Speaker 1: Noah Feldman here. I'm excited to tell you about a
Speaker 1: special five part Deep Background mini series called Deep Bench.
Speaker 1: The first episodes will appear in your feed on Saturday,
Speaker 1: October seventeenth. The battle for the Supreme Court has become
Speaker 1: a huge issue in the presidential election. In many ways,
Speaker 1: it's a culmination of a conservative legal revolution spearheaded by
Speaker 1: the Federalist Society. Deep Bench is the inside story of
Speaker 1: how these legal conservatives gained power and how, at the
Speaker 1: height of their influence, they're actually in danger of splitting apart.
Speaker 1: But first, we're presenting an episode from Pushkin Industry's newest show,
Speaker 1: Brave New Planet. Every day we see how powerful technologies
Speaker 1: are advancing at a breathtaking pace. They have amazing potential upsides,
Speaker 1: but if we're not careful, some might leave us a
Speaker 1: lot worse off. In Brave New Planet, doctor Eric Lander
Speaker 1: and his guests weigh the pros and cons of a
Speaker 1: wide range of powerful innovations in science and technology. Doctor
Speaker 1: Lander directs the Broad Institute of MIT and Harvard. He
Speaker 1: was a leader of the Human Genome Project for eight years.
Speaker 1: He served as a science advisor to President Obama's White House.
Speaker 1: In this episode, doctor Lander explores deep fakes. Deep fakes
Speaker 1: can be useful in art, education and therapy, but could
Speaker 1: they be weaponized to provoke international conflicts or swing elections?
Speaker 1: And where does the right to free speech fit in?
Speaker 1: Every episode of Brave New Planet will grapple with opportunities
Speaker 1: and challenges that are too big to fit in a tweet,
Speaker 1: but will shape our future. You can subscribe in Apple Podcasts.
Speaker 1: Here's the brilliant Eric Lander and Brave New Planet. Your
Speaker 1: to Brave New Planet, a podcast about amazing new technologies
Speaker 1: that could dramatically improve our world, or, if we don't
Speaker 1: make wise choices, could leave us a lot worse off
Speaker 1: Utopia or dystopia. It's up to us. On July sixteenth,
Speaker 1: nineteen sixty nine, Apollo eleven blasted off from the Kennedy
Speaker 1: Space Center near Cape Canaveral, Florida. Twenty five million Americans
Speaker 1: watched on television as the spacecraft ascended toward the heavens,
Speaker 1: carrying Commander Neil Armstrong, Lunar Module pilot Buzz Aldron, and
Speaker 1: Command Module pilot Michael Collins their mission to be the
Speaker 1: first humans in history to set foot on the Moon.
Speaker 1: Four days later, on Sunday, July twentieth, The lunar module
Speaker 1: separated from the command ship and soon fired its rockets
Speaker 1: to begin its lunar descent. Five minutes later, disaster struck
Speaker 1: about a mile above the Moon's surface. Program alarms twelve
Speaker 1: oh one and twelve O two sounded loudly, indicating that
Speaker 1: the mission computer was overloaded, and then well, every American
Speaker 1: knows what happened next lost date of five good evening,
Speaker 1: my fellow Americans. President Richard Nixon addressed a grieving nation.
Speaker 1: Fates has ordained that the men who went to the
Speaker 1: Moon to explore in peace will stay on the Moon
Speaker 1: to rest in peace. These men, Neil Armstrong and Edwin Auburn,
Speaker 1: know that there is no pope for their recovery, but
Speaker 1: they also know that there is hook for mankind in
Speaker 1: their sacrifice. He ended with the now famous words for
Speaker 1: every human being who looks up at the Moon and
Speaker 1: the nights to come, will know that there is some
Speaker 1: pun or another word that is forever mankind. Wait a minute,
Speaker 1: that never happened. The Moon mission was a historic success.
Speaker 1: The three astronauts returned safely to ticker tape parades and
Speaker 1: a celebratory thirty eight day World Tour. Those alarms actually
Speaker 1: did sound, but they turned out to be harmless. Nixon
Speaker 1: never delivered that speech. His speech writer had written it,
Speaker 1: but it sat in a folder labeled an event of
Speaker 1: Moon disaster until now. The Nixon you just heard is
Speaker 1: a deep fake, part of a seven minute film created
Speaker 1: by artificial intelligence deep learning algorithms. The fake was made
Speaker 1: by the Center for Advanced Virtuality at the Massachusetts Institute
Speaker 1: of Technology as part of an art exhibit to raise
Speaker 1: awareness about the power of synthesized media. Not long ago,
Speaker 1: something like this would have taken a lot of time
Speaker 1: and money, But now it's getting easy. You can make
Speaker 1: new paintings in the style of French Impressionism, revived dead
Speaker 1: movie stars, help patience with ner degenerative disease, or soon
Speaker 1: maybe take a class on a tour of ancient Rome.
Speaker 1: But as the technology quickly becomes democratized, we're getting to
Speaker 1: the point where almost anyone can create a fake video
Speaker 1: of a friend, an ex lover, a stranger, or a
Speaker 1: public figure that's embarrassing, pornographic, or perhaps capable of causing
Speaker 1: international chaos. Some argue that in a culture where faked
Speaker 1: news spreads like wildfire, and political leaders deny the veracity
Speaker 1: of hard facts. Deep faked media may do a lot
Speaker 1: more harm than good. Today's big question will synthesized media
Speaker 1: unleash a new wave of creativity or will it erode
Speaker 1: the already tenuous role of truth in our democracy? And
Speaker 1: is there anything we can do to keep it in check.
Speaker 1: My name is Eric Lander. I'm a scientist who works
Speaker 1: on ways to improve human health. I helped lead the
Speaker 1: Human Genome Project, and today I lead the Broad Institute
Speaker 1: of MIT and Harvard. In the twenty first century, powerful
Speaker 1: technologies have been peering at a breathtaking pace related to
Speaker 1: the Internet, artificial intelligence, genetic engineering, and more. They have
Speaker 1: amazing potential upsides, but we can't ignore the risks that
Speaker 1: come with them. The decisions aren't just up to scientists
Speaker 1: or politicians. Whether we like it or not, we all
Speaker 1: of us are the stewards of a brave New planet.
Speaker 1: This generation's choices will shape the future as never before.
Speaker 1: Coming up on today's episode of Brave New Planet, I
Speaker 1: speak with some of the leaders behind advances in synthesized media.
Speaker 1: You could, certainly, by the way, generate stories that could
Speaker 1: be fresh and interesting and new and personal for every child.
Speaker 1: We got emails from people who were quadruplegic and they
Speaker 1: asked us if we could make them dance. We hear
Speaker 1: from experts about some of the frightening ways that bad
Speaker 1: actors can use deep fakes. Creditors would chime in and say,
Speaker 1: you can absolutely make a deep fake sex video of
Speaker 1: your ex with thirty pictures. I've done it with twenty.
Speaker 1: Here's the things that keep me up at night. Right
Speaker 1: a video of Donald Trump saying I've launched nuclear weapons
Speaker 1: against Iran. And before anybody gets around to figuring out
Speaker 1: whether this is real or not, we have global nuclear
Speaker 1: mountdown and we explore how we might prevent the worst abuses.
Speaker 1: It's important that younger people advocate for the Internet that
Speaker 1: they want. We have to fight for it. We have
Speaker 1: to ask for different things. Stay with us, Chapter one,
Speaker 1: Abraham Lincoln's Head. To begin to understand the significance of
Speaker 1: deep fake technology, I went to San Francisco to speak
Speaker 1: with a world expert on synthetic media. My name is
Speaker 1: alexey Or sometimes called alyosha Eros, and I'm a professor
Speaker 1: at UC Berkeley and Computer Science and Electrical Engineering Department.
Speaker 1: My research is on computer vision, computer graphics, machine learning,
Speaker 1: various aspects of artificial intelligence. Where'd you grow up? I
Speaker 1: grew up in Saint Petersburg in Russia. I was one
Speaker 1: of those geeky kids playing around with computers or dreaming
Speaker 1: about computers. My first computer was actually the first Soviet
Speaker 1: personal computer. So you actually are involved in making sort
Speaker 1: of synthetic content, synthetic media. That's right. Alexei has invented
Speaker 1: powerful artificial intelligence tools, but his lab also has a
Speaker 1: wonderful ability to use computers to enhance the human experience.
Speaker 1: I was struck by a remarkable video on YouTube created
Speaker 1: by his team at Berkeley. So this was a project
Speaker 1: that actually was done by my students who didn't even
Speaker 1: think of this as anything but a silly little toy
Speaker 1: project of trying to see if we could get a
Speaker 1: geeky computer science student to move like a ballerina. In
Speaker 1: the video, one of the students, Carolyn cham dances with
Speaker 1: a skill and grace of a professional, despite never having
Speaker 1: studied ballet. The idea is you take a source actor
Speaker 1: like a ballerina. There is a way to detect the
Speaker 1: limbs of the dancer have a kind of a skeleton
Speaker 1: extracted and also have my student just move around and
Speaker 1: do some geeky moves. And now we're basically just going
Speaker 1: to try to sympathize the appearance of my student driven
Speaker 1: by the skeleton of the ballerina. Put it all together,
Speaker 1: and then we have our grad student dancing pirouets like
Speaker 1: a ballerina through artificial intelligence. Carolyn's body is puppeteered by
Speaker 1: the dancer. We weren't even going to publish it, but
Speaker 1: we just released a video on YouTube called Everybody Dance Now,
Speaker 1: and somehow it really touched the nerve. Well, there's been
Speaker 1: an explosion recently a new ways to manipulate media. Alexei
Speaker 1: notes that the idea itself isn't new, it has a
Speaker 1: long history. I can't help but ask, given that you
Speaker 1: come from Russia. One of the premier users of doctoring
Speaker 1: photographs I think was Stalin, who used the ability to
Speaker 1: manipulate images for political effect. How did they do that?
Speaker 1: Can you think of examples of this and like, what
Speaker 1: was the technology? Then? The urge to change photographs has
Speaker 1: been around basically since the invention of photography. For example,
Speaker 1: there is a photograph of Abraham Lincoln that still hangs
Speaker 1: in may classrooms. That's fake. It's actually Calhoun with Lincoln's
Speaker 1: head attached it. Alexei's referring to John C. Calhoun, the
Speaker 1: South Carolina senator and champion of slavery. A Civil War
Speaker 1: portrait artist superimposed a photo of Lincoln's head onto an
Speaker 1: engraving of Calhoun's body because they thought Lincoln's gangly frame
Speaker 1: wasn't dignified enough, and so they just said, okay, we
Speaker 1: can use Calhoun. Let's slap the Lincoln's head on his body.
Speaker 1: And then, of course, as soon as you go into
Speaker 1: the twentieth century, as soon as you get to dictatorships,
Speaker 1: this is a wonderful toy for a dictator to use.
Speaker 1: So again, Stalin was a big fan of this. He
Speaker 1: would get rid of people in photographs once they were
Speaker 1: out of favor or once they got jailed or killed.
Speaker 1: He would just basically get them scratched out with reasonably cruetics.
Speaker 1: Hitler did it, Mao did it, Castro did it, Bresnev
Speaker 1: did it. I'm sure US agencies have done it also.
Speaker 1: We have always manipulated images with a desire to change history.
Speaker 1: This is Honi for Reed. He's also a professor at
Speaker 1: Berkeley and a friend of Alexei's. I'm a professor of
Speaker 1: computer science and I'm an expert in digital forensics, where
Speaker 1: Alexei works on making synthetic media. Honey has devoted his
Speaker 1: career to identifying when synthetic media is being used to
Speaker 1: fool people, that is spotting fakes. He regularly collaborates on
Speaker 1: this mission with Alexei. So I met Alyosha efros ten
Speaker 1: twenty years ago. He is a really incredibly creative and
Speaker 1: clever guy, and he has done what I consider some
Speaker 1: of the most interesting work in computer vision and computer
Speaker 1: graphics over the last two decades. And if you really
Speaker 1: want to do forensics, well, you have to partner with
Speaker 1: somebody like Alyosha. You have to partner with world class
Speaker 1: mind who knows how to think about the synthesiside so
Speaker 1: that you can synthesize the absolute best content and then
Speaker 1: think about how to detect it. I think it's interesting
Speaker 1: that if you're somebody on the synthesis side and developing
Speaker 1: the forensic there's a little bit of a jekylin hide there,
Speaker 1: and I think it's really fascinating. You know, the idea
Speaker 1: of altering photos, it's not entirely new How far back
Speaker 1: does this go? So we used to have in the
Speaker 1: days of Stalin, highly talented, highly skilled, time consuming, difficult
Speaker 1: process of manipulating images, removing somebody, erasing something from the image,
Speaker 1: splicing faces together. And then we moved into the digital age,
Speaker 1: where now a highly talented digital artist could remove one
Speaker 1: face and add another face, but it was still a
Speaker 1: time consuming and required scale. In nineteen ninety four, the
Speaker 1: makers of the movie Forrest Gump won an Oscar for
Speaker 1: Visual Effects for their representations of the title character interacting
Speaker 1: with historical figures like President John F. Kennedy gratulate. How
Speaker 1: does it feel to be an all Americans? Very good gratulation?
Speaker 1: How do you feel? I believe it. Now computers are
Speaker 1: doing all of the heavy lifting of what used to
Speaker 1: be relegated to talented artists. The average person now can
Speaker 1: use sophisticated technology to not just capture the recording, but
Speaker 1: also manipulate it and then distribute it. The tools used
Speaker 1: to create synthetic media have grown by leaps and bounds,
Speaker 1: especially in the past few years, and so now we
Speaker 1: have technology broadly called deep fake, but more specifically should
Speaker 1: be called synthesized content where you point an image or
Speaker 1: a video or an audio to an AI or machine
Speaker 1: learning system, and it will replace the face for you.
Speaker 1: I mean it can do that in an image, it
Speaker 1: can do that in a video, or it can synthesize
Speaker 1: audio for you in a particular person's voice. It's becomes
Speaker 1: straightforward to swap people's faces. There's a popular YouTube video
Speaker 1: that features tech pioneer Elon Musk's adult face on a
Speaker 1: baby's body, and there's a famous meme where actor Nicholas
Speaker 1: Cage's face replaces those of leading movie actors, both male
Speaker 1: and female. You can put words into people's mouths and
Speaker 1: make them jump and dance and run. You can even
Speaker 1: resurrect powerful figures and have them deliver a fake speech
Speaker 1: about a fake tragedy. From an Altered History, Chapter two,
Speaker 1: Creating Nixon. The text of Nixon's Moon disaster speech that
Speaker 1: we heard at the top of the show is actually
Speaker 1: not fake. As I mentioned, it was written for President
Speaker 1: Nixon as a contingency speech and thankfully never had to
Speaker 1: be delivered. It's an amazing piece of writing. It was
Speaker 1: written by Bill Safire, who was one of Nixon's speech writers.
Speaker 1: This is artist in journalist Francesco Panetta. She's the co
Speaker 1: director of the Nixon Fake or MTS Moon Disaster Team.
Speaker 1: She's also the creative director in MIT's Center for Advanced Virtuality.
Speaker 1: I was doing experimental journalism at the Guardian newspaper. I
Speaker 1: ran the Guardians Virtual Reality studio for the last three years.
Speaker 1: The second half of the Moon Disaster team is sound
Speaker 1: artist Halsey Bergund. My name is Halsey Bergund. I'm a
Speaker 1: sound artist and technologist, and I've had a lot of
Speaker 1: experience with lots of sorts of audio enhanced with technology,
Speaker 1: though this is my first experience with synthetic media, especially
Speaker 1: since I typically focus on authenticity of voice and now
Speaker 1: I'm kind of doing the opposite. So together Halsey and
Speaker 1: Francesca chose to automate a tragic moment in history that
Speaker 1: never actually happened. I think it all started with it
Speaker 1: being the fiftieth anniversary of the Moon landing last year,
Speaker 1: and add on top of that an election cycle in
Speaker 1: this country, and dealing with this information, which is obviously
Speaker 1: very important in election cycles. It was like light bulbs
Speaker 1: went on and we got very excited about pursuing it.
Speaker 1: It's possible to make mediocre fakes pretty quickly and cheaply,
Speaker 1: but Francesca and Halsey wanted high production values. So how
Speaker 1: does one go about making a first rate fake presidential address.
Speaker 1: There are two components. There's the visuals and there's the audio,
Speaker 1: and they are completely different processes. So we decided to
Speaker 1: go with a video dialogue replacement company called Kenny Ai,
Speaker 1: who would do the visuals for us, and then we
Speaker 1: decided to go with Respeecher, who are a dialogue replacement
Speaker 1: company for the voice of Nixon. They tackled the voice first,
Speaker 1: the more challenging of the two mediums. What we were
Speaker 1: told to do was to get two to three hours
Speaker 1: worth of Nixon talking. That was pretty easy because the
Speaker 1: Nixon Library has hours and hours of Nixon, mainly giving
Speaker 1: Vietnam's speeches. The Communist armies of North Vietnam launched a
Speaker 1: massive inversion of South Vietnam. That audio was then chopped
Speaker 1: up into chunks between one and three seconds long. We
Speaker 1: found this incredibly patient actor called Lewis D. Wheeler. Lewis
Speaker 1: would listen to the one second clip and then he
Speaker 1: would repeat that and do what I believe was right. Speech.
Speaker 1: Would say to us things like we need to change
Speaker 1: the diagonal attention, which meant nothing to us. Yes, we
Speaker 1: have a whole lot of potential band names going forward. Yeah,
Speaker 1: Synthetic Nixon is another good one. So once we have
Speaker 1: our Nixon model made out of these thousands of tiny clips,
Speaker 1: it means that whatever our act says will come out
Speaker 1: then in Nixon's voice. So then what we did was
Speaker 1: record the contingency speech of Nixon, and it meant that
Speaker 1: we got Lewis's actually performance, but in Nixon's voice. What
Speaker 1: about the video part, I mean, the video was much easier.
Speaker 1: We're talking a couple of days here and a tiny
Speaker 1: amount of data just with Lewis's iPhone. We filmed him
Speaker 1: reading the contingency speech once a couple of minutes of
Speaker 1: him just chatting to camera, and that was it fate
Speaker 1: that the men who went to the Moon to explore
Speaker 1: will stay on the moon. You know, we were told
Speaker 1: by Kenny Ai that everything would be the same in
Speaker 1: the video apart from just the area around the mouth.
Speaker 1: So every gesture of the hand, every blink, every time
Speaker 1: he moved his face, all of that would stay the same,
Speaker 1: but just the mouth basically would change. So we used
Speaker 1: Nixon's resignation speech. To have served in this office, it
Speaker 1: is to have felt a very personal sense of It
Speaker 1: was the speech of Nixon that looked to the most
Speaker 1: somber way. He seemed to have the most emotion in
Speaker 1: his face. So what actually went on in the computer?
Speaker 1: Artificial intelligence sometimes sounds inscrutable, but the basic ideas are
Speaker 1: quite simple. In this case, it uses a type of
Speaker 1: computer program called an auto encoder. It's trained to take
Speaker 1: complicated things, say spoken sentences or pictures, encode them in
Speaker 1: a much simpler form, and then decode them to recover
Speaker 1: the original as best it can. The encoder tries to
Speaker 1: reduce things to their essence, throwing away most of the
Speaker 1: information but keeping enough to do a good job of
Speaker 1: reconstructing it to make a deep fake. Here's the trick.
Speaker 1: Train a speech auto encode for Nixon to Nixon, and
Speaker 1: a speech auto encoder for actor to actor, but force
Speaker 1: them to use the same encoder. Then you can input
Speaker 1: actor and decoded as Nixon. If you have enough data.
Speaker 1: It's a piece of cake. Around their carefully created video,
Speaker 1: the Moon Disaster team created an entire art installation a
Speaker 1: nineteen sixties living room with a fake vintage newspaper sharing
Speaker 1: the fake tragic news while a fake Nixon speak solemnly
Speaker 1: on a vintage black and white television. Some people, when
Speaker 1: they were watching the installation, they watched a number of times.
Speaker 1: You'd see them, they'd watch it once, then they would
Speaker 1: watch it again, staring at the lips to see if
Speaker 1: they could see any lack of synchronicity. We had some
Speaker 1: people who thought that perhaps Nixon had actually recorded this
Speaker 1: speech as a contingency speech for it to go onto television.
Speaker 1: Lots of folks who were listening, viewing, and even press
Speaker 1: folks just immediately said, oh, the voice is real or whatever,
Speaker 1: you know, said these things that weren't accurate because they
Speaker 1: just felt like there wasn't even a question. I suppose
Speaker 1: that is what we wanted to achieve. But at the
Speaker 1: same time, it's it was a little bit eye opening
Speaker 1: and like a little scary, you know that that could happen.
Speaker 1: Chapter three, Everybody dance. What do you see as just
Speaker 1: the wonderful upsides of having technologies like this? Yeah, I
Speaker 1: mean ai A art is becoming a whole field in itself,
Speaker 1: so creatively there is enormous potential. One of the potential
Speaker 1: positive educational uses of deep fake technology would be to
Speaker 1: bring historical figures back to life to make learning more durable.
Speaker 1: I think one could do that with bringing Abraham Lincoln
Speaker 1: back to life and having him deliver speeches. Film companies
Speaker 1: and radios cited about re enactments. We're already beginning to
Speaker 1: see this in films like Star Wars, when we're bringing
Speaker 1: people like Carrie Fisher back to life. I mean that
Speaker 1: is at the moment not being done through deep fake technologies.
Speaker 1: This is using fairy traditional techniques of CGI at the moment.
Speaker 1: So we still have to see our first deep fake
Speaker 1: big cinema screen release, but this is just to come.
Speaker 1: Like the technology is getting better and better. Not only
Speaker 1: will we be able to potentially bring back actors and
Speaker 1: actresses who are no longer alive and have them star movies,
Speaker 1: but an actor could make a model of their own
Speaker 1: voice and then sell the use of that voice to
Speaker 1: anybody to do a voiceover of whatever is wanted, and
Speaker 1: so they could have twenty of these going on at
Speaker 1: the same time, and the sort of restriction of their
Speaker 1: physical presence is no longer there. And that might mean
Speaker 1: that you know, Brad Pitt is in everything, or it
Speaker 1: might just mean that lower budget films can afford to
Speaker 1: have some of the higher cost talent. That point, you know,
Speaker 1: the top twenty actors could just do everything. Yes, there's
Speaker 1: no doubt that there will be winners and losers from
Speaker 1: these technologies, but the potential of synthetic media goes way
Speaker 1: beyond the arts. There are possible medical and therapeutic applications.
Speaker 1: There are companies that are working very hard to allow
Speaker 1: people who have either lost their voice or who never
Speaker 1: had a voice to be able to speak in a
Speaker 1: way that is either how they used to speak or
Speaker 1: in a way that isn't a canned voice that everybody has.
Speaker 1: Alexei ePROs and his students discovered potential uses of synthetic
Speaker 1: media and medicine quite unintentionally while working on their Everybody
Speaker 1: Danced Now project that could turn anyone into a ballerina.
Speaker 1: We were kind of surprised for all the positive feedback.
Speaker 1: We god, we've got emails from people who were quadruplegic
Speaker 1: and they asked us if we could make them dance,
Speaker 1: and it was very unexpected. So now we are trying
Speaker 1: to get the soft where to be in a state
Speaker 1: where people can use that because yeah, it's somehow it
Speaker 1: did hit a nerve with folks. Chapter four Unicorns in
Speaker 1: the Andes. The past few years have seen amazing advances
Speaker 1: in the creation of synthetic media through artificial intelligence. The
Speaker 1: technology now goes far beyond fitting one face over another
Speaker 1: face in a video. A recent breakthrough has made it
Speaker 1: possible to create entirely new and very convincing content out
Speaker 1: of thin air. The breakthrough, called generative adversarial networks or GAMS,
Speaker 1: came from a machine learning researcher at Google named Ian Goodfellow.
Speaker 1: Like auto encoders, the basic idea is simple but brilliant.
Speaker 1: Suppose you want to create amazingly realistic photos of people
Speaker 1: who don't exist. While you build a GAM consisting of
Speaker 1: two computer programs, a photo generator that learns to generate
Speaker 1: fake photos and a photo discriminator that learns to discriminate
Speaker 1: or identify fake photos from a vast collection of real photos.
Speaker 1: You then let the two programs compete, continually tweaking their
Speaker 1: code to outsmart each other. By the time they're done,
Speaker 1: the GAN can generate amazingly convincing fakes. You can see
Speaker 1: for yourself if you go to the website this person
Speaker 1: does not exist dot com. Every time you refresh the page,
Speaker 1: you're shown a new uncanny image of a person who,
Speaker 1: as the website says, does not and never did exist.
Speaker 1: Francescan I actually tried out the website. This young Asian woman.
Speaker 1: She's got great complexion, envious of that, neat black hair,
Speaker 1: with a fringe pink lipstick and a slightly dreamy look
Speaker 1: as she's kind of gazing off to her left. Oh,
Speaker 1: here's a woman who looks like she could be a
Speaker 1: neighbor of mine in Cambridge, probably about sixty five. She's
Speaker 1: got nice wire framed glasses, layered hair. Her earrings don't
Speaker 1: actually match, but that could just be her distinctive style.
Speaker 1: I mean, of course, she doesn't really exist. It's hard
Speaker 1: to argue that gans aren't creating original art. In fact,
Speaker 1: an artist collective recently used a GAM to create a
Speaker 1: French Impressionist style portrait. When Christie's sold it at auction,
Speaker 1: it fetched an eye popping four hundred and thirty two
Speaker 1: thousand dollars. Alexeiefros, the Berkeley professor, recently pushed gans a
Speaker 1: step further, creating something called cycle gans by connecting two
Speaker 1: gans together in a clever way. Cycle. Gans can transform
Speaker 1: a money painting into what's seemingly a photograph of the
Speaker 1: same scene, or turn a summer landscape into a winter
Speaker 1: landscape of the same view. Alexei's psychogans seem like magic.
Speaker 1: If you were to add in virtual reality, the possibilities
Speaker 1: become mind blowing. You maybe reminiscing about walking down Saint
Speaker 1: German and Paris, and with a few clicks, you are there,
Speaker 1: and you're walking down the boulevard and you're looking at
Speaker 1: all the buildings, and maybe you can even switch to
Speaker 1: a different year. And I think that is I think
Speaker 1: very exciting as a way to mentally travel to different places.
Speaker 1: So if you do this in VR, I mean imagine
Speaker 1: classes going on a class visit to ancient Rome. That's right.
Speaker 1: You could imagine from how a particular city like Chrome
Speaker 1: looks now. Trying to extrapolate we looked into past. It
Speaker 1: turns out that gans aren't just transforming images. I spoke
Speaker 1: with a friend who's very familiar with another remarkable application
Speaker 1: of the technology. My name is Reid Hoffman. I'm a
Speaker 1: podcaster of Master's a Scale. I'm a partner at Greylock,
Speaker 1: which is where we're sitting right now co founder of
Speaker 1: LinkedIn and then a variety of other eccentric hobbies. Reid
Speaker 1: is a board member of an unusual organization called open ai.
Speaker 1: Open a Eyes is highly concerned with artificial general intelligence
Speaker 1: human level intelligence. I helped Sam Altman and Elon Musk
Speaker 1: standing up. The basic concern was that if one company
Speaker 1: created and deployed that that could be disbalancing in all
Speaker 1: kinds of ways. And so the thought is, if it
Speaker 1: could be created, we should make sure that there is
Speaker 1: essentially a nonprofit that is creating this and that can
Speaker 1: make that technology available at selective time slices to industry
Speaker 1: as a whole government sut C. Last year, open ai
Speaker 1: released a program that uses gams to write language from
Speaker 1: a short opening prompt. The system, called GPT two, can
Speaker 1: spin a convincing article or story instead of a deep
Speaker 1: fake video. It's deep fake text. It's pretty amazing. Actually.
Speaker 1: For example, open ai researchers gave the program the following prompt.
Speaker 1: In a shocking finding, scientists discovered a herd of unicorns
Speaker 1: living in a remote, previously unexplored valley in the Andes Mountains.
Speaker 1: Even more surprising to the researchers was the fact that
Speaker 1: the unicorns spoke perfect English. GPT two took it from there,
Speaker 1: delivering nine crisp paragraphs on the landmark discovery. I asked
Speaker 1: Fran to read a bit from the story. Doctor Jorge Perez,
Speaker 1: an evolutionary biologist from the University of Lapaz, and several
Speaker 1: companions were exploring the these mountains when they found a
Speaker 1: small valley with no other animals or humans. Perez noticed
Speaker 1: that the valley had what appeared to be a natural
Speaker 1: fountains surrounded by two peaks of rock and silver snow.
Speaker 1: Perez and the others then ventured further into the valley.
Speaker 1: By the time we reached the top of one peak,
Speaker 1: the water looked blue with some crystals on top, said Perez.
Speaker 1: Perez and his friends were astonished to see the unicorn herd.
Speaker 1: Don't tell me some of the great things you can
Speaker 1: do with language generation, well, say, for example, entertainment, Generate
Speaker 1: stories that could be fresh and interesting and new and
Speaker 1: personal for every child. Embed educational things in those stories
Speaker 1: so they're drawn into the fact that the story is
Speaker 1: involving them and their friends, but also now brings in
Speaker 1: grammar and math and other kinds of things. As are
Speaker 1: doing it. Generate explanatory material of this kind of education
Speaker 1: that works best for this audience, for this kind of people,
Speaker 1: like we want to have this kind of math or
Speaker 1: this kind of physics, or this kind of history or
Speaker 1: this kind of poetry explained in the right way. And
Speaker 1: also the sile language right like you know native city
Speaker 1: x language. When open ai announced its breakthrough program for
Speaker 1: text generation, it took the unusual step of not releasing
Speaker 1: the full powered version because it was worried about the
Speaker 1: possible consequences. Now, part of the open ai decision to
Speaker 1: say we're going to release a smaller model than the
Speaker 1: one we did is because we think that the deep
Speaker 1: fake problem hasn't been solved. And by the way, some
Speaker 1: people complained about that because they said, well, you're slowing
Speaker 1: down our ability to do progress and so far. The
Speaker 1: answer to say, look, when these are released to the
Speaker 1: entire public, we cannot control the downside as well as
Speaker 1: the upsides. Downsides from art to therapy to virtual time travel,
Speaker 1: personalized stories and education, Synthetic media has amazing upsides. What
Speaker 1: could possibly go wrong? Chapter five? What could possibly go wrong?
Speaker 1: The downsides are actually not hard to find. The ability
Speaker 1: to reshape reality brings extraordinary power, and people inevitably use
Speaker 1: power to control other people. It should be no surprise, therefore,
Speaker 1: that ninety six percent of fake videos posted online are
Speaker 1: non consensual pornography videos, almost always of women manipulated to
Speaker 1: depict sex acts that never actually occurred. I spoke with
Speaker 1: a professor who studies deep fakes, including digital attempts to
Speaker 1: control women's bodies. I'm Danielle Citron, and I am a
Speaker 1: law professor at Boston University School of Law. I write
Speaker 1: about privacy, technology, automation. My newest work and my next
Speaker 1: book is going to be about sexual privacy. So I've
Speaker 1: worked in and around consumer privacy, individual rights, civil rights.
Speaker 1: I write a lot about free speech and then automated systems.
Speaker 1: When did you first become aware of deep fakes? Do
Speaker 1: you remember when this cross your rit? I did so.
Speaker 1: There was a Reddit thread devoted to, you know, fake
Speaker 1: pornography movies of Galjado Emma Watson. But the reddit thread
Speaker 1: sort of spooled not just from celebrities but ordinary people,
Speaker 1: and so you had rereditors asking each other, how do
Speaker 1: I make a deep fake sex video of max girlfriend?
Speaker 1: I have thirty pictures, and then other reditors would chime
Speaker 1: in and say, look at this YouTube tutorial. You can
Speaker 1: absolutely make a deep fake sex video of your ex
Speaker 1: with thirty pictures. I've done it with twenty. In November
Speaker 1: twenty seventeen, an anonymous redditor began posting synthesized porn videos
Speaker 1: under the pseudonym deep fakes, perhaps a nod to the
Speaker 1: deep learning technology you used to create them as well
Speaker 1: as the nineteen seventies porn film throat. The Internet quickly
Speaker 1: adopted the term deep fakes and broadened its meanings beyond pornography.
Speaker 1: To create the videos, he used celebrity faces from Google
Speaker 1: image search and YouTube videos, and then trains an algorithm
Speaker 1: on that content together with pornographic videos. Have you seen
Speaker 1: deep fake pornography videos? Yes, so still pretty crude, so
Speaker 1: you probably can tell that it's a fake, But for
Speaker 1: the person who's inserted into pornography, it's devastating. You use
Speaker 1: the neural network technology, the artificial intelligence technology to create
Speaker 1: out of digital whole cloth pornography videos using probably real
Speaker 1: pornography and then inserting the person in the pornography so
Speaker 1: they become the female actress. If it's a female, it's
Speaker 1: usually a female in that video. My name is Noel
Speaker 1: Martin and I am an activist and Lauraform campaigner in Australia.
Speaker 1: Noel is twenty six years old and she lives in Perth, Australia.
Speaker 1: So the first time that I discovered myself on pornographic
Speaker 1: sites was when I was eighteen and out of curiosity,
Speaker 1: decided to Google image reverse search myself. In an instant,
Speaker 1: like in a less than a millisecond, my life completely changed.
Speaker 1: At first, it started with photos still images stolen from
Speaker 1: Noel's social media accounts. They were then doctoring my face
Speaker 1: from ordinary images and superimposing those onto the bodies of
Speaker 1: women depicting me having sexual intercourse. It proved impossible to
Speaker 1: identify who was manipulating Noel's image in this way. It's
Speaker 1: still unclear today, which made it difficult for her to
Speaker 1: seek legal action. I went to the police soon after,
Speaker 1: I contacted government agencies, tried getting a private investigator. Essentially,
Speaker 1: there's nothing that they could do. The sites are hosted overseas,
Speaker 1: the perpetrators are probably overseas. The reaction was at the
Speaker 1: end of the day, I think you can contact the
Speaker 1: webmasters to try and get things deleted. You know, you
Speaker 1: can adjust your privacy setting so that you know nothing
Speaker 1: is available to anyone publicly. It was an unwinnable situation.
Speaker 1: Then things started to escalate. In twenty eighteen, who all
Speaker 1: saw a synthesized pornographic video of herself And I believe
Speaker 1: that it was done for the purposes of silencing me
Speaker 1: because I've been very public about my story and advocating
Speaker 1: for change. So I had actually gotten email from a
Speaker 1: fake email address, and you know, I clicked the link
Speaker 1: I was actually at work. It was a video of
Speaker 1: me having sexual intercourse. The title had my name, the
Speaker 1: face of the woman in it was edited so that
Speaker 1: it was my face, and you know, all the tags
Speaker 1: were like Noel Martin Australia, Feminist, and it didn't look real,
Speaker 1: but the context of everything, with the title my face,
Speaker 1: with the tags all points to me being depicted in
Speaker 1: this video. The fakes were of poor quality, but poor
Speaker 1: and consumers aren't a discriminating lot, and many people reacted
Speaker 1: to them as if they were real. The public reaction
Speaker 1: was horrifying to me. I was a victim, blamed and
Speaker 1: slut shamed, and it's definitely limited the course of where
Speaker 1: I can go in terms of career and employment. Noel
Speaker 1: finished a degree in law and began a peening to
Speaker 1: criminalize this sort of content. My advocacy and my activism
Speaker 1: started off because I had a lived experience of this,
Speaker 1: and I experienced it at a time where it wasn't
Speaker 1: criminalized in Australia, the distribution of altered intimate images or
Speaker 1: altered intimate videos, and so I had to petition, meet
Speaker 1: with my politicians in my area. I wrote a number
Speaker 1: of articles, I spoke to the media, and I was
Speaker 1: involved in the law reform in Australia in a number
Speaker 1: of jurisdictions in Western Australia and New South Wales, and
Speaker 1: I ended up being involved in two press conferences with
Speaker 1: the Attorney generals of each state at the announcement of
Speaker 1: the law that was criminalizing this abuse. Today, in part
Speaker 1: because of Noel's activism, it is illegal in Australia to
Speaker 1: distribute intimate images without and scent, including intimate images and
Speaker 1: videos that have been altered. Although it doesn't encompass all
Speaker 1: malicious synthetic media, Noel has made a solid start. Chapter six,
Speaker 1: Scissors and Glue. The videos depicting Noel Martin were nowhere
Speaker 1: near as sophisticated as those made by the Moon disaster team.
Speaker 1: They were more cheap fakes than deep fakes, and yet
Speaker 1: the point didn't have to be perfect to be devastating.
Speaker 1: The same turns out to be true in politics. To
Speaker 1: understand the power of fakes, you have to understand human psychology.
Speaker 1: It turns out that people are pretty easy to fool.
Speaker 1: John Kerry I was running for president of the US.
Speaker 1: His stance on the Vietnam War was controversial. Jane Fond,
Speaker 1: of course, was a very controversial figure back then because
Speaker 1: of her anti war stand. What have we become as
Speaker 1: a nation if we call the men heroes that were
Speaker 1: used by the had have gone to try to exterminate
Speaker 1: an entire people? What business have we to try to
Speaker 1: exterminate a people? And somebody had created a photo of
Speaker 1: the two of them sharing a stage and an anti
Speaker 1: war rally with the hopes of damaging the Carry campaign.
Speaker 1: The photo was fake. They had never shared a stage together.
Speaker 1: They just took two images, probably put it into some
Speaker 1: standard photo editing software like a photoshop, and just put
Speaker 1: a headline around it, and out to the world it went.
Speaker 1: And I will tell you I remember the most fascinating
Speaker 1: interview I've heard in a long time was right after
Speaker 1: the election, Carry, of course lost, and a voter was
Speaker 1: being interviewed and asked how they voted, and he said
Speaker 1: he couldn't vote for Carry, and the interview said, well
Speaker 1: why not? And the gentleman said, I couldn't get that
Speaker 1: photo of John Carry and Jane Fonda out of my head.
Speaker 1: And the interview said, well, you know that photo is fake,
Speaker 1: and the guy said, much to my surprise, yes, but
Speaker 1: I couldn't get it out of my mind. And this
Speaker 1: is shows you the power of visual imagery, Like even
Speaker 1: after I tell you something is fake, it still had
Speaker 1: an impact on somebody. And I thought, wow, we're in
Speaker 1: a lot of trouble because it's very very hard to
Speaker 1: put the cat back into the bag. Once that content
Speaker 1: is out there, you can't undo it. So seeing is believing,
Speaker 1: even above thinking, Yeah, that seems to be the rule.
Speaker 1: There is very good evidence from the social science literature
Speaker 1: that it's very very difficult to correct the record after
Speaker 1: the mistakes are out there. Law professor Danielle Citram also
Speaker 1: notes that humans tend to pass on information without thinking,
Speaker 1: which triggers what she calls information cascades. Information cascades is
Speaker 1: a phenomenon where we have so much information overload that
Speaker 1: when someone sends us something, some information, and we trust
Speaker 1: that person, we pass it on. We don't even check
Speaker 1: its veracity, and so information can go viral fairly quickly
Speaker 1: because we're not terribly reflective, because we act on impulse.
Speaker 1: Danielle says that information cascades have been given new life
Speaker 1: in the twenty first century through social media. Think about
Speaker 1: the twentieth century phenomenon where we get most of our
Speaker 1: information from trusted sources, trusted newspapers, trusted major couple of
Speaker 1: TV channels. Growing up, we only had to you know,
Speaker 1: we didn't have a million, and they were adhering to
Speaker 1: journalistic ethics and commitments to truth and neutrality and notion
Speaker 1: that you can't publish something without checking it. Now we
Speaker 1: are publishing information that most people say. We're lying on
Speaker 1: our peers and our friends. Social media platforms are designed
Speaker 1: to tailor our information diet to what we want and
Speaker 1: to our pre existing views, so we're locked in a
Speaker 1: digital echo chamber. We think everybody agrees with us. We
Speaker 1: pass on that information we haven't checked the veracity, it
Speaker 1: goes wild. And we're especially likely to pass it on
Speaker 1: if it's negative and novel. Why's that, It's just like
Speaker 1: it's one of our weaknesses. We know how gossip goes
Speaker 1: like wildfire online. So like Hillary Clinton as running a
Speaker 1: sex ring. That's crazy. Oh my god, Eric, did you
Speaker 1: hear about that? I'll post it on Facebook. Eric, you
Speaker 1: pass it on. We just can't help ourselves. And it
Speaker 1: is much in the way that we love suits and
Speaker 1: fats and pizza. You know, we indulge. We don't think
Speaker 1: not some sense. This phenomenon is an old phenomenon, right,
Speaker 1: there's the famous observation by Mark Twain about how a
Speaker 1: lie gets halfway around the world before the truth gets
Speaker 1: its pants hang. Yeah, the truth is still in the
Speaker 1: bedroom getting dressed, and we often will see the lie,
Speaker 1: but the rebuttal is not seen. It's often lost in
Speaker 1: the noise of the defamatory statements. That is not new.
Speaker 1: But what is new is a number of things about
Speaker 1: our information ecosystem are our force multipliers. Chapter seven, Truth decay.
Speaker 1: Many experts are worried that the rapid advances in making fakes,
Speaker 1: combined with a catalyst of information cascades, will undermine democracy.
Speaker 1: The biggest concerns have focused on elections. Globally, we are
Speaker 1: looking at highly polarized situations where this kind of manipulated
Speaker 1: media can be used as a weapon. One of the
Speaker 1: main reasons Francesca and Halsey made their Nixon deep fake
Speaker 1: was to spread awareness about the risks of misinformation campaigns
Speaker 1: before the twenty twenty US presidential election. Similarly, a group
Speaker 1: showcased the power of deep fakes by making videos in
Speaker 1: the run up to the UK parliamentary election showing the
Speaker 1: two bitter rivals Boris Johnson and Jeremy Corman, each endorsing
Speaker 1: the other. I wish to rise above this divide and
Speaker 1: indorse my worthy opponent, the right Honorable Jeremy Corbyn, to
Speaker 1: be Prime Minister of our United Kingdom back Boris Johnson
Speaker 1: to continue as our Prime Minister. But you know what,
Speaker 1: don't listen to me. I think I may be one
Speaker 1: of the thousands of deep fakes on the Internet, using
Speaker 1: powerful technologies to tell stories that aren't so. This just
Speaker 1: kind of indicates how candidates and political figures can be misrepresented,
Speaker 1: and you just need to feed them into people's social
Speaker 1: media feeds for them to be seeing this. At times
Speaker 1: when the stakes are pretty high. So far, we haven't
Speaker 1: yet seen sophisticated deep fakes in US or UK politics.
Speaker 1: That might be because fakes will be most effective if
Speaker 1: they're time for maximum chaos, say close to election day,
Speaker 1: when newsrooms won't have the time to investigate and debunk them.
Speaker 1: But another reason might be that well cheap fakes made
Speaker 1: with basic video editing software or actually pretty effective. Remember
Speaker 1: the video that surfaced of how Speaker Nancy Pelosi, in
Speaker 1: which she appeared intoxicated and confused. We want to give
Speaker 1: this president the opportunity do something historic for our country.
Speaker 1: Both President Trump and Rudy Giuliani shared the video as
Speaker 1: fact on Twitter. The video is just a cheap fake,
Speaker 1: just slowed down Pelosi's speech to make her seem incompetent.
Speaker 1: But maybe elections won't be the biggest targets. Some people
Speaker 1: worry the deep fakes could be weaponized to foment international conflict.
Speaker 1: Berkeley professor Honey f Reed has been working with US
Speaker 1: government's Media Forensics program to address this issue. DARPA, on
Speaker 1: the Defense Department's Research arm, has been pouring a lot
Speaker 1: of money over the last five years into this program.
Speaker 1: They are very concerned about how this technology can be
Speaker 1: a threat to national security and also how when we
Speaker 1: get images and videos from around the world in areas
Speaker 1: of conflict, do we know if they're real or not?
Speaker 1: Is this really an image of a US soldier who
Speaker 1: has been taken hostage? How do we know? So? What
Speaker 1: do you see as some of the worst case scenarios.
Speaker 1: Here's the things that keep me up at night right
Speaker 1: a video of Donald Trump saying I've launched nuclear weapons
Speaker 1: against Iran, and before anybody gets around to figuring out
Speaker 1: whether this is real or not, where we have global
Speaker 1: nuclear mountdown. And here's the thing. I don't think that
Speaker 1: that's likely, but I also don't think that the probability
Speaker 1: of that is zero. And that should worry us because
Speaker 1: while it's not likely, the consequences are spectacularly bad. Lawyer
Speaker 1: Danielle Citrom worries about an even more plausible scenario. Imagine
Speaker 1: a deep fake of a well known American general burning
Speaker 1: a Koran, and it is timed at a very tense
Speaker 1: moment in a particular most country, whether it's agha unerstand.
Speaker 1: It could then lead to physical violence. And you think
Speaker 1: this could be made. No general, no Quran actually used
Speaker 1: in the video just programmed. You can use the technology
Speaker 1: to mine existing photographs. Kind of easy, especially with someone
Speaker 1: could take Jim Mattis when he was our defense secretary.
Speaker 1: Of Jim Mattis, you know, actually taking a Koran and
Speaker 1: ripping it in half and say all Muslims should die.
Speaker 1: Imagine the chaos in diplomacy, the chaos of our soldiers
Speaker 1: abroad in Muslim countries. It would be inciting violence without question. Well,
Speaker 1: we haven't yet seen spectacular fake videos used to disrupt
Speaker 1: elections or create international chaos. We have seen increasingly sophisticated
Speaker 1: attacks on public policymaking. So we've got an example in
Speaker 1: twenty seventeen where the FCC solicited public comment on the
Speaker 1: proposal to repeal net neutrality. Net neutrality is the instable
Speaker 1: that Internet service providers should be a neutral public utility.
Speaker 1: They shouldn't discriminate between websites, say slowing down Netflix streaming
Speaker 1: to encourage you to purchase a different online video service.
Speaker 1: As President Barack Obama described in twenty fourteen, there are
Speaker 1: no gatekeepers deciding which sites you get to access. There
Speaker 1: are no toll roads on the information super Highway. Federal
Speaker 1: communications policy had long supported net neutrality, but in twenty seventeen,
Speaker 1: the Trump administration favored repealing the policy. There were twenty
Speaker 1: two million comments that the e SEC received, but ninety
Speaker 1: six percent of those were actually fake. The interesting thing
Speaker 1: is the real comments were opposed to repeal, whereas the
Speaker 1: fake comments were in favor. A Wall Street Journal investigation
Speaker 1: exposed that the fake public comments were generated by bots.
Speaker 1: It found similar problems with public comments about payday lending.
Speaker 1: The bots varied their comments in a combinatorial fashion so
Speaker 1: that the content wasn't identical. With a little sleuthing, though,
Speaker 1: you could see that they were generated by computers. But
Speaker 1: with a technology increasingly able to generate completely original writing,
Speaker 1: like open ayes program that wrote the story about unicorns
Speaker 1: in the ANDES, it's going to become hard to spot
Speaker 1: the fakes. So there was this Harvest student, Max Weiss,
Speaker 1: who used GPT two to kind of demonstrate this, And
Speaker 1: I went on his site yesterday and he's got this
Speaker 1: little test where you need to decide whether a comment
Speaker 1: is real or fake. So you go on and you
Speaker 1: read it, and you decide whether it's been written by
Speaker 1: a bot or by a human. So I did this,
Speaker 1: and the ones that seemed to be really well written
Speaker 1: and quite narrative and discussive. Generally, I was picking them
Speaker 1: as human. I was wrong almost all the time. It
Speaker 1: was amazing and a lot. In our democracy, public commons
Speaker 1: have been an important way in which citizens can make
Speaker 1: their voices heard. But now it's becoming easy to drown
Speaker 1: out those voices with millions of fake opinions. Now the
Speaker 1: downfall of truth likely won't come with a bang, but
Speaker 1: a whimper, a slow, steady erosion that some call truth decay.
Speaker 1: If you can't believe anything you read, or hear or
Speaker 1: see anymore, I don't know how you have a democracy,
Speaker 1: an I don't know, frankly, how we have civilized society
Speaker 1: if everybody's going to live in an echo chamber, believing
Speaker 1: their own version of events. How do we have a
Speaker 1: dialogue if we can't agree on basic facts. In the end,
Speaker 1: the most insidious impact of deep fakes may not be
Speaker 1: the deep fake content itself, but the ability to claim
Speaker 1: that real content is fake. It's something that Danielle Citron
Speaker 1: refers to as the liars dividend. The liars dividend is
Speaker 1: that the more you educate people about the phenomenon of
Speaker 1: deep fix, the more the wrongdoer can disclaim reality. Think
Speaker 1: about what President Trump did with the Access Hollywood tape.
Speaker 1: You know, I'm automatically attracted to be in the work.
Speaker 1: I just started kissing them. It's like a magnet. You
Speaker 1: just I don't even know. And when you were a start,
Speaker 1: they let you do it. You can do anything whatever
Speaker 1: you want, grabbed by the I can do anything. Initially,
Speaker 1: Trump apologized for the remarks. Anyone who knows me knows
Speaker 1: these words don't reflect who I am. I said it.
Speaker 1: I was wrong, and I apologize. But in twenty seventeen,
Speaker 1: a year after his initial apology and with the idea
Speaker 1: of deep fake content starting to gain attention, Trump changed
Speaker 1: his tune. Upon reflection, he said, they're not real. That
Speaker 1: wasn't me. I don't think that was my voice. That's
Speaker 1: the liar's dividend. In practice, the Trump commented about Access
Speaker 1: Hollywood was remarkable. Slightly more subtle than that, He said,
Speaker 1: I'm not sure that was me. Right. Well, that's the
Speaker 1: corrosive gas lighting. Chapter eight, A Life Stored in the Cloud.
Speaker 1: Deep fakes have the potential to devastate individuals and harms society.
Speaker 1: The question is can we stop them from spreading before
Speaker 1: they get out of control. To do so, we need
Speaker 1: reliable ways to spot deep fakes. So the good news
Speaker 1: is there are still artifacts in the synthesized content, whether
Speaker 1: those are images, audio, or a video, that we as
Speaker 1: the experts, can tell apart. So when for example, the
Speaker 1: New York Times wants to run a story with a video,
Speaker 1: we can help them validate it. What are the real
Speaker 1: sophisticated experts looking gat So the eyes are really wonderful
Speaker 1: forensically because they reflect back to you what is in
Speaker 1: the scene. I'm sitting now right now in a studio.
Speaker 1: There's maybe about a dozen or so lights around me,
Speaker 1: and you can see this very complex set of reflections
Speaker 1: in my eyes. So we can analyze fairly complex lighting patterns,
Speaker 1: for example, to determine if this is one person's head
Speaker 1: spliced onto another person's body, or if the two people
Speaker 1: standing next to each other were digitally inserted from another photograph.
Speaker 1: I could spend another hour telling you about the many
Speaker 1: different forensic techniques that we've developed. There's no silver bullet here.
Speaker 1: Really is a sort of a time consuming and deliberate
Speaker 1: and thoughtful and it requires many many tools, and it
Speaker 1: requires people with a fair amount of skill to do this.
Speaker 1: Honey Freed also has quite a few detection techniques that
Speaker 1: he won't speak about publicly for fear of the deep
Speaker 1: fake creators will learn how to beat his tests. I
Speaker 1: don't create a GitHub repository and give my code to
Speaker 1: all my adversaries. I don't have just one forensic techniques.
Speaker 1: I have a couple dozen of them. So that means you,
Speaker 1: as the person creating this now have to go back
Speaker 1: and implement twenty different techniques. You have to do it
Speaker 1: just perfectly, and that makes the landscape a little bit
Speaker 1: more tricky for you to manage. As technology makes it
Speaker 1: easier to create deep fakes, a big problem will be
Speaker 1: the sheer amounts of content to review. So the average
Speaker 1: person can download software repositories, and so it's getting to
Speaker 1: the point now where the average person can just run
Speaker 1: these as if they're running any standard piece of software.
Speaker 1: There's also websites that have propped up where you can
Speaker 1: pay them twenty bucks and you tell them, please put
Speaker 1: this person's face into this person's video, and they will
Speaker 1: do that for you. And so it doesn't take a
Speaker 1: lot to get access to these tools. Now, I will
Speaker 1: say that the output of those are not quite as
Speaker 1: good as what we can create inside the lab. And
Speaker 1: you just know what the trend is. You just know
Speaker 1: it's going to get better and cheaper and faster and
Speaker 1: easier to use. Detecting few fakes will be a never
Speaker 1: ending cat and mouse game. Remember how generative adversarial networks
Speaker 1: or gams are built by training a fake generator to
Speaker 1: outsmart a detector. Well, as detectors get better, fake generators
Speaker 1: will be trained to keep pays still. Detectives like Honey
Speaker 1: and platforms like Facebook are working to develop automated ways
Speaker 1: to spot deep fakes rapidly and reliably. That's important because
Speaker 1: more than five hundred additional hours of video are being
Speaker 1: uploaded to YouTube every minute. I don't mean to sound
Speaker 1: defeatist about this, but I'm going to lose this war.
Speaker 1: I know this because it's always going to be easier
Speaker 1: to create content than it is to detect it. But
Speaker 1: here's where I will win. I will take it out
Speaker 1: of the hands of the average person. So think about,
Speaker 1: for example, the creation of counterfeit currency. With the latest
Speaker 1: innovations brought on by the Treasure Department, it is hard
Speaker 1: for the average person to take their inkjet printer and
Speaker 1: create compelling fake currency, and I think that's going to
Speaker 1: be the same trend here is that if you're using
Speaker 1: some off the shelf tool, if you're paying somebody on
Speaker 1: the website, we're going to find you, and we're going
Speaker 1: to find you quickly. But if you are a deadated
Speaker 1: highly skilled of the time and the effort to create it,
Speaker 1: we are going to have to work really hard to
Speaker 1: detect those. Given the challenges of detecting fake content, some
Speaker 1: people envision a different kind of techno fix. They proposed
Speaker 1: developing airtight ways for content creators to mark their own
Speaker 1: original video as real. That way, we could instantly recognize
Speaker 1: an altered version if it wasn't identical. Now, there's ways
Speaker 1: of authenticating at the point of recording, and these are
Speaker 1: what it called control capture system. So here's the idea.
Speaker 1: You use a special app on your mobile device that
Speaker 1: at the point of capture a cryptographically signs the image
Speaker 1: of the video or the audio. It puts that signature
Speaker 1: onto the blockchain. And the only thing you have to
Speaker 1: know about the blockchain is that it is an immutable
Speaker 1: distributed ledger, which means that that signature is essentially impossible
Speaker 1: to manipulate, and now all of that happened at the
Speaker 1: point of recording. If I was running a campaign today
Speaker 1: and I was worried about candidates likeness being misused, absolutely
Speaker 1: every public event that they were at, I would record
Speaker 1: with a control capture system, and I'd be able to
Speaker 1: prove what they actually said or did at any point
Speaker 1: in the future. So this approach would shift the burden
Speaker 1: of authentication to the people creating the videos rather than
Speaker 1: publishers or consumers. Law professor Danielle Citron has explored how
Speaker 1: this solution could quickly become dystopium. We might see the
Speaker 1: emergence of an essentially an audit trail of everything you
Speaker 1: do and say all of the time. Danielle refers to
Speaker 1: the business model as immutable lifelogs in the cloud. In
Speaker 1: a way we sort of already seen it. There are
Speaker 1: health plans that if you wear a fitbit all the
Speaker 1: time and you let yourself be monitored, it lowers your
Speaker 1: insurance your health insurance rates. But you can see how
Speaker 1: if the incentives are there in the market to self surveil,
Speaker 1: whether it's for health insurance, life insurance, car insurance, we're
Speaker 1: going to see the unraveling of Brian to say by ourselves.
Speaker 1: You know, corporations may very well, because the CEO is
Speaker 1: so valuable, they may say, you've got to have a log,
Speaker 1: an immutable audit trail of everything you do and say
Speaker 1: so when that deep fake comes up the night before
Speaker 1: the IPO, you can say, look, the CEO wasn't taking
Speaker 1: the bribe, wasn't having sex with a prostitute. And so
Speaker 1: we have proof, we have an auto trail, we have
Speaker 1: a log. So when we were imagining, we were imagining
Speaker 1: a business model that hasn't quite come up, but we
Speaker 1: have gotten a number of requests from insurance companies as
Speaker 1: well as companies to say we're interested in this idea.
Speaker 1: So how much has to be in that log? Does
Speaker 1: this have to be a whole video of your life?
Speaker 1: That is a great question, one that terrifies us. So
Speaker 1: it may be that you're logging locate geolocation, you're logging videos.
Speaker 1: You see people talking and who they're interacting with, and
Speaker 1: that might be good enough to prevent the miss Jeff
Speaker 1: that would hijack the IPM. Your whole life online, yes,
Speaker 1: stored securely, clock down, protected in the cloud. It is
Speaker 1: at least for a privacy scholar. There are so many
Speaker 1: reasons why we ought to have privacy that aren't about
Speaker 1: hiding things. It's about creating spaces and managing boundaries around
Speaker 1: ourselves and our intimates and our loved ones. So I
Speaker 1: worry that if we entirely unravel privacy a in the
Speaker 1: wrong hands is very dangerous right B It changes how
Speaker 1: we think about ourselves and humanity. Chapter nine, Section two thirty.
Speaker 1: So techno fixes are complicated. What about passing laws to
Speaker 1: band deep fakes or at least deep fakes that don't
Speaker 1: disclose their fake So the video and audio is speech?
Speaker 1: In our First Amendment doctrine is very much a protective
Speaker 1: of free speech, and the Supreme Court has explained that
Speaker 1: lies just lies themselves without harm is protected speech. When
Speaker 1: lies cause certain kinds of harm, we can regulate it
Speaker 1: defamation of private people, threats, incitement, fraud, impersonation of government officials.
Speaker 1: What about lies concerning public figures like politicians? California and Texas,
Speaker 1: for instance, recently pass laws making it illegal to publish
Speaker 1: deep fakes of a candidate in the weeks leading up
Speaker 1: to an election. It's not clear yet whether the laws
Speaker 1: will pass constitutional muster. As you're saying in an American content,
Speaker 1: we are just not going to be able to outlaw fakes. Yeah,
Speaker 1: we can't have a flat van, and I don't think
Speaker 1: we should. It would fail on doctrinal grounds, but ultimately
Speaker 1: it would prevent the positive uses. Interestingly, in January twenty twenty, China,
Speaker 1: which has no First Amendment protecting free speech, promulgated regulations
Speaker 1: banning deep fakes. The use of AI or virtuality now
Speaker 1: needs to be clearly marked in a prominent manner, and
Speaker 1: the failure to do so is considered a criminal offense.
Speaker 1: To explore other options for the US, I went to
Speaker 1: speak with a public policy expert. My name is Joan Donovan,
Speaker 1: and I work at Harvard Kennedy Shorenstein Center, where I
Speaker 1: lead a team of researchers looking at medium manipulation and
Speaker 1: disinformation campaigns. Joan is head of the Technology and Social
Speaker 1: Change Research Project, and her staff studies how social media
Speaker 1: gives rise to hoaxes and scams. Her team is particularly
Speaker 1: interested and precisely how misinformation spreads across the Internet. Ultimately,
Speaker 1: underneath all of this is the distribution mechanism, which is
Speaker 1: social media and platforms and platforms have to rethink the
Speaker 1: open of their design because that has now become a
Speaker 1: territory for information warfare. In early twenty twenty, Facebook announced
Speaker 1: a major policy change about synthesized content. Facebook preissued policies
Speaker 1: now on deep fakes, saying that if it is an
Speaker 1: AI generated video and it's misleading in some other contextual way,
Speaker 1: then they will remove it. Interestingly, Facebook banned the Moon
Speaker 1: Disaster Team's Nixon video even though it was made for
Speaker 1: educational purposes, but didn't remove the slowed down version of
Speaker 1: Nancy Pelosi, which was made to mislead the public. Why
Speaker 1: because the Pelosi video wasn't created with artificial intelligence. For now,
Speaker 1: Facebook is choosing to target deep fakes, but not cheap fakes.
Speaker 1: One way to push platforms to take a stronger stance
Speaker 1: might be to remove some of the legal protections that
Speaker 1: they currently enjoy. Undersection two thirty of the Communication's Decency
Speaker 1: Act past in nineteen ninety six, platforms aren't legally liable
Speaker 1: for content posted by its users. The fact that platforms
Speaker 1: have no responsibility for the content they host has an upside.
Speaker 1: It's led to the massive diversity of online content we
Speaker 1: enjoyed today. But it also allows a dangerous escalation of
Speaker 1: fake news. Is it time to change section to thirty
Speaker 1: to create incentives for platforms to police false content? I
Speaker 1: asked the former head of a major platform, LinkedIn co
Speaker 1: founder Reid Hoffman. For example, let's take my view of
Speaker 1: what the response to the christ Church shooting should be
Speaker 1: is to say, well, we want you to solve not
Speaker 1: having terrorism, murderer or murderers displayed to people. So we're
Speaker 1: simply going to do a fine of ten thousand dollars
Speaker 1: per view. Two shootings occurred at mosques in Christchurch, New
Speaker 1: Zealand in March twenty nineteen. Graphic videos of the event
Speaker 1: were soon posted online. Five people saw it, that's fifty
Speaker 1: thousand dollars. But if he becomes a meme and a
Speaker 1: million people see it, that's ten billion dollars. Yes, right,
Speaker 1: So what's really trying to do is get you to say,
Speaker 1: let's make sure that the meme never happens. Okay, So
Speaker 1: that's a governance mechanism there is you find the channel
Speaker 1: the platform based on number of views would be a
Speaker 1: very general way to say, now you guys have to solve.
Speaker 1: Now you solve, you figure it out. What about other solutions.
Speaker 1: If we are to make regulation, it should be about
Speaker 1: the amount of staff in proportion to the amount of
Speaker 1: users so that they can get a handle on the content.
Speaker 1: But can they be fast enough. Maybe the viral spread
Speaker 1: should be slowed down enough to allow them to moderate.
Speaker 1: Let's put it this way. The stock market has certain
Speaker 1: governor is built in when there's massive changes in a
Speaker 1: stock price. There are decelerators that kick in, breaks that
Speaker 1: kick in. Should the platforms have breaks that kick in
Speaker 1: before something can go fully viral? So in terms of deceleration,
Speaker 1: there are things that they do already that accelerate the
Speaker 1: process that they need to think differently about, especially when
Speaker 1: it comes to something turning into a trending topic. So
Speaker 1: there needs to be an intervening moment before things get
Speaker 1: to the homepage and get to trending, where there is
Speaker 1: a content review. So much to say here, but I
Speaker 1: want to think particularly about listeners who are in their
Speaker 1: twenties and thirties and are very tech savvy. They're going
Speaker 1: to be part of the solution here. What would you
Speaker 1: say to them about what they can do? I think
Speaker 1: it's important that younger people advocate for the Internet that
Speaker 1: they want we have to fight for it. We have
Speaker 1: to ask for different things, and that kind of agitation
Speaker 1: can come in the form of posting on the platform,
Speaker 1: writing letters, joining groups like Fight for the Future, and
Speaker 1: trying to work on getting platforms to do better and
Speaker 1: to advocate for the kind of content that you want
Speaker 1: to see more of. The important thing is that our
Speaker 1: society is shaped by these platforms, and so we're not
Speaker 1: going to do away with them, but we don't have
Speaker 1: to make do with them either. Conclusion, choose your planet.
Speaker 1: So there you have it, Stewards of the Brave New Planet.
Speaker 1: Synthetic media or deep fakes, have been manipulating content for
Speaker 1: more than a hundred years, but recent advances in AI
Speaker 1: have taken it to a whole new level of verisimilitude.
Speaker 1: The technology could transform movies and television, favored actors from
Speaker 1: years past starring in new narratives, along with actors who
Speaker 1: never existed, patients regaining the ability to speak in their
Speaker 1: own voices, personalized stories created on demand for any child
Speaker 1: around the globe, matching their interests, written in their dialect,
Speaker 1: representing their communities. But there's also great potential for harm.
Speaker 1: The ability to cast anyone in a pornographic video, weaponized
Speaker 1: media dropping days before an election, or provoking international conflicts.
Speaker 1: Are we going to be able to tell fact from fiction?
Speaker 1: Will truth survive? And what does it mean for our democracy? Better?
Speaker 1: Fake detection may help, but it'll be hard for it
Speaker 1: to keep up, and logging our lives in blockchain to
Speaker 1: protect against misrepresentation doesn't sound like an attractive idea. Outright
Speaker 1: bands on deep fakes are being tried in some countries,
Speaker 1: but they're tricky in the US given our constitutional protections
Speaker 1: for free speech. Maybe the best solution is to put
Speaker 1: the liability on platforms like Facebook and YouTube. If we
Speaker 1: can joan Donovan's right to get the future you want,
Speaker 1: you're going to have to fight for it. You don't
Speaker 1: have to be an expert, and you don't have to
Speaker 1: do it alone. When enough people get engaged, we make
Speaker 1: wise choices. Deep fakes are a problem that everyone can
Speaker 1: engage with. Brainstorm with your friends about what should be done.
Speaker 1: Use social media. Tweet at your elected representatives to ask
Speaker 1: if they're working on laws, like in California and Texas,
Speaker 1: And if you work for a tech company, ask yourself
Speaker 1: and your colleagues if you're doing enough. You can find
Speaker 1: lots of resources and ideas at our website Brave New
Speaker 1: Planet dot org. It's time to choose our planet. The
Speaker 1: future is up to us. Brave New Planet is a
Speaker 1: co production of the Broad Institute of MT and Harvard
Speaker 1: Pushkin Industries in the Boston Globe, with support from the
Speaker 1: Alfred P. Sloan Foundation. Our show is produced by Rebecca
Speaker 1: Lee Douglas with Mary Doo theme song composed by Ned Porter,
Speaker 1: mastering and sound designed by James Garver, fact checking by
Speaker 1: Joseph Fridman, and a Stitt and Enchant. Special Thanks to
Speaker 1: Christine Heenan and Rachel Roberts at Clarendon Communications, to Lee McGuire,
Speaker 1: Kristen Zarelli and Justine Levin Allerhand at the Broad, to
Speaker 1: Milobelle and Heather Faine at Pushkin, and to Eli and
Speaker 1: Edy Brode, who made the Brode Institute possible. This is
Speaker 1: Brave New Planet. I am her Aclander