Speaker 1: Welcome to tech Stuff, a production from iHeartRadio. He there
Speaker 1: and welcome to tech Stuff. I'm your host, Jonathan Strickland.
Speaker 1: I'm an executive producer with iHeartRadio. And how the tech
Speaker 1: are you. In April twenty twenty three, the lawyers for
Speaker 1: Tesla CEO Elon Musk argued that submitted recordings of their
Speaker 1: client from twenty sixteen might have been deep bakes, so
Speaker 1: the ongoing case is an emotionally charged one. In twenty eighteen,
Speaker 1: a man named Walter Huang died in a car accident.
Speaker 1: The Tesla he was in was a Tesla Model X
Speaker 1: and it was engaged in autopilot mode at the time
Speaker 1: of the crash. His family contends that the Tesla's safety
Speaker 1: systems failed and the vehicle steered itself into a concrete median,
Speaker 1: and the family's lawyer submitted a recording of Elon Musk
Speaker 1: as evidence that Wang was led to believe his vehicle
Speaker 1: had greater capabilities than it actually possessed. So, in this recording,
Speaker 1: Elon Musk said of Tesla vehicles, quote a Model S
Speaker 1: and Model X at this point can drive autonomously with
Speaker 1: greater safety than a person right now. End quote In response,
Speaker 1: Musk's lawyers said, the recording could be faked. Now, not
Speaker 1: to waffle about this, but if we're speaking solely on
Speaker 1: a technical level, the recording could be faked. And by
Speaker 1: that I mean there are technologies that are sophisticated enough
Speaker 1: to create a fake recording. But just because something could
Speaker 1: be faked doesn't mean it actually was faked. And the
Speaker 1: judge in the Tesla Casevet Pennypacker, who has an amazing name,
Speaker 1: said that this argument is a truly dangerous one. The
Speaker 1: judge said that it implies quote that because mister Musk
Speaker 1: is famous and might be more of a target for
Speaker 1: deep fakes, his public statements are immune quote. In other words,
Speaker 1: if you're notable enough or notorious enough, you have a
Speaker 1: carte blanche excuse for anything that you are recorded as saying,
Speaker 1: because maybe someone just targeted you and created a fake
Speaker 1: version to discredit you. In twenty eighteen, Danielle Citron and
Speaker 1: Robert Chesney wrote a paper in which they predicted this
Speaker 1: sort of situation. They dubbed it the liar's dividend. That
Speaker 1: when there is a proliferation of technology that can create
Speaker 1: misinformation or outright disinformation, the liars out there reap the benefits,
Speaker 1: because what is the truth anyway? When you can't trust
Speaker 1: the evidence, everything falls apart. This is just one of
Speaker 1: the many challenges deep fake technology presents. There are potentially
Speaker 1: harmless or perhaps even beneficial uses of this technology, but
Speaker 1: it doesn't take much imagination to come up with ways
Speaker 1: to cause harm. Let's talk a second about the entertainment industry.
Speaker 1: With deep fake technology, it becomes possible to create videos
Speaker 1: and audio recordings that simulate celebrities, which potentially allows a
Speaker 1: director to cast a film with people who otherwise would
Speaker 1: be very much unavailable. Using sufficiently sophisticated deep fakes, you
Speaker 1: could create a movie that combines a cast of modern
Speaker 1: and classic film stars. Maybe you want the Marx Brothers
Speaker 1: running around with Will Ferrell, Maybe you want Lon Cheney
Speaker 1: Junior to show up in your modern werewolf movie. Or
Speaker 1: maybe you're doing something slightly less extreme, maybe you're using
Speaker 1: the technology to generate a younger version of your current
Speaker 1: star ala Harrison Ford in the upcoming Indiana Jones and
Speaker 1: the Dial of Destiny film. So it doesn't have to
Speaker 1: be more or sinister, but it does bring into question
Speaker 1: concepts like the right to personality or the right to
Speaker 1: identity or the right to publicity. Presumably filmmakers wouldn't want
Speaker 1: to move forward on any project with a computer generated
Speaker 1: simulation of a real film star without the permission from
Speaker 1: that person or their family. But it's possible to do it,
Speaker 1: and depending on the movie, maybe they do go ahead
Speaker 1: without securing permission first. Maybe it's a edgy parody film,
Speaker 1: and the buzz around their decision to do this could
Speaker 1: end up being a boost to marketing. People would say
Speaker 1: how dare they do this, and then go buy tickets
Speaker 1: to see the fallout of it. For actors, there's a
Speaker 1: real concern that this technology could rob them of work,
Speaker 1: that if they turned down a role, the filmmaker could
Speaker 1: just get a computer generated version of them in there,
Speaker 1: or that they could appear to you know, appear in
Speaker 1: projects that they don't actually agree with, and perhaps most
Speaker 1: importantly for many actors out there, that this could all
Speaker 1: happen without compensation for the original actor. I know that
Speaker 1: it could be tough to feel sympathetic toward big Hollywood stars,
Speaker 1: but keep in mind the vast majority of working actors
Speaker 1: out there are not raking in the huge movie deals.
Speaker 1: They're just as worried about AI biting into their work
Speaker 1: as the rest of us are. Then there's the world
Speaker 1: of audio performance. Earlier this year, a TikTok user with
Speaker 1: the handle ghost Writer nine seven seven wrote and produced
Speaker 1: a song called Heart on My Sleeve. But ghost Writer
Speaker 1: nine seven seven didn't provide the vocals for this track. Instead,
Speaker 1: they used AI generated deep fake vocal simulations of artists
Speaker 1: Drake and The Weekend. The songwriter then posted the release
Speaker 1: on multiple platforms and it quickly went viral. Universal music
Speaker 1: groups sprung into action right away and claimed copyright infringement.
Speaker 1: And I am no legal expert, but in my mind
Speaker 1: that's a weak argument. After all, the song itself was
Speaker 1: an original, it was not a cover. It had not
Speaker 1: been stolen from someone's discography. You cannot copyright the sound
Speaker 1: of a voice. Universal Music Group doesn't own the vocal
Speaker 1: quality of Drake or the Weekend, and I'm sure those
Speaker 1: artists would be concerned to learn otherwise. And even if
Speaker 1: the agreement between the label and the artists did go
Speaker 1: all Ursula from the Little Mermaid and claim ownership of
Speaker 1: the voices themselves, there's not really a legal foundation to
Speaker 1: use that as a deterrent against Deep fakes. Universal Music
Speaker 1: Group did argue that the deep fake voices used tons
Speaker 1: of recorded material to train itself to sound like those artists.
Speaker 1: That is most certainly the case. We'll dive into deep
Speaker 1: fake techniques a bit later in this episode, but it
Speaker 1: often boils down to machine learning and using a lot
Speaker 1: of training material to educate a model about what it
Speaker 1: is you want it to do. The more material you
Speaker 1: can submit in training, the better, and Universal Music Group
Speaker 1: said quote the training of generative AI using our artist's music,
Speaker 1: which represents both a breach of our agreements and a
Speaker 1: violation of copyright law end quote, before going on to
Speaker 1: suggest that allowing Heart on my sleeve to exist as
Speaker 1: akin to powering up skynet so that the terminators will
Speaker 1: become real. I'm exaggerating only a little bit, and again
Speaker 1: I am not a copyright expert, but it's hard for
Speaker 1: me to imagine how training an AI model on music
Speaker 1: is in itself a violation of copyright law. After all,
Speaker 1: every musician, every artist, heck, every person who has been
Speaker 1: around other people has been influenced by the work of
Speaker 1: other people. Sometimes you can actually hear the influences in music.
Speaker 1: You might hear an artist play and say, oh, that
Speaker 1: reminds me of Johnny Cash or something like that. The
Speaker 1: history of art is one in which succeeding generations iterate
Speaker 1: on the works of those who came before them. Sometimes
Speaker 1: they make drastic departures from the generations that came before them,
Speaker 1: but even that is in response to the influence of
Speaker 1: the earlier art. So, if you make the argument that
Speaker 1: training AI on specific works is wrong, how do you
Speaker 1: differentiate that from someone who gets their start playing song
Speaker 1: covers or maybe writing their own stuff, but with musical
Speaker 1: influences from identifiable artists. Because art is not created in
Speaker 1: a vacuum, obviously, using AI is different. It can lead
Speaker 1: to the creation of a near perfect simulation of the
Speaker 1: original artist, But the method of training the AI isn't
Speaker 1: really that different from a budding musician voraciously devouring the
Speaker 1: entire discography of their favorite artists before emulating those artists
Speaker 1: in their own work. It is a sticky wicket, no
Speaker 1: question about it, and we're in the early stages of
Speaker 1: figuring out how to handle it, which is particularly unfortunate
Speaker 1: since the technology is already here. But how did we
Speaker 1: get here? Well. An exhaustive history of deep fake technology
Speaker 1: would require a full series of episodes about the history
Speaker 1: of artificial intelligence and machine learning in general and computer
Speaker 1: vision in particular, as well as text to speech and
Speaker 1: lots of other related technologies. But for our purposes, we'll
Speaker 1: simply acknowledge that countless computer scientists and programmers had spent
Speaker 1: endless hours advancing computer technology with the goal of finding
Speaker 1: ways to make machines quote unquote under and data. This
Speaker 1: is easier said than done, so let's take images as
Speaker 1: an example, as that will factor heavily in our discussion today.
Speaker 1: We humans can glance at a photo and we can
Speaker 1: immediately identify what is an object versus just a background.
Speaker 1: So if you have a red mug placed in front
Speaker 1: of a white cinder block wall, we can see what
Speaker 1: is a mug and what is a wall. But we
Speaker 1: have to teach computers how to do that, and when
Speaker 1: you're talking about technologies that generate moving images, it becomes
Speaker 1: even more complicated. So, for lack of a clear beginning,
Speaker 1: I am somewhat arbitrarily going to start in nineteen ninety seven. Now,
Speaker 1: a couple of things happened that year that would be
Speaker 1: important for us to talk about, and one was not
Speaker 1: quite deep baked technology, but it did illustrate some potential
Speaker 1: ethical issues we had to think about. And that was
Speaker 1: a commercial that aired during a big old American football game.
Speaker 1: You know one that happens every year, You know, the
Speaker 1: one I can't I can't call it by name for
Speaker 1: you know, legal reasons. Anyway, one famous feature of this
Speaker 1: big old American football game is that brands will shell
Speaker 1: out huge amounts of money to air commercials during it.
Speaker 1: And one brand to do that in nineteen ninety seven
Speaker 1: was the Dirt Devil vacuum cleaner company. Now, those of
Speaker 1: you across the pond would call it a hoover, not
Speaker 1: a vacuum cleaner, but a hoover is a different brand altogether,
Speaker 1: so stop confusing me. In the commercial, famous actor and
Speaker 1: dancer Fred Astaire is shown dancing with Dirt Devil vacuum cleaners.
Speaker 1: But here's the thing. Fred Astaire had died a decade earlier.
Speaker 1: The footage was taken from his films, with Dirt Devil
Speaker 1: inserting the imagery of its products into the footage to
Speaker 1: make it seem as if Astaire had actually shot commercials
Speaker 1: this way and really danced with vacuum cleaners. So in
Speaker 1: this case, the footage of a stare was legitimate. It
Speaker 1: was the appearance of the vacuum cleaners that had been
Speaker 1: inserted into it. But the use of footage of performers
Speaker 1: who have passed away prompted a debate about the ethics
Speaker 1: of that practice, and people began to speculate about what
Speaker 1: might happen once technology reached a point where a computer
Speaker 1: simulation of a person would be indistinguishable from the real thing. Meanwhile,
Speaker 1: also in nineteen ninety seven, a group of computer scientists
Speaker 1: published and important work. The scientists were Christoph Or, Chris Bregler,
Speaker 1: Michel Covell, and Malcolm Stanley. The paper's title is Video
Speaker 1: Rewrite Driving Visual Speech with Audio. This work built on
Speaker 1: top of a lot of other previous work. For example,
Speaker 1: base interpretation was already a discipline in computer science. It
Speaker 1: traces its history all the way back to the nineteen sixties.
Speaker 1: Ditto for technology that could generate speech from texts. That
Speaker 1: two dates back to the nineteen sixties. Computer animation had
Speaker 1: been around for a while by nineteen ninety seven, so
Speaker 1: creating a three D model of lips, one that you
Speaker 1: could subsequently animate that was also a thing already. But
Speaker 1: what these researchers did was they brought all these elements together.
Speaker 1: It was a convergence of technologies that resulted in a
Speaker 1: new application, one which would allow for computer generated synthetic
Speaker 1: video of real people. The team created the video rewrite software,
Speaker 1: and they also showed what it was capable of doing
Speaker 1: in some very very short video clips. The results are
Speaker 1: primitive by today's standards, but nonetheless impressive. In one two
Speaker 1: second clip, President JFK appears to say I never met
Speaker 1: Forrest Gump. It's a cheeky reference to the nineteen ninety
Speaker 1: four film, which included a segment in which the titular
Speaker 1: character Forst Gump appears to meet JFK and then informs
Speaker 1: him that he needs to rush off to the restroom.
Speaker 1: Video Rewrite served as a foundation for technologies that we
Speaker 1: could refer to as deep fake tech. So just a
Speaker 1: few years later, in two thousand and one, Christopher J. Taylor,
Speaker 1: Gareth J. Edwards, and Timothy. My middle initial is F
Speaker 1: and not Jay, which actually upsets Jonathan. Because of a
Speaker 1: lack of consistency, Coots published a paper that was titled
Speaker 1: Active Appearance Models. The abstract for this paper reads, in
Speaker 1: part quote, we describe a new method of matching statistical
Speaker 1: models of appearances to images. End quote now in plain English.
Speaker 1: This paper describes a method in which computer vision relies
Speaker 1: on statistical models to more accurately identify elements within the image.
Speaker 1: So let's consider facial recognition technology. As I mentioned earlier,
Speaker 1: computers do not inherently understand image. If presented with a
Speaker 1: picture of a face, a computer cannot naturally determine what
Speaker 1: the various features of that face are. Only through proper
Speaker 1: programming and machine learning can you start to do this
Speaker 1: and train a computer to recognize features like a nose,
Speaker 1: a mouth, eyebrows, eyes, et cetera. And by training machines
Speaker 1: on millions of faces, you can reach a point where
Speaker 1: the machine can examine a new face, one that has
Speaker 1: never before been submitted to the machine, and attempt to
Speaker 1: identify those features. This is a necessary step with a
Speaker 1: lot of deep fake technology. See to call all deep
Speaker 1: fakes computer generated is a little misleading. Often what is
Speaker 1: happening is a computer is replacing an existing person or
Speaker 1: face in a video with someone else's features. In order
Speaker 1: to do that, you first have to be able to
Speaker 1: map and identify the original person that was in the video,
Speaker 1: you need to be able to match the synthesized face
Speaker 1: with the movements of the original face. To do that,
Speaker 1: the computer first has to encode the original face, essentially
Speaker 1: to break it down into lots of smaller shapes. Then
Speaker 1: it has to be able to match the synthesized face
Speaker 1: to the original one with a similar encoded approach, and
Speaker 1: then decode that into the synthesized face that replaces the
Speaker 1: original one and then follows the various motions of the
Speaker 1: original face. So you're replacing one person with another through
Speaker 1: the use of a computer, and as part of that,
Speaker 1: the computer has to break down the original person into
Speaker 1: points of data that the computer can handle. So with
Speaker 1: this technology, I could stand facing a camera and deliver
Speaker 1: a speech and then, using software designed to follow the
Speaker 1: steps I just laid out, replace my image with that
Speaker 1: of someone else. If I also used a program designed
Speaker 1: to create a vocal impersonation of that someone else, well
Speaker 1: I could create a video where some celebrity says things
Speaker 1: that they would never say, Like maybe I could create
Speaker 1: a video of Keanu Reeves saying tech Stuff is my
Speaker 1: favorite podcast. Jonathan is such a cool host. I wish
Speaker 1: I could hang out with him. For the record, mister Reeves,
Speaker 1: I would never actually do that. I'm just saying I
Speaker 1: could do it. Of course, creating a video image of
Speaker 1: Keanu Reeves would just be one part of the equation.
Speaker 1: Another would be replicating his voice. Now, I could try
Speaker 1: and do my own impersonation, but this would so clearly
Speaker 1: be fake that I would never achieve my goal of
Speaker 1: trying to make it appear as though Keanu Reeves knows
Speaker 1: who I am and wants to hang out with me.
Speaker 1: I can't even say WHOA the way he does. To
Speaker 1: achieve my dreams, I would need a voice synthesis program
Speaker 1: that I could train on Keano's voice and then produce
Speaker 1: a computer generated impersonation. The history of voice synthesis is
Speaker 1: crazy long. I mean, if we really, we really wanted
Speaker 1: to dive into it, we could go all the way
Speaker 1: back to the late seventeen hundreds. But we won't because
Speaker 1: I can't keep you here that long. Text to speech
Speaker 1: technologies brings us a bit closer to modern day, but
Speaker 1: then we're still talking about the nineteen sixties or thereabouts.
Speaker 1: As I mentioned earlier in this episode. To get to
Speaker 1: a point where computers are capable of producing an imitation
Speaker 1: of a specific person's voice. Then we're getting up to
Speaker 1: like the last decade or so, researchers built tools that,
Speaker 1: after training on how a specific person produces different sounds phonemes.
Speaker 1: If we want to think of it in terms of
Speaker 1: language and the sounds of language, well, then we have
Speaker 1: applications that can take text, interpret that text as a
Speaker 1: series of sounds, pull upon the computer knowledge of how
Speaker 1: a particular person makes those specific sounds, and then voila,
Speaker 1: we have ourselves a copy. Now, early versions of this
Speaker 1: technology were understandibly a bit limited. You would end up
Speaker 1: with speech that on a service level sounded like the
Speaker 1: person in question, the synthesized person, but it would typically
Speaker 1: come across as flat or using incorrect inflection to emphasize
Speaker 1: a point. So think of that kind of robotics sound
Speaker 1: you would get with early personal assistance, right, like if
Speaker 1: you were using a GPS system, which I realized I
Speaker 1: just used a repetition there, like ATM machine. But let's
Speaker 1: say you're using a GPS and it has a voice
Speaker 1: associated with it. Older ones were very robotic, and they
Speaker 1: could also say things that were hilariously wrong. I'll never
Speaker 1: forget the time I was riding in a car and
Speaker 1: the GPS told us to turn right on Oak Doctor
Speaker 1: instead of Oak Drive. But over time the models improved
Speaker 1: and things started to sound a bit more natural. So
Speaker 1: those early ones not so good. Not mistake them for
Speaker 1: being a real person. It would sound like a robot
Speaker 1: in making an impersonation of that person, But models would
Speaker 1: grow in sophistication, and training sessions would include examples where
Speaker 1: the target's expressions would be associated with specific emotions like anger,
Speaker 1: or happiness or sadness. You can actually use a voice
Speaker 1: synthesizer yourself and train it. And as part of that,
Speaker 1: you're typically told to read out sentences with different emotional
Speaker 1: weight to them, So using a bit of appropriate text,
Speaker 1: then maybe some metadata to indicate what emotion should be
Speaker 1: used to read out that text. It then becomes possible
Speaker 1: to craft vocal performances that were and are difficult to
Speaker 1: distinguish from the real thing. We're going to take a
Speaker 1: quick break to thank our sponsor, and then I'll be
Speaker 1: back to talk more about the history and impact of
Speaker 1: deep fake technology. Back to our history of video deep fakes.
Speaker 1: We left off at two thousand and one, and for
Speaker 1: nearly two decades computer scientists continued to work on systems
Speaker 1: that would push forward the capabilities of synthesized video content.
Speaker 1: By the time we get up to twenty seventeen, a
Speaker 1: pair of papers explained that the advancements in consumer computers
Speaker 1: had reached a point where it was actually possible to
Speaker 1: achieve synthesized video using off the shelf computer systems, and
Speaker 1: that would be a huge game changer. No longer would
Speaker 1: you need access to incredibly powerful systems with specialized software.
Speaker 1: Now you could potentially create or access an application on
Speaker 1: an off the shelf computer to do the same thing.
Speaker 1: So the tools to generate computer synthesized video now we're
Speaker 1: within the grasp of the average computer user. With cloud
Speaker 1: based services that could augment these efforts, it became possible
Speaker 1: for a creative person to make videos that appear to
Speaker 1: show people doing and saying things that they never actually did.
Speaker 1: And again, there are multiple uses for such technology. Not
Speaker 1: all of them are sinister, but it doesn't take much
Speaker 1: imagination to come up with scenarios where things get grim,
Speaker 1: And indeed, many early uses of this tech once it
Speaker 1: became accessible, were bad. One big one was using face
Speaker 1: swapping technology to make it appear as though someone famous
Speaker 1: or otherwise was appearing in an adult video. And I
Speaker 1: think it goes without saying that this is a total
Speaker 1: violation of the victim. It robs them of agency and
Speaker 1: they may end up suffering consequences despite not being remotely
Speaker 1: responsible for the content. So imagine facing judgment for something
Speaker 1: that not only you did not do, but you had
Speaker 1: no way of preventing. Honestly, it's impossible for me to
Speaker 1: communicate how devastating this can be. There are several accounts
Speaker 1: online written by people who have been the victim of
Speaker 1: this sort of activity, and they are worth your time.
Speaker 1: They are harrowing to read, but it is important their
Speaker 1: words will far more effectively explain how traumatizing this experience
Speaker 1: can be. And just as a reminder, the rise of
Speaker 1: social networks means that we've all been sharing a lot
Speaker 1: of images of ourselves, videos of ourselves. There's a lot
Speaker 1: of content out there that could be used to train
Speaker 1: various machine models. So it's something to keep in mind
Speaker 1: that even if you aren't concerned right now, there's nothing
Speaker 1: to say that you couldn't become a victim tomorrow. Deep
Speaker 1: fakes also pose a risk to organizations it's not just individuals.
Speaker 1: So imagine for a moment that you see you have
Speaker 1: a voicemail at work, and you pull it up, and
Speaker 1: you listen to the voicemail, and it sounds like your boss,
Speaker 1: and your boss is telling you that you need to
Speaker 1: transfer company funds from the company account into a different one.
Speaker 1: And perhaps they say that it's in order for you
Speaker 1: to pay off some third party vendor for a project
Speaker 1: that you're not really familiar with. But then maybe it
Speaker 1: turns out that voicemail wasn't from your boss after all.
Speaker 1: Maybe it was the result of spearfishing. Maybe a nefarious
Speaker 1: thief has identified you as a possible key to stealing
Speaker 1: money from your organization and has used tech to impersonate
Speaker 1: your boss and direct you toward facilitating a crime. You
Speaker 1: unknowingly have become an accomplice. There's actually been a case
Speaker 1: where this sort of thing was alleged to have happened.
Speaker 1: Now I have to say alleged, because there were questions
Speaker 1: about whether or not it really was a case of
Speaker 1: a synthesized voice, or if maybe this was more of
Speaker 1: a straightforward embezzlement issue, and the deep fake defense aka
Speaker 1: the liar's dividend came into play. Deep fakes have come
Speaker 1: a long way in a few short years. However, they
Speaker 1: are not perfect. There can be telltale signs that a
Speaker 1: video is fake, though they can sometimes be too subtle
Speaker 1: for the human eye to detect. Sometimes there's a dead giveaway.
Speaker 1: You're watching a video and you think this person is
Speaker 1: blinking too frequently or not frequently enough, or maybe their
Speaker 1: eyes don't look quite right, or they movements you're seeing
Speaker 1: don't line up. That a person is turning their head
Speaker 1: one way, their eyes are shifting another in a way
Speaker 1: that just doesn't seem natural. There are those sorts of
Speaker 1: things that people can pick up on, there's some that
Speaker 1: are far more subtle, and deep fake detection tools are
Speaker 1: growing in importance as a result of this. There are
Speaker 1: tools that are trained to spot signs of fakery, sometimes
Speaker 1: ones that are far too subtle for us to notice.
Speaker 1: So it may be things like inconsistencies in lighting and
Speaker 1: the quality of reflections within the frame. Things like that
Speaker 1: may end up being an indication that a video was
Speaker 1: manufactured artificially rather than an actual recording, and they're becoming
Speaker 1: more and more important for people and for organizations. So
Speaker 1: in addition to those tools, Organization leaders should really prepare
Speaker 1: employees for the possibility of encountering deep fakes. Critical thinking
Speaker 1: is a big part of uncovering deception, as is preparation. Heck,
Speaker 1: depending on the organization, you might go so far as
Speaker 1: to set up a phrase or question as an authentication
Speaker 1: process at the top of an official phone call or
Speaker 1: video meeting, so that the person on the other end
Speaker 1: of the line can verify that things are legit. I
Speaker 1: know it sounds like you're going a far away, but
Speaker 1: as this technology gets more sophisticated, as people deploy it
Speaker 1: in ways that are potentially harmful, you have to start
Speaker 1: to think about these things. What we do not want
Speaker 1: to do is to enter into an era where we
Speaker 1: can no longer reliably determine the real from the fate.
Speaker 1: But there is no putting the cat back in the bag,
Speaker 1: or the genie in the bottle or baby in the corner.
Speaker 1: The technology isn't going away. It will not disappear. It
Speaker 1: will continue to evolve and to improve, and so it
Speaker 1: falls upon us to educate ourselves as best we can
Speaker 1: in preparation for encountering it, and to think about how
Speaker 1: we can address the flagrant misuses of the technology to
Speaker 1: attempt to dissuade people from using it in that way,
Speaker 1: because again, the victimization element of this can be really
Speaker 1: severe and really traumatizing and incredibly disruptive to a person's life.
Speaker 1: We should not forget that either. So in conclusion, I
Speaker 1: will say that this technology is truly impressive and again
Speaker 1: it can have some really incredible uses. I don't want
Speaker 1: to paint it as just being a bad thing. It
Speaker 1: is not good or bad. It is how we use
Speaker 1: it that determines whether or not the end result is
Speaker 1: a positive one or a negative one. But only by
Speaker 1: learning about it can we prepare for what is to come.
Speaker 1: So I hope that you found this episode informative, that
Speaker 1: you have a deeper appreciation for what this technology does
Speaker 1: and what it is capable of, and I will speak
Speaker 1: to you again really soon. Tech Stuff is an iHeartRadio production.
Speaker 1: For more podcasts from iHeartRadio, visit the iHeartRadio app, Apple Podcasts,
Speaker 1: or wherever you listen to your favorite shows.