Speaker 1: Welcome to tech Stuff, a production from iHeartRadio. Hey there,
Speaker 1: and welcome to tech Stuff. I'm your host, Jonathan Strickland.
Speaker 1: I'm an executive producer with iHeartRadio, and I love all
Speaker 1: things tech and I owe you guys an apology. We're
Speaker 1: you're having a rerun episode today, So this is a
Speaker 1: rerun from twenty nineteen about machine consciousness, and it's again
Speaker 1: one of those tricky concepts. Consciousness is a difficult thing
Speaker 1: to define, even for humans. So sit back and enjoy
Speaker 1: this rerun. I'll talk to you again at the end
Speaker 1: of the episode. There's a topic I have touched on
Speaker 1: on several occasions in past episodes, but I really wanted
Speaker 1: to dig down today into this topic because it's one
Speaker 1: of those that's fascinating and is an underpinning for tons
Speaker 1: of speculative fiction and horror stories. And since we're now
Speaker 1: in October, I fig this would be kind of thematically
Speaker 1: linked to Halloween text style. It turns out that's pretty
Speaker 1: hard to do Halloween technology stories. I've already covered stuff
Speaker 1: like haunted house technology. So today we're going to talk
Speaker 1: about consciousness and whether or not it might be possible
Speaker 1: that machines could one day achieve consciousness. Now, I could
Speaker 1: start this off by talking about the Turing test, which
Speaker 1: many people have used as the launch point for machine
Speaker 1: intelligence and machine consciousness debates. The way we understand that
Speaker 1: test today, which by the way, is slightly different from
Speaker 1: the test that Alan Turing first proposed, is that you
Speaker 1: have a human interviewer who, through a computer interface, asks
Speaker 1: questions of a subject, and the subject might be another human,
Speaker 1: or it might be a computer program posing as a human,
Speaker 1: and the interviewer just sees text on a screen. So
Speaker 1: if the interviewer is unable to pass a certain threshold
Speaker 1: of being able to tell the difference, to be able
Speaker 1: to determine whether it was a machine or a person,
Speaker 1: then the program or machine that's being tested is said
Speaker 1: to have passed the Turing test. It doesn't mean the
Speaker 1: program or machine is conscious or even intelligent, but rather
Speaker 1: says that to outward appearances, it seems to be intelligent
Speaker 1: and conscious. See, we humans can't be absolutely sure that
Speaker 1: other humans are conscious and intelligent. We assume that they
Speaker 1: are because each of us knows of our own consciousness
Speaker 1: and our own intelligence. We have a personal experience with
Speaker 1: that direct personal experience, and other people seem to display
Speaker 1: behaviors that indicate they too possess those traits, and they
Speaker 1: too have a personal experience. But we cannot be those
Speaker 1: other people, and so we have to grant them the
Speaker 1: consideration that they too are conscious and intelligent. And I
Speaker 1: agree that is very big of us. This is actually
Speaker 1: called the problem of other minds in the field of philosophy,
Speaker 1: and the problem is this, it is impossible for any
Speaker 1: one of us to step outside of ourselves and into
Speaker 1: any other person's consciousness. We cannot feel what other people
Speaker 1: are feeling or experience their thoughts firsthand. We are aware
Speaker 1: of our own abilities, but we are only aware of
Speaker 1: the appearance that other people share those abilities. So assuming
Speaker 1: that other people also experience consciousness rather than imitating it,
Speaker 1: really really, well, that's a step we all have to take.
Speaker 1: Turing's point is that if we do grant that consideration
Speaker 1: to other people, why would we not do it to
Speaker 1: machines as well. I mean, the machine appears to possess
Speaker 1: the same qualities as a human. This is a hypothetical machine,
Speaker 1: so we cannot experience what that machine is going through,
Speaker 1: just as we can't experience what another person is going through.
Speaker 1: At least not on the intrinsic personal level. So why
Speaker 1: would we not grant the machine the same consideration that
Speaker 1: we would grant to people? And Touring was being a
Speaker 1: little cheeky. But while I just gave kind of a
Speaker 1: super fast, high level description of the Turing test, that's
Speaker 1: not actually where I want to start. I want to
Speaker 1: begin with the concept of consciousness itself. Now, the reason
Speaker 1: I want to do this isn't just to make a
Speaker 1: longer podcast. It's because I think one of the most
Speaker 1: fundamental problems with the discussion about AI intelligence, self awareness,
Speaker 1: and consciousness is that there tends to be a pretty
Speaker 1: large disconnect between the biologists and the doctors who specialize
Speaker 1: in neuroscience, particularly cognitive neuroscience, and thus have some understanding
Speaker 1: about the nature of consciousness and people. And then you
Speaker 1: have computer scientists who have a deep understanding of how
Speaker 1: computers process information. And while we frequently will compare brains
Speaker 1: to computers, that comparison is not one to one. It
Speaker 1: is largely a comparison of convenience, and in some cases
Speaker 1: you could argue it's not terribly useful, it might actually
Speaker 1: be counterproductive. And so I think at least some of
Speaker 1: the speculation about machine consciousness is based on a lack
Speaker 1: of understanding of how complicated and mysterious this topic is
Speaker 1: in the first place, and this ends up being really tricky.
Speaker 1: Consciousness isn't an easily defined quality or quantity. Some people
Speaker 1: like to say, we don't so much define consciousness by
Speaker 1: what it is, but rather what it isn't. And this
Speaker 1: will also will kind of bring us into the realm
Speaker 1: of philosophy. Now, I'm going to be honest with you, guys.
Speaker 1: The realm of philosophy is not one I'm terribly comfortable in.
Speaker 1: I'm pretty pragmatic, and philosophy deals with a lot of
Speaker 1: stuff that is, at least for now, unknowable. Philosophy sometimes
Speaker 1: asks questions that we do not and cannot have the
Speaker 1: answer to, and in many cases we may never be
Speaker 1: able to answer those questions. And the pragmatist em says, well,
Speaker 1: why bother asking the question if you can never get
Speaker 1: the answer. Let's just focus on the stuff we actually
Speaker 1: can answer. Now, I realize this is a limitation on
Speaker 1: my part. I'm owning that I'm not out to upset
Speaker 1: the philosophical apple cart. I'm just of a different philosophical meant,
Speaker 1: and I realize that just because we can't answer some
Speaker 1: questions right now, that doesn't necessarily mean they will all
Speaker 1: go unanswered for all time. We might glean a way
Speaker 1: of answering at least some of them, though I suspect
Speaker 1: a few will be forever unanswered. If we go with
Speaker 1: the Basic Dictionary definition of consciousness, it's quote the state
Speaker 1: of being awake and aware of one's end quote. But
Speaker 1: what this doesn't tell us is what's going on that
Speaker 1: lets us do that. It also doesn't talk about being
Speaker 1: aware of oneself, which we largely consider consciousness to be
Speaker 1: part of. Is not just aware of your surroundings, but
Speaker 1: aware that you exist within those surroundings, your relationship to
Speaker 1: your surroundings, and things that are going on within you, yourself,
Speaker 1: your feelings, and your thoughts. The fact that you can
Speaker 1: process all of this, you can reflect upon yourself. We
Speaker 1: tend to group that into consciousness as well. So how
Speaker 1: is it that we can feel things and be aware
Speaker 1: of those feelings? How is it that we can have
Speaker 1: intentions and be aware of our intentions. We are more
Speaker 1: complex than beings that simply react to sensory input. We
Speaker 1: are more than beings that respond to stuff like hunger, fear,
Speaker 1: or the desire to procreate. We have motivations, sometimes really
Speaker 1: complex motivations, and we can reflect on those, we can
Speaker 1: examine them, we can question them, we can even change them.
Speaker 1: So how do we do this? Now? We know this
Speaker 1: is special because some of the things we can do
Speaker 1: are shared among a very few species on Earth. For example,
Speaker 1: we humans can recognize our own reflections in a mirror.
Speaker 1: Starting at around age two or so, we can see
Speaker 1: the mirror image and we recognize the mirrorage image is
Speaker 1: of us. Now, there are only eight species that can
Speaker 1: do this that we know about anyway. Those species are
Speaker 1: the great apes. So you've got humans, gorillas orangutans, bonobos,
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 in a mirror shows a sense
Speaker 1: of self awareness, literally, of awareness of one's self. There
Speaker 1: are a lot of great resources online and offline that
Speaker 1: go into the theme of consciousness. Heck, there are numerous
Speaker 1: college level courses and graduate level courses dedicated to this topic.
Speaker 1: So I'm not going to be able to go into
Speaker 1: all the different hypotheses, arguments, counter arguments, etc. In this episode,
Speaker 1: but I can cover some basics. Also, I highly recommend
Speaker 1: you check out V. Sauce's video on YouTube that's titled
Speaker 1: what is Consciousness? Because it's really good. And no, I
Speaker 1: don't know Michael. I have no connection to him. I've
Speaker 1: never met him. This is just an honest recommendation from me,
Speaker 1: and I have no connection whatsoever to that video series.
Speaker 1: The video includes a link to what V Sauce dubs
Speaker 1: a lean back, which is a playlist of related videos
Speaker 1: on the subject at hand, in this case, consciousness. Those
Speaker 1: are also really fascinating. But I do want to point
Speaker 1: out that, at least at the time of this recording,
Speaker 1: a couple of the videos in that playlist since been
Speaker 1: delisted from YouTube for whatever reason, So there are a
Speaker 1: couple of blank spots in there. But what those videos show,
Speaker 1: and what countless papers and courses and presentations also show,
Speaker 1: is that the brain is so incredibly complex and nuanced
Speaker 1: that we don't know what we don't know. We do
Speaker 1: know that there are some pretty funky things going up
Speaker 1: in the gray matter up in our noggins, and we
Speaker 1: also know that many of the explanations given to describe
Speaker 1: consciousness rely upon some assumptions that we don't have any
Speaker 1: substantial evidence for. You can't really assert something to be
Speaker 1: true if it's based on a premise that you also
Speaker 1: don't know to be true. That's not how good science works.
Speaker 1: This is also why I reject the arguments around stuff
Speaker 1: like ghost hunting equipment. The use of that equipment is
Speaker 1: predicated on the argument that ghosts exist and they have
Speaker 1: certain influences on their environment. But we haven't proven that
Speaker 1: ghosts exist in the first place, let alone that they
Speaker 1: can affect the environment. So selling a meter that supposedly
Speaker 1: detects a ghostly presence from electromagnetic fluctuations makes no logical sense.
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: at 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 this other thing that we've never
Speaker 1: proven to exist makes it exist. Well, that you've just
Speaker 1: made it harder to even prove anything. And we have
Speaker 1: evidence that also shows that that whole idea doesn't hold water.
Speaker 1: The evidence comes in the form of brain disorders, brain diseases,
Speaker 1: and brain damage. We have seen that disease and damage
Speaker 1: to the brain affects consciousness, which suggests that consciousness manifests
Speaker 1: from the actual form and function of our brains, not
Speaker 1: from any mysterious force. Our ability to perceive, to process information,
Speaker 1: to have an understanding of the self, to have an
Speaker 1: accurate reflection of what's going on around us within our
Speaker 1: own conceptual reality, all of that appears to be predicated
Speaker 1: primarily upon the brain. Now, originally I was planning to
Speaker 1: give a rundown on some of the prevailing theories about consciousness.
Speaker 1: In other words, I want to summarize the various schools
Speaker 1: of thought about how consciousness actually arises. But as I
Speaker 1: dove down into the research, it became apparent really quickly
Speaker 1: that such a discussion would require so much groundwork and
Speaker 1: more importantly, a much deeper understanding on my part than
Speaker 1: would be practical for this podcast. So instead of talking
Speaker 1: about the higher order theory of consciousness versus the general
Speaker 1: workspace theory versus integrated information theory, I'll take a step
Speaker 1: back and I'll say there's a lot of ongoing debate
Speaker 1: about the subject, and no one has conclusively proven that
Speaker 1: any particular theory or argument is most likely true. Each
Speaker 1: theory has its strengths and its weaknesses, and complicating matters
Speaker 1: further is that we haven't refined our language around the
Speaker 1: concepts enough to differentiate various ideas. That means you can't
Speaker 1: talk about an organism being conscious of something and that
Speaker 1: degree of consciousness is somehow inherently specific, it's not. That's
Speaker 1: the issue. So, for example, I could say a rat
Speaker 1: is conscious of a rat terrier type of that hunts
Speaker 1: down rats, and so as a result of this consciousness
Speaker 1: of the rat terrier, the rat attempts to remain hidden
Speaker 1: so as not to be killed. But does that mean
Speaker 1: the rat merely perceives the rat terrier and thus is
Speaker 1: trying to stay out of its way, and that's as
Speaker 1: far as the consciousness goes. Or does it mean that
Speaker 1: the rat actually has a deeper, more meaningful awareness of
Speaker 1: the rat terrier? The language is in 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 ready for this, but like consciousness,
Speaker 1: there's no single agreed upon definition or theory of intelligence.
Speaker 1: In general, we use the word intelligence to describe the
Speaker 1: ability to think, to learn, to absorb knowledge, and to
Speaker 1: make use of it to develop skills. Intelligence is what
Speaker 1: allowed humans to learn how to make basic tools, to
Speaker 1: gain an understanding of how to cultivate plants and develop agriculture,
Speaker 1: to develop architecture, to understand mathematic principles and all sorts
Speaker 1: of stuff. So in humans, we tend to lump consciousness
Speaker 1: and intelligence together we tend to think in terms of
Speaker 1: being intelligent and being self aware, but the two need
Speaker 1: not necessarily go hand in hand. There are many people
Speaker 1: who believe that it could be possible to construct an
Speaker 1: artificial intelligence or an artificial consciousness independently of one another.
Speaker 1: When we come back, i'll explain more, but first let's
Speaker 1: take a quick break. So, in a very general sense,
Speaker 1: the group of hypotheses that fall into the integrated information
Speaker 1: theory umbrella state that consciousness emerges through linking elements in
Speaker 1: our brains. These would be neurons processing large amounts of information,
Speaker 1: and that it's the scale of this endeavor that then
Speaker 1: leads to consciousness. In other words, if you have enough
Speaker 1: processors working on enough information and they're all interconnected with
Speaker 1: each other and it's very complicated, bang, you get consciousness. Now,
Speaker 1: it is clear our brains process a lot of information.
Speaker 1: If you do a search in textbooks or online, you'll
Speaker 1: frequently encounter the stat that their brains have around one
Speaker 1: hundred billion neurons in them and ten times as many
Speaker 1: glial cells. Neurons are like the processors in a computer system,
Speaker 1: and glial cells would be the support systems and insulators
Speaker 1: for those processors. Anyway, those numbers have since come under
Speaker 1: some dispute. As an associate professor at Vanderbilt University named
Speaker 1: Susanna Herculano Husel. She explained that the old way of
Speaker 1: estimating how many neurons the brain had appeared to be
Speaker 1: based on taking slices of the brain, estimating the number
Speaker 1: of neurons in that slice, and then kind of extrapolating
Speaker 1: that number to apply across the brain in general. But
Speaker 1: that ignores stuff like the density of cells and the
Speaker 1: distribution of the cells across the brain. So what she did,
Speaker 1: and this also falls into the category of Halloween horror stories,
Speaker 1: is she took a brain and she freaking dissolved it.
Speaker 1: She could then get account of the neuron nuclei that
Speaker 1: was in the soupy mix. By her accounting, the brain
Speaker 1: has closer to eighty six billion neurons and just as
Speaker 1: many glial cells. Still a lot of cells, mind you,
Speaker 1: But you got to admit it's a bit of a
Speaker 1: blow to lose fourteen billion neurons overnight. Still, we're talking
Speaker 1: about billions of neurons that interconnect through an incredibly complex
Speaker 1: system in our brains, with different regions of the brain
Speaker 1: handling different things, and so, yeah, we're processing a lot
Speaker 1: of information all the time, and we do happen to
Speaker 1: be conscious. So could it be possible that with a
Speaker 1: sufficiently powerful computer system, perhaps made up of hundreds or
Speaker 1: thousands or tens of thousands of individual computers, each with
Speaker 1: hundreds of processors, that you could end up with an
Speaker 1: emergent consciousness, Or, as some people have proposed, could the
Speaker 1: Internet itself become conscious due to the fact that it
Speaker 1: is an enormous system of interconnected nodes that is pushing
Speaker 1: 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 is personal to me. So a machine
Speaker 1: might objectively be able to return data about stuff like
Speaker 1: what is a color of a piece of paper? It
Speaker 1: analyzes 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, 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 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 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 perform some form of
Speaker 1: operation on it and then send that resulting data the
Speaker 1: answer after they've processed this information onto other nodes in
Speaker 1: the network. It's a non linear approach to computing, and
Speaker 1: by adjusting the processes each node performs. This is also
Speaker 1: known as adjusting the weight of the nodes, you can
Speaker 1: tweak the outcomes. Now, this is incredibly useful. If you
Speaker 1: already know the outcome you want, you can tweak the
Speaker 1: system so that it learns or is trained to recognize
Speaker 1: something specific. For example, you could train a computer system
Speaker 1: to recognize faces, so you would feed it images. Some
Speaker 1: of the images would have faces in them, some would
Speaker 1: not have faces in them. Some might have something that
Speaker 1: could be a face, but it's hard to tell. Maybe
Speaker 1: it's a shape in a picture. That looks kind of
Speaker 1: 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 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 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 casual
Speaker 1: glance to think. This was helped in no small part
Speaker 1: by the very public display of Watson competing on special
Speaker 1: episodes of Jeopardy, and which it went up against human
Speaker 1: opponents who were former Jeopardy champions themselves. Watson famously couldn't
Speaker 1: call upon the Internet to search for answers. All the
Speaker 1: data the computer could access was self contained in its
Speaker 1: undeniably voluminous storage, and the computer had to parse what
Speaker 1: the clues in Jeopardy were actually looking for, then come
Speaker 1: up with an appropriate response. And to make matters more tricky,
Speaker 1: the computer wasn't returning a guaranteed right answer. The computer
Speaker 1: had to come to a judgment on how confident it
Speaker 1: was that the answer it had arrived at was the
Speaker 1: correct one. If the confidence met a certain threshold, then
Speaker 1: Watson would submit an answer. If it did not meet
Speaker 1: 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 humans 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 understands Chinese symbols. They can
Speaker 1: recognize the significance of whatever symbol was contributed, was sent
Speaker 1: in through the mail slot, and then match it to
Speaker 1: whatever the corresponding data is for that particular symbol, and
Speaker 1: then return that to the user. So to the outside observer,
Speaker 1: it appears as though whatever is inside the room comprehends
Speaker 1: what it is doing. But argues Serle, that's only an
Speaker 1: illusion because the person inside the room doesn't know what
Speaker 1: any of those symbols actually means. So if this is you,
Speaker 1: you have no context. You don't know what any individual
Speaker 1: symbol stands for, nor do you understand why any symbol
Speaker 1: would be prepared with any other symbol. You don't know
Speaker 1: the reasoning behind that. All you have is a book
Speaker 1: of rules, But the rules only state what your response
Speaker 1: should be given a specific input. The rules don't tell
Speaker 1: you why, either on a granular level of what the
Speaker 1: symbols actually mean, or on a larger scale when it
Speaker 1: comes to what you're actually accomplishing in this endeavor. All
Speaker 1: you are doing is filling a physical action over and
Speaker 1: over based on a set of rules you don't understand.
Speaker 1: And Searrele then uses this argument to say that essentially
Speaker 1: we have to think the same way about machines. The
Speaker 1: machines process information based on the input they receive and
Speaker 1: the program that they are following. That's it. They don't
Speaker 1: have awareness or understanding of what the information is. Searle
Speaker 1: was taking aim at a particular concept in AI, often
Speaker 1: dubbed strong AI or general AI. It's a sort of
Speaker 1: general artificial intelligence. So it's something that we could or
Speaker 1: would compare directly to human intelligence, even if it didn't
Speaker 1: work the same way as our intelligence works. The argument
Speaker 1: is that the capacity and the outcomes would be similar
Speaker 1: enough for us to make the comparison. This is the
Speaker 1: type of intelligence that we see in science fiction doomsday scenarios,
Speaker 1: where the machines have rebelled against humans, or the machines
Speaker 1: appear to misinterpret simple requests, or the machines come to
Speaker 1: conclusions that, while logically sound, spell doom for us all.
Speaker 1: The classic example of this, by the way, is appealing
Speaker 1: to a super smart artificial intelligence and you say, could
Speaker 1: you please bring about world peace because we're all sorts
Speaker 1: of messed up, and the intelligence processes this and then
Speaker 1: concludes that while there are at least two humans, there
Speaker 1: can never be a guarantee for peace because there's always
Speaker 1: the opportunity for disagreement and violence between two humans. And
Speaker 1: so to achieve true piece, the computer then goes on
Speaker 1: a killing spree to wipe out all of humanity. Now,
Speaker 1: Cyril is not necessarily saying that computers won't contribute to
Speaker 1: a castrophic 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 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 status 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: Serle'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 involve romanizing words from
Speaker 1: other language in which the letter Q represents a sound
Speaker 1: that's not natively present in English. So you could program
Speaker 1: a machine to follow the basic rule that the symbol
Speaker 1: Q should be followed by the symbol you, assuming you're
Speaker 1: eliminating all those exceptions I just mentioned. But that doesn't
Speaker 1: lead to a grasp of semantics, which is actual meaning. Moreover,
Speaker 1: Searle asserts that it's impossible to come to a of
Speaker 1: semantics merely through a mastery of syntax. You might know
Speaker 1: those rules flawlessly, but Searle argues, you still wouldn't understand
Speaker 1: why there are rules, or what the output of those
Speaker 1: rules means, or even what the input means. There are
Speaker 1: some general counter arguments that philosophers have made to Searle's
Speaker 1: 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 Cerle 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 this 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 but
Speaker 1: if the computer system either could simulate the operation of
Speaker 1: a brain, perhaps with billions of nodes, approaching the complexity
Speaker 1: of a human brain with billions of neurons, or if
Speaker 1: the system were to inhabit a robotic body that could
Speaker 1: have direct interaction with its environment, then the system could
Speaker 1: manifest intelligence. The third group rejects Searle's arguments more thoroughly
Speaker 1: and on the basis of various grounds, ranging from Searle's
Speaker 1: experiment being too narrow in scope to an argument about
Speaker 1: what the word understand actually means. This is where things
Speaker 1: get a bit more loosey goosey, And sometimes I feel
Speaker 1: like arguments in this group amount to oh yeah, but again,
Speaker 1: I'm pragmatic, so I tend to have a pretty strong
Speaker 1: bias against these arguments, and I recognize that this means
Speaker 1: I'm not giving them fair consideration because of those biases.
Speaker 1: A few of these arguments take issue with Searle's assertion
Speaker 1: that one cannot grasp semantics through an understanding syntax. And
Speaker 1: here's something that I find really interesting. Searle originally published
Speaker 1: this argument way back in nineteen eighty. It's been nearly
Speaker 1: forty years since he first proposed it, and to this
Speaker 1: day there is no consensus on whether or not his
Speaker 1: argument is sound. So why is that? Well, it's because,
Speaker 1: as I've covered in this episode, the concepts of intelligence
Speaker 1: and more to the point, consciousness are whibley wobbly, though
Speaker 1: not as far as I can tell, timey whymy. When
Speaker 1: we can't even nail down specific definitions for words like understand,
Speaker 1: it becomes difficult to even tell when we're agreeing or
Speaker 1: disagreeing on certain topics. It could be that while people
Speaker 1: are in a debate and are using words in different ways,
Speaker 1: it turns out they're actually in agreement with one another.
Speaker 1: Such is the messiness that is intelligence. Further, we've not
Speaker 1: yet observed anything in the machine world that seems, upon
Speaker 1: closer examination and 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
Speaker 1: figure 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.
Speaker 1: Now I believe that because I believe our own intelligence
Speaker 1: and our own consciousness is firmly rooted in our brains.
Speaker 1: I don't think there's 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 sequence 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: that 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 matter.
Speaker 1: If we never create devices that have true or human
Speaker 1: like intelligence, we might be able to accomplish just as
Speaker 1: much with something that does not have those capabilities. And
Speaker 1: in other words, this is a very complicated top one
Speaker 1: 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'll be an existential threat to
Speaker 1: humanity or whatever it may be. And I think a
Speaker 1: lot of that is predicated upon a deep misunderstanding or
Speaker 1: underestimation of how complicated cognitive neuroscience actually is and how
Speaker 1: little we really understand when it comes to our own consciousness,
Speaker 1: let alone how we would bring about such a thing
Speaker 1: in a different device. I hope you enjoyed that rerun,
Speaker 1: and as always, I also hope that you are all
Speaker 1: well and I will talk to you again really soon.
Speaker 1: Tech Stuff is an Iheartreate Radio production. For more podcasts
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