Speaker 1: Folks, Tonight's classic episode is one that is near and
Speaker 1: dear and frightening to all of us. It's a question,
Speaker 1: will Google start impersonated you?
Speaker 2: Yes?
Speaker 3: Wow, Oh the days of July twenty eighteen, we were
Speaker 3: so naive.
Speaker 2: Oh the salad days of twenty eighteen. Well, we were so.
Speaker 1: Young, right, We had no idea what was going to happen,
Speaker 1: because even DARPA can't fully predict the future yet. But
Speaker 1: you know, I remember going back to because I lived
Speaker 1: really close to our old office for a while. I
Speaker 1: remember going back in during the pandemic and there was
Speaker 1: this scary moment where I saw somewhat one of our
Speaker 1: coworker's calendars left on like March thirteenth, or like the
Speaker 1: day everything shut down.
Speaker 2: That's like zombie movie stuff. That's eerie. I think.
Speaker 3: Was it on Robert's desk, I don't. I just remember
Speaker 3: seeing that too, Ben like, oh wow.
Speaker 1: It close sped. It was our buddy Seth, who yeah,
Speaker 1: for a long time worked on stuff to blow your mind.
Speaker 1: But yeah, it's the dystopian. It's the very last of us.
Speaker 2: And you know, what we're talking about in this episode
Speaker 2: is a tech that I don't know if it made
Speaker 2: it all the way through to fruition. It certainly not
Speaker 2: what we associate with this kind of stuff, But Google
Speaker 2: was developing something called duplex that was a tech for
Speaker 2: conducting natural conversations to carry out real world talks over
Speaker 2: the phone. Now, of course the name of the game
Speaker 2: for that is chat GPT and all of the many
Speaker 2: worms that come in that bag, that worm bag, badger bag.
Speaker 2: But this is kind of like almost like a peak,
Speaker 2: a sneak peek into like maybe what could be a
Speaker 2: precursor to that, right, guys.
Speaker 1: One hundred percent yeah, Because this this idea of automating
Speaker 1: ones day to day routine interactions. It feels cool. But
Speaker 1: I am certain that in episodes like this one of
Speaker 1: us will will all inevitably reference that scary scene with
Speaker 1: Mickey as a wizard in Fantasia. You know what I mean.
Speaker 1: You get a mop and at first it's easy, it's
Speaker 1: a lot of fun.
Speaker 2: Just brooms were psychotic.
Speaker 3: So come with us back to those happy days of
Speaker 3: twenty eighteen, and let's discuss what AI might become.
Speaker 2: Knowing what it has become.
Speaker 1: From UFOs to psychic powers and government conspiracies, history is
Speaker 1: riddled with unexplained events. You can turn back now or
Speaker 1: learn this stuff they don't want you to know.
Speaker 2: Hello, and welcome back to the show. I'm Noel standing
Speaker 2: in Formatt with that line, because whenever Matt's not here,
Speaker 2: Ben and I always look at each other for a
Speaker 2: few minutes and realize that we're not quite sure how
Speaker 2: to start the show.
Speaker 1: However, Never fear Met is off on a very special
Speaker 1: secret project that we cannot wait to tell you about.
Speaker 1: We will have to tell you sometime soon, so look
Speaker 1: forward to that. No spoilers. In the meantime, they call
Speaker 1: me Ben. We are joined with our super producer Paul
Speaker 1: the Personal Digital Assistant decade. Yeah, PDA, but most importantly,
Speaker 1: you are you. You are here, and that makes this
Speaker 1: stuff they don't want you to know. We're pretty excited
Speaker 1: about today's episode.
Speaker 2: What did PDA stand for when it was like a
Speaker 2: pom pilot? Was it personal digital assistant?
Speaker 3: Uh?
Speaker 1: Yes, I believe or was it Okay, so there was
Speaker 1: public display of affections?
Speaker 2: Well there's that, But they used to call like BlackBerrys
Speaker 2: PDAs back before the advent of the iPhone and the
Speaker 2: more tablet like device.
Speaker 1: Personal digital assistant. That's what it has to be.
Speaker 2: Isn't that funny though, because that's kind of become a
Speaker 2: new thing, because today we're talking about the kinds of
Speaker 2: personal didit assistance that you can talk to and can
Speaker 2: potentially talk back.
Speaker 1: Yes, So let's look at some of the context. It's
Speaker 1: a very common trope in science fiction robots impersonating human
Speaker 1: beings with increasing levels of fidelity, and we see it
Speaker 1: in pop culture all the time, and some stories, like
Speaker 1: in every episode of The Terminator everything related to the
Speaker 1: Terminator franchise, Machine Consciousness tries to mimic humanity exclusively as
Speaker 1: a means of waging war, and in other places or
Speaker 1: other series such as The Matrix, Artificial Intelligence or AI
Speaker 1: attempts to surround us meat bags with an impersonation of reality,
Speaker 1: complete with individual machine minds that can pass for humans.
Speaker 1: And then in other cases, humans work together to build
Speaker 1: technologies that can impersonate other human beings in any number
Speaker 1: of ways.
Speaker 2: Yeah, sometimes for sexy times reasons, sure, sometimes for just companionship,
Speaker 2: and we've seen that go awry. I mean the Terminator.
Speaker 2: I think the whole point of that series was that
Speaker 2: humans created Skynet or whatever for their own to serve
Speaker 2: their own purposes. And then Skynet said, what up, humans,
Speaker 2: We're done with you or going to do our own thing,
Speaker 2: or like in West World m hm.
Speaker 1: And this goes, like you said, in any number of ways,
Speaker 1: from increasingly lifelike androids to entities that exist purely in
Speaker 1: the digital sphere, able to hold genuine, seeming conversations and
Speaker 1: functioning at or above what we would consider the average
Speaker 1: level of human intelligence. As history has proven, science fiction
Speaker 1: is often prescient, and it's not uncommon for authors to
Speaker 1: spin fantastic tales, only for those tales to move years
Speaker 1: decades later from the realm of science fiction to the
Speaker 1: world of science fact. And the quest for this human
Speaker 1: like technology in real life is no different.
Speaker 2: Yeah, I mean, I actually saw a YouTube video a
Speaker 2: bunch of clips of times that science fiction films have
Speaker 2: predicted technology that it totally a thing right now. Like
Speaker 2: Star Trek, for example, there's little communicators, they're basically iPhones.
Speaker 2: And remember that scene in Total Recall where they're walking
Speaker 2: through a full body scanner at the airport and you
Speaker 2: see like their skeletons and you can see the weapons
Speaker 2: hiding and stuff. That's pretty much what we do now
Speaker 2: at the airport. We got to put our hands over
Speaker 2: our heads and stand in those full body scanners where
Speaker 2: hopefully they just draw a little cartoon of us with
Speaker 2: the naughty bits scrubbed out. But in theory they can
Speaker 2: see everything. So too is AI and the kinds of
Speaker 2: things we're talking about today.
Speaker 1: Yeah, and the concept of what we call artificial intelligence.
Speaker 1: Longtime listeners, you may recall, as we use this phrase,
Speaker 1: you may recall that some former guests of ours have
Speaker 1: objected to the term artificial intelligence with a very good question,
Speaker 1: what makes it artificial? What makes it if we're talking
Speaker 1: about consciousness, what makes it any less of a consciousness
Speaker 1: than our own?
Speaker 2: Right?
Speaker 1: And we think that's a good We think that's a
Speaker 1: very good and valid point. We tend to agree with it.
Speaker 1: But for the sake of brevity, we're just going to
Speaker 1: go with AI as a non pejorative thing. It's just
Speaker 1: easier to say it that way. The concept of AI
Speaker 1: has surprisingly old roots in our culture, especially if we
Speaker 1: consider those ancient tales of non human entities impersonating human beings,
Speaker 1: the fairy stories of changelings switched out at birth, or
Speaker 1: God's changing shape to breed with animals or people, or
Speaker 1: shape shifters. In the twentieth century, the concept of this artificial,
Speaker 1: inorganic thinking life form was popularized, just like the point
Speaker 1: we made with science fiction through culture and fiction. If
Speaker 1: you think of one of the earliest artificial intelligences that
Speaker 1: blew up in the Western world, it's the tin man
Speaker 1: in The Wizard of Oz.
Speaker 3: Yeah.
Speaker 2: I never thought of him like that, but I guess
Speaker 2: it's true. It's a good stand in for that because
Speaker 2: even the heart that he gets at the end spoiler
Speaker 2: alert for it, the Wizard of Oz sure is like
Speaker 2: a clockwork hert, you know, it's like something to add
Speaker 2: to his mechanations. It's not actually a physical heart. So
Speaker 2: he's absolutely meant to be like an automaton, which is
Speaker 2: kind of the earliest form of robotics that go back
Speaker 2: to ancient times, even where you have these incredibly intricate
Speaker 2: creations that move through a series of gears and pulleys
Speaker 2: and what have you, but aren't necessarily imbued with any
Speaker 2: kind of like ability to make choices. But there are
Speaker 2: some that can even like play games. I think there's
Speaker 2: one that's like a writer.
Speaker 1: The mechanical Turk Well yeah, yeah, yeah, And they were
Speaker 1: lauded for their ability to appear to do human esque
Speaker 1: things right, but people generally did not think they had
Speaker 1: a soul for sustenance.
Speaker 2: And it kind of goes back to you were mentioning
Speaker 2: we'd had some folks say you had to take issue
Speaker 2: with the term of artificial intelligence. We've also had some
Speaker 2: folks right and taking issue with the term the idea
Speaker 2: of machine learning, because you know, it is a matter
Speaker 2: of we're still at a place where we have to
Speaker 2: program machines to do what we want. We certainly have
Speaker 2: have yet to fully experience, and it's this idea of
Speaker 2: a singularity where the machine takes what we've imbued it
Speaker 2: with and develops its ability to go outside of that
Speaker 2: or like make decisions outside of the parameters that we've
Speaker 2: read programmed. Very rarely have we seen that, and when
Speaker 2: we do see it, it typically gets shut down.
Speaker 1: It's yeah, metacognition, right, so thinking about thinking rather so.
Speaker 1: By the nineteen fifties, scientists and mathematicians and philosophers were
Speaker 1: familiar with this concept of quote unquote artificial intelligence, and
Speaker 1: our species began to cognitively migrate from this world of
Speaker 1: fantasy toward a world increasingly grounded in fact and we
Speaker 1: can hit some of the high points of artificial intelligence here.
Speaker 1: In nineteen fifty, the famous codebreaker Alan Turing made one
Speaker 1: of the most significant early steps in real life AI
Speaker 1: when he and some of his colleagues created what we
Speaker 1: now commonly call the Touring Test. It's named after him,
Speaker 1: of course. He wrote this paper called Computing Machine Intelligence
Speaker 1: that laid the groundwork both for the means of constructing
Speaker 1: AI and for the ways in which we could measure
Speaker 1: the intelligence of that AI or our success building it,
Speaker 1: which might kind of be two different things. And sadly, well,
Speaker 1: amazingly and sadly this line of thought was ahead of
Speaker 1: the curve. Turing could not get right to work building
Speaker 1: these human like minds, or more specifically, he couldn't get
Speaker 1: to work building minds that could fool humans into thinking
Speaker 1: they were also human minds, because the technology at the
Speaker 1: time had hard limits, like to the point you make
Speaker 1: know about the mechanical turk. Up until nineteen forty nine
Speaker 1: or so, computers couldn't really store commands we can say
Speaker 1: commands are decisions here. They could only execute them. And
Speaker 1: this meant that the computers we could build at that
Speaker 1: time were unable to satisfy a key prerequisitive intelligence. They
Speaker 1: couldn't remember past events, past information, and therefore they could
Speaker 1: not use this memory to inform present commands or decisions.
Speaker 1: And in this way, computers began at that tabula rasa state,
Speaker 1: that blank slate state that so many mystic, spiritualist and
Speaker 1: philosophers spend their lives attempting to attain. I think that's fascinating. Computers,
Speaker 1: like people who practice hardcore meditation, existed mentally only in
Speaker 1: the present.
Speaker 2: Yeah, And we'll get back to that concept in a
Speaker 2: little bit in terms of some of the newer computers
Speaker 2: and how they are able to quote unquote figure things
Speaker 2: out some better than others. But let's just go back
Speaker 2: to the fifties for a second, when computers were insanely expensive.
Speaker 2: I think this is no secret they ran. You would
Speaker 2: least one, you couldn't even own it, and that wasn't
Speaker 2: a thing for about two hundred K a month, which
Speaker 2: I believe been in today's standards. That would be about
Speaker 2: two million dollars for one month of computer use. Yeah,
Speaker 2: eighteen dollars a lot on Netflix subscriptions, right, my friend,
Speaker 2: And only, of course, the most prestigious universities and huge
Speaker 2: technology corporations could even you know, afford the cost of entry.
Speaker 2: So for Turing and his ILK to actually be able
Speaker 2: to succeed in building something resembling anywhere close to artificial intelligence,
Speaker 2: they would have to be part of some network of
Speaker 2: high profile, very wealthy and influential funders of research. Yeah,
Speaker 2: that would they would be able to receive just an
Speaker 2: absolute boatload of money from to get this kind of
Speaker 2: work done.
Speaker 1: And imagine, you know, that's a hard sell. It seems really
Speaker 1: interesting now, but back on what we know, Yeah, yeah,
Speaker 1: but back then it was literally walking up to people
Speaker 1: and saying, hey, we're good at math, and do you
Speaker 1: remember the tin man from Wizard of Oz. We sort
Speaker 1: of want to make that. Can we have all of
Speaker 1: the money. So that's that's tough, but the soldiers on.
Speaker 1: In nineteen fifty five, another groundbreaking event occurred. There was
Speaker 1: the premiere of a program called The Logic Theorist. It
Speaker 1: was supposed to mimic the problem solving skills of a
Speaker 1: human being, and it was funded by one of our
Speaker 1: favorite shady boogeymen, the Research and Development Corporation known today
Speaker 1: as RAND.
Speaker 2: A RAND corporation that sounds like something out of a
Speaker 2: sci fi movie in and of itself, and it's still around.
Speaker 2: I'm doing pretty interesting and secretive work because I believe
Speaker 2: they ended up having a relationship with the US. Oh yeah, government,
Speaker 2: big time.
Speaker 1: You're absolutely right. This Logic Theorist is considered by many
Speaker 1: people to be the first technically the first artificial intelligence program.
Speaker 1: And there was a big conference in nineteen fifty six
Speaker 1: hosted by John McCarthy and a guy named Marvin Minsky,
Speaker 1: and in this conference they presented the Logic Theorist as
Speaker 1: a proof of concept. The conference is called the Dartmouth
Speaker 1: Summer Research Project on Artificial Intelligence. And you know, Noel,
Speaker 1: I thought of you with this because I know how
Speaker 1: how we both love acronyms.
Speaker 2: Well, I feel like in this one, the D would
Speaker 2: be silence. So I'm just going to call it SURPI.
Speaker 1: That's great. That sounds better because the DED, you.
Speaker 2: Know, you can't really do a DS sound.
Speaker 1: So the conference itself fell short of the original very
Speaker 1: ambitious aims of the organizers. They wanted to bring together
Speaker 1: the world's best and brightest subject matter experts and by god,
Speaker 1: make an artificial mind of a weekend, right right, They
Speaker 1: said it took god seven days, let's uh, what do
Speaker 1: you say? We could do it in four something like that.
Speaker 1: But the problem was pretty much everybody disagreed on how
Speaker 1: exactly you would make a human like intelligence. And at
Speaker 1: this point they're still thinking in terms of artificial intelligence
Speaker 1: being like humans, which is a huge assumption. But they
Speaker 1: unanimously agreed for the very first time on a single
Speaker 1: crucial point that it was possible to make AI, and
Speaker 1: this set the stage for the next two decades of research.
Speaker 2: So, from nineteen fifty seven to nineteen seventy four, artificial
Speaker 2: intelligence interest in it or research and it really flourished.
Speaker 2: Computers they could stick, you know, they were improving by
Speaker 2: leaps and bounds. They could store more information, which is crucial,
Speaker 2: and of course they became faster and cheaper and more accessible.
Speaker 2: Machine learning algorithms also began to improve, and people got
Speaker 2: better at knowing which algorithm to use for their particular purposes,
Speaker 2: which was also important because it wasn't you know that
Speaker 2: there was sort of an established language of algorithms that
Speaker 2: people could pick and choose from to suit their particular problems.
Speaker 2: Early successes like the General problem solver Ben tell me
Speaker 2: a little bit more about the general problem solver.
Speaker 1: It does what it says under ten. You could give
Speaker 1: it a variety of problems, okay, and generally speaking it
Speaker 1: would attempt to solve them.
Speaker 2: Not necessarily going to give you the most creative solution,
Speaker 2: but that Yeah.
Speaker 1: And again that's that's like an early day's thing. So
Speaker 1: at the time it was very impressive, but of course
Speaker 1: it was just the beginning. And you know, you had
Speaker 1: another example I think of AI application in that time.
Speaker 2: Yeah, this is when Alexa was first invented.
Speaker 1: Oh yes, yes, I'm kidding.
Speaker 2: It's called Eliza. But this is like a spoken language interpreter,
Speaker 2: which I don't know. I wonder if Alexa is a
Speaker 2: is a nod to Eliza. What do you think, Ben,
Speaker 2: that would be pretty cool. Only a couple letters off.
Speaker 2: It was a spoken language interpreter that helped convince the
Speaker 2: government to really, Okay, this is something that we're into
Speaker 2: and this was really important for our story today.
Speaker 1: Yeah, it convinced the government, specifically DARPA here in the
Speaker 1: United States to start funding artificial intelligence DARPA. As you
Speaker 1: know if you are a longtime listener of this show,
Speaker 1: DARPA is the the resident mad Science Department of the
Speaker 1: United States government and it stands for Defense Advanced Research
Speaker 1: Projects Agency. They're the ones who do all the X
Speaker 1: files level or fringe level stuff you hear about in
Speaker 1: the news or often it's going to be. They're a
Speaker 1: forefront of it. And then there's also stuff like Matt's
Speaker 1: favorite public private partnership, skunk Works.
Speaker 2: It's just a good name, I mean, if you can
Speaker 2: like it for nothing other than that, But they do
Speaker 2: all the secret Air Force projects and you know, spyplanes
Speaker 2: and all kinds of stuff. It's kind of technology you
Speaker 2: think about that is probably years ahead of anything we've
Speaker 2: seen out there in the world today.
Speaker 1: It's mistaken for UFOs, right right. This made people very gassed,
Speaker 1: very optimistic, because that's another very human thing that we
Speaker 1: haven't quite learned how to program optimism. In nineteen seventy,
Speaker 1: Marvin Minsky, that guy who co hosted this conference, he
Speaker 1: told Life magazine from three d eight years, we will
Speaker 1: have a machine with the general intelligence of an average
Speaker 1: human being. He was wrong, But to your point about
Speaker 1: suppressed technology novel, who's wrong? So far as we know.
Speaker 2: It's interesting though too not to get too far ahead
Speaker 2: of ourselves. But with everything that's going on with big
Speaker 2: public facing companies like Google and Amazon and Apple and stuff,
Speaker 2: you start to get a sense that maybe the secret
Speaker 2: government stuff isn't quite as far ahead as it once was, right.
Speaker 2: I mean, that's just my opinion. I don't know.
Speaker 1: I think it's a good opinion. It's something we want
Speaker 1: to hear from you about, ladies and gentlemen, because it's
Speaker 1: proven that in terms of physical hardware, material of war,
Speaker 1: weapons and aviation and stuff, it is proven that the
Speaker 1: US and most other governments want to keep that stuff
Speaker 1: under wraps.
Speaker 2: Absolutely. I guess what I'm saying is when you're looking
Speaker 2: at a company that's trying to sell you something and
Speaker 2: you see how far they advance with each update every year, right,
Speaker 2: you kind of get a sense that maybe this is
Speaker 2: about where they're actually at.
Speaker 1: That's that's the point. So I bring up the aviation
Speaker 1: stuff and the weapons of war stuff to contrast it here,
Speaker 1: because what we're seeing also is that the governments of
Speaker 1: the world. This is true, the governments of the world
Speaker 1: usually can't pay the best and brightest as much as
Speaker 1: the private entity skin, so they're grabbing some of the
Speaker 1: best workers unless those people are severely ideological and then
Speaker 1: they're making most of the progress which they would later
Speaker 1: sell to a government. So I think maybe there is
Speaker 1: a little more transparency.
Speaker 2: I think so, which is a which is an interestingly
Speaker 2: positive thing.
Speaker 1: But let's let's get share.
Speaker 2: Yes, keep, let's keep, let's keep humming right along.
Speaker 1: So the quest for human like artificial intelligence soldiered on,
Speaker 1: but it still had tremendous obstacles. Although computers could now
Speaker 1: store information, they couldn't store enough of it, and they
Speaker 1: could not process it at a fast enough pace. So
Speaker 1: funding dwindled until the eighties. And that's when AI experienced
Speaker 1: a renaissance, which we'll get to after a word from
Speaker 1: our sponsor.
Speaker 2: So in the nineteen eighties, new tools and methods gave AI,
Speaker 2: the field of AI, that renaissance that Ben mentioned, that
Speaker 2: kick in the digital pant and a guy named John
Speaker 2: Hopfield and another fellow named David Rommelhart popularized this concept
Speaker 2: of deep learning, which is a technique that allowed computers
Speaker 2: to learn using experiences, using paying attention to surroundings. For example,
Speaker 2: the idea of the fact that our phones are serving
Speaker 2: us ads because we're talking about stuff, and it's taking
Speaker 2: that information in and using it to do something and
Speaker 2: learning our habits right, and this is a really important
Speaker 2: part of things like these personal digital assistance that we
Speaker 2: talked about at the top of.
Speaker 1: The show, right m and on the other side of
Speaker 1: the brain here too, or I guess. In a parallel approach,
Speaker 1: a guy named Edward Figenbaum introduced expert systems that mimicked
Speaker 1: the decision making process of a human expert. So what
Speaker 1: happens is the program will ask an expert in the
Speaker 1: field like it would ask super producer Paul or Richard
Speaker 1: Feinman a question about production or physics, and then they
Speaker 1: would say, how do you respond in this given situation?
Speaker 1: And it could and it would take this for every
Speaker 1: situation it could soak up, and then non experts could
Speaker 1: later receive advice from that program about that information. It
Speaker 1: sounds basic now. It sounds like how a search engine
Speaker 1: can know, with increasingly accurate levels of fidelity, what question
Speaker 1: you mean to ask when you ask question? Remember when
Speaker 1: Google used to be super passive aggressive yeah, and say
Speaker 1: did you mean?
Speaker 2: Well, Now it just fills it in for you, yeah,
Speaker 2: Google instantly. And the reason for that is that it
Speaker 2: has access to every entry that anyone has ever put
Speaker 2: into Google, and so it combines all that information and
Speaker 2: makes the best guess as to what it thinks you're
Speaker 2: probably searching for based on what everyone else that has
Speaker 2: started searching for that kind of thing has also done,
Speaker 2: which is that same deep learning stuff that is coming
Speaker 2: into play.
Speaker 1: Which makes predictive text so funny.
Speaker 2: It can be. I guess it depends on the platform
Speaker 2: you're on, and we'll give that a little bit too,
Speaker 2: because sometimes it can be just really boneheaded. And haven't
Speaker 2: you figured this out yet, Siri spoiler alert, No he hasn't.
Speaker 2: But this was all really put to the test in
Speaker 2: the nineties and two thousands when we really hit some
Speaker 2: big landmark moments and high water mark moments in artificial intelligence,
Speaker 2: like with Gary Kasprov, who I know that you are
Speaker 2: fascinated with, although he is a problematic figure at times.
Speaker 1: Yeah, yeah, raging anti semitism aside. Yeah, chess grandmasters. The
Speaker 1: human ones at least right are immensely, even fractally compelling,
Speaker 1: because there's always some other layer to their personality and
Speaker 1: you have to wonder about the purported correlation between mental
Speaker 1: instability and high thresholds of intelligence, because well, that's a
Speaker 1: story for another day. We should do an episode. Yeah,
Speaker 1: might get a little close to home for us, but
Speaker 1: we can do it.
Speaker 3: So.
Speaker 1: Yeah, as you're seeing, he was defeated by Deep Blue,
Speaker 1: built by IBM, a computer just built to play chess.
Speaker 1: This was a John Henry moment for the human race.
Speaker 1: And then a scientist named Cynthia Brazel created Kismet, a
Speaker 1: program of recognizing and displaying emotion. Now, for all our
Speaker 1: techno futurists philosophers in the crowd, does Kismet recognizing and
Speaker 1: displaying emotion automatically mean that Kismet experiences emotion? Story for
Speaker 1: another day. But these person versus program one on one
Speaker 1: matches didn't stop with the game of chess. There was
Speaker 1: also Jeopardy, right, And then there was one other one
Speaker 1: that had tremendous implications for the world of programming.
Speaker 2: Yeah, and that was much more recently with Google's Alpha Go.
Speaker 2: That's Go, as in the ancient Chinese strategy game of Go,
Speaker 2: and it successfully defeated a Go champion Kaig and Go
Speaker 2: is a notoriously complex game, very difficult to predict, and
Speaker 2: always have to be many many moves ahead of your opponent.
Speaker 2: So that's pretty cool. That's almost a step further. Wouldn't
Speaker 2: you say, Ben that Go would be more challenging for
Speaker 2: a computer too, who beat a human opponent? Then chess?
Speaker 2: Even Like, Yes, this is like a real serious leap forward.
Speaker 1: I would absolutely agree, And a lot of people were
Speaker 1: even more skeptical watching that game. People understood Go. We
Speaker 1: can also go out there and say, Paul, I don't
Speaker 1: know if you are familiar with this, but are you
Speaker 1: Are you a Go enthusiast? Do you play this game? Not?
Speaker 2: Really, Noel, I've never played it. I remember it from
Speaker 2: the movie Pie. Remember Pie, Yeah, the one of Darren
Speaker 2: Aronofski's first movies, which people kind of crap on. But
Speaker 2: I enjoyed it a lot when I was young anyway.
Speaker 2: But that movie is about a kind of a mad
Speaker 2: computer scientist who realizes that the Kabbala contains some kind
Speaker 2: of code involving the number for pie and gets goes
Speaker 2: down a crazy rabbit hole of insanity in paranoia. But
Speaker 2: Go as a recurring game in that movie, implying that
Speaker 2: it's all about high level, very very high level thinking.
Speaker 1: Yeah, I think that's I think that's excellent. You know,
Speaker 1: Pie is a fantastic but jarring film, you know, and
Speaker 1: so we see a common trend in these again, we'll
Speaker 1: call them John Henry moments. And people generally know the
Speaker 1: story of John Henry, right, nol you think they do.
Speaker 2: I know he was a steel driving man, right, give
Speaker 2: me some more.
Speaker 1: Well John Henry for anyone who is outside of the US.
Speaker 1: And I don't think we know for sure how common
Speaker 1: this story is in the States anymore. He was in folklore.
Speaker 1: He was an African American steel driving man, as Noel said,
Speaker 1: that's absolutely true. And his job was to hammer a
Speaker 1: steel drill into rock to make holes for explosives, right,
Speaker 1: And they would blast rock constructing a railroad tunnel. And
Speaker 1: in the legend he was pitted against a new fangled
Speaker 1: steam powered hammer. It was a man against machine race.
Speaker 1: And the historical aspect of whether this did or didn't
Speaker 1: happen is did people go back and forth on it?
Speaker 1: But what we're experiencing now with AI is a series
Speaker 1: of increasingly high stakes John Henry moments. Key G you said,
Speaker 1: Gary kasprov was it was it Ken Jennings who played
Speaker 1: Deep Blue? Yeah, it was our own buddy.
Speaker 2: Colleague, Ken Jennings of Omnibus fame, and then other things. Yeah,
Speaker 2: he does.
Speaker 3: Yeah.
Speaker 1: Yeah, in addition to you know, his big tent item,
Speaker 1: which is hanging hanging out with us at how stuff works.
Speaker 2: Right.
Speaker 1: Uh So, now we are on the cusp of a
Speaker 1: world that is both brave to quote Alta Watson, Watson, Watson,
Speaker 1: I said, deep Blue, and that's the chess.
Speaker 2: On the chess one. Watson was the quiz the quiz
Speaker 2: bot also by IBM though, right, that's correct.
Speaker 1: Okay, yes, so Ken Jennings versus Watson, caspar Off versus
Speaker 1: Deep Blue, and now we are on the cusp of
Speaker 1: a new world that is both brave to quote Aldus Huxley,
Speaker 1: and strange. The average citizen in a developed country interacts
Speaker 1: with some form of artificial intelligence on an increasingly frequent basis,
Speaker 1: even if it's indirect. I mean, think about it. When
Speaker 1: is the last time you called a large company and
Speaker 1: didn't first go through an automated line, a rough impersonation
Speaker 1: of a conversation with a computer.
Speaker 2: I just always start mashing zero right away. Just need
Speaker 2: to pass because they it's so annoying, because it's like,
Speaker 2: if I thought that a computer simulation could help me
Speaker 2: solve the problem, I probably would have just done this
Speaker 2: online right in the fact that I'm calling the company
Speaker 2: in the first place, not to derail it with my
Speaker 2: sure might get off my lawn moment. No, please do seriously,
Speaker 2: It's like they're never that helpful. You always have to
Speaker 2: repeat yourself over and over again, and you usually do
Speaker 2: not get the result you're looking for, which could change
Speaker 2: with some of the new technology we're talking about today,
Speaker 2: which I think we can get into right now.
Speaker 1: Possibly maybe maybe it'll change because one of the big
Speaker 1: tasks for artificial intelligence right now, what we use these
Speaker 1: applications for now is big data. We have built so
Speaker 1: many ways to scoop up information that are poor. Old
Speaker 1: primate brains, which are built for foraging and living in
Speaker 1: small bands and forests, cannot analyze and process all this
Speaker 1: stuff ourselves, so we built machines and codes. In this case,
Speaker 1: codes could just be standing for lines of thought to
Speaker 1: decipher all this data for us. And the application of
Speaker 1: artificial intelligence in this regard has already done amazing things
Speaker 1: in industries like technology, banking, marketing, entertainment.
Speaker 2: Well, it's automation of like sweeping through giant sets of
Speaker 2: data sure so that humans don't have to do it,
Speaker 2: and using particular what's the word algorithms, I guess is
Speaker 2: one way of putting it, or just rules saying, look
Speaker 2: for this thing in this set of data, parse out
Speaker 2: this file. If then whatever. So that's kind of been
Speaker 2: the primary use of that up to this point. In
Speaker 2: all of those things. You said, Yeah, so what's next?
Speaker 2: Right right?
Speaker 1: Because right now it doesn't matter if whether the algorithms
Speaker 1: improve much, it doesn't matter whether there is a c change.
Speaker 1: The basic concept is there. And this massive computing approach,
Speaker 1: this vacuum cleaner approach, for instance, that the NSA uses,
Speaker 1: just allows artificial intelligence to learn through brute force. In
Speaker 1: the future, we're going to see more variation in AI.
Speaker 1: We'll see autonomous vehicles, we'll see predictive ordering services, which
Speaker 1: I know most of us will hate, and soon it
Speaker 1: will seem strange not to have some sort of artificial
Speaker 1: intelligence existing in some aspect of your everyday life. But
Speaker 1: today's question, and a spooky one, is how close to
Speaker 1: human can these programs actually get?
Speaker 2: And I think we'll get to that after just one
Speaker 2: more quick little sponsor break.
Speaker 1: Excuse where it gets crazy.
Speaker 2: See how I hear you? Hi, I'd like to reserve
Speaker 2: a table for Wednesday.
Speaker 1: The seventh.
Speaker 2: For seven people. It's for four people, four people when
Speaker 2: Wednesday at six pm? Oh, actually we leave her for
Speaker 2: like apra, like a bye people for before you can come.
Speaker 2: How long is the wait usually to be seated for
Speaker 2: when tomorrow or weekey or for next Wednesday the seventh?
Speaker 3: Oh no, it's not too easy.
Speaker 2: You can com okay, Oh I gotcha. Thanks. But then
Speaker 2: that that didn't sound like anything at all. That was
Speaker 2: just a conversation between a young man and the proprietor
Speaker 2: of a Chinese restaurant.
Speaker 1: Right, and who was who is? Whoever has a great
Speaker 1: time making restaurant reservation?
Speaker 2: Now you know, no, because then we have open table
Speaker 2: for that, am I? Right? Ben?
Speaker 1: Right? I, like many people in our generation, hate talking
Speaker 1: on the phone.
Speaker 2: It's true. There's even a term for that. I think
Speaker 2: was a telephoneophobia. Yes, yes, that's a good one.
Speaker 1: But no I found Yeah, when I found that one,
Speaker 1: I was like, how can you make a can you
Speaker 1: just make anything a phobia? And if you can, I
Speaker 1: think it's very American English.
Speaker 2: Now I have microphoneophobia, which is weird considering that I
Speaker 2: sit in front of one all the time. Right now,
Speaker 2: I'm in utter terror. But no, I was being coy.
Speaker 2: That clip was not between two humans. I think you
Speaker 2: can probably guess which side of the conversation as the
Speaker 2: non human yep, because if you listen back to it,
Speaker 2: I think you'll notice a couple of tells in that
Speaker 2: there was a few There were a few kind of
Speaker 2: wrenches thrown into that exchange where the person was trying
Speaker 2: to call to make a reservation, and there was some
Speaker 2: confusion from the person on the other end about how many,
Speaker 2: about what day they wanted the reservation to be, how
Speaker 2: many people were in the party, and it ultimately ended
Speaker 2: up with, well, you don't really need a reservation. So
Speaker 2: the person on the other of the phone was not equipped.
Speaker 2: It was not something they would typically do. Reservations seemed
Speaker 2: like not really a thing in this restaurant, and so
Speaker 2: the caller kind of repeated himself.
Speaker 1: Specific things like the specific time and day, and then
Speaker 1: there was a momentary pause when they said we we
Speaker 1: only do reservations for groups of five those ah, gotcha,
Speaker 1: which still sounded very human.
Speaker 2: It did.
Speaker 1: It was not, though. It was a conversation between an
Speaker 1: unsuspected restaurant employee, as you mentioned, and a computer program.
Speaker 1: This is an example of Google's Duplex system, a personal
Speaker 1: assistant designed to make users' lives easier by handling standard
Speaker 1: phone calls, so like doctor's appointments, reservations, and so on,
Speaker 1: breaking up with a loved one without troubling you or
Speaker 1: possibly even letting the person on the other end of
Speaker 1: the phone know that it's not actually you making the call.
Speaker 2: Yeah, and we get into all kinds of ethical quandaries
Speaker 2: with this that we'll get into first, first and foremost, though,
Speaker 2: I think it's interesting that the idea, it's inherently tricksy
Speaker 2: the whole affair, right, The idea is to not inconvenience
Speaker 2: either party, but you or you are genuinely kind of
Speaker 2: tricking someone into believing they're talking to a person.
Speaker 1: Yes, and you're giving up a lot of stuff. So
Speaker 1: at first this seems amazing because, like we established earlier, telephoneophobia,
Speaker 1: in addition to being really fun to say, is a
Speaker 1: genuine thing. I would argue, increasingly people are resorting to text.
Speaker 2: Well, I mean, how often do you talk on the
Speaker 2: phone if you're not talking to like your mom or dad,
Speaker 2: like on a preappointed time.
Speaker 1: I mean, if I'm doing interviews for a podcast.
Speaker 2: Well that's true, but that's a very specific task. Not
Speaker 2: for fun, never for fun, and unless I just feel
Speaker 2: like I could suss something out a lot quicker on
Speaker 2: the phone, real quick than I could usually for business though,
Speaker 2: for work stuff sometimes or conference calls, the dreaded conference
Speaker 2: call that's.
Speaker 1: Also works, the thing that still happens.
Speaker 2: But yeah, for fun, not very often, you know, But
Speaker 2: for other people in the mix. There's totally a dark
Speaker 2: side to this idea of this super convenient, amazing idea
Speaker 2: of my phone being able to make calls for me
Speaker 2: and set up my you know, my waxing appointment or whatever,
Speaker 2: because the implications are incredibly far reaching, aren't they been?
Speaker 1: Yeah, they are. Noal it's not just a it's not
Speaker 1: just an automated phone online saying press one if you
Speaker 1: like to make a payment, press two if you'd like
Speaker 1: to hear a mailing address. It's not just impersoning a
Speaker 1: generic human. Now, it's a program capable of impersonating specific humans,
Speaker 1: namely you, capital y oh, you specifically you listening to this,
Speaker 1: and it's got a it's got a lot of opponents already.
Speaker 2: Now, we're not saying that it mimics your voice. That's
Speaker 2: not what this is. Implying not yet. Not yet, But
Speaker 2: the idea is that it is. It has access to
Speaker 2: all your information right It has access to your date
Speaker 2: books and your phone numbers, and knows everything about you
Speaker 2: because it's tied into this little thing you carry around
Speaker 2: with you that is basically your life in a wallet
Speaker 2: form you.
Speaker 1: Know more specifically with Google. This this initial fear increases,
Speaker 1: or the potential for misuse increases. This journalist, who wrote
Speaker 1: an excellent article in her name's Alex Kranz. To her,
Speaker 1: the dangers are threefold. First, similar to what you're proposing,
Speaker 1: this is a program from Google from Alphabet, and Google
Speaker 1: already knows a ton of personal information about you, whether
Speaker 1: or not you use an Android phone. This means that
Speaker 1: Duplex doesn't just know what time you want to meet
Speaker 1: your tender date at that Tapas place. It also potentially
Speaker 1: knows every other single thing Google knows about your life.
Speaker 1: And Google is so widespread that you do not have
Speaker 1: to have a Google account for this to touch you. Second,
Speaker 1: a criminal, a government, a stalker, a prankster, some Internet
Speaker 1: troll could potentially cheat Duplex out of giving up your
Speaker 1: information in a phone call. Imagine Duplex somehow being fooled
Speaker 1: into thinking it's calling a restaurant to make a delivery
Speaker 1: order and then boom, somebody has your credit card information,
Speaker 1: expiration date, and security code.
Speaker 2: There were actually a really great, a whole bunch of
Speaker 2: great comments in the comment section on this gizmoto article
Speaker 2: by Alex Kranz, and one of them was the idea of, like,
Speaker 2: how this technology could be used by this term I
Speaker 2: love bad actors, you know, so for example, like what
Speaker 2: if you had a pizza place that wanted to use
Speaker 2: this kind of technology to flood arrival pizza company with
Speaker 2: fake calls and keep their phones tied up? And I
Speaker 2: don't know that was a particularly one in sort of
Speaker 2: a far fetched one, but the idea, the implications being
Speaker 2: that you could use this for scamming easily, big time,
Speaker 2: especially with older folks that maybe aren't going to key
Speaker 2: in to the fact that this is not a real person,
Speaker 2: and you could possibly automate and scale scamming in a
Speaker 2: way that would far surpass the way it's done now,
Speaker 2: because even the least tech savvy people can tell that
Speaker 2: you're hearing a automated voice, you know, spamming you to
Speaker 2: try to, you know, sign up for a cruise or
Speaker 2: something like that.
Speaker 1: Absolutely, and to follow up on Kranz's because we said
Speaker 1: it was threefold, So to follow up on the third thing,
Speaker 1: which I found perhaps the most disturbing here, No forget,
Speaker 1: perhaps it is the most disturbing thing. A person could
Speaker 1: hijack your duplex account and essentially function as you until
Speaker 1: such a point as they're caught. So imagine learning your
Speaker 1: credit and savings have been wiped out because your duplex
Speaker 1: called in a series of untraceable transfers to some Caribbean island,
Speaker 1: some offshore account. There's nothing that anyone could do because
Speaker 1: legally that thing would be functioning as you. Crans and
Speaker 1: other critics of this technology are also concerned that at
Speaker 1: the initial presentation, Google build this as another NEDO feature
Speaker 1: and they didn't say anything about privacy or concerns. It
Speaker 1: was very much, look what we can do, not hey
Speaker 1: should we Just.
Speaker 2: Like it's like that line from Jurassic Park. Your scientists
Speaker 2: were spending so much time thinking whether they could, They
Speaker 2: never thought if they should.
Speaker 1: And crime finds a way.
Speaker 2: It does. It does. But you know, and you may
Speaker 2: think this is an alarmist, but it's just like we're
Speaker 2: just a hop skipping a jump away from this stuff.
Speaker 2: And obviously this technology is not ready for market. No, no, no,
Speaker 2: one's saying that this is just kind of like a
Speaker 2: Nido party trick they did at their Io Developers conference.
Speaker 2: But yeah, you're right, because if it's literally the voice
Speaker 2: inside your phone, then it's going to have access to
Speaker 2: all the data that's in your phone. And depending on
Speaker 2: how careful you are with this stuff, it could have
Speaker 2: every bit of identifying information that a human banker would
Speaker 2: ask you to give to confirm this crazy wire transfer
Speaker 2: that you want to do. What do they ask you for?
Speaker 2: Ben they ask you for like the last four digits
Speaker 2: of your social security number, your mailing address, your name,
Speaker 2: maybe your mother's maiden name, or some secret word some
Speaker 2: security going to be on your phone in some way,
Speaker 2: shape or form.
Speaker 1: Likely, and many people email themselves those answers so when
Speaker 1: they're online and they can check back, including passwords. So
Speaker 1: it's a very important point to make that it has
Speaker 1: this duplex. Stuff has not been assigned any kind of
Speaker 1: roll out date. People are still speculating just how close
Speaker 1: or how far Google is from making this technology viable
Speaker 1: outside of testing situations and later available to the public.
Speaker 1: But the problem is this is not the only game
Speaker 1: in town. Sure, audio impersonation is spooky, but what about video.
Speaker 1: Do you remember back during the hunt for Osama bin
Speaker 1: Laden where various people were arguing that the bin Laden
Speaker 1: and propaganda videos wasn't the real guy, but someone else
Speaker 1: impersonating him or.
Speaker 2: Something like that. Sure, or like a Snapchat filter. Right, Yes,
Speaker 2: it's kidding, but I'm not because all of the crazy tech.
Speaker 2: It blows my mind how much research and development they
Speaker 2: put into the Snapchat filter. Oh yeah, and the more
Speaker 2: you see them, the more realistic they get, and the
Speaker 2: more I could see them turning into some pretty nefarious
Speaker 2: ways of mapping people's faces and you know, making you
Speaker 2: look like someone else entirely and making it not just
Speaker 2: a silly mustache, but totally believable doppelganger.
Speaker 1: Yeah, and we've seen Look, visual manipulation is a tail
Speaker 1: as old as journalism. We've seen doctor photos of plenty
Speaker 1: purporting to be evidence of UFOs, giant skeletons, ghosts, and
Speaker 1: we as a species have known for a long time
Speaker 1: that photos can be faked. Someone with a handy knack
Speaker 1: for photoshop, Paul can really work. Wonders and nowadays, we've
Speaker 1: gone beyond the touch ups of still images.
Speaker 2: Have you seen that doctored stuff They don't want you
Speaker 2: to know logo there's some something fishy going on with
Speaker 2: that hand.
Speaker 1: Suspicious indeed, but again no spoilers, right, So if you're
Speaker 1: wondering fans of Westworld, yes, that maze is for you.
Speaker 1: As The Verge reported in July of last year, twenty seventeen,
Speaker 1: we're recording this twenty eighteen July, researchers at the University
Speaker 1: of Washington invented a tool that takes audio files, converts
Speaker 1: them to realistic mouth movements, and then graphs those onto
Speaker 1: existing video. The end result of this is someone saying
Speaker 1: something or appearing to say something that they never actually said.
Speaker 1: And the scary part is it's really convincing. It's not
Speaker 1: like meme level give Funny a lot of their earlier
Speaker 1: examples used footage of former President Barack Obama. Did you
Speaker 1: see this stuff?
Speaker 2: Well, dude, there, I mean, and I don't know, I
Speaker 2: keep harping on this, but there was a Barack Obama
Speaker 2: snapchat filter and it it does have that Uncanny Valley look.
Speaker 2: But first of all, problematic, you know, for a lot
Speaker 2: of reasons sure to put on the visage of someone
Speaker 2: that's a different race than you and play a character
Speaker 2: and you know.
Speaker 1: Oh, just Snapchat users were using it.
Speaker 2: That's what I'm saying. It was literally a Snapchat film.
Speaker 2: There's also another one that was a Bob Marley you know,
Speaker 2: with like but but it's pretty damn realistic. Other than
Speaker 2: there's things they probably did on purpose to make it
Speaker 2: less realistic. But that kind of mapping quality that you're
Speaker 2: talking about, the implications there are nutty.
Speaker 1: Yeah, And luckily for everybody I just got spooked out,
Speaker 1: We have some good news here. First, they didn't just
Speaker 1: choose Barack Obama because they had some sort of ideological
Speaker 1: thing or they were like we like this guy or
Speaker 1: we hate this guy or anything. They did it because
Speaker 1: a high profile individual, like a celebrity or a president
Speaker 1: will have a ton more high quality video and audio
Speaker 1: footage to pull from. And also, this is this thing
Speaker 1: takes it's a huge attrition process. They had to have
Speaker 1: at least seventeen hours of footage just to get started.
Speaker 1: And they say, so.
Speaker 2: There's another neural net kind of learning.
Speaker 1: Exactly yap learning stuffing. So this is a problem. They
Speaker 1: say that their goals are wholly good hearted. We've got
Speaker 1: a quote here the team behind the works say they
Speaker 1: hope it could be used to improve video chat tools
Speaker 1: like Skype. Users could collect footage of themselves speaking, use
Speaker 1: it to train the software, and then when they need
Speaker 1: to talk to someone video on their side would be
Speaker 1: generated automatically, just use their voice. This would help in
Speaker 1: situations where someone's Internet is shaky, or if they're trying
Speaker 1: to save mobile data.
Speaker 2: But that's what Google's saying about Duplex. It's just a
Speaker 2: nifty little, handy dandy tool to help you book your
Speaker 2: hair appointment so you don't have to talk to people
Speaker 2: on the phone. They never talk about the.
Speaker 1: Yeah, yeah, exactly, and this look can't. We can't ascribe motive.
Speaker 1: We can't say these people are lying to you, but
Speaker 1: we can say that this sort of technology is a
Speaker 1: Pandora's jar, And once that lid is unscrewed, there's no
Speaker 1: There is absolutely no realistic way to prevent both the
Speaker 1: spread of these faked segments yeah, and the spread of
Speaker 1: things being accused unjustly of fake segments. This poses some
Speaker 1: inherent dangers for journalism. We already see how easily a
Speaker 1: completely fake story can proliferate on Facebook.
Speaker 2: A bot generated story, yeah times like literally an AI
Speaker 2: generated Twitter account, for example, that can mimic the style
Speaker 2: aisle of someone of a particular ideology or whatever. Here's
Speaker 2: another aspect of it. There's an article from the Information
Speaker 2: dot Com. Google's controversial voice assistant could talk its way
Speaker 2: into call centers because, like we're saying earlier, if you
Speaker 2: get an automated voice on the phone, you know it's
Speaker 2: not going to be actually very helpful at all. You're
Speaker 2: probably just going to blast past it. And they know that.
Speaker 2: And there are a lot of people that have their
Speaker 2: jobs being that person that it gets passed off too.
Speaker 2: So if there were a more successful voice recognition and
Speaker 2: communication tool like this, it could put a lot of
Speaker 2: people out of jobs.
Speaker 1: That's a very good point. It could get us closer
Speaker 1: to not the post work economy, but the post worker economy.
Speaker 1: And up to now we've talked about these two forms
Speaker 1: of impersonation is discrete in different things, right, audio on
Speaker 1: one side, video on another. But what if they become combined.
Speaker 1: What if a digital impersonation of a human being, even
Speaker 1: you knowl or even you Paul, or even me could
Speaker 1: exist online with no one but you and the people
Speaker 1: you meet in person knowing the difference. I mean, let's
Speaker 1: think about it. It would sound like us, it would
Speaker 1: look like us, and if it pulled from our online
Speaker 1: data footprint, it would also know a lot of stuff
Speaker 1: about us, including the relationships we already have with other
Speaker 1: people and how we interact with them. So it's possible,
Speaker 1: for instance, that a fake version of Matt Frederick writs
Speaker 1: to us from Massachusetts foreshadowing there and responds to the
Speaker 1: three of us the way that the real Matt would,
Speaker 1: and we wouldn't know the difference. We'd be sending weird
Speaker 1: memes and thumbs up and doing our inside jokes, and
Speaker 1: it would already know all of those.
Speaker 2: Well, let's be honest. I mean a lot of times
Speaker 2: text conversation and email conversations are already kind of in
Speaker 2: shorthand or in some kind of a little bit more terse,
Speaker 2: not mean, but just kind of like we're trying to
Speaker 2: get it done. That's why we depend on that method
Speaker 2: of communication over talking, because it's a lot more boom
Speaker 2: boom boom, let's get it done and move on. I
Speaker 2: would think it would be easy ish for an AI
Speaker 2: to mimic those kinds of little quick exchanges and not
Speaker 2: raise a red flag for you or I.
Speaker 1: That's a great point because even if they get something wrong,
Speaker 1: we would just think, oh, Matt must.
Speaker 2: Be in a hurry typo or you know, fat finger whatever.
Speaker 1: Yeah, and auto correct.
Speaker 2: Autocorrect exactly like I've sent texts and like seen how
Speaker 2: mangled they were, But be like, oh, he'll know what
Speaker 2: I mean. Yes, like let let it ride.
Speaker 1: Yeah, yeah, yeah. Because again it goes to time. You know,
Speaker 1: it would be alarmist for us to say this would
Speaker 1: plausibly happen to the average person in the near future,
Speaker 1: but for a high value target like a politician, a celebrity,
Speaker 1: a controversial business person and so on, it's completely within
Speaker 1: the realm of possibility. And speaking of alarmists, let's open
Speaker 1: wide the doors of science fiction and do a bit
Speaker 1: of speculation here, completely unfounded. Imagine a world where the
Speaker 1: only communication you can trust becomes face to face in person.
Speaker 1: Imagine a world where you are accused of a crime
Speaker 1: you didn't commit, but you don't have a rock solid
Speaker 1: alibi of your activities at the time of the alleged crime,
Speaker 1: and surprise, surprise, they have you on camera committing it
Speaker 1: and then confessing to it, and you cannot prove it
Speaker 1: is not you would this mean that eventually video evidence
Speaker 1: becomes inadmissible in court? Does this technology pose an existential
Speaker 1: threat to the fabric of digital reality? Is it really
Speaker 1: us talking to you right now?
Speaker 2: Yeah, man, I don't even know anymore.
Speaker 1: I don't know.
Speaker 2: And to answer your hypothetical question earlier, yes, I think
Speaker 2: this is absolutely a threat, and we rely on things
Speaker 2: like video evidence. But I could see far enough removed
Speaker 2: from this particular technological time and place that could be
Speaker 2: something that is just a thing of the past. Man,
Speaker 2: you know what I mean?
Speaker 1: Yeah?
Speaker 2: Can you picture it like I'm trying to think of
Speaker 2: of an analog of something that used to be infallible
Speaker 2: and now as a completely up for grabs, like.
Speaker 1: I don't know, polygraphs.
Speaker 2: Yeah, exactly, a great example. I don't know. This is
Speaker 2: a little bit more of an ephemeral example. But even
Speaker 2: something like the press, or like the news you know now,
Speaker 2: you know, used to to be able to depend on
Speaker 2: some level of rigor and truth in any kind of
Speaker 2: news reporting you see, And now, as you say, because
Speaker 2: of the Internet, we can see stuff that's completely generated
Speaker 2: by artificial intelligence that tricks people all the time into
Speaker 2: believing that it's you know, God's truth.
Speaker 1: And it's insane. So I wonder you know, I have
Speaker 1: questions for you to No, I don't want to put
Speaker 1: you on the spot, but this is this is one
Speaker 1: of the only conversations we can know is actually happening
Speaker 1: between us, right.
Speaker 2: I mean, we are sitting here looking at each other
Speaker 2: in the eyes. We are definitely both humans because as
Speaker 2: far as I know, we don't have human stand ins
Speaker 2: that are believable enough to trick either one of us.
Speaker 1: It's like in Terminator, the first ones you could tell
Speaker 1: the difference, right, So, so what do you have any
Speaker 1: thoughts on the likelihood of this kind of technolog progressing
Speaker 1: or being used to disrupt the spread of reliable information.
Speaker 1: Do you think it's a definite Do you think it
Speaker 1: might happen? Do you think it's a little bit sensationalistic?
Speaker 1: What do you think?
Speaker 2: I think it absolutely could happen. I think it's another
Speaker 2: one of those things that's kind of dangled out there
Speaker 2: like a piece of bait on a fishing hook for
Speaker 2: the American consumer to bite onto and be oh, this
Speaker 2: will change my life for the better. This will make
Speaker 2: it so easy for me to not have to make
Speaker 2: hair appointments or schedule oil changes or what have you. Yeah,
Speaker 2: and then you you know, once the buy in happens,
Speaker 2: then it started. It opens that Pandora's jar that you
Speaker 2: keep talking about. So yeah, no, there's no doubt about it.
Speaker 2: The more access we give our devices and the more
Speaker 2: centralized our information is. And I guess by centralized, I
Speaker 2: just mean within a particular service where it has access
Speaker 2: to all of it. And that's what that's what it takes. Right,
Speaker 2: Why does series suck? Okay, let's just I just want to get
Speaker 2: into this because I was a rag on Apple for
Speaker 2: a minute. Siri is not a good personal assistant because
Speaker 2: it doesn't learn. It doesn't keep track of what you say.
Speaker 2: It's starting from scratch every time you ask it a question. Right,
Speaker 2: This is Siri, Apple's personal assistant, which is one of
Speaker 2: the first ones to hit the market but fizzled ridiculously
Speaker 2: because of Google's and Amazon's Alexa and stuff. Because those
Speaker 2: actually learn and are connected to the Internet and learn
Speaker 2: your vocal patterns and learn your preferences and have access
Speaker 2: to that bigger network, whereas Siri is just kind of dumb.
Speaker 2: It doesn't really do that.
Speaker 1: They can tell you stuff about Apple, you can.
Speaker 2: Tell you a lot of stuff about the history of
Speaker 2: the company, and it can do Google searches for you.
Speaker 2: But if you said, hey, Siri, what time is this movie?
Speaker 2: It'll probably give you some tangentially related answers, but not
Speaker 2: the exact answer you want.
Speaker 1: Time was invented shortly after The Big Bang.
Speaker 2: I say, Siri, tell me a joke. That's always fun.
Speaker 2: But no, but Google assistants will do that because it
Speaker 2: learns your habits and figure years out what kinds of
Speaker 2: questions you're asking. So that's what I'm saying. When we
Speaker 2: start getting into that where we're tapped into this larger network,
Speaker 2: it does seem like a slippery slope. But yeah, people,
Speaker 2: even the most paranoid of my friends, some of them,
Speaker 2: are all about these home assistants. And it's thing, this
Speaker 2: conversation we always have where it's like, I'll give it
Speaker 2: up if I can tell a little box to turn
Speaker 2: on my lights for me.
Speaker 1: Ah. Yeah, I was given one of those things, and
Speaker 1: I plug it in when I'm cooking and unplug it afterwards.
Speaker 1: So it probably thinks that I am always cooking, or
Speaker 1: just don't have power at my house, which I'm fine with,
Speaker 1: But we want to hear from you, assuming again to
Speaker 1: the earlier question, is really us talking to you now?
Speaker 1: What would you like to tell us? Are the robot
Speaker 1: versions of us impersonating the biological versions of us? Do
Speaker 1: you have a home assistant? Do you use this facial
Speaker 1: recognition software that is so often marketed as a free
Speaker 1: recreational pursuit. Do you believe that the future will be
Speaker 1: fraught with things that make us question digital reality? Is
Speaker 1: there a way to combat it? Should there be? You
Speaker 1: can find us on Instagram. You can find us on Facebook.
Speaker 1: You can find us on Twitter. You can find some
Speaker 1: version of us. I feel like now we have to
Speaker 1: say you can find some version of us on there.
Speaker 1: You can also, of course, visit one of our favorite
Speaker 1: places on the internet, our Facebook page. Here's where it
Speaker 1: gets crazy, where you can see Matt Knowle and I, well, what.
Speaker 2: Do we do there? Where on the internet?
Speaker 1: Here's where it gets great?
Speaker 2: Oh, all kinds of stuff. We lurk sometimes sometimes we
Speaker 2: respond and hop around in the threads and post our
Speaker 2: own little gifts and memes, and we have a good
Speaker 2: time in there. It's a lot of fun and.
Speaker 3: That's the end of this classic episode. If you have
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