127000
3000
19:05
to this kind of area.
459
1130000
3000
19:08
Benoit has been particularly interested
460
1133000
2000
19:10
in nested patterns, in fractals and so on,
461
1135000
2000
19:12
where the structure is something
462
1137000
2000
19:14
that's kind of tree-like,
463
1139000
2000
19:16
and where there's sort of a big branch that makes little branches
464
1141000
2000
19:18
and even smaller branches and so on.
465
1143000
3000
19:21
That's one of the ways
466
1146000
2000
19:23
that you get towards true complexity.
467
1148000
3000
19:26
I think things like the Rule 30 cellular automaton
468
1151000
3000
19:29
get us to a different level.
469
1154000
2000
19:31
In fact, in a very precise way, they get us to a different level
470
1156000
3000
19:34
because they seem to be things that are
471
1159000
2000
19:37
capable of complexity
472
1162000
3000
19:40
that's sort of as great as complexity can ever get ...
473
1165000
3000
19:44
I could go on about this at great length, but I won't. (Laughter) (Applause)
474
1169000
3000
19:47
CA: Stephen Wolfram, thank you.
475
1172000
2000
19:49
(Applause)
476
1174000
2000

▲Back to top

ABOUT THE SPEAKER
Stephen Wolfram - Scientist, inventor
Stephen Wolfram is the creator of Mathematica and Wolfram|Alpha, the author of A New Kind of Science, and the founder and CEO of Wolfram Research.

Why you should listen

Stephen Wolfram published his first scientific paper at the age of 15, and received his PhD in theoretical physics from Caltech by the age of 20. Having started to use computers in 1973, Wolfram rapidly became a leader in the emerging field of scientific computing.

In 1981 Wolfram became the youngest recipient of a MacArthur Prize Fellowship. He then set out on an ambitious new direction in science aimed at understanding the origins of complexity in nature. Wolfram's first key idea was to use computer experiments to study the behavior of simple computer programs known as cellular automata. This allowed him to make a series of startling discoveries about the origins of complexity.

Wolfram founded the first research center and the first journal in the field, Complex Systems, and began the development of Mathematica. Wolfram Research soon became a world leader in the software industry -- widely recognized for excellence in both technology and business.

Following the release of Mathematica Version 2 in 1991, Wolfram began to divide his time between Mathematica development and scientific research. Building on his work from the mid-1980s, and now with Mathematica as a tool, Wolfram made a rapid succession of major new discoveries, which he described in his book, A New Kind of Science.

Building on Mathematica, A New Kind of Science, and the success of Wolfram Research, Wolfram recently launched Wolfram|Alpha -- an ambitious, long-term project to make as much of the world's knowledge as possible computable, and accessible to everyone.

More profile about the speaker
Stephen Wolfram | Speaker | TED.com