Speaker 1: Welcome to Tech Stuff, a production of I Heart Radios
Speaker 1: How Stuff Works. Hey there, and welcome to tech Stuff.
Speaker 1: I'm your host, Jonathan Strickland. I'm an executive producer with
Speaker 1: How Stuff Works in My Heart Radio, and I love
Speaker 1: all things tech. And there's a topic I have touched
Speaker 1: on on several occasions in past episodes, but I really
Speaker 1: wanted to dig down today into this topic because it's
Speaker 1: one of those that's fascinating and is an underpinning for
Speaker 1: tons of speculative fiction and horror stories. And since we're
Speaker 1: now in October, I think here this would be kind
Speaker 1: of thematically linked to Halloween text style. It turns out
Speaker 1: that's pretty hard to do. Halloween technology stories have already
Speaker 1: covered stuff like haunted house technology. So today we're going
Speaker 1: to talk about consciousness and whether or not it might
Speaker 1: 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: tested is said to have passed the Turing test. It
Speaker 1: doesn't mean the program or machine is conscious or even intelligent,
Speaker 1: but rather says that to outward appearances, it seems to
Speaker 1: be intel, religent, and conscious. See, we humans can't be
Speaker 1: absolutely sure that other humans are conscious and intelligent. We
Speaker 1: assume that they are because each of us knows of
Speaker 1: our own consciousness and our own intelligence. We have a
Speaker 1: personal experience with that direct personal experience, and other people
Speaker 1: seem to display behaviors that indicate they too possess those traits,
Speaker 1: and they too have a personal experience. But we cannot
Speaker 1: be those other people, and so we have to grant
Speaker 1: them the consideration that they too are conscious and intelligent.
Speaker 1: And I agree that is very big of us. This
Speaker 1: is actually called the problem of other minds in the
Speaker 1: field of philosophy, and the problem is this, it is
Speaker 1: impossible for any one of us to step outside of
Speaker 1: ourselves and into any other person's consciousness. We cannot feel
Speaker 1: 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 Herring 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 be able to answer those questions. And the pragmatist
Speaker 1: emmy says, well, why bother asking the question if you
Speaker 1: can never get the answer. Let's just focus on the
Speaker 1: stuff we actually can answer. Now, I realize this is
Speaker 1: a limitation on my part. I'm owning that I'm not
Speaker 1: out to upset the philosophical apple cart. I'm just of
Speaker 1: a different philosophical bent. And I realized that just because
Speaker 1: we can't answer some questions right now, that doesn't necessarily
Speaker 1: mean they will all go unanswered for all time. We
Speaker 1: might glean a way of answering at least some of them,
Speaker 1: though I suspect a few will be forever unanswered. If
Speaker 1: we go with the basic Dictionary definition of consciousness. It's
Speaker 1: quote the state of being awake and aware of one's
Speaker 1: surroundings end quote. But what this doesn't tell us is
Speaker 1: what's going on that lets us do that. It also
Speaker 1: doesn't talk about being aware of oneself, which we largely
Speaker 1: consider consciousness to be part of. Is not just aware
Speaker 1: of your surroundings, but aware that you exist within those surroundings,
Speaker 1: your relationship to your surroundings, and things that are going
Speaker 1: on within you, yourself, your feelings, and your thoughts. The
Speaker 1: fact that you can process all of this, you can
Speaker 1: reflect upon yourself. We tend to group that into consciousness
Speaker 1: as well. So how is it that we can feel
Speaker 1: things and be aware of those feelings? How is it
Speaker 1: that we can have intentions and be aware of our intentions.
Speaker 1: We are more complex than beings that simply react to
Speaker 1: sensory input. We are more than beings that respond to
Speaker 1: stuff like hunger, fear, or the desire to procreate. We
Speaker 1: have motivations, sometimes really complex motivations, and we can reflect
Speaker 1: on those, We can examine them, we can question them,
Speaker 1: we can even change them. So how do we do this? Now?
Speaker 1: We know this is special because some of the things
Speaker 1: we can do are shared among a very few species
Speaker 1: on Earth. For example, we humans can recognize our own
Speaker 1: reflections in a mirror, starting it around age two or so.
Speaker 1: We can see the mirror image and we recognize the
Speaker 1: mirriage images of us. Now, there are only eight species
Speaker 1: that can do this that we know about anyway. Those
Speaker 1: species are the great apes. So you've got humans, gorillas orangutans, binobos,
Speaker 1: and chimpanzees, the magpie, the dolphin, and that's it. Oh,
Speaker 1: and the magpies are birds, right, That's that's all of them.
Speaker 1: Recognizing one's own form and a mirror shows a sense
Speaker 1: of self awareness, literally, of awareness of one's self. Now,
Speaker 1: there are a lot of great resources online and offline
Speaker 1: that go into the theme of consciousness. Heck, there are
Speaker 1: numerous college level courses and graduate level courses dedicated to
Speaker 1: this topic. So I'm not going to be able to
Speaker 1: go into all the different hypotheses, arguments, counter arguments, et
Speaker 1: cetera in this episode, but I can cover some basics. Also,
Speaker 1: highly recommend you check out v Sauces video on YouTube
Speaker 1: that's titled what is Consciousness? Because it's really good. And no,
Speaker 1: I don't know, Michael, I have no connection to him.
Speaker 1: I've never met him. This is just an honest recommendation
Speaker 1: from me, and I have no connection whatsoever to that
Speaker 1: video series. The video includes a link to what v
Speaker 1: Sauce dubs a lean back, which is a playlist of
Speaker 1: related videos on the subject at hand, in this case, consciousness.
Speaker 1: Those are also really fascinating. But I do want to
Speaker 1: point out that, at least at the time of this recording,
Speaker 1: a couple of the videos in that playlist have since
Speaker 1: been delisted from YouTube for whatever reason. So there are
Speaker 1: a couple of blank spots in there. But what those
Speaker 1: videos show, and what countless papers and courses and presentations
Speaker 1: also show, is that the brain is so incredibly complex
Speaker 1: and nuanced that we don't know what we don't know.
Speaker 1: We do know that there are some pretty funky things
Speaker 1: going up in the gray matter up in our noggins,
Speaker 1: and we also know that many of the explanations given
Speaker 1: to describe consciousness rely upon some assumptions that we don't
Speaker 1: have any substantial evidence for. You can't really assert something
Speaker 1: to be true if it's based on a premise that
Speaker 1: you also don't know to be true. That's not how
Speaker 1: good science works. This is also why I reject the
Speaker 1: arguments around stuff like ghost hunting equipment. The use of
Speaker 1: that equipment is predicated on the argument the ghosts exist
Speaker 1: and they have certain influences on their environment. But we
Speaker 1: haven't proven that ghosts exist in the first place, let
Speaker 1: alone that they can affect the environment. So selling a
Speaker 1: meter that supposedly detects a ghostly presence from electromagnetic fluctuations
Speaker 1: makes no logical sense. For us to know that to
Speaker 1: be true, we would already have to have established that
Speaker 1: one ghosts are real and two that they have these
Speaker 1: electromagnetic fluctuation effects, and we haven't done that. It's like
Speaker 1: working science in reverse. That's not how it works. Anyway.
Speaker 1: There are a lot of arguments about consciousness that suggests
Speaker 1: perhaps there's some ineffable force that informs it. You can
Speaker 1: call it the spirit or the soul or whatever. So
Speaker 1: that argument suggests that this thing we've never proven to
Speaker 1: have existed is what gives consciousness its own and that's
Speaker 1: a problem. We can't really state that. I mean, you
Speaker 1: can't say the reason this thing exists is that this
Speaker 1: other thing that we've never proven to exist makes it exist. Well,
Speaker 1: that you've just made it harder to even prove anything,
Speaker 1: and we have evidence that also shows that that whole
Speaker 1: idea doesn't hold water. The evidence comes in the form
Speaker 1: of brain disorders, brain diseases, and brain damage. We have
Speaker 1: seen that disease and damage to the brain affects consciousness,
Speaker 1: which suggests that consciousness manifests from the actual form and
Speaker 1: function of our brains, not from any mysterious force. Our
Speaker 1: ability to perceive, to process information, to have an understanding
Speaker 1: of the self, to have an accurate reflection of what's
Speaker 1: going on around us within our own conceptual reality, all
Speaker 1: of that appears to be predicated primarily upon the brain. Now,
Speaker 1: originally I was planning to give a rundown on some
Speaker 1: of the prevailing theories about consciousness. In other words, I
Speaker 1: want to summarize the various schools of thought about how
Speaker 1: consciousness actually arises. But as I dove down into the research,
Speaker 1: it became apparent really quickly that such a discussion would
Speaker 1: require so much groundwork and more importantly, a much deeper
Speaker 1: understanding on my part than would be practical for this podcast.
Speaker 1: So instead of talking about the higher order theory of
Speaker 1: consciousness versus the general workspace theory versus integrated information theory.
Speaker 1: I'll take a step back, and I'll say there's a
Speaker 1: lot of ongoing debate about the subject, and no one
Speaker 1: has conclusively proven that any particular theory or argument is
Speaker 1: most 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 perceives the rat
Speaker 1: terrier and thus is trying to stay out of its way,
Speaker 1: And that's as far as the consciousness goes, or doesn't
Speaker 1: mean that the rat actually has a deeper, more meaningful
Speaker 1: awareness of the rat terrier. The language isn't much help here,
Speaker 1: and moreover, there's debate about what degrees of consciousness there
Speaker 1: even are. Also While I've been harping on consciousness, that's
Speaker 1: not the only concept we have to consider. Another is intelligence,
Speaker 1: which is distinct from consciousness, and there are some similarities.
Speaker 1: Like consciousness, intelligence is predicated upon brain functions. Again, a
Speaker 1: long history of investigating brain disorders and brain damage indicates this,
Speaker 1: as it can affect not just consciousness but also intelligence.
Speaker 1: So what is intelligence? Well, get ready for this, But
Speaker 1: like consciousness, there's no single agreed upon definition or theory
Speaker 1: of intelligence. In general, we use the word intelligence to
Speaker 1: 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 or 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 Husel, she explained that
Speaker 1: the old way of estimating how many neurons the brain
Speaker 1: had appeared to be based on taking slices of the brain,
Speaker 1: estimating the number of neurons in that slice, and then
Speaker 1: kind of extrapolating that number to apply across the brain
Speaker 1: in general. But that ignores stuff like the density of
Speaker 1: cells and the distribution of the cells across the brain.
Speaker 1: So what she did, and this also falls into the
Speaker 1: category of Halloween horror stories, she took a brain and
Speaker 1: she freaking dissolved it. She could then get account of
Speaker 1: the neuron nuclei that was in the soupy mix. By
Speaker 1: her accounting, the brain has closer to eighty six billion
Speaker 1: neurons and just as many glial cells. Still a lot
Speaker 1: of cells, mind you, But you gotta admit it's a
Speaker 1: bit of a blow to lose fourteen billion neurons overnight. Still,
Speaker 1: we're talking about billions of neurons that interconnect through an
Speaker 1: incredibly complex system in our brains, with different regions of
Speaker 1: the brain handling different things. And so, yeah, we're processing
Speaker 1: a lot of information all the time, and we do
Speaker 1: happen to be conscious. So could it be possible that
Speaker 1: with a sufficiently powerful computer system, perhaps made up of
Speaker 1: hundreds or thousands or tens of thousands of individual computers,
Speaker 1: each with hundreds of processors that you could end up
Speaker 1: with an emergent consciousness, or, as some people have proposed,
Speaker 1: could the Internet itself become conscious due to the fact
Speaker 1: that it is an enormous system of interconnected nodes that's
Speaker 1: pushing around incredible amounts of information. Well, maybe maybe it's possible.
Speaker 1: But here's the kicker. This theory doesn't actually explain the
Speaker 1: mechanism by which the consciousness emerges. See, it's one thing
Speaker 1: to process information, it's another thing to be aware of
Speaker 1: that experience. So when I perceive a color, I'm not
Speaker 1: just perceiving a color. I'm aware that I'm experiencing that color.
Speaker 1: Or to put it in another way, I can relate
Speaker 1: something to how it makes me feel, or some other
Speaker 1: subjective experience that's personal to me. So a machine might
Speaker 1: objectively be able to return data about stuff like what
Speaker 1: is a color of a piece of paper? It analyzes
Speaker 1: the light that's being reflected off that piece of paper,
Speaker 1: it compares that light to a spectrum of colors. But
Speaker 1: that's still not the same thing as having the subjective
Speaker 1: experience of perceiving the color. And there may well be
Speaker 1: some connection between the complexity of the interconnected neurons in
Speaker 1: our brains and the amount of information that we're processing
Speaker 1: and our sense of consciousness. But the theory doesn't actually
Speaker 1: 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. Wine
Speaker 1: is one caused 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 consciousness
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 and 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. I 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 data.
Speaker 1: Your training data. Now, once you do have your computer
Speaker 1: model trained up. You've tweaked all the nodes so that
Speaker 1: it is reliably producing results that say, yes, this is
Speaker 1: a face or no, this isn't. You can now feed
Speaker 1: that same computer model brand new images that it has
Speaker 1: never seen before, and it can perform the same functions.
Speaker 1: You have taught the computer model how to do something.
Speaker 1: But this isn't like spontaneous intelligence, 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 art official neural
Speaker 1: network to use. There are lots of others, and there
Speaker 1: are also systems like IBM S Watson, which also appears
Speaker 1: at you know, casual glance to think. This was helped
Speaker 1: in no small part by the very public display of
Speaker 1: Watson competing on special episodes of Jeopardy, and which went
Speaker 1: up against human opponents who were former Jeopardy champions themselves.
Speaker 1: Watson 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 in 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 philosopher who questioned whether we could
Speaker 1: say a machine, even one so proficient that could deliver
Speaker 1: reliable answers on demand, would ever truly be intelligent at
Speaker 1: least on a level similar to what we human identify
Speaker 1: as being intelligent. It's called the Chinese room argument, which
Speaker 1: Searle included in his article titled Minds, Brains, and Programs
Speaker 1: for the Behavioral and Brain Sciences Journal. Here's the premise
Speaker 1: of the thought experiment. Imagine that you are in a
Speaker 1: 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 Searle, 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 eve and the program that they are following.
Speaker 1: That's it. They don't have awareness or understanding of what
Speaker 1: the information is. Searle was taking aim at a particular
Speaker 1: concept in AI, often dubbed strong AI or general AI.
Speaker 1: It's a sort of general artificial intelligence. So it's something
Speaker 1: that we could or would compared directly to human intelligence,
Speaker 1: even if it didn't work the same way as our
Speaker 1: intelligence works. The argument is that the capacity and the
Speaker 1: outcomes would be similar enough for us to make the comparison.
Speaker 1: This is the type of intelligence that we see in
Speaker 1: science fiction doomsday scenarios where the machines have rebelled against humans,
Speaker 1: or the machines appear to misinterpret simple requests or the
Speaker 1: machines come to conclusions that, while logically sound, spelled 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 reveals themselves to
Speaker 1: be nothing more than a very complex series of mathematical processes.
Speaker 1: You can 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 stays 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: Searle'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 speci of a component of
Speaker 1: a larger system. And if we consider the system, or
Speaker 1: maybe a virtual mind that exists due to the system,
Speaker 1: that does have an understanding, this is sort of like
Speaker 1: saying a neuron in the brain doesn't understand anything. It
Speaker 1: sends along signals that collectively and through mechanisms we don't
Speaker 1: fully understand, become thoughts that we can become conscious of.
Speaker 1: So in this argument, the person in the room is
Speaker 1: just a component of an overall system, and the system
Speaker 1: possesses intelligence even if the component does not. The second
Speaker 1: group argues that if the computer system either could simulate
Speaker 1: the operation of a brain, perhaps with billions of nodes,
Speaker 1: approaching the complexity of a human brain with billions of neurons,
Speaker 1: or if the system were to inhabit a robotic body
Speaker 1: that could have direct interaction with its environment, then the
Speaker 1: system could manifest intelligence. The third group rejects Cearl's arguments
Speaker 1: more thoroughly and on the basis of various grounds, ranging
Speaker 1: from Searle's experiment being too narrow in scope to an
Speaker 1: argument about what the word understand actually means. This is
Speaker 1: where things get a bit more lucy goosey, And sometimes
Speaker 1: I feel like arguments in this group amount to oh yeah,
Speaker 1: but again, I'm pragmatic, so I tend to have a
Speaker 1: pretty strong bias against these arguments, and I recognize that
Speaker 1: this means I'm not giving them fair consideration because of
Speaker 1: those biases. A few of these arguments take issue with
Speaker 1: Searle's assertion that one cannot grasp semantics through an understanding
Speaker 1: of syntax. And here's something that I find really interesting.
Speaker 1: Searle originally published this argument way back in nineteen It's
Speaker 1: been nearly forty years since he first proposed it, and
Speaker 1: to this day there is no consensus on whether or
Speaker 1: not his argument is sound. So why is that? Well,
Speaker 1: it's because, as I've covered in this episode, the concepts
Speaker 1: of intelligence and more to the point, consciousness are wibbly wobbly,
Speaker 1: though not as far as I can tell, Timey, whymy.
Speaker 1: When we can't even nail down specific definitions for words
Speaker 1: like understand, it becomes difficult to even tell when we're
Speaker 1: agreeing or disagreeing on certain topics. It could be that
Speaker 1: while people are in a debate and are using words
Speaker 1: in different ways, it turns out they're actually in agreement
Speaker 1: with one another. Such is the messiness that is intelligence. Further,
Speaker 1: we've not yet observed anything in the machine world that seems,
Speaker 1: upon closer examination, to reflect true intelligence and consciousness, at
Speaker 1: least as the way we experience it. In fact, we
Speaker 1: can't say that we've seen any artificial constructs that have
Speaker 1: experienced anything, because, as far as we know, no such
Speaker 1: device has any awareness of itself. Now, I'm not sure
Speaker 1: if we'll ever create a machine that will have true
Speaker 1: intelligence and consciousness, using the word true here to mean
Speaker 1: human like. Now, I feel pretty confident that if it
Speaker 1: is possible, we will get around to it eventually. It
Speaker 1: might take way more resources than we currently estimate, or
Speaker 1: maybe it will just require a different computational approach. Maybe
Speaker 1: it'll rely on bleeding edge technologies like quantum computing. I figure,
Speaker 1: if it's something we can do, we will do it.
Speaker 1: It's just a question of time, really, And further, it's
Speaker 1: hard for me to come to a conclusion other than
Speaker 1: it will 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 anything mystical involved. And while we don't
Speaker 1: have a full picture of how it happens in our brains,
Speaker 1: we at least know that it does happen, and we
Speaker 1: know some of the questions to ask and have some
Speaker 1: ideas on how to search for answers. It's not a
Speaker 1: complete picture, and we still have a very long way
Speaker 1: to go, but I think it's if it's possible to
Speaker 1: build a full understanding of how our brains work with
Speaker 1: regard to intelligence and consciousness, we'll get there too, sooner
Speaker 1: or later. Probably later, I suppose there's still the chance
Speaker 1: that we could create an intelligent and or conscious machine
Speaker 1: just by luck or accident. And while I intuitively feel
Speaker 1: that this is unlikely, I have to admit that intuition
Speaker 1: isn't really reliable in these matters. It feels to me
Speaker 1: like it is the longest of long shots, but that's
Speaker 1: entirely based on the fact that we haven't managed to
Speaker 1: do it up until now, and including now. Maybe the
Speaker 1: right sequence of events is right around the corner. Just
Speaker 1: because it hasn't happened yet doesn't mean it can't or
Speaker 1: won't happen at all, And it's good to remember the
Speaker 1: machines don't need to be particular early 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
Speaker 1: fiction and also just a lot of speculative prognostications about
Speaker 1: the future. I mean, you'll see a lot of videos
Speaker 1: about how in the future AI is going to perform
Speaker 1: a more intrinsic role, or maybe it will be an
Speaker 1: existential threat to humanity or whatever it may be. And
Speaker 1: I think a lot of that is predict cated upon
Speaker 1: a deep misunderstanding or underestimation of how complicated cognitive neuroscience
Speaker 1: actually is and how little we really understand when it
Speaker 1: comes to our own consciousness, let alone how we would
Speaker 1: bring about such a thing in a different device. What
Speaker 1: do you guys think? And do you think that maybe
Speaker 1: I'm overstating the complexity? Do you think that I'm off base?
Speaker 1: Do you agree with me? And do you have any
Speaker 1: other topics you would like me to cover. I invite
Speaker 1: you to let me know. Send me an email. The
Speaker 1: address is tech stuff at how stuff works dot com,
Speaker 1: or drop me a line on Facebook or Twitter. The
Speaker 1: handle it both of those is tech Stuff h s W.
Speaker 1: Don't forget to go to our website that's tech Stuff
Speaker 1: podcast dot com. That's where we have an archive of
Speaker 1: all of our past episodes, as well as a link
Speaker 1: to our online store, where every purchase you make goes
Speaker 1: to help the show. We greatly appreciate it, and I
Speaker 1: will talk to you again really soon. Text Stuff is
Speaker 1: a production of I heart Radio's How Stuff Works. For
Speaker 1: more podcasts from I heart Radio, visit the i heart
Speaker 1: Radio app, Apple Podcasts, or wherever you listen to your
Speaker 1: favorite shows.