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Now on BBC News,
it is time for Click. | 0:00:00 | 0:00:00 | |
This week, the app that helps record
and report sexual assault. The AI | 0:00:08 | 0:00:15 | |
going after cancer. And the man who
wants us all to the four other. -- | 0:00:15 | 0:00:24 | |
live forever. | 0:00:24 | 0:00:27 | |
As International Women's Day was
marked this week, it brought with it | 0:00:45 | 0:00:52 | |
further focus on the many issues
still to be faced in bringing about | 0:00:52 | 0:00:55 | |
true gender equality in all walks of
life. The technology industry, of | 0:00:55 | 0:01:00 | |
course, has its own issues, as we
will hear later. Silicon Valley's | 0:01:00 | 0:01:04 | |
culture and its treatment of women
raises a lot of questions. But tech | 0:01:04 | 0:01:08 | |
can also be a force for good. It has
been just six months or so since the | 0:01:08 | 0:01:15 | |
Me Too movement gave a voice to so
many women around the world, who | 0:01:15 | 0:01:19 | |
used social media to expose just how
widespread sexual harassment and | 0:01:19 | 0:01:22 | |
assault is. Many women feel that
reporting sexual assault can also be | 0:01:22 | 0:01:27 | |
really traumatic, and the
experiences of some women in Silicon | 0:01:27 | 0:01:31 | |
Valley had spurred them to create
something that may make that just a | 0:01:31 | 0:01:35 | |
little bit easier. Our correspondent
has travelled to Stanford University | 0:01:35 | 0:01:41 | |
to meet survivors of sexual abuse,
and the creators of Callisto. Every | 0:01:41 | 0:01:46 | |
tattoo tells a story. For this
Stanford University student, the | 0:01:46 | 0:01:52 | |
story is bittersweet. She was one of
50 sexual assault survivors invited | 0:01:52 | 0:01:57 | |
to share the stage with Lady Gaga
during the 2016 Oscars. While we | 0:01:57 | 0:02:03 | |
were rehearsing, I remember at one
point we were all crying and hugging | 0:02:03 | 0:02:07 | |
each other, and someone just said we
need to get a tattoo to commemorate | 0:02:07 | 0:02:12 | |
this and to give us strength.
Something to look at when you are | 0:02:12 | 0:02:16 | |
feeling down, and you know that
you're alone. Lin says that a few | 0:02:16 | 0:02:22 | |
days after she was assaulted by a
friend in 2015, she told the title | 0:02:22 | 0:02:27 | |
nine office which investigate sexual
misconduct. I decided to report | 0:02:27 | 0:02:30 | |
because I didn't want him to do it
to anyone else, and later I did find | 0:02:30 | 0:02:35 | |
out that he had done a lot of... A
lot of harassment, stalking, and | 0:02:35 | 0:02:38 | |
also assault on someone else. What
was that experience like? It was | 0:02:38 | 0:02:43 | |
awful. My GPA dropped down, and I
was fighting with the school back | 0:02:43 | 0:02:47 | |
and forth every single hour of every
single day. Lin, now an activist, | 0:02:47 | 0:02:54 | |
wants greater transparency in the
adjudication process. They are | 0:02:54 | 0:02:58 | |
trying to cover up the number of
sexual assault that happen on their | 0:02:58 | 0:03:01 | |
campus, because that makes your
university look safer. It's better | 0:03:01 | 0:03:05 | |
for your public relations. Stanford
told Click... | 0:03:05 | 0:03:09 | |
Stanford changed its title nine
process in 2016, and has begun | 0:03:14 | 0:03:20 | |
reporting case numbers. In the US,
one in five women is sexually | 0:03:20 | 0:03:25 | |
assaulted while in college. I was
sexually assaulted by a friend. Over | 0:03:25 | 0:03:30 | |
a year after that happened, I
decided to report my assault, and I | 0:03:30 | 0:03:34 | |
ended up finding the process of
reporting to be more traumatic than | 0:03:34 | 0:03:38 | |
the event itself. Feeling not
believed by the people who I thought | 0:03:38 | 0:03:43 | |
were there to protect me was
incredibly destabilising. Jessica | 0:03:43 | 0:03:53 | |
Lad's ordeal spurred her to create
Callisto, so survivors would have a | 0:03:53 | 0:03:58 | |
way of reporting sexual assault.
They can have a timestamp, doesn't | 0:03:58 | 0:04:04 | |
have to go anywhere if they don't
want to, send it to authorities in | 0:04:04 | 0:04:08 | |
their school, or three, just say
what happened to them for now but | 0:04:08 | 0:04:11 | |
report electronically if someone
else makes the same assailant. You | 0:04:11 | 0:04:15 | |
can think of it as an international
information as scroat agency. | 0:04:15 | 0:04:20 | |
Students often report to protect
others. This matching feature helps | 0:04:20 | 0:04:24 | |
do that by detecting repeat
offenders. People might use | 0:04:24 | 0:04:29 | |
different names, they may look
different. How do you make sure you | 0:04:29 | 0:04:32 | |
have got the right person? We ask
victims to put in not just the name | 0:04:32 | 0:04:37 | |
of their perpetrator, but also a
series of unique identifiers. | 0:04:37 | 0:04:41 | |
Currently, Facebook profiles I used
to match. Some students want more | 0:04:41 | 0:04:45 | |
ways to idea, and Callisto may add
mobile numbers and e-mail addresses | 0:04:45 | 0:04:49 | |
in the future. 12 US colleges use
Callisto. The University of San | 0:04:49 | 0:04:54 | |
Francisco was the first. We knew
students won't reporting. If you | 0:04:54 | 0:04:59 | |
look at our numbers from once we
first started with Callisto, three | 0:04:59 | 0:05:03 | |
years ago, to now, there is
definitely an increase in reporting. | 0:05:03 | 0:05:07 | |
Callisto allows for our students to
write what happened, to write about | 0:05:07 | 0:05:11 | |
the incident. And sometimes just
riding your perpetrator's name gives | 0:05:11 | 0:05:15 | |
people power. Seeing the need for
Callisto, she led an effort to bring | 0:05:15 | 0:05:21 | |
it to Stanford. It is just available
24 - seven, and they have seen | 0:05:21 | 0:05:27 | |
spikes in usage during times like
spring break, when the title nine | 0:05:27 | 0:05:31 | |
office might not be available, but
students wanted to file a report, or | 0:05:31 | 0:05:35 | |
during the weekend, when no one is
staffing. With Callisto, survivors | 0:05:35 | 0:05:39 | |
recount what happened at the own
pace, privately. Often victims | 0:05:39 | 0:05:46 | |
including in-person interviews what
is incredible, so they want to fill | 0:05:46 | 0:05:49 | |
in the details and tell a wonderful
story arc. At that is not how memory | 0:05:49 | 0:05:54 | |
works, and that is particularly not
how memory works in the event of | 0:05:54 | 0:05:57 | |
trauma. So being able to allow
somebody to say I don't know the | 0:05:57 | 0:06:01 | |
centre, I am not sure that, and only
record think that they are sure of, | 0:06:01 | 0:06:05 | |
is really essential to make sure
that that time stamped record isn't | 0:06:05 | 0:06:08 | |
later used against them. Students
must create a username, password and | 0:06:08 | 0:06:12 | |
pass phrase that be recovered. Not
quite a one click sign up. That | 0:06:12 | 0:06:18 | |
deters some users. For Callisto, it
ensures drivers see. Because then we | 0:06:18 | 0:06:22 | |
would have to be storing the
password, which means that we could | 0:06:22 | 0:06:26 | |
potentially decrypt the data, and we
want to make sure that even we can't | 0:06:26 | 0:06:30 | |
view it. Since students choose
whether to report assaults, some | 0:06:30 | 0:06:34 | |
records are never seen by schools.
They are still useful. We provide | 0:06:34 | 0:06:38 | |
our institutions with at an
aggregate data report that gives | 0:06:38 | 0:06:42 | |
them a better sense of what is
happening in that record, what type | 0:06:42 | 0:06:50 | |
of years are assault reporting, what
classy as are involved? Others are | 0:06:50 | 0:06:54 | |
also working to make reporting less
daunting. The spot app creates a | 0:06:54 | 0:06:59 | |
record from the user's conversation
with a chat bot, while all will let | 0:06:59 | 0:07:07 | |
them report electronically. Lin is
in short electronic reporting would | 0:07:07 | 0:07:10 | |
have changed her was handled, but
she see the potential. What I think | 0:07:10 | 0:07:15 | |
Callisto is great for is to track
perpetrators, if they decide to | 0:07:15 | 0:07:19 | |
apply for grad school or transfer
schools, I think that is where this | 0:07:19 | 0:07:22 | |
can really come in and have a very
powerful effect. as we grow, we want | 0:07:22 | 0:07:26 | |
to create one system, one database
that allows us to track any | 0:07:26 | 0:07:30 | |
perpetrator, even as they move
through space and time. Which would | 0:07:30 | 0:07:33 | |
give survivors away to find out if
there are seven is a repeat | 0:07:33 | 0:07:37 | |
offender, something Jessica Ladd
says she wonders to this day. That | 0:07:37 | 0:07:51 | |
was Sumi Das, at Stanford
University. Now, while Callisto was | 0:07:51 | 0:07:54 | |
created a team of mostly women, that
is rare. Even right here in Silicon | 0:07:54 | 0:07:59 | |
Valley. Whenever I have been to
visit, I have found it all too easy | 0:07:59 | 0:08:05 | |
to think of the valley as sharing
the Progressive values of San | 0:08:05 | 0:08:09 | |
Francisco, where all colours and
genders seem welcome. But the people | 0:08:09 | 0:08:14 | |
I have met, those in charge of the
start-ups and attack giants, have | 0:08:14 | 0:08:17 | |
been mainly men. It is quite obvious
to us that women are | 0:08:17 | 0:08:22 | |
underrepresented here, and there are
those who feel that Silicon Valley | 0:08:22 | 0:08:26 | |
is just as full of sexism and
masculine culture as anywhere else. | 0:08:26 | 0:08:31 | |
It is the social challenge... Emily
Chang is a San Francisco journalist, | 0:08:31 | 0:08:35 | |
and a host of Bloomberg Technology.
And in her new book, Brotopia, she | 0:08:35 | 0:08:44 | |
has written about the industry that
has always self-selected for men. | 0:08:44 | 0:08:47 | |
First came the antisocial nerd who
suddenly became part of the ruling | 0:08:47 | 0:08:52 | |
class, and now she says it is the
time of the cocky, self-confident | 0:08:52 | 0:08:57 | |
risk taker, the bro. Silicon Valley
is the heart of the most powerful | 0:08:57 | 0:09:01 | |
industry in the world, and that is
the technology industry. This is a | 0:09:01 | 0:09:04 | |
world that is controlling what we
see, what we read, how we shop, how | 0:09:04 | 0:09:09 | |
we communicate, how we get around.
The reality is, the exclusion from | 0:09:09 | 0:09:13 | |
this incredible and aggressive
industry was not inevitable. It | 0:09:13 | 0:09:16 | |
didn't have to be this way. I think
of all the women out there who might | 0:09:16 | 0:09:21 | |
have started the next Facebook or
the next Google or the next Apple, | 0:09:21 | 0:09:24 | |
but never got the chance because
they didn't look the part. And that | 0:09:24 | 0:09:28 | |
is something that needs to change.
Women hold just 25% of jobs across | 0:09:28 | 0:09:33 | |
the computing industry. They account
for 7% of investors. Women led | 0:09:33 | 0:09:37 | |
companies get just 2% of venture
capital funding. The most important | 0:09:37 | 0:09:42 | |
thing that we need to do is to
acknowledge that Silicon Valley has | 0:09:42 | 0:09:48 | |
become toxic for women. So what is
bro culture? Well, it is exactly | 0:09:48 | 0:09:53 | |
what it sounds like. Fratty parties,
beer, behaviour that is alienating | 0:09:53 | 0:10:00 | |
towards women. I had 12 women over
at my home most them engineers who | 0:10:00 | 0:10:04 | |
work at companies like Uber and
Google and the Uber engineers told | 0:10:04 | 0:10:13 | |
me they would often be invited to
strip clubs and bondage clubs in the | 0:10:13 | 0:10:17 | |
middle of the day, and so much of
the working culture gets done | 0:10:17 | 0:10:21 | |
outside of the office, so at the
bar, at the conference, in a hotel | 0:10:21 | 0:10:24 | |
lobby, and they are stuck in sort of
an impossible Catch-22. If they | 0:10:24 | 0:10:28 | |
attend, they are disrespected and
discredited. If they don't, they are | 0:10:28 | 0:10:31 | |
shut out of important business and
networking opportunities, because | 0:10:31 | 0:10:33 | |
very powerful people, very powerful
men, are at these parties. So many | 0:10:33 | 0:10:39 | |
female entrepreneurs that I have
spoken to have not just one story, | 0:10:39 | 0:10:43 | |
but several stories to tell about
how an investor has crossed a line. | 0:10:43 | 0:10:46 | |
You know, one of the most sort of
egregious examples that I have found | 0:10:46 | 0:10:50 | |
is an investor who... A very
prominent investor in countries to | 0:10:50 | 0:10:55 | |
make companies like Twitter and Uber
who often hosted hot tub parties at | 0:10:55 | 0:11:01 | |
his home. Well, what female driven
wants to get in a hot tub and future | 0:11:01 | 0:11:05 | |
business while wearing a bikini and
drinking beer? These are the kinds | 0:11:05 | 0:11:09 | |
of activities that have been very
alienating the women, and | 0:11:09 | 0:11:13 | |
unfortunately have created a very
unlevel playing field in Silicon | 0:11:13 | 0:11:15 | |
Valley. I think about how different
the world might be if women had been | 0:11:15 | 0:11:22 | |
at the creation of some of these
companies from the start. I sat down | 0:11:22 | 0:11:26 | |
with Twitter co-founder Ed Williams,
and I asked him, F women had been | 0:11:26 | 0:11:31 | |
involved in the founding of Twitter,
would online harassment and trolling | 0:11:31 | 0:11:34 | |
be such a problem? And he said he
doesn't think so. They were thinking | 0:11:34 | 0:11:38 | |
about that when they were building
Twitter. They were thinking about | 0:11:38 | 0:11:41 | |
all the wonderful and amazing things
that can be done with Twitter, they | 0:11:41 | 0:11:45 | |
won't thinking about how it can be
used to send death threats or rape | 0:11:45 | 0:11:49 | |
threats. And as a result, online
harassment is one of the biggest | 0:11:49 | 0:11:52 | |
problems plaguing internet today. If
women had been more involved in | 0:11:52 | 0:11:57 | |
building these products, and
building these services, maybe | 0:11:57 | 0:12:00 | |
online harassment and trolling
wouldn't be such a problem. I fully | 0:12:00 | 0:12:06 | |
believe that the people who have
already changed the world in so many | 0:12:06 | 0:12:10 | |
wondrous ways, the people who are
taking us to Mars, the people who | 0:12:10 | 0:12:14 | |
are building self driving cars, the
people who have given us right at | 0:12:14 | 0:12:17 | |
the push of a button, if they can do
all that, they can change this too. | 0:12:17 | 0:12:27 | |
Hello, and welcome to the week
intact. It was the week that Sony | 0:12:31 | 0:12:37 | |
blocked the videogame Super Seducer
for being released on the | 0:12:37 | 0:12:42 | |
PlayStation 4. It has been
criticised as too sleazy and for | 0:12:42 | 0:12:46 | |
promoting toxic behaviour. Dyson
announced they will not be making | 0:12:46 | 0:12:51 | |
plug-in vacuum cleaners any more,
they will be working on their | 0:12:51 | 0:12:55 | |
cordless range. The dating app
Bumble has been band uses from | 0:12:55 | 0:13:02 | |
posing with guns, though an
exception has been made for military | 0:13:02 | 0:13:06 | |
and law enforcement. And a robot
managed to solve a Rubik 's cube | 0:13:06 | 0:13:10 | |
under a second. Link you will miss
it. Here it is in slow mode. It was | 0:13:10 | 0:13:15 | |
the week that mobile companies Three
and Vodafone came under | 0:13:15 | 0:13:19 | |
investigation over the way they
handle data on their network. Com is | 0:13:19 | 0:13:23 | |
looking at whether they are
intentionally slowing down internet | 0:13:23 | 0:13:27 | |
speeds while customers are abroad.
Internet others invaded New York's | 0:13:27 | 0:13:31 | |
Museum of modern Art, transforming
the Jackson Pollock room into their | 0:13:31 | 0:13:36 | |
own augmented reality gallery
without the museum's permission. The | 0:13:36 | 0:13:40 | |
project was called hello, we are
from the internet. And finally, | 0:13:40 | 0:13:44 | |
Flippy the robot has been working at
a restaurant in Los Angeles. Their | 0:13:44 | 0:13:48 | |
job, as you guessed it, is to flip
burgers. Using imaging and heat | 0:13:48 | 0:13:54 | |
sensing to flip, Flippy is being
installed at 50 locations, but it is | 0:13:54 | 0:13:58 | |
not cheap, at $60,000 a robot. Hope
those burgers taste good. | 0:13:58 | 0:14:04 | |
The idea of personalised or
precision medicine is really gaining | 0:14:07 | 0:14:10 | |
ground, and in the not too distant
future, every single time we're | 0:14:10 | 0:14:15 | |
prescribed something, exactly what
that is could be dependent on our | 0:14:15 | 0:14:18 | |
height, weight, sex and even our
genetic make up. | 0:14:18 | 0:14:28 | |
Personalising your medication
doesn't always need complex | 0:14:28 | 0:14:31 | |
biomedical data to be beneficial,
though. What I have here is the Beta | 0:14:31 | 0:14:35 | |
version of the app. You input your
data first, your height, your | 0:14:35 | 0:14:40 | |
weight, your sex and details of any
other medication you're taking, | 0:14:40 | 0:14:43 | |
because that could have an affect.
After you've done that you can put | 0:14:43 | 0:14:48 | |
in information on what drug you're
about to take. And this is how you | 0:14:48 | 0:14:52 | |
do it. So this is paracetamol. Now,
I would probably take two 500 mg | 0:14:52 | 0:14:58 | |
tablets, so let's see the effect
that would be likely to have. It's | 0:14:58 | 0:15:01 | |
going to last about four hours,
which is pretty much what I would | 0:15:01 | 0:15:05 | |
have expected, but where this dark
blue is showing it shows I could be | 0:15:05 | 0:15:09 | |
ever so slightly overdosing, so
someone of my height and weight | 0:15:09 | 0:15:13 | |
maybe doesn't need to be taking to
tablets in one go. While the dark | 0:15:13 | 0:15:17 | |
blue may represent a little more
than needed, when you're clearly | 0:15:17 | 0:15:21 | |
taking too much the dial will turn
fully read. With a simple | 0:15:21 | 0:15:27 | |
questionnaire on you, on your
environment, on your body, with | 0:15:27 | 0:15:31 | |
something like five or six seconds
we can cover something like 90% of | 0:15:31 | 0:15:35 | |
the questions and for the ten
remaining % we need complimentary | 0:15:35 | 0:15:41 | |
information, like RUH smoker or not,
which kind of regimented you have, | 0:15:41 | 0:15:46 | |
of course genetics. Weitering
pharmacists in the future Strood | 0:15:46 | 0:15:50 | |
have an important role to play in
this ecosystem that we think. Not | 0:15:50 | 0:15:54 | |
only by selling drugs but by selling
the exact drugs and the exact dose | 0:15:54 | 0:15:59 | |
for the patients. Artificial
intelligence is at the forefront of | 0:15:59 | 0:16:04 | |
this revolution, analysing massive
quantities of biomedical data that | 0:16:04 | 0:16:07 | |
could transform treatment. Well,
imagine you're a scientist and you | 0:16:07 | 0:16:13 | |
could read every piece of
information that had ever been | 0:16:13 | 0:16:17 | |
written about biomedicine. You could
store that information and then you | 0:16:17 | 0:16:21 | |
could use it to make new discoveries
in diseases. There are billions of | 0:16:21 | 0:16:27 | |
potential combinations of genes,
diseases and drugs and here hugely | 0:16:27 | 0:16:31 | |
powerful algorithms are at work to
establish the best combinations. | 0:16:31 | 0:16:38 | |
Genetics will also play a central
role in personalising what you're | 0:16:38 | 0:16:42 | |
prescribed in future. Astrazeneca
are analysing genomes from over 2 | 0:16:42 | 0:16:46 | |
million people, and this data could
soon be at your GP's fingertips. | 0:16:46 | 0:16:53 | |
Patients will actually be at the
point where maybe they'll be able to | 0:16:53 | 0:16:56 | |
go into the clinic, go into your
local GP, and have that information | 0:16:56 | 0:17:02 | |
already available. The doctor will
then be able to look up not only the | 0:17:02 | 0:17:07 | |
type of genotyping but also your
individual Jelic I, and match that | 0:17:07 | 0:17:11 | |
to the best medicine for you. Some
people are even talking about doing | 0:17:11 | 0:17:16 | |
this at birth so when you develop a
disease, the Doctor's already got | 0:17:16 | 0:17:23 | |
your DNA. So any one size fits all
approach to medicine could soon | 0:17:23 | 0:17:28 | |
become a thing of the past, with
your prescription always being | 0:17:28 | 0:17:32 | |
specific to your needs. That was
Lara looking at the very special | 0:17:32 | 0:17:42 | |
techniques that may soon treat the
diseases none of us want to face. | 0:17:42 | 0:17:47 | |
But there are those who are going
further, they're not just trying to | 0:17:47 | 0:17:52 | |
treat, manage and queue up life
shortening diseases, they're | 0:17:52 | 0:17:55 | |
actually trying to lengthen the
human lifespan. In and I'm assuming | 0:17:55 | 0:18:01 | |
research facility in Silicon Valley,
I met Aubrey De Gray, who is | 0:18:01 | 0:18:08 | |
treating a project that treats
ageing itself as a disease which can | 0:18:08 | 0:18:12 | |
be cured. And he's made some
seemingly quite outlandish claims in | 0:18:12 | 0:18:16 | |
the past. Am I right you are the guy
that said the world's verse 1000 | 0:18:16 | 0:18:22 | |
-year-old has already been born? I
always make clear that I think it's | 0:18:22 | 0:18:29 | |
credible. I do think it is credible.
If we look at the care, it is very | 0:18:29 | 0:18:36 | |
straightforward, the risk of dying
in your 20s is low. If you get to | 0:18:36 | 0:18:40 | |
your 26th birthday your chance of
reaching your 27th birthday is very | 0:18:40 | 0:18:45 | |
high. The only reason that people
don't live to 1000 already is | 0:18:45 | 0:18:52 | |
because of ageing, because their
probability of death in the coming | 0:18:52 | 0:18:56 | |
year goes up. It happens to go up by
10% each year. 10% each year? 10% | 0:18:56 | 0:19:05 | |
more likely to die at the age of 63
than you are at the age of 62 and so | 0:19:05 | 0:19:10 | |
on. That's quite high. But if we can
fix this damage that doesn't happen | 0:19:10 | 0:19:14 | |
any more, the probability of dying
stays only being limited by the | 0:19:14 | 0:19:18 | |
probability of things that don't
have to do with how long ago you | 0:19:18 | 0:19:21 | |
were born. What you're saying is
within the lifetime of someone who | 0:19:21 | 0:19:26 | |
has just been born you will learn
how to effectively cancel out | 0:19:26 | 0:19:30 | |
ageing? We've developed ways at the
molecular and cellular level to | 0:19:30 | 0:19:34 | |
repair the damage the body does to
itself throughout life. This is | 0:19:34 | 0:19:40 | |
called the longevity escape
velocity. We're not talking about | 0:19:40 | 0:19:44 | |
massively lengthening human life,
we're talking about massively | 0:19:44 | 0:19:47 | |
lengthening people's healthy lives.
If people can stay healthy for | 0:19:47 | 0:19:54 | |
longer then there would generally be
improvements in society. What do you | 0:19:54 | 0:19:59 | |
see society being like if everyone
had massively extended lifespan is? | 0:19:59 | 0:20:06 | |
That's not what I focus on, what I
get out of bed for is I don't like | 0:20:06 | 0:20:11 | |
people getting sick. I don't like
the fact 100,000 people a day die of | 0:20:11 | 0:20:16 | |
ageing. I would like to hasten the
defeat of that problem. Also you got | 0:20:16 | 0:20:21 | |
to take into account people are only
going to get older one year per | 0:20:21 | 0:20:25 | |
year. We're not going to have any
1000 -year-old people or at least | 0:20:25 | 0:20:29 | |
900 years whatever happens, and
that's quite a long time to figure | 0:20:29 | 0:20:33 | |
out what to do about it. The
enigmatic Aubrey De Grey. Can you | 0:20:33 | 0:20:38 | |
imagine living in a world where we
all might live to be more than 1000? | 0:20:38 | 0:20:42 | |
That would be strange. Mind you,
first we all have to get past the | 0:20:42 | 0:20:46 | |
year 2049, which according to the
recent Blade Runner movie, is a | 0:20:46 | 0:20:50 | |
pretty bleak time. That film won the
Oscar for Best visual effects last | 0:20:50 | 0:20:54 | |
weekend and to celebrate we thought
we would bring you a bit of extra | 0:20:54 | 0:20:59 | |
behind-the-scenes science that went
into making that world. I think you | 0:20:59 | 0:21:02 | |
found him. That's not possible. If
this gets out... We've bought | 0:21:02 | 0:21:09 | |
ourselves a war.
There's obviously a huge | 0:21:09 | 0:21:14 | |
responsibility to deliver something
for the audience of Blade Runner for | 0:21:14 | 0:21:18 | |
the first one. The expectation
visually making everything look cool | 0:21:18 | 0:21:21 | |
is on our mind every day. The
demands of Vegas and the expectation | 0:21:21 | 0:21:26 | |
of making something that was based
on what we know Vegas now but what | 0:21:26 | 0:21:30 | |
it would be in the future, so we
started with the US Geo data, the | 0:21:30 | 0:21:36 | |
Vegas Valley and the city itself. So
a simple model in the computer. | 0:21:36 | 0:21:42 | |
David Gassner, the art director of
the film, add a simple model of | 0:21:42 | 0:21:46 | |
Vegas with buildings placed around
and so forth, we took those two and | 0:21:46 | 0:21:51 | |
smashed them together initially. We
look for ways very subtly of how to | 0:21:51 | 0:21:55 | |
bring in the human element into the
shots, how to sell that scale, | 0:21:55 | 0:22:02 | |
analysing the work and how to use
graphics on the face of the | 0:22:02 | 0:22:06 | |
buildings, how here in a lot of his
paintings used human scale | 0:22:06 | 0:22:10 | |
futuristic items. We build all that
stuff and placed it around the city | 0:22:10 | 0:22:14 | |
in an organised way to make it look
like people were there at one time, | 0:22:14 | 0:22:18 | |
even though we see no one, and
that's what made it look real or | 0:22:18 | 0:22:21 | |
looked like a place people could
have been in. To build trash may | 0:22:21 | 0:22:25 | |
soak was based on the idea that
everyone had moved to the city and | 0:22:25 | 0:22:30 | |
all the structures are outside the
city had been pretty much abandoned. | 0:22:30 | 0:22:35 | |
No power, no water outside to the
trash is generated for the city was | 0:22:35 | 0:22:39 | |
dumped on the buildings outside the
city. Again, we're trying to based | 0:22:39 | 0:22:44 | |
things, as much reality as we could
so we started with the landscape of | 0:22:44 | 0:22:48 | |
current day California from Los
Angeles versus San Diego and we | 0:22:48 | 0:22:54 | |
determined Icelander was the place
to photograph the ground skate and | 0:22:54 | 0:22:58 | |
the beach. So through aerial
photography of that we placed in the | 0:22:58 | 0:23:02 | |
two on top of each other and then
the sequence of the ships was based | 0:23:02 | 0:23:06 | |
on the Bangladesh ship harvesting
yard where they recapture all the | 0:23:06 | 0:23:10 | |
metal and so forth that happens now.
A lot of the ships and the pieces of | 0:23:10 | 0:23:15 | |
the ships and the idea of these
little tiny human beings working on | 0:23:15 | 0:23:19 | |
these massive structures sort of
growth that look through the middle | 0:23:19 | 0:23:22 | |
of that sequence. So it's a matter
of grabbing all these components | 0:23:22 | 0:23:28 | |
that were based on today's reality,
scaling them so did have this sort | 0:23:28 | 0:23:32 | |
of massive relationship between Kay
and his little spinner in this | 0:23:32 | 0:23:38 | |
enormous landscape and these huge
amounts of trash. It was a matter of | 0:23:38 | 0:23:42 | |
pulling off that scale and distance,
which was just a massive | 0:23:42 | 0:23:48 | |
pulling off that scale and distance,
which was just a massive, the | 0:23:48 | 0:23:48 | |
undertaking in terms of the amount
of data and assets we had to build | 0:23:48 | 0:23:52 | |
and things we had to manage in
itself to pull that off. The future | 0:23:52 | 0:23:57 | |
of the species is finally unearthed.
It is a brilliant film, absolutely | 0:23:57 | 0:24:03 | |
superb. Blade Runner 2049, a
well-deserved Oscar winner there. | 0:24:03 | 0:24:09 | |
That's it from us for this week.
Don't forget we live on Twitter and | 0:24:09 | 0:24:13 | |
Facebook. Thank you very much for
watching and we will see you soon. | 0:24:13 | 0:24:18 |