How we use astrophysics to study earthbound problems | Federica Bianco

40,126 views ใƒป 2019-10-10

TED


ืื ื ืœื—ืฅ ืคืขืžื™ื™ื ืขืœ ื”ื›ืชื•ื‘ื™ื•ืช ื‘ืื ื’ืœื™ืช ืœืžื˜ื” ื›ื“ื™ ืœื”ืคืขื™ืœ ืืช ื”ืกืจื˜ื•ืŸ.

ืชืจื’ื•ื: Tal Hemu ืขืจื™ื›ื”: hila scherba
00:13
I am an astrophysicist.
0
13317
2492
ืื ื™ ืืกื˜ืจื•ืคื™ื–ื™ืงืื™ืช.
00:15
I research stellar explosions across the universe.
1
15833
3055
ืื ื™ ื—ื•ืงืจืช ืคื™ืฆื•ืฆื™ื ื›ื•ื›ื‘ื™ื™ื ื‘ืจื—ื‘ื™ ื”ื™ืงื•ื.
00:19
But I have a flaw:
2
19603
1200
ืื‘ืœ ื™ืฉ ื‘ื™ ืคื’ื:
00:21
I'm restless, and I get bored easily.
3
21325
2483
ืื ื™ ื—ืกืจืช ืžื ื•ื—ื” ื•ืื ื™ ืžืฉืชืขืžืžืช ื‘ืงืœื•ืช.
00:24
And although as an astrophysicist, I have the incredible opportunity
4
24356
3196
ื•ืœืžืจื•ืช ืฉื‘ืชื•ืจ ืืกื˜ืจื•ืคื™ื–ื™ืงืื™ืช ื™ืฉ ืœื™ ืืช ื”ื”ื–ื“ืžื ื•ืช ื”ืžื“ื”ื™ืžื”
00:27
to study the entire universe,
5
27576
1932
ืœืœืžื•ื“ ืืช ื”ื™ืงื•ื ื›ื•ืœื•,
00:29
the thought of doing only that, always that,
6
29532
3348
ื”ืžื—ืฉื‘ื” ืœืขืฉื•ืช ืจืง ืืช ื–ื”, ื›ืœ ื”ื–ืžืŸ ืืช ื–ื”,
00:32
makes me feel caged and limited.
7
32904
2231
ื’ื•ืจืžืช ืœื™ ืœื”ืจื’ื™ืฉ ื›ืœื•ืื” ื•ืžื•ื’ื‘ืœืช.
00:36
What if my issues with keeping attention and getting bored
8
36762
3914
ืžื” ืื ื”ื‘ืขื™ื•ืช ืฉื™ืฉ ืœื™ ื‘ืœื”ื™ืฉืืจ ืžืจื•ื›ื–ืช ื•ืœื ืœื”ืฉืชืขืžื
00:40
were not a flaw, though?
9
40700
1482
ืœื ื”ื™ื• ืคื’ื ืื—ืจื™ ื”ื›ืœ?
00:42
What if I could turn them into an asset?
10
42206
2667
ืžื” ืื ื™ื›ื•ืœืชื™ ืœื”ืคื•ืš ืื•ืชืŸ ืœื ื›ืก?
00:45
An astrophysicist cannot touch or interact with
11
45830
2645
ืืกื˜ืจื•ืคื™ื–ื™ืงืื™ ืœื ื™ื›ื•ืœ ืœื’ืขืช ืื• ืœืชืงืฉืจ
00:48
the things that she studies.
12
48499
1538
ืขื ื”ื“ื‘ืจื™ื ืฉื”ื•ื ืœื•ืžื“.
00:50
No way to explode a star in a lab to figure out why or how it blew up.
13
50061
3713
ืื™ืŸ ื“ืจืš ืœื—ืงื•ืจ ื›ื•ื›ื‘ ื‘ืžืขื‘ื“ื” ื‘ืฉื‘ื™ืœ ืœืœืžื•ื“ ืžื“ื•ืข ืื• ืื™ืš ื”ื•ื ื”ืชืคื•ืฆืฅ.
00:54
Just pictures and movies of the sky.
14
54164
2530
ืจืง ืชืžื•ื ื•ืช ื•ืกืจื˜ื™ื ืฉืœ ื”ืฉืžื™ื™ื.
00:57
Everything we know about the universe,
15
57339
2221
ื›ืœ ืžื” ืฉืื ื• ื™ื•ื“ืขื™ื ืขืœ ื”ื™ืงื•ื,
00:59
from the big bang that originated space and time,
16
59584
3091
ื”ื—ืœ ืžื”ืžืคืฅ ื”ื’ื“ื•ืœ ืฉื”ื•ืœื™ื“ ืืช ื”ืžืจื—ื‘ ื•ื”ื–ืžืŸ,
01:02
to the formation and evolution of stars and galaxies,
17
62699
2794
ื”ื™ื•ื•ืฆืจื•ืช ื•ืื‘ื•ืœื•ืฆื™ืช ื”ื›ื•ื›ื‘ื™ื ื•ื”ื’ืœืืงืกื™ื•ืช,
01:05
to the structure of our own solar system,
18
65517
2460
ื•ืขื“ ืœืžื‘ื ื” ืฉืœ ืžืขืจื›ืช ื”ืฉืžืฉ ืฉืœื ื•,
01:08
we figured out studying images of the sky.
19
68001
2800
ื›ืœ ื–ืืช ื”ืกืงื ื• ืžืชืžื•ื ื•ืช ืฉืœ ื”ืฉืžื™ื™ื.
01:12
And to study a system as complex as the entire universe,
20
72006
3952
ื•ื‘ืฉื‘ื™ืœ ืœืœืžื•ื“ ืžืขืจื›ืช ืžื•ืจื›ื‘ืช ื›ืžื• ื–ื• ืฉืœ ื”ื™ืงื•ื ื›ื•ืœื•,
01:15
astrophysicists are experts at extracting simple models and solutions
21
75982
4705
ืืกื˜ืจื•ืคื™ื–ื™ืงืื™ื ืžื•ืžื—ื™ื ื‘ืœื—ืœืฅ ื“ื’ืžื™ื ืคืฉื•ื˜ื™ื ื•ืคืชืจื•ื ื•ืช
01:20
from large and complex data sets.
22
80711
2339
ืžืชื•ืš ืžื‘ื ื™ ื ืชื•ื ื™ื ืขื ืงื™ื™ื ื•ืžื•ืจื›ื‘ื™ื.
01:23
So what else can I do with this expertise?
23
83705
2425
ืื ื›ืš, ืžื” ืขื•ื“ ืื ื™ ืžืกื•ื’ืœืช ืœืขืฉื•ืช ืขื ื”ืžื•ืžื—ื™ื•ืช ื”ื–ื•?
01:28
What if we turned the camera around towards us?
24
88030
4095
ืžื” ืื ื ืกื•ื‘ื‘ ืืช ื”ืžืฆืœืžื” ืœื›ื™ื•ื•ื ื ื•?
01:33
At the Urban Observatory, that is exactly what we are doing.
25
93057
2991
ื‘"ืžืฆืคื” ื”ื›ื•ื›ื‘ื™ื ื”ืขื™ืจื•ื ื™" ื–ื” ื‘ื“ื™ื•ืง ืžื” ืฉืื ื• ืขื•ืฉื™ื.
01:36
Greg Dobler, also an astrophysicist
26
96072
2492
ื’ืจื’ ื“ื•ื‘ืœืจ, ืืกื˜ืจื•ืคื™ื–ืงืื™ ื’ื ื›ืŸ,
01:38
and my husband,
27
98588
1167
ื•ื‘ืขืœื™,
01:39
created the first urban observatory in New York University in 2013,
28
99779
3960
ื™ืฆืจ ืืช ืžืฆืคื” ื”ื›ื•ื›ื‘ื™ื ื”ืขื™ืจื•ื ื™ ื”ืจืืฉื•ืŸ ื‘ืื•ื ื™ื‘ืจืกื™ื˜ืช ื ื™ื•-ื™ื•ืจืง ื‘-2013,
01:43
and I joined in 2015.
29
103763
1556
ื•ืื ื™ ื”ืฆื˜ืจืคืชื™ ื‘-2015.
01:45
Here are some of the things that we do.
30
105752
1929
ื”ื ื” ื›ืžื” ืžืŸ ื”ื“ื‘ืจื™ื ืฉืื ื• ืขื•ืฉื™ื.
01:48
We take pictures of the city at night
31
108196
2270
ืื ื—ื ื• ืžืฆืœืžื™ื ืชืžื•ื ื•ืช ืฉืœ ื”ืขื™ืจ ื‘ืœื™ืœื”
01:50
and study city lights like stars.
32
110490
2589
ื•ื—ื•ืงืจื™ื ืืช ืื•ืจื•ืช ื”ืขื™ืจ ื›ืžื• ื›ื•ื›ื‘ื™ื.
01:53
By studying how light changes over time
33
113514
2012
ื”ื•ื“ื•ืช ืœื›ืš ืฉืื ื• ืœื•ืžื“ื™ื ื›ื™ืฆื“ ืื•ืจ ืžืฉืชื ื” ืœืื•ืจืš ื–ืžืŸ
01:55
and the color of astronomical lights,
34
115550
2074
ื•ืืช ืฆื‘ืขื ืฉืœ ืื•ืจื•ืช ืืกื˜ืจื•ื ื•ืžื™ื™ื,
01:57
I gain insight about the nature of exploding stars.
35
117648
2813
ืื ื™ ืžืฉื™ื’ื” ืชื•ื‘ื ื•ืช ืขืœ ื˜ื‘ืขื ืฉืœ ื›ื•ื›ื‘ื™ื ืžืชืคื•ืฆืฆื™ื.
02:00
By studying city lights the same way,
36
120934
2270
ื‘ืืžืฆืขื•ืช ืœืžื™ื“ื” ืฉืœ ืื•ืจื•ืช ื”ืขื™ืจ ื‘ืื•ืชื” ื”ื“ืจืš,
02:03
we can measure and predict how much energy the city needs and consumes
37
123228
4682
ืื ื• ื™ื›ื•ืœื™ื ืœืžื“ื•ื“ ื•ืœืฆืคื•ืช ื›ืžื” ืื ืจื’ื™ื” ื”ืขื™ืจ ื–ืงื•ืงื” ื•ืฆื•ืจื›ืช
02:07
and help build a resilient grid
38
127934
1847
ื•ืœืขื–ื•ืจ ืœื‘ื ื•ืช ืจืฉืช ืขืžื™ื“ื”
02:09
that will support the needs of growing urban environments.
39
129805
3299
ืฉืชืชืžื•ืš ื‘ืฆืจื›ื™ื ืฉืœ ืกื‘ื™ื‘ื•ืช ืขื™ืจื•ื ื™ื•ืช ืžืชืคืชื—ื•ืช.
02:14
In daytime images, we capture plumes of pollution.
40
134283
3357
ื‘ืชืžื•ื ื•ืช ืฉืœ ืื•ืจ ื™ื•ื, ืื ื• ืœื•ื›ื“ื™ื ืขืžื•ื“ื™ ืขืฉืŸ ืฉืœ ื–ื™ื”ื•ื.
02:18
Seventy-five percent of greenhouse gases in New York City
41
138274
3470
ืฉื‘ืขื™ื ื•ื—ืžื™ืฉื” ืื—ื•ื– ืžื’ื–ื™ ื”ื—ืžืžื” ื‘ืขื™ืจ ื ื™ื•-ื™ื•ืจืง
02:21
come from a building like this one, burning oil for heat.
42
141768
3632
ืžื’ื™ืขื™ื ืžื‘ื ื™ื™ื ื™ื ื›ืžื• ื–ื”, ื”ืฉื•ืจืคื™ื ืฉืžืŸ ืœื—ื™ืžื•ื.
02:26
You can measure pollution with air quality sensors.
43
146477
2395
ื ื™ืชืŸ ืœืžื“ื•ื“ ื–ื™ื”ื•ื ื‘ืืžืฆืขื•ืช ื—ื™ื™ืฉื ื™ ืื™ื›ื•ืช ืื•ื•ื™ืจ.
02:28
But imagine putting a sensor on each New York City building,
44
148896
3842
ืืš ืชืชืืจื• ืœื›ื ื”ืชืงื ืช ื—ื™ื™ืฉืŸ ืขืœ ื›ืœ ื‘ื ื™ื™ืŸ ื‘ื ื™ื•-ื™ื•ืจืง,
02:32
reading in data from a million monitors.
45
152762
2706
ืงืจื™ืื” ืฉืœ ืžื™ื“ืข ืžืžืœื™ื•ื ื™ ื—ื™ื™ืฉื ื™ื.
02:35
Imagine the cost.
46
155492
1326
ืชืชืืจื• ืœื›ื ืืช ื”ืขืœื•ื™ื•ืช.
02:38
With a team of NYU students, we built a mathematical model,
47
158048
3428
ื‘ืขื–ืจืชื” ืฉืœ ืงื‘ื•ืฆื” ืžืื•ื ื™ื‘ืจืกื™ื˜ืช ื ื™ื•-ื™ื•ืจืง ื‘ื ื™ื ื• ื“ื’ื ืžืชืžื˜ื™,
02:41
a neural network that can detect and track these plumes
48
161500
3389
ืจืฉืช ืขืฆื‘ื™ืช ื”ื™ื›ื•ืœื” ืœื–ื”ื•ืช ื•ืœืขืงื•ื‘ ืื—ืจ ืขืžื•ื“ื™ ื”ืขืฉืŸ ื”ืืœื•
02:44
over the New York City skyline.
49
164913
1690
ืžืขืœ ืงื• ื”ืจืงื™ืข ืฉืœ ื ื™ื•-ื™ื•ืจืง.
02:46
We can classify them --
50
166627
1482
ืื ื• ื™ื›ื•ืœื™ื ืœืกื•ื•ื’ ืื•ืชื --
02:48
harmless steam plumes, white and evanescent;
51
168133
3015
ืขืžื•ื“ื™ ืื“ื™ื ืœื ืžื–ื™ืงื™ื - ืœื‘ื ื™ื ื•ื—ื•ืœืคื™ื;
02:51
polluting smokestacks, dark and persistent --
52
171172
3516
ืืจื•ื‘ื•ืช ืžื–ื”ืžื•ืช - ืฉื—ื•ืจื•ืช ื•ืขืงื‘ื™ื•ืช;
02:54
and provide policy makers with a map of neighborhood pollution.
53
174712
3759
ื•ืœืกืคืง ืœืงื•ื‘ืขื™ ื”ืžื™ื“ื™ื ื™ื•ืช ืžืคื” ืฉืœ ืฉื›ื•ื ื•ืช ืžื–ื”ืžื•ืช.
02:59
This cross-disciplinary project created transformational solutions.
54
179777
3889
ื”ืคืจื•ื™ื™ืงื˜ ื—ื•ืฆื”-ื”ืชื—ื•ืžื™ ื”ื–ื” ื™ืฆืจ ืคืชืจื•ื ื•ืช ืžืฉื ื™ ืขื•ืœื.
03:05
But the data analysis methodologies we use in astrophysics
55
185942
2913
ืืš ื”ืฉื™ื˜ื•ืช ืœื ื™ืชื•ื— ื ืชื•ื ื™ื ื‘ื”ืŸ ืื ื• ืžืฉืชืžืฉื™ื ื‘ืืกื˜ืจื•ืคื™ื–ื™ืงื”
03:08
can be applied to all sorts of data,
56
188879
1961
ื™ื›ื•ืœื•ืช ืœืฉืžืฉ ื‘ื›ืœ ืกื•ื’ื™ ื”ืžื™ื“ืข,
03:10
not just images.
57
190864
1150
ืœื ืจืง ืชืžื•ื ื•ืช.
03:12
We were asked to help a California district attorney
58
192450
2484
ื”ืชื‘ืงืฉื ื• ืœืขื–ื•ืจ ืœืคืจืงืœื™ื˜ ืžื—ื•ื– ืงืœื™ืคื•ืจื ื™ื”
03:14
understand prosecutorial delays in their jurisdiction.
59
194958
3421
ืœื”ื‘ื™ืŸ ืขื™ื›ื•ื‘ื™ ืชื‘ื™ืขื” ื‘ืชื—ื•ื ื”ืฉื™ืคื•ื˜ ืฉืœื”ื.
03:18
There are people on probation or sitting in jail,
60
198839
2667
ื™ืฉื ื ืื ืฉื™ื ื‘ืชืงื•ืคืช ืžื‘ื—ืŸ ืื• ื™ื•ืฉื‘ื™ื ื‘ื›ืœื,
03:21
awaiting for trial sometimes for years.
61
201530
2611
ืฉืœืขื™ืชื™ื ืžื—ื›ื™ื ืœืžืฉืคื˜ ื‘ืžืฉืš ืฉื ื™ื.
03:24
They wanted to know what kind of cases dragged on,
62
204165
2444
ื”ื ืจืฆื• ืœื“ืขืช ืื™ืœื• ืกื•ื’ื™ ืชื™ืงื™ื ื ื’ืจืจื™ื,
03:26
and they had a massive data set to explore to understand it,
63
206633
3124
ื•ื”ื™ื” ืœื”ื ื›ืžื•ื™ื•ืช ืื“ื™ืจื•ืช ืฉืœ ืžื™ื“ืข ืœื—ืงื•ืจ ื•ืœื”ื‘ื™ืŸ,
03:29
but didn't have the expertise
64
209781
1412
ืืš ืœื ื”ื™ื™ืชื” ืœื”ื ืืช ื”ืžื•ืžื—ื™ื•ืช
03:31
or the instruments in their office to do so.
65
211217
2548
ืื• ืืช ื”ื›ืœื™ื ื‘ืžืฉืจื“ื ืœืขืฉื•ืช ืืช ื–ื”.
03:33
And that's where we came in.
66
213789
1746
ื•ื›ืืŸ ืื ื—ื ื• ื ื›ื ืกื ื• ืœืชืžื•ื ื”.
03:35
I worked with my colleague, public policy professor Angela Hawken,
67
215559
3397
ืขื‘ื“ืชื™ ืขื ื”ืงื•ืœื’ื” ืฉืœื™, ืคืจื•ืคืกื•ืจ ืœืžื“ื™ื ื™ื•ืช ืฆื™ื‘ื•ืจื™ืช, ืื ื’'ืœื” ื”ื•ืงืŸ,
03:38
and our team first created a visual dashboard
68
218980
3486
ื•ื”ืฆื•ื•ืช ืฉืœื ื• ื”ื›ื™ืŸ ืชื—ื™ืœื” ืœื•ื— ื•ื™ื–ื•ืืœื™,
03:42
for DAs to see and better understand the prosecution process.
69
222490
3900
ืขืœ ืžื ืช ืฉืชื•ื‘ืขื™ื ืžื—ื•ื–ื™ื™ื ื™ื‘ื™ื ื• ื˜ื•ื‘ ื™ื•ืชืจ ืืช ืชื”ืœื™ืš ื”ืชื‘ื™ืขื”.
03:46
But also, we ourselves analyzed their data,
70
226997
2929
ืืš ื’ื, ืื ื• ืขืฆืžื ื• ื ื™ืชื—ื ื• ืืช ื”ืžื™ื“ืข,
03:49
looking to see if the duration of the process
71
229950
2520
ืขืœ-ืžื ืช ืœืจืื•ืช ื”ืื ืื•ืจืš ื”ืชื”ืœื™ืš
03:52
suffered from social inequalities in their jurisdiction.
72
232494
3190
ืกื‘ืœ ืžืื™-ืฉื™ื•ื•ื™ื•ืŸ ื—ื‘ืจืชื™ ื‘ืฉื™ืคื•ื˜ ืฉืœื”ื.
03:56
We did so using methods
73
236367
1542
ืขืฉื™ื ื• ื–ืืช ื‘ืืžืฆืขื•ืช ืฉื™ื˜ื•ืช
03:57
that I would use to classify thousands of stellar explosions,
74
237933
2973
ืฉืื ื™ ื”ืฉืชืžืฉืชื™ ื‘ื”ืŸ ื›ื“ื™ ืœืกื•ื•ื’ ืืœืคื™ ืคื™ืฆื•ืฆื™ื ื›ื•ื›ื‘ื™ื™ื,
04:00
applied to thousands of court cases.
75
240930
2655
ืฉืขื›ืฉื™ื• ืžื™ื•ืฉืžื•ืช ืขืœ ืืœืคื™ ืชื™ืงื™ื ืžืฉืคื˜ื™ื™ื.
04:03
And in doing so,
76
243609
1151
ื•ื‘ื›ืš ืฉืขืฉื™ื ื• ื–ืืช,
04:04
we built a model that can be applied to other jurisdictions
77
244784
2833
ื‘ื ื™ื ื• ื“ื’ื ื”ื™ื›ื•ืœ ืœืฉืžืฉ ืื™ื–ื•ืจื™ ืฉื™ืคื•ื˜ ื ื•ืกืคื™ื
04:07
who are willing to explore their biases.
78
247641
2190
ื”ืžืขื•ื ื™ื™ื ื™ื ืœื—ืงื•ืจ ืืช ื”ื ื˜ื™ื•ืช ืฉืœื”ืŸ.
04:09
These collaborations between domain experts and astrophysicists
79
249855
3246
ืฉื™ืชื•ืคื™ ื”ืคืขื•ืœื” ื”ืืœื• ื‘ื™ืŸ ืžื•ืžื—ื™ื ื‘ืชื—ื•ื ืœืืกื˜ืจื•ืคื™ื–ื™ืงืื™ื
04:13
created transformational solutions
80
253125
2055
ื™ืฆืจื• ืคืชืจื•ื ื•ืช ืžืฉื ื™ ืกื“ืจื™-ืขื•ืœื
04:15
to help improve people's quality of life.
81
255204
2400
ืขืœ-ืžื ืช ืœืฉืคืจ ืืช ืื™ื›ื•ืช ื—ื™ื™ ื”ืื ืฉื™ื.
04:19
But it is a two-way road.
82
259426
1484
ืืš ื–ื”ื• ื›ื‘ื™ืฉ ื“ื•-ืกื˜ืจื™.
04:20
I bring my astrophysics background to urban science,
83
260934
2543
ืื ื™ ืžื‘ื™ืื” ืืช ื”ืจืงืข ืฉืœื™ ื‘ืืกื˜ืจื•ืคื™ื–ื™ืงื” ืœืžื“ืข ืขื™ืจื•ื ื™,
04:23
and I bring what I learn in urban science back to astrophysics.
84
263501
3841
ื•ืœื•ืงื—ืช ืืช ืžื” ืฉืœืžื“ืชื™ ื‘ืžื“ืข ืขื™ืจื•ื ื™ ื—ื–ืจื” ืืœ ืืกื˜ืจื•ืคื™ื–ื™ืงื”.
04:27
Light echoes:
85
267930
1852
ืื•ืจ ืžื”ื“ื”ื“:
04:30
the reflections of stellar explosions onto interstellar dust.
86
270461
4567
ื—ื–ืจืช ื”ืื•ืจ ืžืคื™ืฆื•ืฆื™ื ื›ื•ื›ื‘ื™ื™ื ืขืœ ืื‘ืง ื‘ื™ืŸ-ื›ื•ื›ื‘ื™.
04:36
In our images, these reflections appear as white, evanescent, moving features,
87
276046
5785
ื‘ืชืžื•ื ื•ืช ืฉืœื ื•, ื”ื—ื–ืจืช ื”ืื•ืจ ื”ื–ื• ืžื•ืคื™ืขื” ื›ืกื™ืžื ื™ื ืœื‘ื ื™ื, ืžืชื—ืœืคื™ื ื•ื ืขื™ื,
04:41
just like plumes.
88
281855
1150
ื‘ื“ื™ื•ืง ื›ืžื• ืขืžื•ื“ื™ ืขืฉืŸ.
04:43
I am adapting the same models that detect plumes in city images
89
283363
3895
ืื ื™ ืžืชืื™ืžื” ืืช ืื•ืชื ื”ื“ื’ืžื™ื ื”ืžื–ื”ื™ื ืขืžื•ื“ื™ ืขืฉืŸ ื‘ืชืžื•ื ื•ืช ืขื™ืจื•ื ื™ื•ืช
04:47
to detect light echoes in images of the sky.
90
287282
2928
ื›ื“ื™ ืœื–ื”ื•ืช ื”ื“ื”ื•ื“ื™ ืื•ืจ ื‘ืชืžื•ื ื•ืช ืฉืœ ื”ืฉืžื™ื™ื.
04:52
By exploring the things that interest and excite me,
91
292290
3293
ื‘ื›ืš ืฉื—ืงืจืชื™ ืืช ื”ื“ื‘ืจื™ื ืฉืžืขื ื™ื™ื ื™ื ื•ืžืœื”ื™ื‘ื™ื ืื•ืชื™,
04:55
reaching outside of my domain,
92
295607
1796
ื›ืฉืื ื™ ื™ื•ืฆืืช ืืœ ืžื—ื•ืฅ ืœืชื—ื•ื ืฉืœื™,
04:57
I did turn my restlessness into an asset.
93
297427
2876
ื”ืคื›ืชื™ ืืช ื—ื•ืกืจ ื”ืžื ื•ื—ื” ืฉืœื™ ืœื ื›ืก.
05:01
We, you, all have a unique perspective that can generate new insight
94
301031
5047
ืœื ื•, ืœื›ื, ืœื›ื•ืœื ื• ื™ืฉ ื ืงื•ื“ืช ืžื‘ื˜ ืฉื™ื›ื•ืœื” ืœื™ืฆื•ืจ ืชื•ื‘ื ื” ื—ื“ืฉื”
05:06
and lead to new, unexpected, transformational solutions.
95
306102
4183
ื•ืœื”ื•ื‘ื™ืœ ืืœ ืคืชืจื•ื ื•ืช ืžืฉื ื™ ืขื•ืœื, ื—ื“ืฉื™ื ื•ื‘ืœืชื™-ืฆืคื•ื™ื™ื.
05:10
Thank you.
96
310944
1162
ืชื•ื“ื” ืœื›ื.
05:12
(Applause)
97
312130
4158
(ืžื—ื™ืื•ืช ื›ืคื™ื™ื)
ืขืœ ืืชืจ ื–ื”

ืืชืจ ื–ื” ื™ืฆื™ื’ ื‘ืคื ื™ื›ื ืกืจื˜ื•ื ื™ YouTube ื”ืžื•ืขื™ืœื™ื ืœืœื™ืžื•ื“ ืื ื’ืœื™ืช. ืชื•ื›ืœื• ืœืจืื•ืช ืฉื™ืขื•ืจื™ ืื ื’ืœื™ืช ื”ืžื•ืขื‘ืจื™ื ืขืœ ื™ื“ื™ ืžื•ืจื™ื ืžื”ืฉื•ืจื” ื”ืจืืฉื•ื ื” ืžืจื—ื‘ื™ ื”ืขื•ืœื. ืœื—ืฅ ืคืขืžื™ื™ื ืขืœ ื”ื›ืชื•ื‘ื™ื•ืช ื‘ืื ื’ืœื™ืช ื”ืžื•ืฆื’ื•ืช ื‘ื›ืœ ื“ืฃ ื•ื™ื“ืื• ื›ื“ื™ ืœื”ืคืขื™ืœ ืืช ื”ืกืจื˜ื•ืŸ ืžืฉื. ื”ื›ืชื•ื‘ื™ื•ืช ื’ื•ืœืœื•ืช ื‘ืกื ื›ืจื•ืŸ ืขื ื”ืคืขืœืช ื”ื•ื•ื™ื“ืื•. ืื ื™ืฉ ืœืš ื”ืขืจื•ืช ืื• ื‘ืงืฉื•ืช, ืื ื ืฆื•ืจ ืื™ืชื ื• ืงืฉืจ ื‘ืืžืฆืขื•ืช ื˜ื•ืคืก ื™ืฆื™ืจืช ืงืฉืจ ื–ื”.

https://forms.gle/WvT1wiN1qDtmnspy7