Speaker 1: Hey, welcome to Science Stuff, a production of iHeartRadio. I'm
Speaker 1: horehit Champ, and today we're talking about artificial intelligence, or AI,
Speaker 1: and specifically images and videos made by AI, which some
Speaker 1: people call AI slop, and how it affects us, our brains,
Speaker 1: our society, and our sense of reality. We're going to
Speaker 1: be talking to an AI expert about this, a neuroscientist,
Speaker 1: and somebody who's looked into the dangers of AI generated
Speaker 1: pictures of cute, fuzzy animals. Yeah, you think they're perfectly safe,
Speaker 1: but they're not. So ask chat GPT to clear your schedule,
Speaker 1: claude your way to our show and mid journey with
Speaker 1: us as we answer the question what is AI slop
Speaker 1: doing to us? Enjoy? Hey? Everyone, So you've probably noticed
Speaker 1: that AI is everywhere, and you've probably seen and heard
Speaker 1: images and videos made by AI programs in your social
Speaker 1: media feed, online, and even on TV, sometimes without knowing
Speaker 1: it was made by AI. And this trend has exploded
Speaker 1: in just the last few years. So I was curious
Speaker 1: to know two things. First, what happened a few years
Speaker 1: ago that led to this sudden explosion of AI content
Speaker 1: and apps and services. Do we suddenly discover a magical
Speaker 1: algorithm or alien artifact that suddenly gives machines this much power.
Speaker 1: And second, I wanted to know what effect all this
Speaker 1: fake AI made content has on our brains and how
Speaker 1: we see the world. So we'll answer both questions, and
Speaker 1: we'll start with the first. What happened a few years
Speaker 1: ago that started this recent explosion in AI? And is
Speaker 1: there a limit to it? To answer these questions, I
Speaker 1: reached out to this guy I met at lunch the
Speaker 1: other day, who I'm pretty sure is human. Well, thank
Speaker 1: you doctor Tam for joining us.
Speaker 2: Thank you JORGEV for having me. So my name is
Speaker 2: doctor ed Tam. I am a postdoctoral fellow at Stanford University.
Speaker 2: A lot of my work concerns the uncertainty around AI
Speaker 2: and what are the limitations of AI and how we
Speaker 2: can potentially overcome those.
Speaker 1: And how can I make sure you're real and not
Speaker 1: an AI.
Speaker 2: Well, we've met in person, so you know that.
Speaker 1: I met someone who looks like you a role I know,
Speaker 1: although it was over at lunch.
Speaker 2: So yes, a robot that can eat and joke and drink.
Speaker 1: Okay, doctor ed Tam is going to tell me what
Speaker 1: happened a few years ago that made AI swimmingly explode
Speaker 1: in what it's able to do. But to do that,
Speaker 1: we have to go back to where it all started,
Speaker 1: which is when scientists had the idea for AI in
Speaker 1: the first place.
Speaker 2: It goes back to like the last century at least.
Speaker 3: So the idea is that the human brain is composed
Speaker 3: of cells that are called neurons, and these neurons connect
Speaker 3: with each other and these different productivity patterns in the
Speaker 3: brain gives us the rich functionality of the human brain.
Speaker 3: So back in the nineteen hundred, scientists we're thinking, can
Speaker 3: we somehow mimic the biological human neural network with a computer,
Speaker 3: And so there are some early models that kind of
Speaker 3: try to tries to do this. One of the earliest
Speaker 3: and famous one is called the perceptron, and it's just
Speaker 3: this kind of like three layer no network with some
Speaker 3: nonlinear function and coded. So that was kind of like
Speaker 3: the first model, but it was already kind of like
Speaker 3: a mini breakthrough.
Speaker 1: It sounds like a transformer like the toys.
Speaker 3: Oh yes, yes, megatron.
Speaker 2: I don't know why they call it the perceptron.
Speaker 1: So, starting in the nineteen forties, scientists and engineers had
Speaker 1: an idea of how the human brain worked. It was
Speaker 1: a humongous network of neurons connected to each other, and
Speaker 1: so the scientists and engineers wondered if we could make
Speaker 1: an artificial brain. So they programmed on a computer how
Speaker 1: a neuron reacts. It takes signals from other neurons, weighs
Speaker 1: each input depending on some numbers or parameters, and then
Speaker 1: if it all adds up to a certain amount, it
Speaker 1: fires off a single signal to other neurons. And at
Speaker 1: first these networks with layers of neurons were pretty simple,
Speaker 1: but amazingly they could easily recognize patterns, meaning like recognize
Speaker 1: numbers or images kind.
Speaker 3: Of that's exactly right, like telling apart of human digits,
Speaker 3: can you tell a part a cat from a dog.
Speaker 3: There are all these image benchmark data sets that people
Speaker 3: work on, and then it turns out when you stack
Speaker 3: them on top of each other, it can encode really
Speaker 3: really rich patterns.
Speaker 1: So things progress somewhat slowly. Over the next few decades,
Speaker 1: people started to make bigger and bigger networks of these
Speaker 1: artificial neurons. Instead of a few neurons, it started to
Speaker 1: become dozens, and then hundreds, and then thousands of these
Speaker 1: neurons and dozens of these layers, and it was pretty powerful.
Speaker 1: You could give the machine a whole bunch of images
Speaker 1: of dogs and they could learn to recognize dogs. You
Speaker 1: could give it a whole bunch of human fingerprints or faces,
Speaker 1: and they could recognize fingerprints and faces.
Speaker 3: So that was maybe in like the twenty tens or
Speaker 3: something like that. When you go on Facebook, you know how,
Speaker 3: if you post a picture on Facebook, it actually tags
Speaker 3: your face and it tags the faces of your friends,
Speaker 3: and I remember that being like magic.
Speaker 2: I was like, wow, like it recognizes me.
Speaker 3: And that's the type of AI back then ten years
Speaker 3: ago that can do that.
Speaker 1: So ten years ago, AIS were really good at recognizing things,
Speaker 1: but that's kind of all they could do. They couldn't
Speaker 1: make anything up or talk to you or write anything
Speaker 1: for you. They were smart, but they were passive. But
Speaker 1: then in twenty seventeen, some engineers at Google discovered a
Speaker 1: special way to make these networks of neurons that changed everything.
Speaker 1: And it kind of started when they said, you know what,
Speaker 1: maybe we should stop trying to copy the human brain.
Speaker 3: So at the very beginning, people try to come up
Speaker 3: with very simple computer models, like the perceptron that tries
Speaker 3: to mimic the human brain, and then gradually this neural
Speaker 3: network or deep learning morphed into something completely different that
Speaker 3: is no longer trying to mimic the human brain.
Speaker 1: Yeah, you can imagine that if the History of the
Speaker 1: Human Race was a movie, this might be the part
Speaker 1: of the movie that foreshadows that it's not gonna end. Well,
Speaker 1: what the engineers at Google had invented was something called
Speaker 1: the transformer. In this case, it's not related to the
Speaker 1: robot toys.
Speaker 2: It is not related to the robot toys at all.
Speaker 2: I guess I've been in the AI.
Speaker 3: World for so long that transformers used to me in
Speaker 3: the movie and me Tron and all that souff now
Speaker 3: means something completely different to me.
Speaker 1: They reprogrammed your brain.
Speaker 2: They reprogrammed my brain. Yeah, okay.
Speaker 1: What this transformer way of connecting neural networks that the
Speaker 1: Google engineers invented in twenty seventeen does is that it
Speaker 1: uses an old ticnique from language translation called attention. And
Speaker 1: basically what it means is that when an AI is learning, say,
Speaker 1: a piece of text from a book, it doesn't just
Speaker 1: learn what each word means. It learns the context of
Speaker 1: the word. For each word, it sort of learns what
Speaker 1: word it usually has after it and before it, and
Speaker 1: it also learns what word it usually has two words
Speaker 1: after it and two words before it, and so on
Speaker 1: and so on. Let's here's an example. So like the
Speaker 1: word sit from sitting, usually it comes a few words
Speaker 1: before chair or sofa.
Speaker 3: Right right right, So I sit on a sofa. I
Speaker 3: sit on a sofa is very logical, very sensible. The
Speaker 3: key work that is important in terms of me understanding
Speaker 3: what sofa means is the I in the sentence. So
Speaker 3: it's not a dog sitting on the sofa, it's not
Speaker 3: cat sitting on the sofa, it's I that is sitting
Speaker 3: on the sofa. And so the attention mechanism allows you
Speaker 3: to kind of learn that type of correlation. And there
Speaker 3: are technical details about position encodings, but the main thing
Speaker 3: is just you want to learn the meaning of a
Speaker 3: word in the context of the words that surround it.
Speaker 1: This sort of simple and kind of common sense sounding
Speaker 1: trick is basically what revolutionized AI. You can apply it
Speaker 1: to text, images, music videos, and it's what makes chat GPT, Gemini, Grock,
Speaker 1: mid Journey, Claude, Nano, Banana, Copilot and all those AI
Speaker 1: agents out there work. And that's because using this transformer
Speaker 1: architecture and paying attention to the context of the data
Speaker 1: clout AIS to teach themselves.
Speaker 2: Yeah, so it's called self supervised learning.
Speaker 1: Oh that sounds scary.
Speaker 2: Yeah. Yeah. So traditional machine learning or AI systems, they
Speaker 2: rely on supervised learning, which means that a human has
Speaker 2: to tell you what is right or what is wrong. Now,
Speaker 2: while that's great, it's not very scalable because a human
Speaker 2: can only tell you what's right or wrong for a
Speaker 2: thousand things or two thousand things or whatnot. You can't
Speaker 2: scale it to a billion things. Whereas with this self
Speaker 2: supervised paradigm, you don't need human supervision. You just need
Speaker 2: some large purposes of unstructured data, and then the machine
Speaker 2: teaches itself just by predicting what the next token is
Speaker 2: and seeing whether that matches reality in terms of the
Speaker 2: real data.
Speaker 1: What doctor Tam saying is that this new type of
Speaker 1: AI could teach itself by predicting what the next word
Speaker 1: or part of an image should be according to its
Speaker 1: context and then checking with the real thing. And do
Speaker 1: ability of AIS to learn contexts and teach themselves, combined
Speaker 1: with big companies putting billions of dollars into huge computer
Speaker 1: farms meant that you could suddenly have ais teach themselves
Speaker 1: the entire Internet.
Speaker 3: So the idea is that very surprising things happen when
Speaker 3: you scale something to really really make sizes. They perform
Speaker 3: way way better in terms of producing realistic samples and texts.
Speaker 3: So there is some type of phase transition in terms
Speaker 3: of how intelligent or how realistic the samples that it
Speaker 3: generates will look like something.
Speaker 1: Click. It suddenly activated some potential in these systems that
Speaker 1: experts were not expecting. Right. So t wecap ais were
Speaker 1: originally meant to copy the human brain, and they were
Speaker 1: really good at just recognizing things patterns, faces, numbers. But
Speaker 1: then some engineers at Google decided, you know what, let's
Speaker 1: forget the human brain and just push this through the limit.
Speaker 1: And what they came up with created ais they could
Speaker 1: understand context, and more importantly, they could teach themselves. And
Speaker 1: once that happened, they scaled up, gubbled up the Internet
Speaker 1: and made a leap that's making a lot of people,
Speaker 1: uh concerned right now. So when we come back, we're
Speaker 1: going to talk about how these new ais can affect
Speaker 1: your brain, and how they could even make endangered species
Speaker 1: of animals go to think, don't go anywhere, We'll be
Speaker 1: right back. Hey, welcome back. We're talking about AI slop
Speaker 1: or AI made images and videos that are swamping the
Speaker 1: Internet right now, and what effect it's having on us
Speaker 1: so far. Talking about what happened in twenty seventeen that
Speaker 1: suddenly made these AI systems be able to talk to
Speaker 1: you and make the kind of realistic images everyone sees
Speaker 1: in their social media feeds. Now we're gonna talk about
Speaker 1: the effect it's having on our brains. Is it possible
Speaker 1: for AI generated content to rewire how we think? To
Speaker 1: tell us about that, I reached out to my friend,
Speaker 1: neuroscientist Dwayne Godwin, a professor at Wake Forest University and
Speaker 1: the co author of the book Out of Your Mind,
Speaker 1: which he co wrote with me. Doctor Godwin recently wrote
Speaker 1: an article about this topic online, so I thought he
Speaker 1: was the perfect person to give us the neural perspective
Speaker 1: on this topic. Here's my conversation with doctor Dwayne Godwin. Hey, Dwayne,
Speaker 1: how are you?
Speaker 4: I'm doing well? Orgey, how are you?
Speaker 1: I'm good? Now? Just to make sure this is the
Speaker 1: real you, right, I'm not talking to an AI version
Speaker 1: of you.
Speaker 4: No, I AMZ I'm the real deal man.
Speaker 1: And is there an intelligence artificial or is organic?
Speaker 4: It's augmented by caffeine.
Speaker 1: Augmented intelligence? There you go. Well, I wonder if there's
Speaker 1: a way for people to know if this conversation is
Speaker 1: generated by one of those you know, chat GBT things
Speaker 1: that mix podcasts.
Speaker 4: You know, if we were really sneaky, we might have
Speaker 4: generated an AI podcast of the two of us going
Speaker 4: back and forth and then tested our audience in their
Speaker 4: ability to tell the difference. But we're not that sneaky.
Speaker 1: Of course, this is.
Speaker 4: The real thing.
Speaker 1: All right, let's get into it. I guess the first
Speaker 1: question I had was how good are we at telling
Speaker 1: is something is AI generated?
Speaker 4: We're not very good. People tend to overestimate their AI radar.
Speaker 4: A lot of obvious tells, like you know, extra fingers,
Speaker 4: robotic phrasing, those are getting all smoothed out. And we're
Speaker 4: looking at these things often on very tiny screens, and
Speaker 4: so what's plausible quickly becomes good enough or believable wow,
Speaker 4: And the result of that is there's kind of a
Speaker 4: confidence gap. We feel very certain that we can tell
Speaker 4: fake stuff when we see it, but often we're not
Speaker 4: doing much better than chance.
Speaker 1: Are there any studies that kind of tell us how
Speaker 1: good we are at spotting fake ais?
Speaker 4: Yeah, there have been studies that have been done. There
Speaker 4: was a study where people are essentially two groups. They're
Speaker 4: presented with both real images and AI generated images, and
Speaker 4: it turns out that they're about fifty percent you know,
Speaker 4: it's like fifty to fifty that they can tell the difference.
Speaker 1: Basically, they can't tell the difference.
Speaker 4: Yeah, so they're really about at the level of chance.
Speaker 1: Yikes. Okay, and this was probably done a few couple
Speaker 1: of years ago, right.
Speaker 4: Yeah, that's right, And so things are only better now.
Speaker 4: So you can imagine that right now, with the quality
Speaker 4: of generative AI, that this is even going to be
Speaker 4: more true.
Speaker 1: Yes, we all like to think we're good at spotting
Speaker 1: fake images or videos. I mean, we've all seen bad
Speaker 1: computer graphic effects movies, so we think we know what
Speaker 1: fake looks like. But actually the sign says that we
Speaker 1: are not much better than guessing randomly if something is
Speaker 1: fake or not. And according to doctor Godwin, there are
Speaker 1: three reasons our brains are kind of helpless against this
Speaker 1: onslaught of fake content coming at us on a daily basis.
Speaker 1: The first reason is that your brain sort of wants
Speaker 1: to believe things are real. I guess, as a neuroscientists
Speaker 1: and as a psychologist, why do you think our brains
Speaker 1: are so easy to fool?
Speaker 4: Well, a couple of reasons. You know, our brains are
Speaker 4: built for speed and survival and not for fact checking.
Speaker 1: Right, And I guess that means out in the wild,
Speaker 1: if you think you see a bear or a tiger
Speaker 1: coming at you, your brain is not going to have
Speaker 1: wanted like fact check that there really is a tiger
Speaker 1: coming at you. You want your brain to just react
Speaker 1: to the idea that maybe there's a tiger coming at you.
Speaker 4: Yes, that's sedaptive. You're going to go with bear if
Speaker 4: it looks anything.
Speaker 1: Good. Because I guess we don't have all information all
Speaker 1: the time, so our brains are sort of have trained to, like,
Speaker 1: do as much as you can with as little information
Speaker 1: as possible.
Speaker 4: Yeah, there's a concept in psychology called heuristics, which is
Speaker 4: basically making decisions on the basis of very limited information,
Speaker 4: and our brains are doing that constantly, so you know,
Speaker 4: we have to make those very quick decisions, so we
Speaker 4: can pass our genes onto the next generation.
Speaker 1: I see, and so you can exploit that. I guess
Speaker 1: because we see something that sort of looks real, your
Speaker 1: mind will basically make the conclusion that it is real.
Speaker 4: Yeah, it will create the story that it is real.
Speaker 4: You know, what is the purpose of our brains. Our
Speaker 4: brains are prediction machines, and so we've learned certain facts
Speaker 4: about the world, about how things normally go. So if
Speaker 4: you're in the woods and you encounter something it seems
Speaker 4: like a bear, your brain already knows that that's probably
Speaker 4: not a great thing, and so you're going to extrapolate
Speaker 4: that story and you're going to want to avoid that
Speaker 4: bear as much as possible, right.
Speaker 1: I guess we kind of had a natural mechanism against that,
Speaker 1: which was this idea of the Uncanny Valley.
Speaker 4: Yeah, I'm familiar with the Uncanny Valley. It's that sort
Speaker 4: of eerie feeling that you get when you see something
Speaker 4: that's trying to be real but it's not real. It
Speaker 4: makes me uncomfortable because I know that it's not supposed
Speaker 4: to look like that exactly.
Speaker 1: Yeah, So, like we did have a brain mechanism to
Speaker 1: defense against fake things. Right, But it seems like AI
Speaker 1: today has basically blown past that defense, like it's now
Speaker 1: past the uncanny valley.
Speaker 4: Yeah, it has jumped across the uncanny valley for sure.
Speaker 1: In other words, our brain has a built in BS
Speaker 1: meter called the uncanny valley. But now AI content is
Speaker 1: so good it's blown past that last line of defense
Speaker 1: or your brain. Okay. The second reason our brains are
Speaker 1: easy to fool by AI generated content is that it's
Speaker 1: not built to withstand the constant stream of it we
Speaker 1: get every day. The second thing you said was repetition.
Speaker 4: Yeah, that's right. So your resistance is being reduced in
Speaker 4: two ways. So first is just the repetition of seeing
Speaker 4: the same thing over and over.
Speaker 1: Meaning like, if I'm scrolling through these online feeds and
Speaker 1: most of what I see has that sort of feeling
Speaker 1: of an AI generated thing, then my brain is just
Speaker 1: gonna normalize it. Is that kind of what you're saying, Like,
Speaker 1: it's yeah, it is.
Speaker 4: There's another more insidious part of that, which is, let's
Speaker 4: use the you know, the cute animal AI generated cute
Speaker 4: animal videos you'll see in the comments underneath those people
Speaker 4: reacting to it as if it's real, and so part
Speaker 4: of that is, Hey, everybody else thinks it's real, and
Speaker 4: I'm a person too, shouldn't I be thinking that this
Speaker 4: is real? And so there's this sort of desire on
Speaker 4: the part of people to reach a consensus with your group,
Speaker 4: and so if you start identifying with that group, then
Speaker 4: your brain might be bent toward making a decision that, hey,
Speaker 4: maybe this is real, even if you might have had
Speaker 4: initial doubts. Yeah, maybe you start to fall into line.
Speaker 1: Like we all like to think we're independent thinkers and
Speaker 1: critical thinkers, but part of our brain is sort of
Speaker 1: wired to kind of basically follow the mob.
Speaker 4: Yeah, a little bit of mob mentality. Yeah, we like
Speaker 4: to think of ourselves as logical beings, but the reality
Speaker 4: is we're all challenged by biases, and those biases can
Speaker 4: be very adaptive in the case of things like heuristics.
Speaker 1: And then the third reason our brains are easy targets
Speaker 1: for AI slob is that a lot of that SLOB
Speaker 1: is made to play with our emotions. And then you
Speaker 1: said the third one was that we tend to believe
Speaker 1: things that kind of affect our emotions.
Speaker 4: Yes, that's probably one of the most insidious things, is
Speaker 4: that things that ring our emotional bell tend to make
Speaker 4: it through without the kind of critical thought processing that
Speaker 4: we would normally apply to something that's just factual or techation.
Speaker 4: So think about politics. Politics is very polarizing. So if
Speaker 4: you see something in your social media feed and it
Speaker 4: confirms what your emotions tell you should be true based
Speaker 4: on your political leanings, then that we'll get through the
Speaker 4: normal cognitive criticism that you would apply to that information.
Speaker 1: Yikes, Like, we let our emotions get in the way
Speaker 1: of our critical thinking, so our brains are ill equipped
Speaker 1: to deal with all this AI slot because it tends
Speaker 1: to believe what it sees. We're inundated with this content
Speaker 1: and it's all meant to trigger our emotions and things
Speaker 1: doctor Godwin says are just getting worse.
Speaker 4: They're getting worse because it's getting better. There's a lot
Speaker 4: of money being put into making these things basically indistinguishable
Speaker 4: from reality, and we were already suffering under the idea
Speaker 4: that we could possibly tell the difference, but it's becoming
Speaker 4: a parent that we cannot, and it's going to be
Speaker 4: even harder.
Speaker 1: It kind of seems like it's also getting worse because
Speaker 1: it seems like people are more willing to post fake
Speaker 1: AI generated things, like just the other day, the White
Speaker 1: House official account posted some AI altered image of some
Speaker 1: activist protester, and they were totally unapologetic about it.
Speaker 4: Yeah, the tools are more readily available, and your ability
Speaker 4: to manipulate those tools doesn't even require that you have
Speaker 4: any sort of computer science degree.
Speaker 1: Yeah, it's just an app on your phone.
Speaker 4: Yeah. This stuff is scalable too, So it's not just
Speaker 4: that a person can go in and do like a
Speaker 4: one off. A single person could generate a lot of
Speaker 4: this content and get it out there, and even use
Speaker 4: AI to generate bots that would distribute that content for you.
Speaker 2: Yeah.
Speaker 4: So it's only going to get worse, I think.
Speaker 1: Yeah, it's not looking so good for us organic and
Speaker 1: they naturally made squishy intelligences out here. But according to
Speaker 1: doctor Godwin, there are some things you can do to
Speaker 1: protect your neurons. What can we do to protect our
Speaker 1: brains from the defect of this AI slut?
Speaker 4: Well, it's hard. I wish I had an easy answer,
Speaker 4: because what I've told you so far it's pretty hopeless. Right,
Speaker 4: it sounds hopeless.
Speaker 1: Maybe I should look it up and chat GBT.
Speaker 4: But I think there are things that we can do,
Speaker 4: and part of it is just slowing down. The thing
Speaker 4: we can't do is there's no way we're going to
Speaker 4: become a forensic AI detector. So what we have to
Speaker 4: do is really add some friction, build some simple verification habits,
Speaker 4: and try to shrink our exposure to this fire hose
Speaker 4: of bad stuff. I think a good rule is if
Speaker 4: it hits you emotionally in the first two seconds, then
Speaker 4: just pause and then just ask the question, Okay, made this,
Speaker 4: where did it come from? And can I confirm that
Speaker 4: this is real? Somewhere boring and reputable.
Speaker 1: I see, look for sources that you can trust, and
Speaker 1: maybe shift your consumption from the wild west of social
Speaker 1: media to some of these sources that you trust more
Speaker 1: and that are known to give you objective truth.
Speaker 4: That's right. So if something seems to perfectly confirm your
Speaker 4: own biases, that's the time to tell yourself, you know,
Speaker 4: I need to dig a little deeper before I accept this.
Speaker 1: I see, check other websites, maybe even the ones that
Speaker 1: you don't agree with.
Speaker 4: That's right. Part of it is if it's real, then
Speaker 4: it should exist somewhere other than your social media feed.
Speaker 4: And I think the other part of this is there
Speaker 4: is an algorithm that is trying to cater information to you.
Speaker 4: It's called a social media feed for a reason, because
Speaker 4: it's being fed to you.
Speaker 1: Right, I see, ye realize that it's a feed.
Speaker 4: So you have a choice. In x dot com, for example,
Speaker 4: it'll have a curated feed, and then it'll have a
Speaker 4: feed of people that you follow, and I always turn
Speaker 4: off the curated feed and only go for the accounts
Speaker 4: that I have actually chosen to follow.
Speaker 1: Yeah, well that's a good one. Well, hopefully people will listen.
Speaker 1: But here's the twist, ending, Dwayne, this was AI generated. Yes, No,
Speaker 1: we're just kidding. No, we actually had this conversation. How
Speaker 1: can we prove it? Dwayne, say something that an AI
Speaker 1: would never say?
Speaker 4: What an AI would never say? Never listen to an AI?
Speaker 1: All right, let me come back. We're going to talk
Speaker 1: about another way in which AI slob is affecting us,
Speaker 1: one that is kind of unexpected. That is through the
Speaker 1: insidious use of pictures of cute animals. You think they're harmless,
Speaker 1: but actually they could lead to a lot of death
Speaker 1: and destruction. So stay with us. We'll be right back.
Speaker 1: Welcome back. I am not an AI or am I
Speaker 1: We're talking about the effects AI slop or AI generated
Speaker 1: content has on us. And so far we learn how
Speaker 1: AI slop works and why it's so easy for our
Speaker 1: brains to believe it's real. Now we're going to explore
Speaker 1: how we can affect our actions by focusing on one
Speaker 1: specific example, which is pictures and videos of cute animals.
Speaker 1: You've probably seen this in your social media feed. Some
Speaker 1: cute little hummingbirds taking shelter inside a flower, or a
Speaker 1: cute kitten plucking gummy bears off a gummy bear tree,
Speaker 1: or a cow scratching itself with the room. Actually one
Speaker 1: of those is real, but the fact that you don't
Speaker 1: know which of them it is is kind of the
Speaker 1: whole problem, and, according to the next person I talked to,
Speaker 1: can kind of be a serious problem with potentially dire consequences.
Speaker 1: Ketterina Zimmer is a science journalist who writes about biology
Speaker 1: and the environment, and she recently published an article about
Speaker 1: aislop in Atmos magazine. Well, thank you, Katrina for joining us.
Speaker 5: Thanks so much for having me.
Speaker 1: Now, how can I be sure that you're real and
Speaker 1: not AI generated?
Speaker 5: That's a good question. I guess you'll have to trust
Speaker 5: me on this.
Speaker 2: One.
Speaker 1: Oh no, Well, you've written a lot about the impact
Speaker 1: of AI and AI generated images on us, but from
Speaker 1: a different angle, So tell us about the article that
Speaker 1: you wrote.
Speaker 6: Yes, the story began sometime last year when I started
Speaker 6: seeing this wave of AI generated images of animals on Instagram,
Speaker 6: which is my poison and of social media use.
Speaker 5: And so for context, I'm a big animal lover and.
Speaker 6: I write journalistic articles about nature and wildlife for living,
Speaker 6: So my algorithms know that and they show me a
Speaker 6: lot of animal content.
Speaker 5: But these AI generated images started to.
Speaker 6: Worry me a bit because they were usually very captivating,
Speaker 6: often very beautiful images, but they're not real and I
Speaker 6: guess I could only tell that they were AI generated
Speaker 6: because they were depicting animals doing things that they usually don't,
Speaker 6: or moving not quite the way you'd expect, or sometimes
Speaker 6: they had this like glossy photoshop sheen to them that's
Speaker 6: characteristic of some AI imagery.
Speaker 5: And what alarmed me.
Speaker 6: Was that a lot of people, judging by the comments
Speaker 6: sections on some of these posts, didn't seem to know
Speaker 6: that they were a fake because they look so realistic.
Speaker 1: Wow, what would they say? So?
Speaker 6: One example is an I generated video of a pair
Speaker 6: of hummingbirds sheltering from the rain inside a rose. I
Speaker 6: asked an ornithologist about this. Hummingbirds do not do that,
Speaker 6: and people are still commenting things like oh, how cute lovebirds,
Speaker 6: how adorable, and it's got three point one million likes.
Speaker 1: Wow, So millions of people were believing that these were
Speaker 1: real hummingbirds.
Speaker 6: Yeah, And as a truth abiding journalist, I felt it
Speaker 6: was important to call this out for what it is,
Speaker 6: misinformation and also explore what impact does it have when
Speaker 6: you have millions of people believing this kind of imagery
Speaker 6: is real? How could this affect our perceptions of nature?
Speaker 1: Amazing? What are some other examples of PI generated images
Speaker 1: of animals you've seen out there?
Speaker 6: Yeah, so there are different genres of this kind of imagery.
Speaker 5: So birds in beautiful settings.
Speaker 6: I've seen a lot of videos showing animals like bears
Speaker 6: or sometimes seals being rescued from the ocean. They're also
Speaker 6: AI concoctions of animals that don't exist, like there are
Speaker 6: some like tadpole type creatures with big googly eyes that
Speaker 6: I saw going viral as well. And there are also
Speaker 6: a lot of videos showing like eagles or lions attacking
Speaker 6: children or dogs, that kind of thing.
Speaker 1: Why do you think people make these images and videos?
Speaker 6: Yeah, I think there's a lot of motivations that feed
Speaker 6: into this. The most obvious one is just wanting more likes, clicks, followers,
Speaker 6: which is of course like the currency of status for
Speaker 6: social media.
Speaker 1: People just want to get popular on social media.
Speaker 6: I think so in some cases, they're also financial incentives,
Speaker 6: so social media platforms like TikTok will actually pay creators
Speaker 6: for videos that go viral. I had a conversation with
Speaker 6: one cre who makes AI generated videos, and he had
Speaker 6: a motivation that really surprised me. For him, part of
Speaker 6: the fun is deliberately testing whether people can recognize these
Speaker 6: as fake. So he'll often make his videos to look
Speaker 6: like they were shot from an iPhone, like there is
Speaker 6: one showing a giraffe walking through a mall in Dubai.
Speaker 1: Now you wrote a little bit about why we're vulnerable
Speaker 1: to these images, like why are we prone to believe
Speaker 1: these fake animals?
Speaker 6: Yeah, I think a lot of the spoils down to
Speaker 6: our growing lack of knowledge about animals among many societies today.
Speaker 6: So a lot of us are living in cities, We're
Speaker 6: being less exposed in nature. I think we're losing touch
Speaker 6: on a large scale, and I mean a lot of us,
Speaker 6: including me, I should say, struggle to identify species of
Speaker 6: plants and animals around us. There is one study of
Speaker 6: students at Oxford University that showed that half of them
Speaker 6: couldn't five British bird species. If I recall correctly, those
Speaker 6: were actually biology students.
Speaker 1: Wow.
Speaker 5: And I think we're.
Speaker 6: Especially susceptible to biology related misinformation in particular because, I mean,
Speaker 6: scientists are always discovering new species and new behaviors. Just
Speaker 6: the other week, there's that story about Veronica, this Austrian
Speaker 6: cow who used a broom to scratch herself that was
Speaker 6: not AI generated because that information came from reputable news outlets.
Speaker 1: For a second, I thought you were mentioning something that
Speaker 1: was not real.
Speaker 5: No, No, I'm pretty sure that was real.
Speaker 1: This was a cow that grabbed a broom and scratched
Speaker 1: itself with it.
Speaker 6: Yeah, she had figured out to use a broom to
Speaker 6: scratch herself in particular ways, and I think it was
Speaker 6: described as the first case of tool use in cows
Speaker 6: by scientists.
Speaker 1: Bonus points. Do you if you correctly guessed the cows
Speaker 1: scratching itself with a broom is the real image and
Speaker 1: not AI generated. It's true, look it up on a
Speaker 1: real new site. Okay, now let's get to how these
Speaker 1: AI made images can affect us. And then in the
Speaker 1: article you wrote about how this can affect how we
Speaker 1: relate to animals, what did you learn.
Speaker 6: Yeah, so a lot of these impacts are just unfolding
Speaker 6: at the moment, so we don't have concrete data on this.
Speaker 6: But the experts I spoke to had a few concerns.
Speaker 6: One of them is that as we see more images
Speaker 6: of a certain animal, that can make us more likely
Speaker 6: to believe that that animal is in fact more abundant
Speaker 6: in the real world. So there's some interesting research showing
Speaker 6: that seeing images of endangered lions or giraffes can cause
Speaker 6: us to overestimate their abundance in the wild. And I
Speaker 6: don't know the exact numbers, but I think there's only
Speaker 6: like a little over one hundred and ten thousand wild
Speaker 6: giraffes in the world, and only a little more than
Speaker 6: twenty thousand lions. But a lot of people don't know
Speaker 6: that because we see these animals all the time in
Speaker 6: travel commercials or children's toys cartoons, and so some of
Speaker 6: the AI generated videos of birds that I've seen featured
Speaker 6: birds of paradise, many of which are threatened by habitat loss.
Speaker 6: So I think seeing these images a lot can cause
Speaker 6: us to believe they're actually quite abundant and make us
Speaker 6: blind to the conservation crisis these creatures are in.
Speaker 2: I see.
Speaker 1: So that's one way, hey, I slap can change our
Speaker 1: view of animals. It can distort how abundant or how
Speaker 1: safe from extinction they are. It can also distort our
Speaker 1: view of how safe or dangerous so animals are.
Speaker 6: And there are some other concerns and more related to
Speaker 6: specific kinds of videos. So one expert mentioned that seeing
Speaker 6: human like depictions of animals like polar bears could lead
Speaker 6: us to believe that they're cute, fluffy, and approachable creatures,
Speaker 6: and that kind of information could lead people to seek
Speaker 6: out encounters with those animals in the wild. There actually
Speaker 6: are cases of people in the US going out to
Speaker 6: try and pet mountain lions and bears and unfortunately ending
Speaker 6: they end up getting moulled.
Speaker 1: Are you trying to tell me that polar bears don't
Speaker 1: really drink Coca cola and wear scars?
Speaker 5: I'm afraid not. Sorry to bust that myth.
Speaker 1: Say she didn't go up to a polar bear with
Speaker 1: us pop drink.
Speaker 5: Definitely not. You should never approach a polar bear.
Speaker 1: Yes, fake images can make some animals seem safer than
Speaker 1: they really are, and you can also have the opposite effect.
Speaker 1: You can make some animals seem more dangerous than they
Speaker 1: really are.
Speaker 6: There are a lot of videos showing animals like eagles
Speaker 6: or lions attacking humans or dots, which could make people
Speaker 6: more likely to support the culling of those animals or
Speaker 6: less likely to support their conservation.
Speaker 1: Kindarina says. Another big genre of AI generated images or
Speaker 1: videos are those of exotic or endangered animals walking around
Speaker 1: in people's houses.
Speaker 6: Seeing a lot of AI generated imagery also features exotic
Speaker 6: animals being.
Speaker 5: Kept as pets, such as Kappi virus.
Speaker 6: Seeing that kind of imagery could make people more likely
Speaker 6: to want to have them as pets and potentially even
Speaker 6: fuel the capture of these animals in the wild.
Speaker 1: But one of the worst consequences of all this AI slob,
Speaker 1: Katerina argues, is that it makes us doubt what's actually real.
Speaker 6: I want to add the big impact that worries me
Speaker 6: personally is that as people begin to cotton on that
Speaker 6: a lot of this imagery isn't real, they might start
Speaker 6: to dismiss wildlife imagery altogether. So you could have photographers
Speaker 6: spending years trying to get a significant image of a
Speaker 6: bird doing something interesting, only to have people on social
Speaker 6: media say, oh, that's fake news. I'm not going to
Speaker 6: care about that. And the same thing could happen with
Speaker 6: scientists who discover new species or new behaviors. And I
Speaker 6: think that's really what worries me the most is that
Speaker 6: it could further alienate us from the natural.
Speaker 1: World because we don't know what to believe anymore, and
Speaker 1: so therefore we will tend to not care about.
Speaker 5: It, right exactly.
Speaker 6: Yeah, And I think that's really sad because this is
Speaker 6: a time when species really need our attention. So twenty
Speaker 6: eight percent of plant and animal species that have been
Speaker 6: studied are in some way threatened with extinction, so basically
Speaker 6: a third. And to me, I think that's the saddest
Speaker 6: thing about this is that not only are we not
Speaker 6: paying attention to these real threats that animals are facing,
Speaker 6: but that we're instead looking at these fake images of
Speaker 6: them on our phones that don't reflect reality. It's like
Speaker 6: the world's on fire and we're looking at these hummingbirds
Speaker 6: and roses and going, oh, how cute. That's wonderful, and
Speaker 6: there are actually some hummingbird species that are in need
Speaker 6: of conservation attention.
Speaker 1: Meaning that it's sort of detaching, is further from reality,
Speaker 1: and the reality could be kind of bad out there. Yeah, exactly,
Speaker 1: like the world's on fire and people are looking at
Speaker 1: the pictures of the fire and going, oh, that's ais.
Speaker 6: Yeah, exactly, all right.
Speaker 1: Well, hopefully that gives you a pretty sense of why
Speaker 1: these AI generated images are so powerful all of a sudden,
Speaker 1: why our brain is so easily fooled by them, and
Speaker 1: how even pictures and videos of cute animals can impact
Speaker 1: our sense of reality. Some of you might be wondering, well,
Speaker 1: what can we do about this, and the answer is
Speaker 1: not that complicated. According to our experts. You should a
Speaker 1: not believe everything you see on the Internet or your
Speaker 1: social media feed and look for reliable and reputable sources. B.
Speaker 1: You should keep pushing companies to do a better job
Speaker 1: of labeling AI generated content, and c lobby your elected
Speaker 1: officials to put in place more rules and regulations around
Speaker 1: this stuff. But most definitely you should listen to this
Speaker 1: podcast every week. Wait did I say that or did
Speaker 1: AI say that? Either way, if you're hearing this and
Speaker 1: you're human, thanks for joining us. See you next time
Speaker 1: you've been listening to Science Stuff. The production of iHeartRadio
Speaker 1: written and produced by me or hych Ham, credited by
Speaker 1: Rose Seguda, executive producer Jerry Rowland, and audio engineer and
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