Deepfakes and the Future of Truth

It’s getting easy to create convincing—but false—videos through artificial intelligence. These “deepfakes” can have interesting applications in art and education, but they can also cause great harm—from ruining the reputation of an ex-partner to provoking international conflicts or swinging elections. When seeing is not believing, who can we trust, and can democracy and truth survive? 

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Episode Transcript

Speaker 1 (00:15):
Pushkin, you're listening to Brave New Planet, a podcast about
amazing new technologies that could dramatically improve our world. Or
if we don't make wise choices, could leave us a
lot worse off. Utopia or dystopia. It's up to us

(00:45):
pash the eye. On July sixteenth, nineteen sixty nine, Apollo
eleven blasted off from the Kennedy Space Center near Cape Canaveral, Florida.
Twenty five million Americans watched on television as the spacecraft
ascended toward the heavens, carrying commander Neil Armstrong, Lunar Module

(01:08):
pilot Buzz Aldron, and Command Module pilot Michael Collins their
mission to be the first humans in history to set
foot on the Moon. Four days later, on Sunday, July twentieth,
the lunar module separated from the command ship and soon
fired its rockets to begin its lunar descent. Five minutes later,

(01:35):
disaster struck about a mile above the Moon's surface. Program
alarms twelve O one and twelve O two sounded loudly,
indicating that the mission computer was overloaded, and then, well,
every American knows what happened next. Lost date of five

(02:05):
good evening, my fellow Americans, President Richard Nick addressed a
grieving nation. Fades has ordained that the men who went
to the Moon to explore in peace will stay on
the Moon to rest in peace. These brave men, Neil

(02:26):
Armstrong and Edwin Auburn, know that there's no hope for
their recovery, but they also know that there is hope
for mankind in their sacrifice. He ended with the now
famous words for every human being who looks up at

(02:46):
the Moon and the nights to come, will know that
there is some corner another word that is forever mankind.
Wait a minute, that never happened. The Moon mission was
a historic success. The three astronauts returned safely to ticker

(03:07):
tape parades and a celebrity thirty eight day world tour.
Those alarms actually did sound, but they turned out to
be harmless. Nixon never delivered that speech. His speechwriter had
written it, but it sat in a folder labeled an
event of Moon disaster until now. The Nixon you just

(03:30):
heard is a deep fake, part of a seven minute
film created by artificial intelligence deep learning algorithms. The fake
was made by the Center for Advanced Virtuality at the
Massachusetts Institute of Technology as part of an art exhibit
to raise awareness about the power of synthesized media. Not

(03:51):
long ago, something like this would have taken a lot
of time and money, But now it's getting easy. You
can make new paintings in the style of French Impressionism,
revived dead movie stars, help patients with nor degenerative disease,
or soon maybe take a class on a tour of
ancient rome. But as the technology quickly becomes democratized, we're

(04:14):
getting to the point where almost anyone can create a
fake video of a friend, an ex lover, a stranger,
or a public figure that's embarrassing, pornographic, or perhaps capable
of causing international chaos. Some argue that in a culture
where fake news spreads like wildfire and political leaders deny

(04:36):
the veracity of hard facts, deep fake media may do
a lot more harm than good. Today's big question will
synthesized media unleash a new wave of creativity or will
it erode the already tenuous role of truth in our democracy?

(04:57):
And is there anything we can do to keep it
in check. My name is Eric Lander. I'm a scientist
who works on ways to improve human health. I helped
lead the Human Genome Project, and today I lead the

(05:17):
Broad Institute of MIT and Harvard. In the twenty first century,
powerful technologies have been appearing at a breathtaking pace related
to the Internet, artificial intelligence, genetic engineering, and more. They
have amazing potential upsides, but we can't ignore the risks
that come with them. The decisions aren't just up to

(05:38):
scientists or politicians. Whether we like it or not, we
all of us are the stewards of a brave New Planet.
This generation's choices will shape the future as never before.
Coming up on today's episode of Brave New Planet, I
speak with some of the leaders behind advances in synthesized media.

(06:02):
You could, certainly, by the way, generate stories that could
be fresh and interesting and new and personal for every child.
We got emails from people who were quadruplegic and they
asked us if we could make them dance. We hear
from experts about some of the frightening ways that bad
actors can use deep fakes. Creditors would chime in and say,

(06:24):
you can absolutely make a deep fake sex video of
your ex with thirty pictures. I've done it with twenty.
Here's the things that keep me up at night right
a video of Donald Trump saying I've launched nuclear weapons
against Iran, and before anybody gets around to firing out
whether this is real or not, we have global nuclear
outdown and we explore how we might prevent the worst abuses.

(06:46):
It's important that younger people advocate for the Internet that
they want. We have to fight for it. We have
to ask for different things. Stay with us, Chapter one,
Abraham Lincoln's Head. To begin to understand and the significance

(07:10):
of deep fake technology, I went to San Francisco to
speak with a world expert on synthetic media. My name
is Alexei or sometimes called Alyosha Afros, and I'm a
professor at UC Berkeley and Computer Science and Lexical Engineering Department.
My research is on computer vision, computer graphics, machine learning,

(07:33):
various aspects of artificial intelligence. Where'd you grow up. I
grew up in Saint Petersburg in Russia. I was one
of those geeky kids playing around with computers or dreaming
about computers. My first computer was actually the first Soviet

(07:55):
personal computer. So you actually are involved in making sort
of synthetic content, synthetic media, that's right. Alexei has invented
powerful artificial intelligence tools, but his lab also has one
full ability to use computers to enhance the human experience.
I was struck by a remarkable video on YouTube created

(08:17):
by his team at Berkeley. So this was a project
that actually was done by my students who didn't even
think of this as anything but a silly little toy
project of trying to see if we could get a
geeky computer science student to move like a ballerina. In

(08:39):
the video, one of the students, Carolyn cham dances with
a skill and grace of a professional despite never having
studied ballet. The idea is, you take a source actor
like a ballerina. There is a way to detect the
limbs of the dancer, have a kind of a skeleton extracted,

(09:00):
and also have my student just move around and do
some geeky moves. And now we're basically just going to
try to sympathize the appearance of my student driven by
the skeleton of the ballerina. Put it all together, and
then we have our grad student dancing pirouets like a ballerina.

(09:23):
Through artificial intelligence, Carolyn's body is puppeteered by the dancer.
We weren't even going to publish it, but we just
released a video on YouTube called Everybody Dance Now, and
somehow it really touched the nerve. Well, there's been an
explosion recently a new ways to manipulate media. Alexei notes

(09:44):
that the idea itself isn't new, It has a long history.
I can't help but ask, given that you come from Russia.
One of the premier users of doctoring photographs I think
was Stalin, who used the ability to manipulate images for
political effect. How did they do that? Can you think

(10:07):
of examples of this and like what was the technology? Then?
The urge to change photographs has been around basically since
the invention of photography. For example, there is a photograph
of Abraham Lincoln that still hangs in many classrooms. That's fake.
It's actually Calhoun with Lincoln's head attached to it. Alexei's

(10:30):
referring to John C. Calhoun, the South Carolina senator and
champion of slavery. A Civil War portrait artist superimposed a
photo of Lincoln's head onto an engraving of Calhoun's body
because he thought Lincoln's gangly frame wasn't dignified enough, and
so they just said Okay, we can use Calhoun. Let's

(10:52):
slap the Lincoln's head on his body. And then, of course,
as soon as you go into the twentieth century, as
soon as you get to dictatorships, this is a wonderful
toy for a dictator to use. So again, Stalin was
big fan of this. He would get rid of people
in photographs once they were out of favor, or once

(11:15):
they got jailed or killed. He would just basically get
them scratched out with reasonably crude techniques. Hitler did it,
Mao did it, Castro did it, Bresnev did it. I'm
sure US agencies have done it. Also, we have always
manipulated images with a desire to change history. This is

(11:36):
Honi f Reed. He's also a professor at Berkeley and
a friend of Alexey's. I'm a professor of computer science
and I'm an expert in digital forensics, where Alexei works
on making synthetic media. Honey has devoted his career to
identifying when synthetic media is being used to fool people,
that is, spotting fakes. He regularly collaborates on this mission

(12:00):
with Alexey so I, met Alyosha efros Ten, twenty years ago.
He is really incredibly creative and clever guy, and he
has done what I consider some of the most interesting
work in computer vision and computer graphics over the last
two decades. And if you really want to do forensics, well,

(12:20):
you have to partner with somebody like Aliosha. You have
to partner with a world class mind who knows how
to think about the synthesis side so that you can
synthesize the absolute best content and then think about how
to detect it. I think it's interesting that if you're
somebody on the synthesis side and developing the forensic there's
a little bit of a jekylin hide there, and I
think it's really fascinating. You know, the idea of altering photos,

(12:44):
it's not entirely new. How far back does this go?
So we used to have in the days of Stalin,
highly talented, highly skilled, time consuming, difficult process of manipulating images,
removing somebody, erasing something from the image, splicing faces together.
And then we moved into the digital age where now

(13:06):
a highly talented digital artist could remove one face and
add another phase, but it was still a time consuming
and required scale. In nineteen ninety four, The makers of
the movie Forrest Gump won an Oscar for Visual Effects
for their representations of the title character interacting with historical
figures like President John F. Kennedy gratulating how do the

(13:29):
field being all Americans? It's very good congratulation. How do
you feel I got? I believe that he had. Now
computers are doing all of the heavy lifting of what
used to be relegated to talented artists. The average person
now can use sophisticated technology to not just capture the recording,

(13:50):
but also manipulate it and then distribute it. The tools
used to create synthetic media have grown by leaps and bounds,
especially in the past few years, and so now we
have technology broadly called deep fake, but more specifically should
be called synthesized content, where you point an image or
a video or an audio to an AI or machine

(14:11):
learning system and it will replace the face for you.
I mean it can do that in an image, it
can do that in a video, or it can synthesize
audio for you in a particular person's voice. It's becomes
straightforward to swap people's faces. There's a popular YouTube video
that features tech pioneer Elon Musk's adult face on a

(14:35):
baby's body, and there's a famous meme where actor Nicholas
Cage's face replaces those of leading movie actors, both male
and female. You can put words into people's mouths and
make them jump and dance and run. You can even
resurrect powerful figures and have them deliver a fake speech

(14:55):
about a fake tragedy. From an Altered History, Chapter two,
Creating Nixon. The text of Nixon's Moon disaster speech that
we heard at the top of the show is actually
not fake. As I mentioned, it was written for President

(15:16):
Nixon as a contingency speech and thankfully never had to
be delivered. It's an amazing piece of writing. It was
written by Bill Safire, who was one of Nixon's speech writers.
This is artist in journalist Francesca Panetta. She's the co
director of the Nixon Fake or MIT's Moon Disaster Team.

(15:37):
She's also the creative director in MIT's Center for Advanced Virtuality.
I was doing experimental journalism at the Guardian newspaper. I
ran the Guardians Virtual Reality studio for the last three years.
The second half of the Moon Disaster team is sound
artist Halsey Bergund. My name is Halsey Bergund. I am

(15:57):
a sound artist and technologist, and I've had a lot
of experience with lots of sorts of audio enhanced with technology,
though this is my first experience with synthetic media, especially
since I typically focus on authenticity of voice and now
I'm kind of doing the opposite. So together, Halsey and
Francesca chose to automate a tragic moment in history that

(16:19):
never actually happened. I think it all started with it
being the fiftieth anniversary of the moon landing last year,
and add on top of that an election cycle in
this country, and dealing with this information, which is obviously
very important in election cycles. It was like lightbulbs went
on and we got very excited about pursuing it. It's

(16:40):
possible to make mediocre fakes pretty quickly and cheaply, but
Francesca and Halsey wanted high production values. So how does
one go about making a first rate fake presidential address?
There are two components. There's the visuals and there's the audio,
and they are completely different processes. So we decided to
go with a video dialogue replacement company called Kenny Ai,

(17:04):
who would do the visuals for us and then we
decided to go with re Speech, who are a dialogue
replacement company for the voice of Nixon. They tackled the
voice first, the more challenging of the two mediums. What
we were told to do was to get two to
three hours worth of Nixon talking. That was pretty easy

(17:25):
because the Nixon Library has hours and hours of Nixon,
mainly giving Vietnam's speeches. The Communist armies of North Vietnam
launched a massive inversion of South Vietnam. That audio was
then chopped up into chunks between one and three seconds long.
We found this incredibly patient actor called Lewis D. Wheeler.

(17:47):
Lewis would listen to the one second clip and then
he would repeat that and do what I believe was right.
Re Speech would say to us things like we need
to change the diagonal attention, which meant nothing to us. Yes,

(18:07):
we have a whole lot of potential band name going forward. Yeah,
Synthetic Nixon is another good one. So once we have
our Nixon model made out of these thousands of tiny clips,
it means that whatever our actor says will come out
then in Nixon's voice. So then what we did was

(18:29):
record the contingency speech of Nixon, and it meant that
we got Lewis's actually performance but in Nixon's voice. What
about the video part? I mean, the video was much easier.
We're talking a couple of days here and a tiny
amount of data just with Lewis's iPhone. We filmed him

(18:50):
reading the contingency speech once a couple of minutes of
him just chatting to camera, and that was it fate
that the men who went to the Moon to explore
in peace will stay on. You know. We were told
by Kenny Ai that everything would be the same in

(19:11):
the video apart from just the area around the mouth.
So every gesture of the hand, every blink, every time
he moved his face, all of that would stay the same,
but just the mouth basically would change. So we used
Nixon's resignation speech to have served in this office, it's
to have felt a very personal sense of it was

(19:35):
the speech of Nixon that looked the most somber, where
he seemed to have the most emotion in his face.
So what actually went on in the computer? Artificial intelligence
sometimes sounds inscrutable, but the basic ideas are quite simple.
In this case, it uses a type of computer program
called an auto encoder. It's trained to take complicated things,

(19:59):
say spoken sentences or pictures, encode them in a much
simpler form, and then decode them to recover the original
as bested. Care the encoder tries to reduce things to
their essence, throwing away most of the information but keeping
enough to do a good job of reconstructing it to

(20:19):
make a deep fake. Here's the trick. Train a speech
auto encoder for Nixon to Nixon, and a speech auto
encoder for actor to actor, but force them to use
the same encoder. Then you can input actor and decoded
as Nixon. If you have enough data. It's a piece

(20:41):
of cake around there. Carefully created video, the Moon Disaster
team created an entire art installation a nineteen sixties living
room with a fake vintage newspaper sharing the fake tragic
news while a fake Nixon speaks solemnly on a vintage
black and white television. Some people, when they were watching

(21:05):
the installation, they watched a number of times. You'd see them,
they'd watch at once, then they would watch it again,
staring at the lips to see if they could see
any lack of synchronicity. We had some people who thought
that perhaps Nixon had actually recorded this speech as a
contingency speech for it to go onto television. Lots of
folks who were listening, viewing, and even press folks just

(21:28):
immediately said, oh, the voice is real or whatever you
said these things that weren't accurate because they just felt
like there wasn't even a question. I suppose that is
what we wanted to achieve, But at the same time,
it was a little bit eye opening and like a
little scary. You know that that could happen. Chapter three,

(21:51):
Everybody dance. What do you see as just the wonderful
upside of having technologies like this? Yeah, I mean a
aion art is becoming a whole field in itself, so creatively,
there is enormous potential. One of the potential positive educational
uses of deep fake technology would be to bring historical

(22:14):
figures back to life to make learning more durable. I
think one could do that with bringing Abraham Lincoln back
to life and having him deliver speeches. Film companies are
really excited about re enactments. We're already beginning to see
this in films like Star Wars, when we're bringing people
like Carrie Fisher back to life. I mean that is
at the moment not being done through deep fake technologies.

(22:37):
This is using fatty traditional techniques of CGI at the moment,
So we still have to see our first deep fake
big cinema screen release. But this is just to come
like the technology is getting better and better. Not only
will we be able to potentially bring back actors and
actresses who are no longer alive and have them star
in movies, but an actor could make a model of

(22:59):
their own voice and then sell the use of that
voice to anybody to do a voiceover of whatever is wanted,
and so they could have twenty of the is going
on at the same time, and the sort of restriction
of their physical presence is no longer there. And that
might mean that, you know, Brad Pitt is in everything,
or it might just mean that lower budget films can

(23:21):
afford to have some of the higher cost talent. At
that point, you know, the top twenty actors could just
do everything. Yes, there's no doubt that there will be
winners and losers from these technologies, but the potential of
synthetic media goes way beyond the arts. There are possible
medical and therapeutic applications. There are companies that are working
very hard to allow people who have either lost their

(23:44):
voice or who never had a voice, to be able
to speak in a way that is either how they
used to speak or in a way that isn't a
canned voice that everybody has. Alexei ePROs and his students
discovered potential uses of synthetic media and medicine quite unintentionally
while working on their Everybody Dance Now project that could

(24:06):
turn anyone into a ballerina. Were kind of surprised for
all the positive feedback we got. We've got emails from
people who were quadriplegic and they asked us if we
could make them dance, and it was very unexpected. So
now we are trying to get the software to be
in a state where people can use it, because yeah,

(24:27):
it's somehow it did hit a nerve with folks. Chapter
four Unicorns in the Andes. The past few years have
seen amazing advances in the creation of synthetic media through
artificial intelligence. The technology now goes far beyond fitting one
face over another face in a video. A recent breakthrough

(24:51):
has made it possible to create entirely new and very
convincing content out of thin air. The breakthrough called generative
adversarial networks or GAMS, came from a machine learning researcher
at Google named Ian Goodfellow. Like auto encoders, the basic
idea is simple but brilliant. Suppose you want to create

(25:16):
amazingly realistic photos of people who don't exist. While you
build a GAN consisting of two computer programs, a photo
generator that learns to generate fake photos and a photo
discriminator that learns to discriminate or identify fake photos from
a vast collection of real photos. You then let the

(25:38):
two programs compete, continually tweaking their code to outsmart each other.
By the time they're done, the GAN can generate amazingly
convincing fakes. You can see for yourself if you go
to the website this Person does Not Exist dot com.
Every time you refresh the page, you're shown a new

(26:00):
uncanny image of a person who, as the website says,
does not and never did exist. Francescan I actually tried
out the website. This young Asian woman. She's got great complexion.
Envious of that neat black hair with a fringe pink

(26:20):
lipstick and a slightly dreamy look as she's kind of
gazing off to her left. Oh, here's a woman who
looks like she could be a neighbor of mine in Cambridge,
probably about sixty five. She's got nice wire framed glasses,
layered hair. Her earrings don't actually match, but that could

(26:43):
just be her distinctive style. I mean, of course, she
doesn't really exist. It's hard to argue that gams aren't
creating original art. In fact, an artist collective recently used
a GAM to create a French Impressionist style portrait. When
Christie's sold it at auction, it fetched an eye popping

(27:07):
four hundred and thirty two thousand dollars. Alexei Efros, the
Berkeley professor, recently pushed gans a step further, creating something
called cycle gans. By connecting two gans together in a
clever way, cycle gans can transform a monet painting into
what's seemingly a photograph of the same scene, or turn

(27:31):
a summer landscape into a winter landscape of the same view.
Alexei's cycle gans seem like magic. If you were to
add in virtual reality, the possibilities become mind blowing. You
may be reminiscing about walking down Saint German and Paris

(27:51):
and with a few clicks. You are there, and you're
walking down the boulevard, and you're looking at all the buildings,
and maybe you can even switch to a different year.
And I think that is I think very exciting as
a way to mentally travel to different places. So if
you do this in VR, I mean, can you imagine

(28:13):
classes going on a class visit to ancient Rome. That's right,
you could imagine from how a particular city like Chrome.
Luke's now trying to extrapolate to how it looked in
the past. It turns out that gans aren't just transforming images.
I spoke with a friend who's very familiar with another
remarkable application of the technology. My name is Reid Hoffman.

(28:37):
I'm a podcaster of Master's Scale. I'm a partner at Greylock,
which is where we're sitting right now, co founder of LinkedIn,
and then a variety of other eccentric hobbies. Reid is
a board member of an unusual organization called open AI.
Open a Eyes is highly concerned with artificial general intelligence
human level intelligence. I helped Sam Altman and Elon Musk

(29:00):
standing up. The basic concern was that if one company
created and deployed that that could be is balancing in
all kinds of ways. And so the thought is, if
it could be created, we should make sure that there
is essentially a nonprofit that is creating this and that
can make that technology available at selective time, slices to

(29:24):
industry as a whole, government, etc. Last year, open ai
released a program that uses gams to write language from
a short opening prompt. The system, called GPT two, can
spin a convincing article or story instead of a deep
fake video. It's deep fake text. It's pretty amazing actually.

(29:47):
For example, open ai researchers gave the program the following prompt.
In a shocking finding, scientists discovered a herd of unicorns
living in a remote, previously unexplored valley in the Andes Mountains.
Even more surprising to the researches was the fact that
the unicorns spoke perfect to English. GPT two took it

(30:08):
from there, the livering nine crisp paragraphs on the landmark discovery.
I asked Franz to read a bit from the story.
Doctor Jorge Perez, an evolutionary biologists from the University of
Lapez and several companions, were exploring the Andes Mountains when
they found a small valley with no other animals or humans.

(30:29):
Perez noticed that the valley had what appeared to be
a natural fountains surrounded by two peaks of rock and
silver snow. Perez and the others then ventured further into
the valley. By the time we reached the top of
one peak, the water looked blue with some crystals on top,
said Perez. Perez and his friends were astonished to see
the unicorn. Heard. Tell me some of the great things

(30:55):
you can do with language generation, well, say, for example, entertainment,
generate stories that could be fresh and interesting and new
and personal for every child. Embed educational things in those
stories of the on into the fact that the story
is involving them and their friends, but also now brings
in grammar and math and other kinds of things as

(31:19):
the doing it generate explanatory material of this kind of
education that works best for this audience, for this kind
of people, like we want to have this kind of
math or this kind of physics, or this kind of
history or this kind of poetry explained in the right way,
and also the style of language right like you know
native city x language. When open ai announced its breakthrough

(31:41):
program for text generation, it took the unusual step of
not releasing the full powered version because it was worried
about the possible consequences. Now, part of the open AI
decision to say we're going to release a smaller model
than the one we did is because we think that
the deep fake problem hasn't been solved. And by the way,

(32:02):
some people complained about that, because they said, well, you're
slowing down our ability to do progress. And so for
the answer and say, look, when these are at least
to the entire public, we cannot control the downside as
well as upsides. Downsides from art to therapy to virtual
time travel, personalized stories and education, synthetic media has amazing upsides.

(32:27):
What could possibly go wrong? Chapter five? What could possibly
go wrong? The downsides are actually not hard to find.
The ability to reshape reality brings extraordinary power, and people
inevitably use power to control other people. It should be

(32:49):
no surprise, therefore, that ninety six percent of fake videos
posted online are non consensual pornography videos, almost always of
women manipulated to depict sex acts that never actually occurred.
I spoke with a professor who studies deep fakes, including
digital attempts to control women's bodies. I'm Danielle Citron and

(33:12):
I am a law professor at Boston University School of Law.
I write about privacy, technology, automation. My newest work and
my next book is going to be about sexual privacy.
So I've worked in and around consumer privacy, individual rights,
civil rights. I write a lot about free speech and
then automated systems. When do you first become aware of

(33:35):
deep fakes? Do you remember when this cross rit I did? So,
there was a Reddit thread devoted to, you know, fake
pornography movies of Gal Jadot Emma Watson. But the reddit
thread sort of spooled not just from celebrities but ordinary people,
and so you had rereditors asking each other, how do
I make a deep fake sex video of max girlfriend?

(33:55):
I have thirty pictures? And then other redditors would chime
in and say, look at this YouTube tutorial. You can
absolutely make a deep fake sex video of your ex
with thirty pictures. I've done it with twenty. In November
two thousand seventeen, an anonymous reditor began posting synthesized porn
videos under the pseudonym deep fakes, perhaps a nod to

(34:18):
the deep learning technology used to create them as well
as the nineteen seventies porn film deep Throat. The Internet
quickly adopted the term deep fakes and broadened its meanings
beyond pornography. To create the videos, he used celebrity faces
from Google image search and YouTube videos and then trains

(34:39):
an algorithm on that content together with pornographic videos. Have
you seen deep fake pornography videos? Yes, so still pretty crude,
so you probably can tell that it's a fake, but
for the person who's inserted into pornography, it's devastating. You
use the neural network technology, the artificial intelligence technology to

(35:03):
create out of digital whole cloth pornography videos using proba
real pornography and then inserting the person in the pornography
so they become the female actress. If it's a female,
it's usually a female in that video. My name is
Noel Martin and I am an activist and Laura Form

(35:26):
campaigner in Australia. Noel is twenty six years old and
she lives in Perth, Australia. So the first time that
I discovered myself on pornographic sites was when I was
eighteen and out of curiosity, decided to Google image reverse

(35:48):
search myself in an instant, like in a less than
a millisecond, my life completely changed. At first, it started
with photos still images stolen from Noel's social media accounts.
They were then doctoring my face from ordinary images and

(36:09):
superimposing those onto the bodies of women depicting me having
sexual intercourse. It proved impossible to identify who was manipulating
Nowell's image in this way. It's still unclear today, which
made it difficult for her to seek legal action. I
went to the police soon after, I contacted government agencies,

(36:32):
tried getting a private investigator. Essentially, there's nothing that they
could do. The sites are hosted overseas, the perpetrators are
probably overseas. The reaction was at the end of the day,
I think you can contact the webmasters to try and
get things deleted. You know, you can adjust your privacy
setting so that nothing is available to anyone publicly. It

(36:56):
was an unwinnable situation. Then things started to escalate. In
twenty eighteen, who Well saw a synthesized pornographic video of
herself and I believe that it was done for the
purposes of silencing me because I've been very public about
my story and advocating for change. So I had actually

(37:20):
gotten email from a fake email address, and you know,
I clicked the link. I was actually at work. It
was a video of me having sexual intercourse. The title
had my name, the face of the woman in it
was edited so that it was my face, and you know,

(37:41):
all the tags were like Noel Martin Australia, feminist, and
it didn't look real, but the context of everything with
the title my face, with the tags all points to
me being depicted in this video. The fakes were of
poor quality, but poor and consumers are in a discriminating lot,

(38:05):
and many people reacted to them as if they were real.
The public reaction was horrifying to me. I was a victim,
blamed and slut shamed, and it's definitely limited the course
of where I can go in terms of career and employment.
Noel finished a degree in law and began campaigning to
criminalize this sort of content. My advocacy and my activism

(38:29):
started off because I had a lived experience of this,
and I experienced it at a time where it wasn't
criminalized in Australia. The distribution of altered intimate images or
altered intimate videos and so I had to petition, meet
with my politicians in my area. I wrote a number

(38:53):
of articles, I spoke to the media, and I was
involved in the law reform in Australia in a number
of jurisdictions in Western Australia and New South Wales, and
I ended up being involved in two press conferences with
the Attorney generals of each state at the announcement of
the law that was criminalizing this abuse. Today, in part

(39:18):
because of Noel's activism, it is illegal in Australia to
distribute intimate images without consent, including intimate images and videos
that have been altered. Although it doesn't encompass all malicious
synthetic media, Noel has made a solid start. Chapter six,

(39:40):
Scissors and Glue. The videos depicting Noel Martin were nowhere
near as sophisticated as those made by the Moon Disastered team.
They were more cheap fakes than deep fakes, and yet
the point didn't have to be perfect to be devastating.
The same turns out to be true in politics. To

(40:00):
understand the power of fakes, you have to understand human psychology.
It turns out that people are pretty easy to fool.
Carry I was running for President of the US. His
stance on the Vietnam War was controversial. Jane Fonda, of course,
was a very controversial figure back then because of her
anti war stand. What have we become as a nation

(40:21):
if we call the men heroes that were used by
the Pentagon to try to exterminate an entire people? What
business have we to try to exterminate a people? And
somebody had created a photo of the two of them
sharing a stage and an anti war rally with the
hopes of damaging the Carry campaign. The photo was fake.
They had never shared a stage together. They just took
two images, probably put it into some standard photo editing

(40:42):
software like a Photoshop, and just put a headline around it,
and out to the world it went. And I will
tell you I remember the most fascinating interview I've heard
in a long time was right after the election, Kerry
of course lost, and a voter was being interviewed and
asked how they voted, and he said he couldn't vote
for Carry, and the interview said, well why not? And

(41:04):
the gentleman said, I couldn't get that photo of John
Carry and Jane Fonda out of my head. And the interviews, well,
you know, that photo is fake, and the guy said,
much to my surprise, yes, but I couldn't get it
out of my mind. And this shows you the power
of visual imagery, Like even after I tell you something
is fake, it still had an impact on somebody, and

(41:24):
I thought, Wow, we're in a lot of trouble because
it's very very hard to put the cat back into
the bag. Once that content is out there, you can't
undo it. So seeing is believing, even above thinking Yeah,
that seems to be the rule. There is very good
evidence from the social science literature that it's very very
difficult to correct the record after the mistakes are out there.

(41:46):
Law professor Danielle Citram also notes that humans tend to
pass on information without thinking, which triggers what she calls
information cascades. Information cascades is a phenomenon where we have
so much information overload that when someone sends us something,
some information, and we trust that person, we pass it on.
We don't even check it's veracity, and so information can

(42:10):
go viral fairly quickly because we're not terribly reflective, because
we act on impulse. Danielle says that information cascades have
been given new life in the twenty first century through
social media. Think about the twentieth century phenomenon, where do
we get most of our information from trusted sources, trusted newspapers,

(42:33):
trusted major couple of TV channels. Growing up, we only
had you know, we didn't have a million, and they
were adhering to journalistic ethics and commitments to truth and
neutrality and notion that you can't publish something without checking it.
Now we are publishing information that most people say. We're
lying on our peers and our friends. Social media platforms

(42:56):
are designed to tailor our information diet to what we
want and to our pre existing views, so we're locked
in a digital echo chamber. We think everybody agrees with us.
We pass on that information. We haven't checked the veracity.
It goes wild and we're especially likely to pass it
on if it's negative and novel. Why's that? It's just

(43:19):
like it's one of our weaknesses. We know how gossip
goes like wildfire online. So like Hillary Clinton as running
a sex ring. That's crazy. Oh my god, Eric, did
you hear about that. I'll post it on Facebook. Eric,
you pass it on. We just can't help ourselves, and
it is much in the way that we love suits

(43:40):
and fats and pizza. You know, we indulge. We don't think.
On some sense, this phenomenon is an old phenomenon. Right
is the famous observation by Mark Twain about how a
lie gets halfway around the world before the truth gets
its pants. Hall. Yeah, the truth still in the bedroom
getting dressed, and we often will see the lie, but

(44:03):
the rebuttal is not seen. It's often lost in the
noise ways of the defamatory statements. That is not new.
But what is new is a number of things about
our information ecosystem are our force multipliers Chapter seven, Truth Decay.

(44:30):
Many experts are worried that the rapid advances in making fakes,
combined with a catalyst of information cascades, will undermine democracy.
The biggest concerns have focused on elections Globally, we are
looking at highly polarized situations where this kind of manipulated

(44:50):
media can be used as a weapon. One of the
main reasons Francesca and Halsey made their Nixon deep fake
was to spread awareness about the risks of misinformation campaigns
before the twenty twenty US presidential election. Similarly, a group
showcased the power of deep fakes by making videos the
run up to the UK parliamentary election showing the two

(45:12):
bitter rivals, Boris Johnson and Jeremy Corman, each endorsing the other.
I wish to rise above this divide and indorse my
worthy opponent, the right Honorable Jeremy Corbyn. SIPI Prime Minister
of our United Kingdom, back Boris Johnson to continue as
our Prime Minister. But you know what, don't listen to me.

(45:33):
I think I may be one of the thousands of
deep fakes on the Internet, using powerful technologies to tell
stories that aren't so. This just kind of indicates how
candidates and political figures can be misrepresented, and you just
need to feed them into people's social media feeds for

(45:54):
them to be seeing this at times when the stakes
are pretty high. So far, we haven't yet seen sophisticated
deep fakes in US or UK politics. That might be
because fakes will be most effective if they're tim for
XM chaos, say close to election day, when newsrooms won't
have the time to investigate and debunk them. But another

(46:16):
reason might be the cheap fakes made with basic video
editing software are actually pretty effective. Remember the video that
surfaced of how speaker Nancy Pelosi, in which she appeared
intoxicated and confused. We want to give this president the
opportunity to something historic for our country. Both President Trump

(46:42):
and Rudy Giuliani shared the video as fact on Twitter.
The video is just a cheap fake, just slowed down
Pelosi's speech to make her seem incompetent. But maybe elections
won't be the biggest targets. Some people worry that deep
fakes could be weaponized to foment international conflict. Berkeley professor

(47:03):
Honey f Reed has been working with US government's Media
Forensics program to address this issue. DARPA, the Defense Department's
research arm, has been pouring a lot of money over
the last five years into this program. They are very
concerned about how this technology can be a threat to
national security and also how when we get images and

(47:24):
videos from around the world in areas of conflict, do
we know if they're real or not? Is this really
an image of a US soldier who has been taken hostage?
How do we know? So? What do you see as
some of the worst case scenarios. Here's the things that
keep me up at night. Right. A video of Donald
Trump saying I've launched nuclear weapons against Iran, and before

(47:45):
anybody gets around to figuring out whether this is real
or not, where we have global nuclear moutdown. And here's
the thing. I don't think that that's likely, but I
also don't think that the probability of that is zero.
And that should worry us because while it's not likely,
the consequences are spectacularly bad. Lawyer Danielle Citram worries about

(48:06):
an even more plausible scenario. And imagine a deep fake
of a well known American general burning a koran and
it is timed at a very tense moment in a
particular most you know country, whether it's Afghanistan. It could
then lead to physical violence. And you think this could

(48:28):
be made. No general, no qoran actually used in the video,
just programmed. You can use the technology to mine existing photographs.
Kind of easy, especially with someone like take Jim Mattis
when he was our defense secretary. Of Jim Mattis, you know,
actually taking a koran and ripping it in half and
say all Muslims should die. Imagine the chaos in diplomacy,

(48:53):
the chaos of our soldiers abroad in Muslim countries. It
would be inciting violence without question. Well, we haven't yet
seen spectacular fake videos used to disrupt elections or create
international chaos. We have seen recingly sophisticated attacks on public policymaking.

(49:13):
So we've got an example in twenty seventeen where the
FEC solicited public comment on the proposal to repeal net neutrality.
Net neutrality is the principle that internet service providers should
be a neutral public utility. They shouldn't discriminate between websites,
say slowing down Netflix streaming to encourage you to purchase

(49:35):
a different online video service. As President Barack Obama described
in twenty fourteen, there are no gatekeepers deciding which sites
you get to access. There are no toll roads on
the information super Highway. Federal Communications Policy had long supported
net neutrality, but in twenty seventeen, the Trump administration favored

(49:56):
repealing the policy. There were twenty two million comments that
the FEC received, but ninety six percent of those were
actually fake. The interesting thing is the real comments were
opposed to repeal, whereas the fake comments were in favor.
A Wall Street Journal investigation exposed that the fake public

(50:19):
comments were generated by bots. It found similar problems with
public comments about pay data lending. The bots varied their
comments in a combinatorial fashion so that the content wasn't identical.
With a little sleuthing, though, you could see that they
were generated by computers. But with the technology increasingly able

(50:40):
to generate completely original writing, like open aiyes program that
wrote the story about unicorns in the Andes, it's going
to become hard to spot the fakes. So there was
this Harvest student, Max Weiss, who used GPT two to
kind of demonstrate this, And I went on his site
yesterday and he's got this little test where you need

(51:02):
to decide whether a comment is real or fake. So
you go on and you read it and you decide
whether it's been written by a bot or by a human.
So I did this, and the ones that seemed to
be really well written and quite narrative discussive, generally I
was picking them as human. I was wrong almost all
the time. It was amazing and alarming. In our democracy,

(51:26):
public comments have been an important way in which citizens
can make their voices heard, but now it's becoming easy
to drown out those voices with millions of fake opinions. Now,
the downfall of truth likely won't come with a bang,
but a whimper, a slow, steady erosion that some call

(51:47):
truth decay. If you can't believe anything you read, or
hear or see anymore, I don't know how you have
a democracy a I don't know, frankly, how we have
civilized society if everybody's going to live in an echo
chamber believing their own version of events. How do we
have a dialogue if we can't agree on basic facts.
In the end, the most insidious impact of deep fakes

(52:07):
may not be the deep fake content itself, but the
ability to claim that real content is fake. It's something
that Danielle Citron refers to as the liar's dividend. The
liar's dividend is that the more you educate people about
the phenomenon of deep fags, the more the wrongdoer can
disclaim reality. Think about what President Trump did with the

(52:31):
Access Hollywood tape. You know, I'm automatically attracted to beautiful
I just started kissing them. It's like a magnet, kid.
I don't even know it. And when you're started, they
let you do it. You can do anything whatever you want.
Grab them by the pro I can do anything. Initially,
Trump apologized for the remarks. Anyone who knows me knows

(52:51):
these words don't reflect who I am. I said it,
I was wrong, and I apologize. But in twenty seventeen,
a year after his initial apology and with the idea
of deep fake content starting to gain attention, Trump changed
his tomb upon reflection, he said, they're not real. That

(53:11):
wasn't me. I don't think that was my voice. That's
the liar's dividend. In practice, the Trump comments about excess
Hollywood was remarkable. Slightly more subtle than that, he said,
I'm not sure that was me. Right, Well, that's the
corrosive gas lighting. Chapter eight, A Life Stored in the Cloud.

(53:44):
Deep fakes have the potential to devastate individuals and harms society.
The question is can we stop them from spreading before
they get out of control. To do so, we'd need
reliable ways to spot deep fakes. So the good news
is there are still artifacts in the synthesized content, whether
those are images, audio, or a video, that we as

(54:06):
the experts, can tell apart. So when, for example, The
New York Times wants to run a story with a video,
we can help them validate it. What are the real
sophisticated experts looking. Yeah, so the eyes are really wonderful
forensically because they reflect back to you what is in
the scene. So I'm sitting now right now in a studio,

(54:26):
there's maybe about a dozen or so lights around me,
and you can see this very complex set of reflections
in my eyes. So we can analyze fairly complex lighting patterns,
for example, to determine if this is one person's head
spliced onto another person's body, or if the two people
standing next to each other were digitally inserted from another photograph.
I could spend another hour telling you about the many

(54:48):
different forensic techniques that we've developed. There's no silver bullet here.
Really is a sort of a time consuming and deliberate
and thoughtful and it requires many many tools, and it
requires people with a fair amount of skill to do this.
Honey Freed also has quite a few detection techniques that
he won't speak about publicly for fear of the deep
fake creators will learn how beat his tests. I don't

(55:10):
create a GitHub repository and give my code to all
my adversaries. I don't have just one forensic techniques. I
have a couple dozen of them. So that means you,
as the person creating this now have to go back
and implement twenty different techniques and you have to do
it just perfectly, and that makes the landscape a little
bit more tricky for you to manage. As technology makes

(55:30):
it easier to create deep fakes, a big problem will
be the sheer amounts of content to review. So the
average person can download software repositories, and so it's getting
to the point now where the average person can just
run these as if they're running any standard piece of software.
There's also websites that have propped up where you can
pay them twenty bucks and you tell them, please put

(55:52):
this person's face into this person's video, and they will
do that for you. And so it doesn't take a
lot to get access to these tools. Now, I will
say that the output of those are not quite as
good as what we can create inside the lab. And
you just know what the trend is. You just know
it's going to get better and cheaper and faster and
easy to use. Detecting dow fakes will be a never
ending cat and mouse game. Remember how generative adversarial networks

(56:17):
or gams are built by training a fake generator to
outsmart a detector. Well. As detectors get better, fake generators
will be trained to keep pays still. Detectives like Honey
and platforms like Facebook are working to develop automated ways
to spot deep fakes rapidly and reliably. That's important because

(56:40):
more than five hundred additional hours of video are being
uploaded to YouTube every minute. I don't mean to sound
defeatist about this, but I'm going to lose this war.
I know this because it's always going to be easier
to create content than it is to detect it. But
here's where I will win. I will take it out
of the hands of the average person. So think about,

(57:03):
for example, the creation of counterfeit currency. With the latest
innovations brought on by the Treasure Department, it is hard
for the average person to take their inkjet printer and
create compelling fake currency. And I think that's going to
be the same trend here is that if you're using
some off the shelf tool, if you're paying somebody on
the website, we're going to find you, and we're going
to find you quickly. But if you are a dedicated,

(57:24):
highly skilled of the time and the effort to create it,
we are going to have to work really hard to
detect those. Given the challenges of detecting fake content, some
people envision a different kind of techno fix. They propose
developing airtight ways for content creators to mark their own
original video as real. That way, we get instantly recognize

(57:48):
an altered version if it wasn't identical. Now there's ways
of authenticating at the point of recording, and these are
what it's called control capture system. So here's the idea.
You use a special app on your mobile device that,
at the point of capture, a cryptographically signs the image
of the video or the audio. It puts that signature
onto the blockchain. The only thing you have to know

(58:09):
about the blockchain is that it is an immutable distributed ledger,
which means that that signature is essentially impossible to manipulate.
And now all of that happened at the point of recording.
If I was running a campaign today and I was
worried about my candidates likeness being misused, absolutely every public
event that they were at, I would record with a
control capture system and I'd be able to prove what

(58:31):
they actually said or did at any point in the future.
So this approach would shift the burden of authentication so
the people creating the videos rather than publishers or consumers.
Law professor Danielle Citron has explored how this solution could
quickly become dystopium. We might see the emergence of an

(58:52):
essentially an audit trail of everything you do and say
all of the time. Danielle refers to the business model
as immutable lifelogs in the cloud. In a way we
sort of already seen it. There are health plans that
if you wear a fitbit all the time and you
let yourself be monitored as your insurance, you know your
health insurance rates. But you can see how if the

(59:13):
incentives are there in the market to self surveil, whether
it's for health insurance, life insurance, car insurance, we're going
to see the unraveling of bribe to say by ourselves.
You know, corporations may very well, because the CEO is
so valuable, they may say you've got to have a log,

(59:35):
an immutable audit trail of everything you do and say.
So when that deep fake comes up the night before
the IPO, you can say, look, the CEO wasn't taking
the bribe wasn't having sex with a prostitute, and so
we have proof because we have an auto trail, we
have a log. So when we were imagining, we were
imagining a business model that hasn't quite come up, but

(59:58):
we have gotten a number of requests from insurance companies
as well as companies to say we're interested in this idea.
So how much has to be in that log? Does
this have to be a whole video of your life?
That is a great question, one that terrifies us. So
it may be that you're logging locate geolocation, you're logging videos,

(01:00:19):
you see people talking and who they're interacting with, and
that might be good enough to prevent the mischief that
would hijack the IP. Your whole life online, yes, stored securely,
our clock down, protected in the cloud. It is, at
least for a privacy scholar. There are so many reasons
why we ought to have privacy that aren't about hiding things.

(01:00:42):
It's about creating spaces and managing boundaries around ourselves and
our intimates and our loved ones. So I worry that
if we entirely unravel privacy A in the wrong hands
is very dangerous. Right B It changes how we think
about ourselves and humanity? Chapter nine, Section two thirty. So

(01:01:10):
technofixes are complicated. What about passing laws to ban deep
fakes or at least deep fakes that don't disclose their
fake So the video and audio is speech in our
First Amendment doctrine is very much a protective of free speech,
and the Supreme Court has explained that lies just lies
themselves without harm is protected speech. When lies cause certain

(01:01:34):
kinds of harm, we can regulate it. Defamation of private people, threats, incitement, fraud,
impersonation of government officials. What about lies concerning public figures
like politicians? California and Texas, for instance, recently pass laws
making it illegal to publish deep fakes of a candidate

(01:01:56):
in the weeks leading up to an election. It's not
clear yet whether the laws will pass constitutional muster. As
you're saying in an American content, we are just not
going to be able to law great fakes. Yeah, we
can't have a flat van, and I don't think we should.
It would fail on doctrinal grounds, but ultimately it would

(01:02:17):
prevent the positive uses. Interestingly, in January twenty twenty, China,
which has no First Amendment protecting free speech promulgated regulations
banning deep fakes. The use of AI or virtuality now
needs to be clearly marked in a prominent manner, and
the failure to do so is considered a criminal offense.

(01:02:40):
To explore other options for the US, I went to
speak with a public policy expert. My name is Joan Donovan,
and I work at Harvard Kennedy Shorenstein Center, where I
lead a team of researchers looking at medium manipulation and
disinformation campaigns. Joan is head of the Technology and Social
Change Research Project, and her staff studies how social media

(01:03:03):
gives rise to hoaxes and scams. Her team is particularly
interested and precisely how misinformation spreads across the Internet. Ultimately,
underneath all of this is the distribution mechanism, which is
social media and platforms. And platforms have to rethink the

(01:03:23):
openness of their design because that has now become a
territory for information warfare. In early twenty twenty, Facebook announced
a major policy change about synthesized content. Facebook preissued policies
now on deep fake saying that if it is an
AI generated video and it's misleading in some other contextual way,

(01:03:47):
then they will remove it. Interestingly, Facebook ban the Moon
Disaster Team's Nixon video even though it was made for
educational purposes, but didn't remove the slowed down version of
Nancy Pelosi, which was made to mislead the public. Why
because the Pelosi video wasn't created with artificial elligience. For now,

(01:04:11):
Facebook is choosing to target deep fakes, but not cheap fakes.
One way to push platforms to take a stronger stance
might be to remove some of the legal protections that
they currently enjoy. Under Section two thirty of the Communication's
Decency Act past in nineteen ninety six, platforms aren't legally
liable for content posted by its users. The fact that

(01:04:34):
platforms have no responsibility for the content they host has
an upside. It's led to the massive diversity of online
content we enjoyed today, but it also allows a dangerous
escalation of fake news. Is it time to change section
two thirty to create incentives for platforms to police false content?

(01:04:57):
I ask the former head of a major platform, LinkedIn
co founder Reid Hoffman. For example, let's take my view
of what the response to the Christchurch shooting should be
as to say, well, we want you to solve, not
having terrorism, murderer or murderers displayed to people. So we're
simply going to do a fine of ten thousand dollars

(01:05:19):
per view. Two shootings occurred at mosques in Christchurch, New
Zealand in March twenty nineteen. Graphic videos of the event
were soon posted online. Five people saw it, that's fifty
thousand dollars. But if he becomes a meme and a
million people see it, that's ten billion dollars. Yes, right,

(01:05:39):
So what's really trying to do is get you to say,
let's make sure that the meme never happens. Okay, So
that's a governance mechanism there. Yes, you find the channel
the platform based on number of views would be a
very general way to say. Now you guys have to solve.
Now you solve, you figure it out. What about other solutions?

(01:06:01):
If we are to make regulation, it should be about
the amount of staff in proportion to the out of
users so that they can get a handle on the content.
But can they be fast enough. Maybe the viral spread
should be slowed down enough to allow them to moderate.
Let's put it this way. The stock market has certain

(01:06:25):
governors built in when there's massive changes in a stock price.
There are decelerators that kick in, breaks that kick in
should the platforms have breaks that kick in before something
can go fully viral. So in terms of deceleration, there
are things that they do already that accelerate the process
that they need to think differently about, especially when it

(01:06:48):
comes to something turning into a trending topic. So there
needs to be an intervening moment before things get to
the homepage and get to trending, where there is a
content review. So much to say here, but I want
to think particularly about listeners who are in their twenties
and thirties, are very tech savvy. They're going to be

(01:07:12):
part of the solution here. What would you say to
them about what they can do? I think it's important
that younger people advocate for the Internet that they want.
We have to fight for it, We have to ask
for different things, and that kind of agitation can come

(01:07:33):
in the form of posting on the platform, writing letters,
joining groups like Fight for the Future, and trying to
work on getting platforms to do better and to advocate
for the kind of content that you want to see
more of. The important thing is that our society is

(01:07:56):
shaped by these platforms and so we're not going to
do away with them, but we don't have to make
do with them either. Conclusion, choose your planet. So there

(01:08:17):
you have it. Stewards of the Brave New Planet. Synthetic
media or deep fakes. People have been manipulating content for
more than a hundred years, but recent advances in AI
have taken it to a whole new level of verisimilitude.
The technology could transform movies and television, favored actors from

(01:08:38):
years past starring in new narratives, along with actors who
never existed, patients regaining the ability to speak in their
own voices, personalized stories created on demand for any child
around the globe, matching their interests, written in their dialect,
representing their communities. But there's also great potential for harm,

(01:09:02):
the ability to cast anyone in a pornographic video, weaponized
media dropping days before an election, or provoking international conflicts.
Are we going to be able to tell fact from fiction?
Will truth survive? And what does it mean for our democracy? Better?
Fake detection may help, but it'll be hard for it

(01:09:25):
to keep up, and logging our lives in blockchain to
protect against misrepresentation doesn't sound like an attractive idea. Outright
bands on deep fakes are being tried in some countries,
but they're tricky in the US given our constitutional protections
for free speech. Maybe the best solution is to put

(01:09:45):
the liability on platforms like Facebook and YouTube. If we
can joan Donovan's right to get the future you want,
you're going to have to fight for it. You don't
have to be an expert, and you don't have to
do it alone. When enough people get engaged, we make
wise choices. Deep fakes are a problem that everyone can

(01:10:07):
engage with. Brainstorm with your friends about what should be done.
Use social media. Tweet at your elected representatives to ask
if they're working on laws, like in California and Texas.
And if you work for a tech company, ask yourself
and your colleagues if you're doing enough. You can find

(01:10:27):
lots of resources and ideas at our website Brave New
Planet dot org. It's time to choose our planet. The
future is up to us. Brave New Planet is a

(01:10:49):
coproduction of the Broad Institute of MT and Harvard Pushkin
Industries in the Boston Globe, with support from the Alfred P.
Sloan Foundation. Our show is produced by Rebecca Lee Douglas
with Mary Doo theme song composed by Ned Porter, mastering
and sound designed by James Garver, fact checking by as
If Fridman and a Stitt and Enchant. Special thanks to

(01:11:13):
Christine Heenan and Rachel Roberts at Clarendon Communications, to Lee McGuire,
Kristen Zarelli and Justine Levin Allerhans at the Broad, to
mil Lobell and Heather Faine at Pushkin, and to Eliah
Edie Brode who made the Broad Institute possible. This is
brave new planet. I'm Eric Lander.