How we can find ourselves in data
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0:01 - 0:04This is what my last week looked like.
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0:05 - 0:06What I did,
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0:06 - 0:08who I was with,
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0:08 - 0:11the main sensations I had
for every waking hour ... -
0:12 - 0:15If the feeling came as I thought of my dad
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0:15 - 0:16who recently passed away,
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0:16 - 0:20or if I could have just definitely
avoided the worries and anxieties. -
0:20 - 0:22And if you think I'm a little obsessive,
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0:22 - 0:24you're probably right.
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0:24 - 0:27But clearly, from this visualization,
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0:27 - 0:30you can learn much more about me
than from this other one, -
0:30 - 0:33which are images you're
probably more familiar with -
0:33 - 0:36and which you possibly even have
on your phone right now. -
0:36 - 0:38Bar charts for the steps you walked,
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0:38 - 0:40pie charts for the quality
of your sleep -- -
0:40 - 0:42the path of your morning runs.
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0:43 - 0:46In my day job, I work with data.
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0:46 - 0:48I run a data visualization design company,
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0:48 - 0:51and we design and develop ways
to make information accessible -
0:51 - 0:54through visual representations.
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0:54 - 0:57What my job has taught me over the years
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0:57 - 1:01is that to really understand data
and their true potential, -
1:01 - 1:04sometimes we actually
have to forget about them -
1:04 - 1:06and see through them instead.
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1:06 - 1:10Because data are always
just a tool we use to represent reality. -
1:10 - 1:13They're always used
as a placeholder for something else, -
1:13 - 1:15but they are never the real thing.
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1:15 - 1:17But let me step back for a moment
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1:17 - 1:20to when I first understood
this personally. -
1:20 - 1:24In 1994, I was 13 years old.
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1:24 - 1:26I was a teenager in Italy.
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1:26 - 1:28I was too young
to be interested in politics, -
1:28 - 1:31but I knew that a businessman,
Silvio Berlusconi, -
1:31 - 1:33was running for president
for the moderate right. -
1:34 - 1:36We lived in a very liberal town,
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1:36 - 1:39and my father was a politician
for the Democratic Party. -
1:39 - 1:44And I remember that no one thought
that Berlusconi could get elected -- -
1:44 - 1:46that was totally not an option.
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1:47 - 1:48But it happened.
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1:48 - 1:50And I remember the feeling very vividly.
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1:50 - 1:52It was a complete surprise,
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1:52 - 1:57as my dad promised that in my town
he knew nobody who voted for him. -
1:59 - 2:00This was the first time
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2:00 - 2:05when the data I had gave me
a completely distorted image of reality. -
2:05 - 2:09My data sample was actually
pretty limited and skewed, -
2:09 - 2:12so probably it was because of that,
I thought, I lived in a bubble, -
2:12 - 2:15and I didn't have enough chances
to see outside of it. -
2:16 - 2:20Now, fast-forward to November 8, 2016
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2:20 - 2:21in the United States.
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2:22 - 2:23The internet polls,
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2:23 - 2:25statistical models,
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2:25 - 2:29all the pundits agreeing on a possible
outcome for the presidential election. -
2:30 - 2:32It looked like we had
enough information this time, -
2:32 - 2:36and many more chances to see outside
the closed circle we lived in -- -
2:36 - 2:38but we clearly didn't.
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2:38 - 2:40The feeling felt very familiar.
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2:40 - 2:42I had been there before.
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2:42 - 2:45I think it's fair to say
the data failed us this time -- -
2:45 - 2:47and pretty spectacularly.
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2:47 - 2:49We believed in data,
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2:49 - 2:50but what happened,
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2:50 - 2:53even with the most respected newspaper,
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2:53 - 2:58is that the obsession to reduce everything
to two simple percentage numbers -
2:58 - 3:00to make a powerful headline
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3:00 - 3:02made us focus on these two digits
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3:02 - 3:03and them alone.
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3:04 - 3:06In an effort to simplify the message
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3:06 - 3:09and draw a beautiful,
inevitable red and blue map, -
3:09 - 3:11we lost the point completely.
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3:11 - 3:14We somehow forgot
that there were stories -- -
3:14 - 3:16stories of human beings
behind these numbers. -
3:17 - 3:19In a different context,
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3:19 - 3:20but to a very similar point,
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3:21 - 3:24a peculiar challenge was presented
to my team by this woman. -
3:24 - 3:27She came to us with a lot of data,
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3:27 - 3:31but ultimately she wanted to tell
one of the most humane stories possible. -
3:31 - 3:33She's Samantha Cristoforetti.
-
3:33 - 3:36She has been the first
Italian woman astronaut, -
3:36 - 3:38and she contacted us before being launched
-
3:38 - 3:42on a six-month-long expedition
to the International Space Station. -
3:42 - 3:44She told us, "I'm going to space,
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3:44 - 3:47and I want to do something meaningful
with the data of my mission -
3:47 - 3:49to reach out to people."
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3:50 - 3:52A mission to the
International Space Station -
3:52 - 3:54comes with terabytes of data
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3:54 - 3:57about anything you can possibly imagine --
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3:57 - 3:58the orbits around Earth,
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3:58 - 4:00the speed and position of the ISS
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4:00 - 4:04and all of the other thousands
of live streams from its sensors. -
4:05 - 4:08We had all of the hard data
we could think of -- -
4:08 - 4:10just like the pundits
before the election -- -
4:10 - 4:13but what is the point
of all these numbers? -
4:13 - 4:16People are not interested
in data for the sake of it, -
4:16 - 4:18because numbers are never the point.
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4:18 - 4:20They're always the means to an end.
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4:21 - 4:23The story we needed to tell
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4:23 - 4:25is that there is a human being
in a teeny box -
4:25 - 4:27flying in space above your head,
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4:27 - 4:32and that you can actually see her
with your naked eye on a clear night. -
4:32 - 4:35So we decided to use data
to create a connection -
4:35 - 4:39between Samantha and all of the people
looking at her from below. -
4:39 - 4:42We designed and developed
what we called "Friends in Space," -
4:42 - 4:47a web application that simply
lets you say "hello" to Samantha -
4:47 - 4:48from where you are,
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4:48 - 4:51and "hello" to all the people
who are online at the same time -
4:51 - 4:53from all over the world.
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4:54 - 4:57And all of these "hellos"
left visible marks on the map -
4:57 - 4:59as Samantha was flying by
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4:59 - 5:03and as she was actually
waving back every day at us -
5:03 - 5:04using Twitter from the ISS.
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5:05 - 5:10This made people see the mission's data
from a very different perspective. -
5:10 - 5:15It all suddenly became much more
about our human nature and our curiosity, -
5:15 - 5:16rather than technology.
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5:16 - 5:19So data powered the experience,
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5:19 - 5:21but stories of human beings
were the drive. -
5:23 - 5:26The very positive response
of its thousands of users -
5:26 - 5:28taught me a very important lesson --
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5:28 - 5:31that working with data
means designing ways -
5:31 - 5:34to transform the abstract
and the uncountable -
5:34 - 5:38into something that can be seen,
felt and directly reconnected -
5:38 - 5:40to our lives and to our behaviors,
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5:40 - 5:42something that is hard to achieve
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5:42 - 5:46if we let the obsession for the numbers
and the technology around them -
5:46 - 5:47lead us in the process.
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5:49 - 5:53But we can do even more to connect data
to the stories they represent. -
5:54 - 5:56We can remove technology completely.
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5:56 - 5:58A few years ago, I met this other woman,
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5:58 - 6:00Stefanie Posavec --
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6:00 - 6:06a London-based designer who shares with me
the passion and obsession about data. -
6:06 - 6:07We didn't know each other,
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6:07 - 6:10but we decided to run
a very radical experiment, -
6:10 - 6:13starting a communication using only data,
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6:13 - 6:14no other language,
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6:14 - 6:19and we opted for using no technology
whatsoever to share our data. -
6:19 - 6:22In fact, our only means of communication
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6:22 - 6:25would be through
the old-fashioned post office. -
6:25 - 6:27For "Dear Data," every week for one year,
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6:27 - 6:31we used our personal data
to get to know each other -- -
6:31 - 6:34personal data around weekly
shared mundane topics, -
6:34 - 6:36from our feelings
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6:36 - 6:37to the interactions with our partners,
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6:37 - 6:41from the compliments we received
to the sounds of our surroundings. -
6:41 - 6:45Personal information
that we would then manually hand draw -
6:45 - 6:48on a postcard-size sheet of paper
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6:48 - 6:50that we would every week
send from London to New York, -
6:51 - 6:52where I live,
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6:52 - 6:54and from New York to London,
where she lives. -
6:54 - 6:58The front of the postcard
is the data drawing, -
6:58 - 6:59and the back of the card
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7:00 - 7:02contains the address
of the other person, of course, -
7:02 - 7:05and the legend for how
to interpret our drawing. -
7:06 - 7:08The very first week into the project,
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7:08 - 7:11we actually chose
a pretty cold and impersonal topic. -
7:11 - 7:14How many times do we
check the time in a week? -
7:15 - 7:17So here is the front of my card,
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7:17 - 7:19and you can see that every little symbol
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7:19 - 7:22represents all of the times
that I checked the time, -
7:22 - 7:25positioned for days
and different hours chronologically -- -
7:26 - 7:28nothing really complicated here.
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7:28 - 7:30But then you see in the legend
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7:30 - 7:33how I added anecdotal details
about these moments. -
7:33 - 7:38In fact, the different types of symbols
indicate why I was checking the time -- -
7:38 - 7:39what was I doing?
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7:39 - 7:41Was I bored? Was I hungry?
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7:41 - 7:42Was I late?
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7:42 - 7:45Did I check it on purpose
or just casually glance at the clock? -
7:45 - 7:48And this is the key part --
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7:48 - 7:51representing the details
of my days and my personality -
7:51 - 7:53through my data collection.
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7:53 - 7:58Using data as a lens or a filter
to discover and reveal, for example, -
7:58 - 8:00my never-ending anxiety for being late,
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8:00 - 8:02even though I'm absolutely always on time.
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8:04 - 8:08Stefanie and I spent one year
collecting our data manually -
8:08 - 8:13to force us to focus on the nuances
that computers cannot gather -- -
8:13 - 8:14or at least not yet --
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8:14 - 8:18using data also to explore our minds
and the words we use, -
8:18 - 8:20and not only our activities.
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8:20 - 8:21Like at week number three,
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8:21 - 8:25where we tracked the "thank yous"
we said and were received, -
8:25 - 8:30and when I realized that I thank
mostly people that I don't know. -
8:30 - 8:34Apparently I'm a compulsive thanker
to waitresses and waiters, -
8:34 - 8:37but I definitely don't thank enough
the people who are close to me. -
8:39 - 8:40Over one year,
-
8:40 - 8:45the process of actively noticing
and counting these types of actions -
8:45 - 8:46became a ritual.
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8:46 - 8:48It actually changed ourselves.
-
8:48 - 8:51We became much more
in tune with ourselves, -
8:51 - 8:54much more aware of our behaviors
and our surroundings. -
8:55 - 8:58Over one year, Stefanie and I
connected at a very deep level -
8:58 - 9:00through our shared data diary,
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9:00 - 9:04but we could do this only because
we put ourselves in these numbers, -
9:04 - 9:08adding the contexts
of our very personal stories to them. -
9:08 - 9:10It was the only way
to make them truly meaningful -
9:11 - 9:13and representative of ourselves.
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9:14 - 9:18I am not asking you
to start drawing your personal data, -
9:18 - 9:20or to find a pen pal across the ocean.
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9:20 - 9:23But I'm asking you to consider data --
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9:23 - 9:25all kind of data --
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9:25 - 9:26as the beginning of the conversation
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9:26 - 9:28and not the end.
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9:28 - 9:31Because data alone
will never give us a solution. -
9:31 - 9:34And this is why data failed us so badly --
-
9:34 - 9:37because we failed to include
the right amount of context -
9:37 - 9:39to represent reality --
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9:39 - 9:42a nuanced, complicated
and intricate reality. -
9:43 - 9:45We kept looking at these two numbers,
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9:45 - 9:47obsessing with them
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9:47 - 9:49and pretending that our world
could be reduced -
9:49 - 9:52to a couple digits and a horse race,
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9:52 - 9:53while the real stories,
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9:53 - 9:55the ones that really mattered,
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9:55 - 9:56were somewhere else.
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9:56 - 10:00What we missed looking at these stories
only through models and algorithms -
10:00 - 10:03is what I call "data humanism."
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10:04 - 10:06In the Renaissance humanism,
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10:06 - 10:07European intellectuals
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10:07 - 10:12placed the human nature instead of God
at the center of their view of the world. -
10:13 - 10:15I believe something similar
needs to happen -
10:15 - 10:17with the universe of data.
-
10:17 - 10:20Now data are apparently
treated like a God -- -
10:20 - 10:23keeper of infallible truth
for our present and our future. -
10:24 - 10:27The experiences
that I shared with you today -
10:27 - 10:32taught me that to make data faithfully
representative of our human nature -
10:32 - 10:35and to make sure they will not
mislead us anymore, -
10:35 - 10:39we need to start designing ways
to include empathy, imperfection -
10:39 - 10:41and human qualities
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10:41 - 10:44in how we collect, process,
analyze and display them. -
10:45 - 10:48I do see a place where, ultimately,
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10:48 - 10:52instead of using data
only to become more efficient, -
10:52 - 10:54we will all use data
to become more humane. -
10:55 - 10:56Thank you.
-
10:56 - 11:01(Applause)
- Title:
- How we can find ourselves in data
- Speaker:
- Giorgia Lupi
- Description:
-
Giorgia Lupi uses data to tell human stories, adding nuance to numbers. In this charming talk, she shares how we can bring personality to data, visualizing even the mundane details of our daily lives and transforming the abstract and uncountable into something that can be seen, felt and directly reconnected to our lives.
- Video Language:
- English
- Team:
- closed TED
- Project:
- TEDTalks
- Duration:
- 11:13
Brian Greene edited English subtitles for How we can find ourselves in data | ||
Brian Greene edited English subtitles for How we can find ourselves in data | ||
Joanna Pietrulewicz accepted English subtitles for How we can find ourselves in data | ||
Joanna Pietrulewicz edited English subtitles for How we can find ourselves in data | ||
Joanna Pietrulewicz edited English subtitles for How we can find ourselves in data | ||
Leslie Gauthier edited English subtitles for How we can find ourselves in data | ||
Leslie Gauthier edited English subtitles for How we can find ourselves in data | ||
Leslie Gauthier edited English subtitles for How we can find ourselves in data |