Speaker 1: Welcome to tech Stuff, a production from I Heart Radio.
Speaker 1: Hey there, and welcome to tech Stuff. I'm your host,
Speaker 1: Jonathan Strickland. I'm an executive producer with I Heart Radio,
Speaker 1: and I love all things tech and I owe you
Speaker 1: guys an apology where you're having a rerun episode today.
Speaker 1: That's because I came down with food poisoning. You could
Speaker 1: probably hear. I don't quite sound like my normal self.
Speaker 1: I apologize for that as well, but it completely laid
Speaker 1: me low and I was unable to do any work,
Speaker 1: and so I wasn't able to research right and record
Speaker 1: an episode, and I feel badly about that. I really
Speaker 1: love you guys, and I love being able to create
Speaker 1: great podcasts for you. But yeah, I just I was
Speaker 1: in bed with a fever pretty much all day yesterday.
Speaker 1: So this is a rerun from two thousand nineteen about
Speaker 1: machine consciousness. I figured this is a good companion piece
Speaker 1: to some of the things we've talked about recently with
Speaker 1: artificial intelligence and machine learning and artificial neural networks and
Speaker 1: all of that sort of stuff. And um, it's it's
Speaker 1: again one of those tricky concepts. Consciousness is a difficult
Speaker 1: thing to define, even for humans. So sit back and
Speaker 1: enjoy this rerun, and I am going to go and
Speaker 1: eat some apple sauce. I'll talk to you again at
Speaker 1: the end of the episode. There's a topic I have
Speaker 1: touched on on several occasions in past episodes, but I
Speaker 1: really wanted to dig down today into this topic because
Speaker 1: it's one of those that's fascinating and is an underpinning
Speaker 1: for tons of speculative fiction and horror stories. And since
Speaker 1: we're now in October, I think here this would be
Speaker 1: kind of thematically linked to Halloween text style. It turns
Speaker 1: out that's pretty hard to do. Halloween tech anology stories
Speaker 1: have already covered stuff like haunted house technology. So today
Speaker 1: we're going to talk about consciousness and whether or not
Speaker 1: it might be possible that machines could one day achieve consciousness. Now,
Speaker 1: I could start this off by talking about the Turing test,
Speaker 1: which many people have used as the launch point for
Speaker 1: a machine intelligence and machine consciousness debates. The way we
Speaker 1: understand that test today, which by the way, is slightly
Speaker 1: different from the test that Alan Turing first proposed, is
Speaker 1: that you have a human interviewer who, through a computer interface,
Speaker 1: asks questions of a subject, and the subject might be
Speaker 1: another human, or it might be a computer program posing
Speaker 1: as a human and the the interviewer just sees text
Speaker 1: on a screen. So if the interviewer is unable to
Speaker 1: pass a certain threshold of being able to tell the difference,
Speaker 1: to be able to determine whether it was a machine
Speaker 1: or a person, then the program or machine that's being
Speaker 1: test did is said to have passed the Turing test.
Speaker 1: It doesn't mean the program or machine is conscious or
Speaker 1: even intelligent, but rather says that to outward appearances, it
Speaker 1: seems to be intelligent and conscious. See, we humans can't
Speaker 1: be absolutely sure that other humans are conscious and intelligent.
Speaker 1: We assume that they are because each of us knows
Speaker 1: of our own consciousness and our own intelligence. We have
Speaker 1: a personal experience with that direct personal experience, and other
Speaker 1: people seem to display behaviors that indicate they too possess
Speaker 1: those traits, and they too have a personal experience. But
Speaker 1: we cannot be those other people, and so we have
Speaker 1: to grant them the consideration that they too are conscious
Speaker 1: and intelligent. And I agree that is very big of us.
Speaker 1: This is actually called the problem of other minds in
Speaker 1: the field of philosophy, and the problem is this, It
Speaker 1: is impossible for any one of us to step outside
Speaker 1: of ourselves and into any other person's consciousness. We cannot
Speaker 1: feel what other people are feeling or experience their thoughts firsthand.
Speaker 1: We are aware of our own abilities, but we are
Speaker 1: only aware of the appearance that other people share those abilities.
Speaker 1: So assuming that other people also experience consciousness rather than
Speaker 1: imitating it, really, really, well, that's a step we all
Speaker 1: have to take. Turings point is that if we do
Speaker 1: grant that consideration to other people, why would we not
Speaker 1: do it to machines as well? I mean, the machine
Speaker 1: appears to possess the same qualities as a human. This
Speaker 1: is a hypothetical machine, so we cannot experience what that
Speaker 1: machine is going through, just as we can't experience what
Speaker 1: another person is going through, at least not the intrinsic
Speaker 1: personal level. So why would we not grant the machine
Speaker 1: the same consideration that we would grant to people? And
Speaker 1: touring was being a little cheeky, But while I just
Speaker 1: gave kind of a super fast, high level description of
Speaker 1: the touring test, that's not actually where I want to start.
Speaker 1: I want to begin with the concept of consciousness itself. Now.
Speaker 1: The reason I want to do This isn't just to
Speaker 1: make a longer podcast. It's because I think one of
Speaker 1: the most fundamental problems with the discussion about AI intelligence,
Speaker 1: self awareness, and consciousness is that there tends to be
Speaker 1: a pretty large disconnect between the biologists and the doctors
Speaker 1: who specialize in neuroscience, particularly cognitive neuroscience, and does have
Speaker 1: some understanding about the nature of consciousness and people. And
Speaker 1: then you have computer scientists who have a deep understanding
Speaker 1: of how computers process information. And while we frequently will
Speaker 1: compare brains to computers, that comparison is not one to one.
Speaker 1: It is largely a comparison of convenience, and in some
Speaker 1: cases you could argue it's not terribly useful, it might
Speaker 1: actually be counterproductive. And so I think at least some
Speaker 1: of the speculation about machine consciousness is based on a
Speaker 1: lack of understanding of how complicated and mysterious this topic
Speaker 1: is in the first place, and this ends up being
Speaker 1: really tricky. Consciousness isn't an easily defined quality or quantity.
Speaker 1: Some people like to say, we don't so much define
Speaker 1: consciousness by what it is, but rather what it isn't,
Speaker 1: And this will also will will kind of bring us
Speaker 1: into the realm of philosophy. Now, I'm gonna be honest
Speaker 1: with you, guys, the realm of philosophy is not one
Speaker 1: I'm terribly comfortable in. I'm pretty pragmatic, and philosophy deals
Speaker 1: with a lot of stuff that is, at least for now, unknowable.
Speaker 1: Philosophy sometimes asks questions that we do not and cannot
Speaker 1: have the answer to, and in many cases we may
Speaker 1: never ever be able to answer those questions. And the
Speaker 1: pragmatist in me says, well, why bother asking the question
Speaker 1: if you can never get the answer. Let's just focus
Speaker 1: on the stuff we actually can answer. Now, I realize
Speaker 1: this is a limitation on my part. I'm owning that
Speaker 1: I'm not out to upset the philosophical apple cart. I'm
Speaker 1: just of a different philosophical bent. And I realized that
Speaker 1: just because we can't answer some questions right now, that
Speaker 1: doesn't necessarily mean they will all go unanswered for all time.
Speaker 1: We might glean a way of answering at least some
Speaker 1: of them, though I suspect a few will be forever unanswered.
Speaker 1: If we go with the Basic Dictionary definition of consciousness,
Speaker 1: it's quote the state of being awake and aware of
Speaker 1: one's surroundings end quote. But what this doesn't tell us
Speaker 1: is what's going on that lets us do that. It
Speaker 1: also doesn't talk about being aware of oneself, which we
Speaker 1: largely consider consciousness to be part of. Is not just
Speaker 1: aware of your surroundings, but aware that you exist within
Speaker 1: those surroundings, your relationship to your surroundings, and things that
Speaker 1: are going on within you, yourself, your feelings, and your thoughts.
Speaker 1: The fact that you can process all of this, you
Speaker 1: can reflect upon yourself. We tend to group that into
Speaker 1: consciousness as well. So how is it that we can
Speaker 1: feel things and be aware of those feelings? How is
Speaker 1: it that we can have intentions and be aware of
Speaker 1: our intentions. We are more complex than beings that simply
Speaker 1: react to sensory input. We are more than beings that
Speaker 1: respond to stuff like hunger, fear, or the desire to procreate.
Speaker 1: We have motivations, sometimes really complex motivations, and we can
Speaker 1: reflect on those. We can examine them, we can question them,
Speaker 1: we can even change them. So how do we do
Speaker 1: this now? We know this is special because some of
Speaker 1: the things we can do are shared among a very
Speaker 1: few species on Earth. For example, we humans can recognize
Speaker 1: our own reflections in a mirror, starting it around age
Speaker 1: two or so, we can see the mirror image and
Speaker 1: we recognize the mirriage images of us. Now, there are
Speaker 1: only eight species that can do this that we know
Speaker 1: about anyway. Those species are the great apes. So you've
Speaker 1: got humans, guerrillas, orangutans, binobos, and chimpanzees, the magpie, the dolphin,
Speaker 1: and that's it. Oh and the magpies are birds, right,
Speaker 1: that's that's all of them. Recognizing one's own form and
Speaker 1: a mirror shows a sense of self awareness, literally, of
Speaker 1: awareness of one's self. Now, there are a lot of
Speaker 1: great resources online and offline that go into the theme
Speaker 1: of consciousness. Heck, there are numerous college level courses and
Speaker 1: graduate level courses dedicated to this topic. So I'm not
Speaker 1: going to be able to go into all the different hypotheses, arguments,
Speaker 1: counter arguments, et cetera in this episode, but I can
Speaker 1: cover some basics. Also, highly recommend you check out v
Speaker 1: Sauces video on YouTube that's titled what Is Consciousness? Because
Speaker 1: it's really good. And No, I don't know Michael, I
Speaker 1: have no connection to him. I've never met him. This
Speaker 1: is just an honest recommendation from me. And I have
Speaker 1: no connection whatsoever to that video series. The video includes
Speaker 1: a link to what v Sauce dubs a lean back,
Speaker 1: which is a playlist of related videos on the subject
Speaker 1: at hand, in this case, consciousness. Those are also really fascinating.
Speaker 1: But I do want to point out that, at least
Speaker 1: at the time of this recording, a couple of the
Speaker 1: videos in that playlist have since been delisted from YouTube
Speaker 1: for whatever reason. So there are a couple of blank
Speaker 1: spots in there. But what those videos show, and what
Speaker 1: countless papers and courses and presentations also show, is that
Speaker 1: the brain is so incredibly complex and nuanced that we
Speaker 1: don't know what we don't know. We do know that
Speaker 1: there are some pretty funky things going up in the
Speaker 1: gray matter up in our noggins, and we also know
Speaker 1: that many of the explanations given to describe consciousness rely
Speaker 1: upon some assumptions that we don't have any substantial evidence for.
Speaker 1: You can't really assert something to be true if it's
Speaker 1: based on a premise that you also don't know to
Speaker 1: be true. That's not how good science works. This is
Speaker 1: also why I reject the arguments around stuff like ghost
Speaker 1: hunting equipment. The use of that equipment is predicated on
Speaker 1: the argument the ghosts exist and they have certain influences
Speaker 1: on their environment. But we haven't proven that ghosts exist
Speaker 1: in the first place, let alone that they can affect
Speaker 1: the environment. So selling a meter that supposedly detects a
Speaker 1: ghostly presence from electromagnetic fluctuations makes no logical sense. Pens
Speaker 1: For us to know that to be true, we would
Speaker 1: already have to have established that one ghosts are real
Speaker 1: and two that they have these electromagnetic fluctuation effects, and
Speaker 1: we haven't done that. It's like working science in reverse.
Speaker 1: That's not how it works. Anyway. There are a lot
Speaker 1: of arguments about consciousness that suggests perhaps there's some ineffable
Speaker 1: force that informs it. You can call it the spirit
Speaker 1: or the soul or whatever. So that argument suggests that
Speaker 1: this thing we've never proven to have existed is what
Speaker 1: gives consciousness its own and that's a problem. We can't
Speaker 1: really state that. I mean, you can't say the reason
Speaker 1: this thing exists is that this other thing that we've
Speaker 1: never proven to exist makes it exist. Well, that you've
Speaker 1: just made it harder to even prove anything. And we
Speaker 1: have evidence that also shows that that whole idea doesn't
Speaker 1: hold water. The evidence comes in the form of brain disorders,
Speaker 1: brain diseases, and brain image. We have seen that disease
Speaker 1: and damage to the brain affects consciousness, which suggests that
Speaker 1: consciousness manifests from the actual form and function of our brains,
Speaker 1: not from any mysterious force. Our ability to perceive, to
Speaker 1: process information, to have an understanding of the self, to
Speaker 1: have an accurate reflection of what's going on around us
Speaker 1: within our own conceptual reality, all of that appears to
Speaker 1: be predicated primarily upon the brain. Now, originally I was
Speaker 1: planning to give a rundown on some of the prevailing
Speaker 1: theories about consciousness. In other words, I want to summarize
Speaker 1: the various schools of thought about how consciousness actually arises.
Speaker 1: But as I dove down into the research, it became
Speaker 1: apparent really quickly that such a discussion would require so
Speaker 1: much groundwork and more importantly, a much deeper understanding on
Speaker 1: my part than would be practical for this podcast. So
Speaker 1: instead of talking about the higher order theory of consciousness
Speaker 1: versus the general workspace theory versus integrated information theory, I'll
Speaker 1: take a step back and I'll say there's a lot
Speaker 1: of ongoing debate about the subject, and no one has
Speaker 1: conclusively proven that any particular theory or argument is most
Speaker 1: likely true. Each theory has its strengths and its weaknesses,
Speaker 1: and complicating matters further is that we haven't refined our
Speaker 1: language around the concepts enough to differentiate various ideas. That
Speaker 1: means you can't talk about an organism being conscious of
Speaker 1: something and that degree of consciousness is somehow inherently specific,
Speaker 1: it's not. That's the issue. So, for example, I could
Speaker 1: say a rat is conscious of a rat terrier, type
Speaker 1: of dog that hunts down rats, and so as a
Speaker 1: result of this consciousness of the rat terrier, the rat
Speaker 1: attempts to remain hidden so as not to be killed.
Speaker 1: But does that mean the rat merely perceived eaves the
Speaker 1: rat terrier and thus is trying to stay out of
Speaker 1: its way, And that's as far as the consciousness goes,
Speaker 1: or doesn't mean that the rat actually has a deeper,
Speaker 1: more meaningful awareness of the rat terrier. The language isn't
Speaker 1: much help here, and moreover, there's debate about what degrees
Speaker 1: of consciousness there even are. Also, while I've been harping
Speaker 1: on consciousness, that's not the only concept we have to consider.
Speaker 1: Another is intelligence, which is distinct from consciousness, and there
Speaker 1: are some similarities. Like consciousness, intelligence is predicated upon brain functions. Again,
Speaker 1: a long history of investigating brain disorders and brain damage
Speaker 1: indicates this as it can affect not just consciousness but
Speaker 1: also intelligence. So what is intelligence? Well, get ready for this,
Speaker 1: But like consciousness, there's no single agreed upon definition or
Speaker 1: theory of intelligence. In general, we use the word intelligence
Speaker 1: to describe the ability to think, to learn, to absorb knowledge,
Speaker 1: and to make use of it to develop skills. Intelligence
Speaker 1: is what allowed humans to learn how to make basic tools,
Speaker 1: to gain an understanding of how to cultivate plants and
Speaker 1: develop agriculture, to develop architecture, to understand mathematic principles, and
Speaker 1: all sorts of stuff. So in humans, we tend to
Speaker 1: lump consciousness and intelligence together. We tend to think in
Speaker 1: terms of being intelligent and being self aware, but the
Speaker 1: two need not necessarily go hand in hand. There are
Speaker 1: many people who believe that it could be possible to
Speaker 1: construct an artificial intelligence or an artificial consciousness independently of
Speaker 1: one another. When we come back, I'll explain more, but
Speaker 1: first let's take a quick break. So in a very
Speaker 1: general sense. The group of hypotheses that fall into the
Speaker 1: integrated information theory umbrella state that consciousness emerges through linking
Speaker 1: elements in our brains. These would be neurons processing large
Speaker 1: amounts of information, and that it's the scale of this
Speaker 1: endeavor that then leads to consciousness. In other words, if
Speaker 1: you have enough processors working on enough information and they're
Speaker 1: all interconnected with each other and it's very complicated, bang,
Speaker 1: you get consciousness. Now, it is clear our brains process
Speaker 1: a lot of information. If you do a search in
Speaker 1: textbooks or online, you'll frequently encounter the stat their brains
Speaker 1: have around one hundred billion neurons in them and ten
Speaker 1: times as many glial cells. Neurons are like the processors
Speaker 1: in a computer system, and glial cells would be the
Speaker 1: support systems and insulators with those processors. Anyway, those numbers
Speaker 1: have since come under some dispute, as an associate professor
Speaker 1: at Vanderbilt University named Susanna Herculano who sell She explained
Speaker 1: that the old way of estimating how many neurons the
Speaker 1: brain had appeared to be based on taking slices of
Speaker 1: the brain. Estimating the number of neurons in that slice
Speaker 1: and then kind of extrapolating that number to apply across
Speaker 1: the brain in general. But that ignores stuff like the
Speaker 1: density of cells and the distribution of the cells across
Speaker 1: the brain. So what she did, and this also falls
Speaker 1: into the category of Halloween horror stories, is she took
Speaker 1: a brain and she freaking dissolved it. She could then
Speaker 1: get account of the neuron nuclei that was in the
Speaker 1: soupy mix. By her accounting, the brain has closer to
Speaker 1: eighties six billion neurons and just as many glial cells.
Speaker 1: Still a lot of cells, mind you, But you gotta
Speaker 1: admit it's a bit of a blow to lose fourteen
Speaker 1: billion neurons overnight. Still, we're talking about billions of neurons
Speaker 1: that interconnect through an incredib doably complex system in our brains,
Speaker 1: with different regions of the brain handling different things. And so, yeah,
Speaker 1: we're processing a lot of information all the time, and
Speaker 1: we do happen to be conscious. So could it be
Speaker 1: possible that with a sufficiently powerful computer system, perhaps made
Speaker 1: up of hundreds or thousands or tens of thousands of
Speaker 1: individual computers, each with hundreds of processors, that you could
Speaker 1: end up with an emergent consciousness, or, as some people
Speaker 1: have proposed, could the Internet itself become conscious due to
Speaker 1: the fact that it is an enormous system of interconnected
Speaker 1: nodes that's pushing around incredible amounts of information. Well maybe,
Speaker 1: maybe it's possible. But here's the kicker. This theory doesn't
Speaker 1: actually explain the mechanism by which the consciousness emerges. See,
Speaker 1: it's one thing to process information, it's another thing to
Speaker 1: be aware of that experience. So when I perceive a color,
Speaker 1: I'm not just perceiving a color. I'm aware that I'm
Speaker 1: experiencing that color. Or to put it in another way,
Speaker 1: I can relate something to how it makes me feel,
Speaker 1: or some other subjective experience that's personal to me. So
Speaker 1: a machine might objectively be able to return data about
Speaker 1: stuff like what is a color of a piece of paper?
Speaker 1: That analyzes the light that's being reflected off that piece
Speaker 1: of paper, it compares that light to a spectrum of colors.
Speaker 1: But that's still not the same thing as having the
Speaker 1: subjective experience of perceiving the color. And there may well
Speaker 1: be some connection between the complexity of the interconnected neurons
Speaker 1: in our brains and the amount of information that we're
Speaker 1: processing and our sense of consciousness, But the theory doesn't
Speaker 1: actually explain what that connection is. It's more like saying, hey,
Speaker 1: maybe this thing we have, this consciousness experience, is also
Speaker 1: linked to this other thing, without actually making the link
Speaker 1: between the two. It appears to be correlative but not
Speaker 1: necessarily causal to relate that to our personal experience. Imagine
Speaker 1: that you've just poofed into existence. You have no prior
Speaker 1: knowledge of the world, or the physics in that world,
Speaker 1: or basic stuff like that, So you're drawing conclusions about
Speaker 1: the world around you based solely on your observations as
Speaker 1: you wander around and do stuff. And at one point
Speaker 1: you see an interesting looking rock on the path, so
Speaker 1: you bend over and you pick up the rock, and
Speaker 1: when you do, it starts to rain, and you think, well,
Speaker 1: maybe I caused it to rain because I picked up
Speaker 1: this rock. And maybe it happens a few times where
Speaker 1: you pick up a rock and it starts to rain,
Speaker 1: which seems to support your thesis. But does that mean
Speaker 1: you're actually causing the effects that you are observing. If so,
Speaker 1: what is it about picking up the rock that's making
Speaker 1: it rain? Now, even in this absurd case, that I'm making.
Speaker 1: You could argue that if there's never an instance in
Speaker 1: which picking up the rock wasn't immediately followed by rain,
Speaker 1: there's a lot of evidence to suggest the two are linked,
Speaker 1: but you still can't explain why they are linked, why
Speaker 1: does one cause the other. And that's a problem because
Speaker 1: without that piece, you're never really totally sure that you're
Speaker 1: on the right track. That's kind of where we are
Speaker 1: with consciousness. We've got a lot of ideas about what
Speaker 1: makes it happen, but those ideas are mostly missing key
Speaker 1: pieces that explain why it's happening. Now, it's possible that
Speaker 1: we cannot reduce consciousness any further than we already have,
Speaker 1: and maybe that means we never really get a handle
Speaker 1: on what makes it happen. It's also possible that we
Speaker 1: could facilitate the emergence of consciousness and machines without knowing
Speaker 1: how we did it. Essentially, that would be like stumbling
Speaker 1: upon the phenomenon by luck. We just happened to create
Speaker 1: the conditions necessary to allow some form of artificial ansciousness
Speaker 1: to emerge. Now, I think this might be possible, but
Speaker 1: it strikes me as a long shot. I think of
Speaker 1: it like being locked in a dark warehouse filled with
Speaker 1: every mechanical part you can imagine, and you start trying
Speaker 1: to put things together in complete darkness, and then the
Speaker 1: lights come on and you see that you have created
Speaker 1: a perfect replica of an F fifteen fighter jet. Is
Speaker 1: that possible? Well, I mean, yeah, I guess, but it
Speaker 1: seems overwhelmingly unlikely. But again, this is based off ignorance.
Speaker 1: It's based off the fact that it hasn't happened yet,
Speaker 1: so I could be totally wrong here. Now, on the
Speaker 1: flip side of that, programmers, engineers, and scientists have created
Speaker 1: computer systems that can process information in intricate ways to
Speaker 1: come up with solutions to problems that seem, at least
Speaker 1: at first glance, to be similar to how we humans think.
Speaker 1: We even have names for systems that reflect biological systems,
Speaker 1: like artificial neural networks. Now the name might make it
Speaker 1: sound like it's a robot brain, but it's not quite that. Instead,
Speaker 1: it's a model for computing in which components in the
Speaker 1: system act kind of like neurons. They're interconnected and each
Speaker 1: one does a specific process. The nodes in the computer
Speaker 1: system connect to other nodes, so you feed the system
Speaker 1: input whatever it is you want to process, and then
Speaker 1: the nodes that accept that input performs some form of
Speaker 1: operation on it, and then send that resulting data the
Speaker 1: the answer after they've processed this information onto other nodes
Speaker 1: in the network. It's a nonlinear approach to computing, and
Speaker 1: by adjusting the processes each node performs this is also
Speaker 1: like known as adjusting the weight of the nodes, you
Speaker 1: can tweak the outcomes. Now, this is incredibly useful. If
Speaker 1: you already know the outcome you want, you can tweak
Speaker 1: the system so that it learns or is trained to
Speaker 1: recognize something specific. For example, you could train a computer
Speaker 1: system to recognize faces, so you would feed it images.
Speaker 1: Some of the images would have faces in them, some
Speaker 1: would not have faces in them. Some might have something
Speaker 1: that could be a face, but it's hard to tell.
Speaker 1: Maybe it's a shape in a picture that looks kind
Speaker 1: of like a face, but it's not actually someone's face. Anyway,
Speaker 1: you train the computer model to try and separate the
Speaker 1: faces from the non faces, and it might take many
Speaker 1: iterations to get the model trained up using your starting
Speaker 1: data your training data. Now, once you do have your
Speaker 1: computer model trained up, you've tweaked all the nodes so
Speaker 1: that it is reliably producing results that say, yes, this
Speaker 1: is a face or no, this isn't. You can now
Speaker 1: feed that same computer model brand new images that it
Speaker 1: has never seen before, and it can perform the same
Speaker 1: functions you have taught the computer model how to do something.
Speaker 1: But this isn't like spontaneous intelligens and it's not connected
Speaker 1: to consciousness. You couldn't really call it thinking so much
Speaker 1: as just being trained to recognize specific patterns. Pretty well. Now,
Speaker 1: that's just one example of putting an artificial neural network
Speaker 1: to use. There are lots of others, and there are
Speaker 1: also systems like IBM S Watson, which also appears at
Speaker 1: you know, casual glance to think. This was helped in
Speaker 1: no small part by the very public display of Watson
Speaker 1: competing on special episodes of Jeopardy, and which went up
Speaker 1: against human opponents who were former Jeopardy champions themselves. Watson
Speaker 1: famously couldn't call upon the Internet to search for answers.
Speaker 1: All the data the computer could access was self contained
Speaker 1: in its undeniably voluminous storage, and the computer had to
Speaker 1: parse what the clues and Jeopardy were actually looking for
Speaker 1: then come up with an appropriate response, And to make
Speaker 1: matters more tricky, the computer wasn't returning a guaranteed right answer.
Speaker 1: The computer had to come to a judgment on how
Speaker 1: confident it was that the answer it had arrived at
Speaker 1: was the correct one. If the confidence met a certain threshold,
Speaker 1: then Watson would submit an answer. If it did not
Speaker 1: meet that threshold, Watson would remain silent. It's a remarkable achievement,
Speaker 1: and it has lots of potential applications, many of which
Speaker 1: are actually in action today. But it's still not quite
Speaker 1: at the level of a machine thinking like a human,
Speaker 1: and I don't think anyone at IBM would suggest that
Speaker 1: it possesses any sense of consciousness. When we come back,
Speaker 1: i'll talk about a famous thought experiment that really starts
Speaker 1: to examine whether or not machines could ever attain intelligence
Speaker 1: and consciousness. But first let's take another quick break. And
Speaker 1: now this brings me to a famous thought experiment proposed
Speaker 1: by John Searle, a philosoph of her who questioned whether
Speaker 1: we could say a machine, even one so proficient that
Speaker 1: could deliver reliable answers on demand, would ever truly be intelligent,
Speaker 1: at least on a level similar to what we humans
Speaker 1: identify as being intelligent. It's called the Chinese room argument,
Speaker 1: which Searle included in his article titled Minds, Brains, and
Speaker 1: Programs for the Behavioral and Brain Sciences Journal. Here's the
Speaker 1: premise of the thought experiment. Imagine that you are in
Speaker 1: a simple room. The room has a table and a chair.
Speaker 1: There's a ream of blank paper, there's a brush, there's
Speaker 1: some ink, and there's also a large book within the
Speaker 1: room that contains pairs of Chinese symbols. In the book.
Speaker 1: Oh and we also have to imagine that you don't
Speaker 1: understand or recognize these Chinese symbols. They mean nothing to you.
Speaker 1: There's also a door to the room, and the door
Speaker 1: has a mail slot, and every now and again someone
Speaker 1: slides a piece of paper through the slot. The piece
Speaker 1: of paper has one of those Chinese symbols printed on it,
Speaker 1: and it's your job to go through the book and
Speaker 1: find the matching symbol in the book, plus the corresponding
Speaker 1: symbol in the pair, because remember I said there were
Speaker 1: symbols that were paired together. You then take a blank
Speaker 1: sheet of paper, You draw the corresponding symbol from that
Speaker 1: pair onto the sheet of paper, and finally you slip
Speaker 1: that piece of paper through the mail slot, presumably to
Speaker 1: the person who gave you the first piece of paper
Speaker 1: and the original part of this problem. So to an
Speaker 1: outside observer, let's say it's actually the person who's slipping
Speaker 1: the piece of paper to you, it would seem that
Speaker 1: whomever is inside the door actually understands Chinese symbols. They
Speaker 1: can recognize the significance of whatever symbol was was contributed,
Speaker 1: was sent in through the mail slot, and then match
Speaker 1: it to whatever the corresponding data is for that particular symbol,
Speaker 1: and then return that to the user. So to the
Speaker 1: outside observer, it appears as though whatever is inside the
Speaker 1: room comprehends what it is doing. But argues sirle, that's
Speaker 1: only an illusion because the person inside the room doesn't
Speaker 1: know what any of those symbols actually means. So, if
Speaker 1: if this is you, you have no context. You don't
Speaker 1: know what any individual symbol stands for, nor do you
Speaker 1: understand why any symbol would be prepared with any other symbol.
Speaker 1: You don't know the reasoning behind that. All you have
Speaker 1: is a book of rules. But the rules only state
Speaker 1: what your response should be given a specific input. The
Speaker 1: rules don't tell you why either on a granular level
Speaker 1: of what the symbols actually mean or on a larger
Speaker 1: scale when it comes to what you're actually accomplishing in
Speaker 1: this endeavor. All you are doing is filling a physical
Speaker 1: action over and over based on a set of rules
Speaker 1: you don't understand. And Searle then uses this argument to
Speaker 1: say that, essentially we have to think the same way
Speaker 1: about machines. The machines process information based on the input
Speaker 1: they receive and the program that they are following. That's it.
Speaker 1: They don't have awareness or understanding of what the information is.
Speaker 1: Searle was taking aim at a particular concept in AI,
Speaker 1: often dubbed strong AI or general AI. It's a sort
Speaker 1: of general artificial intelligence. So it's something that we could
Speaker 1: or would compared directly to human intelligence, even if it
Speaker 1: didn't work the same way as our intelligence works. The
Speaker 1: argument is that the capacity and the outcomes would be
Speaker 1: similar enough for us to make the comparison. This is
Speaker 1: the type of intelligence that we see in science fiction
Speaker 1: doomsday scenarios where the machines have rebelled against humans, or
Speaker 1: the machines appear to misinterpret simple requests, or the machines
Speaker 1: come to conclud lusions that, while logically sound, spell doom
Speaker 1: for us all. The classic example of this, by the way,
Speaker 1: is appealing to a super smart artificial intelligence and you say,
Speaker 1: could you please bring about world peace because we're we're
Speaker 1: all sorts of messed up, and the intelligence processes this
Speaker 1: and then concludes that while there are at least two humans,
Speaker 1: there can never be a guarantee for peace because there's
Speaker 1: always the opportunity for disagreement and violence between two humans,
Speaker 1: and so to achieve true peace, the computer then goes
Speaker 1: on a killing spree to wipe out all of humanity. Now,
Speaker 1: Cerl is not necessarily saying that computers won't contribute to
Speaker 1: a catastrophic outcome for humanity. Instead, he's saying they're not
Speaker 1: actually thinking or processing information in a truly intelligent way.
Speaker 1: They are arriving in outcomes through a series of processes
Speaker 1: that might appear to be intelligent at first glance, but
Speaker 1: when you break them down, it all reveal themselves to
Speaker 1: be nothing more than a very complex series of mathematical processes.
Speaker 1: You could even break it down further into binary and
Speaker 1: say that ultimately each apparent decision would just be a
Speaker 1: particular sequence of switches that are in the on or
Speaker 1: off position, and the stats of each switch would be
Speaker 1: determined by the input and the program you were running,
Speaker 1: not some intelligent artificial creation that is reasoning through a problem. Essentially,
Speaker 1: Cearle's argument boils down to the difference between syntax and semantics.
Speaker 1: Syntax would be the set of rules that you would
Speaker 1: follow with those symbols. For example, in English, the letter
Speaker 1: Q is nearly always followed by the letter you. The
Speaker 1: few exceptions to this rule mostly involved romanizing words from
Speaker 1: other language, uh, in which the letter Q represents a
Speaker 1: sound that's not natively present in English. So you could
Speaker 1: program a machine to follow the basic rule that the
Speaker 1: symbol Q should be followed by the symbol you, assuming
Speaker 1: you're eliminating all those exceptions I just mentioned. But that
Speaker 1: doesn't lead to a grasp of semantics, which is actual meaning. Moreover,
Speaker 1: Searle asserts that it's impossible to come to a grasp
Speaker 1: of semantics merely through a mastery of syntax. You might
Speaker 1: know those rules flawlessly, but Searle argues, you still wouldn't
Speaker 1: understand why there are rules, or what the output of
Speaker 1: those rules means, or even what the input means. There
Speaker 1: are some general counter arguments that philosophers have made to
Speaker 1: Searle's thought experiment, and according to the Stanford Encyclopedia of Philosophy,
Speaker 1: which is a phenomenal resource, it's also incredibly dense. But
Speaker 1: these counter arguments tend to fall into three groups. The
Speaker 1: first group agrees with Searle that the person inside the
Speaker 1: room clearly has no understanding of the Chinese symbols. But
Speaker 1: the group counters the notion that the system as a
Speaker 1: whole can't understand it. In fact, they say the opposite.
Speaker 1: They say, yes, the person inside the room doesn't understand,
Speaker 1: but you're looking at a specific component of a larger system.
Speaker 1: And if we consider the system, or maybe a virtual
Speaker 1: mind that exists due to the system, that does have
Speaker 1: an understanding, this is sort of like saying a neuron
Speaker 1: in the brain doesn't understand anything. It sends along signals
Speaker 1: that collectively and through mechanisms we don't fully understand, become
Speaker 1: thoughts that we can become conscious of. So in this argument,
Speaker 1: the person in the room is just a component of
Speaker 1: an overall system, and the system possesses intelligence even if
Speaker 1: the component does not. The second group argues that if
Speaker 1: the computer system either could simulate the operation of a brain,
Speaker 1: perhaps with billions of nodes, approaching the complexity of a
Speaker 1: human brain with billions of neurons, or if the system
Speaker 1: were to inhabit a robotic body that could have direct
Speaker 1: interaction with its environment, then the system could manifest intelligence.
Speaker 1: The third group rejects Searle's arguments more thoroughly and on
Speaker 1: the basis of various grounds, ranging from Searle's experiment being
Speaker 1: too narrow in scope to an argument about what the
Speaker 1: word understand actually means. This is where things get a
Speaker 1: bit more loosey goosey, and sometimes I feel like arguments
Speaker 1: in this group amount to oh yeah, but again, I'm pragmatic,
Speaker 1: so I tend to have a pretty strong bias against
Speaker 1: these arguments, and I recognize that this means I'm not
Speaker 1: giving them fair consideration because of those biases. A few
Speaker 1: of these arguments take issue with Searle's assertion that one
Speaker 1: cannot grasp semantics through an understanding of syntax. And here's
Speaker 1: something that I find really interesting. Searle originally published this
Speaker 1: argument way back in nineteen It's been nearly forty years
Speaker 1: since first proposed it, and to this day, there is
Speaker 1: no consensus on whether or not his argument is sound.
Speaker 1: So why is that? Well, it's because, as I've covered
Speaker 1: in this episode, the concepts of intelligence and more to
Speaker 1: the point, consciousness are wibbly wobbly, though not as far
Speaker 1: as I can tell, Timey, Whymy. When we can't even
Speaker 1: nail down specific definitions for words like understand, it becomes
Speaker 1: difficult to even tell when we're agreeing or disagreeing on
Speaker 1: certain topics. It could be that while people are in
Speaker 1: a debate and are using words in different ways, it
Speaker 1: turns out there actually in agreement with one another. Such
Speaker 1: is the messiness that is intelligence. Further, we've not yet
Speaker 1: observed anything in the machine world that seems, upon closer examination,
Speaker 1: to reflect true intelligence and consciousness, at least as the
Speaker 1: way we experience it. In fact, we can't say that
Speaker 1: we've seen any artificial constructs that have experienced any thing, because,
Speaker 1: as far as we know, no such device has any
Speaker 1: awareness of itself. Now, I'm not sure if we'll ever
Speaker 1: create a machine that will have true intelligence and consciousness,
Speaker 1: using the word true here to mean human like. Now,
Speaker 1: I feel pretty confident that, if it is possible, we
Speaker 1: will get around to it eventually. It might take way
Speaker 1: more resources than we currently estimate, or maybe it will
Speaker 1: just require a different computational approach, maybe it'll rely on
Speaker 1: bleeding edge technologies like quantum computing. I figure, if it's
Speaker 1: something we can do, we will do it. It's just
Speaker 1: a question of time, really, and further, it's hard for
Speaker 1: me to come to a conclusion other than it will
Speaker 1: ultimately prove possible to make an intelligent conscious construct. Now.
Speaker 1: I believe that because I believe our own intelligence and
Speaker 1: our own consciousness is firmly rooted in our brains. I
Speaker 1: don't think there's an anything mystical involved. And while we
Speaker 1: don't have a full picture of how it happens in
Speaker 1: our brains, we at least know that it does happen,
Speaker 1: and we know some of the questions to ask and
Speaker 1: have some ideas on how to search for answers. It's
Speaker 1: not a complete picture, and we still have a very
Speaker 1: long way to go, but I think it's if it's
Speaker 1: possible to build a full understanding of how our brains
Speaker 1: work with regard to intelligence and consciousness, we'll get there too,
Speaker 1: sooner or later. Probably later, I suppose there's still the
Speaker 1: chance that we could create an intelligent and or conscious
Speaker 1: machine just by luck or accident. And while I intuitively
Speaker 1: feel that this is unlikely, I have to admit that
Speaker 1: intuition isn't really reliable in these matters. It feels to
Speaker 1: me like it is the longest of long shots, but
Speaker 1: that's entirely based on the fact that we haven't managed
Speaker 1: to do it up until now, and including now. Maybe
Speaker 1: the right see points of events is right around the corner.
Speaker 1: Just because it hasn't happened yet doesn't mean it can't
Speaker 1: or won't happen at all, And it's good to remember
Speaker 1: the machines don't need to be particularly intelligent or conscious
Speaker 1: to be useful or potentially dangerous. We can see examples
Speaker 1: of that playing out already with devices that have some
Speaker 1: limited or weak AI. And by limited I mean it's
Speaker 1: not general intelligence. I don't mean that the AI itself
Speaker 1: is somehow unsophisticated or primitive. So it may not even
Speaker 1: matter if we never create devices that have true or
Speaker 1: human like intelligence. We might be able to accomplish just
Speaker 1: as much with something that does not have those capabilities.
Speaker 1: And in other words, this is a very complicated topic,
Speaker 1: one that I think gets oversimplified, and a lot of fiction,
Speaker 1: and also just a lot of speculative prognostications about the future.
Speaker 1: I mean, you'll see a lot of videos about how
Speaker 1: in the future AI is going to perform a more
Speaker 1: intrinsic role, or maybe it will be an existential threat
Speaker 1: to humanity or whatever it may be. And I think
Speaker 1: a lot of that is predicated upon, uh, a deep
Speaker 1: misunderstanding or underestimation of how complicated cognitive neuroscience actually is
Speaker 1: and how little we really understand when it comes to
Speaker 1: our own consciousness, let alone how we would bring about
Speaker 1: such a thing in a different device. I hope you
Speaker 1: enjoyed that rerun, uh, and I promise will be back
Speaker 1: to new episodes very soon. I hope to have a
Speaker 1: news episode for you tomorrow. That's the plan. I have
Speaker 1: an interview I have to do for another show today,
Speaker 1: but after that, I plan on jumping on the Tech
Speaker 1: Stuff news episode. So, uh, trying to get back running.
Speaker 1: You know, I'm not a percent yet, but gosh darn ittt,
Speaker 1: it's this. This show is really what keeps me going.
Speaker 1: So we're gonna We're gonna soldier on the show, as
Speaker 1: they say, must keep on going. I know, I make
Speaker 1: that joke a lot all right. Well, that's it. If
Speaker 1: you have any suggestions for future episodes of tech Stuff,
Speaker 1: please reach out to me. The best way to do
Speaker 1: that is on Twitter. The handle is tech stuff h
Speaker 1: s W you guys, take care. I can tell you
Speaker 1: of food poisoning is no fun. Uh, but the show
Speaker 1: sometimes is. All Right, that's it for me. Bye, I'll
Speaker 1: talk to you again really soon. Text Stuff is an
Speaker 1: I Heart Radio production. For more podcasts from my Heart Radio,
Speaker 1: visit the i Heart Radio app, Apple Podcasts, or wherever
Speaker 1: you listen to your favorite shows.