Speaker 1: Welcome to Tech Stuff, a production from I Heart Radio.
Speaker 1: Hey there, and welcome to text Stuff. I'm your scary
Speaker 1: but not Harry host Jonathan Strickland, executive producer with I
Speaker 1: Heart Radio and how the tech are You. We are
Speaker 1: continuing our spooky episode series and the lead up to
Speaker 1: Halloween twenty twenty two. Apologies if you're from the future
Speaker 1: listening back on this episode. That's why the bizarre theme
Speaker 1: is popping up. We've already talked recently about stuff like
Speaker 1: vampire power and zombie computers where I'm really grasping at
Speaker 1: tenuous connections to horror stuff for tech. But now it's
Speaker 1: time to tackle the ghost in the machine. These days,
Speaker 1: that phrase is frequently bandied about with relation to stuff
Speaker 1: like artificial intelligence, But the the man who coined the
Speaker 1: phrase itself was Gilbert Ryle in nineteen nine, and he
Speaker 1: wasn't talking about artificial intelligence at all. He was talking about,
Speaker 1: you know, real intelligence, and he was critiquing a seventeenth
Speaker 1: century philosopher's argument about the human mind. That philosopher was
Speaker 1: Renee des Cartes, who famously wrote cogito ergo sum I drink. Therefore,
Speaker 1: I am sorry it's gonna be impossible for me to
Speaker 1: talk about philosophers 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 de Cartes believed in dualism. Now, I don't mean
Speaker 1: that he believed in showing up at dawn to sword
Speaker 1: fight other philosophers. Though, if someone wants to make a
Speaker 1: philosopher version of Highlander, I am down with that and
Speaker 1: I will back your Kickstarter. No. No, deke Hart 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 noggains. 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 too great effect, ethereally independent of the brain itself.
Speaker 1: And Ryle rejects that notion. And you know, Ryle appears
Speaker 1: to be right. We know this because there are plenty
Speaker 1: of 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 often transform people and
Speaker 1: change their behaviors and their underlying personalities. So the damage
Speaker 1: to the physical stuff inside our heads can change who
Speaker 1: we are as people. That seems to dismiss this concept
Speaker 1: of dualism, that the mind and the brain are not
Speaker 1: separate entities. Take that decartes anyway. That's where we get
Speaker 1: the phrase the ghost in the machine. The machine in
Speaker 1: this case is a biological one in the original sense
Speaker 1: of the phrase. In nineteen sixty seven, Arthur Kustler he
Speaker 1: wrote a book called The Ghost in the Machine and
Speaker 1: 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 towards 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 one and we get the title of what
Speaker 1: was the fourth studio album of the Police, you know,
Speaker 1: Sting and the Police. Now I say this in case
Speaker 1: you were thinking that Sting and Company were naming their
Speaker 1: album off the technological use of the term ghost of
Speaker 1: the Machine, but they were not. Sting good Old Gordy
Speaker 1: had read Cussler's book and he took the album's title
Speaker 1: from the book title. So in case you're wondering, that's
Speaker 1: also the album that brought us songs like every little
Speaker 1: thing she does is magic and spirits in the material world.
Speaker 1: For a really recent exploration of due all is um well,
Speaker 1: arguably it's more than dualism. I guess you can actually
Speaker 1: watch Pixars 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? Okay, never mind? Anyway. In Inside Out,
Speaker 1: we learned that our emotions are actual anthropomorphic entities living
Speaker 1: inside our heads that share the controls to our behavior
Speaker 1: that we are in effect governed by our emotions, and
Speaker 1: our emotions in turn are the responsibilities of entities like
Speaker 1: joy and anger and disgust. They cart probably would have
Speaker 1: been thrilled, and Ralph likely 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 dualism 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 the situation in which
Speaker 1: a technological device or construct behaves in a way counter
Speaker 1: to what we expect or want. Uh. At least that
Speaker 1: was the way it was used for quite some time.
Speaker 1: So for example, let's say that you've got yourself a
Speaker 1: robotic vacuum cleaner, and you've set the schedule so that
Speaker 1: it's only going to run early in the afternoon. And
Speaker 1: then one fight, you wake up hearing the worrying and
Speaker 1: bumping of your room ba as it aimlessly wanders your
Speaker 1: home in search of dirt and dust to consume, and
Speaker 1: you spend a few sleepy moments wondering if there's some
Speaker 1: sort of conscientious intruder who's made their way into your
Speaker 1: house and now they're tidying up before you realize, no,
Speaker 1: it's that darn robot vacuum cleaner. There's a ghost in
Speaker 1: the machine. It's decided on its own to come awake
Speaker 1: and start working. Now, alternatively, maybe you just goofed up
Speaker 1: when you create the schedule and you mixed up your A,
Speaker 1: M S and your PMS. That's also a possibility. But
Speaker 1: you know, sometimes technology does behave in a way that
Speaker 1: we don't expect. Either there's a malfunction or it just
Speaker 1: encounters some sort of scenario that it was not designed for,
Speaker 1: and so the result it produces is not the one
Speaker 1: we want, and we tend to try and kind of
Speaker 1: cover that up with this. This blanket explanation of ghosts
Speaker 1: in the machine kind of stands as a placeholder until
Speaker 1: we can really suss out what's going on underneath. Programmers
Speaker 1: sometimes use the phrase ghost in the machine to describe
Speaker 1: moments where you get something unexpected while you're coding, Like
Speaker 1: you get an unexpected result, like you've coded something to
Speaker 1: produce a specific outcome and something else happens instead. So
Speaker 1: the programmer didn't intend for this result to happen, and
Speaker 1: so therefore the cause must be external, right, It's got
Speaker 1: to be some sort of ghost of the machine that's
Speaker 1: causing this to go wrong. Now I'm joshing a bit here.
Speaker 1: Of course, Usually this is a way for a programmer
Speaker 1: to kind of acknowledge that things are not going to plan,
Speaker 1: and that they need to go back and look over
Speaker 1: their code much more closely to find out what's going on.
Speaker 1: Where did things go wrong? Does encoding all it takes
Speaker 1: is like a skipped step where you know you just
Speaker 1: you just missed a thing and you you went on
Speaker 1: one step beyond where you thought you were, or maybe
Speaker 1: you may to typo you got some missed key strokes
Speaker 1: in there. That can be all it takes to make
Speaker 1: a program misbehave, and so then you have to hunt
Speaker 1: down the bugs they're causing the problem. But you know,
Speaker 1: if a program is acting very oddly, you might call
Speaker 1: it a ghost of the machine scenario. Now, I'm not
Speaker 1: sure about the timeline for when folks in the tech
Speaker 1: space began to appropriate the phrase ghost in the machine
Speaker 1: for their work, because when it comes to stuff like this,
Speaker 1: you're really entering the world of folklore, and folklore is
Speaker 1: largely an oral tradition, where you are passing ideas along
Speaker 1: one person to the next, speaking about it. There's not
Speaker 1: necessarily a firm written record, at least not one where
Speaker 1: you can point to something and say this is where
Speaker 1: it began, not like Ryle's version of the phrase goes
Speaker 1: to the machine itself, which was published, so we can
Speaker 1: point to that. They're saying, this is where the phrase
Speaker 1: comes from. This would be what Richard Dawkins would refer
Speaker 1: to as a meme, a little nugget of culture that
Speaker 1: gets passed on from person to person. But it also
Speaker 1: has been used in literature to refer to technological situations.
Speaker 1: Arthur C. Clark, whom I've referenced many times in this show,
Speaker 1: as he's the guy who explained that any sufficiently advanced
Speaker 1: technology is indistinguishable from magic. He also used the phrase
Speaker 1: ghost in the Machine to talk about AI. Specifically, he
Speaker 1: used it in his follow up to his novel, his
Speaker 1: work of fiction, two thousand one, A Space Odyssey. The
Speaker 1: follow up is called, fittingly enough, two thousand ten Odyssey to.
Speaker 1: Chapter forty two of two thousand ten is titled the
Speaker 1: Ghost in the Machine, and the focal point for that
Speaker 1: chapter is characters discussing how that's the AI system from
Speaker 1: two thousand one that caused all the trouble. So quick
Speaker 1: recap of two thousand one for those of you not
Speaker 1: familiar with the story, UH and two thousand and one.
Speaker 1: The film, the Stanley Kubrick film gets pretty loosey goosey,
Speaker 1: So we're just gonna focus on the main narrative. Here.
Speaker 1: You have a crew aboard a spacecraft, an American spacecraft
Speaker 1: called Discovery one, which is on its way towards Jupiter. Now,
Speaker 1: the ship has a really sophisticated computer system called HOW
Speaker 1: nine thousand that controls nearly everything on board. UH. Also
Speaker 1: fun little trivia effect, how h L 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 as 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 Al wants to murder everyone,
Speaker 1: is 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 allowed.
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 Too, 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 ghosts of
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: we're also collected in a book called I Robot, uses
Speaker 1: the phrase ghost of 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 Dr Alfred Lanning, who actually does appear in asthmov 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 in gender, 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 simulate should become the bitter mote of a soul?
Speaker 1: End quote. There's some fun references in there too. Difference engine,
Speaker 1: for example, refers back to Charles Babbage, who created analytical
Speaker 1: engines that predate the concept of electrical computers. Now this idea, this,
Speaker 1: this idea of of consciousness or the appearance of consciousness
Speaker 1: emerging out of technology, is one that often pops up
Speaker 1: in discussions about artificial intelligence, even within our world outside
Speaker 1: of fiction, though usually we talk about this on a
Speaker 1: more hypothetical basis, unless you're Blake Lemoyne or Lemois, the
Speaker 1: former Google engineer who maintains that Google's language model for
Speaker 1: dialogue applications a K A lambda is sentient. That's a
Speaker 1: claim that most other people dispute, By the way, so
Speaker 1: maybe I'll do another episode about it to really kind
Speaker 1: of dig into it. But Lemoyne or LEMOI and I
Speaker 1: apologize because I don't know how his last name is
Speaker 1: is pronounced. Has said a few times that he believes
Speaker 1: that this particular program has gained sentience. But it brings
Speaker 1: us to another favorite topic among tech fans, which is,
Speaker 1: of course, the Turing test. All right, So the Turing
Speaker 1: test was Alan Turing, who he was kind of like
Speaker 1: the father of computer science in many ways. It was
Speaker 1: his response to the question can machines think? Turing's answer
Speaker 1: was that question has no meaning, and you r s
Speaker 1: L E person goodbye. I am, of course paraphrasing, but
Speaker 1: as a sort of thought experiment, Turing proposed taking an
Speaker 1: older concept called the imitation game and applying it to machines,
Speaker 1: really to demonstrate how meaningless the question of can machines think? Is? So,
Speaker 1: what is the imitation game? Well, the name kind of
Speaker 1: gives it away. It's a game in which you have
Speaker 1: a player who asks at least two different people questions
Speaker 1: to determine which of them is an imitator. So, for example,
Speaker 1: you could have an imitation game in which one of
Speaker 1: the people is a sailor and the other is not
Speaker 1: a sailor, and the player would take turns asking each
Speaker 1: person questions to try and suss out which one is
Speaker 1: actually a sailor and which one is merely pretending to
Speaker 1: be a sailor. So the game depends both on the
Speaker 1: strength of the imitator's skill of deception as well as
Speaker 1: the player's ability to come up with really good questions.
Speaker 1: And you could do this with all sorts of scenarios,
Speaker 1: and indeed there are tons of game shows that use
Speaker 1: this very premise. Turings thought experiment was to create a
Speaker 1: version of this in which a player would present questions
Speaker 1: to two other entities, one a human and one a computer.
Speaker 1: The player would only know these entities as X and Y,
Speaker 1: so they could ask questions of X, and they could
Speaker 1: ask questions of why and it replies, So the player
Speaker 1: would not be able to see or hear the other entities.
Speaker 1: All questions would have to be done in writing, you know,
Speaker 1: for example, typed and printed out, and at the end
Speaker 1: of the interview session, the player would be tasked with
Speaker 1: deciding if X was a machine or a human, or
Speaker 1: if why was the machine or the human. Touring was
Speaker 1: suggesting that as computers and systems get more sophisticated, and
Speaker 1: things like chat programs get better at processing natural language
Speaker 1: and formulating responses, though that was a little past during
Speaker 1: time that it would be increasingly difficult for a person
Speaker 1: to determine if any given entity on the other end
Speaker 1: of a chat session was actually a person or a machine,
Speaker 1: And Touring also rather cheekly suggests that we might as
Speaker 1: well assume the machine has consciousness at that point, because
Speaker 1: when you meet another human being, you assume that that
Speaker 1: other human being possesses consciousness, even though you're incapable of
Speaker 1: stepping into that person's actual experience. So you can't take
Speaker 1: over that person and find out, oh, yes, they do
Speaker 1: have consciousness, You just assume they do. So if you
Speaker 1: and I were to meet, I assume you would believe
Speaker 1: I am in fact sentient and conscious even on my
Speaker 1: bad days. So if we're willing to agree to this
Speaker 1: while simultaneously being unable to actually experience and therefore prove it,
Speaker 1: then should we not grant the same consideration to machines
Speaker 1: that give off the appearance of sentience and consciousness. Do
Speaker 1: we have to prove it or do we just go
Speaker 1: ahead and treat them as if they are, because that's
Speaker 1: what we would do if it was a human. Now,
Speaker 1: Touring was being a bit dismissive about the concept of
Speaker 1: machines thinking. His point was that they might get very
Speaker 1: very good at simulating thinking, and that might well be
Speaker 1: enough for us to just go ahead and say that's
Speaker 1: what they're doing. Even if you could, you know, push
Speaker 1: machines through the finest of sieves and find not one
Speaker 1: grain of actual consciousness within it. Now, it doesn't hurt
Speaker 1: that defining consciousness, even in human terms, is something that
Speaker 1: we can't really do, or at least we don't have
Speaker 1: a unifying definition that everybody agrees upon. Sometimes we define
Speaker 1: consciousness by what it doesn't include rather than what it is.
Speaker 1: This is why I get antsy in philosophical discussions, because
Speaker 1: being sort of a pragmatic dullard myself, it's hard for
Speaker 1: me to keep up. But let's jump ahead and talk
Speaker 1: about a related concept. This is also one that I've
Speaker 1: covered a few times on tech stuff that also points
Speaker 1: to this ghost in the machine idea. And this is
Speaker 1: the argument against machine consciousness and strong AI. It is
Speaker 1: called the Chinese Room John Searle a philosopher but for
Speaker 1: this argument back in nineteen eighty and that argument goes
Speaker 1: something like this. Let's say we've got ourselves a computer,
Speaker 1: and this can eater can accept sheets of paper that
Speaker 1: have Chinese characters written on the paper, and the computer
Speaker 1: can then produce new sheets of paper. It can print
Speaker 1: out sheets that are also covered in Chinese characters, that
Speaker 1: are in response to the input sheets that were fed
Speaker 1: to it. These responses are sophisticated, they are relevant. They're
Speaker 1: good enough that a native Chinese speaker would be certain
Speaker 1: that someone fluent in Chinese was creating the responses, someone
Speaker 1: who understood what was being fed to it was producing
Speaker 1: the output. So, in other words, this system would pass
Speaker 1: the Turing test. But does that mean the system actually
Speaker 1: understands Chinese. Cele's argument is no, it doesn't. He says,
Speaker 1: imagine that you are inside a room, and for the
Speaker 1: purposes of this scenario, you do not understand Chinese. So
Speaker 1: if you do understand Chinese, pretend you don't. Okay. So
Speaker 1: there is 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 sent 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 appears you understand.
Speaker 1: But if someone were to ask you to translate anything
Speaker 1: you have done, you wouldn't be able to do it.
Speaker 1: So Sarl is arguing against what is called strong AI. Generally,
Speaker 1: we define strong AI as artificial intelligence that processes information
Speaker 1: in a way that is similar to how our human
Speaker 1: brains process information. Strong AI may or may not include
Speaker 1: semi related concepts like sentience and self awareness and consciousness
Speaker 1: and motivations and the ability to experience things, etcetera. So
Speaker 1: Sarah is saying that machines, even incredibly sophisticated machines, are
Speaker 1: incapable of reaching a level of understanding that true intelligence
Speaker 1: can that we humans can grasp things on a level
Speaker 1: machines simply are unable to reach, even if the machines
Speaker 1: can process information faster and in greater quantities than humans
Speaker 1: are able to. Another way of putting this is a
Speaker 1: calculator can multiply two very large numbers and get a
Speaker 1: result much faster than a human could do, but the
Speaker 1: calculator doesn't understand any significance behind the numbers, or even
Speaker 1: if there's a lack of significance, the calculator doesn't have
Speaker 1: that capability. Now, maybe Searle's argument is valid, and maybe,
Speaker 1: as Touring suggests, it doesn't even matter. So let's talk
Speaker 1: about machine learning for a moment. Machine learning encompasses a
Speaker 1: broad scope of applications and approaches and disciplines, but I'll
Speaker 1: focus on one approach from a very high level. It's
Speaker 1: called generative adversarial networks or g A N s GANS. Okay,
Speaker 1: as the name suggests, this model uses two systems in
Speaker 1: opposition of one another. On one side, you have a
Speaker 1: system that is generative, that is, it generates something. Maybe
Speaker 1: it generates pictures of cats, doesn't really matter, we'll use
Speaker 1: cats for this example. So what does matter is that
Speaker 1: this model is trying to create something that is indistinguishable
Speaker 1: from the real version of that thing. So on the
Speaker 1: other side, you have a system called the discriminator. So
Speaker 1: this is a system that looks for fakes. Its job
Speaker 1: is to sort out real versions of whatever it's designed
Speaker 1: to look for and to flag ones that were generated
Speaker 1: or not real. So with cats as our starting point,
Speaker 1: the discriminator is meant to tell the difference between real
Speaker 1: pictures that have cats and fake pictures of cats, or
Speaker 1: maybe just pictures that don't have cats in them at all.
Speaker 1: So first you have to train up your models, and
Speaker 1: you might do this by setting the task. So let's
Speaker 1: start with the generative system, and you create a system
Speaker 1: that is meant to analyze a bunch of images of cats,
Speaker 1: and you just feed the housands of pictures of cats,
Speaker 1: all these different cats, different sizes and colors and orientations
Speaker 1: and activity. And then you tell the system to start
Speaker 1: making new pictures of cats. And let's say that first
Speaker 1: round that the generative system does is horrific. HP Lovecraft
Speaker 1: would wet himself if he saw the images that this
Speaker 1: computer had created. You see that these horrors from the
Speaker 1: Great Beyond are in no way shape or form cats.
Speaker 1: So you go into the model and you start tweaking
Speaker 1: settings so that the system produces something you know less eldredge.
Speaker 1: And you go again, and you do this lots and
Speaker 1: lots of times, like thousands of times, until the images
Speaker 1: start to look a lot more a cat ish. You
Speaker 1: do something similar with the discriminator model. You feed it
Speaker 1: a bunch of images, some with cats, some without, or
Speaker 1: maybe some with like crudely drawn cats or whatever, and
Speaker 1: you see how many of the system is able to
Speaker 1: suss out. And maybe it doesn't do that good a job.
Speaker 1: Maybe it doesn't identify certain real images of cats properly.
Speaker 1: Maybe it misidentifies images that don't have cats in them.
Speaker 1: So you go into the Discriminator's model and you start
Speaker 1: tweaking it so it gets better and better at identifying
Speaker 1: images that do not have real cats in them. And
Speaker 1: then you set these two systems against each other. The
Speaker 1: generative system is trying to create images that will fool
Speaker 1: the discriminator. The discriminator is trying to identify generated images
Speaker 1: of cats and only allow real images of cats through.
Speaker 1: It is a zero sum game. Winner takes all, and
Speaker 1: the two systems compete against each other, with the models
Speaker 1: for each updating repeatedly so that each gets a bit
Speaker 1: better between sessions. If the generative model is able to
Speaker 1: consistently fool the discriminator, like off the time, the generative
Speaker 1: model is pretty reliably creating good examples. This, by the way,
Speaker 1: is a ridiculous oversimplification of what's going on with generative
Speaker 1: adversarial networks, but you get the idea. This form of
Speaker 1: machine learning starts to feel kind of creepy to some
Speaker 1: of us. Like the ability of a machine to learn
Speaker 1: to do something better seems to be a very human quality,
Speaker 1: something that makes us special. But if we can give
Speaker 1: machines that capability, well, then how are we special or
Speaker 1: are we special at all? That's something I'm going to
Speaker 1: tackle as soon as we come back from this next
Speaker 1: break Okay, we're back now. I would argue that we
Speaker 1: are special. Before the break, I was asking can we
Speaker 1: be special if machines are capable of learning? I think
Speaker 1: we are, and that we're able to do stuff that
Speaker 1: machines as of right now either cannot do, or they
Speaker 1: can do, but they don't do it very well and
Speaker 1: they can only attempt it after a ludicrous amount of time.
Speaker 1: For example, let's talk about opening doors. Several years ago,
Speaker 1: I was at south By Southwest. I attended a panel
Speaker 1: about robotics and artificial intelligence and human computer interactions. In
Speaker 1: that panel, Layla taka Yama, 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 taka
Speaker 1: Yama didn't know is that the robot was processing how
Speaker 1: to open that door, staring at the door and trying
Speaker 1: to 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 indoors that open
Speaker 1: to an exterior location, like inside schools and stuff. You
Speaker 1: push on them to get out. Maybe it's a revolving door,
Speaker 1: which adds in a whole new level of complexity. But
Speaker 1: you get my point. There are a lot of different
Speaker 1: kinds of doors. Now, we humans pick up on how
Speaker 1: doors work pretty darn quickly. I mean, sure, we might
Speaker 1: be like that one kid in the Far Side cartoon
Speaker 1: where the kids 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 build on that. Even if the thing we're facing
Speaker 1: is mostly new to us. We might recognize elements that
Speaker 1: give us a hint on how to proceed. Robots and
Speaker 1: AI aren't really good at doing that. They're also not
Speaker 1: good at associative thinking, which is where we start to
Speaker 1: draw connections between different ideas to come up with something new.
Speaker 1: It's a really important step in the creative process. I
Speaker 1: find myself free associating whenever I'm not actively thinking about something.
Speaker 1: So if I'm doing a mundane task, like if I'm
Speaker 1: washing dishes or I'm mowing the lawn, my brain is
Speaker 1: going nuts free associating ideas and creating new ones. Machines
Speaker 1: are not very good at that for now. Anyway. They
Speaker 1: are not that at mimicking it, but they can't actually
Speaker 1: do it. So, getting back to Laila Takeyama, one of
Speaker 1: the really fascinating bits about that panel I went to
Speaker 1: was a discussion on social cues that robots could have
Speaker 1: in order to alert us humans in that same space.
Speaker 1: What the robot was up to. This was not for
Speaker 1: the robots benefit, but for our benefit. The whole point
Speaker 1: is that these cues would give us an idea of
Speaker 1: what was going on with the robots, So that we
Speaker 1: don't accidentally, you know, interrupt the robots. So you know,
Speaker 1: it might be like the robots in that hallway and
Speaker 1: it's looking at a door, and you're wondering, why is
Speaker 1: this robot shut down in the hallway. But then maybe
Speaker 1: the robot reaches up to apparently kind of scratch its
Speaker 1: head and sort of a huh, what's going on kind
Speaker 1: of gesture, and that might tell you, Oh, the robot
Speaker 1: is actively analyzing something. Don't know exactly what it is,
Speaker 1: but it's clearly working. So maybe I'll step around the
Speaker 1: robot behind it and not interrupt its vision of the
Speaker 1: door it's staring at. The Whole point is that the
Speaker 1: social cues can help us interact more naturally with robots
Speaker 1: and coexist with them within human spaces, so that both
Speaker 1: the humans and the robots can operate well with one another. Also,
Speaker 1: it helps to explain what the robot is doing, because
Speaker 1: if you don't have that, the robots end up being mysterious. Right.
Speaker 1: We can't see into them, we don't understand what they
Speaker 1: are currently trying to do, and mystery can breed distrust.
Speaker 1: That leads to yet another concept in AI that gets
Speaker 1: to this ghost in the machine concept, which is the
Speaker 1: black box. So in this context, a black box refers
Speaker 1: to any system where it is difficult or impossible to
Speaker 1: see how the system works internally. Therefore, the is no
Speaker 1: way of knowing how the system is actually producing any
Speaker 1: given output, or even if the output is the best
Speaker 1: it could do. So with a black box system, you
Speaker 1: feed input into the system and you get output out
Speaker 1: of it, but you don't know what was happening in
Speaker 1: the middle. You don't know what the system did to
Speaker 1: turn the input into output. Maybe there's a sophisticated computer
Speaker 1: in the middle of that system that's doing all the processing.
Speaker 1: Maybe there's a person who doesn't understand Chinese stuck in there.
Speaker 1: Maybe there's a magical ferry that waves a wand and
Speaker 1: produces the result. The problem is we don't know, and
Speaker 1: by not knowing, you cannot be certain that the output
Speaker 1: you're getting is actually the best or even relevant or
Speaker 1: the most likely to be correct based upon the input
Speaker 1: you fed it. So you start making decisions based on
Speaker 1: this output. But because you're not sure that the output
Speaker 1: is actually good, you therefore can't be sure that the
Speaker 1: decisions you're making are the best, and that leads to
Speaker 1: really difficult problems. So let's take a theoretical example. Let's
Speaker 1: say we've built a complex computer model that's designed to
Speaker 1: project the effects of climate change. And let's say this
Speaker 1: model is so complex and so recursive on itself that
Speaker 1: it effectively becomes impossible for us to know whether or
Speaker 1: not the model is actually working properly. Well, that would
Speaker 1: mean we wouldn't really be able to rely on any
Speaker 1: predictions or projections made by this model. I mean, maybe
Speaker 1: the projections are accurate, but maybe they're not. The issue
Speaker 1: is there's no way for us to be certain, and
Speaker 1: yet we have a need to act. Climate change is
Speaker 1: a thing, and we need to make changes to reduce
Speaker 1: its impact or to mitigate it. It's possible that any
Speaker 1: decisions we make based upon the output of the system
Speaker 1: will exacerbate the problem, or maybe it'll just be, you know,
Speaker 1: less effective than alternative decisions would be. Further, we're getting
Speaker 1: closer to that Arthur C. Clark statement about sufficiently advanced
Speaker 1: technologies being indistinguishable from magic. If we produce systems that
Speaker 1: are so complicated that it's impossible for us to understand
Speaker 1: them fully, we might begin to view those technologies as
Speaker 1: being magical. Or the very least greater than the some
Speaker 1: of their parts, and this can lead to some illogical decisions.
Speaker 1: This kind of brings me to talk about the church
Speaker 1: of Ai called the Way of the Future, which was
Speaker 1: founded and then later dissolved by Anthony Lewandowski. You may
Speaker 1: have heard Lewandowski's name if you followed the drama of
Speaker 1: his departure from Google and his eventual employment and subsequent
Speaker 1: termination from Uber. And then there was also the fact
Speaker 1: that he was sentenced to go to prison for stealing
Speaker 1: UH company secrets and then later received a presidential pardon
Speaker 1: from Donald Trump. So quick recap on Lewandowski. Lewandowski worked
Speaker 1: within Google's autonomous vehicle division, which would eventually become a
Speaker 1: full subsidiary of Google's parent company, Alphabet, and that subsidiary
Speaker 1: is called Weymo. So when Lewandowski left Google, he brought
Speaker 1: with him a whole lot of data, data that Google
Speaker 1: claimed belonged to the company and was proprietary and nature
Speaker 1: and thus constituted company secrets. Lewandowski eventually began working with
Speaker 1: Uber in that company's own driverless vehicle initiative, but the
Speaker 1: Google slash Weymo investigation would lead to Uber hastily firing
Speaker 1: Lewandowski and sort of an attempt to kind of disentangle
Speaker 1: Uber from this matter, which only worked a little bit anyway.
Speaker 1: In the midst of all this Weymo slash Uber drama,
Speaker 1: in two thousand seventeen, Wired ran an article that explained
Speaker 1: that this same Anthony Lewandowski had formed a church called
Speaker 1: Way of the Future a couple of years earlier. In
Speaker 1: twenty he placed himself at the head of this church
Speaker 1: with the title of Dean, and he also became the
Speaker 1: CEO of the nonprofit organization designed to run the church.
Speaker 1: The aim of the church was to see quote the realization, acceptance,
Speaker 1: and worship of a Godhead based on artificial intelligence AI
Speaker 1: developed through computer hardware and software end quote. This is
Speaker 1: according to the founding documents that were filed with the
Speaker 1: US Internal Revenue Service or i r S. Further, Lewandowski
Speaker 1: planned to start seminars based on this very idea later
Speaker 1: in by twenty Lewandowski's jumped from Google to Uber escalated
Speaker 1: into a prison sentence of eighteen months uh and it
Speaker 1: was because he had been found guilty of stealing trade secrets.
Speaker 1: Trump would pardon Lewandowski in January one kind of you know,
Speaker 1: after the insurrection on January six, but before Trump would
Speaker 1: leave office in late January. As for the Way of
Speaker 1: the Future, Lewandowski actually began to shut that down in
Speaker 1: June of and it was dissolved by the end of
Speaker 1: but not reported on until like February. He directed the
Speaker 1: assets of the church some dollars to be donated to
Speaker 1: the Inn double a CP. Lewandowski has said that the
Speaker 1: beliefs behind the church are ones that he still adheres to,
Speaker 1: that AI has the potential to tackle very challenging problems
Speaker 1: like taking care of the planet, which Lewandowski says, obviously
Speaker 1: we humans are incapable of doing that. You know, we
Speaker 1: would put on this system taking care of things that
Speaker 1: we understand to be important, but we seem to be
Speaker 1: uh incapable of of handling ourselves, almost like we're children.
Speaker 1: Thus looking at AI like a godhead, so we should
Speaker 1: seek out solutions with AI rather than locking AI away
Speaker 1: and saying, oh, we can't push a eyes development further
Speaker 1: in these directions because of the potential existential dangers that
Speaker 1: could emerge from AI becoming super intelligent. I don't think
Speaker 1: there are actually that many folks who are trying to
Speaker 1: lock AI away at all. Mostly I see tons of
Speaker 1: efforts to improve aspects of AI from a million different angles.
Speaker 1: I think most serious AI researchers and scientists aren't really
Speaker 1: focused on strong AI at all. They're looking at very
Speaker 1: particular applications of artificial intelligence, very particular implementations of it.
Speaker 1: But not like a strong AI that acts like deep
Speaker 1: thought from the Hitchhacker's Guide to the Galaxy. Anyway, maybe
Speaker 1: Lewandowski's vision will eventually lead us not to a ghost
Speaker 1: in the machine, but a literal deos x makina that
Speaker 1: means God out of the machine. That seems to be
Speaker 1: how Lewandowski views the potential of a I that are
Speaker 1: unsolvable problems are almost magically fixed thanks to this robotic
Speaker 1: or computational savior you know. In fiction, du s x
Speaker 1: makina is often seen as a cop out. Right, You've
Speaker 1: got your characters in some sort of iron clad disastrous situation,
Speaker 1: there's no escape for them, and then in order to
Speaker 1: get that happy ending, you have some unlikely savior or
Speaker 1: unlikely event happened and everyone gets saved. And it might
Speaker 1: be satisfying because you've got the happy ending, but upon
Speaker 1: critical reflection, you think, well, that doesn't really make sense.
Speaker 1: There are a lot of stories that get a lot
Speaker 1: of flak for using d S X makina uh. The
Speaker 1: m edge I always have is from classical theater, where
Speaker 1: you've got all the mortal characters in a terrible situation
Speaker 1: and then a an actor standing in as a god
Speaker 1: is literally lowered from the top of the stage on
Speaker 1: pulleys to descend to the moral realm and fix everything
Speaker 1: so that you can have a comedy play with a
Speaker 1: happy ending. For Lewandowski, it's really about turning the ghost
Speaker 1: of the machine into a god. I'm not so sure
Speaker 1: about that myself. I don't know if that's a realistic vision.
Speaker 1: I can see the appeal of it, because we do
Speaker 1: have these very difficult problems that we need to solve,
Speaker 1: and we have had very little progress on many of
Speaker 1: those problems for multiple reasons, not just a lack of information,
Speaker 1: but a lack of motivation or conflicting motivations, where we
Speaker 1: have other needs that have to be met that conflict
Speaker 1: with the solving of a tough problem like climate change.
Speaker 1: Right we have energy needs that need to be met.
Speaker 1: There are places in the developing world that would be
Speaker 1: disproportionately affected by massive policies that were meant to mitigate
Speaker 1: climate change, and it's tough to address that. Right There
Speaker 1: are these real reasons why it's a complicated issue beyond
Speaker 1: just it's hard to understand. So I see the appeal
Speaker 1: of it, but it also kind of feels like a
Speaker 1: cop out to me, like this idea of will engineer
Speaker 1: our way out of this problem, because that just puts
Speaker 1: off doing anything about the problem until future you can
Speaker 1: get around to it. I don't know about any of you,
Speaker 1: but I am very much guilty of the idea. You
Speaker 1: know what this is Future Jonathan will take care of this.
Speaker 1: Jonathan right now has to focus on these other things.
Speaker 1: Future Jonathan will take it. Future Jonathan, by the way,
Speaker 1: hates Jonathan of right now and really hates Jonathan in
Speaker 1: the past, because it's just putting things off until it
Speaker 1: gets to a point where you can't do it anymore,
Speaker 1: and by then it might be too late. So that's
Speaker 1: what I worry about with this particular approach, this idea
Speaker 1: of we'll figure it out, we'll science our way out,
Speaker 1: we'll engineer our way out, because it's it's it's projecting
Speaker 1: all that into the future and not doing anything in
Speaker 1: the present anyway. That's the episode on ghost in the machine.
Speaker 1: There's there are other uh interpretations as well. There's some
Speaker 1: great ones in fiction where sometimes you actually have a
Speaker 1: literal ghost in the machine, like there's a haunted machine.
Speaker 1: But maybe I'll wait and tackle that for a future,
Speaker 1: more like entertainment focused episode where it's not so much
Speaker 1: about the technology but kind of a critique of the
Speaker 1: entertainment itself, because there's only so much you can say
Speaker 1: about you know, I don't know a ghost calculator. That's
Speaker 1: it for this episode. If you have suggestions for future
Speaker 1: episode topics or anything else that you would like to
Speaker 1: communicate to me, that a couple of ways you can
Speaker 1: do that. One is you can download the I Heart
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Speaker 1: click on that, you can leave a voice message up
Speaker 1: to thirty seconds in length and let me know what
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