Speaker 1: Welcome to tech Stuff, a production from iHeartRadio. Hey there,
Speaker 1: and welcome to tech Stuff. I'm your host, Jonathan Strickland.
Speaker 1: I'm an executive producer with iHeart Podcasts and how the
Speaker 1: tech are you. I'm actually a little under the weather today.
Speaker 1: I got my vaccinations for fluent COVID and so I've
Speaker 1: got a few symptoms, nothing serious, but it is hard
Speaker 1: for me to talk, which unfortunately is one of the
Speaker 1: pivotal aspects of podcasting. So while I continue to work
Speaker 1: on episodes, I thought I would bring you an episode
Speaker 1: from just a couple of years ago because spooky times
Speaker 1: are coming up, and with spooky times coming up, I
Speaker 1: thought we could have a spooky times kind of episode,
Speaker 1: at least, you know, tangentially. So I bring to you
Speaker 1: an episode that published on October twenty four, twenty twenty two.
Speaker 1: It is titled The Ghost in the Machine. I hope
Speaker 1: you enjoy. We are continuing our spooky episode series and
Speaker 1: the lead up to Halloween twenty twenty two. Apologies if
Speaker 1: you're from the future listening back on this episode. That's
Speaker 1: why the bizarre theme is popping up. We've already talked
Speaker 1: recently about stuff like Van Bier power and zombie computers
Speaker 1: where I'm really grasping at tenuous connections to horror stuff
Speaker 1: for tech. But now it's time to tackle the ghost
Speaker 1: in the machine. These days, that phrase is frequently bandied
Speaker 1: about with relation to stuff like artificial intelligence. But the
Speaker 1: man who coined the phrase itself was Gilbert Ryle in
Speaker 1: nineteen forty nine, and he wasn't talking about artificial intelligence
Speaker 1: at all. He was talking about, you know, real intelligence,
Speaker 1: and he was critiquing a seventeenth century philosopher's argument about
Speaker 1: the human mind. That philosopher was Renee Descartes, who famously wrote,
Speaker 1: cogito ergo sum, I drink therefore I am sorry. It's
Speaker 1: going to be impossible for me to talk about philosophers
Speaker 1: without quoting Monty Python because I'm a dork. Okay, No,
Speaker 1: cogito ergo sum actually means I think, therefore I am,
Speaker 1: or that's how we interpret it. But that wasn't really
Speaker 1: the bit that Gilbert Ryle was all riled up about.
Speaker 1: See Descartes believed in dualism. Now, I don't mean that
Speaker 1: he believed in showing up at dawn to sword fight
Speaker 1: other philosophers. Though, if someone wants to make a philosopher
Speaker 1: version of Highlander. I am one hundred percent down with that,
Speaker 1: and I will back your kickstarter. No. No, Descartes believed
Speaker 1: that the mind and you know, consciousness and sentience and
Speaker 1: all this kind of stuff are separate from the actual
Speaker 1: gray matter that resides in our noggins. So, in other words,
Speaker 1: intelligence and awareness and all that other stuff that makes
Speaker 1: you you exists independently of your physical brain. That there
Speaker 1: is this this component that is beyond the physical Now,
Speaker 1: Ryle referred to this concept as the ghost in the machine,
Speaker 1: that somehow all the things that represent you are you know,
Speaker 1: to great effect, ethereally independent of the brain itself. And
Speaker 1: Ryle rejects that notion, and you know, Ryle appears to
Speaker 1: be right. We know this because there are plenty of
Speaker 1: case studies focusing on people who have experienced brain injuries,
Speaker 1: either from some physical calamity or a disease or something
Speaker 1: along those lines. And these events all often transform people
Speaker 1: and change their behaviors and their underlying personalities. So the
Speaker 1: damage to the physical stuff inside our heads can change
Speaker 1: who we are as people. That seems to dismiss this
Speaker 1: concept of dualism that the mind and the brain are
Speaker 1: not separate entities. Take that descartes anyway. That's where we
Speaker 1: get the phrase the ghost in the machine. The machine
Speaker 1: in this case is a biological one in the original
Speaker 1: sense of the phrase. In nineteen sixty seven, Arthur Kustler
Speaker 1: he wrote a book called The Ghost in the Machine
Speaker 1: and or at least it was published in nineteen sixty seven,
Speaker 1: and he was a Hungarian born, Austrian educated journalist. So
Speaker 1: his book The Ghost of the Machine was an attempt
Speaker 1: to examine and explain humanity's tendency toward violence, as well
Speaker 1: as going over the mind body problem that Ryle had
Speaker 1: addressed when he coined the phrase to begin with in
Speaker 1: nineteen forty nine. All right, then flash forward a couple
Speaker 1: of decades, nineteen eighty one, and we get the title
Speaker 1: of what was the fourth studio album of the Police,
Speaker 1: you know Sting and the Police. Now I say this
Speaker 1: in case you were thinking that Sting and Company were
Speaker 1: naming their album off the technological use of the term
Speaker 1: ghost of the machine, but they were not. Sting good
Speaker 1: Old Gordy had read Kussler's book, and he took the
Speaker 1: album's title from the book title. So in case you're
Speaker 1: wondering That's also the album that brought us songs like
Speaker 1: every little thing she does as magic and spirits in
Speaker 1: the material world. For a really recent exploration of dualism, well,
Speaker 1: arguably it's more than dualism. I guess you can actually
Speaker 1: watch Pixar's Inside Out. That's a film that solidified my
Speaker 1: reputation as an unfeeling monster among my friends because I
Speaker 1: didn't feel anything when bing Bong meets his final fate.
Speaker 1: I just couldn't care. I mean, he's not even real
Speaker 1: in the context of the film, let alone in my world,
Speaker 1: so why would I know? Okay, never mind anyway. In
Speaker 1: Inside Out, we learn that our emotions are actual anthropomorphic
Speaker 1: entities living inside our heads that share the controls to
Speaker 1: our behavior. That we are in effect governed by our emotions,
Speaker 1: and our emotions, in turn are the responsibilities of entities
Speaker 1: like joy and anger and disgust. Descartes probably would have
Speaker 1: been thrilled and rale Likeli would have rolled his eyes. Anyway,
Speaker 1: The trope of having a little voice inside your head
Speaker 1: that is somehow separate from you and also you is
Speaker 1: a really popular one. I always think of Homer Simpson,
Speaker 1: who will often find himself arguing with his own brain
Speaker 1: for comedic effect. It's another example of doue in popular culture.
Speaker 1: But the idiom the ghost of the machine survived its
Speaker 1: initial philosophical and journalistic trappings, and now folks tend to
Speaker 1: use it to describe stuff that's actually in the technological world.
Speaker 1: We're talking about machines, as in the stuff that we
Speaker 1: humans make, as opposed to the stuff that we are
Speaker 1: generally in tech. The phrase describes a situation in which
Speaker 1: a technological device or construct behaves in a way counter
Speaker 1: to what we expect or want. At least that was
Speaker 1: the way it was used for quite some time. So
Speaker 1: for example, let's say that you've got yourself a robotic
Speaker 1: vacuum cleaner, and you've set the schedule so that it's
Speaker 1: only going to run early in the afternoon, and then
Speaker 1: one night you wake up hearing the worrying and bumping
Speaker 1: of your rumba as it aimlessly wanders your home in
Speaker 1: search of dirt and dust to consume, and you spend
Speaker 1: a few sleepy moments wondering if there's some sort of
Speaker 1: conscientious intruder who's made their way into your house and
Speaker 1: now they're tidying up. Before you realize no, it's that
Speaker 1: darn robot vacuum cleaner. There's a ghost in a machine.
Speaker 1: It's decided on its own to come awake and start working. Now, Alternatively,
Speaker 1: maybe you just goofed up when you created the schedule
Speaker 1: and you mixed up your AMS and your pms. That's
Speaker 1: also a possibility. But you know, sometimes technology does behave
Speaker 1: in a way that we don't expect. Either there's a
Speaker 1: malfunction or it just encounters some sort of scenario that
Speaker 1: it was not designed for, and so the result it
Speaker 1: produces is not the one we want, and we tend
Speaker 1: to try and kind of cover that up with this
Speaker 1: blanket explanation of ghosts in the machine kind of stands
Speaker 1: as a placeholder until we can really suss out what's
Speaker 1: going on underneath. Programmers sometimes use the phrase ghost in
Speaker 1: the machine to describe moments where you get something unexpected
Speaker 1: while you're coding, Like you get an unexpected result, like
Speaker 1: you've coded something to produce a specific outcome, and something
Speaker 1: else happens instead. So the programmer didn't intend for this
Speaker 1: result to happen, and so therefore the cause must be external, Right,
Speaker 1: It's got to be some sort of ghost in the machine.
Speaker 1: That's causing this to go wrong. Now I'm joshing a
Speaker 1: bit here. Of course, Usually this is a way for
Speaker 1: a programmer to kind of acknowledge that things are not
Speaker 1: going to plan and that they need to go back
Speaker 1: and look over their code much more closely to find
Speaker 1: out what's going on. Where did things go wrong? Does
Speaker 1: encoding all it takes is like a skipped step where
Speaker 1: you know you've just you just missed a thing, and
Speaker 1: you you went on one step beyond where you thought
Speaker 1: you were, or maybe you made a typo, you got
Speaker 1: some missed keystrokes in there. That can be all it
Speaker 1: takes to make a program misbehave, and so then you
Speaker 1: have to hunt down the bugs they're causing the problem.
Speaker 1: But you know, if a program is acting very oddly,
Speaker 1: you might call it a ghost of the machine scenario. Now,
Speaker 1: I'm not sure about the timeline for when folks in
Speaker 1: the tech space began to appropriate the phrase ghost in
Speaker 1: the machine for their work, because when it comes to
Speaker 1: stuff like this, you're really entering the world of folklore.
Speaker 1: And folklore is largely an oral tradition where you are
Speaker 1: passing ideas along one person to the next, speaking about it.
Speaker 1: There's not necessarily a firm written record, at least not
Speaker 1: one where you can point to something and say this
Speaker 1: is where it began, Not like Ryle's version of the
Speaker 1: phrase ghost in the Machine itself, which was published, so
Speaker 1: we can point to that as saying this is where
Speaker 1: the phrase comes from. This would be what Richard Dawkins
Speaker 1: would refer to as a meme, a little nugget of
Speaker 1: culture that gets passed on from person to person. But
Speaker 1: it also has been used in literature to refer to
Speaker 1: technological situations. Arthur's Clark, whom I've referenced many times in
Speaker 1: this show, as he's the guy who explained that any
Speaker 1: sufficiently advanced technology is indistinguishable from magic. He also used
Speaker 1: the phrase ghost in the Machine to talk about AI. Specifically,
Speaker 1: he used it in his follow up to his novel,
Speaker 1: his work of fiction two thousand and one, A Space Odyssey.
Speaker 1: The follow up is called, fittingly enough, twenty ten Odyssey two.
Speaker 1: Chapter forty two of twenty ten is titled the Ghost
Speaker 1: in the Machine, and the focal point for that chapter
Speaker 1: is characters discussing how that's the AI system from two
Speaker 1: thousand and one that caused all the trouble so quick
Speaker 1: recap of two thousand and one for those of you
Speaker 1: not familiar with the story and two thousand and one
Speaker 1: the film, the Stanley Kubrick film gets pretty lucy goosey,
Speaker 1: So we're just going to focus on the main narrative here.
Speaker 1: You have a crew aboard a spacecraft and a mayormer
Speaker 1: in spacecraft called Discovery one, which is on its way
Speaker 1: toward Jupiter. Now, the ship has a really sophisticated computer
Speaker 1: system called HOW nine thousand that controls nearly everything on board.
Speaker 1: Also fun little trivia fact, how HL means that the
Speaker 1: initials are each one letter off from IBM, though Arthur C.
Speaker 1: Clark would claim that that was not intentional. Anyway, How
Speaker 1: begins to act erratically in the mission. At one point,
Speaker 1: How insists there's a malfunction in a system that appears
Speaker 1: to be perfectly functional, that it's working just fine. Then
Speaker 1: How systematically begins to eliminate the crew after learning they
Speaker 1: plan to disconnect the computer system because they suspect something's
Speaker 1: going wrong. How figures out that plan by monitoring a
Speaker 1: conversation that a couple of crew members have in a
Speaker 1: room where there are no microphones, so How can't listen
Speaker 1: in on this conversation. But How is able to direct
Speaker 1: a video feed to that room and is able to
Speaker 1: read the lips of the crew members as they talk
Speaker 1: about their plan. So How continues to try and wipe
Speaker 1: everybody out, and he explains or it I shouldn't give
Speaker 1: him a gender. How explains that the computer systems being
Speaker 1: turned off would jeopardize the mission, and How cannot allow
Speaker 1: that to happen. How's prime directive is to make certain
Speaker 1: the mission is a success, so anything that would threaten
Speaker 1: its own existence has to be eliminated. There's also the
Speaker 1: implication that How does not want to cease to exist,
Speaker 1: that How has a personal motivation beyond seeing the mission
Speaker 1: to completion, and so How has no choice but to
Speaker 1: kill everyone. It's not that How wants to murder everyone.
Speaker 1: It's just that in order to complete the mission, that's
Speaker 1: the only outcome that makes sense. Eventually, one of the crew,
Speaker 1: Dave Bowman, manages to turn off How, and How wonders allowil.
Speaker 1: What will happen afterward? Will its consciousness continue once its
Speaker 1: circuits are powered down? Will I dream? It? Says? Well? Anyway.
Speaker 1: In Odyssey two, you now have this group of astronauts
Speaker 1: and cosmonauts in a Soviet American joint effort that are
Speaker 1: trying to figure out what happened with How. Was there
Speaker 1: something inherently flawed in How's programming? Did some external element
Speaker 1: cause how to malfunction? Did How's apparent consciousness emerge spontaneously
Speaker 1: all on its own? Was it all just a sophisticated
Speaker 1: trick And How never really had any sort of consciousness?
Speaker 1: It only appeared to. So the crew are kind of
Speaker 1: left to ponder this themselves. They don't have any easy answers.
Speaker 1: That's just one example of the ghost of the machine
Speaker 1: concept being handled in entertainment. When we come back, I'll
Speaker 1: talk about a different one. But first let's take this
Speaker 1: quick break. Okay, before the break, I talked about Arthur C.
Speaker 1: Clark and his work with the concept of ghost in
Speaker 1: the machine. Let's now leap over to Isaac Asimov, or
Speaker 1: at least an adaptation of Asimov's work. So the film
Speaker 1: version of I Robot, which really bears only a passing
Speaker 1: resemblance to the short stories that Isaac Asimov wrote that
Speaker 1: were also collected in a book called I Robot, uses
Speaker 1: the phrase ghost in the machine. Isaac Asimov, by the way,
Speaker 1: in case you're not familiar with his work, he's the
Speaker 1: guy who also proposed the basic laws of robotics, which
Speaker 1: are pretty famous as well. So in the film the
Speaker 1: character doctor Alfred Lanning, who actually does appear in Asimov's stories,
Speaker 1: but he's a very different version than the one that
Speaker 1: appears in the film. He says in a voiceover quote,
Speaker 1: there have always been ghosts in the machine, random segments
Speaker 1: of code that have grouped together to form unexpected protocols.
Speaker 1: Unanticipated these free radicals engender questions of free will, creativity,
Speaker 1: and even the nature of what we might call the soul.
Speaker 1: Why is it that when some robots are left in darkness,
Speaker 1: they will seek out the light? Why is it that
Speaker 1: when robots are stored in an empty space they will
Speaker 1: group together rather than stand alone. How do we explain
Speaker 1: this behavior random segments of code? Or is it something more?
Speaker 1: When does a perceptual schematic become consciousness? When does a
Speaker 1: difference engine become the search for truth? When does a
Speaker 1: personality simulation become the bitter mote of a soul? End quote.
Speaker 1: There's some fun references in there too. Difference engine, for example,
Speaker 1: refers back to Charles Babbage, who create analytical engines that
Speaker 1: pre date the concept of electrical computers. Now, this idea,
Speaker 1: this idea of consciousness or the appearance of consciousness emerging
Speaker 1: out of technology, is one that often pops up in
Speaker 1: discussions about artificial intelligence, even within our world outside of fiction,
Speaker 1: though usually we talk about this on a more hypothetical basis,
Speaker 1: ununless you're Blake Lemoine or Lemoine, the former Google engineer
Speaker 1: who maintains that Google's language model for dialogue applications aka Lambda,
Speaker 1: is sentient. That's a claim that most other people dispute,
Speaker 1: by the way, So maybe I'll do another episode about
Speaker 1: it to really kind of dig into it. But Lemoine
Speaker 1: or Lemoine, and I apologize because I don't know how
Speaker 1: his last name is pronounced, has said a few times
Speaker 1: that he believes that this particular program has gained sentience.
Speaker 1: But it brings us to another favorite topic among tech fans,
Speaker 1: which is, of course the Turing test. All right, So
Speaker 1: the Turing test was Alan Turing, who he was kind
Speaker 1: of like the father of computer science in many ways.
Speaker 1: It was his response to the question can machines think?
Speaker 1: Turing's answer was that question has no meaning, and you
Speaker 1: are a silly person, goodbye. I am, of course paraphrasing,
Speaker 1: but as a sort of thought experiment, Turing proposed taking
Speaker 1: an older concept called the imitation game and applying it
Speaker 1: to machines, really to demonstrate how meaningless the question of
Speaker 1: can machines think is. So, what is the imitation game? Well,
Speaker 1: the name kind of gives it away. It's a game
Speaker 1: in which you have a player who asks at least
Speaker 1: two different people questions to determine which of them is
Speaker 1: an imitator. So, for example, you could have an imitation
Speaker 1: game in which one of the people is a sailor
Speaker 1: and the other is not a sailor, and the player
Speaker 1: would take turns asking each person questions to try and
Speaker 1: suss out which one is actually a sailor and which
Speaker 1: one is merely pretending to be a sailor. So the
Speaker 1: game depends both on the strength of the imitator's skill
Speaker 1: of deception as well as the player's ability to come
Speaker 1: up with really good questions. And you could do this
Speaker 1: with all sorts of scenarios, and indeed there are tons
Speaker 1: of game shows that use this very premise. Turing's thought
Speaker 1: experiment was to create a version of this in which
Speaker 1: a player would present questions to two other entities, one
Speaker 1: a human and one a computer. The player would only
Speaker 1: know these entities as X and Y, so they could
Speaker 1: ask questions of X, and they could ask questions of
Speaker 1: why and get replies. So the player would not be
Speaker 1: able to see or hear the other entities. All questions
Speaker 1: would have to be done in writing, you know, for example,
Speaker 1: typed and printed out, and at the end of the
Speaker 1: interview session, the player would be tasked with deciding if
Speaker 1: X was a machine or a human, or if why
Speaker 1: was the machine or the human. Turing was suggesting that
Speaker 1: as computers and systems get more sophisticated and things like
Speaker 1: chat programs get better at processing natural language and formulating responses,
Speaker 1: though that was a little past Turing's time, that it
Speaker 1: would be increasingly difficult for a person to determine if
Speaker 1: any given entity on the other end of a chat
Speaker 1: session was actually a person or a machine. And Turing
Speaker 1: also rather cheekily suggests that we might as well assume
Speaker 1: the machine has consciousness at that point, because when you
Speaker 1: meet another human being, you assume that that other human
Speaker 1: being possesses consciousness, even though you're incapable of stepping into
Speaker 1: that person's actual experience. So you can't take over that
Speaker 1: person and find out, oh, yes, they do have consciousness,
Speaker 1: You just assume they do. So if you and I
Speaker 1: were to meet, I assume you would believe I am
Speaker 1: in fact sentient and conscious even on my bad days.
Speaker 1: So if we're willing to agree to this while simultaneously
Speaker 1: being unable to actually experience and therefore prove it, then
Speaker 1: should we not grant the same consideration to machines that
Speaker 1: give off the appearance of sentience and consciousness. Do we
Speaker 1: have to prove it or do we just go ahead
Speaker 1: and treat them as if they are because that's what
Speaker 1: we would do if it was a human. Now, Turing
Speaker 1: was being a bit dismissive about the concept of machines thinking.
Speaker 1: His point was that they might get very very good
Speaker 1: at simulating thinking, and that might well be enough for
Speaker 1: us to just go ahead and say that's what they're doing,
Speaker 1: even if you could, you know, push machines through the
Speaker 1: finest of sieves and find not one grain of actual
Speaker 1: consciousness within it. Now, it doesn't hurt that defining consciousness,
Speaker 1: even in human terms, is something that we can't really do,
Speaker 1: or at least we don't have a unifying definition that
Speaker 1: everybody agrees upon. Sometimes we define consciousness by what it
Speaker 1: doesn't include, rather than what it is. This is why
Speaker 1: I get antsy in philosophical discussions, because being sort of
Speaker 1: a pragmatic Dullered myself, it's hard for me to keep up.
Speaker 1: But let's jump ahead and talk about a related concept.
Speaker 1: This is also one that I've covered a few times
Speaker 1: on tech stuff that also points to this ghost and
Speaker 1: the machine idea. And this is the argument against machine
Speaker 1: consciousness and strong AI. It is called the Chinese room.
Speaker 1: John Searle, a philosopher, put for this argument back in
Speaker 1: nineteen eighty and that argument goes something like this. Let's
Speaker 1: say we've got ourselves a computer, and this computer can
Speaker 1: accept sheets of paper that have Chinese characters written on
Speaker 1: the paper, and the computer can then produce new sheets
Speaker 1: of paper. It can print out sheets that are also
Speaker 1: covered in Chinese characters, that are in response to the
Speaker 1: input sheets that were fed to it. These responses are sophisticated,
Speaker 1: they are relevant. They're good enough that a native Chinese
Speaker 1: speaker would be certain that someone fluent in Chinese was
Speaker 1: creating the responses. Someone who understood what was being fed
Speaker 1: to it was producing the output. So, in other words,
Speaker 1: this system would pass the Turing test. But does that
Speaker 1: mean the system actually understands Chinese. Cirtle's argument is no,
Speaker 1: it doesn't. He says, Imagine that you are inside a room,
Speaker 1: and for the purposes of this scenario, you do not
Speaker 1: understand Chinese. So if you do understand Chinese, pretend you don't. Okay.
Speaker 1: So there's a slot on the wall, and through this
Speaker 1: slot you occasionally get sheets of paper, and there are
Speaker 1: Chinese symbols on the sheets of paper. You cannot read these,
Speaker 1: You don't know what they stand for. You don't know
Speaker 1: anything about it other than they're clearly Chinese characters on
Speaker 1: the paper. However, what you do have inside this room
Speaker 1: with you is this big old book of instructions that
Speaker 1: tells you what to do when these papers come in,
Speaker 1: and you use the instructions to find the characters that
Speaker 1: are on the input sheet of paper, and you follow
Speaker 1: a path of instructions to create the corresponding response. Step
Speaker 1: by step, you do it all the way until you
Speaker 1: have created the full response to whatever was said to you.
Speaker 1: Then you push the response back out the slot. Now,
Speaker 1: the person on the other side of the slot is
Speaker 1: going to get a response that appears to come from
Speaker 1: someone who is fluent in Chinese. But you're not. You're
Speaker 1: just following a preset list of instructions. You don't have
Speaker 1: any actual understanding of what's going on. You still don't
Speaker 1: know the meaning of what was given to you. You
Speaker 1: don't even know the meaning of what you produced. You're
Speaker 1: just ignorantly following an algorithm. So externally it appear here's
Speaker 1: you understand. But if someone were to ask you to
Speaker 1: translate anything you had done, you wouldn't be able to
Speaker 1: do it. So Ceyrele is arguing against what is called
Speaker 1: strong AI. Generally, we define strong AI as artificial intelligence
Speaker 1: that processes information in a way that is similar to
Speaker 1: how our human brains process information. Strong AI may or
Speaker 1: may not include semi related concepts like sentience and self
Speaker 1: awareness and consciousness and motivations and the ability to experience things,
Speaker 1: et cetera. So Cearrel is saying that machines, even incredibly
Speaker 1: sophisticated machines, are incapable of reaching a level of understanding
Speaker 1: that true intelligence can, that we humans can grasp things
Speaker 1: on a level that machines simply are unable to reach
Speaker 1: even if the machines can process information faster and in
Speaker 1: greater quantities than humans are able to. Another way of
Speaker 1: putting this is a calculator can multiply two very large
Speaker 1: numbers and get a result much faster than a human
Speaker 1: could do, but the calculator doesn't understand any significance behind
Speaker 1: the numbers, or even if there's a lack of significance,
Speaker 1: the calculator doesn't have that capability. Now, maybe Seerle's argument
Speaker 1: is valid, and maybe, as Touring suggests, it doesn't even matter.
Speaker 1: So let's talk about machine learning for a moment. Machine
Speaker 1: learning encompasses a broad scope of applications and approaches and disciplines,
Speaker 1: but I'll focus on one approach from a very high level.
Speaker 1: It's called generative adversarial networks or gans GANS. Okay, As
Speaker 1: the name suggests, this model uses two systems in opposition
Speaker 1: of one another. On one side, you have a system
Speaker 1: that is generative, that is, it generates something. Maybe it
Speaker 1: generates pictures of cats, doesn't really matter. We'll use cats
Speaker 1: for this example. So what does matter is that this
Speaker 1: model is trying to create something that is indistinguishable from
Speaker 1: the real version of that thing. So on the other side,
Speaker 1: you have a system called the discriminator. So this is
Speaker 1: a system that looks for fakes. Its job is to
Speaker 1: sort out real versions of whatever it's designed to look
Speaker 1: for and to flag ones that were generated or not real.
Speaker 1: So with cats as our starting point, the discriminator is
Speaker 1: meant to tell the difference between real pictures that have
Speaker 1: cats and fake pictures of cats, or maybe just pictures
Speaker 1: that don't have cats in them at all. So first
Speaker 1: you have to train up your models, and you might
Speaker 1: do this by setting the task. So let's start with
Speaker 1: the generative system, and you create a system that is
Speaker 1: meant to analyze a bunch of images of cats, and
Speaker 1: you just feed the housands of pictures of cats, all
Speaker 1: these different cats, different sizes and colors and orientations and activity,
Speaker 1: and then you tell the system to start making new
Speaker 1: pictures of cats. And let's say that first round that
Speaker 1: the generative system does is horrific. HP Lovecraft would wet
Speaker 1: himself if he saw the images that this computer had created.
Speaker 1: You see that these horrors from the Great Beyond are
Speaker 1: in no way shape or form cats. So you go
Speaker 1: into the model and you start tweaking settings so that
Speaker 1: the system produces something you know less eldritch, and you
Speaker 1: go again, and you do this lots and lots of times,
Speaker 1: like thousands of times, until the images start to look
Speaker 1: a lot more cat ish. You do something similar with
Speaker 1: the discriminator model. You feed it a bunch of images,
Speaker 1: some with cats, some without, or maybe some with like
Speaker 1: crudely drawn cats or whatever, and you see how many
Speaker 1: of the system is able to suss out. And maybe
Speaker 1: if he doesn't do that good a job, maybe it
Speaker 1: doesn't identify certain real images of cats properly. Maybe it
Speaker 1: misidentifies images that don't have cats in them. So you
Speaker 1: go into the discriminator's model and you start tweaking it
Speaker 1: so it gets better and better at identifying images that
Speaker 1: do not have real cats in them. And then you
Speaker 1: set these two systems against each other. The generative system
Speaker 1: is trying to create images that will fool the discriminator.
Speaker 1: The discriminator is trying to identify generated images of cats
Speaker 1: and only allow real images of cats through. It is
Speaker 1: a zero sum game, winner takes all, and the two
Speaker 1: systems compete against each other, with the models for each
Speaker 1: updating repeatedly so that each gets a bit better Between sessions.
Speaker 1: If the generative model is able to consistently fool the
Speaker 1: discriminator like half the time, the generative model is pretty
Speaker 1: reliably creating good examples. This, by the way, is a
Speaker 1: ridiculous oversimplification of what's going on with generative adversarial networks,
Speaker 1: but you get the idea. This form of machine learning
Speaker 1: starts to feel kind of creepy to some of us.
Speaker 1: Like the ability of a machine to learn to do
Speaker 1: something better seems to be a very human quality, something
Speaker 1: that makes us special. But if we can give machines
Speaker 1: that capability, well, then how are we special or are
Speaker 1: we special at all? That's something I'm going to tackle
Speaker 1: as soon as we come back from this next break. Okay,
Speaker 1: we're back now. I would argue that we are special.
Speaker 1: Before the break, I was asking, can we be special
Speaker 1: if machines are capable of learning? I think we are
Speaker 1: in that we're able to do stuff that machines as
Speaker 1: of right now either cannot do or they can do,
Speaker 1: but they don't do it very well and they can
Speaker 1: only attempt it after a lootic Chris amount of time.
Speaker 1: For example, let's talk about opening doors. Several years ago
Speaker 1: twenty sixteen, I was at south By Southwest. I attended
Speaker 1: a panel about robotics and artificial intelligence and human computer interactions.
Speaker 1: In that panel, Laila Takayama, a cognitive and social scientist,
Speaker 1: talked about working in the field of human computer interaction
Speaker 1: and she mentioned how she was once in an office
Speaker 1: where a robot was in the middle of a hallway,
Speaker 1: sitting motionless. It was just facing a door. What Takeyama
Speaker 1: didn't know is that the robot was processing how to
Speaker 1: open that door, staring at the door and trying to
Speaker 1: figure out how to open it for days on end.
Speaker 1: This was taking a lot of time, obviously. Now when
Speaker 1: you think about doors, you realize there can be quite
Speaker 1: a few options, right, Maybe you need to pull on
Speaker 1: a handle to open the door. Maybe you need to
Speaker 1: push on the door. Maybe there's a door knob that
Speaker 1: first you have to turn before you pull or push.
Speaker 1: Maybe there's a crash bar, also known as a panic bar.
Speaker 1: Those are the horizontal bars on exit doors that you
Speaker 1: push on to open. Frequently, they're seen in doors that
Speaker 1: open to an exterior location, like inside schools and stuff.
Speaker 1: You push on them to get out. Maybe it's a
Speaker 1: revolving door, which adds in a whole new level of complexity.
Speaker 1: But you get my point. There are a lot of
Speaker 1: different kinds of doors. Now. We humans pick up on
Speaker 1: how doors work pretty darn quickly. I mean sure, we
Speaker 1: might be like that one kid in the Farside cartoon
Speaker 1: where the kid's going to the School for the Gifted
Speaker 1: and he's pushing as hard as he can on a
Speaker 1: door that is labeled pull. That could be us sometimes,
Speaker 1: but we figure it out right, we do a quick push,
Speaker 1: we realize, oh, it's not opening. We pull. Robots it's
Speaker 1: more challenging for them. They are not good at extrapolating
Speaker 1: from past experience, at least not in every field. We
Speaker 1: humans can apply our knowledge from earlier encounters, and we
Speaker 1: can even if the thing we're facing is mostly new
Speaker 1: to us, we might recognize elements that give us a
Speaker 1: hint on how to proceed. Robots and AI aren't really
Speaker 1: good at doing that. They're also not good at associative thinking,
Speaker 1: which is where we start to draw connections between different
Speaker 1: ideas to come up with something new. It's a really
Speaker 1: important step in the creative process. I find myself free
Speaker 1: associating whenever I'm not actively thinking about something. So if
Speaker 1: I'm doing a mundane task, like if I'm washing dishes
Speaker 1: or I'm mowing the lawn, my brain is going nuts
Speaker 1: free associating ideas and creating new ones. Machines are not
Speaker 1: very good at that for now anyway. They are not
Speaker 1: bad at mimicking it, but they can't actually do it. So,
Speaker 1: getting back to Laila Takeyama, one of the really fascinating
Speaker 1: bits about that panel I went to was a discussion
Speaker 1: on social cues that robots could have in order to
Speaker 1: alert us humans in that same space what the robot
Speaker 1: was up to. This was not for the robots benefit,
Speaker 1: but for our benefit. The whole point is that these
Speaker 1: cues would give us an idea of what was going
Speaker 1: on with the robot so that we don't accidentally, you know,
Speaker 1: interrupt the robot. So you know, it might be like
Speaker 1: the robots in that hallway and it's looking at a door,
Speaker 1: and you're wondering, why is this robot shut down in
Speaker 1: the hallway, But then maybe the robot reaches up to
Speaker 1: apparently kind of scratch its head in sort of a huh,
Speaker 1: what's going on kind of gesture, and that might tell you, oh,
Speaker 1: the robot is actively analyzing something. Don't know exactly what
Speaker 1: it is, but it's clearly working, So maybe I'll step
Speaker 1: around the robot behind it and not interrupt its vision
Speaker 1: of the door it's staring at. The whole point is
Speaker 1: that the social cue whose can help us interact more
Speaker 1: naturally with robots and coexist with them within human spaces,
Speaker 1: so that both the humans and the robots can operate
Speaker 1: well with one another. Also, it helps to explain what
Speaker 1: the robot is doing, because if you don't have that,
Speaker 1: the robots end up being mysterious. Right we can't see
Speaker 1: into them, we don't understand what they are currently trying
Speaker 1: to do, and mystery can breed distrust. That leads to
Speaker 1: yet another concept in AI that gets to this ghost
Speaker 1: in the machine concept, which is the black box. So,
Speaker 1: in this context, a black box refers to any system
Speaker 1: where it is difficult or impossible to see how the
Speaker 1: system works internally. Therefore, there's no way of knowing how
Speaker 1: the system is actually producing any given output, or even
Speaker 1: if the output is the best it could do. So
Speaker 1: with a black box system, you feed input into the
Speaker 1: system and you get out output out of it, but
Speaker 1: you don't know what was happening in the middle. You
Speaker 1: don't know what the system did to turn the input
Speaker 1: into output. Maybe there's a sophisticated computer in the middle
Speaker 1: of that system that's doing all the processing. Maybe there's
Speaker 1: a person who doesn't understand Chinese stuck in there. Maybe
Speaker 1: there's a magical theory that waves a wand and produces
Speaker 1: the result. The problem is we don't know, and by
Speaker 1: not knowing, you cannot be certain that the output you're
Speaker 1: getting is actually the best, or even relevant or the
Speaker 1: most likely to be correct based upon the input you
Speaker 1: fed it. So you start making decisions based on this output.
Speaker 1: But because you're not sure that the output is actually good,
Speaker 1: you therefore can't be sure that the decisions you're making
Speaker 1: are the best, and that leads to really difficult problems.
Speaker 1: So let's take a theoretical example. Let's say we've built
Speaker 1: a complex computer model that's designed to project the effects
Speaker 1: of climate change. And let's say this model is so
Speaker 1: complex and so recursive on itself that it effectively becomes
Speaker 1: impossible for us to know whether or not the model
Speaker 1: is actually working properly. Well, that would mean we wouldn't
Speaker 1: really be able to rely on any predictions or projections
Speaker 1: made by this model. I mean, maybe the projections are accurate,
Speaker 1: but maybe they're not. The issue is there's no way
Speaker 1: for us to be certain, and yet we have a
Speaker 1: need to act. Climate change is a thing, and we
Speaker 1: need to make changes to reduce its impact or to
Speaker 1: mitigate it. It's possible that any decisions we make based
Speaker 1: upon the output of the system will exacerbate the problem,
Speaker 1: or maybe it'll just be less effective than alternative decisions
Speaker 1: would be. Further, we're getting closer to that Arthur C.
Speaker 1: Clark statement about sufficiently advanced technologies being indistinguishable from magic.
Speaker 1: If we produce systems that are so complicated that it's
Speaker 1: impossible for us to understand them fully, we might begin
Speaker 1: to view those technologies as being magical or the very
Speaker 1: least greater than the sum of their parts, and this
Speaker 1: can lead to some illogical decisions. This kind of brings
Speaker 1: me to talk about the Church of Ai called the
Speaker 1: Way of the Future, which was founded and then later
Speaker 1: dissolved by Anthony Lewandowski. You may have heard Lewandowski's name
Speaker 1: if you followed the drama of his departure from Google
Speaker 1: and his eventual employment and subsequent termination from Uber. And
Speaker 1: then there was also the fact that he was sentenced
Speaker 1: to go to prison for stealing company secrets and then
Speaker 1: later received a presidential pardon from Donald Trump. So quick
Speaker 1: recap on Lewandowski. Lewandowski worked within Google's autonomous vehicle division,
Speaker 1: which would eventually become a full subsidiary of Google's parent company, Alphabet,
Speaker 1: and that subsidiary is called Weimo. So when Lewandowski left Google,
Speaker 1: he brought with him a whole lot of data, data
Speaker 1: that Google claimed belonged to the company and was proprietary
Speaker 1: in nature and thus constituted company secrets. Lewandowski eventually began
Speaker 1: working with Uber in that company's own driverless vehicle initiative,
Speaker 1: but the Google slash Weimo investigation would lead to Uber
Speaker 1: hastily firing Lewandowski in sort of an attempt to kind
Speaker 1: of disentangle Uber from this matter, which only worked a
Speaker 1: little bit anyway. In the midst of all this Weimo
Speaker 1: slash Uber drama, in twenty seventeen, Wired ran an article
Speaker 1: that explained that this same Anthony Lewandowski had formed a
Speaker 1: church called Way of the Future a couple of years earlier.
Speaker 1: In twenty fifteen, he placed himself at the head of
Speaker 1: this church with the title of dean, and he also
Speaker 1: became the CEO of the nonprofit organization designed to run
Speaker 1: the church. The aim of the church was to see
Speaker 1: quote the realization, acceptance, and worship of a Godhead based
Speaker 1: on artificial intelligence AI developed through computer hardware and software
Speaker 1: end quote. This is according to the founding documents that
Speaker 1: were filed with the US Internal Revenue Service or IRS. Further,
Speaker 1: Lewandowski planned to start seminars based on this very idea
Speaker 1: later in twenty seventeen. By twenty twenty, Lewandowski's jumped from
Speaker 1: Google to Uber escalated into a prison sentence of eighteen months,
Speaker 1: and it was because he had been found guilty of
Speaker 1: stealing trade secrets. Trump would pardon Lewandowski in January twenty
Speaker 1: twenty one, kind of you know, after the insurrection on
Speaker 1: January sixth, but before Trump would leave office in late January.
Speaker 1: As for the Way of the Future, Lewandowski actually began
Speaker 1: to shut that down in June of twenty twenty and
Speaker 1: it was dissolved by the end of twenty twenty, but
Speaker 1: not reported on until like February twenty twenty one. He
Speaker 1: directed the assets of the church some one hundred and
Speaker 1: seventy five thousand dollars to be donated to the NAACP.
Speaker 1: Lewandowski has said that the beliefs behind the church are
Speaker 1: ones that he still adheres to, that AI has the
Speaker 1: potential to tackle very challenging problems like taking care of
Speaker 1: the planet, which Lewandowski says, obviously we humans are incapable
Speaker 1: of doing that. You know, we would put on this
Speaker 1: system taking care of things that we understand to be important,
Speaker 1: but we seem to be incapable of handling ourselves, almost
Speaker 1: like we're children. Thus looking at AI like a godhead,
Speaker 1: so we should seek out solutions with AI rather than
Speaker 1: locking AI away and saying, oh, we can't push AI's
Speaker 1: development further in these directions because of the potential existential
Speaker 1: dangers that could emerge from AI becoming super intelligent. I
Speaker 1: don't think there are actually that many folks who are
Speaker 1: trying to lock AI away at all. Mostly I see
Speaker 1: tons of efforts to improve aspects of AI from a
Speaker 1: million different angles. I think most serious AI researchers and
Speaker 1: scientists aren't really focused on strong AI at all. They're
Speaker 1: looking at very particular applications of artificial intelligence, very particular
Speaker 1: implementations of it, but not like a strong AI that
Speaker 1: acts like deep thought from the Hitchhacker's Guide to the Galaxy. Anyway,
Speaker 1: maybe Lewandowski's vision will eventually lead us not to a
Speaker 1: ghost in the machine, but a literal dios x makina
Speaker 1: that means god out of the machine. That seems to
Speaker 1: be how Levendal's Ski views the potential of AI that
Speaker 1: are unsolvable problems are almost magically fixed thanks to this
Speaker 1: robotic or computational savior. Know In fiction, deos ex machina
Speaker 1: is often seen as a cop out. Right, You've got
Speaker 1: your characters in some sort of ironclad disastrous situation, there's
Speaker 1: no escape for them, and then in order to get
Speaker 1: that happy ending, you have some unlikely savior or unlikely
Speaker 1: event happen and everyone gets saved, and it might be
Speaker 1: satisfying because you've got the happy ending, but upon critical
Speaker 1: reflection you think, well, that doesn't really make sense. There
Speaker 1: are a lot of stories that get a lot of
Speaker 1: flak for using deosx machina. The image I always have
Speaker 1: is from classical theater, where you've got all the mortal
Speaker 1: characters in a terrible situation and then an actor standing
Speaker 1: in as a god is literally lowered from the top
Speaker 1: of the stage on pulleys to descend to the moral
Speaker 1: realm and fix everything so that you can have a comedy,
Speaker 1: a play with a happy ending. For Lewandowski, it's really
Speaker 1: about turning the ghost of the machine into a god.
Speaker 1: I'm not so sure about that myself. I don't know
Speaker 1: if that's a realistic vision. I can see the appeal
Speaker 1: of it, because we do have these very difficult problems
Speaker 1: that we need to solve, and we have had very
Speaker 1: little progress on many of those problems for multiple reasons,
Speaker 1: not just a lack of information, but a lack of
Speaker 1: motivation or conflicting motivations where we have other needs that
Speaker 1: have to be met that conflict with the solving of
Speaker 1: a tough problem like climate change. Right, we have energy
Speaker 1: needs that need to be met. There are places in
Speaker 1: the developing world that would be disproportionately affected by massive
Speaker 1: policies that were meant to mitigate climate change, and it's
Speaker 1: tough to address that. Right. There are these real reasons
Speaker 1: why it's a complicated issue beyond just it's hard to understand.
Speaker 1: So I see the appeal of it, but it also
Speaker 1: kind of feels like a cop out to me, Like
Speaker 1: this idea of will engineer our way out of this problem,
Speaker 1: because that just puts off doing anything about the problem
Speaker 1: until future you can get around to it. I don't
Speaker 1: know about any of you, but I am very much
Speaker 1: guilty of the idea of, you know what this is,
Speaker 1: future Jonathan will take care of this. Jonathan right now
Speaker 1: has to focus on these other things. Future Jonathan will
Speaker 1: take it. Future Jonathan, by the way, hates Jonathan of
Speaker 1: right now and really hates Jonathan in the past because
Speaker 1: it's just putting things off until it gets to a
Speaker 1: point where you can't do it anymore, and by then
Speaker 1: it might be too late. So that's what I worry
Speaker 1: about with this particular approach, this idea of we'll figure
Speaker 1: it out, will science our way out, we'll engineer our
Speaker 1: way out, because it's projecting all that into the future
Speaker 1: and not doing anything in the present anyway. That's the
Speaker 1: episode on ghost in the Machine. There are other interpretations
Speaker 1: as well. There's some great ones in fiction where sometimes
Speaker 1: you actually have a literal ghost in the machine, like
Speaker 1: there's a haunted machine. But maybe I'll wait and tackle
Speaker 1: that for a future, more like entertainment focused episode where
Speaker 1: it's not so much about the technology but kind of
Speaker 1: a critique of the entertainment itself, because there's only so
Speaker 1: much you can say about. You know, I don't know
Speaker 1: a ghost calculator. That's it for this episode. If you
Speaker 1: have suggestions for future episode topics or anything else that
Speaker 1: you would like to communicate to me, there are a
Speaker 1: couple of ways you can do that. One is you
Speaker 1: can download the iHeartRadio app and navigate over to tech Stuff.
Speaker 1: Just put tech Stuff in the little search field and
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Speaker 1: tech Stuff page and there's a little microphone icon. Click
Speaker 1: on that. You can leave a voice message up to
Speaker 1: thirty seconds in length and let me know what you
Speaker 1: would like to hear in future episodes. And if you like,
Speaker 1: you can even tell me if I can use your
Speaker 1: voice message in a future episode. Just let me know.
Speaker 1: I'm all about opt in. I'm not gonna do it automatically,
Speaker 1: or if you prefer, you can reach out on Twitter.
Speaker 1: The handle for the show is tech Stuff HSW and
Speaker 1: I'll talk to you again really soon. Tech Stuff is
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