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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. You've heard

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Speaker 1: the rumors before, perhaps and whispers written between the lines

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Speaker 1: of the textbooks. Conspiracies, paranormal events, all those things that

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Speaker 1: disappear from the official explanations. Tune in and learn more

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Speaker 1: of the stuff they don't want you to know in

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Speaker 1: this video podcast from how stuff works dot com. Hello everybody,

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Speaker 1: and welcome to tech stuff. My name is Chris Pouette,

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Speaker 1: and I'm the tech editor here at how stuff works

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Speaker 1: dot com. Sitting across from me as usual, is Senior

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Speaker 1: Ryan Jonathan Strickland's up, y'all very nice, changed up a

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Speaker 1: little bit, Okay, I'm fine with that. So before we

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Speaker 1: get started, I just wanted to mentioned something. I saw

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Speaker 1: this email going out about a new show that's coming

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Speaker 1: up on the Science Channel. Yeah, it's called The Road

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Speaker 1: to Punkin Chunkin. And then immediately it's followed by punkin Chunkin.

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Speaker 1: Oh yeah, yeah, yeah, you saw this email too. Yeah.

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Speaker 1: It's a Thanksgiving night here in the United States, so yeah,

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Speaker 1: starts at eight. That's the Road to Punkin Chunkin, followed

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Speaker 1: by Punkin Chunkin at nine. And if you don't know

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Speaker 1: what this is about, it's really about designing machines that

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Speaker 1: can throw a pumpkin a really far distance. Yeah yeah, yeah, yeah.

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Speaker 1: They used to do that with with tribute sches and

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Speaker 1: stuff like that, but now they have these air powered cannons. Yeah,

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Speaker 1: it's gonna be pretty crazy. So, uh, you should definitely

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Speaker 1: check it out. I'm gonna be watching it myself. I

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Speaker 1: think there's nothing I like more than a little pumpkin destruction. Awesome. Well,

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Speaker 1: so what are we gonna talk about today? Today's topic

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Speaker 1: comes to us courtesy of a little listener mail. Well,

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Speaker 1: all right, then this is list Your mail comes from

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Speaker 1: our ninth grader listener named Alexandra. I'm sure we have

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Speaker 1: other ninth graders, but this one in particular road to us.

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Speaker 1: Thank you, Alexandra, and here is the message. Hey you guys,

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Speaker 1: I have a suggestion for a topic, Artificial intelligence. My

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Speaker 1: favorite show is Night Writer two thousand eight. Kit the

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Speaker 1: Talking Car is said to have the most advanced AI

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Speaker 1: in the world. Is AI in cars and other devices

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Speaker 1: coming soon? Will they have personalities? Thanks for a great podcast.

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Speaker 1: P s. I like the eighties Night Writer too. So

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Speaker 1: you're talking about night Writer, you're talking about KIT, which

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Speaker 1: of course is the Night Industries two thousand artificially intelligent car.

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Speaker 1: By the way, do you remember what Kit's arch nemesis

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Speaker 1: car was called? Oh, we'll see I now now. I

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Speaker 1: didn't remember what it stood for. I knew that it

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Speaker 1: was car, Yes, Car K A r R. It stands

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Speaker 1: for Night Automated Roving Robot. Do you remember the main

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Speaker 1: character's name? Besides Kit and Night Writer met the main

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Speaker 1: character Michael, Yes, Michael Night. Who do you remember what

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Speaker 1: his name was before became Michael Knight? Oh? No, actually

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Speaker 1: I don't. Michael Arthur Long undercover agents shot in the face.

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Speaker 1: He had to be rehabilitated. They rebuilt his face, gave

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Speaker 1: it David Hasselhoss face for some reason. He's huge in

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Speaker 1: Germany and U. He became Michael Knight because it was

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Speaker 1: Night Industries. Did I just have one question for you?

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Speaker 1: Did you did you look that up? Or did you

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Speaker 1: know that off the top of your head? I wrote

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Speaker 1: all that down from memory. I like Alexandra, I was

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Speaker 1: a bit of a fan of the nineteen eighties night Writers. Well,

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Speaker 1: I thought I was, but apparently I was a lower

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Speaker 1: case F fan and not an upper case I also

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Speaker 1: wanted to throw out throw out the phrase I was saying,

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Speaker 1: the the little joke I made to you before we

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Speaker 1: started recording this podcast, Michael, let's drive to Congress and

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Speaker 1: if any of you know why I said it that

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Speaker 1: way and what the whole Congress thing is, reference to

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Speaker 1: write me tech stuff at how stuff works dot com.

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Speaker 1: First person to get it right gets a kudos. Just

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Speaker 1: not a granola bar. Not a granola bar. I mean,

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Speaker 1: if I encounter you on the street and I happen

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Speaker 1: to have a granola bar, I will be happy to

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Speaker 1: hand it over to you. If I don't have a

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Speaker 1: granola bar, we're kind of ada luck on that one.

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Speaker 1: So this brings us to our topic granola bars. No,

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Speaker 1: I'm sorry, artificial intelligence. I got off on a tangent there,

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Speaker 1: So artificial intelligence now, she asked, is artificial intelligence coming

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Speaker 1: to other devices and cars as well? And will they

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Speaker 1: have personalities? Um, here's the interesting thing about artificial intelligence.

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Speaker 1: The definition is somewhat vague. For example, Chris, how would

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Speaker 1: you define artificial intelligence? I mean, nope, there are no

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Speaker 1: right or wrong answers. Just when you hear the term,

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Speaker 1: what is it that you think of? Well, you know,

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Speaker 1: of course there's the movie Okay, yes, there's the film

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Speaker 1: that was started by Stanley Kubrick completed by Steven Spielberg

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Speaker 1: that lasted thirty minut it's too long and should have

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Speaker 1: ended with a Little Boy in the Ocean spoiler alert.

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Speaker 1: But beyond that, you know this, This is the effect

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Speaker 1: of Odyssey of the Mind on my brain, because I'm

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Speaker 1: trained to think of everything but the actual answer, Um, no,

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Speaker 1: I want I When I think of artificial intelligence, um,

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Speaker 1: you know, actually, one of the first things that comes

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Speaker 1: to mind is games because people talk about the AI

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Speaker 1: in a particular game, all the AI and this game

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Speaker 1: is really good. Yeah, and uh, you know of course

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Speaker 1: robots mostly androids, things like that. Yeah, but yeah, I

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Speaker 1: mean those things and and and walking talking. You know.

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Speaker 1: It's always the movie version of our official intelligence where

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Speaker 1: there's you know, something and of course it's evil. It's

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Speaker 1: usually evil example, you know, and it's thinking, and it's

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Speaker 1: it's decided that we're irrelevant and it's going to kill

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Speaker 1: us all. I mean that, isn't it nice? It's pretty

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Speaker 1: much gonna happen. But no, no, no, so here's here's

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Speaker 1: here's my ulswer. Yeah, but to know that it's coming

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Speaker 1: and it will kill me. Yes, my Hunter Civic will

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Speaker 1: eventually do me in. Um No, here's what I think

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Speaker 1: of when I think artificial intelligence. I think of a

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Speaker 1: an artificial construct. So some sort of man made thing

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Speaker 1: that is capable of sensing its environment, reacting to its environment,

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Speaker 1: and learning from its experiences. Now, to me, the learning

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Speaker 1: part is the most important because if it's if it's

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Speaker 1: only taking in it's uh, you know, some sort of

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Speaker 1: sensory input and then reacting to it, there's not really

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Speaker 1: any thinking going on there. I mean, essentially, you're just

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Speaker 1: it's it's stimulus response, right, Yeah, And that's pretty much

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Speaker 1: how I carry out my day. I just some of

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Speaker 1: us are a little higher functioning than Chris, and we

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Speaker 1: we occasionally spare a thought or two for something. Um No,

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Speaker 1: Chris is actually a very intelligent person. I I people

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Speaker 1: get on to me about picking on you. You know that, right,

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Speaker 1: I don't. Chris and I are actually good buddies here. Yeah, yeah,

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Speaker 1: so at any rate, at any rate, So yeah, I think, uh,

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Speaker 1: I think learning is an important element in artificial intelligence,

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Speaker 1: so that not only are you able to react to

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Speaker 1: something that's in your environment, but you learn from that

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Speaker 1: experience and you are able to handle similar experiences in

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Speaker 1: a better way in the future. So, for example, you

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Speaker 1: encounter if if you have an artificially intelligent construct that

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Speaker 1: is UH susceptible to melting and it encounters a heat

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Speaker 1: source that is higher than it's melting point, and it

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Speaker 1: detects this and then backs away and then has to

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Speaker 1: try and UH factor in a route around this heat

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Speaker 1: source so that it can continue doing whatever it was

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Speaker 1: it was doing beforehand. UM. After encountering that, it might

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Speaker 1: be able to build on that experience and be able

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Speaker 1: to solve a similar problem in half the time, and

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Speaker 1: ideally you have this increminal improvement. UM. It's also called

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Speaker 1: self recursive improvement, where you have an experience, you learn

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Speaker 1: from it, and you are able to do it more

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Speaker 1: efficiently or effectively, however you want to quantify it in

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Speaker 1: the future. So right now, using that definition, we don't

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Speaker 1: really have a truly good example of artificial intelligence. There's

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Speaker 1: some that are really close, UM, but there's nothing out

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Speaker 1: there that's that's like a computer that can understand how

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Speaker 1: computers work and therefore design its own successor to be

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Speaker 1: a more powerful version of itself. So um, yeah, no, uh,

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Speaker 1: what was the computer in Hitchhiker's Guide Deep Thought? So

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Speaker 1: deep Thought? Yeah, deep Thought? And uh in Hitticker's Guide

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Speaker 1: to the Galaxy, there's this computer called deep Thought that

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Speaker 1: is so intelligent that it is able to come up

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Speaker 1: with the answer to life, the universe, and everything. It

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Speaker 1: is not able to come up with the question it's

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Speaker 1: the answer to a question, but it's not. But it

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Speaker 1: can actually get the question. To get the question, it

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Speaker 1: has to build a computer even smarter than itself, but

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Speaker 1: it's able to do that. So it can't solve the problem,

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Speaker 1: but it can build a tool that can solve the

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Speaker 1: problem that ends up being the planet Earth in that

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Speaker 1: in that series of books. Now, that's actually an idea

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Speaker 1: that's being explored in computer science. It's an idea of

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Speaker 1: can we build a machine that can understand how machines

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Speaker 1: work and therefore build a better version of the machine

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Speaker 1: because it can make these calculations far faster and more

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Speaker 1: effectively than any human can. And once you reach that point,

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Speaker 1: that's when we hit what we've We've talked about this

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Speaker 1: in the past. We've talked about in another podcast. But

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Speaker 1: the singularity, which is where you've reached the point where

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Speaker 1: machines or or even humans. It doesn't have to be

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Speaker 1: a machine. It could be like somehow we have biologically

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Speaker 1: altered ourselves so that we can do this to ourselves.

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Speaker 1: But we the improvements come so quickly that uh, that

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Speaker 1: there's no way to distinguish one moment from the next

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Speaker 1: as far as technological advancement goes, because it's just going

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Speaker 1: easing fast and we've reached a brand new reality, at

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Speaker 1: least as far as our perspective is concerned. Uh, to

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Speaker 1: the outside observer, it wouldn't be quite the same. And

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Speaker 1: I did mention that we have some stuff that's coming close.

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Speaker 1: The one I wanted to talk about just mention, uh

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Speaker 1: was the Cornell Research um UH project where they had

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Speaker 1: the computer that analyzed the pendulum. Do you remember reading

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Speaker 1: the story There was a new story. It was back

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Speaker 1: earlier this year. There was a group of Cornell computer scientists.

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Speaker 1: They had designed a computer and all the computer was

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Speaker 1: doing was observing the movements of a pendulum and then

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Speaker 1: from those movements working out the laws of physics, so

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Speaker 1: it would observe the way the pendulum moved, the speed

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Speaker 1: at which it moved, how much it slowed down, and

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Speaker 1: it started to work out the laws of thermodynamics. It's

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Speaker 1: you know, physical laws, classical physics. And it turned out

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Speaker 1: to be really good at it. It didn't get it

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Speaker 1: exactly right at the very beginning, but it kept observing

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Speaker 1: just like humans would. It kept observing the the phenomena

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Speaker 1: and then making guesses and and then testing those guesses,

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Speaker 1: throwing out anything that didn't fit over time, and keeping

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Speaker 1: everything that did fit, and eventually it was able to

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Speaker 1: work out the basic laws of physics. Now that I

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Speaker 1: had not read that. That's a pretty cool example of

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Speaker 1: artificial intelligence, and the idea that this was able to

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Speaker 1: observe something in its environment and draw conclusions from it.

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Speaker 1: Um Now, it had been programmed to be able to observe,

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Speaker 1: so I mean, it wasn't like it had developed capabilities

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Speaker 1: beyond its programming. I think that's another sign of artificial intelligence.

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Speaker 1: When you've created something that can go beyond what you

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Speaker 1: had originally programmed it for. That's another sign of intelligence.

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Speaker 1: You know a lot of people think that used to

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Speaker 1: the if you will, the classical definition of artificial intelligence.

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Speaker 1: You know, the touring tests. Yes, the Touring tests Alan Touring,

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Speaker 1: and I was a little surprised you didn't start with him,

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Speaker 1: to be honest, Yeah, I I thought about it, um,

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Speaker 1: because this is a when you get to artificial intelligence,

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Speaker 1: it really is a philosophical matter. It's not just technological,

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Speaker 1: it's not just computer science. It is philosophy. Well, you know, uh,

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Speaker 1: when I was doing research for the podcast, I found

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Speaker 1: a great resource at Stanford. And it's funny because when

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Speaker 1: I think of Alan Touring, I think of computer science,

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Speaker 1: but it's actually the Stanford Encyclopedia of Philosophy where I

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Speaker 1: encountered his thoughts on artificial intelligence and the imitation game,

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Speaker 1: which is the when I think of you know, when

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Speaker 1: when I actually read it, that's what I think of

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Speaker 1: as the Touring test where they put a machine and

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Speaker 1: a human being, um and sort of a blind test

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Speaker 1: with someone asking questions, and theoretically the person should be

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Speaker 1: able to ask the machine and the person questions and

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Speaker 1: they should be able to give answers, and the person

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Speaker 1: asking the question would be unable to determine which one

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Speaker 1: was real and which one was meant, I mean, which

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Speaker 1: one was the machine. It's hard to say when you're

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Speaker 1: using the word real is kind of tricky, but yeah,

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Speaker 1: I understand which was human and which was a human? Like? Yes, yes,

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Speaker 1: which one was human and which one was was a machine? Right?

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Speaker 1: So ideally you would have these these the computer and

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Speaker 1: the uh the other test subject, the human test subject

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Speaker 1: um separated. They would be completely isolated from the person

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Speaker 1: asking questions, and at the end of the list of questions,

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Speaker 1: you would ask the question er, all right, which one

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Speaker 1: was human, which one was computer? And if the questionnaire

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Speaker 1: was unable to identify the two roles better than chance.

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Speaker 1: So you know, you have to do this test many

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Speaker 1: times over right, right. If they were unable to identify

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Speaker 1: computer versus human uh to a point where it takes

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Speaker 1: chance out of the fact the whole factor, then um,

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Speaker 1: you can say that the computer passes the Turing test,

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Speaker 1: that the computer is, by all intents and purpose this

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Speaker 1: indistinguishable from a human being when it comes to communication. Now, um,

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Speaker 1: there have been some touring tests that that sort of

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Speaker 1: achieved this in a way, one of them being one

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Speaker 1: that focused on um, a computer that simulated the thought

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Speaker 1: processes of someone suffering from schizophrenia, and they had a

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Speaker 1: group of psychiatrists ask questions over, you know, through text

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Speaker 1: um to both a real person and this computer that

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Speaker 1: was simulating someone with schizophrenia and something like of them

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Speaker 1: weren't able to tell the difference. They weren't able to

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Speaker 1: reliably identify which one was computer which one was human.

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Speaker 1: So that's a that's right around the chance level, right,

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Speaker 1: so that you could say that that computer passes the

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Speaker 1: touring test. However, that is of course a very narrow

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Speaker 1: set of parameters, and some people have criticized the test

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Speaker 1: saying that, okay, but when you're getting to someone who

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Speaker 1: is operating on are a set of faculties that are

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Speaker 1: not considered the normal set, because schizophrenia is outside the norm,

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Speaker 1: then it wouldn't really The computer doesn't need to understand

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Speaker 1: what it's saying, right, The computer doesn't have to be

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Speaker 1: able to comprehend that the words it's putting together are

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Speaker 1: following a certain you know, pattern, are making sense. Um,

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Speaker 1: you could just as easily have a jumble of words.

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Speaker 1: The computer could randomly pair them up and create nonsensical sentences,

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Speaker 1: and it would be almost as effective because you're not

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Speaker 1: working with the full set of human faculties. Um. If

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Speaker 1: you were, then the presumably the percentage rate of identifying

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Speaker 1: the computer versus the human would be much different. You

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Speaker 1: would be able to probably accurately describe them, and much

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Speaker 1: more often because computers are not terribly good at carrying

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Speaker 1: on a truly human conversation, or at least not the

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Speaker 1: kind of conversations I have with other humans. Did you

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Speaker 1: did you ever here? Did you know of dr Spates? So, uh,

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Speaker 1: it's a sound blaster thing. The the old sound blaster

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Speaker 1: cards came with a software called dr Spates, So asked

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Speaker 1: Dr SPATESO. And it was this computer voice. You could

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Speaker 1: type questions to it. It would respond to you. It

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Speaker 1: would actually talk to you, and there were times where

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Speaker 1: it kind of felt a little uncanny that wow, that

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Speaker 1: was actually a really good response. But then it was

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Speaker 1: just a database full of responses and it would pick

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Speaker 1: whichever one was most likely going to apply to whatever

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Speaker 1: question you had typed in, So it wasn't really artificial intelligence.

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Speaker 1: But I remember that was probably my first encounter in

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Speaker 1: a personal level, apart from the movies. Well, one of

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Speaker 1: the things that surprised me as I was doing research

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Speaker 1: because I was familiar with Alan touring in the touring

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Speaker 1: test um, but I was a little surprised that Renee

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Speaker 1: des Cartes had actually made some suggestions. I have lyrics

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Speaker 1: going through my head. But no. In sixty eight, of course, again,

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Speaker 1: according to the Stanford Encyclopedia Philosophy, UM the cards said

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Speaker 1: that if there were a machine that could walk and

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Speaker 1: talk like you know, a person could. Um, I'm assuming

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Speaker 1: he didn't have some kind of time machine. Was guessing

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Speaker 1: that there will be androids in the future. But Um

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Speaker 1: basically said that these machines would need to be able

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Speaker 1: to put together a conversation like a person in order

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Speaker 1: to really be defined as intelligent. Um. But he said

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Speaker 1: that he didn't really believe that it was possible for

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Speaker 1: a machine to exhibit that kind of intelligence because they

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Speaker 1: would also need and I'm not quite sure what he

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Speaker 1: means here, all the organs necessary to live. I mean

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Speaker 1: to react to everything that they would encounter. I'm assuming

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Speaker 1: that there he's saying. You know, you wouldn't have skin,

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Speaker 1: so you wouldn't be able to go, oh, we have

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Speaker 1: that panics hot whorse, that would be a lot more. Again,

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Speaker 1: this would suggest again that he does not have a

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Speaker 1: time machine, because yeah, actually, now there are sensors that

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Speaker 1: you could embed in Yeah, there's actually there's there's so

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Speaker 1: much advancement in as far as being able to sense

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Speaker 1: the environment. Is concerned that we have some rudimentary artificial

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Speaker 1: intelligence examples out there, things that are able to uh

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Speaker 1: to examine and react to their environments. Um, things like

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Speaker 1: you know as Amo. As Amo, the robot from Honda

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Speaker 1: can walk around the room and recognize an obstacle in

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Speaker 1: its path and then reroute its path so that it

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Speaker 1: can get to its end point um by you know,

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Speaker 1: walking around or whatever. Uh it, But it still has limitations.

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Speaker 1: It can't it can't deal with anything completely new. It's

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Speaker 1: not capable of doing that. And even something like the

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Speaker 1: it's able to go up and downstairs, which was a

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Speaker 1: big deal, like it was able to balance while going

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Speaker 1: up and downstairs, right, Daleks can't. That's all that takes

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Speaker 1: to ruin the machinations of the Daleks. A staircases. Um,

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Speaker 1: although I've been told that can fly, yeah, but I mean,

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Speaker 1: come on a stairwell. It's not gonna fly up a

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Speaker 1: stairwell anyway. So the stairs have to be pre programmed

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Speaker 1: right into the Asimo. It can't. It can't encounter a

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Speaker 1: new staircase and be able to walk up or down

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Speaker 1: that staircase all on its own yet, but it could

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Speaker 1: encounter a new room and be able to walk from

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Speaker 1: one point to the other and recognize that there's something

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Speaker 1: in the way and move around it. So there are

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Speaker 1: limitations even now. But um, you know, we we're seeing

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Speaker 1: advancements all the time. And uh, I do think it's

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Speaker 1: just a question of when we get to the point

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Speaker 1: of something that is capable of learning from its environment,

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Speaker 1: like the Cornell example kind of proves that. Um, whether

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Speaker 1: or not it develops a personality is a totally different question.

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Speaker 1: And I actually think that the Decart example was fascinating.

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Speaker 1: I want or if perhaps part of the problem with

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Speaker 1: defining artificial intelligence is that we have a very you know,

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Speaker 1: we have a very narrow perspective of what intelligence is.

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Speaker 1: You know, we can't we're humans. We can't really conceive

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Speaker 1: of an intelligence outside of our own human intelligence in

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Speaker 1: the sense that there might be some way of having

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Speaker 1: intelligence that is so foreign to us that we would

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Speaker 1: not even recognize it as such. Yeah, we're we're basing

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Speaker 1: it on our own understanding of what intelligence is. You

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Speaker 1: have to there, there's no way we can't because we

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Speaker 1: are human beings. But that's that's a good point to make,

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Speaker 1: is that there could be let's say that ten years

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Speaker 1: down the road, we have an artificially intelligent device, but

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Speaker 1: we can't really recognize it as such because it's not

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Speaker 1: intelligent the same way human beings are. And I do

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Speaker 1: think there are artificial intelligences. Yeah, I've met a lot

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Speaker 1: of them, mostly on election day. Oh man. Now, I

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Speaker 1: mean but I think, you know, going back to your

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Speaker 1: to your Asimo example, Yeah, um, I I think they

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Speaker 1: are intelligent. The thing, the things that we manufacture that

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Speaker 1: have an artificial intelligence to them are artificially intelligent within

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Speaker 1: the boundaries that we set for them, like there are

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Speaker 1: you know, I've played games that have the ones that

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Speaker 1: are described as having good artificial intelligences, and the ones

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Speaker 1: that are that are really good are the ones that

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Speaker 1: seem to adapt to the way that you play. They say, Okay,

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Speaker 1: I know he's gonna come at me from this angle.

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Speaker 1: Now I'm going to do this to defend myself and

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Speaker 1: go in this direction. Um. But of course, if you

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Speaker 1: wanted it to make you a grill cheese sandwich, you know,

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Speaker 1: really that's not gonna help much. Yeah. The intelligence intelligence

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Speaker 1: is limited to the set of instructions that were given

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Speaker 1: to the intelligence at the very beginning, right, So Asimo

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Speaker 1: couldn't direct an orchestra, but it could climb a pair

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Speaker 1: a set of stairs, and then they programmed it to

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Speaker 1: conduct in order. Was just about to say conduct it did,

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Speaker 1: but it was actually it was actually um mimicking someone

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Speaker 1: else's movement, accorded someone else's movements, programmed that into Asimo.

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Speaker 1: So asthma was not that was that That was like

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Speaker 1: putting a tape into a VCR or a CD into

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Speaker 1: a CD player and pushing play. There was no way

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Speaker 1: for Asimo to depart from that series of movements. It

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Speaker 1: could not It couldn't interpret the music. It couldn't stop

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Speaker 1: and try and hold a note a little for a

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Speaker 1: little more time to get the biggest emotional impact of

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Speaker 1: the piece. It could only conduct at the the tempo

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Speaker 1: and the all the motions that were involved. Um that

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Speaker 1: we're programmed into it beforehand. So that's a very good example.

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Speaker 1: One thing that that Honda's robot will do. I mean,

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Speaker 1: since it is from Honda, it can reach in a chord.

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Speaker 1: Oh my gosh, uh Michael, um so that I see.

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Speaker 1: That was that was not Honda. I was gonna talk

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Speaker 1: a little bit about capture as well, and it's the

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Speaker 1: way it plays into artificial intelligence. Now, for those of

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Speaker 1: you who aren't familiar, capture, of course, is the uh

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Speaker 1: it's it's used a lot in verification and when you're

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Speaker 1: you know, signing up for a new account with something,

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Speaker 1: um it's quiggly alphabetical characters that look like they've been

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Speaker 1: type written around a piece of string or something. It's

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Speaker 1: it's some Usually it's a It can be either a

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Speaker 1: string of random characters or if you're lucky, it's a word.

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Speaker 1: Because some of these can get so difficult to understand

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Speaker 1: that you know, you can't like, is that a B

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Speaker 1: or is that a you know A Yeah, if it's

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Speaker 1: a word, you can at least you know, your brain

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Speaker 1: will put that together, which is as another good example,

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Speaker 1: like if a computer is not able to figure out

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Speaker 1: a word based upon a couple of you know, letters

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Speaker 1: in there, then obviously that shows that it fails that test.

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Speaker 1: So the idea mind capture is that it's supposed to

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Speaker 1: be a test that human beings can easily pass, but

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Speaker 1: computers cannot, And the purpose for capture is to try

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Speaker 1: and avoid having automated bots register tons and tons and

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Speaker 1: tons of spam accounts for whatever, you know, whatever the

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Speaker 1: thing is like. Ticketmaster is a good example. Let's say

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Speaker 1: you're buying tickets on Ticketmaster. There's a capture section where

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Speaker 1: you have to fill it out before you can complete

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Speaker 1: your order. This helps prevent scalpers from setting up an

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Speaker 1: automated program to buy all the tickets from for a

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Speaker 1: certain venue for a certain show, and then scalping the

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Speaker 1: heck out of those tickets. All right, so it makes

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Speaker 1: sense now, Um, there are people who are figuring out

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Speaker 1: ways to program computers to recognize captures, and in a

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Speaker 1: way you might say, oh, well, this is terrible because

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Speaker 1: it means that the security now has taken a back

427
00:24:35,760 --> 00:24:37,240
Speaker 1: step and now they're gonna have to figure out a

428
00:24:37,240 --> 00:24:38,760
Speaker 1: new way of doing this, which is gonna make it

429
00:24:38,760 --> 00:24:42,560
Speaker 1: even more difficult for humans to figure out the damn word. Right, Well,

430
00:24:42,600 --> 00:24:46,199
Speaker 1: but you're you're talking about. The silver lining is that

431
00:24:46,359 --> 00:24:50,080
Speaker 1: it means that it's an advance in artificial intelligence. So

432
00:24:50,520 --> 00:24:53,440
Speaker 1: on the one hand, computer security has gotten a little

433
00:24:53,480 --> 00:24:55,920
Speaker 1: more difficult. On the other hand, artificial intelligence has gotten

434
00:24:55,920 --> 00:24:58,520
Speaker 1: a little better. Now. Of course, this doesn't apply if

435
00:24:58,560 --> 00:25:02,960
Speaker 1: you happen to employ hundred people out in Indo, China

436
00:25:03,080 --> 00:25:07,920
Speaker 1: to manually go and type these captures in because that's

437
00:25:07,960 --> 00:25:10,320
Speaker 1: what a lot of you know, quote unquote hackers are

438
00:25:10,359 --> 00:25:13,440
Speaker 1: actually doing. They're not creating a program that understands capture.

439
00:25:13,640 --> 00:25:16,000
Speaker 1: They're just hiring someone who will work for pennies on

440
00:25:16,040 --> 00:25:19,840
Speaker 1: the hour to type these things out. Um, that's obviously

441
00:25:19,840 --> 00:25:23,920
Speaker 1: not artificial intelligence. That's just being a scumbag. Uh, because

442
00:25:23,920 --> 00:25:26,040
Speaker 1: there are better ways to help people in need than

443
00:25:26,119 --> 00:25:30,840
Speaker 1: to employ them to create junk mail accounts. Um, well

444
00:25:30,840 --> 00:25:35,120
Speaker 1: there goes my business model. Yes. So yeah, So that's

445
00:25:35,119 --> 00:25:37,320
Speaker 1: the whole idea behind the capture is that even when

446
00:25:37,359 --> 00:25:40,880
Speaker 1: it fails, it's still a success. It's just a success

447
00:25:40,880 --> 00:25:44,879
Speaker 1: in a different field. Alright. Then it's kind of it's

448
00:25:44,960 --> 00:25:46,400
Speaker 1: kind of a weird way of thinking of it though

449
00:25:46,600 --> 00:25:48,600
Speaker 1: you would think that would be sort of a negative

450
00:25:48,640 --> 00:25:50,320
Speaker 1: all the way around. And and you know, we've seen

451
00:25:50,359 --> 00:25:53,760
Speaker 1: other elements of artificial intelligence pop up in devices. You

452
00:25:53,800 --> 00:25:57,359
Speaker 1: have things like the collision detection in cars, where it

453
00:25:57,400 --> 00:26:01,520
Speaker 1: alerts you before you actually were to uh collide with something.

454
00:26:02,680 --> 00:26:05,520
Speaker 1: And then there's it appears that you're trying to write

455
00:26:05,560 --> 00:26:10,679
Speaker 1: a letter, Yes, there's Clippy Clippy, which is not really

456
00:26:11,400 --> 00:26:16,359
Speaker 1: that intelligent. No, it's artificially incredibly irritating. Actually, you know,

457
00:26:16,400 --> 00:26:18,520
Speaker 1: I haven't seen Clippy around. What's he doing these days?

458
00:26:19,200 --> 00:26:23,359
Speaker 1: Hopefully hard time because I hated that thing. Oh my gosh,

459
00:26:23,400 --> 00:26:26,720
Speaker 1: I give you a hard time. Yeah. Yeah, you type

460
00:26:26,760 --> 00:26:29,280
Speaker 1: in three words and it suddenly decided what you've tried

461
00:26:29,280 --> 00:26:30,800
Speaker 1: to do, and it's trying to be helpful even though

462
00:26:30,800 --> 00:26:32,600
Speaker 1: you didn't ask for help and you didn't want it.

463
00:26:33,480 --> 00:26:36,840
Speaker 1: I hate Clippy. It appears that you're trying to extort

464
00:26:36,880 --> 00:26:40,560
Speaker 1: some money. It appears that you're a Nigerian prince. Who is?

465
00:26:41,840 --> 00:26:45,880
Speaker 1: That's just thanks Clippy, you've made scamming people so much easier.

466
00:26:46,800 --> 00:26:54,160
Speaker 1: But artificial intelligence, it's one of those truly multidiscipline type

467
00:26:54,160 --> 00:26:56,920
Speaker 1: of uh fields, right, I mean it has it has

468
00:26:56,960 --> 00:27:01,719
Speaker 1: everything that it's everything from philosophy, computer science and euroscience. Uh,

469
00:27:01,760 --> 00:27:06,840
Speaker 1: I mean biology even because there are people who are

470
00:27:06,880 --> 00:27:09,119
Speaker 1: staying the human brain and trying to figure out how

471
00:27:09,160 --> 00:27:12,600
Speaker 1: that works and apply that somehow to computer science. That's

472
00:27:12,640 --> 00:27:15,520
Speaker 1: the tricky tricky thing because what what we know about

473
00:27:15,520 --> 00:27:19,880
Speaker 1: the computer the human brain is so incredibly it's such

474
00:27:19,880 --> 00:27:23,320
Speaker 1: a tiny fraction of what there is to know from

475
00:27:23,320 --> 00:27:24,880
Speaker 1: what we can tell at any rate. I mean, there's

476
00:27:24,880 --> 00:27:27,960
Speaker 1: so much we don't understand. What is the mind versus

477
00:27:28,000 --> 00:27:30,959
Speaker 1: the brain? We can't answer that question, which is kind

478
00:27:30,960 --> 00:27:33,119
Speaker 1: of ironic when you think about it. That would be

479
00:27:33,160 --> 00:27:37,240
Speaker 1: philosophy right there, Yes, philosophy, but it's also biology. I mean,

480
00:27:37,280 --> 00:27:41,520
Speaker 1: it's the same thing. It crosses over. The mind versus

481
00:27:41,600 --> 00:27:45,440
Speaker 1: the brain is one of those questions that various disciplines

482
00:27:45,440 --> 00:27:48,600
Speaker 1: have been trying to answer for for generations. And um

483
00:27:48,760 --> 00:27:53,879
Speaker 1: there's some who who believe that a and artificial intelligence

484
00:27:53,920 --> 00:27:58,600
Speaker 1: could develop a mind, even if we don't ourselves understand

485
00:27:58,640 --> 00:28:01,120
Speaker 1: how the mind works. If we build a brain that's

486
00:28:01,119 --> 00:28:05,760
Speaker 1: good enough, a mind could spontaneously generate from that brain.

487
00:28:05,840 --> 00:28:08,360
Speaker 1: If the brain was effective enough and could could make

488
00:28:08,400 --> 00:28:12,200
Speaker 1: its own decisions, it could develop its own mind. This

489
00:28:12,240 --> 00:28:15,840
Speaker 1: is completely theoretical because we don't have the ability to

490
00:28:15,920 --> 00:28:19,520
Speaker 1: make a device that complex right now to test this out.

491
00:28:20,000 --> 00:28:23,280
Speaker 1: But it's also kind of terrifying because then we arrive

492
00:28:23,440 --> 00:28:27,960
Speaker 1: at the scenario you mentioned at the beginning. The intelligence

493
00:28:28,000 --> 00:28:31,119
Speaker 1: takes a look around, looks at what it needs, decides

494
00:28:31,160 --> 00:28:34,040
Speaker 1: whether or not humans are needed. If humans are not needed,

495
00:28:34,160 --> 00:28:36,560
Speaker 1: or should they be eliminated. If they should be eliminated,

496
00:28:36,640 --> 00:28:39,040
Speaker 1: what's the most effective way of doing this. If it's

497
00:28:39,080 --> 00:28:41,760
Speaker 1: got the self recursive improvement, it's going to figure out

498
00:28:41,840 --> 00:28:44,520
Speaker 1: better and better ways of getting rid of people until

499
00:28:44,600 --> 00:28:47,000
Speaker 1: you don't have people anymore, or you get assimilated and

500
00:28:47,040 --> 00:28:50,080
Speaker 1: you become part of the borg. Well, you know, that's

501
00:28:50,120 --> 00:28:53,720
Speaker 1: a cheery outlook on it. Or it absorbs our intelligence

502
00:28:53,760 --> 00:28:57,480
Speaker 1: in some way and we all become virtual people and

503
00:28:57,800 --> 00:29:02,160
Speaker 1: are fleshy, meaty bar These eventually die and decompose and

504
00:29:03,160 --> 00:29:06,280
Speaker 1: say farewell and shovel off the mortal coil. But our

505
00:29:06,760 --> 00:29:10,640
Speaker 1: intelligence lives on. Now that's kind of an interesting thought.

506
00:29:10,640 --> 00:29:12,880
Speaker 1: It also is kind of depressing because he realized that

507
00:29:12,920 --> 00:29:15,320
Speaker 1: it's not the intelligence that's living in you right now.

508
00:29:15,400 --> 00:29:19,440
Speaker 1: It would be a copy of you. You know, I'm

509
00:29:19,480 --> 00:29:22,160
Speaker 1: guessing that when Alexander wrote to us that she had

510
00:29:22,280 --> 00:29:24,320
Speaker 1: no idea whatsoever that we were going to get into

511
00:29:24,320 --> 00:29:27,840
Speaker 1: this deep conversation about this stuff, that's because I'm not

512
00:29:27,920 --> 00:29:30,160
Speaker 1: sure that I and my brain is kind of hurting

513
00:29:30,160 --> 00:29:32,520
Speaker 1: at this point. Yeah, it's well, this is a this

514
00:29:32,600 --> 00:29:38,360
Speaker 1: is a tough topic. Now we're all to answer Alexandra's

515
00:29:38,440 --> 00:29:43,080
Speaker 1: question artificial intelligence, to some extent, is already in a

516
00:29:43,160 --> 00:29:47,280
Speaker 1: host of different products, from cars to even things like smartphones.

517
00:29:47,760 --> 00:29:50,640
Speaker 1: Smartphones able to detect where you are, how fast you're moving,

518
00:29:50,680 --> 00:29:53,880
Speaker 1: all this kind of stuff. Um, it hasn't gotten to

519
00:29:53,880 --> 00:29:55,840
Speaker 1: the point where they can start to make their own

520
00:29:55,840 --> 00:30:02,520
Speaker 1: decisions or or improve upon pastic sperience. But I don't

521
00:30:02,520 --> 00:30:05,440
Speaker 1: think we're that far off. There are people who believe

522
00:30:05,520 --> 00:30:09,280
Speaker 1: that artificial intelligence may maybe there may be a breakthrough

523
00:30:09,480 --> 00:30:12,560
Speaker 1: as early as next year. Of course they said that

524
00:30:12,720 --> 00:30:15,680
Speaker 1: last year, right, Well, actually in two thousand and seven

525
00:30:15,720 --> 00:30:18,520
Speaker 1: they said any time between three and seven years, So

526
00:30:18,760 --> 00:30:20,640
Speaker 1: next year would be the first year for that major

527
00:30:20,680 --> 00:30:25,440
Speaker 1: breakthrough and then for the singularity you're looking at around

528
00:30:26,760 --> 00:30:30,040
Speaker 1: depending upon who you ask. So uh, I mean that

529
00:30:30,920 --> 00:30:33,080
Speaker 1: whether we actually hit the singularity, A lot of that

530
00:30:33,120 --> 00:30:36,080
Speaker 1: depends on if other things hold true, like Moore's law. Yeah,

531
00:30:36,280 --> 00:30:39,000
Speaker 1: but of course you know, Ray Kurtzweil and others are

532
00:30:39,000 --> 00:30:42,440
Speaker 1: counting on it. Yeah, they've they've got an entire lecture

533
00:30:42,480 --> 00:30:45,560
Speaker 1: series devoted to it. And they are very smart people,

534
00:30:46,560 --> 00:30:49,560
Speaker 1: I would say, far smarter than I am. So it

535
00:30:49,640 --> 00:30:52,600
Speaker 1: may be that they're onto something. Um, it's hard for

536
00:30:52,640 --> 00:30:55,720
Speaker 1: me to say because it's you know, I also look

537
00:30:55,720 --> 00:31:00,640
Speaker 1: at other extremely complex machinery that doesn't yet work. Yeah,

538
00:31:00,880 --> 00:31:05,360
Speaker 1: like you know large Hadron collider. Well, there are theories

539
00:31:05,400 --> 00:31:07,800
Speaker 1: as to why that's We're going to get into that,

540
00:31:07,840 --> 00:31:09,880
Speaker 1: though we can't. We can't talk about that. That's coming

541
00:31:09,960 --> 00:31:13,040
Speaker 1: up in an upcoming episode. I wonder if artificial intelligence

542
00:31:13,080 --> 00:31:17,760
Speaker 1: is behind that. Sounds like a conspiracy to me. That's

543
00:31:17,800 --> 00:31:20,920
Speaker 1: a hint. All right, Well, let's uh, I think are

544
00:31:21,320 --> 00:31:23,400
Speaker 1: do you have anything else to add on artificial intelligence?

545
00:31:23,920 --> 00:31:26,640
Speaker 1: This was a very philosophical discussion, very different from our

546
00:31:26,680 --> 00:31:28,520
Speaker 1: normal episode. I hope you guys enjoyed it. We don't

547
00:31:28,520 --> 00:31:31,640
Speaker 1: plan on doing a ton of philosophical discussions, but it's

548
00:31:31,640 --> 00:31:34,160
Speaker 1: gonna do something a little different. It is. It's nice

549
00:31:34,320 --> 00:31:36,800
Speaker 1: thought experiment, which, as it turns out, very important in

550
00:31:36,840 --> 00:31:42,120
Speaker 1: artificial intelligence. But now that we've exhausted that topic, at

551
00:31:42,160 --> 00:31:44,560
Speaker 1: least as far as our understanding goes, you could you

552
00:31:44,560 --> 00:31:48,480
Speaker 1: could take entire college courses in artificial I mean, there's far, far,

553
00:31:48,600 --> 00:31:51,760
Speaker 1: far more out. Yeah, this is not even scratching the surface.

554
00:31:51,800 --> 00:31:54,600
Speaker 1: That would be an insult to the field of artificial intelligence.

555
00:31:54,640 --> 00:31:57,600
Speaker 1: But it does now wrap things up, which leads us

556
00:31:57,640 --> 00:32:04,480
Speaker 1: to our second bout of listener May This listener mail

557
00:32:04,600 --> 00:32:07,320
Speaker 1: comes from Thomas, and Thomas has this to say, Hello,

558
00:32:07,400 --> 00:32:10,280
Speaker 1: Chris and Jonathan. I just recently listened to your podcast

559
00:32:10,360 --> 00:32:12,239
Speaker 1: on a r g S and I was wondering if

560
00:32:12,280 --> 00:32:14,400
Speaker 1: I could get a few pointers. I'm getting started and

561
00:32:14,480 --> 00:32:17,880
Speaker 1: participating in one. So r g s are the alternate

562
00:32:17,880 --> 00:32:21,400
Speaker 1: reality games, which we talked about several episodes ago. This.

563
00:32:21,640 --> 00:32:24,920
Speaker 1: Two sites I would recommend anyone go to if they

564
00:32:24,920 --> 00:32:29,520
Speaker 1: are interested in getting into alternate reality games are www

565
00:32:29,560 --> 00:32:34,760
Speaker 1: Dot a r g N dot com or www dot

566
00:32:34,960 --> 00:32:38,920
Speaker 1: unfiction dot com. Both of those are good place to

567
00:32:39,080 --> 00:32:41,800
Speaker 1: start if you want to look into alternate reality games

568
00:32:41,840 --> 00:32:43,920
Speaker 1: and kind of get an idea of which ones you

569
00:32:43,920 --> 00:32:46,040
Speaker 1: want to play. Some of the old games are still

570
00:32:46,520 --> 00:32:49,400
Speaker 1: semi active, so you could actually play through parts of

571
00:32:49,440 --> 00:32:53,080
Speaker 1: the game now. Granted, anything that realizes on interaction between

572
00:32:53,080 --> 00:32:57,040
Speaker 1: the game master and the players that's long since over right,

573
00:32:57,080 --> 00:32:59,600
Speaker 1: so you can't you can't play that part. But the

574
00:32:59,640 --> 00:33:02,080
Speaker 1: puzzles that are online you may still be able to

575
00:33:02,080 --> 00:33:04,840
Speaker 1: play through, or at least be able to see what

576
00:33:05,040 --> 00:33:07,920
Speaker 1: the puzzles were, how people work them out, get an

577
00:33:07,960 --> 00:33:10,640
Speaker 1: idea for the way the games are played. Um. Both

578
00:33:10,640 --> 00:33:14,120
Speaker 1: of those sites also list active games. When I looked,

579
00:33:14,240 --> 00:33:16,040
Speaker 1: there weren't a whole lot of active games right now,

580
00:33:16,080 --> 00:33:19,760
Speaker 1: but that could change at any time. So thanks Thomas.

581
00:33:20,160 --> 00:33:23,680
Speaker 1: If any of you have any questions, suggestions, criticisms, comments,

582
00:33:23,680 --> 00:33:25,720
Speaker 1: that kind of thing, right to us tech Stuff at

583
00:33:25,760 --> 00:33:28,200
Speaker 1: how stuff works dot com. We have a lot of

584
00:33:28,280 --> 00:33:31,400
Speaker 1: articles on the site that relate to artificial intelligence. Uh

585
00:33:31,440 --> 00:33:35,160
Speaker 1: not one specifically about how AI works, but how it

586
00:33:35,200 --> 00:33:37,760
Speaker 1: relates to things like robots, video games, all that sort

587
00:33:37,800 --> 00:33:39,959
Speaker 1: of stuff. I recommend you check those out. That's at

588
00:33:39,960 --> 00:33:42,160
Speaker 1: how stuff works dot com. Remember we have a live

589
00:33:42,200 --> 00:33:46,920
Speaker 1: show every Tuesday one pm Eastern. Check that out again. Blogs,

590
00:33:47,440 --> 00:33:49,360
Speaker 1: So you would look at the House of Works dot

591
00:33:49,360 --> 00:33:50,800
Speaker 1: com on the right hand side there's the link of

592
00:33:50,840 --> 00:33:53,280
Speaker 1: the blogs. That's where you're gonna find that information. And

593
00:33:53,400 --> 00:33:56,000
Speaker 1: Chris and I will talk to you again really soon

594
00:33:59,040 --> 00:34:01,480
Speaker 1: for more on this and thousands of other topics. Does

595
00:34:01,520 --> 00:34:03,800
Speaker 1: it how stuff works dot com And be sure to

596
00:34:03,880 --> 00:34:05,960
Speaker 1: check out the new tech stuff blog now on the

597
00:34:05,960 --> 00:34:13,360
Speaker 1: House stuff Works homepage. Brought to you by the reinvented

598
00:34:13,400 --> 00:34:16,080
Speaker 1: two thousand twelve camera. It's ready, are you