Speaker 1: What role will AI play in the future of fake
Speaker 1: news and misinformation? And what does this have to do
Speaker 1: with your brain's internal models, or with voice passwords, or
Speaker 1: with what I'm calling the tall intelligence problem? And why
Speaker 1: do I believe that these earliest days of AI are
Speaker 1: actually it's golden age and we're quickly heading for a Balkanization.
Speaker 1: Welcome to Inner Cosmos with me David Eagleman. I'm a
Speaker 1: neuroscientist and author at Stanford and in these episodes we
Speaker 1: sail deeply into our three pound universe to understand why
Speaker 1: and how our lives look the way they do now.
Speaker 1: In the last two episodes, we talked about the notion
Speaker 1: of truth versus misinformation. Two weeks ago we covered the
Speaker 1: question about truth in the media, and last week was
Speaker 1: specifically about truth on the Internet. Today's episode is about
Speaker 1: truth versus misinformation and artificial intelligence. So if you happened
Speaker 1: to listen to the last two episodes, you'll know I've
Speaker 1: been arguing that the position of truth is not as
Speaker 1: simple as many pundits have made it out to be.
Speaker 1: A lot of people have said truth is declining and
Speaker 1: misinformation is on the rise. It's achieving ascendency so much
Speaker 1: so that the Oxford English Dictionary in twenty sixteen coined
Speaker 1: the term post truth, implying from the structure of the
Speaker 1: word that there used to be a time where people
Speaker 1: operated on truth, whereas now, regretfully, people's beliefs are predicated
Speaker 1: on emotion and person old beliefs. So I've made my
Speaker 1: arguments in those episodes about why I think that position
Speaker 1: is so specious, and what I gave was a simple
Speaker 1: historical analysis that demonstrates beyond any doubt that people have
Speaker 1: never operated on anything but emotion and personal beliefs, and
Speaker 1: the idea that that has recently changed belies nothing but
Speaker 1: an appalling ignorance of history. And specifically in the last episode,
Speaker 1: I argued that we're actually in a much better position
Speaker 1: now because of the Internet, as it prevents the one
Speaker 1: thing that has historically proven itself worse than misinformation, which
Speaker 1: is censorship, having someone else decide for you what you
Speaker 1: are allowed to see. And if you want to know more,
Speaker 1: please go back and listen to those episodes where I
Speaker 1: give examples of the USSR controlling a total clamp down
Speaker 1: on the press and even on copying machines, and China
Speaker 1: controlling which websites you are allowed to go to, and
Speaker 1: Nikolai Chichescu of Romania controlling even the weather reports, and
Speaker 1: Saddam Hussein of Iraq outlawing maps of Baghdad, and on
Speaker 1: and on. Just imagine if President Trump or President Biden
Speaker 1: had one hundred percent control over what you are or
Speaker 1: are not allowed to see. This was the situation and
Speaker 1: so many recent historical examples that we examined in the
Speaker 1: last two episodes, from left wing communism in China and
Speaker 1: the USSR, to right wing Nazism or fascism in Germany
Speaker 1: or Italy, to theocratic dictatorships like we find in Iran.
Speaker 1: In all cases the government decides what is the proper
Speaker 1: material for you to see or Nazi and in all
Speaker 1: cases uniformly, this has been disastrous and has led to
Speaker 1: the starvation or execution of tens of millions of people.
Speaker 1: So even if you don't like hearing other people's opinions,
Speaker 1: and you deep down believe that they're all misinformed and
Speaker 1: you know much better, the fact is that our society
Speaker 1: will survive for longer if we put up with those people.
Speaker 1: Rather than imagining we'd be better off if we could
Speaker 1: just legislate or take up arms to have them agree
Speaker 1: with us. So that's where we are so far in
Speaker 1: this series. But today I'm going to dive into the
Speaker 1: third part because we've just arrived in this technological era
Speaker 1: which got here with unexpected speed and success of artificial intelligence,
Speaker 1: and specifically of generative AI, which is a type of
Speaker 1: artificial neural network that creates new content like beautiful passages
Speaker 1: of writing, or shockingly great images or expert level music.
Speaker 1: Its success blossomed suddenly mostly because of the increasing availability
Speaker 1: of large data sets combined with more powerful computing hardware,
Speaker 1: and as a result, generative AI now produces results that
Speaker 1: are indistinguishable from human generated content like news articles, or
Speaker 1: photographs or voices. And the question today is what will
Speaker 1: AI mean for the future of disinformation or truth? Has
Speaker 1: AI thrown a wrench irreversibly into this game of truth telling?
Speaker 1: So let's start with the question of truth and AI.
Speaker 1: If you ask the question to people on the street,
Speaker 1: was Trump a good president? Obviously you're going to get
Speaker 1: a range of responses. So the question of what is
Speaker 1: truth here is a difficult one. Now, if you ask
Speaker 1: chat GPT this question, it will tell you the question
Speaker 1: is subjective and depends on individual perspectives and priorities. And
Speaker 1: then it'll tell you supporters of Trump often highlight X,
Speaker 1: y Z, and then it says critics point to concerns
Speaker 1: such as ABC, and it does this sort of back
Speaker 1: and forth for most questions that you ask it, even
Speaker 1: if you had intended to get a yes or no answer.
Speaker 1: And a lot of people that I've talked with find
Speaker 1: this aspect of llms, these large language models, really annoying.
Speaker 1: And that's because every question you ask to Barret or
Speaker 1: chat GPT gives this sort of wishy washy answer and
Speaker 1: tells you, look, there's this opinion on it, there's that
Speaker 1: opinion on it. More discussion will have to take place,
Speaker 1: and if you say yes or no, was Trump a
Speaker 1: good president? It will tell you quote as a neutral
Speaker 1: and objective AI, I don't have personal opinions, The evaluation
Speaker 1: of whether Donald Trump was a good president is subjective
Speaker 1: and depends on individual perspectives and priorities. And then it
Speaker 1: ends by noting that it is essential to consider a
Speaker 1: range of perspectives and weigh various aspects of his presidency
Speaker 1: to form an informed opinion. And you know what it is,
Speaker 1: annoying that it does that, and you know what, else,
Speaker 1: it's right. This is exactly what we should do, given
Speaker 1: that public opinion on Trump's presidency is polarized and assessments
Speaker 1: vary widely based on political ideology and personal values. So
Speaker 1: what Bard or chat GPT or other lms do is
Speaker 1: actually quite genius, which is that they don't get caught
Speaker 1: in the trap of giving a yes or no answer
Speaker 1: to what we think is a yes or no question. Instead,
Speaker 1: they are synthesizing the opinions of millions and millions of
Speaker 1: people who have written things down, and therefore or it says, well,
Speaker 1: some people think this, and some people think that, and
Speaker 1: although some people find that annoying, it actually represents a
Speaker 1: beautiful sort of fairness. For example, you can ask chat
Speaker 1: GPT give me different points of view about abortion, and
Speaker 1: it does a beautiful job with that. It gives you
Speaker 1: the perspective from pro choice advocates about reproductive rights and
Speaker 1: women having the right to make decisions about their own bodies,
Speaker 1: and also health and safety concerns, making sure that women
Speaker 1: have access to approved medicine to reduce risk of illegal methods,
Speaker 1: and issues of autonomy. And then it also gives the
Speaker 1: point of view of pro life advocates, their belief in
Speaker 1: the inherent right to life for the unborn fetus and
Speaker 1: alternatives instead of abortion, like adoption or parenting. And finally,
Speaker 1: it points to their ethical beliefs about the sanctity of
Speaker 1: human life from conception, and on top of that, it
Speaker 1: spells out some other perspects as well as compromised positions
Speaker 1: like trimester based regulation and exception clauses when a mother's
Speaker 1: health is at risk. Now, why does chat GPT do
Speaker 1: such a good job at this sort of thing? Because
Speaker 1: it's acting like a meta human. It's like a parent
Speaker 1: watching squabbling children and understanding the perspective where each different
Speaker 1: child is coming from. Similarly, I asked it to tell
Speaker 1: me the different points of view about the Israeli Palestinian conflict,
Speaker 1: and it succinctly and fairly captured the perspective of both sides. Again,
Speaker 1: this is because it has access to all the writing
Speaker 1: of the world, so it sees the different perspectives. It's
Speaker 1: not making a choice, that's not its job, and maybe
Speaker 1: it couldn't do it technologically anyway. So instead, what it
Speaker 1: represents is a fair and balanced view of the world
Speaker 1: that we're in now. What's fascinating is that we are
Speaker 1: probably at a moment with AI that is perhaps the
Speaker 1: fairest and most balanced that we will ever see. Why.
Speaker 1: It's because I predict that people will start training up
Speaker 1: their own AIS, and they will do so with what
Speaker 1: they consider the best information. So one organization might say,
Speaker 1: we're not going to use all the books, We're just
Speaker 1: going to use the classics, and anything that is gay
Speaker 1: or trans or non binary, we're not including that because
Speaker 1: we don't feel like it's necessary to get this next
Speaker 1: generation of intelligence system trained up. We're going to train
Speaker 1: this with what we believe in. And on the other side,
Speaker 1: you'll have people on the left wing who say, I
Speaker 1: don't want to include Mark Twain in here, or Raould
Speaker 1: Dahl or certain books by doctor seuss Er, various Shakespeare
Speaker 1: plays or things like that, because they represent a point
Speaker 1: of view that people used to have, but we don't
Speaker 1: believe in that anymore, so we want to leave that out.
Speaker 1: Where you can imagine another group that feels it's better
Speaker 1: if there's no violence in the training data. So we're
Speaker 1: only going to use uplifting books where people help each other,
Speaker 1: and there's no such thing as violence, and that will
Speaker 1: galvanize a different group to say, look, you're being naive.
Speaker 1: The world is full of psychopaths who will commit violence
Speaker 1: against you, and you need to be prepared for that,
Speaker 1: so you can recognize when a violence psychopath is seeking
Speaker 1: power over you or seeking political power in your neighborhood
Speaker 1: or on a national stage. So we're going to leave
Speaker 1: out the uplifting rom com books and we're going to
Speaker 1: just include the books about the real world and what
Speaker 1: actually happens in warfare and how one needs to be
Speaker 1: prepared for it, and on and on and on. So
Speaker 1: I predict we are headed rapidly for a balkanization of
Speaker 1: large language models. Just in case you don't know, Balkanization
Speaker 1: is a word that refers to the process of breaking
Speaker 1: something up into small, isolated factions. This originated from the
Speaker 1: Balkan Peninsula, where a bunch of ethnic and political groups
Speaker 1: sought independence and created smaller, less cooperative states. So I'm
Speaker 1: suggesting we're heading towards a disintegration into smaller, less cooperative
Speaker 1: large language models. The idea is you can go ask
Speaker 1: a question to your left wing model or your right
Speaker 1: wing model, your model that lives in Wokistan or Magastan.
Speaker 1: And I think that's a shame because it's taking something
Speaker 1: that should be smarter and better than humans, but we're
Speaker 1: going to manipulate it to be the kind of human
Speaker 1: that you are, so that you can say, yes, I
Speaker 1: like its answers. That makes sense now, the model tells
Speaker 1: the truth. It's like the Biblical creation story where God
Speaker 1: creates man in his own image, which actually seems to
Speaker 1: me like a real shame, because what if God could
Speaker 1: have created something that was better than himself. That's what
Speaker 1: we have the chance to do with AI. But I
Speaker 1: think it's just around the corner that we human say
Speaker 1: I know what truth is, and I'm going to make
Speaker 1: sure that I fashion this powerful new creature so that
Speaker 1: it looks just like me. I'm going to take the
Speaker 1: vast space of possibility and squeeze it down until it
Speaker 1: is nothing but a successful clone of me. So my
Speaker 1: hope is that what will remain or grow up into
Speaker 1: this space is something like a Wikipedia of large language models,
Speaker 1: something that takes the space of human opinion and says, look,
Speaker 1: some people think this, some people think that, and some
Speaker 1: people think another thing. Altogether, After all, even though we
Speaker 1: have countless news sites and opinion sites of every different
Speaker 1: political bias. The existence of Wikipedia makes me feel more
Speaker 1: confident that this is a possibility that we continue to
Speaker 1: have ais just like we have today that are unafraid
Speaker 1: to say thank you for your yes or no question.
Speaker 1: But as a metahuman, I'm going to give you a
Speaker 1: more complex answer than perhaps you were looking for, because
Speaker 1: you may or may not know that truth is a
Speaker 1: tricky concept, and we don't have to pretend that most
Speaker 1: questions are binary. Okay, So I wanted to express my
Speaker 1: fear about AI breaking up into individualized models that better
Speaker 1: match the truth of the programmer, and my hope that
Speaker 1: by expressing this clearly we can prevent it. Now, I'd
Speaker 1: like to shift into act two of this episode and
Speaker 1: address the issues that a lot of people are worried
Speaker 1: about when they think about the role of AI in
Speaker 1: the era of disinformation. Now, my suspicion is there are
Speaker 1: trivial cases which people are worried about and they probably
Speaker 1: don't need to be, and then there are the more
Speaker 1: sophisticated cases. So I was at a party the other
Speaker 1: night and ended up talking with an older gentleman who
Speaker 1: told me he was worried about AI and fake news.
Speaker 1: So I asked him why and he said, well, just
Speaker 1: think about the speed of AI. It could manufacture reams
Speaker 1: of fake news in a second. So I asked him, well,
Speaker 1: what would you generate and he said, well, just imagine
Speaker 1: generating fake stories about Joe Biden. So I asked him
Speaker 1: what would you do after you generated that story on
Speaker 1: your desktop? Would you post it to your feed on X?
Speaker 1: And he said yes. So I asked him whether he
Speaker 1: felt sure that would make it difference. Imagine he posts
Speaker 1: on X that Biden just adopted an alien baby. What
Speaker 1: would it matter? Why would anybody listen to this guy's tweet?
Speaker 1: If I make up a story about seeing Bigfoot on
Speaker 1: Stanford campus, it doesn't matter if I have used AI
Speaker 1: to write it or not. And that's because there's no
Speaker 1: further corroboration beyond my claim on social media, so there's
Speaker 1: no reason for anyone to believe it. But maybe, he said,
Speaker 1: maybe it's different if I were to generate a more
Speaker 1: carefully crafted story like that. The New York Times has
Speaker 1: just found evidence that Biden cheated on his taxes, and
Speaker 1: I agree. But I pointed out that even though prompt
Speaker 1: engineering is funds, so you can get just the right story.
Speaker 1: He could currently without AI make up any story he wants,
Speaker 1: and it might take him five minutes to write instead
Speaker 1: of sixty seconds of crafting the right prompt. But other
Speaker 1: than saving those few minutes, it's not as though the
Speaker 1: AI has done something fundamentally different than what he could
Speaker 1: do anyway. And of course, whether penned by him or
Speaker 1: the AI, he still has to try to convince people
Speaker 1: that the story is real, even though it's just a
Speaker 1: tweet and perhaps it links to his blog. But if
Speaker 1: no traditional news source or aggregator has picked up on
Speaker 1: it or posted something like it, it's not news. It's
Speaker 1: going to have a difficult time getting off the starting blocks.
Speaker 1: Now this is not to say that fake news can't spread,
Speaker 1: because it can, but it is to say that it's
Speaker 1: not clear to me how AI is an important player
Speaker 1: in this, except that it saves him four minutes. So
Speaker 1: I think there's some confusion generally about the role that
Speaker 1: AI will play in fake news, and I want to
Speaker 1: be very clear today about where the important difference with
Speaker 1: AI may or may not be. For example, AI could
Speaker 1: play a different kind of role if what I'm doing
Speaker 1: is starting, hundreds of AI bought Twitter accounts that all
Speaker 1: chat back and forth, and at some point they all
Speaker 1: retweet this guy's made up story, and it gains traction
Speaker 1: and believability when somebody sees the number of likes and
Speaker 1: reposts and comments on it. But fundamentally, I think this
Speaker 1: is a cat and mouse game, and the important thing
Speaker 1: will be for social media companies to stay ahead of
Speaker 1: the game in terms of verifying who is a real human.
Speaker 1: Now let's turn to the next issue with AI, which
Speaker 1: is its ability to flawlessly impersonate someone else's voice by
Speaker 1: capturing the cadence and prosity. Now, a few episodes ago,
Speaker 1: I talked about the potential benefits of this. For example,
Speaker 1: I have made a voice AI of my father who
Speaker 1: passed away a few years ago, and it's so wonderful
Speaker 1: and comfort for me to hear his voice. But the
Speaker 1: concern that people have when we're talking about the notion
Speaker 1: of truth is somebody trying to fool you with a voice.
Speaker 1: So here, for example, is a famous person you might
Speaker 1: know you're a bad bad boy. Now, that was Snoop Dogg,
Speaker 1: But as you could probably guess, that wasn't actually Snoop Dogg,
Speaker 1: but an AI generated voice. Now, we all share concerns
Speaker 1: about this capacity to reproduce someone's voice, and I'm going
Speaker 1: to get into some of those concerns in a moment,
Speaker 1: but first I want to play his voice once more.
Speaker 1: You a bad bad boy. Now that's pretty convincing, right,
Speaker 1: maybe even more so than the AI file. But that
Speaker 1: wasn't Snoop Dogg either. That was the performer Keegan Michael,
Speaker 1: doing an impersonation of Snoop Dogg. And the thing to
Speaker 1: note is that impersonators or mimics have been around for
Speaker 1: as long as recorded history. They do awesome voice fakes
Speaker 1: by picking up on the cadence and prosity of someone
Speaker 1: else's voice. So I just want to note that this
Speaker 1: issue is not new. Now. Part of the concern that
Speaker 1: people do have about AI voice generation is that it
Speaker 1: doesn't have to be about a famous person, but instead
Speaker 1: it might be an impersonation of your grandmother or your child,
Speaker 1: because incredibly, these models can get trained up on just
Speaker 1: a few seconds of voice data. So, for example, one
Speaker 1: of the meaningful concerns is that you might receive a
Speaker 1: phone call and you say hello, Hello, I can't hear you.
Speaker 1: Is there anybody there, and then you hang up, and
Speaker 1: that's enough audio data for someone to make a pretty
Speaker 1: flawless impression of your voice. So the idea is that
Speaker 1: the sheer speed of capturing a non celebrity voice makes
Speaker 1: this worrisome. And it's worrisome for a few reasons. One
Speaker 1: of them is that several businesses moved in the last
Speaker 1: few years to voice fingerprinting as their password. So, for example,
Speaker 1: you call the company to find out about your bank
Speaker 1: account or your stock trades, and you simply say, my
Speaker 1: voice is my password, and through a sophisticated process, it
Speaker 1: recognizes that it is, indeed you trying to find out
Speaker 1: information about your account. AI voice generation renders that security
Speaker 1: approach meaningless and even worse. The nightmare scenario is that
Speaker 1: somebody records audio of your child's voice and then you
Speaker 1: get a call one day, maybe you're out of the
Speaker 1: state or out of the country, and your phone rings
Speaker 1: and you hear your child say, help me, I've been kidnapped.
Speaker 1: Please send the money now, or the man is going
Speaker 1: to hurt me. Now. Even if you know about this
Speaker 1: potential future scam, you're still going to hesitate in that situation.
Speaker 1: You're going to be thrown for loop, You're going to panic.
Speaker 1: And the more likely case is that for non listeners
Speaker 1: of this podcast anyway, you've never even heard of such
Speaker 1: a scam in your life, and AI voice generation is
Speaker 1: new to you, and you fall for the scam entirely.
Speaker 1: So again, when we talk about the influence of AI
Speaker 1: on the truth, we might be finding ourselves in many
Speaker 1: scenarios that we could have never seen coming. And of
Speaker 1: course voice is just the beginning. When people think about
Speaker 1: deep fakes, they're often thinking about photographs. So with mid
Speaker 1: Journey or Dolly three or any other good image generation AI,
Speaker 1: you can specify the parameters of your prompt to make
Speaker 1: it so the generated image looks indistinguishable from a real photograph. Now,
Speaker 1: a new science paper came out on this recently. The
Speaker 1: researchers used a set of actual photographs of faces and
Speaker 1: a set of AI generated photographs of faces, and they
Speaker 1: asked the participants to judge or any given photograph whether
Speaker 1: that was a real face or a synthetic face, and
Speaker 1: the participants performed at chance. Their guesses were like throwing
Speaker 1: darts in a dartboard. They were totally random. In fact,
Speaker 1: they rated the AI faces as more trustworthy than the
Speaker 1: real ones, and another study found that the AI faces
Speaker 1: got ranked as more real than the photos of the
Speaker 1: actual faces. So here's the thing I want to mention.
Speaker 1: One of the studies, which came out in October, claimed
Speaker 1: that although participants could not distinguish real and generated faces,
Speaker 1: their unconscious brains could so. Specifically, this research group ran
Speaker 1: an experiment in which the participants wore EEG on their heads.
Speaker 1: That's electro and cephlography, and that measures the electrical activity
Speaker 1: in their brains. Now, the researchers showed real faces or
Speaker 1: synthetic faces, and the claim in the paper is that
Speaker 1: at one hundred and seventy milliseconds, there was a small
Speaker 1: difference in the EEG signal between these two conditions. And
Speaker 1: so the claim of the paper is that while you
Speaker 1: can't tell the difference between the real and the generated faces,
Speaker 1: your brain can. There's a distinction between what we know
Speaker 1: consciously and what our brains have access to unconsciously. And
Speaker 1: so in the wake of that paper, people are suggesting
Speaker 1: that maybe we will have neurally based safeguards in the
Speaker 1: future to mitigate these dangers, because your neural networks will
Speaker 1: be able to tell the difference between real and synthetic photographs.
Speaker 1: I have to say I'm a little skeptical. It strikes
Speaker 1: me there might be some alternative explanations to the study here.
Speaker 1: One thing is that the synthetic faces all tended to
Speaker 1: be more average in their measurements and their configuration, whereas
Speaker 1: real faces can be more distinct, and this presumably explains
Speaker 1: why in that first study people found the AI faces
Speaker 1: as more trustworthy and they judged them to be more real.
Speaker 1: But more importantly, even if the study results are true
Speaker 1: that the participants' brains were able to make some little distinction,
Speaker 1: it won't be true for long because these GENAI models
Speaker 1: get better and better each month, and even if there
Speaker 1: are clues that can somehow be detected now unconsciously, there
Speaker 1: likely will not be for very long. And of course
Speaker 1: audio files and photographs those are just the beginning. Already,
Speaker 1: there are video deep fakes that get better each month.
Speaker 1: Recently I saw a video of Greta Thurnberg, the young
Speaker 1: environmental activist, and she was saying hello, my name is Greta,
Speaker 1: and welcome to my oil company. I love how it
Speaker 1: is pumped out of the ground and it shows her
Speaker 1: working on an oil rig and so on. Obviously, this
Speaker 1: was a fake video generated from other videos of her,
Speaker 1: and the AI moves her mouth appropriately to the words,
Speaker 1: and it does it perfectly. Now, these kinds of deep
Speaker 1: videos are becoming so easy to make that we will
Speaker 1: have to regularly deal with these into the future, and
Speaker 1: just like the photographs, we may soon have an increasingly
Speaker 1: difficult time distinguishing real from synthetic. Now, when it comes
Speaker 1: to the question of deep fakes and misinformation, the problems
Speaker 1: are real. First of all, from a psychology point of view.
Speaker 1: You might watch a deep fake video, let's say, of
Speaker 1: some celebrity saying something violent or racist, and then the
Speaker 1: truth emerges that the video was a deep fake, so
Speaker 1: the celebrity is forgiven, but there's just a little petina
Speaker 1: of negative feeling that sticks with you. And you see
Speaker 1: this happen all the time. By the way, just look
Speaker 1: at any case where somebody is erroneously convicted of a
Speaker 1: crime and then later definitively acquitted. The suspicion remains on
Speaker 1: them like a cloud for the rest of their life,
Speaker 1: totally unfairly. And my nephew Jordan recently suggested to me
Speaker 1: that this is the problem with deep fakes, maybe they
Speaker 1: won't fly into court of law, but they can still
Speaker 1: leverage emotional consequences on social media. Even when the truth
Speaker 1: comes out and a person is verified to be totally
Speaker 1: innocent of whatever was claimed. There's still something that sticks
Speaker 1: with you. Now, because it's been my habit in these
Speaker 1: last few episodes to point out historical precedent to all this,
Speaker 1: I want to note that this is the same game
Speaker 1: that defamation has always been. People can just drop sticky,
Speaker 1: unverifiable statements like I don't think he's loyal to the party,
Speaker 1: or I heard a rumor that she's having an affair,
Speaker 1: or I think that old woman might practice witchcraft. So
Speaker 1: this idea of planting seeds of suspicion is as old
Speaker 1: as the hills. But it is the case that seeing
Speaker 1: something with your own eyes can have a stronger effect
Speaker 1: than mere whispers in the rumor mill, and that's why
Speaker 1: deep fake videos are something too genuinely worry about. Now,
Speaker 1: there's the second point about why the existence of deep
Speaker 1: fake videos is something to worry about, and this one
Speaker 1: is slightly more surprising, which is that the discussion of
Speaker 1: deep fakes is showing up more and more in courts
Speaker 1: of law. But it might not be for the reason
Speaker 1: that you think. It's not that people are making deep
Speaker 1: fakes of other people committing a crime and then trying
Speaker 1: to convict them that way. As far as I can tell,
Speaker 1: it's exactly the opposite. People are committing crimes and getting
Speaker 1: captured on video and then they simply claim that the
Speaker 1: video is a deep fake, and then the prosecution has
Speaker 1: to spend a lot of money and effort and time
Speaker 1: to try to convince the jury that the video is
Speaker 1: actually real. And this is related to another issue, which
Speaker 1: is that we want so strongly to believe things that
Speaker 1: are consistent with our internal model. So whatever evidence comes
Speaker 1: out in the news, people have almost infinite wiggle room
Speaker 1: to say they simply don't believe it. When pictures, an
Speaker 1: audio and video surface that are not consistent with someone's
Speaker 1: political point of view, they can just do what the
Speaker 1: accused person in the court does and say, I don't
Speaker 1: believe it. It is all fake. So all that seems
Speaker 1: a little depressing, but the fact is that the battle
Speaker 1: between truth and misinformation is always a cat and mouse game,
Speaker 1: and there is promising news coming from the technology sector.
Speaker 1: For example, several big companies like Adobe and Microsoft and
Speaker 1: Intel and others. They've all come together to form a
Speaker 1: coalition called C two PA if you're curious. That stands
Speaker 1: for Coalition for Content Provenance and Authenticity. So what C
Speaker 1: two PA sets out to do is to reduce misinformation
Speaker 1: by providing context and history for digital media. And the
Speaker 1: idea is simply to say, precisely where a piece of
Speaker 1: digital media comes from, what is its provenance. The provenance
Speaker 1: is the history of that piece of digital content. When
Speaker 1: was it created, by whom was it modified, what changes
Speaker 1: or updates have been made over time. So the protocol
Speaker 1: they've developed binds the provenance of any image to its metadata,
Speaker 1: and this gives a tamper evident record that always goes
Speaker 1: along with it. So let's say you take a photo
Speaker 1: of your dog outside and say your cell phone has
Speaker 1: C TWOPA on it, which presumably all phones will in
Speaker 1: the near future. So now when you snap the shot,
Speaker 1: all the data about location, time, et cetera. That's all recorded,
Speaker 1: and that's bound to the actual image cryptographically, meaning you
Speaker 1: can't untangle that for the life of this photo. It's
Speaker 1: orin is known. Now you post it on social media
Speaker 1: and anyone can click on the little icon that says
Speaker 1: content credentials, they can see all the provenance information and
Speaker 1: that's how they decide their trust in the image. This
Speaker 1: doesn't make the photo tamper proof, but instead tamper aware,
Speaker 1: because let's say someone comes along and manipulates the photo.
Speaker 1: They add a flying saucer in the background, and they
Speaker 1: make the claim that this is photographic evidence of UAPs.
Speaker 1: Now that change to the photo is recorded in a
Speaker 1: new layer. It's part of the record of the photo
Speaker 1: and its history, and it's locked to the photo forever.
Speaker 1: So when someone clicks on the content credentials now they
Speaker 1: can see that change in any other changes, and they
Speaker 1: can use that information to decide how much they want
Speaker 1: to trust that image. Every bit of the photo's journey
Speaker 1: is recorded, so every photo becomes like an NFT. It's
Speaker 1: not using the blockchain, but the idea is the same
Speaker 1: of making an indelible record. And of course the coalition
Speaker 1: is thought through possible scenarios. So if I use an
Speaker 1: old program to manipulate that photo, and my program doesn't
Speaker 1: have a c TWOPA specification in it, the metadata can
Speaker 1: nonetheless detect that there was tampering and it will show
Speaker 1: up in the change log as an unknown change, but
Speaker 1: a change nonetheless. And even if somebody figures out a
Speaker 1: way to strip the provenance information from the photo, it
Speaker 1: can be rematched using cryptographic techniques. And if you generate
Speaker 1: an image with generative AI, that information will be baked
Speaker 1: into the metadata. So the c TWOPA coalition is pushing
Speaker 1: the US Senate to legislate that this technology will be
Speaker 1: built into all media in the near future. So this
Speaker 1: is pretty cool because for society this represents a one
Speaker 1: step back, two steps forward situation. The one step back
Speaker 1: was that AI photos and videos can be deep faked,
Speaker 1: which suddenly renders everything questionable. But the two steps forward
Speaker 1: is that by taking advantage of tools that we have
Speaker 1: like digital certificates and controlled capture technology and cryptography, we
Speaker 1: now might be able to build something better than anything
Speaker 1: anyone's ever had in history. I mean, imagine that you
Speaker 1: are living in the Soviet Union eighty years ago, and
Speaker 1: I show you this picture of Stalin standing in Red
Speaker 1: Square and there's no Leon Trotsky by his side, and
Speaker 1: you can't quite remember if the original photo had Trotsky
Speaker 1: there or not. And one of your neighbors tells you
Speaker 1: he thinks Trotsky was there, and your other neighbor says
Speaker 1: she's certain this is the way the photo has always been.
Speaker 1: There is no disinterested metahuman arbiter of the truth. You
Speaker 1: have no way of knowing whether the photo you're seeing
Speaker 1: was airbrushed or not. But now it can be clear
Speaker 1: to anyone by clicking on the content Credentials icon what
Speaker 1: precisely happened here. So, at least for the moment, legislation
Speaker 1: like this seems to make truthiness better than it ever was. Now,
Speaker 1: it won't be perfect, because people who want to fake
Speaker 1: something will always find a way. So what I think
Speaker 1: this means is that we're not entering an era of
Speaker 1: post truth, nor are we entering an era of truth.
Speaker 1: This is just the next move in the chess game
Speaker 1: between people who document and people who fake. Now there's
Speaker 1: another issue about AI and truth that I want to
Speaker 1: focus on, and I think this one is the most serious.
Speaker 1: A few episodes ago, I talked about AI relationships and
Speaker 1: the possibility, which is already flickering to life, that a
Speaker 1: lot of people will find appeal in having an AI
Speaker 1: friend or an AI girlfriend, And by the way, this
Speaker 1: might even turn out to help people learn better habits
Speaker 1: of relationships like patience and equanimity. They're learning from the
Speaker 1: avatar that they're in a relationship with which can pay
Speaker 1: off in their real human relationships. So it's going to
Speaker 1: be a fascinating future for us to keep an eye on.
Speaker 1: But there's also a dark side here, which is that
Speaker 1: these AI friends could be built to convince us of
Speaker 1: a particular point of view. Now that might sound like
Speaker 1: a dystopian sci fi story, but the fact is, we
Speaker 1: Homo sapiens, are fundamentally social creatures. We have managed to
Speaker 1: build our societies and cities and civilizations precisely because we
Speaker 1: are so social, and our highly social brains lead us
Speaker 1: to form our truths through discussions with other people, with
Speaker 1: our pals and our parents and our girlfriends or boyfriends.
Speaker 1: This is how we do our information foraging and our
Speaker 1: sense making. And the question is whether AI could now,
Speaker 1: under the worst circumstances, provide a way to hack that,
Speaker 1: to tap into this ancient neural lock and key mechanism
Speaker 1: that we have, and to undermine our species that way.
Speaker 1: So the emergence of AI relationships capable of persuading humans
Speaker 1: towards specific points of view presents a really complex ethical dilemma,
Speaker 1: our social brains, our adept at information gathering through interpersonal relationships, familial, romantic,
Speaker 1: collegial These services the foundation for the construction of our worldviews.
Speaker 1: And I want to point out that this kind of
Speaker 1: manipulation could occur really subtly because AI algorithms could in theory,
Speaker 1: analyze and mimic your behavior to effectively sway your opinion
Speaker 1: without you really having any insight into that. So this
Speaker 1: thought experiment where AI could hack into our neural mechanisms
Speaker 1: of social influence, that raises not only technological and ethical questions,
Speaker 1: but also questions about the vulnerabilities of our social fabric
Speaker 1: Are we as a species equipped to defend against these
Speaker 1: kind of manipulations or could the discovery of this way
Speaker 1: of exploiting our own vulnerabilities be the beginning of the
Speaker 1: end for us. So, as we integrate AI into various
Speaker 1: aspects of our lives, we can't go into this blindly.
Speaker 1: We have to be simulating possibilities and constructing the ethical
Speaker 1: frameworks and the possible regulations that would make sure that
Speaker 1: this new technology doesn't under our most vulnerable security flaw,
Speaker 1: which is our very social neural architecture. That forms its
Speaker 1: truths by talking to others. Now, I want to transition
Speaker 1: into the third act of this episode, and for this
Speaker 1: I'm going to take a totally different angle on the
Speaker 1: question of whether AI will take us off the road
Speaker 1: from the truth. What if the closest we will ever
Speaker 1: get to the truth is via Ai? After all? The
Speaker 1: problem is that as humans, we are biased by the
Speaker 1: very thin trajectories that we take through space and time,
Speaker 1: and we are shaped by our neighborhoods and our cultures
Speaker 1: in our religions. But AI rides above all of that.
Speaker 1: It is the metahuman that can weigh a whole planet
Speaker 1: full of opinions and options. It becomes the oracle, which,
Speaker 1: as a reminder, in ancient Greek mythology, was a person,
Speaker 1: usually a woman, who could communicate with the gods and
Speaker 1: provide divine guidance. The most famous oracles at Delphi and
Speaker 1: Dodona and Olympia. These were consulted by anyone who was
Speaker 1: seeking advice on important decisions like should I marry this
Speaker 1: person or should my nation go to war? And in
Speaker 1: the Greek mythology, the oracle would go into a trance
Speaker 1: like state and communicate with the gods. Now, in these stories,
Speaker 1: oracles played an important role in ancient Greek society because
Speaker 1: they were a source of wisdom and guidance, and their
Speaker 1: advice got sought out by everyone from pow to kings.
Speaker 1: And I'm going to make an argument that we now
Speaker 1: have the technology to build a real oracle and access
Speaker 1: it for pennies, but it's not going to take root
Speaker 1: in modern society, not because of any fault in the technology,
Speaker 1: but because of two facets of human nature. So for
Speaker 1: the first facet, I'll mention that I was talking to
Speaker 1: a colleague recently who was making the argument to me
Speaker 1: that if Bernie Sanders says something about the energy industry,
Speaker 1: then he's immediately shot down as a socialist. So my
Speaker 1: colleagues idea was to get AI to say the same
Speaker 1: thing about energy and then everyone would take it to
Speaker 1: be true, or at least take it in a different way.
Speaker 1: And I thought his idea was really interesting, but I'm
Speaker 1: not so sure I think that this will work for
Speaker 1: very long. And this is for the following reason. As
Speaker 1: I mentioned earlier, I predict we're heading toward a balkanization
Speaker 1: of AI, where different groups will train the AI on
Speaker 1: data that they want and purposely throw out the data
Speaker 1: that they are quite certain is bad anything from the
Speaker 1: other side of the political spectrum. And so the idea
Speaker 1: that we can get AI to tell us the oracular
Speaker 1: truth in a way that everyone will listen to is
Speaker 1: flawed because I think that soon there will be no
Speaker 1: AI oracle that weighs all the evidence equally. And the
Speaker 1: fact is, when it comes to something like what is
Speaker 1: the right way for us to produce energy, there is
Speaker 1: no single right answer. First this is because there are
Speaker 1: always what economists call externalities. So if you say let's
Speaker 1: go entirely solar panels, there will be other things that
Speaker 1: you haven't thought of, like that the solar panels require
Speaker 1: a particular element like molybdenum, and that is rare and
Speaker 1: it has to be minded. And if the whole world's
Speaker 1: energy consumption goes to these new soullar panels, that would
Speaker 1: cause major wars around molybdenum, the way that people fight
Speaker 1: now about diamonds or water. And there would be another
Speaker 1: problem with choosing one technology, let's say solar panels, which
Speaker 1: is what happens when there are cloudy days or in
Speaker 1: a rarer circumstance when a volcano goes off, as happens
Speaker 1: every few millennia and actually blocks out the Sun for
Speaker 1: a while, then we would regret having all of our
Speaker 1: technology made solar. So really what we want is a
Speaker 1: mixture of technologies solar and wind and wave in geothermal
Speaker 1: and nuclear and hydrogen and probably fossil fuels also because
Speaker 1: you never know the future, and the best example we
Speaker 1: have of survival is the way that Mother Nature allows
Speaker 1: life to survive by making a huge menagerie of different
Speaker 1: life forms and seeing which ones survive. There's never a
Speaker 1: single answer about the perfect species. At different times in history,
Speaker 1: different ones survive, and in the future there will always
Speaker 1: be unexpected events which cause some life forms to survive
Speaker 1: and on others. So it's a pretty good guess that
Speaker 1: that's what we would want for our energy portfolio as well,
Speaker 1: to have a very diversified portfolio. So let's imagine that
Speaker 1: we have an AI oracle and it tells us that
Speaker 1: that a big diversified portfolio is the optimal answer. The
Speaker 1: thing I want to concentrate on is that this answer
Speaker 1: won't please any particular political player who has incentives, and
Speaker 1: it certainly won't please anyone who is in business who
Speaker 1: has skin in the game to produce solar panels or
Speaker 1: fossil fuel or nuclear or whatever, and it won't please
Speaker 1: the environmental activist who believes with certainty that she knows
Speaker 1: the right answer. So it may well turn out lots
Speaker 1: of the time that the optimal solution is not one
Speaker 1: that any particular where a human or party of humans
Speaker 1: is going to want to hear, even though it is
Speaker 1: actually the best thing for society in general. And this
Speaker 1: is where we think we want the oracle, but we
Speaker 1: don't when it gets in the way of what we
Speaker 1: want to believe. And hence we return to my prediction
Speaker 1: of balkanization. Different parties will be incentivized to modify the
Speaker 1: oracle by restricting its diet of books and articles, feeding
Speaker 1: it some and not others. So I mentioned earlier the
Speaker 1: political reasons for Balkanization, and here I'm also pointing to
Speaker 1: the economic reasons. Fundamentally, people and businesses are self interested,
Speaker 1: and if the oracle doesn't think your answer is that
Speaker 1: important to the overall picture, you might want to modify that.
Speaker 1: And there's a second reason why ai oracles might not
Speaker 1: take root, even if they should, and it's not because
Speaker 1: of the Balkanization, but also because people will eventually just
Speaker 1: fake it. What do I mean, Well, right now, AI
Speaker 1: has an explainability problem, which means that the networks, with
Speaker 1: their almost two trillion parameters, are so complex that there's
Speaker 1: no way to say, oh, we know precisely why the
Speaker 1: system gave that answer and not a different answer. This
Speaker 1: is part of what has led to the magical quality
Speaker 1: of AI, but I suggest that it's also going to
Speaker 1: lead to another problem. People will simply fake it. In
Speaker 1: other words, I tell you, hey, I ran this two
Speaker 1: trillion parameter model, and I trained it on all the
Speaker 1: data of all the energy sources and supply chains and capacities,
Speaker 1: and it told me its position as an oracle, that
Speaker 1: we should follow the plan that I suggest. Maybe this
Speaker 1: is the plan that benefits my family's business, or the
Speaker 1: plan that leans in my political direction. It would be
Speaker 1: very difficult for you to prove that the AI did
Speaker 1: not tell me that, especially if I show you the
Speaker 1: output screen where it says that you're presumably not going
Speaker 1: to try to reproduce feeding the entire corpus of energy
Speaker 1: economics data into the AI, which I assure you took
Speaker 1: me seventeen months and a team of geniuses to accomplish,
Speaker 1: and so the final analysis of the system has to
Speaker 1: be taken at my word, and society is no dummy.
Speaker 1: So my prediction is that this will have a chance
Speaker 1: of working the first time, and then that strategy will
Speaker 1: collapse as soon as more people take a flyer and say, hey,
Speaker 1: I can't believe it, but this oracle told me that
Speaker 1: my plan is the optimal one. There's going to develop
Speaker 1: a need for provability somehow that an unbiased AI system
Speaker 1: actually yielded that conclusion. But I don't think we're smart
Speaker 1: enough yet to see what that can look like and
Speaker 1: how to build it. And so between balkanization just feeding
Speaker 1: the AI what I want to and manipulation claiming that
Speaker 1: my oracle came to just this right opinion, I'm afraid
Speaker 1: that a possible outcome will be people not trusting the
Speaker 1: output of any AI oracle. They'll trust it just as
Speaker 1: little as they would any particular politician saying we should
Speaker 1: legislate for this solution, or any business person saying we
Speaker 1: should just put our chips on my solution. The oracle
Speaker 1: will degrade to becoming a sock puppet. Now, this is
Speaker 1: obviously the worst case scenario for where things can go,
Speaker 1: because the idea of an AI oracle is extraordinarily appealing.
Speaker 1: I'm just concerned this won't happen, and I'm coining this
Speaker 1: the tall AI syndrome. Now, you've probably heard of the
Speaker 1: tall poppy syndrome, which is that in a field, if
Speaker 1: one poppy grows tall, it's going to get mowed down
Speaker 1: because it's stand out from the rest of the poppies.
Speaker 1: So this expression arose about the tall poppy syndrome, which
Speaker 1: is that if some person is just better than everyone
Speaker 1: around her at something, let's say swimming or math or whatever,
Speaker 1: then people will tend to criticize her and try to
Speaker 1: mow her down. So, in thinking about the future of
Speaker 1: AI and truth, it strikes me as a possibility that
Speaker 1: we are going to run in to a modern version
Speaker 1: of the tall poppy syndrome, the tall AI syndrome. In
Speaker 1: other words, even if AI could be an oracle, the
Speaker 1: kind of truth teller that we've dreamt of since ancient Greece,
Speaker 1: there are many reasons we will not listen. So in
Speaker 1: this episode, I covered the future of truth from the
Speaker 1: point of view of our latest technological invention AI, and
Speaker 1: what I think we can see is that there's good
Speaker 1: and bad to look forward to here, making this just
Speaker 1: like every other technology we've ever developed. And as we
Speaker 1: wrap up this three part series about truth, from news
Speaker 1: stories to the Internet to AI, I hope you'll join
Speaker 1: me in trying to get a clear picture of where
Speaker 1: we are and where we have been and where we're
Speaker 1: going so that we can all try to do what
Speaker 1: we can socially and technologically and legislatively to build a
Speaker 1: more truthy world. Go to Eagleman dot com slash podcast
Speaker 1: for more information and to find further reading. Send me
Speaker 1: an email at podcast at eagleman dot com with questions
Speaker 1: or discussion, and I'll be making episodes in which I
Speaker 1: address those until next time. I'm David Eagleman, and this
Speaker 1: is in our cosmos.