I was eight years old when my godfather my Benigno Francisco Pardo jr. died of a
my Benigno Francisco Pardo jr. died of a
my Benigno Francisco Pardo jr. died of a massive heart attack it came swiftly and
massive heart attack it came swiftly and
massive heart attack it came swiftly and suddenly and took his life at the ripe
suddenly and took his life at the ripe
suddenly and took his life at the ripe age of 37 for many years the aspects of
age of 37 for many years the aspects of
age of 37 for many years the aspects of his passions that stuck with me and with
his passions that stuck with me and with
his passions that stuck with me and with my brother or his love of theater film
my brother or his love of theater film
my brother or his love of theater film and the arts and in fact both my brother
and the arts and in fact both my brother
and the arts and in fact both my brother and I pursued careers in those fields
and I pursued careers in those fields
and I pursued careers in those fields but his other love was technology he was
but his other love was technology he was
but his other love was technology he was the first one on his block to have an
the first one on his block to have an
the first one on his block to have an apple 2e computer oh yeah and unlike
apple 2e computer oh yeah and unlike
apple 2e computer oh yeah and unlike many of the commercials at the time that
many of the commercials at the time that
many of the commercials at the time that marketed the personal computer to men
marketed the personal computer to men
marketed the personal computer to men and boys he made sure my brother and I
and boys he made sure my brother and I
and boys he made sure my brother and I had an experience talking to his Apple
had an experience talking to his Apple
had an experience talking to his Apple right we would input that magical green
right we would input that magical green
right we would input that magical green code and on the oh yeah you know what
code and on the oh yeah you know what
code and on the oh yeah you know what I’m talking about and on the other end
I’m talking about and on the other end
I’m talking about and on the other end would appear that awesome parachuting
would appear that awesome parachuting
would appear that awesome parachuting game you know what you wanted to shoot
game you know what you wanted to shoot
game you know what you wanted to shoot down those guys that were falling and to
down those guys that were falling and to
down those guys that were falling and to me I didn’t know I was coding all I knew
me I didn’t know I was coding all I knew
me I didn’t know I was coding all I knew was it was fun and it was magical and I
was it was fun and it was magical and I
was it was fun and it was magical and I was making things happen I never
was making things happen I never
was making things happen I never understood the computer science was a
understood the computer science was a
understood the computer science was a career you could pursue or a field you
career you could pursue or a field you
career you could pursue or a field you could study and in fact after my
could study and in fact after my
could study and in fact after my Godfather passed the only images I ever
Godfather passed the only images I ever
Godfather passed the only images I ever really saw of computer programmers
really saw of computer programmers
really saw of computer programmers hackers you know what I’m talking about
hackers you know what I’m talking about
hackers you know what I’m talking about the nerdy white guy who sits in a closet
the nerdy white guy who sits in a closet
the nerdy white guy who sits in a closet has no friends maybe you know a little
has no friends maybe you know a little
has no friends maybe you know a little antisocial has glasses okay so I have
antisocial has glasses okay so I have
antisocial has glasses okay so I have glasses that’s the one gimme but they
glasses that’s the one gimme but they
glasses that’s the one gimme but they all kind of look like that guy
and so you know didn’t really identify
and so you know didn’t really identify with the field and I’m not alone studies
with the field and I’m not alone studies
with the field and I’m not alone studies have shown that while we’re seeing the
have shown that while we’re seeing the
have shown that while we’re seeing the under-representation of women and black
under-representation of women and black
under-representation of women and black and hispanic boys the United States and
and hispanic boys the United States and
and hispanic boys the United States and computer science it’s because of the
computer science it’s because of the
computer science it’s because of the misperceptions of the field if you can’t
misperceptions of the field if you can’t
misperceptions of the field if you can’t see it you can’t be it you can’t see it
see it you can’t be it you can’t see it
see it you can’t be it you can’t see it you can’t be it and so in 2014 just to
you can’t be it and so in 2014 just to
you can’t be it and so in 2014 just to give you a little data women made up
give you a little data women made up
give you a little data women made up only 17% of computer science graduates
only 17% of computer science graduates
only 17% of computer science graduates in the United States even though we
in the United States even though we
in the United States even though we accounted for 57 percent of total
accounted for 57 percent of total
accounted for 57 percent of total bachelor’s degrees only 17% I’m gonna
bachelor’s degrees only 17% I’m gonna
bachelor’s degrees only 17% I’m gonna come back to that number later so
come back to that number later so
come back to that number later so remember it for blacks and Hispanics
remember it for blacks and Hispanics
remember it for blacks and Hispanics both men and women it was less than 10%
both men and women it was less than 10%
both men and women it was less than 10% each our Google study on women who
each our Google study on women who
each our Google study on women who choose CS showed there were four major
choose CS showed there were four major
choose CS showed there were four major factors as two white girls were not
factors as two white girls were not
factors as two white girls were not pursuing computer science number two
pursuing computer science number two
pursuing computer science number two factor perceptions accounted for almost
factor perceptions accounted for almost
factor perceptions accounted for almost 30 percent of the decision that is only
30 percent of the decision that is only
30 percent of the decision that is only one percentage point lower than the
one percentage point lower than the
one percentage point lower than the number one reason which was adult
number one reason which was adult
number one reason which was adult encouragement take my story I had the
encouragement take my story I had the
encouragement take my story I had the adult encouragement of my Godfather but
adult encouragement of my Godfather but
adult encouragement of my Godfather but after that everything I knew about
after that everything I knew about
after that everything I knew about computer science was stereotyped and
computer science was stereotyped and
computer science was stereotyped and driven by what I saw around me girls in
driven by what I saw around me girls in
driven by what I saw around me girls in the study described computer science
the study described computer science
the study described computer science much like I did nerdy antisocial male
much like I did nerdy antisocial male
much like I did nerdy antisocial male etc right and in more recent studies
etc right and in more recent studies
etc right and in more recent studies that we’ve done both boys and girls say
that we’ve done both boys and girls say
that we’ve done both boys and girls say they rarely see a computer scientist on
they rarely see a computer scientist on
they rarely see a computer scientist on screen that looks like them with girls
screen that looks like them with girls
screen that looks like them with girls 31% of girls saying they never see a
31% of girls saying they never see a
31% of girls saying they never see a computer scientist on the screen that
computer scientist on the screen that
computer scientist on the screen that looks like them think about how much
looks like them think about how much
looks like them think about how much media we consume on a daily basis to
media we consume on a daily basis to
media we consume on a daily basis to never see somebody that looks like you
never see somebody that looks like you
never see somebody that looks like you on screen for Hispanics it’s worse like
on screen for Hispanics it’s worse like
on screen for Hispanics it’s worse like me I’m Hispanic right I’m latina we’re
me I’m Hispanic right I’m latina we’re
me I’m Hispanic right I’m latina we’re one in four of this country under the
one in four of this country under the
one in four of this country under the age of 18 but we rarely see ourselves
age of 18 but we rarely see ourselves
age of 18 but we rarely see ourselves reflected on screen not only computer
reflected on screen not only computer
reflected on screen not only computer science but other roles we have less
science but other roles we have less
science but other roles we have less access to computers and we feel less
access to computers and we feel less
access to computers and we feel less confident learning computer science then
confident learning computer science then
confident learning computer science then what if I told you that by the year 2020
what if I told you that by the year 2020
what if I told you that by the year 2020 which is just three years we will have
which is just three years we will have
which is just three years we will have 1 million computer science jobs unfilled
1 million computer science jobs unfilled
1 million computer science jobs unfilled because we haven’t trained a technical
because we haven’t trained a technical
because we haven’t trained a technical workforce to fill them 1 million jobs so
workforce to fill them 1 million jobs so
workforce to fill them 1 million jobs so if we have a whole lot of people over
if we have a whole lot of people over
if we have a whole lot of people over here they don’t see themselves in
here they don’t see themselves in
here they don’t see themselves in computer science particularly girls of
computer science particularly girls of
computer science particularly girls of all shades and black and Hispanic boys
all shades and black and Hispanic boys
all shades and black and Hispanic boys and over here we have 1 million jobs
and over here we have 1 million jobs
and over here we have 1 million jobs left unfilled then we have a gap a gap
left unfilled then we have a gap a gap
left unfilled then we have a gap a gap of aspiration and application but what
of aspiration and application but what
of aspiration and application but what if we could change all that with a
if we could change all that with a
if we could change all that with a television show what if a digital series
television show what if a digital series
television show what if a digital series a game a movie could somehow change the
a game a movie could somehow change the
a game a movie could somehow change the way we thought about a profession what
way we thought about a profession what
way we thought about a profession what it is who does it and our role in it
it is who does it and our role in it
it is who does it and our role in it well that happened how many of you watch
well that happened how many of you watch
well that happened how many of you watch CSI or one of the four CSIS well when
CSI or one of the four CSIS well when
CSI or one of the four CSIS well when CSI premiered in the year 2000 five
CSI premiered in the year 2000 five
CSI premiered in the year 2000 five years later we saw a 50% increase in the
years later we saw a 50% increase in the
years later we saw a 50% increase in the number of forensic science majors in the
number of forensic science majors in the
number of forensic science majors in the United States 50% with an over indexing
United States 50% with an over indexing
United States 50% with an over indexing in women because of the lead women
in women because of the lead women
in women because of the lead women protagonists in the show this is called
protagonists in the show this is called
protagonists in the show this is called the CSI effect and in fact forensic
the CSI effect and in fact forensic
the CSI effect and in fact forensic science is one of the only STEM fields
science is one of the only STEM fields
science is one of the only STEM fields science technology engineering and math
science technology engineering and math
science technology engineering and math that does not have a parity issue we
that does not have a parity issue we
that does not have a parity issue we have 5050 male/female representation
have 5050 male/female representation
have 5050 male/female representation this has happened in other fields as
this has happened in other fields as
this has happened in other fields as well with other shows including medicine
well with other shows including medicine
well with other shows including medicine and law for those of you back in the day
and law for those of you back in the day
and law for those of you back in the day LA Law and ER and and we’ve also seen it
LA Law and ER and and we’ve also seen it
LA Law and ER and and we’ve also seen it happen with a designated driver campaign
happen with a designated driver campaign
happen with a designated driver campaign and term so in the late 80s the Harvard
and term so in the late 80s the Harvard
and term so in the late 80s the Harvard alcohol project actually worked with the
alcohol project actually worked with the
alcohol project actually worked with the three networks at the time CBS ABC and
three networks at the time CBS ABC and
three networks at the time CBS ABC and NBC to make sure the term designated
NBC to make sure the term designated
NBC to make sure the term designated driver and the idea of drunk driving was
driver and the idea of drunk driving was
driver and the idea of drunk driving was integrated subtly into storylines and
integrated subtly into storylines and
integrated subtly into storylines and characters and now we all use that term
characters and now we all use that term
characters and now we all use that term as if it were second nature they also
as if it were second nature they also
as if it were second nature they also saw a sharp decrease in the number of
saw a sharp decrease in the number of
saw a sharp decrease in the number of drunk driving deaths as well as an
drunk driving deaths as well as an
drunk driving deaths as well as an increase in the term usage itself pretty
increase in the term usage itself pretty
increase in the term usage itself pretty cool
cool
cool in more recent years the Geena Davis
in more recent years the Geena Davis
in more recent years the Geena Davis Institute and gender and media yeah that
Institute and gender and media yeah that
Institute and gender and media yeah that Geena Davis she was really interested in
Geena Davis she was really interested in
Geena Davis she was really interested in archery why archery well in 2012 two
archery why archery well in 2012 two
archery why archery well in 2012 two movies premiered that had central female
movies premiered that had central female
movies premiered that had central female protagonists who were archers Merida
protagonists who were archers Merida
protagonists who were archers Merida from brave and Katniss Everdeen from The
from brave and Katniss Everdeen from The
from brave and Katniss Everdeen from The Hunger Games so working with the u.s.
Hunger Games so working with the u.s.
Hunger Games so working with the u.s. archery organization they did a survey
archery organization they did a survey
archery organization they did a survey girls participation in archery increased
girls participation in archery increased
girls participation in archery increased by 50% since 2012 and seven and ten
by 50% since 2012 and seven and ten
by 50% since 2012 and seven and ten girls seven in ten identified Merida or
girls seven in ten identified Merida or
girls seven in ten identified Merida or Katniss as a key influencer in why they
Katniss as a key influencer in why they
Katniss as a key influencer in why they wanted to take up archery so what about
wanted to take up archery so what about
wanted to take up archery so what about computer science how are we doing right
computer science how are we doing right
computer science how are we doing right remember that 17% number I shared
remember that 17% number I shared
remember that 17% number I shared earlier well across 11 major film
earlier well across 11 major film
earlier well across 11 major film markets the number of computer
markets the number of computer
markets the number of computer scientists technologists characters on
scientists technologists characters on
scientists technologists characters on screen are 17 percent women interesting
screen are 17 percent women interesting
screen are 17 percent women interesting huh 17% guess what women in background
huh 17% guess what women in background
huh 17% guess what women in background and crowd scenes across those same
and crowd scenes across those same
and crowd scenes across those same movies 17% so the extras you see in the
movies 17% so the extras you see in the
movies 17% so the extras you see in the background and in top leadership
background and in top leadership
background and in top leadership positions in the United States across 10
positions in the United States across 10
positions in the United States across 10 major industries so meeting top
major industries so meeting top
major industries so meeting top leadership and board 17% and for blacks
leadership and board 17% and for blacks
leadership and board 17% and for blacks and Hispanics I should add out of those
and Hispanics I should add out of those
and Hispanics I should add out of those 10,000 characters that were studied 12
10,000 characters that were studied 12
10,000 characters that were studied 12 percent black six percent Latino leaving
percent black six percent Latino leaving
percent black six percent Latino leaving very little room for any profession let
very little room for any profession let
very little room for any profession let alone computer science right so this is
alone computer science right so this is
alone computer science right so this is this is where we’re presented an
this is where we’re presented an
this is where we’re presented an incredible opportunity right literally
incredible opportunity right literally
incredible opportunity right literally our on screen realities are affecting
our on screen realities are affecting
our on screen realities are affecting our off screen realities we are creating
our off screen realities we are creating
our off screen realities we are creating stories on screen that reinforce
stories on screen that reinforce
stories on screen that reinforce negatively often or falsely portray the
negatively often or falsely portray the
negatively often or falsely portray the world we live in normalizing under
world we live in normalizing under
world we live in normalizing under representation so that it feels normal
representation so that it feels normal
representation so that it feels normal off screen let me say that
off screen let me say that
off screen let me say that and that normalizes under-representation
and that normalizes under-representation
and that normalizes under-representation so it feels normal to us off screen so
so it feels normal to us off screen so
so it feels normal to us off screen so can we do for computer science with CSI
can we do for computer science with CSI
can we do for computer science with CSI did for forensic science I think so my
did for forensic science I think so my
did for forensic science I think so my team and I work on that at Google
team and I work on that at Google
team and I work on that at Google Courtney McCarthy and drea green are my
Courtney McCarthy and drea green are my
Courtney McCarthy and drea green are my teammates and we work across multiple
teammates and we work across multiple
teammates and we work across multiple different genres and platforms because
different genres and platforms because
different genres and platforms because unlike the designated driver campaign
unlike the designated driver campaign
unlike the designated driver campaign we’re not really dealing with those
we’re not really dealing with those
we’re not really dealing with those three networks anymore it’s about 500
three networks anymore it’s about 500
three networks anymore it’s about 500 now and we introduced them to our
now and we introduced them to our
now and we introduced them to our Googlers who actually share their
Googlers who actually share their
Googlers who actually share their stories as engineers we’ve worked with
stories as engineers we’ve worked with
stories as engineers we’ve worked with everybody from Empire on Fox 2 miles
everybody from Empire on Fox 2 miles
everybody from Empire on Fox 2 miles from Tomorrowland on Disney jr. to free
from Tomorrowland on Disney jr. to free
from Tomorrowland on Disney jr. to free forms the Fosters to Cartoon Network’s
forms the Fosters to Cartoon Network’s
forms the Fosters to Cartoon Network’s Powerpuff Girls to ready jet go on PBS
Powerpuff Girls to ready jet go on PBS
Powerpuff Girls to ready jet go on PBS Kids and most recently with Netflix’s
Kids and most recently with Netflix’s
Kids and most recently with Netflix’s project MC squared so an example of how
project MC squared so an example of how
project MC squared so an example of how we do this is with Powerpuff Girls for
we do this is with Powerpuff Girls for
we do this is with Powerpuff Girls for example last year we had five of our
example last year we had five of our
example last year we had five of our Google engineers all women on the
Google engineers all women on the
Google engineers all women on the rooftop of the Cartoon Network building
rooftop of the Cartoon Network building
rooftop of the Cartoon Network building in Burbank California and we did a
in Burbank California and we did a
in Burbank California and we did a speed-dating event with their writers
speed-dating event with their writers
speed-dating event with their writers artists producers and executives and
artists producers and executives and
artists producers and executives and what that means is that each of the
what that means is that each of the
what that means is that each of the engineers were prepared to give a seven
engineers were prepared to give a seven
engineers were prepared to give a seven minutes Ted style talk and then rotate
minutes Ted style talk and then rotate
minutes Ted style talk and then rotate and once they got through all the
and once they got through all the
and once they got through all the different groups they then would share
different groups they then would share
different groups they then would share back and have a Q&A session out of those
back and have a Q&A session out of those
back and have a Q&A session out of those conversations just by merely having the
conversations just by merely having the
conversations just by merely having the storytellers talk to the engineers
storytellers talk to the engineers
storytellers talk to the engineers bubbles became a coder on the show if
bubbles became a coder on the show if
bubbles became a coder on the show if you can believe it yes she coated her
you can believe it yes she coated her
you can believe it yes she coated her sisters out of that video game to save
sisters out of that video game to save
sisters out of that video game to save the world
the world
the world back in June and it actually became one
back in June and it actually became one
back in June and it actually became one of the top-rated episodes
of the top-rated episodes
of the top-rated episodes thus far of the Powerpuff Girls relaunch
thus far of the Powerpuff Girls relaunch
thus far of the Powerpuff Girls relaunch I should also note that Cartoon Network
I should also note that Cartoon Network
I should also note that Cartoon Network gave 30 million dollars in a commitment
gave 30 million dollars in a commitment
gave 30 million dollars in a commitment as part of President Obama’s CS for all
as part of President Obama’s CS for all
as part of President Obama’s CS for all initiative and they’re one of the few
initiative and they’re one of the few
initiative and they’re one of the few media companies to do that really
media companies to do that really
media companies to do that really incredible we can’t wait to see what
incredible we can’t wait to see what
incredible we can’t wait to see what comes out of that I should say we don’t
comes out of that I should say we don’t
comes out of that I should say we don’t do this alone we work with incredible
do this alone we work with incredible
do this alone we work with incredible partners like the science entertainment
partners like the science entertainment
partners like the science entertainment exchange who’s represented here today
exchange who’s represented here today
exchange who’s represented here today they work to normalize representation of
they work to normalize representation of
they work to normalize representation of science and scientists on screen we also
science and scientists on screen we also
science and scientists on screen we also work in the America
work in the America
work in the America Film Institute was Sundance and of
Film Institute was Sundance and of
Film Institute was Sundance and of course with the Geena Davis Institute on
course with the Geena Davis Institute on
course with the Geena Davis Institute on gender and media is a quick aside that
gender and media is a quick aside that
gender and media is a quick aside that is what it looks like when age engineer
is what it looks like when age engineer
is what it looks like when age engineer is talking to writers on the rooftop of
is talking to writers on the rooftop of
is talking to writers on the rooftop of the Cartoon Network building and yeah
the Cartoon Network building and yeah
the Cartoon Network building and yeah yeah is that great look there’s bubbles
yeah is that great look there’s bubbles
yeah is that great look there’s bubbles coding you’re gonna love that and
coding you’re gonna love that and
coding you’re gonna love that and another project we worked on that was
another project we worked on that was
another project we worked on that was really interesting was a film called
really interesting was a film called
really interesting was a film called code girl it was directed by Academy
code girl it was directed by Academy
code girl it was directed by Academy Award winner Lesley Chilcott who
Award winner Lesley Chilcott who
Award winner Lesley Chilcott who approached me in 2014 and she said so
approached me in 2014 and she said so
approached me in 2014 and she said so Juliana I’m doing this film about
Juliana I’m doing this film about
Juliana I’m doing this film about Technovation which is a nonprofit that
Technovation which is a nonprofit that
Technovation which is a nonprofit that works with girls around the world to
works with girls around the world to
works with girls around the world to solve problems in their local community
solve problems in their local community
solve problems in their local community using technology and and she goes I
using technology and and she goes I
using technology and and she goes I don’t think girls are going to the movie
don’t think girls are going to the movie
don’t think girls are going to the movie theater to watch documentaries then like
theater to watch documentaries then like
theater to watch documentaries then like Leslie is anyone going to the movie
Leslie is anyone going to the movie
Leslie is anyone going to the movie theater to watch documentaries and she
theater to watch documentaries and she
theater to watch documentaries and she goes well good point this is why I want
goes well good point this is why I want
goes well good point this is why I want to work with us it great let’s put it on
to work with us it great let’s put it on
to work with us it great let’s put it on YouTube which is exactly what we did we
YouTube which is exactly what we did we
YouTube which is exactly what we did we premiered code girl on YouTube for 5
premiered code girl on YouTube for 5
premiered code girl on YouTube for 5 days for free so the girls around the
days for free so the girls around the
days for free so the girls around the world could see the film we know that
world could see the film we know that
world could see the film we know that almost 1 million people watch the film
almost 1 million people watch the film
almost 1 million people watch the film with a higher average watch time than
with a higher average watch time than
with a higher average watch time than the usual YouTube video a hundred
the usual YouTube video a hundred
the usual YouTube video a hundred thousand girls watched it end-to-end and
thousand girls watched it end-to-end and
thousand girls watched it end-to-end and we had 82 million social impressions and
we had 82 million social impressions and
we had 82 million social impressions and what’s even more interesting was the
what’s even more interesting was the
what’s even more interesting was the survey data we got back from the girls
survey data we got back from the girls
survey data we got back from the girls in the room at an average of about 90%
in the room at an average of about 90%
in the room at an average of about 90% for every response they said that they
for every response they said that they
for every response they said that they were now interested in learning more
were now interested in learning more
were now interested in learning more about coding that they would share the
about coding that they would share the
about coding that they would share the movie with a friend so entertainment
movie with a friend so entertainment
movie with a friend so entertainment people that’s a great statistic around
people that’s a great statistic around
people that’s a great statistic around 90% that they share the movie and that
90% that they share the movie and that
90% that they share the movie and that they agreed with the statement girls can
they agreed with the statement girls can
they agreed with the statement girls can be good at coding and the even more
be good at coding and the even more
be good at coding and the even more interesting statistic was that 2,500 of
interesting statistic was that 2,500 of
interesting statistic was that 2,500 of them registered for the Technovation
them registered for the Technovation
them registered for the Technovation challenge for the following year the
challenge for the following year the
challenge for the following year the year before at that same time the number
year before at that same time the number
year before at that same time the number was zero perceptions matter our
was zero perceptions matter our
was zero perceptions matter our on-screen reality is matter what would
on-screen reality is matter what would
on-screen reality is matter what would happen if everybody saw themselves as a
happen if everybody saw themselves as a
happen if everybody saw themselves as a techie as a computer scientist
techie as a computer scientist
techie as a computer scientist as a part of our technical future could
as a part of our technical future could
as a part of our technical future could we solve the most pressing problems of
we solve the most pressing problems of
we solve the most pressing problems of the day like racial justice like hunger
the day like racial justice like hunger
the day like racial justice like hunger like climate change if we involved
like climate change if we involved
like climate change if we involved everybody in the equation
everybody in the equation
everybody in the equation could we get to Mars faster we run the
could we get to Mars faster we run the
could we get to Mars faster we run the risk of creating a world that does not
risk of creating a world that does not
risk of creating a world that does not work for everyone
work for everyone
work for everyone if we don’t include everyone in making
if we don’t include everyone in making
if we don’t include everyone in making and creating that world we can increase
and creating that world we can increase
and creating that world we can increase inequality cause more problems and in
inequality cause more problems and in
inequality cause more problems and in fact hinder our own innovation and what
fact hinder our own innovation and what
fact hinder our own innovation and what it starts with is a story a line of
it starts with is a story a line of
it starts with is a story a line of dialogue a phrase a character so next
dialogue a phrase a character so next
dialogue a phrase a character so next time you’re with the kids in your life
time you’re with the kids in your life
time you’re with the kids in your life or just by yourself think about did I
or just by yourself think about did I
or just by yourself think about did I ever trade off passions I love like tech
ever trade off passions I love like tech
ever trade off passions I love like tech and art because I thought I had to I
and art because I thought I had to I
and art because I thought I had to I can’t bring my bunny no back but I can
can’t bring my bunny no back but I can
can’t bring my bunny no back but I can certainly keep telling his story about
certainly keep telling his story about
certainly keep telling his story about his love of art and Technology through
his love of art and Technology through
his love of art and Technology through the storytelling we put on the screen
the storytelling we put on the screen
the storytelling we put on the screen and so I ask all of you what story
and so I ask all of you what story
and so I ask all of you what story will you tell thank you
will you tell thank you
will you tell thank you [Applause]
”
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