1
00:00:00,612 --> 00:00:03,374
For the next 16 minutes,
I'm going to take you on a journey

2
00:00:03,398 --> 00:00:06,484
that is probably
the biggest dream of humanity:

3
00:00:06,508 --> 00:00:08,523
to understand the code of life.

4
00:00:09,072 --> 00:00:11,815
So for me, everything started
many, many years ago

5
00:00:11,839 --> 00:00:14,562
when I met the first 3D printer.

6
00:00:14,586 --> 00:00:16,260
The concept was fascinating.

7
00:00:16,284 --> 00:00:18,306
A 3D printer needs three elements:

8
00:00:18,330 --> 00:00:22,464
a bit of information, some
raw material, some energy,

9
00:00:22,488 --> 00:00:25,822
and it can produce any object
that was not there before.

10
00:00:26,517 --> 00:00:28,654
I was doing physics,
I was coming back home

11
00:00:28,678 --> 00:00:32,116
and I realized that I actually
always knew a 3D printer.

12
00:00:32,140 --> 00:00:33,476
And everyone does.

13
00:00:33,500 --> 00:00:34,658
It was my mom.

14
00:00:34,682 --> 00:00:35,683
(Laughter)

15
00:00:35,707 --> 00:00:38,121
My mom takes three elements:

16
00:00:38,145 --> 00:00:42,118
a bit of information, which is between
my father and my mom in this case,

17
00:00:42,142 --> 00:00:46,299
raw elements and energy
in the same media, that is food,

18
00:00:46,323 --> 00:00:48,831
and after several months, produces me.

19
00:00:48,855 --> 00:00:50,667
And I was not existent before.

20
00:00:50,691 --> 00:00:54,453
So apart from the shock of my mom
discovering that she was a 3D printer,

21
00:00:54,477 --> 00:00:59,215
I immediately got mesmerized
by that piece,

22
00:00:59,239 --> 00:01:00,956
the first one, the information.

23
00:01:00,980 --> 00:01:03,231
What amount of information does it take

24
00:01:03,255 --> 00:01:05,191
to build and assemble a human?

25
00:01:05,215 --> 00:01:06,789
Is it much? Is it little?

26
00:01:06,813 --> 00:01:08,993
How many thumb drives can you fill?

27
00:01:09,017 --> 00:01:11,641
Well, I was studying physics
at the beginning

28
00:01:11,665 --> 00:01:17,262
and I took this approximation of a human
as a gigantic Lego piece.

29
00:01:17,286 --> 00:01:21,071
So, imagine that the building
blocks are little atoms

30
00:01:21,095 --> 00:01:25,748
and there is a hydrogen here,
a carbon here, a nitrogen here.

31
00:01:25,772 --> 00:01:27,343
So in the first approximation,

32
00:01:27,367 --> 00:01:31,710
if I can list the number of atoms
that compose a human being,

33
00:01:31,734 --> 00:01:33,121
I can build it.

34
00:01:33,145 --> 00:01:35,174
Now, you can run some numbers

35
00:01:35,198 --> 00:01:38,475
and that happens to be
quite an astonishing number.

36
00:01:38,499 --> 00:01:41,256
So the number of atoms,

37
00:01:41,280 --> 00:01:46,035
the file that I will save in my thumb
drive to assemble a little baby,

38
00:01:46,059 --> 00:01:50,726
will actually fill an entire Titanic
of thumb drives --

39
00:01:50,750 --> 00:01:53,468
multiplied 2,000 times.

40
00:01:53,957 --> 00:01:57,358
This is the miracle of life.

41
00:01:57,382 --> 00:01:59,994
Every time you see from now on
a pregnant lady,

42
00:02:00,018 --> 00:02:02,874
she's assembling the biggest
amount of information

43
00:02:02,898 --> 00:02:04,454
that you will ever encounter.

44
00:02:04,478 --> 00:02:07,428
Forget big data, forget
anything you heard of.

45
00:02:07,452 --> 00:02:10,333
This is the biggest amount
of information that exists.

46
00:02:10,357 --> 00:02:14,190
(Applause)

47
00:02:14,214 --> 00:02:18,858
But nature, fortunately, is much smarter
than a young physicist,

48
00:02:18,882 --> 00:02:22,458
and in four billion years, managed
to pack this information

49
00:02:22,482 --> 00:02:25,187
in a small crystal we call DNA.

50
00:02:25,605 --> 00:02:29,917
We met it for the first time in 1950
when Rosalind Franklin,

51
00:02:29,941 --> 00:02:31,497
an amazing scientist, a woman,

52
00:02:31,521 --> 00:02:32,910
took a picture of it.

53
00:02:32,934 --> 00:02:38,122
But it took us more than 40 years
to finally poke inside a human cell,

54
00:02:38,146 --> 00:02:39,748
take out this crystal,

55
00:02:39,772 --> 00:02:42,852
unroll it, and read it for the first time.

56
00:02:43,615 --> 00:02:46,856
The code comes out to be
a fairly simple alphabet,

57
00:02:46,880 --> 00:02:50,652
four letters: A, T, C and G.

58
00:02:50,676 --> 00:02:54,166
And to build a human,
you need three billion of them.

59
00:02:54,933 --> 00:02:56,112
Three billion.

60
00:02:56,136 --> 00:02:57,715
How many are three billion?

61
00:02:57,739 --> 00:03:00,501
It doesn't really make
any sense as a number, right?

62
00:03:00,525 --> 00:03:04,610
So I was thinking how
I could explain myself better

63
00:03:04,634 --> 00:03:07,684
about how big and enormous this code is.

64
00:03:07,708 --> 00:03:10,762
But there is -- I mean,
I'm going to have some help,

65
00:03:10,786 --> 00:03:14,013
and the best person to help me
introduce the code

66
00:03:14,037 --> 00:03:17,559
is actually the first man
to sequence it, Dr. Craig Venter.

67
00:03:17,583 --> 00:03:20,973
So welcome onstage, Dr. Craig Venter.

68
00:03:20,997 --> 00:03:27,928
(Applause)

69
00:03:27,952 --> 00:03:30,208
Not the man in the flesh,

70
00:03:31,448 --> 00:03:33,793
but for the first time in history,

71
00:03:33,817 --> 00:03:37,279
this is the genome of a specific human,

72
00:03:37,303 --> 00:03:41,063
printed page-by-page, letter-by-letter:

73
00:03:41,087 --> 00:03:45,083
262,000 pages of information,

74
00:03:45,107 --> 00:03:49,471
450 kilograms, shipped
from the United States to Canada

75
00:03:49,495 --> 00:03:54,338
thanks to Bruno Bowden,
Lulu.com, a start-up, did everything.

76
00:03:54,362 --> 00:03:55,825
It was an amazing feat.

77
00:03:55,849 --> 00:04:00,146
But this is the visual perception
of what is the code of life.

78
00:04:00,170 --> 00:04:02,648
And now, for the first time,
I can do something fun.

79
00:04:02,672 --> 00:04:05,219
I can actually poke inside it and read.

80
00:04:05,243 --> 00:04:09,868
So let me take an interesting
book ... like this one.

81
00:04:13,077 --> 00:04:15,611
I have an annotation;
it's a fairly big book.

82
00:04:15,635 --> 00:04:19,362
So just to let you see
what is the code of life.

83
00:04:20,566 --> 00:04:23,957
Thousands and thousands and thousands

84
00:04:23,981 --> 00:04:26,651
and millions of letters.

85
00:04:26,675 --> 00:04:29,071
And they apparently make sense.

86
00:04:29,095 --> 00:04:30,852
Let's get to a specific part.

87
00:04:31,571 --> 00:04:32,933
Let me read it to you:

88
00:04:32,957 --> 00:04:33,978
(Laughter)

89
00:04:34,002 --> 00:04:38,008
"AAG, AAT, ATA."

90
00:04:38,965 --> 00:04:41,032
To you it sounds like mute letters,

91
00:04:41,056 --> 00:04:45,097
but this sequence gives
the color of the eyes to Craig.

92
00:04:45,633 --> 00:04:47,565
I'll show you another part of the book.

93
00:04:47,589 --> 00:04:49,683
This is actually a little
more complicated.

94
00:04:50,983 --> 00:04:53,630
Chromosome 14, book 132:

95
00:04:53,654 --> 00:04:55,744
(Laughter)

96
00:04:55,768 --> 00:04:57,045
As you might expect.

97
00:04:57,069 --> 00:05:00,535
(Laughter)

98
00:05:02,857 --> 00:05:07,364
"ATT, CTT, GATT."

99
00:05:08,329 --> 00:05:10,016
This human is lucky,

100
00:05:10,040 --> 00:05:14,557
because if you miss just
two letters in this position --

101
00:05:14,581 --> 00:05:16,458
two letters of our three billion --

102
00:05:16,482 --> 00:05:18,501
he will be condemned
to a terrible disease:

103
00:05:18,525 --> 00:05:19,965
cystic fibrosis.

104
00:05:19,989 --> 00:05:23,402
We have no cure for it,
we don't know how to solve it,

105
00:05:23,426 --> 00:05:27,181
and it's just two letters
of difference from what we are.

106
00:05:27,585 --> 00:05:30,290
A wonderful book, a mighty book,

107
00:05:31,115 --> 00:05:33,113
a mighty book that helped me understand

108
00:05:33,137 --> 00:05:35,890
and show you something quite remarkable.

109
00:05:36,480 --> 00:05:40,915
Every one of you -- what makes
me, me and you, you --

110
00:05:40,939 --> 00:05:43,893
is just about five million of these,

111
00:05:43,917 --> 00:05:45,145
half a book.

112
00:05:46,015 --> 00:05:47,678
For the rest,

113
00:05:47,702 --> 00:05:50,264
we are all absolutely identical.

114
00:05:51,008 --> 00:05:55,026
Five hundred pages
is the miracle of life that you are.

115
00:05:55,050 --> 00:05:57,581
The rest, we all share it.

116
00:05:57,605 --> 00:06:00,514
So think about that again
when we think that we are different.

117
00:06:00,538 --> 00:06:02,759
This is the amount that we share.

118
00:06:03,441 --> 00:06:06,870
So now that I have your attention,

119
00:06:06,894 --> 00:06:08,253
the next question is:

120
00:06:08,277 --> 00:06:09,428
How do I read it?

121
00:06:09,452 --> 00:06:10,961
How do I make sense out of it?

122
00:06:11,409 --> 00:06:15,649
Well, for however good you can be
at assembling Swedish furniture,

123
00:06:15,673 --> 00:06:19,236
this instruction manual
is nothing you can crack in your life.

124
00:06:19,260 --> 00:06:20,863
(Laughter)

125
00:06:20,887 --> 00:06:23,999
And so, in 2014, two famous TEDsters,

126
00:06:24,023 --> 00:06:26,563
Peter Diamandis and Craig Venter himself,

127
00:06:26,587 --> 00:06:28,514
decided to assemble a new company.

128
00:06:28,538 --> 00:06:29,950
Human Longevity was born,

129
00:06:29,974 --> 00:06:31,344
with one mission:

130
00:06:31,368 --> 00:06:33,229
trying everything we can try

131
00:06:33,253 --> 00:06:36,012
and learning everything
we can learn from these books,

132
00:06:36,036 --> 00:06:37,741
with one target --

133
00:06:38,862 --> 00:06:41,663
making real the dream
of personalized medicine,

134
00:06:41,687 --> 00:06:45,454
understanding what things
should be done to have better health

135
00:06:45,478 --> 00:06:47,761
and what are the secrets in these books.

136
00:06:48,329 --> 00:06:52,579
An amazing team, 40 data scientists
and many, many more people,

137
00:06:52,603 --> 00:06:53,953
a pleasure to work with.

138
00:06:53,977 --> 00:06:56,230
The concept is actually very simple.

139
00:06:56,254 --> 00:06:59,412
We're going to use a technology
called machine learning.

140
00:06:59,436 --> 00:07:03,975
On one side, we have genomes --
thousands of them.

141
00:07:03,999 --> 00:07:07,996
On the other side, we collected
the biggest database of human beings:

142
00:07:08,020 --> 00:07:12,316
phenotypes, 3D scan, NMR --
everything you can think of.

143
00:07:12,340 --> 00:07:15,239
Inside there, on these two opposite sides,

144
00:07:15,263 --> 00:07:17,705
there is the secret of translation.

145
00:07:17,729 --> 00:07:20,201
And in the middle, we build a machine.

146
00:07:20,801 --> 00:07:23,186
We build a machine
and we train a machine --

147
00:07:23,210 --> 00:07:26,420
well, not exactly one machine,
many, many machines --

148
00:07:26,444 --> 00:07:30,988
to try to understand and translate
the genome in a phenotype.

149
00:07:31,362 --> 00:07:34,702
What are those letters,
and what do they do?

150
00:07:34,726 --> 00:07:37,473
It's an approach that can
be used for everything,

151
00:07:37,497 --> 00:07:40,490
but using it in genomics
is particularly complicated.

152
00:07:40,514 --> 00:07:43,790
Little by little we grew and we wanted
to build different challenges.

153
00:07:43,814 --> 00:07:46,546
We started from the beginning,
from common traits.

154
00:07:46,570 --> 00:07:49,173
Common traits are comfortable
because they are common,

155
00:07:49,197 --> 00:07:50,381
everyone has them.

156
00:07:50,405 --> 00:07:52,899
So we started to ask our questions:

157
00:07:52,923 --> 00:07:54,303
Can we predict height?

158
00:07:54,985 --> 00:07:57,162
Can we read the books
and predict your height?

159
00:07:57,186 --> 00:07:58,337
Well, we actually can,

160
00:07:58,361 --> 00:08:00,154
with five centimeters of precision.

161
00:08:00,178 --> 00:08:03,313
BMI is fairly connected to your lifestyle,

162
00:08:03,337 --> 00:08:07,201
but we still can, we get in the ballpark,
eight kilograms of precision.

163
00:08:07,225 --> 00:08:08,456
Can we predict eye color?

164
00:08:08,480 --> 00:08:09,638
Yeah, we can.

165
00:08:09,662 --> 00:08:10,986
Eighty percent accuracy.

166
00:08:11,466 --> 00:08:13,324
Can we predict skin color?

167
00:08:13,348 --> 00:08:15,789
Yeah we can, 80 percent accuracy.

168
00:08:15,813 --> 00:08:17,153
Can we predict age?

169
00:08:18,121 --> 00:08:21,860
We can, because apparently,
the code changes during your life.

170
00:08:21,884 --> 00:08:25,166
It gets shorter, you lose pieces,
it gets insertions.

171
00:08:25,190 --> 00:08:27,745
We read the signals, and we make a model.

172
00:08:28,438 --> 00:08:29,913
Now, an interesting challenge:

173
00:08:29,937 --> 00:08:31,666
Can we predict a human face?

174
00:08:33,014 --> 00:08:34,292
It's a little complicated,

175
00:08:34,316 --> 00:08:37,507
because a human face is scattered
among millions of these letters.

176
00:08:37,531 --> 00:08:40,160
And a human face is not
a very well-defined object.

177
00:08:40,184 --> 00:08:42,235
So, we had to build an entire tier of it

178
00:08:42,259 --> 00:08:44,969
to learn and teach
a machine what a face is,

179
00:08:44,993 --> 00:08:47,030
and embed and compress it.

180
00:08:47,054 --> 00:08:49,302
And if you're comfortable
with machine learning,

181
00:08:49,326 --> 00:08:51,610
you understand what the challenge is here.

182
00:08:52,108 --> 00:08:58,099
Now, after 15 years -- 15 years after
we read the first sequence --

183
00:08:58,123 --> 00:09:01,025
this October, we started
to see some signals.

184
00:09:01,049 --> 00:09:03,504
And it was a very emotional moment.

185
00:09:03,528 --> 00:09:07,273
What you see here is a subject
coming in our lab.

186
00:09:07,619 --> 00:09:09,547
This is a face for us.

187
00:09:09,571 --> 00:09:13,202
So we take the real face of a subject,
we reduce the complexity,

188
00:09:13,226 --> 00:09:15,196
because not everything is in your face --

189
00:09:15,220 --> 00:09:19,006
lots of features and defects
and asymmetries come from your life.

190
00:09:19,030 --> 00:09:22,499
We symmetrize the face,
and we run our algorithm.

191
00:09:23,245 --> 00:09:25,143
The results that I show you right now,

192
00:09:25,167 --> 00:09:28,539
this is the prediction we have
from the blood.

193
00:09:29,596 --> 00:09:31,120
(Applause)

194
00:09:31,144 --> 00:09:32,579
Wait a second.

195
00:09:32,603 --> 00:09:37,295
In these seconds, your eyes are watching,
left and right, left and right,

196
00:09:37,319 --> 00:09:41,249
and your brain wants
those pictures to be identical.

197
00:09:41,273 --> 00:09:43,719
So I ask you to do
another exercise, to be honest.

198
00:09:43,743 --> 00:09:46,030
Please search for the differences,

199
00:09:46,054 --> 00:09:47,415
which are many.

200
00:09:47,439 --> 00:09:50,042
The biggest amount of signal
comes from gender,

201
00:09:50,066 --> 00:09:55,267
then there is age, BMI,
the ethnicity component of a human.

202
00:09:55,291 --> 00:09:59,002
And scaling up over that signal
is much more complicated.

203
00:09:59,026 --> 00:10:02,276
But what you see here,
even in the differences,

204
00:10:02,300 --> 00:10:05,895
lets you understand
that we are in the right ballpark,

205
00:10:05,919 --> 00:10:07,267
that we are getting closer.

206
00:10:07,291 --> 00:10:09,640
And it's already giving you some emotions.

207
00:10:09,664 --> 00:10:12,367
This is another subject
that comes in place,

208
00:10:12,391 --> 00:10:13,800
and this is a prediction.

209
00:10:13,824 --> 00:10:18,420
A little smaller face, we didn't get
the complete cranial structure,

210
00:10:18,444 --> 00:10:21,095
but still, it's in the ballpark.

211
00:10:21,634 --> 00:10:23,858
This is a subject that comes in our lab,

212
00:10:23,882 --> 00:10:25,325
and this is the prediction.

213
00:10:26,056 --> 00:10:30,732
So these people have never been seen
in the training of the machine.

214
00:10:30,756 --> 00:10:33,593
These are the so-called "held-out" set.

215
00:10:33,617 --> 00:10:37,357
But these are people that you will
probably never believe.

216
00:10:37,381 --> 00:10:40,057
We're publishing everything
in a scientific publication,

217
00:10:40,081 --> 00:10:41,232
you can read it.

218
00:10:41,256 --> 00:10:43,600
But since we are onstage,
Chris challenged me.

219
00:10:43,624 --> 00:10:47,250
I probably exposed myself
and tried to predict

220
00:10:47,274 --> 00:10:50,105
someone that you might recognize.

221
00:10:50,470 --> 00:10:54,895
So, in this vial of blood --
and believe me, you have no idea

222
00:10:54,919 --> 00:10:57,799
what we had to do to have
this blood now, here --

223
00:10:57,823 --> 00:11:01,724
in this vial of blood is the amount
of biological information

224
00:11:01,748 --> 00:11:04,025
that we need to do a full genome sequence.

225
00:11:04,049 --> 00:11:06,119
We just need this amount.

226
00:11:06,528 --> 00:11:09,733
We ran this sequence,
and I'm going to do it with you.

227
00:11:09,757 --> 00:11:13,736
And we start to layer up
all the understanding we have.

228
00:11:13,760 --> 00:11:17,110
In the vial of blood,
we predicted he's a male.

229
00:11:17,134 --> 00:11:18,498
And the subject is a male.

230
00:11:18,996 --> 00:11:21,434
We predict that he's a meter and 76 cm.

231
00:11:21,458 --> 00:11:23,850
The subject is a meter and 77 cm.

232
00:11:23,874 --> 00:11:27,984
So, we predicted that he's 76;
the subject is 82.

233
00:11:28,701 --> 00:11:31,333
We predict his age, 38.

234
00:11:31,357 --> 00:11:33,261
The subject is 35.

235
00:11:33,851 --> 00:11:35,975
We predict his eye color.

236
00:11:36,824 --> 00:11:38,035
Too dark.

237
00:11:38,059 --> 00:11:39,614
We predict his skin color.

238
00:11:40,026 --> 00:11:41,436
We are almost there.

239
00:11:41,899 --> 00:11:43,272
That's his face.

240
00:11:45,172 --> 00:11:48,441
Now, the reveal moment:

241
00:11:48,465 --> 00:11:50,235
the subject is this person.

242
00:11:50,259 --> 00:11:52,194
(Laughter)

243
00:11:52,218 --> 00:11:54,276
And I did it intentionally.

244
00:11:54,300 --> 00:11:57,992
I am a very particular
and peculiar ethnicity.

245
00:11:58,016 --> 00:12:00,966
Southern European, Italians --
they never fit in models.

246
00:12:00,990 --> 00:12:06,120
And it's particular -- that ethnicity
is a complex corner case for our model.

247
00:12:06,144 --> 00:12:07,653
But there is another point.

248
00:12:07,677 --> 00:12:11,154
So, one of the things that we use
a lot to recognize people

249
00:12:11,178 --> 00:12:12,900
will never be written in the genome.

250
00:12:12,924 --> 00:12:15,241
It's our free will, it's how I look.

251
00:12:15,265 --> 00:12:18,494
Not my haircut in this case,
but my beard cut.

252
00:12:18,518 --> 00:12:22,071
So I'm going to show you, I'm going to,
in this case, transfer it --

253
00:12:22,095 --> 00:12:24,860
and this is nothing more
than Photoshop, no modeling --

254
00:12:24,884 --> 00:12:26,597
the beard on the subject.

255
00:12:26,621 --> 00:12:30,093
And immediately, we get
much, much better in the feeling.

256
00:12:30,955 --> 00:12:33,664
So, why do we do this?

257
00:12:35,938 --> 00:12:41,078
We certainly don't do it
for predicting height

258
00:12:41,102 --> 00:12:43,474
or taking a beautiful picture
out of your blood.

259
00:12:44,390 --> 00:12:48,408
We do it because the same technology
and the same approach,

260
00:12:48,432 --> 00:12:50,952
the machine learning of this code,

261
00:12:50,976 --> 00:12:54,113
is helping us to understand how we work,

262
00:12:54,137 --> 00:12:55,623
how your body works,

263
00:12:55,647 --> 00:12:57,312
how your body ages,

264
00:12:57,336 --> 00:13:00,105
how disease generates in your body,

265
00:13:00,129 --> 00:13:03,101
how your cancer grows and develops,

266
00:13:03,125 --> 00:13:04,908
how drugs work

267
00:13:04,932 --> 00:13:07,246
and if they work on your body.

268
00:13:07,713 --> 00:13:09,380
This is a huge challenge.

269
00:13:09,894 --> 00:13:11,532
This is a challenge that we share

270
00:13:11,556 --> 00:13:14,135
with thousands of other
researchers around the world.

271
00:13:14,159 --> 00:13:16,381
It's called personalized medicine.

272
00:13:17,125 --> 00:13:20,585
It's the ability to move
from a statistical approach

273
00:13:20,609 --> 00:13:22,641
where you're a dot in the ocean,

274
00:13:22,665 --> 00:13:24,478
to a personalized approach,

275
00:13:24,502 --> 00:13:26,687
where we read all these books

276
00:13:26,711 --> 00:13:29,575
and we get an understanding
of exactly how you are.

277
00:13:30,260 --> 00:13:33,622
But it is a particularly
complicated challenge,

278
00:13:33,646 --> 00:13:37,644
because of all these books, as of today,

279
00:13:37,668 --> 00:13:40,310
we just know probably two percent:

280
00:13:41,027 --> 00:13:44,680
four books of more than 175.

281
00:13:46,021 --> 00:13:49,227
And this is not the topic of my talk,

282
00:13:50,145 --> 00:13:52,743
because we will learn more.

283
00:13:53,378 --> 00:13:56,047
There are the best minds
in the world on this topic.

284
00:13:57,048 --> 00:13:58,882
The prediction will get better,

285
00:13:58,906 --> 00:14:01,159
the model will get more precise.

286
00:14:01,183 --> 00:14:03,041
And the more we learn,

287
00:14:03,065 --> 00:14:07,895
the more we will
be confronted with decisions

288
00:14:07,919 --> 00:14:10,940
that we never had to face before

289
00:14:10,964 --> 00:14:12,399
about life,

290
00:14:12,423 --> 00:14:14,097
about death,

291
00:14:14,121 --> 00:14:15,724
about parenting.

292
00:14:20,626 --> 00:14:25,372
So, we are touching the very
inner detail on how life works.

293
00:14:26,118 --> 00:14:29,276
And it's a revolution
that cannot be confined

294
00:14:29,300 --> 00:14:31,959
in the domain of science or technology.

295
00:14:32,960 --> 00:14:35,204
This must be a global conversation.

296
00:14:35,798 --> 00:14:41,015
We must start to think of the future
we're building as a humanity.

297
00:14:41,039 --> 00:14:45,103
We need to interact with creatives,
with artists, with philosophers,

298
00:14:45,127 --> 00:14:46,637
with politicians.

299
00:14:46,661 --> 00:14:47,819
Everyone is involved,

300
00:14:47,843 --> 00:14:50,668
because it's the future of our species.

301
00:14:51,273 --> 00:14:55,241
Without fear, but with the understanding

302
00:14:55,265 --> 00:14:59,136
that the decisions
that we make in the next year

303
00:14:59,160 --> 00:15:02,949
will change the course of history forever.

304
00:15:03,732 --> 00:15:04,892
Thank you.

305
00:15:04,916 --> 00:15:15,075
(Applause)