Speaker 1: Brought to you by Toyota. Let's go places. Welcome to
Speaker 1: Forward Thinking. Hey there, and welcome to Forward Thinking, the
Speaker 1: podcast that looks at the future and says I'm a
Speaker 1: little intellectual someone who knows it all. I'm Jonathan Strickland,
Speaker 1: and I'm Joe McCormick, and our other host, Lauren voc
Speaker 1: obamb is not with us today. She is in the
Speaker 1: city of New York, within the state of New York. Yes,
Speaker 1: the city of Apples. You were sell what it is
Speaker 1: now according to the I T crowd, you were there
Speaker 1: not long ago. To Joe, yeah, that's right. Uh So
Speaker 1: my co hosts on the other podcast I do here
Speaker 1: Step to blow your mind, Robert Lamb, Christian Saga, and
Speaker 1: I did a live podcast at the Star Trek Mission
Speaker 1: New York convention that was held at the Javits Center.
Speaker 1: That fantastic. I'm so envious that you guys got to
Speaker 1: do that. And then of course Lauren is up there
Speaker 1: with a lot of our crew just kind of exploring
Speaker 1: New York and also taking part in a bunch of
Speaker 1: different activities, including a trivia night. She's having a blast.
Speaker 1: We are having a blast here talking about something that
Speaker 1: could potentially blast us off the face of the planet.
Speaker 1: I knew you were going to make a blast punt
Speaker 1: today to get there. Today we're gonna be talking about
Speaker 1: the potential scenario of an AI arms race. Yeah, and
Speaker 1: this is really going to be a two part series.
Speaker 1: So this first part we're talking mainly about just the
Speaker 1: concept of artificial intelligence, the various definitions that we have
Speaker 1: created to talk about AI. And in our next episode,
Speaker 1: we're really going to dive into some of these various
Speaker 1: scenarios people have proposed that we could see as a
Speaker 1: result of this emerging discipline. And it's funny to call
Speaker 1: an emerging discipline because really, if you look back at
Speaker 1: the history of AI, it's it's more than a century old.
Speaker 1: But I think most people would argue that what what
Speaker 1: the general public would consider artificial intelligence. That's the kind
Speaker 1: of stuff we're just now starting to see kind of
Speaker 1: kind of peek around the corner a little bit. So
Speaker 1: starting that all off, let's start defining artificial intelligence, because
Speaker 1: this is a term that gets used all the time.
Speaker 1: Sometimes it is misused, sometimes liberally misused or misrepresented, and uh,
Speaker 1: to me, it's sort of like virtual reality. It's one
Speaker 1: of those terms that people have heard it, and they
Speaker 1: generally know what it means, but sometimes they have a
Speaker 1: different vision than what was necessarily intended. It means a
Speaker 1: stock image of a sexy robot with a gun, yeah yeah,
Speaker 1: or sometimes a cyborg lady with an ear of corn.
Speaker 1: Ear of corn. You've never seen those, I'm not sure
Speaker 1: the ear of corn, the ear of corn, cyborg lady?
Speaker 1: I uh, I am ashamed to admit I have used
Speaker 1: that image in an in an article for how Stuff
Speaker 1: Works dot com. Uh tangent, And I know we're going
Speaker 1: on a tangent already. Dylan who works here, he actually
Speaker 1: he edits a lot of the podcasts, but he also
Speaker 1: does a lot of photo work, and he held a
Speaker 1: workshop here in the office to talk about the kinds
Speaker 1: of photos that you should and should not use and
Speaker 1: when he starts from stock photo websites in order to
Speaker 1: illustrate your work. And I had to cringe as some
Speaker 1: of the ones he was picking up as ones you
Speaker 1: should not use. I know for a fact I have
Speaker 1: used at least one of the images from that series,
Speaker 1: if not that specific one, then one from that same
Speaker 1: photography series that generated that particular image that he used
Speaker 1: as an example yeah. So if you're ever looking for
Speaker 1: a good belly laugh, go to a stock photo website
Speaker 1: and look up pictures of artificial intelligence or pictures of hacker. Yes,
Speaker 1: and so this, this actually tells me that we don't
Speaker 1: need to worry about artificial intelligence. We need to worry
Speaker 1: about just human intelligence first, at least in my case. Well,
Speaker 1: that's not necessarily all that off the money, according to
Speaker 1: some people. So yeah, so how would we define artificial intelligence?
Speaker 1: One really simple way to characterize it would be intelligence
Speaker 1: possessed or performed by machines. But this is already complicated,
Speaker 1: right because you used a word intelligence to define artificial intelligence. Yeah,
Speaker 1: and it's not that simple because it's there in the
Speaker 1: name artificial intelligence. Now, if it were really just about
Speaker 1: machines meeting some objectively defined criterion of intelligence, there would
Speaker 1: be nothing artificial about it, right. You might just refer
Speaker 1: to as machine intelligence then. Yeah. So that and machine intelligence,
Speaker 1: in fact, is a terminology that many experts writing this
Speaker 1: area seem to prefer. They like it better. But when
Speaker 1: some people use the term artificial intelligence, I think they're
Speaker 1: implying that machines are merely performing some kind of simulation. Generally,
Speaker 1: the simulation of the behavior you're of a human brain, right,
Speaker 1: like the science fiction depiction of artificial intelligence, is a
Speaker 1: robot or computer that thinks, at least on some level,
Speaker 1: similar to the way a human being thinks. Yeah, And
Speaker 1: this does come through in many of the public facing
Speaker 1: criteria for determining what constitutes a successful example of artificial intelligence,
Speaker 1: for example, the so called Turing tests, which we will
Speaker 1: refer to numerous times throughout this and we've talked about
Speaker 1: it on the show before, you've probably heard about it
Speaker 1: from us before. But essentially, a computer program, as it's
Speaker 1: understood today, a computer program is designed to chat back
Speaker 1: and forth with a human via text messages on a
Speaker 1: computer terminal, and ultimately its goal is to trick the
Speaker 1: human into thinking it's not a computer program but another
Speaker 1: live human, right. Essentially the idea of being that you
Speaker 1: are communicating with a series of entities, some of which
Speaker 1: are humans, some of which are computers, and you are
Speaker 1: unable to reliably tell the difference between the two. And
Speaker 1: so the test as conceived today arises out of a
Speaker 1: philosophical concern originally voiced by Alan Turing over essentially our
Speaker 1: inability to tell the difference between a machine that truly
Speaker 1: possesses intelligence and a machine that merely gives the appearance
Speaker 1: of intelligence. Right, we we give people the benefit of
Speaker 1: the doubt that they too are intelligent when we interact
Speaker 1: with them. Correct. Like Joe and I are sitting across
Speaker 1: from one another at a table. We both have microphones
Speaker 1: in front of us. We are interacting, We're having this conversation,
Speaker 1: and Jonathan has no idea that I'm not conscious. Right
Speaker 1: to me, you know you are behaving in a way
Speaker 1: that I would associate with being intelligent. Correct, Like you
Speaker 1: are hearing what I say, you are processing it, you
Speaker 1: are responding with your own thoughts. So to me, that
Speaker 1: is the appearance of intelligence. I know that I am
Speaker 1: intelligent because I have that personal experience. Therefore, I assume
Speaker 1: you also have a personal experience that is similar, if
Speaker 1: not identical, to my own, and you must possess intelligence
Speaker 1: if a computer is able to behave in such a
Speaker 1: way that it also appears to have these these faculties,
Speaker 1: even if you don't know what's going on in the
Speaker 1: back end, Touring would say you should at least extend
Speaker 1: the courtesy to say the machine is intelligent, because you
Speaker 1: would have done the same thing for another human being. Yes,
Speaker 1: but I think this all gives a very human gloss
Speaker 1: to the definition of intelligence. Like interpreted like this, intelligence
Speaker 1: sort of seems to mean that property of a conscious
Speaker 1: mind that is possessed by a normally functioning brain of
Speaker 1: an adult Homo sapiens. Yes. In other words, we have
Speaker 1: couched the word intelligence to be a distinctly human type
Speaker 1: of experience, right like that, Like there is if we're
Speaker 1: talking about artificial intelligence and we're framing it in the
Speaker 1: human experience than machine intelligence almost as meaningless. Yeah, and
Speaker 1: so I what I would be on the search for
Speaker 1: is a definition of intelligence that's more kind of an
Speaker 1: objective definition that could apply to any object, including humans
Speaker 1: or machines, depending on what what they could do. So,
Speaker 1: uh so, do we have anything that's like an objective,
Speaker 1: universal definition of intelligence that doesn't just mean acting like
Speaker 1: a human? Might? I came across one that I like
Speaker 1: a lot. I'll see what you think of this, Jonathan.
Speaker 1: So this comes from the systems theorist David Krakauer, and
Speaker 1: he's got definitions of intelligence and of stupidity that I
Speaker 1: think are actually very illuminating. So, according to krak our
Speaker 1: intelligence is finding very simple solutions to complex problems. And
Speaker 1: he gives an example I'd like to read from a
Speaker 1: piece that's published in Nautilus that that he did. So
Speaker 1: he says, quote, let's take a very simple example, and
Speaker 1: the example I'd like to choose is the Rubik's cube.
Speaker 1: If I give you a Rubik's cube and you try
Speaker 1: to solve it just randomly, so is imagining you're just
Speaker 1: turning in all directions until you hope, by random chance,
Speaker 1: you get solved. He continues, it will take many, many lifetimes.
Speaker 1: There are a billion billion solutions to the Rubik's cube.
Speaker 1: That's several lifetimes. That would be ignorance. That would be
Speaker 1: where you just don't know what to do, and so
Speaker 1: you perform essentially at a random level. Stupidity for the
Speaker 1: Rubik's cube is if you just consistently moved and manipulated
Speaker 1: one face. Maybe if I just rotate this one face forever,
Speaker 1: eventually the cube will be solved, and it will never
Speaker 1: be solved, even in infinite time, unlike the random case,
Speaker 1: which it will. Intelligence is a series of rules manipulations
Speaker 1: that will guarantee that you reach a solution in in
Speaker 1: steps or less. So my way of translating this is
Speaker 1: that he's saying intelligence is the ability to come up
Speaker 1: with strategies for improving one's chance of success at a
Speaker 1: given goal. So, in other word, uh, let's say that
Speaker 1: you to even generalize this, to go beyond like the
Speaker 1: Rubek's Cuban say, just a general problem. Let's say that
Speaker 1: you are faced with a problem and you try to
Speaker 1: come up with a solution. It turns out your solution
Speaker 1: doesn't work, so then you adjust, You try a different
Speaker 1: solution to see if that perhaps is more applicable, and
Speaker 1: eventually you hit on it. You get to a point
Speaker 1: it may not be the most elegant, it may not
Speaker 1: be the most efficient, but you finally find a way
Speaker 1: to solve that problem. Whereas the stupidity issue is you
Speaker 1: go with that first attempt, it doesn't work, and you
Speaker 1: just keep going with that same attempt, you do worse
Speaker 1: than random chance. Yes, it ends up almost falling into
Speaker 1: that same definition. As as the idea of insanity, you know,
Speaker 1: doing the same same thing over and over but expecting
Speaker 1: a different result. Uh, same kind of argument. There, the
Speaker 1: idea that that and you could see this easily with robotics, right,
Speaker 1: a robot that all it can do is pick up
Speaker 1: an object and turn it and set it down. And
Speaker 1: that's all I can do. Uh, it's never going to
Speaker 1: be able to solve a problem that doesn't involve picking
Speaker 1: up an object, turning it, and then setting it down.
Speaker 1: And we do see stupidity in robotics. Sometimes sometimes it's necessary,
Speaker 1: right because because the stupid solution, if you're talking about
Speaker 1: something that is always going to be the same, you
Speaker 1: are never going to have any variance from the approach.
Speaker 1: You want a stupid robot, right right? You just wanted
Speaker 1: to do this this three set are rather the series
Speaker 1: of three steps and that's all it does. Oh well,
Speaker 1: I mean if it's that would be if I'm hearing
Speaker 1: you correctly, that I mean, that's a different thing. Like
Speaker 1: a robot can not have any innovative solutions, but it
Speaker 1: can still do its job correctly because it's been programmed
Speaker 1: to do. You could even have a what I mean
Speaker 1: is a stupidly programmed robot hour since where where it
Speaker 1: performs worse than random performance at a task in like
Speaker 1: the example would be the robot that is trying to
Speaker 1: hand you something but instead stabs you. That would sort
Speaker 1: of be stupid robotics. Yes, yes, I would definitely feel
Speaker 1: stupid for using that robot. Uh. Now, moving beyond just
Speaker 1: trying to define what is intelligence, even beyond what is
Speaker 1: artificial intelligence, you start encountering subsets of artificial intelligence. And
Speaker 1: this is where a lot of other confusion comes in,
Speaker 1: and we're gonna be talking about some of these subsets
Speaker 1: in detail. One thing you may have encountered is the
Speaker 1: concept of weak AI versus strong AI. Yeah, and so
Speaker 1: this distinction is sometimes invoked as a philosophical one rather
Speaker 1: than a technological one, and for good reason. I mean,
Speaker 1: for one thing, we don't have anything that I think
Speaker 1: approaches the strong AI definition, and so we we can't
Speaker 1: say what the technological distinction is because we haven't achieved it, right,
Speaker 1: But let me explain. So, in the philosophical sense, week
Speaker 1: AI is a machine that can simulate or generate the
Speaker 1: outward appearance of intelligence or quote thinking, and strong AI
Speaker 1: is a machine that is quote actually thinking. So I
Speaker 1: would say this distinction is it's sometimes extended to sort
Speaker 1: of assert that strong AI is conscious, you know, like
Speaker 1: that there's something that's like to be that robot. And
Speaker 1: I do find the debate about whether machines can be
Speaker 1: conscious and whether machines can really think. I do find
Speaker 1: that interesting, but I also think that it's sort of
Speaker 1: a different question than the question of implementing AI technology
Speaker 1: in the real world, because you can't know whether another
Speaker 1: person is conscious. So it's just different than talking about
Speaker 1: strategies for how to create an intelligent machine, right, And
Speaker 1: I see your point, and I agree with it mostly.
Speaker 1: I think that that it's important only in the sense
Speaker 1: that you have a lot of people talking in terms
Speaker 1: of a I that, at least from the way they
Speaker 1: are talking about it, seems to indicate strong AI, this
Speaker 1: idea of machines that are quote unquote actually thinking. Uh.
Speaker 1: And there's a lot of debate on whether or not
Speaker 1: we will ever achieve that. There's some people who don't
Speaker 1: think it's achievable at all. There are others who say, well,
Speaker 1: that seems that seems like it's pretty egotistical to suggest
Speaker 1: that that only are gray matter in our heads is
Speaker 1: capable of this, and that we would never be able
Speaker 1: to achieve this in a machine environment. It may may
Speaker 1: be that it's impractical. Some would say, how would you
Speaker 1: know the difference? And in fact, there are lots of
Speaker 1: different philosophical you know, like thought experiment type things. We
Speaker 1: I think we've even mentioned one in a previous episode.
Speaker 1: Have we ever talked about the Chinese Room. Yeah, we
Speaker 1: did a whole episode on the Chinese Room experiments, all
Speaker 1: about whether machines can think right or are they just
Speaker 1: responding in a way that they've been programmed to respond
Speaker 1: so that it seems like they are thinking, that the
Speaker 1: illusion of thought is there, but there's no actual comprehension
Speaker 1: going on. But I agree that overall that conversation is
Speaker 1: not really relevant to most of what we're concentrating on,
Speaker 1: other than the fact that I think at least some
Speaker 1: of the people will chat about appear to have that
Speaker 1: kind of concept of AI in mind when they're talking
Speaker 1: about well, and to be fair to them, I think
Speaker 1: some people would make a not stupid case like it,
Speaker 1: like an interesting case that these these things we're talking about, consciousness,
Speaker 1: something deep about the very human, distinctly human things we
Speaker 1: think of as thinking and consciousness are actually in some
Speaker 1: way crucial to what we're about to talk about, which
Speaker 1: is general intelligence. But another way we could address the
Speaker 1: idea of week AI. This is sort of different, but
Speaker 1: the same terms are used sometimes UM also known as
Speaker 1: narrow AI. It's the AI that you're already familiar with
Speaker 1: any device or computer program that's mulates some mental ability
Speaker 1: of human beings. So you could argue that facial recognition
Speaker 1: in photographs on a computer is a very specific form
Speaker 1: of narrow AI, or those turing chat bots, you know,
Speaker 1: the inner the programs, or another form of narrow AI.
Speaker 1: We're already surrounded by examples of this kind of thing. Yeah.
Speaker 1: In fact, uh, we see advances in this all the
Speaker 1: time in interesting ways that maybe you weren't necessarily associating
Speaker 1: with artificial intelligence. One great example of this is the
Speaker 1: capture series, where you know captures that that that step
Speaker 1: you need to go through in order to prove you're
Speaker 1: not a robot to various online services. Typically, you know,
Speaker 1: early on it was just you read a word and
Speaker 1: then you had to type in what the word was,
Speaker 1: and or maybe it was just a string of letters
Speaker 1: and numbers, whatever it might be, and then the computer
Speaker 1: would verify that you are, in fact the human being
Speaker 1: and not some sort of autumn aided bought that's just
Speaker 1: trying to spam a service. Then there was a point
Speaker 1: where the object recognition or the text recognition capabilities of
Speaker 1: computers began to improve, and that is one aspect of
Speaker 1: artificial intelligence, the ability to recognize characters and understand what
Speaker 1: they correspond to. And so the captions began to get
Speaker 1: more difficult. They were distorted in weird ways or somewhat
Speaker 1: um covered up by other images in order to fool
Speaker 1: these image recognition programs, which then made the people designing
Speaker 1: the image recognition software go a step further so that
Speaker 1: they could recognize those examples too, And it became back
Speaker 1: and forth between security systems and artificial intelligence. And if
Speaker 1: you read up on the folks behind capture, they said, yeah,
Speaker 1: we were kind of thinking like this, this will help
Speaker 1: services figure out that, you know, the people who are
Speaker 1: trying to use their services are in fact human beings.
Speaker 1: But ultimately we wanted to drive the develop element of
Speaker 1: artificial intelligence. It seems so weird because we often think
Speaker 1: of AI as being this much more broad concept, nothing
Speaker 1: so specific as being able to recognize that this string
Speaker 1: of weird images is in fact a series of letters
Speaker 1: and numbers, but that is wrapped up in the concept
Speaker 1: of artificial intelligence and would be very much an example
Speaker 1: of narrow AI. My favorite example, are the ones that say,
Speaker 1: which of these pictures is not a clown? Right? Five
Speaker 1: clowns and then a pocket watch and a tiger right right,
Speaker 1: I've I've seen those two. Yeah. Where you you get
Speaker 1: like a bank of like nine images and about five
Speaker 1: of them tend to be whatever it is that you're
Speaker 1: supposed to be looking for, or or you get one
Speaker 1: that's really kind of makes you feel stupid, like it says,
Speaker 1: pick the images that have a river in them, and
Speaker 1: you're looking at one you see like a house on
Speaker 1: a shore and you're thinking, all right, is that a river?
Speaker 1: Is that a lake? And does this know the difference? Well,
Speaker 1: regular caption makes me feel stupid. Sometimes I can't see
Speaker 1: what those letters are. Yeah, I I sometimes will go there.
Speaker 1: You know, there's audio ones too, and in fact audio
Speaker 1: capture the same thing, like the idea of getting uh,
Speaker 1: the the voice recognition, the audio recognition that plays into
Speaker 1: artificial intelligence, natural language processing plays into artificial intelligence. All
Speaker 1: of these different things. So really, uh, you know, these
Speaker 1: are all aspects of narrow AI. These are all or
Speaker 1: or rather you know, manifestations of narrow AI. And some
Speaker 1: of them are are better than others. Some of them
Speaker 1: are more accurate than others. Um, some of them are
Speaker 1: further along than others, but it shows how diverse this
Speaker 1: discipline is. Yeah, but at the other end of the
Speaker 1: spectrum from narrow AI, you would get to the main
Speaker 1: concept that we're gonna be talking about in these two episodes,
Speaker 1: which is general AI. Artificial general intelligence often abbreviated a
Speaker 1: g I, and so under some usage, I think sometimes
Speaker 1: some people use the term strong AI to mean this, Right,
Speaker 1: they interchange the two terms to mean the same thing,
Speaker 1: but more accurately. Yeah, what we're trying to talk about
Speaker 1: is artificial general intelligence, and it's the property possessed by
Speaker 1: a machine that can apply intelligence to many or even
Speaker 1: all problems, rather than just one problem or some small
Speaker 1: number of them. And it's more like the diverse and
Speaker 1: adaptable problem solving engine within the complex animal brain, except
Speaker 1: it's going to presumably be free from biological and psychological
Speaker 1: limitations that we have in our human brains and probably
Speaker 1: be able to outperform the human brain most if not all,
Speaker 1: problem solving tasks. So this would be the difference between
Speaker 1: an AI implementation that can recognize a face and go
Speaker 1: beyond to one that not only can recognize a face,
Speaker 1: but also know the context of what's going on, also
Speaker 1: even respond to questions that are related to the the
Speaker 1: picture and ones that go beyond like tangential to the pictures.
Speaker 1: So you might say, uh, what is this a picture
Speaker 1: of it? It's a crowd of people, And the answer
Speaker 1: might be, well, that is as a group of human beings,
Speaker 1: all right, well how many how many of them are
Speaker 1: wearing blue T shirts? And it could tell you, and
Speaker 1: then you could start asking more questions that that start
Speaker 1: building off of that. It would continue to answer those,
Speaker 1: and it goes beyond just a single uh, a single
Speaker 1: task or a single um course of action, right. Like
Speaker 1: another great example is that a lot of the robots
Speaker 1: we depend upon today are functional because they focus on
Speaker 1: a very specific task. They don't they don't branch beyond it.
Speaker 1: And we've seen that the there are a lot of
Speaker 1: challenges to create robots that are able to tackle numerous
Speaker 1: challenges and uh, Thus we come to the conclusion that
Speaker 1: coming coming up with like an artificial general intelligence is
Speaker 1: going to be incredibly challenging just from the the baby
Speaker 1: steps that we've seen so far in that arena. And
Speaker 1: and even then like with the darker robotics challenge, which
Speaker 1: we will mention again later on. Uh. Even with the
Speaker 1: advancements that we've seen in that realm, we see the
Speaker 1: limitations that are there, and that's only a small, a tiny,
Speaker 1: tiny slice of everything. Whereas general intelligence we we tend
Speaker 1: to say could deal with any topic or any task.
Speaker 1: Anything humans can do, it should be able to do too,
Speaker 1: and probably some other stuff too. Yes, yes, so yeah.
Speaker 1: I think I'm going to suggest that it makes more
Speaker 1: sense for us to speak of narrow AI and general
Speaker 1: AI for the purpose of these episodes than we k
Speaker 1: AI and strong AI, just so we can avoid these
Speaker 1: side questions about you know, the nature of consciousness in
Speaker 1: the phenomenal logical nature of thinking and stuff. I agree,
Speaker 1: and uh. I also want to point out that general
Speaker 1: artificial intelligence can still refer to a machine that is
Speaker 1: dedicated to a specific set of tasks. It doesn't have
Speaker 1: to mean that you've got, you know, the deep thought
Speaker 1: computer where you can ask any question and it will
Speaker 1: give you the answer. It may be that we look
Speaker 1: at examples like a machine that could exist within the
Speaker 1: medical field is the example I picked, where it helps
Speaker 1: doctors diagnose and treat patients, and IBM s. Watson computer
Speaker 1: is meant to do those sort of things. But I'm
Speaker 1: talking about going a step beyond what Watson can do
Speaker 1: and and be almost like a partner or collaborator with
Speaker 1: the doctors, the medical staff. I've got something to say
Speaker 1: about that in a bit, because there are some critics
Speaker 1: of AI theory who don't like Watson and and it's
Speaker 1: a very under endible. Yeah, I'm glad that you put
Speaker 1: that note in as well. Also, I think that it's
Speaker 1: possible we'll see that there's not a firm line between
Speaker 1: narrow AI and general AI, maybe that that line is
Speaker 1: actually pretty fuzzy, and that it will be a little
Speaker 1: bit difficult once you get to sufficiently complex narrow AI.
Speaker 1: It may be difficult for us to say, well, is
Speaker 1: this still narrow AI, or would we call this general AI.
Speaker 1: It may not be something as simple as, uh, you know,
Speaker 1: well that this is distinctly general AI and this is
Speaker 1: distinctly narrow but it is useful for the purposes of
Speaker 1: this conversation to have that distinction between the two. Okay,
Speaker 1: one more topic we should define before we start getting
Speaker 1: into these debates is the idea of super intelligence. Yeah,
Speaker 1: this is what Superman has no but it is, well, actually,
Speaker 1: I guess Superman does have sort of superhuman intelligence suddenly.
Speaker 1: I mean, if you watched Batman versus Superman, clear really
Speaker 1: he does not, because he would never agree to be
Speaker 1: in that movie if he had super intelligence. Yeah. I
Speaker 1: watched the most of that on the plane back from
Speaker 1: New York. Yeah. Wow, that was a slog. So what
Speaker 1: is super intelligence? That's just intelligence surpassing the most powerful
Speaker 1: biological intelligence of humans, which is clearly that by definition,
Speaker 1: it means we humans cannot possess it unless we've undergone
Speaker 1: some form of enhancement, whether unless you get to your
Speaker 1: trans human kind of brain right right, And you could
Speaker 1: even argue that this this would be degrees, right, it's
Speaker 1: not necessarily that we would be leaps and bounds more
Speaker 1: intelligent front once we hit that point. It may mean
Speaker 1: that we find, through genetic modification, we can improve our
Speaker 1: intelligence by degrees over the course of time as we
Speaker 1: get more and more adept with it. And so it
Speaker 1: may be that it's a transition where it's not like
Speaker 1: the common conception of the singularity, where one day everything
Speaker 1: is different, but rather, over the core of a decent
Speaker 1: amount of time, we get to a stage where we
Speaker 1: no longer can meaningfully define the present, but it does
Speaker 1: mean changing humans in some way if if we want
Speaker 1: to achieve superhuman intelligence, either through medicine or through technology
Speaker 1: or both or whatever. Right, but superhuman intelligence and machines.
Speaker 1: One thing that I think is interesting about this is
Speaker 1: it's always just assumed. It's assumed if we create a
Speaker 1: g I that will lead to super intelligence in machines.
Speaker 1: And I'm not necessarily disputing that assumption, but I do
Speaker 1: think it's interesting that it's always just kind of assumed
Speaker 1: as a given. I think it's funny because we're all
Speaker 1: familiar with how computer services can sometimes mess up. Right
Speaker 1: where you know any service that you might use where
Speaker 1: you start looking into it and you're like, well, this
Speaker 1: is clearly it's it's either unintelligent or the work of
Speaker 1: a crazy person. Great example that is Chef Watson, where
Speaker 1: you start looking at the recipes, You're thinking, all right, well,
Speaker 1: we've got a long way to go because these recipes
Speaker 1: either do not sound appetizing, do not reflect what the
Speaker 1: title of the recipe says, or make no sense, or
Speaker 1: some combination of those. Um But then again, I kind
Speaker 1: of wonder what would it mean for a computer chef
Speaker 1: to actually be super intelligent in the realm of designing recipes,
Speaker 1: I imagine it would have to mean that it was
Speaker 1: just able to make the most nutritious, delicious food covered
Speaker 1: in caso. I mean, caso has to be on top
Speaker 1: of it for it to be super intelligent. Yeah, I don't. Yeah,
Speaker 1: really it is cheating. I any time I make something
Speaker 1: and I realized that I have made a terrible dish,
Speaker 1: I just add melted cheese and that that covers all sins.
Speaker 1: But you know, we've this may also be a fuzzy area,
Speaker 1: right because we can already see that computers are better
Speaker 1: or at least faster at processing certain types of information
Speaker 1: than human beings are. If if it, if they weren't,
Speaker 1: we wouldn't use computers right there. They would be unnecessary.
Speaker 1: They would actually slow things down. But they are better
Speaker 1: than we are for certain types of complex processing. And
Speaker 1: we've built computers that are better at us than lots
Speaker 1: of other or better than us that add a lot
Speaker 1: of tasks like chess, We've got computers and computer programs.
Speaker 1: They can beat the best chess masters human chess masters
Speaker 1: on the planet and go. We've solved Go. Now, the
Speaker 1: computers are better at playing Go than than humans are.
Speaker 1: But these are what I want to know is when
Speaker 1: computers are going to be able to beat humans at
Speaker 1: Magic the Gathering? Yeah, yeah, when they start tapping man
Speaker 1: like crazy. Yeah, I'm already still pretty darn good at clue.
Speaker 1: I think I can. I think I can go up
Speaker 1: against the best computers in a game of clue. Uh
Speaker 1: and not because I cheat. But you gotta keep in
Speaker 1: mind that these computers that we're talking about, they're very
Speaker 1: specific machines running very specific soft where for a very
Speaker 1: specific purpose. Right It's like the the computer that can
Speaker 1: beat you in chess may not be better than you
Speaker 1: are at some other tasks. And there are certain cognitive
Speaker 1: tasks that computers just don't they can't even compete in
Speaker 1: right now. Yeah, you know, especially anything that involves creativity. Um,
Speaker 1: that is one of those issues where we're still way ahead.
Speaker 1: We've talked about that in previous episode and we're going
Speaker 1: to get into that more in a bit. But yeah,
Speaker 1: these these tasks all have very highly specified outcomes. Yes,
Speaker 1: it's very clear what the parameters of success are, and
Speaker 1: thus it's easy for the computer to follow them because
Speaker 1: all it has to do is run step by step
Speaker 1: algorithmic execution. Uh. And it can do it thousands of
Speaker 1: times faster than we can. So yes, it beats us.
Speaker 1: It's essentially brute forcing the um the task, right. It
Speaker 1: looks at the task, it says, all right, well, what
Speaker 1: what are all the different possible moves for this particular situation?
Speaker 1: Which one is the most advantageous. Now we've seen those
Speaker 1: programs become more sophisticated over time, where it becomes less
Speaker 1: of a brute force and more of a probabilistic approach.
Speaker 1: But it's still, you know, it's still going about it
Speaker 1: this way. It's not like innovating. It's not it's not
Speaker 1: creating a new defense or a new attack like you
Speaker 1: would see chess masters do. However, there are computers that
Speaker 1: are capable of actually learning, right, there are There is
Speaker 1: such a thing as machine learning. UM. That's essentially a
Speaker 1: process of trial and error. You give a machine a task,
Speaker 1: it attempts to do this task, You evaluate how the
Speaker 1: machine performed and and indicate in some way in the
Speaker 1: machines programming whether it's succeeded or failed, And then it
Speaker 1: attempts to do it again, maybe perhaps improve upon its performance,
Speaker 1: and through this process of trial and error, which takes
Speaker 1: quite some time even in the machine world, it quote
Speaker 1: unquote learns. It learns how to best approach whatever task
Speaker 1: you've given it. Um it It's it doesn't mean that
Speaker 1: the machine actually understands what it's doing. Right, For the
Speaker 1: example that we've given before, the idea of teaching a
Speaker 1: computer what a cat is like how to recognize a
Speaker 1: cat in a picture. It recognizes cats to some degree
Speaker 1: of of accuracy in photos, but it doesn't know what
Speaker 1: a cat is. It doesn't know how a cat is
Speaker 1: different from a dog. Is only able to recognize, uh,
Speaker 1: the image of a cat inside a picture, and it
Speaker 1: could never understand why cat memes are funny, right, it wouldn't.
Speaker 1: It would just see that there's an awful lot of
Speaker 1: cat content on the Internet, which might lead a a
Speaker 1: stupid computer to believing that cats are the dominant species
Speaker 1: on this planet, which I think most domestic cats would
Speaker 1: agree is the truth. But I I have some you know,
Speaker 1: I don't own a cat, so I dispute us um Now.
Speaker 1: A super intelligent general AI is typically the type we
Speaker 1: see in science fiction stories that pick the man against
Speaker 1: machine skynet. Yeah, that's the big one or or how
Speaker 1: in two thousand one. Uh, but we again, we can't
Speaker 1: be sure that we'll ever be able to create such
Speaker 1: a computer or the software. Really, when we say a
Speaker 1: computer or a machine, we're also we also mean the
Speaker 1: software that would be required to make this happen. And
Speaker 1: in fact it maybe that software becomes the big impediment
Speaker 1: and not not the technological processing part. We don't know,
Speaker 1: but either way, just keep in mind when we talk
Speaker 1: about computers or machines, we're lumping software into that as well. Right,
Speaker 1: So I guess we should transition to talking about where
Speaker 1: we are right now in the development of artificial intelligence.
Speaker 1: And I would say, maybe you can dispute this, but
Speaker 1: the most basic picture of the lay of the land
Speaker 1: today is that we're making lots of progress with individual
Speaker 1: examples of narrow AI and apparently nowhere near anything like
Speaker 1: strong or general AI. I would agree with that. I
Speaker 1: would say that, uh, we've got some great examples of
Speaker 1: very compelling, very accomplished narrow AI stuff that is impressive,
Speaker 1: but then you you just recognize on the face of
Speaker 1: it that it cannot do anything outside of its intended purpose,
Speaker 1: like facial recognition. Again, facial recognition software has gotten really good.
Speaker 1: I mean, just using something like Facebook and going on Facebook.
Speaker 1: The ability for Facebook Facebook's AI, you know, it's facial
Speaker 1: recognition software to identify a person with a pretty good
Speaker 1: accuracy is kind of surprising us. Even if you're you've
Speaker 1: got part of the face obscured, it's pretty good. It's
Speaker 1: it's not perfect. I don't know. It keeps identifying my
Speaker 1: left elbow as Gary Abuse. Well, to be fair, I
Speaker 1: thought I thought for a while that you had actually
Speaker 1: grafted Gary Bucy's head onto your left elbow. Uh so,
Speaker 1: I mean that's really more of a personal problem. I guess. No,
Speaker 1: I guess I shouldn't have gotten that Gary Bucy tattoo. Yeah.
Speaker 1: But uh but again, we wouldn't expect that facial recognition
Speaker 1: software to be able to diagnose a medical issue or
Speaker 1: be able to give us the projection for whether three
Speaker 1: months out, like, none of that would make any sense.
Speaker 1: It doesn't. It doesn't do those things. It is a
Speaker 1: very specific application of AI for a specific purpose, and
Speaker 1: when you think of it that way, a lot of
Speaker 1: our narrow AI is pretty good. Here's some examples I
Speaker 1: would give. I'd say, driverless cars are a good example
Speaker 1: of narrow AI. It's maybe a little more broad than
Speaker 1: your facial recognition is, because it's got to do a
Speaker 1: lot of different stuff. It's got to navigate changing dynamic environments.
Speaker 1: It has to be able to sense things that's going around.
Speaker 1: It has to be able to convert that information in
Speaker 1: in process it in a way that then results in
Speaker 1: actions that it takes, whether that's speeding up or breaking
Speaker 1: or swerving out of the way, whatever that might be.
Speaker 1: There are a lot of different uh um things going
Speaker 1: on here. Now. We have had some fairly tragic, really
Speaker 1: tragic examples of failures of this technology recently that show
Speaker 1: that it has its limitations. It cannot process every scenario
Speaker 1: with perfection. In general, they these driverless cars performed better
Speaker 1: than human drivers. They're able to have full sixty degree
Speaker 1: awareness beyond that really because but it is no longer
Speaker 1: true that they've had no at fault accidents. That was
Speaker 1: true for a long time. Yeah. Now, the in the
Speaker 1: case of the Google one, it's funny because it's kind
Speaker 1: of a backhanded thing where the Google's like, yeah, our
Speaker 1: car assumed that the bus driver would let the car
Speaker 1: in and it turns out that assumption was wrong, which
Speaker 1: is kind of kind of a passive aggressive way of saying,
Speaker 1: it's our fault. They need to adjust their algorithm to
Speaker 1: to include a lower view of human nature. Yes, yeah,
Speaker 1: assume that people are jerk faces. Uh. Then there's the
Speaker 1: Tesla autopilot, which really is more of a driver assist system.
Speaker 1: It's not meant to be a driverless car system. Uh.
Speaker 1: And we've seen several accidents with people in those cars.
Speaker 1: But you know, Tesla, the company itself has said this
Speaker 1: is not meant to be an autonomous car, it's meant
Speaker 1: to be a driver assist feature. Uh. So you could say, well,
Speaker 1: in that case, some of those accidents are at least
Speaker 1: partly the fault of the operators because they were not
Speaker 1: operating the vehicle the way the company had communicated you
Speaker 1: were supposed to, which is like you don't take your
Speaker 1: hands off the wheel, and you maintain awareness and you
Speaker 1: don't just let the car just take over to take over,
Speaker 1: because that wasn't the purpose of the the technology. Beyond that,
Speaker 1: we've got things like virtual assistance. I think we're all
Speaker 1: familiar with these things like Siri or Alexa or Google's
Speaker 1: assistant these are all voice activated interfaces. They involve some
Speaker 1: natural language processing button cheeky jokes, cheeky jokes ultimately, but
Speaker 1: their pre programmed cheeky jokes. They're not coming up with
Speaker 1: new jokes, right. Someone had to record all those things.
Speaker 1: And I mean even if you were to record every
Speaker 1: single word in whatever language you were programming in, uh,
Speaker 1: that would you'd still have to figure out a way
Speaker 1: of placing them in the right order for the syntax
Speaker 1: to make sense. But ultimately, these are interfaces, right. It's
Speaker 1: not any different than say a graphic user interface or
Speaker 1: a gooey or even a text based interface. It's just
Speaker 1: it's just a level of interaction that seems to suggest
Speaker 1: the presence of intelligence, but doesn't actually mean it's intelligence.
Speaker 1: It still involves artificial intelligence because it involves that natural
Speaker 1: language processing and probabilistic determination of which, you know, what
Speaker 1: was it that you were asking for and what's the
Speaker 1: most likely answer to your query so that you get
Speaker 1: what you want. If if that and work, then it
Speaker 1: would be pointless using these things, right, You would ask
Speaker 1: Serie a question and the response would be completely nonsensical
Speaker 1: or irrelevant, and you would never use it again. So
Speaker 1: there's some elements of AI there, but it is very narrow. Uh.
Speaker 1: There's the facial recognition stuff that we've talked about already.
Speaker 1: There's the discipline of machine learning, which is, you know,
Speaker 1: not specific to any one application. It's a discipline, not
Speaker 1: not a specific example, but it does show a concept
Speaker 1: of narrow AI, which is again that trial and error approach. Uh.
Speaker 1: You can also have computer programs that are able to
Speaker 1: infer information based upon some sort of input. UH. Stanford
Speaker 1: researchers showed this off when they had the computer program
Speaker 1: that observed the movements of a pendulum and then was
Speaker 1: able to infer the laws of motion based upon that,
Speaker 1: and it was able to do it in the course
Speaker 1: of like I think, I think, like a maybe a
Speaker 1: day or so. And when you think about how long
Speaker 1: it took humans to put all that together, it's pretty impressive,
Speaker 1: right that a computer was able to put these pieces
Speaker 1: together within a matter of a few hours compared to
Speaker 1: centuries UM. Pretty pretty extraordinary, but again limited in that respect.
Speaker 1: It can't it can't look at anything. It's not like
Speaker 1: you could show it reality television and it could explain
Speaker 1: that to you. Some things are just impossible to understand um.
Speaker 1: And then there, of course robotic platforms designed to observe
Speaker 1: humans as they go through a series of steps and
Speaker 1: then just repeat those series of steps. We've seen these
Speaker 1: in robotics, like pick up this block, move it over here,
Speaker 1: pick up this bottle, poor stuff on the block, move
Speaker 1: the block over here, and then when the robot observes
Speaker 1: it can then repeat. That's the type of machine learning too. Now,
Speaker 1: the further we go from single purpose devices, especially with robotics,
Speaker 1: the more we start to see the limitations of AI. Right.
Speaker 1: The Darker Robotics challenge is a great example of this. Yes,
Speaker 1: and we we've talked about all of the darker robotics
Speaker 1: fails before, and we should emphasize as always that in
Speaker 1: talking about how funny it is to see these robots
Speaker 1: just fall over and be conquered by a door or
Speaker 1: some sand or something, we're not saying that the people
Speaker 1: who created them didn't do an amazing technological accomplishment. I
Speaker 1: mean they did. These things are cutting edge, they're very
Speaker 1: they're very impressive. It's just a testament to how hard
Speaker 1: it is to make a robot that's able to do
Speaker 1: twenty six different physical tasks. Right, Yeah, it's so again,
Speaker 1: it demonstrates that artificial intelligence and robotics designs both are
Speaker 1: our hard problems. They're not not easily conquered, and we
Speaker 1: often forget that when we see really compelling demonstrations of
Speaker 1: either artificial intelligence or robotics, it seems natural that the
Speaker 1: next step forward would be right around the corner. That's
Speaker 1: not necessarily the case. And then, Uh, we got to
Speaker 1: get into how we're treating the whole idea of artificial
Speaker 1: general intelligence in the first place, Like, how do we
Speaker 1: how how would we get to that point? Right? Well,
Speaker 1: we're we're just sort of assuming up until now that
Speaker 1: we can just keep working on the types of artificial
Speaker 1: intelligence projects we've been doing and eventually maybe we'll get
Speaker 1: to some a g I. And that could be a
Speaker 1: very flawed assumption. I mean, it could be that there
Speaker 1: is a problem with our basic approach to a g
Speaker 1: I and we need to go back to ground level
Speaker 1: and start over. For example, the Oxford physicist David Deutsch,
Speaker 1: he he has written about this in a piece from
Speaker 1: October twenties twelve and Eon magazine that I thought was interesting.
Speaker 1: He was talking about our failure to build a g I,
Speaker 1: and Uh, so he says, in principle a g I
Speaker 1: must be possible given the universality of computation. Quote. This
Speaker 1: entails that everything that the laws of physics require a
Speaker 1: physical object to do can, in principle be emulated in
Speaker 1: arbitrarily fine detail by some program on a general purpose computer,
Speaker 1: provided it is given enough time and memory. And so
Speaker 1: he he talks about how the first people to really
Speaker 1: grapple with this were Charles Babbage and Ada Lovelace, who
Speaker 1: did their work with the Difference Engine, which was a
Speaker 1: machine designed to sort of the first computer in many
Speaker 1: ways is enormous, big mechanical computer, right, designed to flawlessly
Speaker 1: replicate the computation power of human quote computers. These people
Speaker 1: who you know, just did computations to fill out out
Speaker 1: mathematical tables. So you'd have a big table had all
Speaker 1: these co signed values or logarithm values in it, and
Speaker 1: they'd make mistakes, and so they wanted, you know, Babbage
Speaker 1: wanted a computer that could It was a machine that
Speaker 1: could put these numbers out without making any mistakes along
Speaker 1: the way. And of course there was a later spiritual
Speaker 1: successor to the Difference Engine, which was the general purpose
Speaker 1: outworking of its principle, the analytical engine. You could use
Speaker 1: an early form of computer memory to reprogram itself for
Speaker 1: any computational tasks, even to the point where Lovelace herself
Speaker 1: had envisioned a future in which music and art could
Speaker 1: be reduced into I shouldn't say reduced, but converted into
Speaker 1: mathematical expressions that a computer would be able to process
Speaker 1: and recreate, which was incredibly prescient. Yeah, so this is
Speaker 1: more than a hundred years ago people having the idea
Speaker 1: of general computer intelligence, and and and it's true what
Speaker 1: what they envisioned is in fact correct, because Deutsch says
Speaker 1: he himself formally proved the principle of universality of computation
Speaker 1: a few decades decades ago using quantum theory of computation.
Speaker 1: So this is now an established principle in in computation theory.
Speaker 1: But so, h G I must in principle be possible,
Speaker 1: and yet we have just made abysmally little progress toward it.
Speaker 1: We we've got all these narrow AI functions, but as
Speaker 1: far as a G I, there's just nothing. Uh. And
Speaker 1: so concerning the study of artificial intelligence, Deutsch writes, I
Speaker 1: cannot think of any other significant field of knowledge in
Speaker 1: which the prevailing wisdom, not only in society at large
Speaker 1: but also among experts is so beset with entrenched, overlapping
Speaker 1: fundamental errors. So that's a that's an intense critique. And
Speaker 1: what does he have to say, Well, well, we'll get
Speaker 1: to this in a second. But he he ties it
Speaker 1: back into some more progress in the history of computing theory.
Speaker 1: So he brings up Alan Turing. He says, Turing came
Speaker 1: along after, you know, much later after Babbage and Lovelace,
Speaker 1: and articulated the idea of the universality of computation that
Speaker 1: any of the distinctive attributes of the human brain could
Speaker 1: be reproduced by a properly designed computer. And then Deutsch
Speaker 1: says that since Turing, so that sort of sparked a debate,
Speaker 1: and since then the intellectual intellectual world has been mostly
Speaker 1: split between two camps. There's one camp who says that
Speaker 1: a g I is impossible, and the other camp says
Speaker 1: it's imminent you know any day now, Yeah, well, and
Speaker 1: and Deutsch says both of these are wrong. And the
Speaker 1: first camp is usually motive motivated either by supernaturalism, you know,
Speaker 1: they say there's something about the mind that is magical right.
Speaker 1: That well, those are the ones who argue the mind
Speaker 1: over the brain, right, the idea that the mind somehow
Speaker 1: is more than the manifestation of activity within within our bodies. Right,
Speaker 1: Or if it's not necessarily a supernatural objection, it's some
Speaker 1: kind of philosophical objection that the Deutsch thinks is incoherent.
Speaker 1: None of these ideas hold water for him, right, like
Speaker 1: like like, For for some inarticulate reason, machines would be
Speaker 1: incapable of possessing this faculty. Huh. And yet Deutsch says,
Speaker 1: the second camp, meanwhile, is has been led severely astray
Speaker 1: because they've failed to understand the primary feature distinguishing human
Speaker 1: minds from other physical objects like computers. And that's creativity, right,
Speaker 1: that idea of of innovation and being able to come
Speaker 1: up with an idea that is not necessarily a product
Speaker 1: of just experimentation or trial and error. I would I
Speaker 1: would argue also that that second camp has largely been
Speaker 1: led astray by equating the idea of progress towards a
Speaker 1: g I with other types of progress like Moore's law,
Speaker 1: where you see Moore's laws progress and it seems to
Speaker 1: have this very steady rate. Yeah, you. You'll see this
Speaker 1: sometimes in like a Kurswilian type thought, where people say,
Speaker 1: look at look at the rate of progress through Moore's law,
Speaker 1: we can extrapolate this to a general progress of computational
Speaker 1: power and thus machine intelligence, and from this rule we
Speaker 1: can predict that the singularity will come in twenty whatever. Yeah,
Speaker 1: but the problem there is that one it does not
Speaker 1: necessarily take into account the progress with software sophistication, which
Speaker 1: does not necessarily keep up with Moore's law. Nor does
Speaker 1: it take into account the fact that artificial intelligence depends
Speaker 1: more on more than just processing power, and that our
Speaker 1: understanding of intelligence in general doesn't follow the same pattern
Speaker 1: as Moore's laws. So there are a lot of other
Speaker 1: factors that could put the brakes on that on that
Speaker 1: journey to a g I not saying that it makes
Speaker 1: it impossible, but rather that the timeline maybe longer than
Speaker 1: what some of these enthusiasts project. Yes, and so so.
Speaker 1: To come back to Deutsch at the end of his argument,
Speaker 1: he seems to suggest, at least the way I read
Speaker 1: him is he's saying we need a philosophical revolution in epistemology,
Speaker 1: and that's the study of how we know things. You
Speaker 1: know how how you know, um a revolution in the
Speaker 1: philosophy of epistemology before we can create a g I,
Speaker 1: because we don't even have a correct philosophical model of
Speaker 1: how humans actually do generate creativity. And I'd agree with
Speaker 1: him there. I think we we don't have a coherent
Speaker 1: understanding of what exactly creativity is. Yeah, and I think
Speaker 1: that's something that we probably I'm not sure I fully
Speaker 1: agree with him that we have to understand that before
Speaker 1: we could create a g I. But I do think
Speaker 1: he makes a strong point. We're trying to make a
Speaker 1: machine that can be as good as a human at
Speaker 1: at creative leaps, and we don't even understand how humans
Speaker 1: make creative leaps, right. We we understand the products of
Speaker 1: that creativity, but we don't understand the mechanism of creativity itself,
Speaker 1: which is and he he illustrates this with common examples.
Speaker 1: You know, the kind of thing that you would You
Speaker 1: can get a computer to do amazing things as long
Speaker 1: as you have specified parameters. You're telling it. Here are
Speaker 1: the rules of go. Here, the types of moves you
Speaker 1: can make. Here, the conditions of winning. Now figure out
Speaker 1: how to win. Okay, Well, that's clear now instead try
Speaker 1: to say, can you figure out what dark matter is?
Speaker 1: Go computer. You can't write a program for that, because
Speaker 1: it requires to to program such a machine, you would
Speaker 1: sort of you would have to know what the parameters
Speaker 1: of success were. And if you knew what the parameters
Speaker 1: of success were, then you would you would have the answer.
Speaker 1: You wouldn't need to ask the computer in the first place. Yeah. Yeah,
Speaker 1: this is where you get into that that issue of uh, well,
Speaker 1: the way to get around that ultimately is assuming you
Speaker 1: get to that first step where you can create a
Speaker 1: machine where you can have it have at least some
Speaker 1: element of a g I, have it designed its own successor,
Speaker 1: and then then you get into the deep thought Earth model.
Speaker 1: You know, that might be a solution. But so anyway
Speaker 1: to finish up with Deutsch, what what he says in
Speaker 1: the end is really we're making no significant progress at
Speaker 1: all on a g I. Not because people aren't working
Speaker 1: hard on the problem, but because we lack a fundamental,
Speaker 1: ground level piece of information that's necessary to designing this
Speaker 1: capability and machines to understanding the nature of the thing
Speaker 1: we call intelligence. And until we make that philosophical breakthrough
Speaker 1: about the nature of creativity in a in epistemology, we're
Speaker 1: just fumbling around in the dark with AI research. Yeah,
Speaker 1: so it would be like landing on a planet and
Speaker 1: someone tells you, hey, it's a planet. That's that's like
Speaker 1: Earth to the point where there's Starbucks. Okay, okay, there's
Speaker 1: only one Starbucks. And you land on that plane, you
Speaker 1: land on that exactly for the bathroom. You land on
Speaker 1: that planet and uh, you have no map, you have
Speaker 1: no coordinates or anything. You're just told you need to
Speaker 1: go to Starbucks. Well, you don't know what direction to
Speaker 1: head in. You don't know where it is in relation
Speaker 1: to where you are, you don't know where to look. Uh,
Speaker 1: the same sort of thing, like like you might find it,
Speaker 1: you might fall upon it, but odds are you would
Speaker 1: just spend a whole lot of time just randomly walking around,
Speaker 1: not making any real progress. That's kind of the same thing.
Speaker 1: Like we're fumbling about because we don't have a big picture,
Speaker 1: we don't have a map, we don't we don't know
Speaker 1: how to get the pathway to the destination we want
Speaker 1: to reach. And this idea is actually it's kind of
Speaker 1: exciting to me because it makes me think about, oh wow,
Speaker 1: how I mean what if somebody figures this out in
Speaker 1: my lifetime? What if somebody discovers here, really, this is
Speaker 1: the best way to characterize what creativity is and how
Speaker 1: it happened in the brain. Wouldn't that be a fascinating
Speaker 1: thing to understand? Well? And it would be neat because
Speaker 1: you could then take that information and think back on
Speaker 1: individuals that we identify as being incredibly creative, right like,
Speaker 1: Like then you could sit there and think, wow, so
Speaker 1: this this who whomever was the author of Shakespeare's Place
Speaker 1: it was William Shakespeare. You could then start to characterize
Speaker 1: in a more kind of analytical way, which would be
Speaker 1: fascinating to me because you could say, like, these are
Speaker 1: the qualities that Shakespeare must have possessed in order no
Speaker 1: oxford Ians come on kidding, insulting my intelligence. That's a
Speaker 1: button that you can push very easily. But then we
Speaker 1: have some other ideas about artificial general intelligence and and
Speaker 1: some of the problems that have come along with misrepresenting,
Speaker 1: misrepresenting either intentionally or otherwise, what our official intelligence is. Yeah,
Speaker 1: another thing I want to mention is a criticism leveled
Speaker 1: that I that I've read before, by the uh, the
Speaker 1: what would you call him and just general, very smart,
Speaker 1: interesting guy. Jaron Lanier. He's a technologist, virtual reality pioneer.
Speaker 1: He writes about technology and culture. Super smart dude. Yeah,
Speaker 1: I And I like him a lot. I really like
Speaker 1: reading what he has to say, even when I don't
Speaker 1: agree with him. He's one of those Yeah, he's one
Speaker 1: of those people who I like to read him. Disagree
Speaker 1: with me, Yeah, yeah, I I I'm the same page.
Speaker 1: But yeah. Anyway, So he says that sometimes the way
Speaker 1: we invoke the very concept of artificial intelligence can have
Speaker 1: negative impacts on research progress and on human culture. And
Speaker 1: so the example he gives, uh, and I'm gonna just
Speaker 1: try to give a summary rather than getting into the details. Uh.
Speaker 1: In this New York Times article of his that I
Speaker 1: read from a while back, called the First Church of Robotics,
Speaker 1: he says that um framing advancements in computer software in
Speaker 1: robotics as quote artificial intelligence often over sells a sort
Speaker 1: of fake theatrical advancement and under sells the real function
Speaker 1: of the advancement. And the example he gives is Watson
Speaker 1: on Jeopardy, And the way I'd interpret him, I think
Speaker 1: what he's saying is that, you know, the Jeopardy competition.
Speaker 1: Watson going on there and beating the human contestants is
Speaker 1: sort of theatrical and misleading. If you're trying to imply
Speaker 1: that Watson has the sort of general conversational intelligence of
Speaker 1: a human who can win at Jeopardy, you're misleading people.
Speaker 1: It doesn't have that. But meanwhile, what IBM did do
Speaker 1: is put together a really interesting, powerful, formidable phrase based
Speaker 1: search engine, which is an achievement that is useful and
Speaker 1: deserves recognition in its own light. And you kind of
Speaker 1: undercut that when you try to frame it as something
Speaker 1: that it isn't. I agree entirely with that, by the way.
Speaker 1: I think that that's an excellent and point, and uh,
Speaker 1: I think we see it all the time in lots
Speaker 1: of different realms of artificial intelligence. And I also think
Speaker 1: it's totally fitting for a guy who was a pioneer
Speaker 1: in virtual reality to bring that up, because virtual reality
Speaker 1: had the exact same thing happen. Right. Virtual reality, when
Speaker 1: it was blossoming in the in the late eighties early
Speaker 1: nineties is just getting started. It fizzled out largely because
Speaker 1: there was a misrepresentation of what virtual reality was. People
Speaker 1: had an idea of what it actually was capable of doing,
Speaker 1: and the reality was so far away from that that
Speaker 1: public interests and funding went away, and it took two
Speaker 1: decades to get back to where we should have been
Speaker 1: by the end of the nineties. Misleading marketing killed public
Speaker 1: interests in something that was genuinely interesting. Uh. Yeah. And
Speaker 1: so another point he makes, and this is kind of
Speaker 1: beside the point, but I do think it's interesting, is
Speaker 1: that he points out that trying to construe the latest
Speaker 1: robot or computer program as in some ways significantly resembling
Speaker 1: human intelligence, we not only overestimate what these machines can do,
Speaker 1: we also tend to start underestimating and devaluing human intelligence
Speaker 1: in personhood. And I think there's something to that too. Yeah,
Speaker 1: And and so here's my take on both Deutsche and
Speaker 1: Lanier's points. So, first, I think and I hesitate to
Speaker 1: say that Deutsch is overlooking anything, because I'm sure Deutsch
Speaker 1: is far more intelligent than I am and has thought
Speaker 1: about this in much greater detail than I have. And
Speaker 1: I've not read all of Deutsch's work, So this is
Speaker 1: but based upon what we looked at, I think there
Speaker 1: is the possibility that you're overlooking, not you Joe, but
Speaker 1: Deutsch is overlooking the potential for us to achieve uh,
Speaker 1: the the task of creating an a g I without
Speaker 1: fully understanding the mechanisms that actually create it. Right. This
Speaker 1: is something that does happen where occasionally someone makes something
Speaker 1: that works, but we don't fully understand why it works
Speaker 1: until later. Right. So it's one of the amazing things
Speaker 1: about human ingenuity. Sometimes we create a thing, it does
Speaker 1: something unexpected, or it does something better than what we
Speaker 1: had anticipated, and uh, and it's not because some sort
Speaker 1: of magical light shone down on that innovation. It's that
Speaker 1: we had a limited understanding of what we were actually doing,
Speaker 1: and we did it, and later on, as we get
Speaker 1: a grander understanding of it, a deeper, more thorough understanding
Speaker 1: of it, then we're we say, oh, well, that's why
Speaker 1: it works that way. We didn't know it at the time.
Speaker 1: So I think it's entirely possible, not necessarily plausible, but
Speaker 1: possible that we would create a general artificial intelligence without
Speaker 1: knowing the secret sauce that makes a general intelligence possible.
Speaker 1: It's it's one of those things that could just arise
Speaker 1: as a product of complexity once we have sufficiently sophisticated,
Speaker 1: narrow AI, and we've banded enough of them together that
Speaker 1: it seems like it's emergent, and it's not necessarily truly emergent,
Speaker 1: and may just seem that way to us because we
Speaker 1: don't fully understand the mechanisms that made it a general
Speaker 1: intelligence UM So I don't necessarily agree that in order
Speaker 1: to get to that point we have to have a
Speaker 1: deeper understanding of what makes human creativity possible. It may
Speaker 1: turn out that way. It may just, by happenstance, turn
Speaker 1: out that we get a better understanding of human creativity
Speaker 1: before we ever get a g I. But I don't
Speaker 1: know that it's necessary. I don't know if that's like
Speaker 1: if there's like a direct path and human creativity is
Speaker 1: a stop that we have to hit before we hit
Speaker 1: a g I. Um As for a linear I do
Speaker 1: agree with your interpretation, uh, And I do think that
Speaker 1: when you have these misrepresentations, whether they were intentional or not,
Speaker 1: and often I think they are unintentional. Often I think
Speaker 1: it's an interpretation that say that a media outlet takes
Speaker 1: or is you know, some some sort of pr person
Speaker 1: has created a communication that is not entirely accurate. Again,
Speaker 1: not through any attempt to mislead the public, but just
Speaker 1: through a miss misunderstanding of what's going on. It is
Speaker 1: harmful in the long run, both because we overestimate where
Speaker 1: where we are in AI and also we don't appreciate
Speaker 1: where where we actually are right and and uh again,
Speaker 1: if you if you do it enough, the same thing
Speaker 1: that happened with VR could happen with AI, and that
Speaker 1: you get people less interested in the discipline. It means
Speaker 1: you have fewer computer scientists going into the field, you
Speaker 1: have less uh funding going into projects that could push
Speaker 1: the field forward, and ultimately we all are left behind
Speaker 1: as a result of it. So to all you journalists
Speaker 1: out there, do your best to make sure you're representing
Speaker 1: AI stories accurately, but also say what's genuinely cool about them?
Speaker 1: Oh yeah, no, don't you know, don't dismiss it, but
Speaker 1: at the same time, don't build it as something that
Speaker 1: it is not. I think people can sometimes, in service
Speaker 1: of trying to be skeptical about things, also sort of
Speaker 1: overcorrect into just kind of pessimism and hating everything. Yes,
Speaker 1: you know, it's yeah, it's very easy to go to
Speaker 1: the extremes where uh again, where you know, you you
Speaker 1: want to be realistic, but you don't want to be uh,
Speaker 1: you don't want you don't want to disregard stuff you
Speaker 1: want don't want to say, like, well, it's not general
Speaker 1: artificial intelligence. That care, right, it's not general artificial intelligence.
Speaker 1: Therefore it's not important. Ye, that's that's not true. So
Speaker 1: so what is it going to take for us to
Speaker 1: get to a g I. Joe, tell me what's the step?
Speaker 1: How do we do it? If we knew we'd do it?
Speaker 1: We don't know. Nobody knows. Well, there goes my plans
Speaker 1: for tomorrow. But there's a few hypotheses. Right, There are
Speaker 1: a few different avenues that we could in theory. Take
Speaker 1: one we can discuss, I think is the the incremental approach, right,
Speaker 1: sort of the additive emergent a g I approach, and
Speaker 1: that would be narrow AI plus narrow AI plus narrow AI,
Speaker 1: and you just keep adding them up somehow. Eventually you
Speaker 1: can add together enough of these narrow AI s that
Speaker 1: you have generated by addition and artificial general intelligence. Is
Speaker 1: this a good hypothesis? And of course we don't know
Speaker 1: the answer to that. It may very well be that
Speaker 1: artificial general intelligence becomes the product of or rather the
Speaker 1: summation of a whole bunch of different types of narrow AI.
Speaker 1: You could think of it in the way if you
Speaker 1: look at a human being and you say, all right, well,
Speaker 1: what are the faculties that uh, that contribute to intelligence
Speaker 1: as we understand them, And you start with the senses,
Speaker 1: all all the stuff that is in that allows us
Speaker 1: to take an input of information, and you say, all right, well,
Speaker 1: we've got to make sure that we can simulate all
Speaker 1: of that in our machine so that it has some
Speaker 1: equivalent to the senses of sight and smell and touch
Speaker 1: and taste and hearing and all this kind of stuff.
Speaker 1: But then also maybe we add on some memory, and
Speaker 1: then maybe also some object recognition, and then also some
Speaker 1: some basic limbic responses, and then you just keep on
Speaker 1: adding things until you get to a level of complexity
Speaker 1: that is uh. It is is enough for you to say, well,
Speaker 1: if this isn't general AI, it's it's a close enough
Speaker 1: to not matter, right, if it's if it's sufficiently sophisticated
Speaker 1: or complicated to the point where it appears to be
Speaker 1: general AI, then it kind of is general AI. Even
Speaker 1: if that wasn't a let's start from square one and
Speaker 1: build up to general AI approach and uh and it
Speaker 1: maybe it will be that that's the path we have
Speaker 1: to take. That that because as Deutsch points out, we
Speaker 1: lack this this key piece of understanding of what makes
Speaker 1: human intelligence so special, at least as far as we
Speaker 1: can tell. I mean, we're the only ones who seem
Speaker 1: to possess it. Um If if we cannot figure that out,
Speaker 1: and that's a necessary step to go from scratch to
Speaker 1: build out a g I, it may very well be
Speaker 1: that it's just through the process of building out a
Speaker 1: machine that has all of these different narrow AI elements
Speaker 1: that are working together in some way. And I mean
Speaker 1: that alone is a monumental task, right, not just to
Speaker 1: develop each type of narrow AI, but then to figure
Speaker 1: out how do you build a framework where these can
Speaker 1: all work together and there can be some sort of
Speaker 1: processing unit that acts as like the conductor that understands
Speaker 1: what all of this data means and assign it importance
Speaker 1: and be able to create responses to it. That's a
Speaker 1: non trivial problem all by itself. But it maybe that
Speaker 1: that's the approach to a g I, or maybe we
Speaker 1: just you know, maybe maybe it just happens accidentally. You know,
Speaker 1: we've talked about this before, to the idea of if
Speaker 1: you were to create a sufficiently complicated neural network that
Speaker 1: perhaps intelligence would just be an emergent faculty, that it
Speaker 1: would it would uh manifest simply through the complexity. Uh.
Speaker 1: Somehow I find that this. I find it intuitively unlikely.
Speaker 1: But then again, what's our intuition worth, you know, I
Speaker 1: mean almost nothing? Yeah, I mean it's it's part of
Speaker 1: You could argue, well, the brain is really a complicated
Speaker 1: organ and maybe if we were to create an artificial
Speaker 1: neural network that had a sufficient level of complexity, wouldn't
Speaker 1: necessar really have to match the human brain, but it
Speaker 1: would have to be at least closer than what we
Speaker 1: can accomplish right now that intelligence would naturally arise from it.
Speaker 1: There have been people who have argued that perhaps the
Speaker 1: Internet itself could become an artificial general intelligence, because if
Speaker 1: you think of all the different nodes on the Internet
Speaker 1: as being elements of a neural network, it's getting more
Speaker 1: and more complicated every single day. But if it is,
Speaker 1: it's a it's a brain that has lots of lots
Speaker 1: of mental issues because those machines aren't always on all
Speaker 1: the time. The communication lines can get fouled by various means,
Speaker 1: and as far as we have been able to tell, uh,
Speaker 1: the Internet has not become an intelligent entity of its own,
Speaker 1: and if it is, it is obsessed with cats to
Speaker 1: a level is clearly unhealthy. I mean, it's just it's
Speaker 1: a problem. But that that's It's got a huge, massive
Speaker 1: subconscious right, you know, only a tiny bit of the
Speaker 1: Internet is what we see. Well, not only that, but
Speaker 1: it has like you know, the the classic depiction of
Speaker 1: the angel and the devil on the shoulders that that
Speaker 1: guide a person's decisions. It has several billion of those,
Speaker 1: and many of them are terrible, terrible, terrible entities that
Speaker 1: comment only on YouTube. Okay, so I've got another scenario.
Speaker 1: What would you call it if you just have, like say,
Speaker 1: some project at Google and they just say, yeah, we
Speaker 1: just built it. Here it is, we built your a
Speaker 1: g I. Yeah, I call this one the Grand Slam.
Speaker 1: The idea that you know, you start off with the
Speaker 1: intent of building artificial general intelligence, and this would mean
Speaker 1: instead of going that narrow AI plus narrow AI approach,
Speaker 1: you identify a strategy to create a general intelligence from
Speaker 1: from scratch, from from a starting point. But again I
Speaker 1: would argue that this this kind of falls into that
Speaker 1: that category that Deutsch was pointing at, saying that without
Speaker 1: that full understanding of human intelligence, it would be really,
Speaker 1: really hard, I think, to go this route because it
Speaker 1: presupposes that we understand enough about intelligence to be able
Speaker 1: to create an artificial version of it. Um. I don't
Speaker 1: know that that is something that we could possibly do
Speaker 1: without that deeper understanding that Deutsch said. We might be
Speaker 1: able to create uh, machines that outwardly appear to possess it,
Speaker 1: and you could argue that we've already done that with
Speaker 1: some of the examples we've already given, but it doesn't
Speaker 1: really have that capability. So one last question before we
Speaker 1: wrap up part one, how do we know once we've
Speaker 1: made it? It seems like it would be obvious, but
Speaker 1: would it be necessarily I don't know, because again, if
Speaker 1: if you have a sufficiently complex device that is capable
Speaker 1: of giving what seems to be until eligent answers to
Speaker 1: various questions, you get back to that Chinese room problem,
Speaker 1: right like, is it actually an intelligent machine? Or is
Speaker 1: it just complicated enough that it appears to be intelligent
Speaker 1: to us and doesn't matter at that point. Well, I mean,
Speaker 1: if we go with the crack our definition of intelligence,
Speaker 1: one way we could check to see if it is
Speaker 1: truly an artificial general intelligence is check to see if
Speaker 1: it's solutions to various general problems are working. That's an
Speaker 1: excellent point. You know, it has it reduced to the
Speaker 1: number of steps or the complications in solving problems and
Speaker 1: multiple areas. If we accept that as the definition, then
Speaker 1: I think that would make it pretty easy to figure
Speaker 1: out because you could, uh, it kind of becomes like
Speaker 1: that Simpson's episode where Homer goes to work for Scorpio
Speaker 1: and he walks, is are you guys working? Yeah? Could
Speaker 1: you work harder? Yes? The same sort of thing, right, like,
Speaker 1: can you solve this Rubik's cube? Going back to that example,
Speaker 1: and then it solves the Rubik's cube and then say
Speaker 1: all right, we're gonna reset it to the state it
Speaker 1: was in when you started. Can you solve it in
Speaker 1: fewer moves than you did in the previous one? And
Speaker 1: can you continue to do that? Can you improve on
Speaker 1: your performance until you reached an ideal version of that? Though?
Speaker 1: I guess the real test for for a g I
Speaker 1: would be not just that, because maybe you could program
Speaker 1: into it some kind of you know, the parameters for
Speaker 1: success in solving a Rubics cube. Yeah, now there are
Speaker 1: algorithms for that are known algorithms, because that's how human
Speaker 1: Rubik's cube competitors. Yeah. I mean, we can design a
Speaker 1: computer program right now to solve Rubik's cubes. And that's
Speaker 1: not all that impressive because it's a specified outcome. We
Speaker 1: know what the what the what all the parameters are.
Speaker 1: The really impressive thing would be to do the thing
Speaker 1: Deutsch said and say, hey, can you figure out what
Speaker 1: the heck dark matter is for us? And it comes
Speaker 1: up with the solution and we say, wow, that's really interesting,
Speaker 1: and we do some empirical tests and we figure out
Speaker 1: the son of a gun it was right. Yeah, yeah,
Speaker 1: that would be if if you if you were to
Speaker 1: argue that that's not a g I, I think at
Speaker 1: that point most people would say, like you, at this point,
Speaker 1: you're arguing semantics at best at best, because the result
Speaker 1: is exactly what we would expect with general artificial general intelligence. Well,
Speaker 1: that kind of wraps up this, uh, this laying the groundwork,
Speaker 1: which is pretty extensive. We we laid on a big
Speaker 1: old foundation for our next episode. Well, next time we're
Speaker 1: gonna be talking. We're gonna build on what we've talked
Speaker 1: about today and specifically talk about the idea of an
Speaker 1: AI arms race, taking this idea of a g I
Speaker 1: one step further into the geopolitical realm. Yeah, and that's
Speaker 1: where things really get messy. So you're gonna wanna tune
Speaker 1: into that and pay attention to to what we have
Speaker 1: to say. We're gonna be talking about some interesting characters
Speaker 1: out there who have a lot of opinions on the matter,
Speaker 1: So tune into that, and remember you can send us
Speaker 1: any questions or comments you might have. Our e knowlogress
Speaker 1: is FW thinking at hell stuff, what's dot com? Or
Speaker 1: drop us a line on Facebook or Twitter. If you
Speaker 1: search f w Thinking in Facebook, our profile will pop
Speaker 1: right up. Our Twitter handle is FW thinking. You look
Speaker 1: forward to hearing from you, and we'll talk to you
Speaker 1: again really some For more on this topic in the
Speaker 1: future of technology, visit forward Thinking dot Com brought to
Speaker 1: you by Toyota. Let's go places