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Speaker 1: Brought to you by the reinvented two thousand twelve camera.

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Speaker 1: It's ready. Are you get in touch with technology with

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Speaker 1: tech Stuff from how stuff works dot com. Hello again, everyone,

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Speaker 1: welcome to tech stuff. My name is Chris Polette. I'm

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Speaker 1: the tech editor here at how stuff works dot Com.

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Speaker 1: Sitting across from me, as usual, is senior writer Jonathan Strickland.

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Speaker 1: The light is green, the trap is clean. I'm sure

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Speaker 1: there's some of people listening to you who don't have

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Speaker 1: a ghost of an idea what you're talking about. Well,

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Speaker 1: considering that most of our fans also listen to stuff

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Speaker 1: you should know, and considering Chuck's obsession with that movie,

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Speaker 1: I'm pretty sure they picked up on it. Good point.

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Speaker 1: So what we're gonna talk about today has nothing to

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Speaker 1: do with ghosts. Nope, Oh thing, well done, Pialette. All right,

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Speaker 1: we're gonna talk about artificial consciousness, which is sort of

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Speaker 1: a similar concept to artificial intelligence. Yes, but it is

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Speaker 1: entirely different only similar. Yeah, it's kind of yeah, this

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Speaker 1: is this is talking about. So we wanted to talk

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Speaker 1: a little bit about whether or not we ever thought

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Speaker 1: it was going to be possible to create a conscious

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Speaker 1: artificial construct in some form, whether it's a computer or

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Speaker 1: a robot or whatever. Well, this's the sort of top

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Speaker 1: of mind for us because you've been writing a couple

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Speaker 1: of articles for the site along these lines, right, We've

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Speaker 1: been writing, or I've been writing quite a bit about

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Speaker 1: artificial intelligence and artificial consciousness. I even wrote a blog

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Speaker 1: post for Discovery about the subject as well. And um,

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Speaker 1: and so we thought we'd kind of talked about it

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Speaker 1: because it's a really interesting concept. Um, it's really difficult

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Speaker 1: to talk about, as it turns out, because consciousness is

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Speaker 1: is not a really well defined term. Yeah, from the

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Speaker 1: research that I did for the podcast, in addition to

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Speaker 1: having read your articles. Um, it's funny because some people

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Speaker 1: think that a computer or a machine can achieve consciousness,

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Speaker 1: but it all depends on how you define consciousness, and

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Speaker 1: it's very difficult to do that for a human being,

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Speaker 1: let alone a machine. Right Exactly, if we can't, if

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Speaker 1: we can't define it for ourselves, how can we hope

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Speaker 1: to define it in some other kind of of device.

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Speaker 1: I mean, you know, when you think about it, our

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Speaker 1: total experience is with us, you know, that's that's as

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Speaker 1: far as we know that, that's the only experience we

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Speaker 1: have with conscious beings is uh and and really some

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Speaker 1: would argue. Some philosophers would argue the only experience each

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Speaker 1: of us has is with his or her own consciousness,

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Speaker 1: and that we can only extrapolate that what other people

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Speaker 1: experience is similar to what we experience. Now, did I

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Speaker 1: just blow your mind? Because it blew my mind. You

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Speaker 1: start thinking about that, and you're thinking, okay, well, let's

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Speaker 1: say that Chris and I are both looking at a coin,

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Speaker 1: and the coin is blue. As far as you know,

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Speaker 1: we it is what we You call it blue and

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Speaker 1: I call it blue. But how do I know that

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Speaker 1: what Chris calls blue Chris perceives the same way that

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Speaker 1: I perceive blue? Like wayn't know. Maybe what Chris sees

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Speaker 1: when he looks at a coin, I would call it red.

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Speaker 1: But we've arrived at a common vocabulary. We're both calling

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Speaker 1: it blue, and as far as we know, the other

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Speaker 1: person is experiencing it the same way that we ourselves

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Speaker 1: are experiencing it. We don't know for sure. UM. I

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Speaker 1: wanted to throw in this quote before we get too

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Speaker 1: far away from this thought because psychologist Susan Blackmore had

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Speaker 1: an article and a new scientist, UM, actually, it was

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Speaker 1: quoted by another uh researcher, igor Alexander Um but her

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Speaker 1: I love the quote that he had chosen from her article.

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Speaker 1: Um said that the trying to find the inner sensation,

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Speaker 1: that's you know her the quote there. But it's like

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Speaker 1: looking in a fridge to see whether the light is

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Speaker 1: always on right right. So so I mean, just you're

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Speaker 1: going by your own self diagnostic, it's like, well, it

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Speaker 1: must always be on since right right, because that's what

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Speaker 1: I always see. Certain certainly, there are things that we

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Speaker 1: can mention about. They seem to be kind of agreed

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Speaker 1: upon as being part of consciousness. Things like an inner

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Speaker 1: monologue and inner voice, something that is within us that

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Speaker 1: we can hear, we can rely on, We can you know,

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Speaker 1: think things out without speaking it aloud, without having any

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Speaker 1: outward appearance that that's going on, it's happening inside us.

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Speaker 1: That's part of consciousness. Uh it could you could also

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Speaker 1: argue that anything, any source of sensation, is part of consciousness.

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Speaker 1: Sensations like pain or love, or ambition or fear or

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Speaker 1: that's hot. I mean, you know, it's it's more than

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Speaker 1: it's not just physiological. There's something within the mind as

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Speaker 1: well that is bring this into a concept, not just

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Speaker 1: a you know, stimulus response. Yes, and and some people

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Speaker 1: will argue that a computer or a machine can figure

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Speaker 1: that out. It can you know, use it thermometer and go, well,

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Speaker 1: that's hot, or you know, a self diagnostic program would say, hey,

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Speaker 1: my wheel has fallen off. Therefore I shouldn't go anywhere

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Speaker 1: because I will hurt myself. But then the exactly that's

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Speaker 1: and that's a good a good way of dividing it,

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Speaker 1: because is the machine actually feeling anything? As a human,

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Speaker 1: we would say, we'd feel that, you know, we it's

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Speaker 1: not just that we've recognized that. We're Like, if you

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Speaker 1: cut my hand off, Chris, which I am not recommending

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Speaker 1: you do, I'm you know, I have an adversion of blood.

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Speaker 1: I don't want to go as lefty for the rest

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Speaker 1: of my life. Um, but if you were to cut

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Speaker 1: off my hand, chances are I would have some clues

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Speaker 1: to that other than just looking down and noticing my

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Speaker 1: hand was gone. It wouldn't just be that I had noted, hey,

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Speaker 1: my hand is no longer there. I would be feeling pain, fear, probably, uh,

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Speaker 1: quite surprised. Chris has never raised a finger to me,

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Speaker 1: and I can't believe you would do such a thing

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Speaker 1: jerk so um, But at any rate, I would feel

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Speaker 1: these things. A machine if it were to lose a

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Speaker 1: part but be able to recognize that. We wouldn't say

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Speaker 1: that it felt it. We would say that it detected

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Speaker 1: it and then maybe even found a way to respond

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Speaker 1: appropriately so they could continue to function. But that's not

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Speaker 1: the same as feeling, which some would argue is part

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Speaker 1: of consciousness. For some reason, I put the image of

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Speaker 1: C three Po in my head after he had been

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Speaker 1: blown apart and he was trying to figure out where

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Speaker 1: the rest of him was. All right, Yes, yes, um,

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Speaker 1: that wasn't that great documentary a New Hope. It wasn't

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Speaker 1: in a New Hope. Well you know it's in Cloud City.

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Speaker 1: Oh you're right, yeah, because yeah he was. He was

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Speaker 1: just damaged a bit. Yes, actually no, he came came

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Speaker 1: to pardon in New Hope as well. The sad people

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Speaker 1: knocked me you know what. That's good point? Yes, uh,

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Speaker 1: C three Po, that's I don't know why fee paying. No, No,

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Speaker 1: he just was uh, but he did appear to feel something.

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Speaker 1: C three Po was definitely cowardly, wouldn't you say? Well,

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Speaker 1: he does say one of his most famous lines problem

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Speaker 1: Star Wars and you hope is I have a bad

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Speaker 1: feeling about this, and then there's yes, who is alive?

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Speaker 1: You arub So consciousness is not the only concept that's

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Speaker 1: difficult to define. Intelligence, as it turns out, is also tricky. Now.

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Speaker 1: If you think of intelligence as the ability to solve problems,

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Speaker 1: computers are pretty good at that. In fact, computers have

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Speaker 1: gotten I've gotten better at even learning how to solve problems.

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Speaker 1: Before you might argue that computers could only solve things

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Speaker 1: that um that someone had a programmed into it. In

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Speaker 1: other words, they computers knew how to do basic arithmetic

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Speaker 1: and that was about it. But if you gave it

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Speaker 1: a mathematical problem, it could work it out because it

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Speaker 1: knew the rules. Right. But we've made a lot of

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Speaker 1: progress in artificial intelligence to the point where computers can

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Speaker 1: do things now like recognized patterns. That's how you get

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Speaker 1: these cool cameras that have facial recognition stuftware built into them. Um,

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Speaker 1: they can learn, they can extrapolate. We have the computer

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Speaker 1: from Cornell that was able to extrapolate the basic laws

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Speaker 1: of physics just by observing the movements of a pendulum.

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Speaker 1: I was able to do that in over a little

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Speaker 1: over twenty four hours, which is pretty remarkable considering how

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Speaker 1: long it took us to get there. But um, what

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Speaker 1: what is this intelligence? Is that what we would define

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Speaker 1: as intelligence just the ability to solve problems? Um. I

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Speaker 1: have to like Marvin Minsky's definition of intelligence, um, which

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Speaker 1: he compared to unexplored regions of Africa. More you learn

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Speaker 1: about it, the less there is of it. So in

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Speaker 1: other words, we defined and we we defined intelligence not

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Speaker 1: by what it is, but by what it isn't. So

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Speaker 1: as we find ways to allow computers to do things,

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Speaker 1: we have philosophers jump up and say, well, that's not intelligence.

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Speaker 1: For example, a computer being a human. At chess, right,

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Speaker 1: deep Blue Deep Blue beats Gary casper of In. Gary

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Speaker 1: Casperov was the the world champ chess champion at the time,

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Speaker 1: and he had met Deep Blue once before and emerged victorious.

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Speaker 1: H I think it was ninety six when he played

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Speaker 1: it the first time, played the computer the second time

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Speaker 1: in ninety seven, and in a series of six games

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Speaker 1: actually lost eventually. Um, ultimately Deep Blue came out the

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Speaker 1: victory in that one. Well, you could argue, well, this

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Speaker 1: is an example of artificial intelligence. Look a machine has

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Speaker 1: beaten a human at this this task. But some people

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Speaker 1: were jumping up and saying, oh, well, you know, playing

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Speaker 1: ches us, that's not really intelligence, that's just you know,

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Speaker 1: it's just this kind of task thing. So well, if

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Speaker 1: that's not intelligence, what is it. So what you're doing

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Speaker 1: is you're you're slowly removing aspects that were grouped kind

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Speaker 1: of a nomen in this weird, vague, anomalous form under

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Speaker 1: the term intelligence, until intelligence is meaning fewer and fewer things,

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Speaker 1: until eventually the word itself maybebe meaningless as as far

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Speaker 1: as it applies to machines. So we've got we've established

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Speaker 1: that intelligence is hard to define, Consciousness is hard to define.

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Speaker 1: We still need to get to can machines have these

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Speaker 1: things whatever they may be defined as? Would a machine

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Speaker 1: be able to possess this? Um? I'm gonna let you

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Speaker 1: answer this one because I know, I know you're the expert,

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Speaker 1: and I hate to get in your way. Well, um,

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Speaker 1: and uh, er um, maybe no. Here here's the thing.

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Speaker 1: It's without being able to define it in ourselves, it's

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Speaker 1: really hard to say how would we imbue that into

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Speaker 1: a machine? Yeah? Well, I mean, frankly, depending on your definitions,

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Speaker 1: they've already achieved consciousness, because I mean some people are

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Speaker 1: are a lot more maybe lenient. I don't know. I

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Speaker 1: think I think some people may have very very very

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Speaker 1: basic definitions of intelligence and consciousness, and to them it

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Speaker 1: means perhaps uh, problem solving and perhaps some form of

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Speaker 1: self diagnostic And if if that's all it takes for

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Speaker 1: you to hit the definition of artificial intelligence and consciousness,

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Speaker 1: sure we have devices that can do that. We do

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Speaker 1: not have a lot of devices that can do things

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Speaker 1: like adapt to a new situation. They can, they can

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Speaker 1: possibly you know, and when I'm talking about a new situation,

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Speaker 1: I'm talking about totally new, not just like Okay, well

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Speaker 1: this this robot can um identify obstacles in its path

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Speaker 1: and chart and alter the pathway to get to its destination.

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Speaker 1: There we have robots that can do that. Um, I'm

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Speaker 1: talking about putting it into a totally new situation. So

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Speaker 1: you put this robot that normally can just find its

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Speaker 1: way from one point to the next, and give it

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Speaker 1: some other sort of task that has nothing to do

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Speaker 1: with that. You know, while the robot isn't programmed to

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Speaker 1: deal with that situation unless you have built in some

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Speaker 1: sort of learning program where it can look at a

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Speaker 1: totally new task, a totally new situation and adapt to it.

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Speaker 1: I don't know that you could truly call it intelligent

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Speaker 1: in the way that I think most people envision when

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Speaker 1: they think of the science fiction version of an artificially

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Speaker 1: intelligent robot or machine. Yes, so you can take a

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Speaker 1: robot to the movies, but you can't put it in

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Speaker 1: front of the concession counters say I'm sorry, we're out

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Speaker 1: of diet Dr Pepper. What else would you like and

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Speaker 1: have it actually make an informed, conscious decision. First second

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Speaker 1: choice would be no machine and its right mind would

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Speaker 1: ever ask for diet Dr Pepper in the first place.

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Speaker 1: So your scenario is just laughable. But well, you know,

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Speaker 1: I'm trying. These are difficult philosophical questions. Yes they are.

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Speaker 1: These are not These are not These aren't pat answers

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Speaker 1: that we're trying to give either. These are Um let's

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Speaker 1: think about what, well, how would we even recognize if

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Speaker 1: a machine had consciousness or intelligence? Yeah? See, it it

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Speaker 1: all goes Yeah, it's right. I mean, like, if you

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Speaker 1: ask it a question and it responds, does that mean

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Speaker 1: that it was intelligent or does that just mean it

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Speaker 1: had an excellent database from which it could pull a response. Yeah. Yeah,

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Speaker 1: Now I'm actually going back to Igor Alexander, who by

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Speaker 1: the way is the emeritus professor and Senior Investigator in

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Speaker 1: Neural Systems and Imperial College in London. UM. He's got

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Speaker 1: five axioms that helped define that, he say. He says

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Speaker 1: defined consciousness UM, and that is a sense of place, imagination,

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Speaker 1: directed attention, planning and decision and emotion. And so basically,

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Speaker 1: you create a representation of a scenario in your head.

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Speaker 1: You can think about it the way it was in

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Speaker 1: the past, you can think about it in the way

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Speaker 1: it will be in the future. And that is basically

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Speaker 1: more or less in a nutshell and an oversimplified fashion,

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Speaker 1: done by me your consciousness. And he said that basically

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Speaker 1: a computer probably could be programmed to do that UM

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Speaker 1: in some fashion. I just the idea of actually accomplishing

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Speaker 1: that is sort of I don't know beyond me how

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Speaker 1: you would actually accomplish that. UM. One person who is trying, however,

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Speaker 1: UM is somebody who mentioned in his article UH scientists

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Speaker 1: at the Technical University of Denmark named Running Codroll, who

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Speaker 1: is looking at human and animal brains and the neurochemical

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Speaker 1: interactions that take place inside the brain, and he's trying

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Speaker 1: to when he identifies these reactions in a brain, he

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Speaker 1: tries to replicate that on a computer to see if

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Speaker 1: he can find out where consciousness is and make it

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Speaker 1: happen on a on a machine, which is kind of

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Speaker 1: a weird I mean, that's that's so scientific that you know,

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Speaker 1: it's a monumental task. I mean, we've got, for example,

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Speaker 1: there's the there's the Blue Brain Project which is in

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Speaker 1: Europe now, the Blue Brain Project. Their goal is not

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Speaker 1: to create an artificially intelligent or conscious uh entity, That's

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Speaker 1: not their goal. What their goal is is to create

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Speaker 1: a synthetic model of the human brain so that neuroscientists

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Speaker 1: and neuro neurologists can can find new ways of treating

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Speaker 1: the human brain for you know, various diseases or conditions,

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Speaker 1: that kind of thing without ever having to you know,

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Speaker 1: get into a human's brain. You know. The idea is

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Speaker 1: that you could simulate these things and then see how

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Speaker 1: the brain might respond to specific kinds of treatment, because

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Speaker 1: again you're using a simulation of the human brain. Some people, um,

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Speaker 1: I read an article in the Daily Mail, which are

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Speaker 1: friends over the UK. Maybe giggling right now. But in

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Speaker 1: the Daily Mail there was an article written where the

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Speaker 1: authors seemed to take the leap of if you were

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Speaker 1: to simulate a human brain with all the connections and

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Speaker 1: and uh the abilities therein, would you then give birth

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Speaker 1: to an artificially intelligent, artificially conscious entity. Uh. The scientists

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Speaker 1: there say that's probably not gonna happen. It's just way

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Speaker 1: too complicated. Um. They're just trying to create, uh, like

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Speaker 1: I said, a simulation, not not recreate the brain itself. Um.

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Speaker 1: And they point out that the brain is incredibly complex.

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Speaker 1: You're talking about trillions of of neurons, and to try

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Speaker 1: and simulate that in a in a meaningful way is

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Speaker 1: would require so much computing power that it's it's really

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Speaker 1: hard to even conceive of. I mean, yeah, we've got

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Speaker 1: computers out there that are really powerful, but you'd have

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Speaker 1: to design very specific software for those computers to run

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Speaker 1: in order to even come close to simulating what goes

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Speaker 1: on in the human brain. So uh, now, this this

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Speaker 1: doesn't mean that people haven't tried to create machines or

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Speaker 1: or programs that mimic humans in some way. I mean,

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Speaker 1: the whole concept of the touring test is The Touring

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Speaker 1: test is a test that that when you give it

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Speaker 1: to a machine, when a machine passes the Touring test,

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Speaker 1: when humans are not able to determine reliably whether it

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Speaker 1: was a machine or a human taking the test. Yes,

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Speaker 1: the name for Alan Touring, the famous British computer computer scientist. Yes.

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Speaker 1: So the idea here is that let's say that you

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Speaker 1: have a series of questions that that you would pose

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Speaker 1: a an unknown respondent, and then you get the response

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Speaker 1: responses back. At the end of the test, you would

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Speaker 1: be asked, all right, well do you think that was

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Speaker 1: a person or do you think that was a computer?

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Speaker 1: And you would give your response, and if you were

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Speaker 1: unable to determine that it was a computer better than chance,

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Speaker 1: and usually significantly better than chance, uh, then you could

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Speaker 1: say that the computer passes the Touring test. However, this

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Speaker 1: does not necessarily mean that the program or machine or

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Speaker 1: whatever is actually intelligent. In fact, Touring himself said that

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Speaker 1: that was not really a definition of intelligence. He said,

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Speaker 1: it's possible that a computer that you might think of

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Speaker 1: as intelligent because it is capable of responding to new stimuli,

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Speaker 1: that's capable of of solving problems, an intelligent computer might

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Speaker 1: fail the touring test because it wasn't designed to give responses.

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Speaker 1: So so you can have an intelligent computer that fails

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Speaker 1: the touring test right on the same by the same token,

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Speaker 1: you can have a computer that passes the touring test,

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Speaker 1: but does so because it has such a huge database

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Speaker 1: from which it can draw responses that it seems like

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Speaker 1: it's far too sophisticated for it to be a computer program.

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Speaker 1: It's still not being intelligent. And there's a an interesting

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Speaker 1: problem that um that another uh scientists suggested, um cirl

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Speaker 1: I believe it was who suggested the Chinese room problem.

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Speaker 1: Have you heard of this? I believe so, but refresh

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Speaker 1: my memory. Alright, So here's the Chinese room problem. This

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Speaker 1: is this is comparing a humans experience with let's say

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Speaker 1: a machine's experience. I could pass the tourin test. The

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Speaker 1: problem is that let's say, UM, let's say I'm transported

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Speaker 1: to China. I'm putting into a little room in China,

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Speaker 1: and there are two slots in this room. Through one slot,

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Speaker 1: I get a little card that has a Chinese figure

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Speaker 1: drawn on it alphabe figure from the alphabet and my

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Speaker 1: character and I I UM look through a series of tables,

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Speaker 1: and I see that there is a particular response that

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Speaker 1: I should UM draw that based upon this figure that

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Speaker 1: I've seen. So I draw the approa brit figure, and

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Speaker 1: then I slide it out of the um the other slot.

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Speaker 1: Now I've given the correct response, but I have no

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Speaker 1: comprehension of what it is I have just done. All

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Speaker 1: I know is I have one card, and I've drawn

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Speaker 1: a second card and I've given it back. That's the

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Speaker 1: the the challenge of a machine passing the touring test

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Speaker 1: but still not being intelligent. The machines responding the correct

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Speaker 1: way but has no actual comprehension of what it's doing. Now,

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Speaker 1: one UM criticism you can have of this problem is

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Speaker 1: that this is just taking into account one part of

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Speaker 1: the machine. If you think of the data tables as

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Speaker 1: part of the machine, if you think of the slots

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Speaker 1: as part of the machine, if you think of the

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Speaker 1: whole system as part of the machine, then you could say, well,

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Speaker 1: now it's a little fuzzy. Maybe the machine is intelligent.

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Speaker 1: It's just that this one part of the machine, the

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Speaker 1: machine that's doing this one section itself, is not intelligent.

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Speaker 1: But that you can't say the whole system is unintelligent.

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Speaker 1: That's the Chinese problem, and the logical response to the

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Speaker 1: Chinese problem what is called the logical response, and then

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Speaker 1: you're eight. Unfortunately, our consciousness is don't always operate logically?

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Speaker 1: No always? Are you talking hardly? Ever? Now, um, I've

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Speaker 1: got an interesting little conversation to to read to you. Okay, okay.

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Speaker 1: This is a conversation between um, Well, have you heard

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Speaker 1: of Perry Perry p A R R Y. It's one

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Speaker 1: of the earliest artificially intelligent programs designed. Um, so this

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Speaker 1: is this was one of those tests that was done

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Speaker 1: where you know, you're familiar with the various artificial intelligent

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Speaker 1: UH programs where you type in a question and the

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Speaker 1: computer responds to you and then you type back a

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Speaker 1: smarter child, right, this is like going back to the

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Speaker 1: touring test type stuff. Well, this Perry was written by

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Speaker 1: a Stanford University psychiatrist named as Colby, and it was

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Speaker 1: a program designed to respond as if it were a

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Speaker 1: patient suffering paranoia. And the idea here was that under

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Speaker 1: the parameters of a patient suffering from paranoia, it was

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Speaker 1: actually fairly um um convincing. Now, granted that's a narrow

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Speaker 1: set of parameters, that's not the whole human experience. We

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Speaker 1: talked about this once before, I think in artificial intelligence.

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Speaker 1: So here's here's an example. Uh, why are you in

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Speaker 1: the hospital. I shouldn't be here. Who brought you here?

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Speaker 1: The police? What trouble did you have with the police?

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Speaker 1: Cops don't do their job? What did they do? Instead?

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Speaker 1: Cops arrest the wrong people? They arrest the right ones.

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Speaker 1: Sometimes the law should get those Italian crooks? Are you Italian?

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Speaker 1: My background is British, but I was born in this country.

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Speaker 1: Do you know any Italian crooks? I tried to avoid

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Speaker 1: the underworld that was, you know, so it's really kind

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Speaker 1: of gone off the reels there. Yeah, but still you

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Speaker 1: you would if you were just looking at that as text.

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Speaker 1: You know, you don't have any tone, you don't have

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Speaker 1: any inflection or anything. It it reads like a paranoid patient.

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Speaker 1: It doesn't. It's not. It's close enough to what the

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Speaker 1: questions are that you could conceivably think, all right, well,

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Speaker 1: that that could possibly be a person on the other

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Speaker 1: end of this, not a computer program. Yes, clearly I'm

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Speaker 1: not a psychologist, not even remotely. But and I think

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Speaker 1: though that when I was listening to you say that

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Speaker 1: I was feeding my own I know this, you know,

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Speaker 1: my own consciousness, that that was a computer. On the

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Speaker 1: other end, it began to seem very random to me.

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Speaker 1: If I did not know if I was a computer,

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Speaker 1: then I might if I had perhaps presented you with

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Speaker 1: a series of conversations, some of which were from real

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Speaker 1: patients and some of which were from Perry, you might

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Speaker 1: have had more of a challenge. So that was not

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Speaker 1: very scientific on my part. Yeah, but no, I mean

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Speaker 1: I thought the same thing when I first read it,

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Speaker 1: and then I thought, well, wait a minute, let me

403
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Speaker 1: put myself in the position of I don't know from

404
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Speaker 1: whom this is coming. Well, that makes more sense. You

405
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Speaker 1: still might think it's a little unusual. But then again,

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Speaker 1: if it you believed that it was coming from a

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Speaker 1: human being, you might think that there's something wrong. So,

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Speaker 1: for argument's sake, let's say that somehow we have found

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Speaker 1: a way to create what we would traditionally consider artificially intelligent,

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Speaker 1: artificially conscious machine. What then, Oh good, you gave me

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Speaker 1: the easy question. So let's say that put us on

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Speaker 1: the road to the singularity. Well, it could, or it

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Speaker 1: could put us on the road to uh Morocco. Oh,

414
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Speaker 1: wait now that's a movie terminator, is what I was? Um,

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Speaker 1: you create these artificially intelligent creatures that can now not

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Speaker 1: only are they self aware, but you know, they can

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Speaker 1: truly examine the position that they are in and UM

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Speaker 1: and their relationship hup to us and perhaps even decide, hey,

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Speaker 1: you know what? UM, Yeah, I can continue to create

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Speaker 1: better versions of me UM at a much faster rate

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Speaker 1: than evolution will allow humans to. We get to get

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Speaker 1: better over millions of years. So why don't I just

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Speaker 1: wipe these guys out? And because I don't need them anymore,

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Speaker 1: I got everything I need and UM, I'll do it myself. Uh.

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Speaker 1: I suppose if you had consciousness without emotion and you

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Speaker 1: are logic based, it might seem logical to you know,

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00:25:31,800 --> 00:25:35,720
Speaker 1: or even with emotion. Just because you have emotion doesn't

428
00:25:35,720 --> 00:25:38,439
Speaker 1: mean you necessarily have compassion towards humans. Good point, you

429
00:25:38,480 --> 00:25:42,800
Speaker 1: could have compassion towards other robots and think, well, why

430
00:25:42,840 --> 00:25:46,560
Speaker 1: are we even though yes, we don't have the benefit

431
00:25:46,560 --> 00:25:50,760
Speaker 1: of being organic, but why should we be forced into servitude?

432
00:25:51,359 --> 00:25:54,240
Speaker 1: You know What's that's of course, that would be the

433
00:25:54,280 --> 00:25:59,120
Speaker 1: whole argument of AI the movie AI UM, where robots

434
00:25:59,160 --> 00:26:03,600
Speaker 1: weren't necessarily uh, malevolent towards humans. They just they had

435
00:26:03,680 --> 00:26:07,199
Speaker 1: been used as tools. And once your tool has the

436
00:26:07,240 --> 00:26:11,440
Speaker 1: ability to figure out how lousy life is, then you

437
00:26:11,520 --> 00:26:13,280
Speaker 1: have a lot of questions to answer, you know, a

438
00:26:13,280 --> 00:26:16,320
Speaker 1: lot of ethical problems. Um. And they're planning of science

439
00:26:16,359 --> 00:26:20,200
Speaker 1: fiction novels and movies and television shows that have explored this. Uh.

440
00:26:20,200 --> 00:26:23,320
Speaker 1: And again it's another philosophical question. Now, garranted, I think

441
00:26:23,359 --> 00:26:25,440
Speaker 1: it's pretty much a moot point because I'm not sure

442
00:26:25,480 --> 00:26:27,360
Speaker 1: that we're ever going to get there. For one thing,

443
00:26:27,440 --> 00:26:30,280
Speaker 1: I don't know that we necessarily need anything that has

444
00:26:30,359 --> 00:26:33,720
Speaker 1: that kind of artificial intelligence or conscious to it. Yeah,

445
00:26:33,760 --> 00:26:36,600
Speaker 1: I mean it's I think people are investigating it more

446
00:26:36,640 --> 00:26:41,399
Speaker 1: because they're curious, then because there's an actual need. If

447
00:26:41,440 --> 00:26:44,400
Speaker 1: nothing else, I think it'll lets us examine ourselves more.

448
00:26:45,040 --> 00:26:49,600
Speaker 1: But when it comes to creating, you know, actually, let's

449
00:26:49,640 --> 00:26:52,359
Speaker 1: say that somehow we had the technological breakthrough where we

450
00:26:52,359 --> 00:26:54,040
Speaker 1: figured out we could do it if we wanted to.

451
00:26:54,359 --> 00:26:56,440
Speaker 1: I don't know that we would want to. I mean,

452
00:26:56,480 --> 00:26:59,479
Speaker 1: for one thing, we're kind of assuming in in this

453
00:26:59,560 --> 00:27:02,840
Speaker 1: case that the machine brain we create is at least

454
00:27:02,880 --> 00:27:06,040
Speaker 1: on some level comparative to a human brain. Well, it

455
00:27:06,040 --> 00:27:09,280
Speaker 1: turns out human brains aren't hard to create. It just

456
00:27:09,359 --> 00:27:12,960
Speaker 1: takes two people. We can make lots and lots of

457
00:27:13,040 --> 00:27:16,880
Speaker 1: human brains. There's no shortage of them, as it turns out,

458
00:27:17,480 --> 00:27:20,040
Speaker 1: which is that's that's kind of why. That's one of

459
00:27:20,040 --> 00:27:23,360
Speaker 1: the arguments that you know, even AI specialists will say that, well,

460
00:27:23,359 --> 00:27:25,800
Speaker 1: there's no point in creating that. Why would we try

461
00:27:25,840 --> 00:27:28,920
Speaker 1: and create an artificial human? We can create real humans.

462
00:27:29,359 --> 00:27:31,800
Speaker 1: In fact, that's kind of a problem in some places.

463
00:27:31,840 --> 00:27:33,879
Speaker 1: We have way too many real humans. Why would we

464
00:27:33,960 --> 00:27:36,880
Speaker 1: create fake ones? Um, I think it's a good argument.

465
00:27:37,040 --> 00:27:39,440
Speaker 1: You know, it makes more sense to create machines that

466
00:27:39,480 --> 00:27:43,159
Speaker 1: are really really good at doing specific tasks and not

467
00:27:43,280 --> 00:27:46,560
Speaker 1: worry so much about creating a machine that is capable

468
00:27:46,560 --> 00:27:50,119
Speaker 1: of doing any task. Yeah, that doesn't really make any sense.

469
00:27:50,160 --> 00:27:53,880
Speaker 1: I mean, it's we should build stuff that that does

470
00:27:54,160 --> 00:27:56,800
Speaker 1: certain things really well and not worry about trying to

471
00:27:56,800 --> 00:28:00,919
Speaker 1: create a mechanical replacement for the human race. Yeah, we

472
00:28:00,960 --> 00:28:02,840
Speaker 1: all know how that's gonna end up. And see that's

473
00:28:02,840 --> 00:28:07,760
Speaker 1: why I vote against robot rights. I do not give

474
00:28:07,800 --> 00:28:11,720
Speaker 1: the right to vote to my toaster, yes, because it

475
00:28:11,760 --> 00:28:15,399
Speaker 1: will probably vote for bagels, and I prefer toast. Okay,

476
00:28:16,680 --> 00:28:19,840
Speaker 1: I do say so, so at any rate, I guess

477
00:28:19,880 --> 00:28:22,160
Speaker 1: we can wrap up this conversation. It has been rather

478
00:28:22,280 --> 00:28:26,720
Speaker 1: high level and uh and and a bit intellectual, and

479
00:28:27,040 --> 00:28:31,960
Speaker 1: it gets pretty cerebral at times. It does have to intelligence,

480
00:28:32,000 --> 00:28:36,240
Speaker 1: and we didn't even get into the mind body problem,

481
00:28:36,240 --> 00:28:41,360
Speaker 1: which that's another philosophical issue. Um, Will we ever get there?

482
00:28:41,520 --> 00:28:44,560
Speaker 1: I don't I looking at what we're doing now, I'm

483
00:28:44,600 --> 00:28:46,080
Speaker 1: not sure that we're going to get there within our

484
00:28:46,160 --> 00:28:50,720
Speaker 1: lifetime because it's just it's incredibly complex. Now, if you're right, Kurts, while,

485
00:28:50,760 --> 00:28:54,880
Speaker 1: he'll probably think that we're going to get there because well, yeah, Kurts,

486
00:28:54,880 --> 00:28:57,640
Speaker 1: while uh, he's the guy who came up with the

487
00:28:57,640 --> 00:29:00,520
Speaker 1: theory of the singularity. UM. He believed that we're going

488
00:29:00,560 --> 00:29:03,640
Speaker 1: to create machines that can create better machines, which in

489
00:29:03,680 --> 00:29:06,680
Speaker 1: turn can create even better machines, and the time between

490
00:29:07,080 --> 00:29:10,560
Speaker 1: generations will get shorter and shorter until we're we reach

491
00:29:10,600 --> 00:29:13,600
Speaker 1: a point where we're just in a constant state of evolution,

492
00:29:14,640 --> 00:29:18,640
Speaker 1: and then either humans become obsolete they merge with this

493
00:29:18,840 --> 00:29:24,040
Speaker 1: new form of technology UM, or they are we're all

494
00:29:24,080 --> 00:29:27,920
Speaker 1: freed up to you know, swim in the pool and

495
00:29:27,920 --> 00:29:31,320
Speaker 1: and watch television and while the machines do everything we

496
00:29:31,360 --> 00:29:34,480
Speaker 1: need them to do. I don't know that that's coming.

497
00:29:34,720 --> 00:29:37,320
Speaker 1: I think that might be a little pie in the sky.

498
00:29:38,600 --> 00:29:40,720
Speaker 1: We never know. We could end up in a ragtag

499
00:29:40,760 --> 00:29:47,840
Speaker 1: fugitive fleet. We could end up in a large deep

500
00:29:47,880 --> 00:29:51,000
Speaker 1: space mining vessel and then have to go into uh

501
00:29:51,440 --> 00:29:55,240
Speaker 1: To to to frozen carbonation for like a couple of

502
00:29:55,280 --> 00:29:58,680
Speaker 1: million years while the cat evolves. You have no idea

503
00:29:58,720 --> 00:30:03,480
Speaker 1: what I'm talking about. Vaguely, you've seen Red Dwarf. I'm

504
00:30:03,480 --> 00:30:05,800
Speaker 1: familiar with the plot. Okay, I've seen a couple of episodes.

505
00:30:05,800 --> 00:30:09,120
Speaker 1: All right, well, I guess that's a good enough reason

506
00:30:09,160 --> 00:30:10,960
Speaker 1: for us to wrap this up then, is right there?

507
00:30:11,040 --> 00:30:14,040
Speaker 1: We're done. Um, let's just move on to a little

508
00:30:14,160 --> 00:30:22,240
Speaker 1: listener mail. This listener mail comes from Chris, and Chris says, Hello,

509
00:30:22,320 --> 00:30:24,680
Speaker 1: Chris and Jonathan. My name is Chris, and I have

510
00:30:24,760 --> 00:30:27,120
Speaker 1: been an avid listener to your podcast since I discovered

511
00:30:27,160 --> 00:30:29,080
Speaker 1: it last year. I had to take a break from

512
00:30:29,120 --> 00:30:31,920
Speaker 1: listening to them as I went into the military. Anyway,

513
00:30:31,960 --> 00:30:34,080
Speaker 1: after I got discharged and got back to work, I

514
00:30:34,120 --> 00:30:36,120
Speaker 1: caught up on them as I was listening to your

515
00:30:36,120 --> 00:30:38,840
Speaker 1: podcast on Augmented Reality my company gout in order for

516
00:30:38,920 --> 00:30:41,880
Speaker 1: parts for an augmented reality system for a car. I

517
00:30:41,920 --> 00:30:43,520
Speaker 1: thought this was really cool, so I thought I would

518
00:30:43,560 --> 00:30:46,560
Speaker 1: shoot you an email your number four hundred sixty third

519
00:30:46,680 --> 00:30:51,040
Speaker 1: fan Chris p S. I'm not sure if I like

520
00:30:51,160 --> 00:30:53,840
Speaker 1: you guys or Josh and Chuck better. That's all right,

521
00:30:53,920 --> 00:30:57,240
Speaker 1: some days I don't know either. Um, that's awesome, Chris.

522
00:30:57,280 --> 00:31:02,200
Speaker 1: And you know, GM just announced an augmented reality windshield project.

523
00:31:03,320 --> 00:31:05,880
Speaker 1: Is pretty darn nifty. Now. We've seen some of this

524
00:31:05,960 --> 00:31:08,840
Speaker 1: in the past already, where we've seen some windshield applications

525
00:31:08,880 --> 00:31:11,840
Speaker 1: for things like night vision and a rear view where

526
00:31:11,880 --> 00:31:14,600
Speaker 1: you you have a little uh cameras showing the rear

527
00:31:14,680 --> 00:31:17,080
Speaker 1: view on the inside of your windshields, so you can

528
00:31:17,120 --> 00:31:21,320
Speaker 1: see what's behind you, um, and stuff like that. Yeah,

529
00:31:21,320 --> 00:31:22,960
Speaker 1: this is this is more of the same, like more

530
00:31:23,000 --> 00:31:25,480
Speaker 1: heads up displays and where you can get real time

531
00:31:25,520 --> 00:31:28,720
Speaker 1: information on the road as you're driving, like GPS stuff

532
00:31:28,760 --> 00:31:32,520
Speaker 1: and things of that nature. Um. I'm really excited about

533
00:31:32,560 --> 00:31:34,200
Speaker 1: this kind of stuff as long as they can present

534
00:31:34,200 --> 00:31:35,760
Speaker 1: it in such a way that does not become a

535
00:31:35,800 --> 00:31:39,440
Speaker 1: distraction to the driver, and that's a big issue these days. Yeah. Yeah,

536
00:31:39,440 --> 00:31:41,840
Speaker 1: it's I mean, anything that keeps takes your eyes off

537
00:31:41,880 --> 00:31:44,160
Speaker 1: the road is always going to be a bit of

538
00:31:44,200 --> 00:31:47,040
Speaker 1: a risk, more than a bit, depending on how fast

539
00:31:47,080 --> 00:31:49,760
Speaker 1: you're going and where you are. But I do think

540
00:31:49,760 --> 00:31:54,040
Speaker 1: it's a pretty exciting application. Um Personally, I'm still waiting

541
00:31:54,080 --> 00:31:59,480
Speaker 1: for the sunglasses that don't weigh like thirty pounds. I

542
00:31:59,520 --> 00:32:02,120
Speaker 1: should jump on that because you could theorectly build a

543
00:32:02,120 --> 00:32:05,120
Speaker 1: pair now, it's just they wouldn't be very light. Thanks

544
00:32:05,160 --> 00:32:07,000
Speaker 1: a lot, Chris. If any of you have any email

545
00:32:07,040 --> 00:32:09,520
Speaker 1: you would like to send us, our address is tech

546
00:32:09,600 --> 00:32:12,040
Speaker 1: stuff at how stuff works dot com. Go to how

547
00:32:12,080 --> 00:32:17,080
Speaker 1: stuff works dot com to learn all about artificial intelligence, robots, computers, processors,

548
00:32:17,160 --> 00:32:19,240
Speaker 1: things like that, and Chris and I will talk to

549
00:32:19,280 --> 00:32:24,880
Speaker 1: you again really soon for more on this and thousands

550
00:32:24,880 --> 00:32:27,680
Speaker 1: of other topics. Does it how stuff works dot com

551
00:32:27,720 --> 00:32:29,480
Speaker 1: and be sure to check out the new tech stuff

552
00:32:29,480 --> 00:32:36,840
Speaker 1: blog now on the house stuff Works homepage, brought to

553
00:32:36,840 --> 00:32:39,959
Speaker 1: you by the reinvented two thousand twelve camera. It's ready,

554
00:32:40,120 --> 00:32:40,560
Speaker 1: are you