Speaker 1: Brought to you by Toyota. Let's go places. Welcome to
Speaker 1: Forward Thinking. Hey there, and welcome to Forward Thinking. The
Speaker 1: podcast at looks at the future and says Confucius has
Speaker 1: a puzzling grace. I'm Jonathan Strickland, I'm La, and I'm
Speaker 1: Joe McCormick. So, um, we were talking like intelligent people
Speaker 1: just now, right, like intelligent people. Sure depends on how
Speaker 1: you define intelligence. Maybe people not. We're not all geniuses
Speaker 1: around here, but we have sort of what you might
Speaker 1: think of as a human intelligence. We're smarter than like
Speaker 1: pigs maybe, or like grasshoppers and stuff like that. I
Speaker 1: flatter myself to be considered smarter than my toaster most days. Yeah,
Speaker 1: so this actually got us into a discussion about being
Speaker 1: smarter than inanimate objects, and so far we're doing quite well. Yeah.
Speaker 1: I want y'all to imagine a scenario. Okay, imagine that
Speaker 1: you have a real good friend Ardia already stretching imagination already. Well,
Speaker 1: I know you don't have good friends, but we just
Speaker 1: pretend you have a really good relationship. But this guy
Speaker 1: named Artie, and he's funny, intelligent, compassionate. Um, not to
Speaker 1: mention the strongest man in the world. Uh. But you
Speaker 1: have been extremely good friends with Artie for many, many years. Um.
Speaker 1: And suddenly one day, while you and Artie are hanging out,
Speaker 1: maybe having some coffee in your living room, a bunch
Speaker 1: of people in lab coats bust into your living room
Speaker 1: and they deactivate Alreadie with a remote control. Uh. And
Speaker 1: the head scientist among this group, she flares her lab
Speaker 1: coat and informs you that Already is in fact and
Speaker 1: artificially intelligent robot. And to prove it to you, she
Speaker 1: opens up Already's head and shows you that it is
Speaker 1: full of dense circuitry where a normal person's brain would be. Pop.
Speaker 1: Quiz is what's your reaction in this scenario? Are you
Speaker 1: a horrified that you were tricked into a fake relationship
Speaker 1: with fake intelligence or are you be amazed that a
Speaker 1: synthetic intelligence is capable of being a real friend. I'm
Speaker 1: gonna go with Cee. I'm amazed they were able to
Speaker 1: burst into my living room because there's no doors, so
Speaker 1: live us through the walls. So I got like a
Speaker 1: wrecking ball comes in and it's got a bunch of
Speaker 1: lab coat people clinging to it, and so like scientist
Speaker 1: versions of Miley Cyrus. Maybe I think that I am
Speaker 1: d upset by this entire situation and that someone has
Speaker 1: just deactivated my friend. I think that I'm concerned about
Speaker 1: artist personally, which probably puts me into a B camp
Speaker 1: if you're if you're really being technical, Okay. But so yeah,
Speaker 1: I it's hard to know how I would feel, But
Speaker 1: I would like to say I think i'd go with B.
Speaker 1: That I wouldn't just be horrified and say like all
Speaker 1: of those years were just a yeah, Now, I've had
Speaker 1: enough of those in in my life from from people who,
Speaker 1: as far as I know, are not robots. So I
Speaker 1: think I would mostly be amazed that someone had achieved
Speaker 1: the technical ability to simulate intelligence to the extent where
Speaker 1: I could not tell the difference between what is simulated
Speaker 1: intelligence and real intelligence. Okay. And so this ties into
Speaker 1: a concept we've talked about on the show before, which
Speaker 1: is the Turing test. What what is the Turing tests? Essentially,
Speaker 1: the basic version of the test is that you have
Speaker 1: a person positing questions over a terminal, so a computer terminal.
Speaker 1: They cannot see whomever or whatever is answering those questions.
Speaker 1: Those questions are being answered either by a person in
Speaker 1: another room at another terminal, who's typing those answers in
Speaker 1: or by a computer program that is responding automatically to
Speaker 1: those questions. If the person asking the questions is unable
Speaker 1: to determine with any real level of of of certain certainty,
Speaker 1: thank you words that don't come to mind certainty, then
Speaker 1: that machine is said to pass the Touring test if
Speaker 1: in fact it was the machine that was answering those questions. Okay,
Speaker 1: And so that's the basic Turing tests, say the back
Speaker 1: and forth text based chatbot. But you could sort of
Speaker 1: look at the Turing test in a larger view and say,
Speaker 1: it's the bigger question of can you simulate human intelligence
Speaker 1: in a way that people can't tell the difference? And
Speaker 1: in fact, Touring went so far as to say that
Speaker 1: if this machine was able to simulate human intelligence to
Speaker 1: that level, we would, in fact, we should, in fact
Speaker 1: say that it is intelligent in the sense that we
Speaker 1: should grant it the same kind of consideration that we do. Right,
Speaker 1: because because we cannot inhabit another person's being, we cannot
Speaker 1: be certain that that person actually possesses intelligence. We assume, yeah,
Speaker 1: we assume that they have the same things that we
Speaker 1: You know that they have the same things that we have,
Speaker 1: so we should extend the same courtesy to machines if
Speaker 1: they are capable of doing such a thing. Right, So
Speaker 1: this is what's known as sort of strong AI. Actually
Speaker 1: this is really known as weak AI because it's simulating intelligence,
Speaker 1: not necessarily possessing intelligence. Well, that what the question is
Speaker 1: about strong AI. The question is is um something that
Speaker 1: displays convincingly intelligent behavior actually intelligent. And here's where we
Speaker 1: come back to Artie. Okay, So Artie, your friend convinced
Speaker 1: you for years that he was a real intelligence. He
Speaker 1: seemed intelligent, he was, you know, funny, He was a
Speaker 1: hundred indistinguishable from every other human you knew. And when
Speaker 1: he was activated, he would insist that he was in
Speaker 1: fact conscious, understanding language, would insist on all the other
Speaker 1: things that we would insist on as part of our
Speaker 1: sort of mental abilities. Was already actually intelligent and did
Speaker 1: already understand the relationship the language, all of these things
Speaker 1: that happened between humans. Or was he merely running programs
Speaker 1: that made it seem as though he was right? So,
Speaker 1: in other words, this is the difference between we weak
Speaker 1: AI and strong AI. Week A I would be running
Speaker 1: programs where you would be taking in various kinds of
Speaker 1: input and producing output that to an independent observer would
Speaker 1: seem to be this kind of natural processing of information
Speaker 1: and reaction in the way a human being would react
Speaker 1: week at week A. I would be the position that
Speaker 1: even though you could simulate it, it wasn't really intelligence, right,
Speaker 1: and strong AI would be that it actually has a
Speaker 1: level of intelligence that's comparable to human intelligence. It might
Speaker 1: not be identical, but it would be. It would truly understand,
Speaker 1: It would know what it knew, and not just be
Speaker 1: able to process information based on some complex series of rules. Right.
Speaker 1: So can a robot or a computer program, any kind
Speaker 1: of machine that's based on symbolic programming and computation actually
Speaker 1: no or understand anything. And this is a question that
Speaker 1: is debated quite heavily in artificial intelligence circles and cognitive science. Yeah,
Speaker 1: what we want to focus on today is one particular
Speaker 1: thought experiment that has tried to answer this question. And
Speaker 1: I would say that this is probably the most hotly
Speaker 1: debated question in AI and cognitive science in the past
Speaker 1: few decades, well at least the past decades certainly. Yeah,
Speaker 1: we're talking about John Searle's Chinese Room thought experiment, which
Speaker 1: is a really uh interesting approach. And there are some
Speaker 1: also interesting counter arguments to the Chinese room thought experiment
Speaker 1: that we'll have to kind of talk about. Yeah. As
Speaker 1: of about, computer scientist Pat Hayes had defined cognitive cognitive
Speaker 1: science as a field as the ongoing research project of
Speaker 1: refuting searles argument. Um. Yeah, So obviously this is a
Speaker 1: huge issue, hotly debated by people a lot smarter than
Speaker 1: we are and who have thought about it a lot more.
Speaker 1: So we're not going to settle it here today. We're
Speaker 1: just going to lay it out. We were just going
Speaker 1: to try to explain it and offer a few of
Speaker 1: the prominent thoughts in this field. Let me give a
Speaker 1: crack at explaining what this thought experiment is. Okay, okay, Well,
Speaker 1: first of all, just let's know where it came from.
Speaker 1: It was from John Searle in his nineteen eighty paper Minds,
Speaker 1: Brains and Programs, and that was published in the Behavioral
Speaker 1: in Brain Science. He has he has updated it a
Speaker 1: couple of times since then, uh, and also responded to
Speaker 1: various criticisms leveled against his argument. But here is the
Speaker 1: basic thought experiment now, imagine that you, Joe. I'm going
Speaker 1: to use you as an example. The Joe, you don't
Speaker 1: happen to know Chinese, do you know? Okay, well, then
Speaker 1: you're a perfect I wanted to use you as an example.
Speaker 1: It's too late. I'm the one giving the example, so
Speaker 1: you are the example. I also don't know Chinese, so
Speaker 1: either one of us would work in Okay, you can
Speaker 1: use me. Okay, So Joe does not know Chinese. Uh,
Speaker 1: he is put into a room. That room has a desk,
Speaker 1: it's got some paper, some pencils or racers, it's got
Speaker 1: some file in cabinets and as a door with a
Speaker 1: slot in it. And the way that Joe interacts with
Speaker 1: the outside world is that occasionally someone puts a piece
Speaker 1: of paper through that slot that has a Chinese character
Speaker 1: drawn on it. Now, Joe also has an enormous book
Speaker 1: full of instructions on what to do every time a
Speaker 1: piece of paper with a character comes in and he
Speaker 1: looks at the character, goes through the book, finds the
Speaker 1: character in the book, follows the instructions on what he's
Speaker 1: supposed to do when that character is inserted, which usually
Speaker 1: involves him drawing a different Chinese character on a on
Speaker 1: a sheet of paper and then slaying that back through
Speaker 1: the slot. Now from anyone who is outside of that rooms,
Speaker 1: a Chinese speaking person who is writing down a specific
Speaker 1: instruction on a sheet of paper and slides it through
Speaker 1: that slot and then gets the result that Joe has
Speaker 1: driven written on the other sheet of paper. To them,
Speaker 1: it appears that whatever is inside that room understands Chinese
Speaker 1: right from their perspective, if if the instructions are good enough, yes, right.
Speaker 1: So what we're sort of a mad jenning is this
Speaker 1: is a room with a person in it that passes
Speaker 1: the Turing test. Yes, yeah, because you the person on
Speaker 1: the outside has no idea what's on the inside of
Speaker 1: the room. All they know is that they have passed
Speaker 1: in instructions that were in Chinese. They've received output that's
Speaker 1: also in Chinese, and everything follows according to a sets
Speaker 1: set of rules, predetermined rules, and and the output is
Speaker 1: precise and convincing enough that the person outside thinks that
Speaker 1: whatever is inside is a true native Chinese speaker. So
Speaker 1: so the question then in this experiment is does this
Speaker 1: mean that Joe actually knows Chinese? And Searles, uh, Well,
Speaker 1: Searles says no, you don't. You don't know Chinese. You
Speaker 1: don't understand Chinese. You're following instructions, but you don't understand
Speaker 1: the actual language. Actually, I might make one changes. I
Speaker 1: think it's the obvious point is that I and that
Speaker 1: thought experiment don't actually know Chinese. The question is does
Speaker 1: this translate to a computer in the same situation, because
Speaker 1: this with this supposed to be an analogy for the
Speaker 1: situation that a computer or turing machine is in sure,
Speaker 1: it receives input, um it has a set of instructions
Speaker 1: that it follows in in computer readable language. It processes
Speaker 1: that input through those instructions, and then it gives output.
Speaker 1: But it doesn't have any actual understanding of what is
Speaker 1: going on at any given time. It it knows to
Speaker 1: follow specific instructions because of a program, but it doesn't
Speaker 1: understand what's happened. It doesn't understand the question more or
Speaker 1: less much less the answer, right. And so that was
Speaker 1: Searle's point, and it wasn't He wasn't against week Ai.
Speaker 1: I mean, he obviously believed that maybe a computer could
Speaker 1: simulate human intelligence so well that we couldn't tell the difference.
Speaker 1: He was just pointing out that UM it seems that
Speaker 1: based on simple programmable instructions and symbolic computation, that a
Speaker 1: computer could never really understand but it was dealing with. Yeah,
Speaker 1: so understanding is the crux of this argument. And also
Speaker 1: it's problematic because often people who level criticisms against it
Speaker 1: say that we need to define what understand means, and
Speaker 1: then it gets a little metaphysical. But we're not going
Speaker 1: to go too far into that because obviously that that
Speaker 1: can take hours of discussion. Well, but I mean, part
Speaker 1: of this problem is the is the semantics of the
Speaker 1: terminology that we use for this kind of thing, And
Speaker 1: and which is a little bit funny because because syntax
Speaker 1: versus semantics is considered um kind of the key point
Speaker 1: of this argument, right, Since what the computer clearly does
Speaker 1: understand is syntax, it's got instructions and data to deal with.
Speaker 1: What's up for debate is whether it gets semantics, whether
Speaker 1: it understands the meaning of the symbol. So, for example,
Speaker 1: we've talked about the semantic web, this idea of a web,
Speaker 1: some way of interacting with the Worldwide Web. I love
Speaker 1: to use that old term because I was around when
Speaker 1: it started, but a way to interact with the web
Speaker 1: where it quote unquote understand ends what you want and
Speaker 1: it's pulling exactly the stuff that you want. But when
Speaker 1: we talk about actually making the semantic web come to pass,
Speaker 1: it's all about things like meta data and including all
Speaker 1: of this information about information so that a computer can
Speaker 1: make sense of it, because a computer does not natively
Speaker 1: possess this ability to understand semantics. And even in that case,
Speaker 1: we would call it the semantic web, but it would
Speaker 1: still be based on program yes, and still be very
Speaker 1: much based on syntax. All right, All the examples that
Speaker 1: we can really come up with are very much based
Speaker 1: in our human world because it's a little bit difficult
Speaker 1: for us to understand, um without metaphor exactly what's going
Speaker 1: on inside of a computer. But it's it's sort of
Speaker 1: talking about the difference between like memorizing versus understanding and
Speaker 1: equation or being tested on memorization versus comprehension. Yeah, okay,
Speaker 1: so well, I want to offer a few of the
Speaker 1: responses I've read about. None of these, of course, are
Speaker 1: original to us. This is something that's been debated over
Speaker 1: and over again. So these are all things that have
Speaker 1: been gust in that philosophy world, and an AI and
Speaker 1: computer science before UM. But I think one of the
Speaker 1: most important things to start with is sort of the
Speaker 1: epistemological question is just going straight back to the Turing skepticism,
Speaker 1: which is that, um, if you can't tell the difference,
Speaker 1: you can't tell the difference. That the idea is, if
Speaker 1: someone appears to you to have understanding, how could you
Speaker 1: say that they don't have understanding? Um? And I think
Speaker 1: that's a really good point to start with. But there
Speaker 1: is also a good response to it, in my mind,
Speaker 1: which is that, um, it still leaves the question of
Speaker 1: what is the basic thing we're talking about? So we
Speaker 1: might not be able to know if other people have understanding,
Speaker 1: but just saying that sentence still acknowledges, well, there is
Speaker 1: such a thing as understanding, and whether or not we
Speaker 1: can know that, the question of whether a program machine
Speaker 1: can in principle attain this state remains. So it's not
Speaker 1: deconstructing the argument, and it's just it's just raising more questions. Yeah,
Speaker 1: and so one point raised by this objection might be that, well,
Speaker 1: what if we can never solve this question, But that's
Speaker 1: not the same as saying that the question is meaningless. Basically, sure,
Speaker 1: another objection or another criticism leveled uses what Searl has
Speaker 1: identified as the system approach. So the system argument is
Speaker 1: about how if you look at the person in the
Speaker 1: room as supposed to you know, the person in the
Speaker 1: room is supposed to represent a computer, then clearly you
Speaker 1: would argue while Joe didn't really learn Chinese, computer being Joe,
Speaker 1: Joe clearly did not learn it. He was following specific
Speaker 1: instructions that were in a giant book that were not
Speaker 1: part of Joe's knowledge. All he was doing was taking
Speaker 1: an input and then applying this set of rules and
Speaker 1: then getting output. But the system approach says, well, you're
Speaker 1: looking at this wrong. You're looking at at the Joe
Speaker 1: at being the representation of the computer, whereas really it
Speaker 1: would be the entire system that you have to take
Speaker 1: into consideration rather than just Joe. Joe would be like
Speaker 1: the processor, the CPU, and that that something to do
Speaker 1: with the room, which has created these instructions, understands Chinese,
Speaker 1: and therefore that that intelligence has happened. So when you
Speaker 1: look at the whole thing together, the room, the English
Speaker 1: instruction manual, me the input and output paper slips that
Speaker 1: are coming through the door, the filing system, which represents memory. Right.
Speaker 1: All of that together, you could say, in a way
Speaker 1: understands Chinese. Right, And it may not be directly analogous
Speaker 1: to the way a person understands Chinese, but that really
Speaker 1: doesn't matter in this argument so much. We're talking about
Speaker 1: strong AI versus weak AI, not whether or not the
Speaker 1: intelligence is exactly like human intelligence. Yeah, and so this
Speaker 1: is probably the I think the most popular response to
Speaker 1: the Chinese room argument. There's some others we're gonna talk
Speaker 1: about in a minute, but I think the dominant one
Speaker 1: is this systems thinking approach. And and this also goes with,
Speaker 1: um a lot of the ways people have come up
Speaker 1: with to think about how intelligence comes out of a
Speaker 1: material mind, sorry, a material brain. Um. So you know,
Speaker 1: it's like you're the brain is not just one discrete
Speaker 1: agent doing one kind of thing, but it's this confabulation
Speaker 1: of tons of different types of events that, in an
Speaker 1: emergent way combined to produce what we think of his consciousness. Right.
Speaker 1: It's also related to another response called the virtual mind reply,
Speaker 1: which which specifies that AI is a machine that has
Speaker 1: created a mind that understands Chinese, not that the AI
Speaker 1: itself understands Chinese. Which is getting back into semantics. Yeah,
Speaker 1: it's getting it's also getting a little metaphysical, to the
Speaker 1: point where Searle's responses puffed. I think that's how you
Speaker 1: would sum it up. But essentially he says like, you
Speaker 1: don't need to bring metaphysics into it. This isn't this,
Speaker 1: that's that's irrelevant. Well, if you want to get really
Speaker 1: hard nosed and practical, there are a couple other ways
Speaker 1: we could go with this. One of them is simply
Speaker 1: the criticism that the thought experiment is too impractical to
Speaker 1: be useful, or it's too unlike the real world. So
Speaker 1: think about it like this. Um It asks us to
Speaker 1: imagine that the person in the room would be able
Speaker 1: to use this English instruction manual, which is the equivalent
Speaker 1: to a computer program, fast enough to generate convincing responses.
Speaker 1: But in reality, it might take a single person in
Speaker 1: a room like billions of years to go through all
Speaker 1: of the instructions in that instruction manual and turn back
Speaker 1: an answer that was satisfactory. A computer could do this
Speaker 1: in less than a second, probably, so Thus there could
Speaker 1: be a fundamental difference between the kind of slow intelligence
Speaker 1: that the thought experiment would display and an actually convincing
Speaker 1: touring ai um, and thus the thought experiment can actually
Speaker 1: comment on what happens with a touring AI. See. I
Speaker 1: I have a problem with this just from a gut level. Obviously,
Speaker 1: I'm not a philosopher nor my scientist. However, I think
Speaker 1: about people who really take their time and answering a question,
Speaker 1: and I think of computers I've used that also really
Speaker 1: take your time and executing a program, And I'm just
Speaker 1: thinking that when it comes down to the time issue,
Speaker 1: I almost see that as irrelevant to me, like that
Speaker 1: doesn't really matter so much to me. But then a
Speaker 1: part of it is also that I'm used to talking
Speaker 1: about stuff that's taking over taking taking place over the
Speaker 1: course of billions of years. So whether it takes you know,
Speaker 1: a month to respond to a request or it takes
Speaker 1: a second, I don't really necessarily see that as an
Speaker 1: indicator or or you know, something that denies the presence
Speaker 1: of intelligence. Yeah, you could certainly say that you know
Speaker 1: that basically no matter how long it takes, the principles
Speaker 1: behind the question in the thought experiment remain the same.
Speaker 1: Um that it's just that's just sort of an irrelevant
Speaker 1: variable that we can exclude for the sake of argument,
Speaker 1: And the question is is it reasonable to exclude it
Speaker 1: for the sake of argument or not? Right, because if
Speaker 1: time is actually a factor, if speed is a factor
Speaker 1: in determining what is is an intelligent then clearly you
Speaker 1: can't just dismiss it. Right, Like if some if for
Speaker 1: some reason, we discover that that the way that we
Speaker 1: exhibit intelligence is dependent partly on the speed at which
Speaker 1: our neural networks work, and then it is. But if
Speaker 1: they did not work at that speed, we would not
Speaker 1: be intelligent, That's what I'm saying. Like, if that were
Speaker 1: the case, then we would you know, well we wouldn't
Speaker 1: be having this argument because we wouldn't be able to.
Speaker 1: But oh, the way you just phrased it made this
Speaker 1: objection suddenly seem more reasonable to me. Actually, Like, what
Speaker 1: if something that did all the same things a human
Speaker 1: brain does, except did them at one one billiont the speed.
Speaker 1: Would that have consciousness in the same way that we do? Well,
Speaker 1: I don't know that we would all ever argue them
Speaker 1: as sument have consciousness the same way. Well, not exactly,
Speaker 1: but you know what I mean, has this intelligence and
Speaker 1: part of this is also part of a separate but
Speaker 1: related argument that talks about exactly how ego centric we're
Speaker 1: being in our definition of intelligence and understanding, because that's
Speaker 1: a huge part of it. I mean, you know, like
Speaker 1: like if if we do we define intelligence based on
Speaker 1: neural connections, because there are things out there who display
Speaker 1: relatively intelligent behavior that don't have that on our planet Earth. Yeah,
Speaker 1: I just think of them as incredibly lucky. They're just
Speaker 1: on a They're just on a series of making their
Speaker 1: saving throw against stupidity. That's all that's happening. Uh. Yeah.
Speaker 1: Let me offer one more objection that's sort of more
Speaker 1: on the practical edge I think, and that's sort of
Speaker 1: the lack of diverse sensory grounding. So system, if a
Speaker 1: system is only receiving input by pieces of paper being
Speaker 1: shoved through a slot, that's extremely limiting. Yeah. Um, and
Speaker 1: so okay. This is also known as the robot response
Speaker 1: to the argument. Yeah so uh. This argument basically says
Speaker 1: that what prevents the person in the room from understanding
Speaker 1: Chinese is the lack of sensory grounding in the outside
Speaker 1: world through diversity of cues like visual, auditory all that. So,
Speaker 1: perhaps a computer designed only to manipulate symbols based on
Speaker 1: instructions wouldn't understand language or wouldn't understand those symbols. But
Speaker 1: if granted diverse sensory and put like video cameras haptic input, Basically,
Speaker 1: if you made that computer into a robot that could
Speaker 1: sample the world and all of the ways that we can,
Speaker 1: the system actually would be able to understand what those
Speaker 1: symbols mean. It's an interesting argument. Uh. Cyril of course
Speaker 1: has his own counter which says that you could have
Speaker 1: a string of numbers that to a person in that
Speaker 1: room seemed to be meaningless, but are actually the readoubts
Speaker 1: that a result of a video camera that's set up
Speaker 1: pointing outside of the room, and it wouldn't magically make
Speaker 1: things more meaningful to the person inside the room, and
Speaker 1: that it would just create more work for him or her. Right,
Speaker 1: you're just yeah, that's exactly what Sarl says, Like, all
Speaker 1: you've done is just made more work. You haven't made it.
Speaker 1: You haven't increased understanding or intelligence, You just increase the workload.
Speaker 1: So that's an interesting counter argument. Although related to that
Speaker 1: is the idea that that man in the room, and
Speaker 1: this is kind of where it becomes a poor example,
Speaker 1: would eventually learn Chinese due to his interactions with the
Speaker 1: Chinese language. Oh so you're saying like that somehow, I mean,
Speaker 1: it seems unlikely. But the person, even through just interacting
Speaker 1: based on these instructions, would somehow gain an understanding that
Speaker 1: given given long enough and giving given enough input, or
Speaker 1: you know, maybe through enough questions about, for example, hamburgers,
Speaker 1: the guy would know what a hamburger is and would
Speaker 1: eventually connect the two things in his brain. See, I
Speaker 1: have a real problem with that. I could I could
Speaker 1: see where Joe in his room starts to receive, over
Speaker 1: a great span of time, enough of one particular character
Speaker 1: that he's remembered how the resulting character already needs to
Speaker 1: be formed without having to look it up. He's just
Speaker 1: he's done it enough times. But that doesn't mean he
Speaker 1: knows what the input is or what the output means.
Speaker 1: He just knows, Oh, I've done this five hundred times.
Speaker 1: I know how to draw this without even having to
Speaker 1: look it up. It's a good question, though, Actually, I
Speaker 1: mean this sort of just intuitively assumes that while the
Speaker 1: part we you can't question is that you could never
Speaker 1: learn anything just from the symbols alone. But I don't know.
Speaker 1: I mean what if you, I mean, imagine something like this,
Speaker 1: So you receive sequences of symbols, and then based on
Speaker 1: over like hundreds of years of doing this in the room,
Speaker 1: you start to notice that certain sequences of symbols mirror
Speaker 1: mirror the syntax of sentences you would recognize, like you
Speaker 1: start to maybe understand part of speech or something well.
Speaker 1: And then maybe from understanding part of speech, you could
Speaker 1: start to understand more complex relationships between meanings. All right,
Speaker 1: I think that part of the thought experiment assumes that
Speaker 1: that that Joe sitting in the room is not thinking
Speaker 1: at all about what he's doing, that he's merely following
Speaker 1: the instructions. Yeah, it just seems to be sort of
Speaker 1: like a condition of this thought experiment that you don't learn, right,
Speaker 1: but you know, but but people do learn, and and
Speaker 1: you know, people like Kurtswile talk a lot about about
Speaker 1: machines also being a naturally emergent um forces just like
Speaker 1: anything else in nature. I'm not going to dwell on
Speaker 1: cards while too much, but but I will say I
Speaker 1: will say that there have been some incredible developments in
Speaker 1: machines learning or being able to derive information based upon
Speaker 1: uh input, like sensory input. So we were talking just
Speaker 1: now about the robot objection, right, the idea that by
Speaker 1: adding the sensory input, the system might be able to
Speaker 1: piece together more things and thus learn and have true intelligence.
Speaker 1: So you guys know about the computer that the Cornell
Speaker 1: people built that was able to derive Newton's laws of
Speaker 1: physics by examining the movements of a pendulum. Right, I
Speaker 1: had not heard about this. So they set a pendulum
Speaker 1: in motion, they have a computer that was set up
Speaker 1: to measure what was going on with the pendulum, and
Speaker 1: the pendulum within or the computer, within about a day's time,
Speaker 1: derived the basic law Newton's basic laws of physics because
Speaker 1: it observed how the the pendulum moved and then extrapolated
Speaker 1: from that what laws must guide the movements. So what's
Speaker 1: it done for us lately? Well, I just think that
Speaker 1: that's an interesting example of how a machine has been
Speaker 1: able to seemingly learn something by observing. Now, again, I
Speaker 1: think Searl would argue that the computer doesn't really know that,
Speaker 1: but that the computer was able to make um conclusions
Speaker 1: based upon input, right, And that all kind of kind
Speaker 1: of ties back to what I was saying at the beginning.
Speaker 1: About are kind of basic, the basic metaphors that we
Speaker 1: use to describe computers and how we're we're essentially misrepresenting
Speaker 1: computer syntax with our ideas of of ones and zeros,
Speaker 1: because really it's all voltage and operations. It's not really reading.
Speaker 1: A computer isn't reading information. It's it's chains of state changes. Um.
Speaker 1: And it's it's hard for us to even conceive of
Speaker 1: how how little computers understand because it seems like such
Speaker 1: a compelling you know. Yeah, well that reminds me of
Speaker 1: one more interesting objection to the Chinese room argument, which
Speaker 1: is that, Okay, so Cearle isn't saying that no machine
Speaker 1: could ever have intelligence. I mean, what cearl is talking
Speaker 1: about is programmable machines, right, that are based on symbolic computation, says,
Speaker 1: you know, essentially our brains are machines. He's not intelligence. Yeah,
Speaker 1: he's not saying that this is magic. He's just saying that,
Speaker 1: like computers in the way we think of computers can't
Speaker 1: do this. Um. So there's another objection there. It's like, well,
Speaker 1: maybe it's true that computers can't do this in the
Speaker 1: way we think of, like programmed computers based on information processing,
Speaker 1: you know ones and zeros, but maybe some other type
Speaker 1: of synthetic machine could. Yeah. And of course, when you
Speaker 1: put it that way, I mean the possibilities are impossible
Speaker 1: for us to even contain. We don't know what that
Speaker 1: would look like. The only types of computers we can
Speaker 1: think of to be based on these symbols. So I
Speaker 1: was thinking, like, when I was thinking about the touring
Speaker 1: test in the Chinese room, there was one computer in
Speaker 1: particular that kept popping up to mind, something that appears
Speaker 1: to be intelligent based upon the way it behaved, And
Speaker 1: I was thinking of IBM S Watson. Obviously, you know,
Speaker 1: Watson being the computer that competed on Jeopardy and now
Speaker 1: is trying to uh help trying doctors are trying to
Speaker 1: use it to help with medical diagnoses and things of
Speaker 1: that nature. But if you watched any of those episodes
Speaker 1: of Jeopardy where wats and was one of the contestants
Speaker 1: and was going up against two former champions, it appeared
Speaker 1: that Watson was fairly intelligent thing. I mean, it was
Speaker 1: able to beat the champions at their own game. Spoiler
Speaker 1: alert for anyone who wasn't watched Jeopardy in the last
Speaker 1: few years, but you know, it was able to parse
Speaker 1: sometimes pretty complicated clues, go through all of the information
Speaker 1: that was stored on the computer because it didn't have
Speaker 1: any connection to the internet during those games, and try
Speaker 1: and determine what the best answer was, or best question
Speaker 1: in the case of Jeopardy was to that particular clue.
Speaker 1: And that's a you know, that's pretty tricky stuff. It's
Speaker 1: one of those things where you know, you would say,
Speaker 1: even smart people have trouble sometimes on Jeopardy. We've seen it.
Speaker 1: So but again Searle would say, yeah, the computer didn't
Speaker 1: necessarily understand what it was doing. It was able to
Speaker 1: break this apart and look for clues using various search
Speaker 1: algorithms and probabilistic models to determine what was probably the
Speaker 1: right answer, but didn't actually understand any of that content.
Speaker 1: That would be Searles or right. Even though even though
Speaker 1: Jeopardy is not you know, direct direct question and answer
Speaker 1: kind of things, it's a lot of it's based on
Speaker 1: cultural understanding or puns. But that's the kind of thing
Speaker 1: that Watson is really good at parsing because it just
Speaker 1: has enough stuff in there. It's got a lot of
Speaker 1: natural language processing ability that I mean that is part
Speaker 1: of artificial intelligence. And again, you know, Searle was never
Speaker 1: saying that artificial intelligence is as a fool's errand, but
Speaker 1: rather that you know, defining it as strong AI versus
Speaker 1: weak AI, and and his skepticism that strong AI is
Speaker 1: something that we could attain with the current version of
Speaker 1: computing as we know it. Right, Okay, So now that
Speaker 1: we've talked about it, Yeah, where do you come down?
Speaker 1: Is already intelligent? Did already understand the friendship you had
Speaker 1: with him? No one understands the friendship they had with me. Um.
Speaker 1: I mean I think I think no, based on our
Speaker 1: based on our basic definitions of intelligence. But I again
Speaker 1: would argue more on the side of of that our
Speaker 1: definitions are flawed and that as we move forward into
Speaker 1: this incredible future, that we maybe need to think really
Speaker 1: hard about what we're calling intelligent and how we're I mean,
Speaker 1: I'm just saying that I don't want, you know, AI
Speaker 1: artificial intelligence like the Steven Spielberg Stanley Kubrick flick too
Speaker 1: to come into reality. What a film? You know, So
Speaker 1: I intuitively feel like already would not actually be intelligent.
Speaker 1: But I also, especially in areas like this where we
Speaker 1: have so little understanding, I feel disinclined to trust my intuitions.
Speaker 1: I think my intuitions very likely could be leading me astray. Well,
Speaker 1: you certainly, I mean, we have a bias, honestly, and
Speaker 1: that's that that's undeniable. So I would say, particularly the
Speaker 1: system's response to CYL makes a lot of sense to me.
Speaker 1: I don't feel it. I don't feel like it's true intuitively,
Speaker 1: but thinking about it abstract ly, I can absolutely see
Speaker 1: how that is a reasonable answer. Well, I mean, the
Speaker 1: example I could give to you on a biological level
Speaker 1: is that, you know, I can speak a little French,
Speaker 1: but if you take any single neuron, or even in
Speaker 1: any single synapse out of my brain, it can't speak French.
Speaker 1: It's you know, you can't. You know. So, judging one
Speaker 1: element of a system and saying that that one element
Speaker 1: proves that the entire system can't do something seems to
Speaker 1: be shortsighted. Cyril, of course, has his own responses to that.
Speaker 1: That's not that's not the end of the argument, and
Speaker 1: I just broken. I don't consider him refuted, But I
Speaker 1: think that objection is interesting. It is interesting, I agree,
Speaker 1: entirely interesting, but not you know, not the end all
Speaker 1: of this, like we said, there are people way smarter
Speaker 1: than us who are continuously debating this and and expanding
Speaker 1: our UH knowledge. And sometimes they're focusing definitions, sometimes they
Speaker 1: are completely scrapping definitions and starting from scratch. So it's
Speaker 1: the best thing about all of this is that it's
Speaker 1: going to inform future engineers who are working on artificial
Speaker 1: intelligence problems. And it may very well be that either
Speaker 1: we will never create any sort of entity that has
Speaker 1: strong AI, or that we do but we have no
Speaker 1: way of knowing that we did it. But no matter what,
Speaker 1: it means that we're going to make improvements in that field.
Speaker 1: We're going to have computers that are better able to
Speaker 1: react to things like natural language commands, and that will
Speaker 1: in turn benefit us. So while this whole discussion might
Speaker 1: have seemed philosophical and that there was no real practical application,
Speaker 1: the reality is exactly the opposite. Well, and if there
Speaker 1: is such a thing as strong AI, you better believe
Speaker 1: this has practical application, and we certainly should be thinking
Speaker 1: about it. I mean, just on the legal and moral basis, right,
Speaker 1: just on the off chance that there is such a
Speaker 1: thing as the understanding and experience of being already I
Speaker 1: think it's worth conser. Still not giving a toaster the
Speaker 1: right to vote, guys, still not doing it. I'm not
Speaker 1: gonna do it, alright. So that wraps up this discussion
Speaker 1: of artificial intelligence of the Chinese Room Thought experiment. And uh,
Speaker 1: while I sound cold, I'm I don't really subscribe to
Speaker 1: the things I say. Sometimes. Oh I always vote for
Speaker 1: toasters day. They believe in caramelization, and that's wonderful. That's okay,
Speaker 1: that's fair. All right. So guys, if you have enjoyed this,
Speaker 1: make sure you go to f W Thinking dot com.
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