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 Marvin, I
Speaker 1: love you. Remember, I'm programmed for you. I'm Jonathan Strickland,
Speaker 1: and I'm Joe McCormick. And our other host, Lauren is
Speaker 1: not with us today, but you you had something to
Speaker 1: say about Marvin there. Now, does that mean today we're
Speaker 1: gonna be talking about somebody named Marvin? We are? Is
Speaker 1: it going to be Lee Marvin? It will not be
Speaker 1: Lee Marvin apart from this very moment where we are
Speaker 1: talking about Lee Marvin. No, Lee Barman wouldn't make a
Speaker 1: lot of sense on this party. No. I mean, you know,
Speaker 1: the lyric refers to Marvin the paranoid android from Hitchhiker's
Speaker 1: Guide to the Galaxy. But we're not talking about that
Speaker 1: Marvin either. Although we're talking about a Marvin who had
Speaker 1: a lot to do with artificial intelligence, which Marvin the
Speaker 1: paranoid Android possessed a great love that was kind of
Speaker 1: a long way of going about that. We're talking about
Speaker 1: Marvin Minsky. Marvin Minsky. So he passed away last month. Yes,
Speaker 1: Marvin Minsky was an artificial intelligence pioneer associated with the
Speaker 1: m I T. And he he passed away on Sunday,
Speaker 1: January four, sixteen, and a bunch of publications that I
Speaker 1: read and I'd seen online had been running some retrospectives
Speaker 1: of his life, looking at his influence on his main field,
Speaker 1: which I guess you would say is artificial intelligence, but
Speaker 1: also on the history of computational theory and on cognitive science.
Speaker 1: You might say, yeah, it's interesting because uh, you know,
Speaker 1: we often will refer to artificial intelligence as being multidisciplinary.
Speaker 1: It's not. You know, you could argue artificial intelligence is
Speaker 1: its own discipline, but within that you have other disciplines.
Speaker 1: It's far more complex than just a label. And really,
Speaker 1: if you're talking about the the entire scope of artificial intelligence,
Speaker 1: it almost necessarily encompasses all of human knowledge. Yeah, you're
Speaker 1: You're not wrong, I mean, and Minsky in many ways
Speaker 1: was kind of a human example of this, because he's
Speaker 1: certainly had a wide variety of interests and um and
Speaker 1: so we wanted to really kind of talk about him,
Speaker 1: and in a way we're thinking about doing occasionally an
Speaker 1: episode about a forward thinker of some sorts. So we
Speaker 1: may in the future do episodes about other Forward thinkers.
Speaker 1: This is sort of a pilot program for that, and uh,
Speaker 1: I know, there are a lot of people we would
Speaker 1: love to talk about in the future, So we're gonna
Speaker 1: kind of start with this one. And if you guys
Speaker 1: out there have people, you know, Forward thinkers you would
Speaker 1: love us to to profile, you should definitely let us know.
Speaker 1: And we'll talk more about that at the end. But
Speaker 1: let's talk more about Minsky. Yeah, and so we we
Speaker 1: thought it'd be good to talk about Minsky because we
Speaker 1: so often talk about artificial intelligence on the program and
Speaker 1: it's one of the great future frontiers that we keep
Speaker 1: coming back to, and that his his influence on the
Speaker 1: development of artificial intelligence in the SA and half of
Speaker 1: the twentieth century has been so profound, and also his
Speaker 1: views on where artificial intelligence had been going over the
Speaker 1: last decade are really interesting. Yeah, we'll conclude with our
Speaker 1: discussion on that, but to start off at the beginning,
Speaker 1: Minsky himself was born in New York City on August nine,
Speaker 1: nineteen seven. Yeah. I so there was a piece that
Speaker 1: we read that was a profile of Minsky from The
Speaker 1: New Yorker in nineteen eighty one that was written by
Speaker 1: the physicist Jeremy Bernstein. It was just shy of a
Speaker 1: full autobiography. I mean it was Yeah, it was really
Speaker 1: comprehensive and and one of the things that makes me
Speaker 1: realize is that we totally do not have space on
Speaker 1: this podcast to cover all of the interesting aspects of
Speaker 1: his life. So we're just going to do a kind
Speaker 1: of highlight reel of some of the things that stuck
Speaker 1: out to us. But if you're interested in the stuff
Speaker 1: we have here, I would highly recommend checking that out
Speaker 1: to learn more about him. But anyway, that piece is
Speaker 1: going to be the source of several quotes that I've
Speaker 1: pulled about Minsky's childhood and and edgy cation that I
Speaker 1: thought would help give you a better picture of sort
Speaker 1: of the color of his personality in life. Right, Yeah,
Speaker 1: because this guy was a lot of people described him
Speaker 1: as being imaginative and humorous and maybe some people would
Speaker 1: say eccentric. Certainly they would say, you know, he was
Speaker 1: very enthusiastic, so a vibrant personality, not like some person
Speaker 1: who would cloister himself away from everybody else in order
Speaker 1: to work on ideas. He strikes me as uh a
Speaker 1: quintessential outside the box thinker, you know what I mean,
Speaker 1: somebody who would always approach a problem in a in
Speaker 1: a strange and usually fruitful way. And he loved to
Speaker 1: incorporate students in his in his thinking. You love to
Speaker 1: collaborate with students because I think, although I don't think
Speaker 1: he ever necessarily articulated it this way, to me, it
Speaker 1: sounds like he loved to talk with people who had
Speaker 1: not yet learned what was impossible, because that meant that
Speaker 1: they didn't put those constraints on their ideas from the getting.
Speaker 1: And that's where you see a lot of innovation. Yeah,
Speaker 1: I know exactly what you mean, and I think it's
Speaker 1: kind of inspiring in that right. I agree, But but
Speaker 1: I want to start with with a little a picture
Speaker 1: of little Marvin. So he's talking about the different interests
Speaker 1: he had in in subjects in school when when he
Speaker 1: was a kid, and he he talks about his interesting
Speaker 1: chemistry and this is his sort of hands on approach
Speaker 1: to doing experiments and learning things firsthand. So he says,
Speaker 1: I've been reading some chemistry books and I thought it
Speaker 1: would be nice to make some chemicals. In particular, I
Speaker 1: had read about ethel merkup Tan, which interested me because
Speaker 1: it was said to be the worst smelling thing around.
Speaker 1: I went to and this is his teacher, Zim mr Zim.
Speaker 1: I went to Zim and told him that I wanted
Speaker 1: to make some. He said, sure, how do you plan
Speaker 1: to do it? We talked about it for a while,
Speaker 1: and he convinced me that if we were going to
Speaker 1: be thorough, we should first make ethanol, from which we
Speaker 1: were to make ethel chloride. I did make the ethanol
Speaker 1: and then the ethyl chloride, which instantly disappeared. It's about
Speaker 1: the most volatile thing there is. I think Zim had
Speaker 1: fooled me into doing this synthesis, knowing that the product
Speaker 1: would evaporate before I had actually got to make that
Speaker 1: awful merkuptan. I remember being sort of mad and deciding
Speaker 1: that chemistry was harder than it looked on paper, because
Speaker 1: when you synthesize something, it can just disappear. I thought
Speaker 1: this was an interesting metaphor also for the way you
Speaker 1: would end up chasing the basis of physical intelligence. Sure, yeah,
Speaker 1: I mean it's it's you know, there's one thing that
Speaker 1: he would refer to, uh, you know. He would say
Speaker 1: that intelligence was sometimes why you would call a suitcase
Speaker 1: word because he would cram so many different concepts into
Speaker 1: the the suitcase of intelligence. And uh, we've also mentioned
Speaker 1: this when he would say the same thing about consciousness.
Speaker 1: But I know that we've on this episode or not
Speaker 1: this episode, but on the show I've talked about how
Speaker 1: consciousness is kind of one of those ideas where you
Speaker 1: almost define it by striking things out from under the
Speaker 1: umbrella of consciousness, right, and then you're like, okay, so
Speaker 1: whatever's left, that's what consciousness is. It's it's just some people,
Speaker 1: some people have made the criticism of of consciousness theory
Speaker 1: that you know, it's almost like when you're saying what
Speaker 1: consciousness is, you're just making a list of all the
Speaker 1: things the brain does and then striking out everything that
Speaker 1: we fully understand or not fully understand, but everything that
Speaker 1: we understand the physical basis, we've got a good grip
Speaker 1: on the actual mechanisms that are going on behind the scenes.
Speaker 1: And so actually, I'm okay with using consciousness as a
Speaker 1: placeholder until we figured everything else out. That will kind
Speaker 1: of come into play with his ideas on what thought
Speaker 1: was all about. But before we get to that, we
Speaker 1: also need to talk that about how when he was
Speaker 1: uh when in the four he was joined the U. S. Navy,
Speaker 1: served in the Navy until nineteen. Yeah. I think he
Speaker 1: explains that he he joined the Navy because he was
Speaker 1: saying that he knew they would send him to electric
Speaker 1: electricians electrical school whatever they called it back then, They
Speaker 1: would send him to school if he if he joined.
Speaker 1: So I think he was going to he was on
Speaker 1: track to be a radar technician or something like that.
Speaker 1: But of course that you know, he was in the military,
Speaker 1: so they had him do basic training, he says. And
Speaker 1: he talks about this group that he was in the
Speaker 1: in the Navy with, and he says, our little group
Speaker 1: was a strange kind of mini Harvard in the middle
Speaker 1: of the Navy. Everything seemed unrealistic. I practiced shooting down
Speaker 1: planes on an anti aircraft simulator. I held the base record.
Speaker 1: I shot down a hundred and twenty planes in a row.
Speaker 1: I realized I had memorized the training tape and knew
Speaker 1: in advanced exactly where each plane would appear. But I
Speaker 1: must have some odd skill in marksmanship. Many years later,
Speaker 1: my wife and I were in Mexico on a trip.
Speaker 1: We came across some kids shooting at things with a rifle.
Speaker 1: I asked them if I could try it, and I
Speaker 1: hit everything. It seems that I have a highly developed
Speaker 1: skill at shooting things for which there is no explanation.
Speaker 1: I also, I also love that he he talks about
Speaker 1: how there were maybe four people in my company who
Speaker 1: are really remarkable, including a mathematician and an astronomer. And
Speaker 1: he started hearing this, you think there's like a nerdy
Speaker 1: version of inglorious bastards that could be made from Minsky's
Speaker 1: experience in the Navy. So instead of being these these
Speaker 1: tough like special forces guys, it's like the brilliant mathematicians
Speaker 1: and scientists who were part of the Navy and then
Speaker 1: went on to go and do other things. Um Minsky,
Speaker 1: after he left the Navy, joined well. He attended Harvard University,
Speaker 1: and this is where we really get a first look
Speaker 1: at how he was interested in so many different fields
Speaker 1: that collectively lent themselves to this idea of artificial intelligence.
Speaker 1: He studied psychology, and he studied neurophysiology and physics. When
Speaker 1: he graduated, his degree was in mathematics. But he was
Speaker 1: interested in all this stuff while he was in school. Yes,
Speaker 1: he moved around a lot like he He says quote.
Speaker 1: I was nominally a physics major, but I also took
Speaker 1: courses in sociology and psychology. I got interested in neurology
Speaker 1: around the end of high school. I started thinking about thinking.
Speaker 1: One of the things that got me started was wondering
Speaker 1: why it was so hard to learn mathematics. You take
Speaker 1: an hour a page to read this thing, and still
Speaker 1: it doesn't make sense. Then suddenly it becomes so easy,
Speaker 1: it's trivial. I had never thought about that before, but
Speaker 1: he's exactly right about understanding math concepts. It's always been
Speaker 1: that way for me. That you can go over how
Speaker 1: to use a certain operator. You know, you're learning a
Speaker 1: new type of mathematical function or operation, and it's just
Speaker 1: banging your head against a wall until you get it.
Speaker 1: And then as soon as you get it, it's it
Speaker 1: seems so simple, it's stupid. Yeah, this was how I
Speaker 1: experienced math when I was in high school. I remember
Speaker 1: by the time I got to trigonometry, uh it was
Speaker 1: it didn't take very long for that switch to click
Speaker 1: in my head where I would see what I was
Speaker 1: supposed to do and understand why I was doing it
Speaker 1: that way. It wasn't until I hit Calculus, and I'm
Speaker 1: not certain what the roadblock was, but for some reason,
Speaker 1: when I hit Calculus, that switch would take longer and
Speaker 1: longer to click. And I would often attribute that to
Speaker 1: the fact that I think the way it was being
Speaker 1: taught was here's how you do this, not here's why
Speaker 1: you do this. So I think it also depends on
Speaker 1: your approach to learning that concept. But I totally get
Speaker 1: what he's saying. Where you look at something and it
Speaker 1: just feels like I could read this for the twentieth time,
Speaker 1: but it's still not going to become any more clear
Speaker 1: to me, and then two hours later, when you're doing
Speaker 1: something totally different, you just think, oh wait, now I
Speaker 1: get it. Yeah, yeah, yeah, it's it says something very
Speaker 1: interesting about human cognition. And I think this insight that
Speaker 1: he mentions here could very well come into play when
Speaker 1: we're talking about how you construct intelligence from base parts, uh,
Speaker 1: because there's something happening here. There's something about intuition and
Speaker 1: about maybe the formation of pathways like you would have
Speaker 1: in your old at work, where you know, once the
Speaker 1: pathway is set, now you can find your way back
Speaker 1: there quite quite easily. Yeah, you could even think of
Speaker 1: that as being, you know, make it an analogy of
Speaker 1: a physical pathway through a forest. Like the first time
Speaker 1: you go and make a path, you're cutting your way through.
Speaker 1: It's a lot of work. Uh. It might even be
Speaker 1: hard for you to retrace it the first time, But
Speaker 1: after you've done it a couple of times, there's a
Speaker 1: pretty worn path there that's much easier to follow. It's
Speaker 1: it's a fitting analogy in many ways. But Minsky also had,
Speaker 1: as we've said, very eclectic interests when he was in school.
Speaker 1: For example, there is all throughout his life he was
Speaker 1: interested in music, and I love what he says about
Speaker 1: music here. This is another interesting thing about cognition that
Speaker 1: I'll get to in this. He says, quote, I had
Speaker 1: also taken a number of music courses with Irving Fine.
Speaker 1: He usually gave me ces or d's, but he kept
Speaker 1: encouraging me to come back. He was a tremendously honest man.
Speaker 1: Is that referring to the season d's. I'm not sure. Uh.
Speaker 1: He says he was a tremendously honest man. I think
Speaker 1: the problem was that I was basically an improviser, one
Speaker 1: of those people who can occasionally improvise an entire fugue
Speaker 1: in satisfactory form without much conscious thought or plan. The
Speaker 1: trouble is, the more I work on a piece deliberately,
Speaker 1: the worse it gets. I can totally get behind this too,
Speaker 1: because you know, we're both writers, and I'm sure that
Speaker 1: I know what he's talking about. There's been experiences where
Speaker 1: you'll sit down and you just you get a nugget
Speaker 1: of inspiration and you just start writing. And why you
Speaker 1: end up whether you may have to go back and
Speaker 1: revise a little bit, but in large part, it's just
Speaker 1: it feels really satisfying. And there are other times when
Speaker 1: you think I have an idea, I'm gonna go ahead
Speaker 1: and start the whole process of outlining all of this
Speaker 1: and then blocking it all out, and then I'll actually
Speaker 1: get around to writeing it, and then, like you know,
Speaker 1: two hours later, you're just like, I don't know, whatever
Speaker 1: made me think this was worth putting down on paper. Yeah,
Speaker 1: I know exactly what you mean. I mean usually, I
Speaker 1: would say for most people and for myself, more work
Speaker 1: leads to improvement, but not all the time. Sometimes you
Speaker 1: can just write a thing to death. The more you
Speaker 1: keep tinkering with it, the less interesting it becomes. Uh So,
Speaker 1: by nineteen fifty one, he had graduated Harvard the year before,
Speaker 1: and then he goes and joins Princeton University for postgraduate studies,
Speaker 1: and uh that same year he built the world's first
Speaker 1: neural network simulator. And this is this is a thing
Speaker 1: that is worth noting. It's a neural network simulator in
Speaker 1: nineteen fifty one, So try to imagine that this is
Speaker 1: not based on microchips. No. Um. Also, it was called SNARK,
Speaker 1: which is great. It's s n A r C. And
Speaker 1: that stands for stochastic Neural analog Reinforcement Calculator, which really
Speaker 1: clears it all up. Uh. Stochastic is one of those
Speaker 1: words that's going to pop up a couple of times
Speaker 1: as we talk about this. In case you aren't familiar
Speaker 1: with the term, it essentially means random. That's that's kind
Speaker 1: of a easy way of translating it. Uh So. He
Speaker 1: graduated Princeton in nineteen fifty four with a doctorate in mathematics.
Speaker 1: The following year, in fifty five, he invented the confocal
Speaker 1: scanning microscope, which actually uses a little spatial penhole inside
Speaker 1: the lens, and the purpose of that is to filter
Speaker 1: out all the light that would not be in focus,
Speaker 1: so it therefore creates a higher resolution image of whatever
Speaker 1: it is you're looking at through the microscope. So it's
Speaker 1: kind of just really improving resolution now. In nineteen fifties seven,
Speaker 1: Marvin Minsky began to work for m I T. Massachusetts
Speaker 1: Institute of Technology, and he was specifically interested in researching
Speaker 1: computers in order to understand human thought, which uh might
Speaker 1: seem counterintuitive to some people, like why would you look
Speaker 1: at computers in order to get a better understanding of
Speaker 1: how humans think? There it was a really good analogy,
Speaker 1: I thought. In um one of the pieces we looked
Speaker 1: at it was on edge dot org that was talking
Speaker 1: about it was interviewing different people with recollections about Mints
Speaker 1: his life. But there was one part of this piece
Speaker 1: that talked about how, even though the analogy was not perfect,
Speaker 1: if you were a person today who wanted to understand
Speaker 1: how birds fly, probably one of the easiest ways to
Speaker 1: start would be to look at how airplanes work. Even
Speaker 1: though airplanes and birds work in a different way, you
Speaker 1: can start getting the principles about what you know how
Speaker 1: things stay aloft in the air by looking at what
Speaker 1: an airplane needs to do in order to not fall.
Speaker 1: And I think the same thing could be true about
Speaker 1: computers and brains. Both do computation, both to information processing.
Speaker 1: So if you look at a thing that's kind of
Speaker 1: graceful and mysterious, like a human mind, and you want
Speaker 1: to try to understand it, it it might be a good
Speaker 1: place to start to say, Okay, how does information processing
Speaker 1: work in a machine? Yeah, I mean, I I'm always
Speaker 1: hesitant about that. I there are a lot of things
Speaker 1: that Minsky talks about that I like a lot, but
Speaker 1: it's because it lates to the mind, not the brain.
Speaker 1: And uh it's because I know that computer's process information
Speaker 1: in a very different way than the way we think
Speaker 1: in general. I mean, if you're talking about classical computers
Speaker 1: and the neural networks that we have in our in
Speaker 1: the wet ware we have in our heads. Uh, So
Speaker 1: I'm always hesitant to make that comparison. However, when you
Speaker 1: go to an abstract level of the human mind as
Speaker 1: opposed to the human brain, then suddenly these conversations make
Speaker 1: a lot more sense to me, and I'm a lot
Speaker 1: more um inclined to agree and engage on that level
Speaker 1: as opposed to just crossing my arms and going yeah, well,
Speaker 1: I mean I think it plays on the same principle
Speaker 1: as the idea of the universal computer, right that if
Speaker 1: you have a touring machine, you know you have a
Speaker 1: basic universal computer. It doesn't matter what the hardware is.
Speaker 1: If you can do the basic computing functions, you can
Speaker 1: do the same job as a different kind of computer
Speaker 1: that uses different hardware, right right, Well, moving on with
Speaker 1: our little biography on Marvin Minsky before we get into
Speaker 1: some more details about his specific ideas in fifty eight
Speaker 1: or fifty nine. And the reason why I put that
Speaker 1: down is because depending on what source you read, some
Speaker 1: site that uh, this happened in nineteen fifty eight, others
Speaker 1: in nineteen fifty nine. My suspicion as this particular thing
Speaker 1: took a long time to happen and probably started in
Speaker 1: fifty eight and became official in fifty nine. Uh. Minsky
Speaker 1: partnered with a man named John McCarthy who was a
Speaker 1: professor of electrical engineering at m I t and together
Speaker 1: they formed the m I t Ai Laboratory. And as
Speaker 1: a side note, John McCarthy is generally attributed as the
Speaker 1: person who actually coined the phrase artificial intelligence in the
Speaker 1: mid nineteen fifties, I didn't know. Yeah, so he was another, uh,
Speaker 1: founding father of the science of artificial intelligence. Like you know,
Speaker 1: if you were to make a list, you'd have people
Speaker 1: like Ada Lovelace and Alan Turing and Marvin Minsky and
Speaker 1: John McCarthy all on that list easily. I mean, you
Speaker 1: would not want to leave them off. He would. Minsky,
Speaker 1: that is, would stay with m i T for the
Speaker 1: rest of his career. He became the Donner Professor of
Speaker 1: Science in nineteen seventy four and the Toshiba Professor of Media,
Speaker 1: Arts and Sciences at the m i T Media Lab
Speaker 1: in nineteen Yeah. Well, I think we should now just
Speaker 1: transition to a more general discussion of what were some
Speaker 1: of Minsky's influential ideas, concepts, and books, because, as we've
Speaker 1: said earlier, he was massively influential. We we don't have
Speaker 1: time to talk about everything, but we want to highlight
Speaker 1: a few interesting things that he brought forward and and uh,
Speaker 1: A lot of his work kind of relates to this
Speaker 1: running theme of the whole and its parts. Yeah, like
Speaker 1: so whole as in w h O L E uh,
Speaker 1: the entirety and its parts, specifically with reference to intelligence.
Speaker 1: Right that that's a running theme throughout a lot of
Speaker 1: his work. One of his early ideas something that he
Speaker 1: called frames. It was this concept that he proposed in
Speaker 1: nineteen and defined frames as the general information a computer
Speaker 1: system would have to possess before it can make specific decisions.
Speaker 1: So what do you mean by that? All Right? So
Speaker 1: let's say that you've you've built yourself a robot and
Speaker 1: you want the robot to do things. In order for
Speaker 1: the robot to to do the things you wanted to do,
Speaker 1: you have to teach the robot certain concepts. First, I
Speaker 1: love that sentence. You want the robot to do things? Yeah, well,
Speaker 1: let's you know, you could just build a robot, right,
Speaker 1: I mean maybe maybe you're rawsom and you're just like,
Speaker 1: I just want some universal robots run around this place.
Speaker 1: But you know, I don't care if they do anything. No,
Speaker 1: you must send it upon the world with a mission.
Speaker 1: But if you here's a simple example, you've got a roomba.
Speaker 1: You've built a roomba. Well, before you can just set
Speaker 1: a rumba down and have it vacuum of a room,
Speaker 1: you've got to teach it general concepts, things like uh, walls,
Speaker 1: you know, Uh, the what happens if you come up
Speaker 1: to allege all this kind of stuff. You have to
Speaker 1: teach it all of this before it can complete eat
Speaker 1: the task it was built for. So one example that
Speaker 1: is commonly sited is imagine you've got a computer system
Speaker 1: and you've got a series of rooms, and these rooms
Speaker 1: are connected to each other through doorways that actually have
Speaker 1: doors on them. In order to have a this computer
Speaker 1: system be able to navigate through those rooms, you know,
Speaker 1: presumably through some sort of robotic form, it would have
Speaker 1: to understand how doors work. What a door is, that
Speaker 1: a door could swing either inward or outward, the various
Speaker 1: mechanisms that might be employed in order to work a door,
Speaker 1: whether it's a door knob or um a handle that's
Speaker 1: got a latch that you have to press down with
Speaker 1: your thumb, or maybe even a bar that you have
Speaker 1: to push or pull. And you have to teach the
Speaker 1: computer system all these things. Now, these are things that
Speaker 1: humans once you teach them. Once humans are really good,
Speaker 1: Like they can recognize get the basic concept of a door.
Speaker 1: You've got pretty much all doors ready to go. Yeah,
Speaker 1: you might get thrown by something like a revolving door
Speaker 1: that you see for the first time. But most most
Speaker 1: of the time, you're gonna see a door and you're
Speaker 1: gonna think, all right, this is either going to open
Speaker 1: inward or outward. It's not going to do anything else
Speaker 1: unless it's Star Trek and then goes But yeah, like
Speaker 1: you said, you're a robot and you come to a
Speaker 1: door with different types of door knobs, or with door
Speaker 1: knobs at different height, or you or you only taught
Speaker 1: the robot how to open a door if it opens outward.
Speaker 1: What if it's a push door and it doesn't have
Speaker 1: a knob. Yeah, And these are all sort of things
Speaker 1: that that we take for granted as humans because we've
Speaker 1: had some experience and we're able to extrapolate. Computer systems
Speaker 1: in general are not good at this. Computer systems are
Speaker 1: very good at performing tasks that they've been programmed to do,
Speaker 1: but they're not so good at doing tasks they haven't
Speaker 1: been programmed to do. Who to thunk it? So um,
Speaker 1: But he was he was using this this idea of
Speaker 1: frames as a way of explaining this concept of These
Speaker 1: are the These are the sort of contextual information buckets
Speaker 1: that you need to each a computer system in order
Speaker 1: for it to be able to do the thing you
Speaker 1: designed it to do, and whatever environment that might be,
Speaker 1: whether it's you know, a robot moving around rooms or
Speaker 1: autonomous some submarine exploring underwater features. Anything. Really though, I
Speaker 1: would suggest that, well, I guess I'd have to guess
Speaker 1: because I don't know for sure, but I would guess
Speaker 1: that Minski would agree that if the human mind can
Speaker 1: figure out things without having to be told them, a
Speaker 1: computer potentially can too. It just needs the right equipment.
Speaker 1: It needs the right process is to be able to
Speaker 1: know things that without being told them it would need
Speaker 1: to be able. It would still need some frames, right
Speaker 1: Like for example, if I, uh, let's say that that
Speaker 1: your mind is completely wiped, Joe. So let's imagine it's
Speaker 1: last Tuesday, because we all know what happened that day. Uh,
Speaker 1: and I were to produce for you a coffee mug
Speaker 1: and I point to this, and I say, this is
Speaker 1: a mug. Sometimes people refer to it as a cup,
Speaker 1: and it holds liquid. This is where the liquid goes.
Speaker 1: As a human being, what's liquid, We've already covered that,
Speaker 1: We've already gotten to that part. Though that stuff we
Speaker 1: already covered. This is actually pretty advanced in the day.
Speaker 1: This is like four pm on Tuesday, and so, but
Speaker 1: at that point you would you would be able to
Speaker 1: recognize another cup, even if it were a different color,
Speaker 1: different size, even if it were a slightly different shape.
Speaker 1: Let's say it's like a novelty cup, so it's in
Speaker 1: the shape of a tartist or something, you would know.
Speaker 1: You might not know that that's a tartist, but you
Speaker 1: would know that that was a cup, and you would
Speaker 1: be able to use it as such. Whereas computer systems,
Speaker 1: or if you haven't built in any sort of machine
Speaker 1: learning so that they can actually start to extrapolate information,
Speaker 1: they can't do that right if it's if it might
Speaker 1: not even recognize the same cup, if the light in
Speaker 1: the room is different, or if it's a little too
Speaker 1: far away from the camera, are a little too close
Speaker 1: because the size will look different to it by perspective.
Speaker 1: So uh, you know, you would still need those frames
Speaker 1: at least to exist for some amount of information so
Speaker 1: that the machine could know what to do. But the
Speaker 1: goal of course and artificial intelligence is to get machines
Speaker 1: sophisticated enough where those frames can be more basic that
Speaker 1: you don't have to map out every single possibility in
Speaker 1: order for a machine to be able to understand that
Speaker 1: the machine itself would be able through perhaps even trial
Speaker 1: and error, learn how things work. Like if you taught
Speaker 1: the uh the the machine how certain doors. Like let's
Speaker 1: say that you've got ten different varieties of doors in
Speaker 1: this other scenario we mentioned, and you teach it about
Speaker 1: five of them and how those five work, and it
Speaker 1: has all the basic information of how all the doors work,
Speaker 1: but the other five are slightly different variations on it.
Speaker 1: And you have taught it how to uh do trial
Speaker 1: and error so that it can actually experiment when it
Speaker 1: owners a door that doesn't fit the five that it
Speaker 1: was taught. That would be more like he will it
Speaker 1: will do science in order to break on through to
Speaker 1: the other side. Right yeah, it might just turn into
Speaker 1: a robotic kool aid man, you know, and just crash through.
Speaker 1: But your goal is so that it actually learns and
Speaker 1: experiments and continues to uh grow its own knowledge. Okay, Well,
Speaker 1: let's look at another one of Minsky's influential ideas, which
Speaker 1: is his society of mind theory. Now, he had a
Speaker 1: book called Society of Mind I think in nineteen eighty five, right, Yeah,
Speaker 1: before that, he had started really playing around with this
Speaker 1: concept all the way back in the nineteen sixties, and
Speaker 1: what really inspired him was that he started to work
Speaker 1: on a very basic robotics system. Uh. And it was
Speaker 1: a very simple exercise and artificial intelligence. Simple in the
Speaker 1: sense that it was elegant, not simple as in it
Speaker 1: was easy to do. Yeah, and Minsky had done some
Speaker 1: work with with robotic motion and the manipulation of of
Speaker 1: arms and claws and stuff like that, right even back
Speaker 1: when he was in school. This was one of the
Speaker 1: great stories from that piece in The New Yorker that
Speaker 1: Minsky tells. So once the Harvard zoology professor John Welsh
Speaker 1: offered Minsky access to his lab and his equipment after
Speaker 1: Minsky found out that scientists didn't know how the nerves
Speaker 1: in crayfish worked and uh Minsky told The New Yorker,
Speaker 1: I became an expert at dissecting and crayfish. At one point,
Speaker 1: I had a crayfish claw mounted on an apparatus in
Speaker 1: such a way that I could operate the individual nerves.
Speaker 1: I could get the several jointed claw to reach down
Speaker 1: and pick up a pencil and wave it around. I'm
Speaker 1: not sure that what I was doing had much scientific value,
Speaker 1: but I did learn which nerve fibers had to be
Speaker 1: excited to inhibit the effects of another fiber so that
Speaker 1: the claw would open. And it got me interested in
Speaker 1: robotic instrumentation, something that I have now returned to, trying
Speaker 1: to build better micro manipulators for surgery and the like. Yeah,
Speaker 1: So in between his UH Frankenstein like experiments with crayfish
Speaker 1: claws and developing UH micro mechanical systems for surgery, he
Speaker 1: was experimenting with this very basic artificial intelligence robotic arm apparatus.
Speaker 1: And it consisted of a computer that did calculations, a
Speaker 1: camera that could focus in on what needed to be manipulated,
Speaker 1: a robotic arm, and then a series of blocks. And
Speaker 1: the idea was that if you could UH teach the
Speaker 1: computer system what certain terms were, like I want you
Speaker 1: to build a tower, that you would then be able
Speaker 1: to teach the robot how to pick up a block,
Speaker 1: how to manipulate it so it's in the right place,
Speaker 1: how to stack the blocks so that they're stable, and
Speaker 1: also to teach them things that people kind of grasp
Speaker 1: pretty quickly once they get out of the infant stage
Speaker 1: of their lives, like if you're trying to build a
Speaker 1: tower and you've got three blocks stacked on one another
Speaker 1: and you need to put you know, your instruction is
Speaker 1: make this tower four blocks high. One solution is not
Speaker 1: to grab the block that's on the bottom of the tower,
Speaker 1: pull it free, and then try and place it at
Speaker 1: the top. Was the one I was thinking, was you
Speaker 1: would it would it necessarily understand that you have to
Speaker 1: lay down the lowest level first, right if you try
Speaker 1: and all, right, well, you know, let's let's start from
Speaker 1: the top and work our way down. That doesn't you
Speaker 1: can't do that. This is something we've talked about before.
Speaker 1: But I do think it's an interesting thing about artificial
Speaker 1: intelligence that's often overlooked is the basic locomotion and physical
Speaker 1: interactions with objects is a kind of intelligence. Absolutely, It's
Speaker 1: it's not at all just like, well, that's the dumb
Speaker 1: thing the robots do, and artificial intelligence is getting them
Speaker 1: to be chatter bots, you know, to pass the Turing
Speaker 1: test and have have conversations. I mean, knowing how to
Speaker 1: move things in your environment in a smart ways absolutely
Speaker 1: artificial intelligence. Sure, yeah, you know, knowing how to handle
Speaker 1: any particular you know, object so that you are not
Speaker 1: damaging it, that you can move it effectively, you might
Speaker 1: even want to program in things where the robot knows
Speaker 1: I cannot move this particular object because either it's too
Speaker 1: delicate or it's too heavy, or whatever it may be. Yeah, okay,
Speaker 1: but back to MINS. So when Minsky was working on this,
Speaker 1: he began to think about all the different elements that
Speaker 1: are necessary in order to make this task possible, and
Speaker 1: he began to look at kind of discrete facets of
Speaker 1: intelligence that are required in order for you to do this,
Speaker 1: and that's where he had this breakthrough, this idea that
Speaker 1: led to the society of mind idea. So in the
Speaker 1: nineteen seventies he began to develop this theory and he
Speaker 1: published a lot of essays on the subject, and he
Speaker 1: worked with an m. I. T. Mathematician named Seymour Papert
Speaker 1: on several of the early ideas. So the book came
Speaker 1: out in ve and the argument he makes is that
Speaker 1: the mind not the brain, that the human mind is
Speaker 1: made up of individual parts called agents and agents It's
Speaker 1: important to note have no mind of their own, So
Speaker 1: agents themselves have no emotion, they have no thought. They
Speaker 1: are aspects of the mind itself, and each agent is
Speaker 1: responsible for a particular aspect of intelligence. It's through their
Speaker 1: cooperation that conscious thought emerges, according to this society of
Speaker 1: mind theory, and it's really about how the mind works
Speaker 1: at a conceptual level as opposed to the biological level.
Speaker 1: This is an idea I've encountered before in cognitive science,
Speaker 1: but I wasn't aware in the past that it really
Speaker 1: came from Minsky. Um. But I think there's a lot
Speaker 1: to this. I think this is a very I would
Speaker 1: consider this a very plausible and convincing way to think
Speaker 1: about what consciousness and intelligence are. And even if you
Speaker 1: are hesitant to argue for that, at the very least,
Speaker 1: it is a very compelling way to think of artificial intelligence.
Speaker 1: How do you get a machine to do any particular
Speaker 1: thing that would require intelligence on behalf of that machine?
Speaker 1: But if if you're not convinced, maybe we should look
Speaker 1: at an example. And this comes straight from the book.
Speaker 1: In fact, I read the book. Um. It's very easy
Speaker 1: to read. Uh. Each idea is about a page long,
Speaker 1: and each chapter is a collection of between eight or
Speaker 1: nine ideas, maybe more a fewer depending upon the chapter
Speaker 1: in their thirty seven chapters. UH and I Love I
Speaker 1: actually also watched the beginning of a lecture that Minsky gave.
Speaker 1: There's an open course on m T where you can
Speaker 1: go to m I t s website and watch a
Speaker 1: lecture series led by Minski himself from two thousand eleven. Oh,
Speaker 1: that sounds fun. I kind of want to get on that.
Speaker 1: It's pretty cool. And at the very beginning he talks
Speaker 1: about how he really liked Society of Mind, and the
Speaker 1: main reason he liked is that so each idea is
Speaker 1: like a page long, and if you don't like it,
Speaker 1: you can totally skip it and go to the next one.
Speaker 1: It's really easy. Like this other book I wrote later,
Speaker 1: the chapters are much longer, and if you don't like
Speaker 1: an idea, you kind of have to just keep going. Uh.
Speaker 1: You can't really hear the students, but I would hope
Speaker 1: there was some good natured chuckling going on at any rate.
Speaker 1: So he gives an example in his book, and he
Speaker 1: presents a very simple scenario, the idea that you are
Speaker 1: told to pick up a cup of tea and you're
Speaker 1: gonna you're gonna drink from this cup occasionally, but it
Speaker 1: is immediately What I'm thinking of is something you mentioned
Speaker 1: on the podcast a few episodes episodes ago, which is
Speaker 1: the office simulatory pick up the cup and throw it.
Speaker 1: Just just start throwing things across the virtual reality office.
Speaker 1: That that video is hilarious, by the way. So from
Speaker 1: his book, he says, let's think about all the elements
Speaker 1: that go into picking up a cup of tea uh
Speaker 1: in the in this idea of society of mind that's
Speaker 1: made up of agents, he says, you're grasping. Agents want
Speaker 1: to keep hold of the cup. And he uses the
Speaker 1: word want as in not not that they have an
Speaker 1: actual motivation, but that's their purpose. So you're grasping. Agents
Speaker 1: want to keep hold of the cup. You're balancing. Agents
Speaker 1: want to keep the tea from spilling out your thirst.
Speaker 1: Agents want you to drink the tea. You're moving, agents
Speaker 1: want to get the cup to your lips. So he
Speaker 1: argues that these four agents working together, although each one
Speaker 1: is independent and that is important, they're independent of one another,
Speaker 1: but they're working together in concert, can accomplish the task
Speaker 1: of allowing you to drink your tea. And more importantly,
Speaker 1: you can do this while doing other things like you could.
Speaker 1: His example was walking around like at a like at
Speaker 1: a tea party type deal, and you're having conversations with people,
Speaker 1: and you're just casually holding your tea and occasionally sipping it.
Speaker 1: But you're not thinking about that, right, at least not
Speaker 1: consciously thinking about it, right. But clearly your brain is
Speaker 1: doing all this work, right, It's not like you're just
Speaker 1: magically holding this cup and keeping the liquid from spilling
Speaker 1: out and all that kind of stuff. But he said
Speaker 1: that you know consciously, you're not really aware of it. Uh.
Speaker 1: So my example, I said, you could, uh, you could
Speaker 1: drink your tea while not interrupting other stuff you might
Speaker 1: be doing, such as telling the Queen of England that
Speaker 1: hilarious story about the time you got drunk on the tube. Um,
Speaker 1: because as soon as I think tea, I'm like, well,
Speaker 1: clearly I'm If I'm drinking tea, I'm obviously having as
Speaker 1: as I am want to do. And so Minsky would
Speaker 1: go on to argue that the concept of agents is
Speaker 1: a necessary concept. He argues that if we cannot and
Speaker 1: this is a quote, explain the mind in terms of
Speaker 1: things that have no thoughts or feelings of their own,
Speaker 1: will only have gone around in a circle. So, in
Speaker 1: other words, he says that if your definition of thinking
Speaker 1: requires you to talk about smaller elements that also think
Speaker 1: you're you're not really describing thinking, You're just you're just
Speaker 1: shifting the definition around two different parts of the brain.
Speaker 1: This is something that parallels uh and analogy that I
Speaker 1: remember coming across in the works of Daniel Dennett, the
Speaker 1: cognitive philosopher, and he so he presents this idea of
Speaker 1: the Cartesian theater. Have you ever heard this, I've heard
Speaker 1: the term. Well, essentially, he says, okay, so some people
Speaker 1: think that look there is what your eyes do is
Speaker 1: that they take in light from your surroundings and they
Speaker 1: paint a picture. And it's like the brain projects that
Speaker 1: picture as a movie screen for you to see. But
Speaker 1: who's doing the seeing? So then you have to imagine
Speaker 1: that really inside your brain is a small is a
Speaker 1: little brain that gets to sit in the movie theater
Speaker 1: of your mind and watch the screen that is made
Speaker 1: by your eyes and so, but who's seeing within that
Speaker 1: brain in that movie theater. So if you keep postulating
Speaker 1: a little person inside you that is the audience of
Speaker 1: your thoughts or the audience of what you are perceiving,
Speaker 1: it's an infinite, infinite grass right right, And that's not
Speaker 1: helpful if you want to have an actual meaningful conversation
Speaker 1: about how is this working? Um. So the book divides
Speaker 1: up concepts into these categories that kind of mentioned that,
Speaker 1: where you have like maybe up to eight or nine
Speaker 1: of these one sheet descriptions collected under these categories. And
Speaker 1: those categories include things like holes and parts, kind of
Speaker 1: what I was referring to earlier, conflict and compromise, the self,
Speaker 1: problems and goals, and lots of other ones like I said,
Speaker 1: They're thirty seven in that book, and each section details
Speaker 1: Minsky's ideas on how the human mind processes this information
Speaker 1: on a conceptual level. So Minski uses the example of
Speaker 1: building blocks early on in the book to demonstrate all
Speaker 1: those all the considerations one has to take in order
Speaker 1: to complete that simple task. So, uh again, you know,
Speaker 1: back to that idea of I want you to build
Speaker 1: a steeple. I don't know what a steeple is. A
Speaker 1: steeple is going to be two green blocks and one
Speaker 1: orange triangle that goes on top. And then once you
Speaker 1: teach it, then it, you know, it knows how to
Speaker 1: do that, but it has to you have to give
Speaker 1: it the all you know to find all the agents
Speaker 1: to identify things like a block versus a triangle, how
Speaker 1: to pick that up, how to place them, the fact
Speaker 1: that the blocks have to go on the bottom and
Speaker 1: the triangle has to go on the top, all this
Speaker 1: kind of stuff. Um. And he says that once you
Speaker 1: break it down into those basic parts, then suddenly these
Speaker 1: these uh advances and artificial intelligence become possible. Um. And
Speaker 1: like I said, you can take an open course on
Speaker 1: the Society of the Mind on the m I T website.
Speaker 1: You can actually find that for free. So if you
Speaker 1: want to check it out, even if you just want
Speaker 1: to see some of the lectures and and hear what
Speaker 1: the man himself had to say about this idea, you
Speaker 1: can go and do that. And I highly recommend checking
Speaker 1: it out at least you know, satisfied your curiosity for
Speaker 1: given a good ten fifteen minutes. The first lecture is
Speaker 1: two hours long, and there are a lot of lectures,
Speaker 1: so um. But yeah, and that this kind of leads
Speaker 1: to that idea of common sense and common sense is
Speaker 1: one of those things that we kind of innately understand
Speaker 1: as human beings. But what does that mean for artificial intelligence? Yeah,
Speaker 1: and this is one of the things that I think
Speaker 1: got mentioned most often, like in the obituaries after he
Speaker 1: passed away, a lot of publications mentioned that he was
Speaker 1: interested in giving computers common sense. But what does that
Speaker 1: really mean from Minsky's point of view, Well, it goes
Speaker 1: back to that that description I talked about earlier, like
Speaker 1: if you're building a tower, you can't we know, you
Speaker 1: can't take a block from the bottom of the tower
Speaker 1: and put it on the top, or you can't start
Speaker 1: at the top and work your way down. Gravity obvious
Speaker 1: to us, but maybe not obvious to a computer. Right,
Speaker 1: So things that are are common sense we often kind
Speaker 1: of dismiss as being easy or simple, or it's just
Speaker 1: a matter of fact, and therefore it's not anything to
Speaker 1: to really worry about, except if you're building an artificial
Speaker 1: system to do those things. The artificial system doesn't know
Speaker 1: any of that, so you have to teach it. And
Speaker 1: I think his point is sort of the common sense
Speaker 1: is not as simple as we think it is. It's
Speaker 1: actually it's actually quite hard. We think common sense is
Speaker 1: something that's very basic or very simple because it's intuitive
Speaker 1: to us, but it's not basic. It's not simple. Common
Speaker 1: sense is incredibly complex. Yeah, he he had a quote
Speaker 1: um that says, a common sense is not a simple thing. Instead,
Speaker 1: it is an immense society of hard earned practical ideas,
Speaker 1: of multitudes of life learned rules and exceptions, dispositions and tendencies,
Speaker 1: balances and checks, which I think is a good way
Speaker 1: of putting it. Like it's stuff that once we humans
Speaker 1: have come in contact with it. You got it right,
Speaker 1: It's like this little, this little box in our brains
Speaker 1: gets checked and we understand that concept from that point forward,
Speaker 1: even if we encounter it in a different context in
Speaker 1: the future. Not so with machines, at least not naturally,
Speaker 1: which is why it's a big problem in artificial intelligence
Speaker 1: that if you can create a machine intelligence that is
Speaker 1: able to mimic that sort of uh feature of human intelligence,
Speaker 1: you're you're way ahead of the game. So um uh yeah,
Speaker 1: it's it's interesting too because you've got this, I like
Speaker 1: your fun fact in here. Well, yes, the fun fact
Speaker 1: is that did you know that Marvin Minsky was consulted
Speaker 1: by Stanley Kubrick as I don't know exactly what you
Speaker 1: call it, maybe sort of a science advisor for two
Speaker 1: thousand one of Space Odyssey. I did, but only because
Speaker 1: Minsky would often have Kubrick over to his house for parties,
Speaker 1: as well as Arthur C. Clark and Isaac Asimov. Minski
Speaker 1: moved in some awesome circles, like like people who were
Speaker 1: really interested in robotics, not just from the academic side,
Speaker 1: but from the literary side. We're all uh in contact
Speaker 1: with him at the time. He taught re Curse Wild,
Speaker 1: didn't he? He may have. I don't know that for
Speaker 1: a fact. I do know that he he had conversations
Speaker 1: with Albert Ein's line and said he couldn't understand a
Speaker 1: word of it. Um he uh. He was friends with Heindline,
Speaker 1: So I mean the guy was like, he was like
Speaker 1: the guy who knew everybody. So it would not surprise
Speaker 1: me to learn that he had taught Kurtzwile we have
Speaker 1: some other stuff to talk about. He has another book
Speaker 1: called The Emotion Machine, which came out in two thousand six,
Speaker 1: and this is sort of following up on some of
Speaker 1: the same ideas earlier in his career. Yeah, it's a
Speaker 1: it's most people refer to it as a sequel to
Speaker 1: Society of Mind. His central argument, and this one is
Speaker 1: that emotions are really just different ways of thinking. Yeah,
Speaker 1: and I've read several quotes of his along these lines
Speaker 1: where he talks about he's sort of urging people not
Speaker 1: to underestimate the the cognitive content of emotions, if that
Speaker 1: makes any sense. He certainly says that you know, the
Speaker 1: ability to have these emotions, whether or not their different
Speaker 1: methods of thinking, uh, hey, they lead to greater intelligence.
Speaker 1: That it creates a new capability of looking at information,
Speaker 1: and it is an interesting way of looking at it right, Like,
Speaker 1: like if you are thinking about something and you're angry,
Speaker 1: you might come to a different conclusion and learn something
Speaker 1: that you otherwise wouldn't have if you were happy or sad. Well,
Speaker 1: it also for me, raises an interesting question, which is
Speaker 1: that we naturally make a distinction between thoughts and feelings.
Speaker 1: We think there are two different species of things. You know,
Speaker 1: I have feelings and then I have thoughts, And I
Speaker 1: can have thoughts about feelings, and I can have feelings
Speaker 1: about thoughts. But are they necessarily different species? Are are
Speaker 1: feelings maybe just another type of thought? Are they just thoughts?
Speaker 1: And this kind of brings us to that amazing documentary
Speaker 1: Inside Out, which a lot of people have said, you
Speaker 1: know it was it's based on some of the most
Speaker 1: current information and scholarship on emotions and memory, and thought, yeah,
Speaker 1: I really haven't seen it. I know, so, I know
Speaker 1: some people who liked it a lot. I I saw
Speaker 1: it so anyway, when I described my reaction to Inside Out,
Speaker 1: which I thought was entertaining, but that's about it. Most
Speaker 1: people think I'm dead inside because like that movie destroyed me,
Speaker 1: I cried, like crazy, like what's wrong with being dead inside? Yeah? Look,
Speaker 1: a lot of Pixar's movies affect me deeply. That was
Speaker 1: just not one of them. However, I did think it
Speaker 1: was a very interesting approach with emotions their connection to
Speaker 1: thought and memory, and it seems very similar in many
Speaker 1: ways to what Minsky was saying. Now, not everybody was
Speaker 1: totally thrilled with this approach. Some people thought it was
Speaker 1: an interesting way of understanding the mind, but a misleading
Speaker 1: way of thinking about how the brain actually works. So
Speaker 1: neurologist Richard Rustack wrote up a review about his you
Speaker 1: know his work on emotions and criticized part of Minsky's approach,
Speaker 1: saying that Ski failed to show how emotional functions relate
Speaker 1: to brain activity. Now, he acknowledged that Minski explains this
Speaker 1: by saying our knowledge of the brain changes so quickly
Speaker 1: that it becomes outdated rapidly. But then Respects says, well,
Speaker 1: how can you possibly draw any meaningful correlation between brains
Speaker 1: and machines if you also are arguing our knowledge of
Speaker 1: the brain changes so quickly as to essentially contradict itself,
Speaker 1: So you can't make any conclusion if part of your
Speaker 1: argument states our knowledge of the brain changes so quickly
Speaker 1: that it changes our understanding. Like, how can you conclude
Speaker 1: anything if at the very start of your argument you say, listen,
Speaker 1: I'm not gonna write about the brain because our knowledge
Speaker 1: of it changes so quickly. Anything I write will be
Speaker 1: out of date by the time this book is published.
Speaker 1: But they're totally like machines like that, he says, You know,
Speaker 1: that's a logical Like there's there's a disconnect there now.
Speaker 1: Respect also wrote that he had some reservations about some
Speaker 1: of Minsky's other assertions, many of which seem to draw
Speaker 1: conclusions of about how the brain works based on how
Speaker 1: large complex computer systems work. So Restack wasn't so sure
Speaker 1: you could support such a connection. But he also said,
Speaker 1: it may turn out that Minski is completely right. We
Speaker 1: just don't have the science to support it one way
Speaker 1: or or you know, deny it one way or the other.
Speaker 1: It's it's just that without knowing, we can't be sure.
Speaker 1: But it may turn out that these are absolutely on target.
Speaker 1: We just we just can't be so so sure of
Speaker 1: it right now. But he did say you could learn
Speaker 1: a lot about how the mind works by reading Minsky's book,
Speaker 1: you just wouldn't learn about how that relates to the
Speaker 1: way your brain functions. So again, the mind being this
Speaker 1: more nebulous platform that rests upon the brain, like it's
Speaker 1: a manifestation of the brain's abilities. Um, and that we
Speaker 1: can learn more about how the mind works, but not
Speaker 1: so much necessarily about the neurology underneath it. Um. Just
Speaker 1: pretty cool, any hope coming through for my theory that
Speaker 1: the brain is just for cooling the blood and we
Speaker 1: really think with our tone nails, h I'm gonna I'm
Speaker 1: gonna say that science does not currently have very much
Speaker 1: support for that particular belief, but shine on you, crazy diamond.
Speaker 1: They also said Galileo was wrong. Okay, and moving on,
Speaker 1: let's talk about Minsky and the concept of free will. Yeah,
Speaker 1: Minsky had some really interesting thoughts on this, and I
Speaker 1: want to read a quote from his paper Matter, Mind
Speaker 1: and Models, and this was cited in another thing I
Speaker 1: read about him. But this quote goes, if one thoroughly
Speaker 1: understands a machine or a program, he finds no urge
Speaker 1: to attribute volition to it. If one does not understand
Speaker 1: it so well, he must supply an incomplete model for explanation.
Speaker 1: Our everyday intuitive models of higher human activity are quite incomplete,
Speaker 1: and many notions in our informal explanations do not tolerate
Speaker 1: close examination. Free will or villa is one such notion.
Speaker 1: People are incapable of explaining how it differs from stochastic caprice,
Speaker 1: but feel strongly that it does so. Stochastic caprice, in
Speaker 1: case you're wondering, would mean random whimsy. Yeah, so he's
Speaker 1: saying that even though we can't really explain, we can't
Speaker 1: give any good account of where free will comes from,
Speaker 1: we insist we must have it, and that it is
Speaker 1: different from just random impulses that we have that we
Speaker 1: act on. Yes, but he continues, I conjectured that this
Speaker 1: idea has its genesis in a strong primitive defense mechanism. Briefly,
Speaker 1: in childhood, we learn to recognize various forms of aggression
Speaker 1: and compulsion, and to dislike them, whether we submit or resist. Older.
Speaker 1: When told that our behavior is controlled by such and
Speaker 1: such set of laws, we insert this fact in our
Speaker 1: model inappropriately, along with other recognizers of compulsion. We resist compulsion,
Speaker 1: no matter from whom and whom is in quote, it's there.
Speaker 1: Although resistance is logically feutile, this resentment persists and is
Speaker 1: rationalized by defective explanations since the alternative is emotionally unacceptable.
Speaker 1: I think that's a very interesting insight. Yeah, And and
Speaker 1: it applies to more than just intelligence, Yeah, you know,
Speaker 1: because it It actually reminds me of any time where
Speaker 1: we encounter something we've never encountered before, and by we,
Speaker 1: I mean humans at large, and we naturally, as curious beings,
Speaker 1: want to understand that thing we've just encountered, and often
Speaker 1: in our first attempts we will we will create explanations
Speaker 1: that don't necessarily correlate to any kind of reality in
Speaker 1: order to explain it. And it's only later on, as
Speaker 1: we start to peel things away that we really see
Speaker 1: what's happening underneath the surface. Yeah, and then totally he
Speaker 1: goes on to apply this same reasoning, uh about the
Speaker 1: origins of our resistance to, you know, the idea of determinism,
Speaker 1: and and our and our tendency toward free will as
Speaker 1: a kind of rebellious impulse against compulsion. Uh. He's like, well,
Speaker 1: hold on, now, if we create intelligent machines and they
Speaker 1: have something like consciousness, will that inherently bring with it
Speaker 1: the illusion of free will and the resistance to the
Speaker 1: idea of compulsion by physical determinism. Would a robot with
Speaker 1: consciousness also insist that it has free will? Yeah, it's
Speaker 1: an excellent question that right now remains in the realm
Speaker 1: of philosophy. One day it will not be though. One
Speaker 1: day it will be a reality whether or not, and
Speaker 1: it may turn out that the answers No, I don't
Speaker 1: need to worry about that. I don't know that that's
Speaker 1: going to be the case, because I I believe very
Speaker 1: strongly that, uh that our our concept of re will
Speaker 1: is based upon ultimately the activity going on in our brains.
Speaker 1: So if we in fact build a system that is
Speaker 1: truly simulating that activity, it stands to reason that whatever
Speaker 1: entity is created from that would also experience that same
Speaker 1: feeling that it possesses free will. Yeah. And even if
Speaker 1: you argued, no, I built you so that you can
Speaker 1: make me toast. It's you know, that's that doesn't matter.
Speaker 1: If someone told me, no, I built you so that
Speaker 1: I can make you toast, I would they say, I'll
Speaker 1: show you why you built me exactly. So, Hey, we
Speaker 1: talked about the future on this podcast. I have to
Speaker 1: assume that Minsky, given all his thoughts about artificial intelligence,
Speaker 1: made some comments somewhere about what he thought the future
Speaker 1: would be like. He talked about the future. And he
Speaker 1: also months before his death, he had there were a
Speaker 1: few different interviews where people were asking him his opinions
Speaker 1: on the current state of artificial intelligence. And I think
Speaker 1: those answers are really interesting too. But uh, you know
Speaker 1: the New Yorker piece that that you dug up from
Speaker 1: nineteen eighty one, it was actually called like it was
Speaker 1: talking about his vision of the future. Although spoiler alert,
Speaker 1: if you read the whole thing, there isn't a lot
Speaker 1: in there about that. It's mostly about a profile of him. Yeah,
Speaker 1: though it is a really good one. It's fascinating. It's
Speaker 1: just that the headline might be a tad misleading, but uh,
Speaker 1: or or it could be more of like a broad
Speaker 1: approach like you know, he he saw this stuff happening
Speaker 1: well before it became reality. In fact, his work is
Speaker 1: what allowed a lot of the artificial intelligence uh developments
Speaker 1: to to take place in the first place. But one
Speaker 1: of the things he talked about was he could envision
Speaker 1: a future and remember this, in which a with a
Speaker 1: relatively small amount of technical improvements in robots would see
Speaker 1: automatic factories in space dead on. And we do have
Speaker 1: robots working in a lot of factories doing automated work,
Speaker 1: just not in space. Oh wait, I forgot. You have
Speaker 1: not seen the factories on the far side of the moon. Well, Jonathan,
Speaker 1: you are in for a pleasant surprise. Yeah. Uh, this
Speaker 1: actually reminds me of a terrible movie by the Asylum
Speaker 1: I once watched, and which robots were being built in
Speaker 1: a space station orbiting the Earth and then sent down
Speaker 1: to Earth. And I thought, what a huge waste of resources.
Speaker 1: Just build them on the Earth. Build them on Earth
Speaker 1: if if that's where they're doing work, why would you
Speaker 1: ever build them in space? It's too expensive? Uh So Yeah,
Speaker 1: that obviously has not panned out. But we are seeing
Speaker 1: a lot more of automated systems in factories around the world,
Speaker 1: a lot more robots being employed to the point where
Speaker 1: we're seeing like the thoughts about robotic drivers and robotic
Speaker 1: drones delivering stuff to us. I mean, it's it's pretty
Speaker 1: far along in that respect. Now, where did Minsky come
Speaker 1: down on that? We talked about the possibility of machines
Speaker 1: developing aciousness? Was was he thumbs up to that? Uh?
Speaker 1: It's interesting. I would argue that his answers, depending upon
Speaker 1: what time of his life you were looking at, Uh,
Speaker 1: went a little back and forth. It was always a
Speaker 1: little vague to me. But he did say that he
Speaker 1: could see a future where machines have minds of their own,
Speaker 1: and their minds would be aware of the various parts
Speaker 1: that make up those minds, kind of the agents, if
Speaker 1: you will, and that what each of those agents would
Speaker 1: be capable of doing, and be able to use that
Speaker 1: knowledge to solve any problems that the machine would encounter.
Speaker 1: It's not the same thing as consciousness, however, Necessarily it
Speaker 1: may just be Oh, I have this task that I've
Speaker 1: never had to do before that I have got to complete,
Speaker 1: but it's similar to all these other things I know
Speaker 1: how to do. Therefore, I'm going to employ all of
Speaker 1: these agents that help me do those similar tasks to
Speaker 1: complete this one. You wouldn't argue that that in itself
Speaker 1: is a manifestation of consciousness. I think if you were
Speaker 1: like that rotten so and so wants me to do
Speaker 1: this thing and didn't even tell me how to do it,
Speaker 1: We'll fine, I'll do it, but I'm not gonna be
Speaker 1: happy about it. Then he'd be like, Okay, that's a
Speaker 1: pretty conscious machine. So I'll show you why you created me, right. So, so,
Speaker 1: you know, I would hate to ascribe a position when
Speaker 1: I myself, I'm not entirely certain where he fell on
Speaker 1: that he may himself have been like, this is a
Speaker 1: philosophical question that fascinates me, but I don't know what
Speaker 1: the answer is, or I don't know what I feel
Speaker 1: the answer will be. Um. So, but you mentioned that
Speaker 1: right before his death he had thoughts about where we
Speaker 1: stand with AI today. Yeah, they were not um complimentary thoughts. Actually,
Speaker 1: you know, he he was very dismissive about certain things,
Speaker 1: Like there was a Washington Post piece that was published
Speaker 1: shortly after his death, and it contained a lot of
Speaker 1: little nuggets about Minsky and what he felt, how he
Speaker 1: felt about certain developments, you know, recent developments and artificial intelligence. So,
Speaker 1: for example, they asked him about UM IBM S Watson.
Speaker 1: I think a lot of people would argue that IBM
Speaker 1: S Watson is a very impressive display of artificial intelligence.
Speaker 1: It's not, or at least a very impressive display of
Speaker 1: word play. And that's kind of what Minsky would say.
Speaker 1: He said he called it an ad hoc question answering machine,
Speaker 1: that it wasn't intelligent, it was just a question answering machine. UM,
Speaker 1: which is I think if you would go to maybe
Speaker 1: the Watson chef style, where it's starting to try and
Speaker 1: invent things based upon other things, it's not doing a
Speaker 1: great job, but it's trying, I think it goes beyond
Speaker 1: question answering machine. But that was his opinion, and he
Speaker 1: also talked about how he felt AI developers were making
Speaker 1: a mistake aiming for what he called the top of
Speaker 1: the AI problem, So, in other words, trying to create
Speaker 1: systems that on their surface appear to be similar to
Speaker 1: human thought, but they lack the foundation of what thought
Speaker 1: is really all about, and therefore it's just it's kind
Speaker 1: of like a chat bot. It's just it's simulating it
Speaker 1: enough so that it seems intelligent, but there's nothing underneath
Speaker 1: it to actually support that supposition. I guess from his
Speaker 1: point of view that the problem might be that it's
Speaker 1: uh that it's lacking these agents, right, the society of mind,
Speaker 1: the basic agents that populate the society that becomes thinking. Right. So,
Speaker 1: in other words, instead of having agents, it's simply uh,
Speaker 1: trying to follow a program that mimics the way humans
Speaker 1: would respond to situations, but without that underlying you know,
Speaker 1: network of agents that are actually making it happen. Uh,
Speaker 1: it's kind of like skipping those those those found that
Speaker 1: foundation in order to just get the result. But that
Speaker 1: means that the underlying system is not as robust as
Speaker 1: what you would need to have a truly intelligent uh computer.
Speaker 1: He also said in an interview with M I T
Speaker 1: Technology Review that the last decade of AI was about
Speaker 1: quote improving systems that aren't very good end quote. So
Speaker 1: he contrasted that with the era of the nineteen sixties,
Speaker 1: the early era of artificial intelligence development, when he said
Speaker 1: that they were having major breakthroughs on the order of
Speaker 1: every couple of days, that he and students would talk
Speaker 1: about these ideas and come up with new approaches and
Speaker 1: new concepts about thought that would lead to enormous potential
Speaker 1: breakthroughs in artificial intelligence. So these days it's every two
Speaker 1: or three years you might see a breakthrough. And part
Speaker 1: of that, he argued, was that we rely too heavily
Speaker 1: on so called experts in AI. He was actually calling
Speaker 1: back for the days when he would work with students who,
Speaker 1: again because they don't know what's impossible, end up asking
Speaker 1: questions and coming up with ideas with those constraints, and
Speaker 1: therefore push forward the discipline much further than people who
Speaker 1: have a preconceived idea of what is and isn't a possibility,
Speaker 1: that have already placed limitations on themselves that they aren't
Speaker 1: aware don't really exist. So it was interesting, I, you know,
Speaker 1: I I can see his point. Also, there is something
Speaker 1: to say about when you get to a when you
Speaker 1: when a new discipline is created, you would probably expect
Speaker 1: advancements in that discipline to be extremely rapid early on,
Speaker 1: because there was nothing before. But as you build and
Speaker 1: build and build by necessity, usually things slow down. You
Speaker 1: just you know, you've you've explored, you you've picked up
Speaker 1: all the and I hate the phrase, but you've got
Speaker 1: you picked all the low hanging fruit. Why do you
Speaker 1: hate the phrase because it's overused. I worked for consultants
Speaker 1: for seven years. I hate low hanging fruits. We come
Speaker 1: up with an alternative expression, the eazy cheese. All the
Speaker 1: easy cheese has been eaten, and it's it's the difficult
Speaker 1: to get cheese. That is, it's the only cheese that's left.
Speaker 1: You've you've picked You've picked up the deli counter cheese. Yeah. Yeah,
Speaker 1: the really good stuff that's like under a heavy glass
Speaker 1: case and it's guarded by wolves. You just haven't been
Speaker 1: able to get to that yet. I go to a
Speaker 1: lot of weird cheese parties. Alright. So that kind of
Speaker 1: wraps up our discussion of Marvin Minsky. Obviously, like you said, Joe,
Speaker 1: there there's so much more we could have talked about. Um,
Speaker 1: the guy was absolutely fascinating. He has had an enormous
Speaker 1: impact on the discipline of artificial intelligence, and I have
Speaker 1: no doubt will continue that impact will continue into the future. Uh.
Speaker 1: And if you guys enjoyed this, let us know if
Speaker 1: you have other people you would like us to talk about.
Speaker 1: If if I mean, I would love to do a
Speaker 1: full episode on Ada Lovelace. I think that she was
Speaker 1: an absolutely phenomenal person and uh, it would be really
Speaker 1: interesting to do a full rundown on on her ideas
Speaker 1: and how how much of a pioneer she was. Uh,
Speaker 1: but you know other people too, like people who are
Speaker 1: still alive would be great too, Like we it doesn't
Speaker 1: have to be someone from I think they must be dead. Okay, alright,
Speaker 1: So if you have an idea of someone you would
Speaker 1: like us to profile, living or dead, let us know
Speaker 1: send us an email. The address is f W Thinking
Speaker 1: at how Stuff Works dot com, or you can always
Speaker 1: drop us a line on Twitter or on Facebook. At
Speaker 1: Twitter we are f W Thinking. Just search fw Thinking
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Speaker 1: leave us a message there. We read all of them,
Speaker 1: and we will talk to you again really soon. For
Speaker 1: more on this topic in the future of technology, I'll
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