Speaker 1: Brought to you by the reinvented two thousand twelve camera.
Speaker 1: It's ready. Are you get in touch with technology with
Speaker 1: tech Stuff from how stuff works dot com. Hello again, everyone,
Speaker 1: Welcome to tech Stuff. My name is Chris Poulette and
Speaker 1: I am an editor at how stuff works dot Com.
Speaker 1: Sitting across from me, as always, is senior writer Jonathan Strickland.
Speaker 1: The game is afoot Okay. This episode is about a
Speaker 1: system created by IBM as a scientific experiment to determine
Speaker 1: whether a computer can beat a human in a game
Speaker 1: of skill and intelligence. Jonathan, what is Watson? That is correct?
Speaker 1: I like all I would too? And Big Bucks? Are you?
Speaker 1: Are you a giant computer? Sorry's really reaching back now.
Speaker 1: I would like to tell you my sob story about
Speaker 1: my life so I can win a new refrigerator. There.
Speaker 1: That's reaching back, and it's really obscure. If you know
Speaker 1: what I'm referring to with that particular game show, let
Speaker 1: me know, sadly I do. So I'm just gonna stay
Speaker 1: out at this. I'm not eligible to win. I read
Speaker 1: the rules. So we're gonna talk today about the Watson computer.
Speaker 1: We actually had a lot of listeners right in about
Speaker 1: this because The announcement of the Watson computer came shortly
Speaker 1: after we are episode on. Actually, I think it might
Speaker 1: have even been just before our episode about Computers Versus
Speaker 1: Humans published, So of course it looked like we had
Speaker 1: a glaring omission. Yes, but by in our defense, we
Speaker 1: didn't know about it yet. Yes, actually we mentioned one
Speaker 1: of Watson's cousins predecessors, is probably a predecessor of processors. Yeah. Actually, um,
Speaker 1: deep Blue, I'm sorry, Deep Blue, Deep Blue, Big Blue
Speaker 1: would be the company that made it. But the the
Speaker 1: we're talking about IBM, and IBM does this thing occasionally
Speaker 1: where they issue Yeah, well it is a thing, I mean,
Speaker 1: it's it's because it's not just Deep Blue, it's not
Speaker 1: just Watson. They issue what they call grand challenges their
Speaker 1: engineering teams. Yes, they've had a series of these, and
Speaker 1: some of them are are more noticeable to the public,
Speaker 1: I guess, and others. Deep Blue would definitely be one
Speaker 1: of those because that made headlines. In the nineties. Deep
Speaker 1: Blue was of course the computer that challenged Gary Kasparov,
Speaker 1: the chess grand Master um to a series of games.
Speaker 1: In the first series of games, Kasparov was emerged victorious,
Speaker 1: and in the second Deep blue one, and so that
Speaker 1: was one of those things that kind of propelled the
Speaker 1: whole idea of computers being able to outwit humans, to
Speaker 1: be able to outperform humans in certain tasks. But there
Speaker 1: were other tasks that humans were still much much more
Speaker 1: capable of completing than computers. And UM, as it turns out,
Speaker 1: Watson is a grand challenge. To answer one of those,
Speaker 1: so to speak, or maybe question one of those would
Speaker 1: be better because you have to put it in the
Speaker 1: form of a question, right, That's that's correct. UM. I
Speaker 1: would imagine that Watson does this flawlessly. But we could
Speaker 1: talk about the differences in a human opponent and a
Speaker 1: computer opponent in a little bit. UM. I wanted to
Speaker 1: get into some of the details. Watson is not actually
Speaker 1: a single computer as I typically think about it. UM.
Speaker 1: It is made of ten racks of IBM power, seven
Speaker 1: fifty servers using the Linux operating system. How many cores
Speaker 1: does it have? Two thousand, eight hundred eight processor cores
Speaker 1: wholly free holies? Have you thought your quad core processor
Speaker 1: was the bees knees? I also thought my, uh my
Speaker 1: computers for gigabytes of RAM were pretty much for what
Speaker 1: I'm doing. But Watson has fifteen terabytes of RAM. A
Speaker 1: terabyte is one thousand, twenty four gigabytes, that's right. Also,
Speaker 1: it computes eight at the rate of eighty tarra flops,
Speaker 1: which is eighty trillion calculations per second. And in fact,
Speaker 1: I understand from reading IBM's website about Watson that it
Speaker 1: has somewhere in the neighborhood of two million books essentially.
Speaker 1: I mean, that's it's it's kind of hard to say
Speaker 1: how much information is in a book, but um more
Speaker 1: or less two million books, and it can scan the
Speaker 1: entirety of information on all of those hard drives in
Speaker 1: that machine in roughly two to three second. Right. The
Speaker 1: idea here is that they needed to create a computer.
Speaker 1: You have, the whole the whole challenge here was to
Speaker 1: create a computer that could compete in a game of
Speaker 1: Jeopardy and compete on a championship level. Yeah. And as
Speaker 1: a matter of fact, when we talked about the computer
Speaker 1: to versus person challenge in that podcast, we were discussing how,
Speaker 1: you know, computers do some things really really well and
Speaker 1: some things they don't do so well. And ib AM
Speaker 1: freely admitted that this was a real toughie. Yeah, because
Speaker 1: as it turns out one of the things computers do
Speaker 1: really well. They do well with things like like logical problems,
Speaker 1: you know, because you follow a very set a series
Speaker 1: of steps, things that that obey specific rules. The English
Speaker 1: language does not obey rules as strictly as a mathematical formula. Yes,
Speaker 1: as a matter of fact, we we sort of go
Speaker 1: with with things that might be tricky for computers to
Speaker 1: understand all the time because we constantly on this show
Speaker 1: do wordplay and puns, um, and computers may not necessarily
Speaker 1: understand the nuances of such things, or or slang, or
Speaker 1: metaphors or metaphors. Um. There's a lot of elements to
Speaker 1: human speech that we naturally understand as we develop our
Speaker 1: language skills. Right speak for yourself, I have no idea
Speaker 1: how this thing works, okay, but most of us figure
Speaker 1: out how to determine what someone is talking about based
Speaker 1: on contextual clues and our knowledge of things like wordplay
Speaker 1: and metaphors. So as we build our vocabulary, as we
Speaker 1: build our ability to create sentences, as we understand concepts
Speaker 1: that are not necessarily concrete, then we are able to
Speaker 1: communicate in a more ambiguous way than a computer would
Speaker 1: necessarily be capable of on any normal computer. That is, So,
Speaker 1: what are you trying to say, Johnny get Yeah, what
Speaker 1: I'm trying to say is that I'm trying to say
Speaker 1: is that the depending on the way you word a sentence, Uh,
Speaker 1: a human might be able to determine immediately what the
Speaker 1: significance is of the sentence. You know, what you just said.
Speaker 1: They'd be able to understand it. A computer, depending upon
Speaker 1: the wording, may not be able to interpret it properly
Speaker 1: because you know, you didn't necessarily say like, the ball
Speaker 1: is blue. You know, you might have used a much
Speaker 1: more poetic way of saying it that a computer just
Speaker 1: can't you know, the computer can't equate that as being
Speaker 1: the ball is blue. But any human listener would be
Speaker 1: able to understand what you were getting at and say, oh,
Speaker 1: it's a blue ball. It was just a really fancy,
Speaker 1: flowery way of saying that. Yes, Um, I watched a
Speaker 1: number of videos on the IBM site and some of
Speaker 1: them are quite amusing. Actually, uh, because the early versions
Speaker 1: of Watson just didn't get it. Yeah, they weren't. They
Speaker 1: weren't the most um accurate. And what what's funny about
Speaker 1: is not that the computer didn't get it. But the
Speaker 1: looks on the engineer's faces and as they were going, yeah, okay, no,
Speaker 1: maybe not not so much. We have to go back
Speaker 1: to the drawing board. But Dr Chris Welty was saying
Speaker 1: the point of this exercise is to do the science
Speaker 1: behind this and and they specifically we're looking forward to
Speaker 1: the challenge of Jeopardy and UM. You know, if you
Speaker 1: if you're unfamiliar with the show UM, which some of
Speaker 1: you maybe uh a lot of the questions. Of course,
Speaker 1: the the the answers are presented first. UH. The contestants
Speaker 1: are given the opportunity to choose one of six categories
Speaker 1: that are on the board at different values UH monetary
Speaker 1: values UM. And so you can expect in these categories
Speaker 1: that the the answers UH you are actually supposed to
Speaker 1: give the question if you are contestant on the game.
Speaker 1: The answers can fall within a certain domain of knowledge UM.
Speaker 1: For example, the infamous Potent Potables category UM is about
Speaker 1: alcoholic drinks, and you can expect that if you are
Speaker 1: fairly knowledgeable about different kinds of drinks that you might
Speaker 1: do well or poorly in the category. So you should
Speaker 1: either choose questions or answers from the category or not. Um. Well,
Speaker 1: you know, if no one has bothered to program that
Speaker 1: information into Watson, Uh, then Watson will do poorly in
Speaker 1: that category. But some of the categories on Jeopardy are
Speaker 1: written with a lot of word smithing involved, so you
Speaker 1: might have to supply an answer that rhymes or unscramble
Speaker 1: the war letters to do to form another word. Now,
Speaker 1: the unscrambling thing might come very easy to a computer, um,
Speaker 1: but the rhyming answer, you'd have to go over a
Speaker 1: lot of synonyms in your head to try to find. Okay, well,
Speaker 1: I know the answer to this question, but it obviously
Speaker 1: isn't going to rhyme right. So um. Dr Welty said,
Speaker 1: you know, this is one of the things that we
Speaker 1: were really looking forward to. We wanted, we wanted to challenge.
Speaker 1: We wanted the computer to be answered able to answer
Speaker 1: questions or question answers that the computer normally wouldn't be
Speaker 1: able to. So they were really looking forward to cracking
Speaker 1: this nut, so to speak. Um. They talked about there
Speaker 1: being five major areas that they had to concentrate on
Speaker 1: in order to make Watson work based upon the way
Speaker 1: Jeopardy works, because again they designed this project with a
Speaker 1: very specific application in mind. It helped give them direction
Speaker 1: as opposed to it just being I just want to
Speaker 1: make a computer that is able to analyze semantics and
Speaker 1: and respond. Um. That's you know, that's a much more
Speaker 1: general approach. By giving them the fact that, okay, well,
Speaker 1: our goal is to be able to create a computer
Speaker 1: that can compete and potentially beat champions in Jeopardy, Uh,
Speaker 1: it provided more focus. So with Jeopardy in mind, they
Speaker 1: said the five things they needed to concentrate on was
Speaker 1: that Jeopardy creates a broad and open domain, which means
Speaker 1: that you don't just get questions about one subject. Yes,
Speaker 1: you're not going to have to know everything there is
Speaker 1: to know about alcoholic drinks and that's the only thing
Speaker 1: you were going to be asked about. Right There might
Speaker 1: be politics, pop culture, sports, literature, all sorts of categories
Speaker 1: that you could potentially come up against. So with that
Speaker 1: in mind, the computer had to be able to answer
Speaker 1: those things. Uh. There were as Chris was saying, there
Speaker 1: was an element of complex language. Jeopardy answers can be tricky.
Speaker 1: They're not necessarily straightforward. It's kind of like the New
Speaker 1: York Times crossword puzzle. If you read the clues to
Speaker 1: that crossword puzzle, they aren't necessarily straightforward. They require you
Speaker 1: to make some You have to bridge some gaps in
Speaker 1: order to get to the right answer yes. And in fact,
Speaker 1: they will ask you even in clues for for that puzzle.
Speaker 1: They will ask you for things in poetic language, and
Speaker 1: you'll have to think about things in a completely different
Speaker 1: way than you might have otherwise. The next area that
Speaker 1: they had to focus on was high precision, so you
Speaker 1: had to be able to narrow down your choices and
Speaker 1: find out which of your potential answers would be the most,
Speaker 1: the most accurate, or the best one to choose. Along
Speaker 1: with that was accurate confidence, which means that the computer
Speaker 1: itself has to be able to determine how likely is
Speaker 1: this answer? How likely is this the right answer? Yes? Right,
Speaker 1: and um. And then the last one was high speed.
Speaker 1: It had to be a really really fast computer in
Speaker 1: order to compete against people, because if you know something,
Speaker 1: you just you just spout it out right, you know,
Speaker 1: you you buzz and you say, who is Marshall brain?
Speaker 1: You know? And then you've got the answer, who is
Speaker 1: Marshal brain? I think only one person can answer that question,
Speaker 1: and he is not in the studio today. UM. But yeah,
Speaker 1: you have to have computers capable of of accessing all
Speaker 1: this information and picking it out as quickly as a
Speaker 1: human would be able to. UM. In fact, I saw
Speaker 1: on one of these videos that uh, if you had
Speaker 1: a two point six giga Hurts core processor a computer
Speaker 1: running one of those Okay, posably, I do own a
Speaker 1: computer with a two point six gigga Hurts process right,
Speaker 1: so you know, kind of a middle of the road
Speaker 1: computer right now. But but two point six gigga Hurts computer.
Speaker 1: If you were to try and answer one question uh,
Speaker 1: and you were going to go through all of Watson's
Speaker 1: UH data in order to find that question, the answer
Speaker 1: to that question and compare all the answers and come
Speaker 1: up with the best result and then presented, it would
Speaker 1: take you two hours for that one computer. It doesn't
Speaker 1: surprise me much. So that's why you have that two
Speaker 1: thousand eight processor. You know that with all the different
Speaker 1: uh the web servers running, you have to have those
Speaker 1: core processors running so that you can solve these questions
Speaker 1: in parallel. Excuse me, And you probably remember us talking
Speaker 1: about parallel computing and other podcasts. That's the idea that
Speaker 1: you try and solve a problem by working on parts
Speaker 1: of the problem all at the same time. In this case,
Speaker 1: Watson gets the the answer from Jeopardy and then goes
Speaker 1: through and tries to process all the potential questions that
Speaker 1: would be the correct response to that answer, and then
Speaker 1: it has to evaluate them and choose the right one,
Speaker 1: and has to do this in just a couple of seconds.
Speaker 1: It's a pretty cool idea. The the challenges are not trivial,
Speaker 1: the answers are, but not the the challenges um and
Speaker 1: like you were saying, the early tests were very amusing
Speaker 1: because Watson just didn't get it. It would it would
Speaker 1: give answers that were obviously related to the question, or
Speaker 1: at least related to words that were within the question,
Speaker 1: but we're not the right answer. It's kind of like
Speaker 1: if you were ever using a search engine and you
Speaker 1: put in certain terms and the results you're getting back
Speaker 1: are related to the terms you put in, but not
Speaker 1: to the subject matter you wanted, because it's maybe using hominem's,
Speaker 1: or it's using synonyms, or it's or maybe you misspelled
Speaker 1: something or whatever. But anyway, you're getting the wrong kind
Speaker 1: of responses, same sort of thing. Yep. And speaking of trivial,
Speaker 1: I did want to point out to that Dr Kelly,
Speaker 1: Dr John E. Kelly the third He is a senior
Speaker 1: vice president of ib i'm in the director of IBM Research.
Speaker 1: Um this the project itself, you know, Yes, they're building
Speaker 1: a computer to win a trivia contest, so that might
Speaker 1: seem trivial. Yes, However, the point is, you know, Dr
Speaker 1: Kelly was saying, Look, the amount of information that is
Speaker 1: being created today is rapidly uh, overcoming our ability to
Speaker 1: identify it, process it, makes sense of it, and and
Speaker 1: and derive knowledge from it. Yeah. In fact, I think
Speaker 1: it is a fifteen petabytes of data raw data get
Speaker 1: generated every day, not just not just from people but
Speaker 1: from machines as well. But that's that's an insane amount
Speaker 1: of information. Yes, yes, now, I mean, the human mind
Speaker 1: is a remarkable thing, and if you have systems in place,
Speaker 1: you can help manage that. But at some point, uh,
Speaker 1: you know, even even people can't keep up with that.
Speaker 1: Even there are remarkable computing machines and our skulls. So uh,
Speaker 1: the idea is to build a tool that can actually
Speaker 1: help people. There will be a tool for people to
Speaker 1: help people make sense of this vast amount of information
Speaker 1: and and to overcome that and get get real help
Speaker 1: I guess from machines and and help people understand or
Speaker 1: navigate the world of information that is rapidly creating. UM.
Speaker 1: One of the cooler videos on this site I think
Speaker 1: was the one where they were explaining, look, there there's
Speaker 1: always been this interconnected system of information going on all
Speaker 1: over the world, but we didn't really understand it nearly
Speaker 1: as well. Until the Internet came around. We could actually
Speaker 1: see what was going on, you know, in seconds, rather
Speaker 1: than you know, having it take hours or days or
Speaker 1: weeks or months or even years in many many years past. UM,
Speaker 1: and it's it's just enabled this and is accelerating the problem.
Speaker 1: So UM, the challenge of creating the computer to play
Speaker 1: the game, well, this is basically, I guess an exercise
Speaker 1: to see can we really do this? Can we create
Speaker 1: uh reasonably intelligent computer that can help us, you know,
Speaker 1: figure out what's going on and where the the answers
Speaker 1: are to our questions? Can can we create a computer
Speaker 1: that can understand natural language so that that you challenge it, right,
Speaker 1: It's it's not it's not that you have to tailor
Speaker 1: your language to the computer so that it understands I mean,
Speaker 1: we were familiar with that. You know, we talked about
Speaker 1: Boollyan logic before, about how if you want to do
Speaker 1: really effective search terms, you need to understand how Booleyan
Speaker 1: logic works so that you can. Because search engines don't
Speaker 1: understand natural language, they'll do their best to try and
Speaker 1: match your query with the right result, but they don't
Speaker 1: understand it. They aren't able to analyze the information. One
Speaker 1: of the concepts that it was really important with Watson
Speaker 1: is one that's going to be very important if we
Speaker 1: ever are to have us semantic web, which is the
Speaker 1: idea that you could talk to your computer, whether you're
Speaker 1: actually speaking or typing or whatever. You you can communicate
Speaker 1: with your computer in a natural way, and the computer
Speaker 1: will be able to understand, at least on some level.
Speaker 1: It may not be a deep level, but be able
Speaker 1: to interpret what you're saying and give you the right result.
Speaker 1: Uh in response, that's right. It just it depends on
Speaker 1: a system of contexts, and without those contexts, and the
Speaker 1: computer has to be able to interpret that well, um,
Speaker 1: you're you know, it's it's not nearly as effective as
Speaker 1: it could be um, So this is this is definitely
Speaker 1: a step in the right direction. Yeah, I think it's
Speaker 1: pretty fascinating the way it talked about how or the
Speaker 1: way the the engineers talked about how the computer comes
Speaker 1: up with its answers. So what it does is it
Speaker 1: will it comes up with candidate answers. This is part
Speaker 1: of that parallel processing where all the potential answers to
Speaker 1: a question pop up, and then it turns each of
Speaker 1: those answers into a hypothesis and then examines each hypothesis
Speaker 1: to determine how likely that hypothesis is in fact the
Speaker 1: right answer, and if it doesn't meet a certain level
Speaker 1: of confidence, then then Watson won't buzz in. So Watson
Speaker 1: is not going to buzz in on every question because
Speaker 1: occasionally there's gonna be a question it's gonna be worded
Speaker 1: in such a way that Watson is not really able
Speaker 1: to interpret what what the answer is or just doesn't
Speaker 1: have the information and database. That's another thing we should
Speaker 1: point out. Watson is completely self contained. Yes, it is
Speaker 1: not hooked up to the Internet, so lest you think
Speaker 1: it is searching on Google, it is not. Right. So
Speaker 1: all the information that Watson has available to it is
Speaker 1: self contained. It doesn't. It cannot get more information during
Speaker 1: the course of a game. Now, in between games, um,
Speaker 1: the people ib folks at IBM where it would update Watson,
Speaker 1: especially with things like pop culture references, so that pop
Speaker 1: so that Watson would be able to interpret questions that
Speaker 1: revolved around pop culture and be able to respond to
Speaker 1: them U or news items, things that just happened in
Speaker 1: the news that would have they'd have to update Watson
Speaker 1: with that information as well. But yeah, the key was
Speaker 1: to be able to let Watson break down a sentence
Speaker 1: and really understand what the sentence was saying, not just
Speaker 1: you know this this must be the object and this
Speaker 1: is the the subject and this is the verb, but
Speaker 1: to really understand what it was saying because uh, context,
Speaker 1: as you were pointing out, is so important. One of
Speaker 1: the elements that they talked about was temporal reasoning. Temporal
Speaker 1: reasoning meaning that, uh, there are different ways of saying
Speaker 1: the same thing. For instance, I could say, uh that, um,
Speaker 1: I graduated twenty years ago, or I could say I graduated,
Speaker 1: or I could say the twenty high school reunion is
Speaker 1: coming up for me. All of those things essentially give
Speaker 1: you the same information. By the way I did not graduate. Um.
Speaker 1: But all that all that information, all those those phrases
Speaker 1: give you the same information that I graduated high school. UM,
Speaker 1: but it's different ways of saying it, and a computer
Speaker 1: does not necessarily know that each of those different sentences
Speaker 1: means the same thing. So they had to find a
Speaker 1: way for Watson to learn that, to learn that there
Speaker 1: are many different ways of conveying the same information using
Speaker 1: totally different sentences. And you'll actually be able to see
Speaker 1: that on on February fourteenth, if you tune in to
Speaker 1: watch the show. That's when it's scheduled to air here
Speaker 1: in the United States. Um. And we we know that,
Speaker 1: we know that it performed pretty well already at least,
Speaker 1: let's kind of get into that. Okay, Sorry, No, I
Speaker 1: just figured after after we you know, we could talk
Speaker 1: about the actual show. It's coming up there. I think
Speaker 1: actually the show itself, uh, this particular episode is going
Speaker 1: to be interesting. But well, I was gonna mention that
Speaker 1: a minute, Okay, uh no, basically one of the things
Speaker 1: that I think is really kind of cool. You're not
Speaker 1: going to be just sitting there watching a box and
Speaker 1: to human opponents, they actually made They actually made an
Speaker 1: interface for people to watch, which I think was probably
Speaker 1: key for Jeopardy because I imagine they would actually want
Speaker 1: to see It's like, well, how do we know what
Speaker 1: it's doing? Um, it could be brewing coffee for all
Speaker 1: we know, um, mr coffee. It has an avatar, then
Speaker 1: you'll see it. It looks kind of like a planet
Speaker 1: with a little uh, I don't know, thought wigglies. What
Speaker 1: do you call those? Illustrated I'd call that Doug's hair. Um. Basically,
Speaker 1: if the computer is feeling I put this in quotes,
Speaker 1: if you don't mind confident, the avatar that you see
Speaker 1: is green, so it has it's feeling pretty sure that
Speaker 1: it's got an answer it can use to to buzz in. However,
Speaker 1: if it doesn't have the correct answer, it will be orange,
Speaker 1: so you will be able to see what's going on,
Speaker 1: and you will also be able to see it thinking
Speaker 1: because as the algorithms are processing information to try to
Speaker 1: find an uh A correct question. It's so weird to
Speaker 1: say in this context, um, the avatar is going to flicker,
Speaker 1: so you'll actually be able to see it in the
Speaker 1: process of trying to determine an answer for itself. Um. Now,
Speaker 1: and in two thousand seven, they started building Watson, which,
Speaker 1: by the way, we didn't mention, I don't think uh uh,
Speaker 1: this is named after IBMS founder Thomas J. Watson nine
Speaker 1: after the h Sir Arthur Arthur Conan Doyle character. Right,
Speaker 1: he's not a doctor who who served in India. Um.
Speaker 1: But yeah, that they actually started working on this problem
Speaker 1: and our project in two thousand seven and didn't really
Speaker 1: have a candidate until that. They were ready to share
Speaker 1: with the Jeopardy producers until late two thousand nine. Now. UM,
Speaker 1: one of the videos, or a couple of videos that
Speaker 1: I saw on the website interviewed one of the producers
Speaker 1: of Jeopardy UM and I had his name, Harry Friedman,
Speaker 1: Executive producer. Uh. And he said, basically, you know, we
Speaker 1: were interested in it, but we didn't want it to
Speaker 1: come off as some kind of stunt. Um. And I
Speaker 1: understand that the Jeopardy has sort of a cache as
Speaker 1: being Uh yes, it's a trivia show. But these people
Speaker 1: are seriously intelligent and they have a lot of domain
Speaker 1: you know, cross domain knowledge. Celebrity Jeopardy accepted, of course,
Speaker 1: we won't go there. Um. Actually some of them are anyway. UM. So,
Speaker 1: but that's always entertaining to there there's an element of entertainment,
Speaker 1: but they also have a certain um cash A yes,
Speaker 1: it's like, yeah, we have seriously smart people on this show.
Speaker 1: We don't we don't want to devolve and cheap in
Speaker 1: the show UM. So they showed it to the producers
Speaker 1: in late two thousand nine, and they have video of
Speaker 1: the producers watching Watson perform in a contest with some
Speaker 1: IBM employees and they seemed pretty impressed. Obviously, they're impressed
Speaker 1: enough to actually go forward with the with the show
Speaker 1: UM now to recruit. They recruited two of the very
Speaker 1: best Jeopardy champions for show UM. You probably have heard
Speaker 1: of both of them. One as Ken Jennings who won
Speaker 1: seventy four games a few years ago one two point
Speaker 1: four million dollars on the show, and Brad Rutter, who
Speaker 1: is the all time money champion who won three million,
Speaker 1: two hundred fifty five thousand, hundred two dollars UM. And
Speaker 1: they stand to win one million dollars. Whomever takes home
Speaker 1: first place will take home a million dollars. Second place
Speaker 1: is good for three hundred thousand dollars, and third is
Speaker 1: to two hundred thousand now that the human contestants I
Speaker 1: have agreed to UH to donate half of that charity,
Speaker 1: and I V will donate all of its prize winnings
Speaker 1: to charity, no matter what place it comes in. Yeah,
Speaker 1: that's pretty phenomenal when you consider how much time and
Speaker 1: effort and money must have been put into this project. Yes, now,
Speaker 1: as Jonathan said, these three have already gone at it
Speaker 1: for a a prep round and Watson did pretty well. Yeah.
Speaker 1: Actually I was doing really really well in the first
Speaker 1: half of the game. It ended up winning. Um. And uh,
Speaker 1: actually they asked Brad Rudder. I read an article in
Speaker 1: in Wired magazine UM by Sam Gustin who who was
Speaker 1: writing who talked to Brad Rudder and said, uh, you
Speaker 1: know that He said, are you scared to be going
Speaker 1: up against his computers? Or nervous? He said, and not
Speaker 1: and this is a quote, not nervous, But I will
Speaker 1: be when Watson's progeny comes back from the future to
Speaker 1: kill me. Yeah. There's been a lot of Skynet jokes
Speaker 1: about this, and how jokes as well. UM, but yeah,
Speaker 1: you know we That's one of the other things that's
Speaker 1: really cool about uh Watson is that you know, I
Speaker 1: mentioned a little bit that it kind of thinks thanks
Speaker 1: being yeah, taken in context, folks. Um, No, that Watson
Speaker 1: looks for answers the same way we do, and that
Speaker 1: it has all this information that's been stored in its database.
Speaker 1: But it's all been stored like in the form of
Speaker 1: books and plays and poems and things like that. Right, Yes,
Speaker 1: So it's not organizing all its information and tables, which
Speaker 1: is typic lee how you would do that in a database,
Speaker 1: you know, it's it's actually searching through contextually, which to
Speaker 1: me is phenomenal. That's one of the reasons why. But
Speaker 1: it's also whether reasons why it does so well because
Speaker 1: it's not looking for specific patterns, it's it's looking through
Speaker 1: the actual information. Um. And it was no small feat
Speaker 1: to design this computer. They had several teams working at IBM.
Speaker 1: Actually I've got I've written down the different teams here
Speaker 1: they had. They had an algorithms team that fifteen people
Speaker 1: on it. By the way, some of these teams had
Speaker 1: just had shared members, like there there would be someone
Speaker 1: who be on more than one team. So in total
Speaker 1: it was around twenty five people who worked on this project,
Speaker 1: but fifteen of them were working on algorithms, and these
Speaker 1: were the ones that would identify the context created by
Speaker 1: the question and and look for the available sources UH
Speaker 1: for answers. UM there was a strategy team, and the
Speaker 1: strategy team actually was in charge of designing Watson's game
Speaker 1: play and betting strategies. Well, that's important, that's um. Yeah again,
Speaker 1: if you haven't watched the show, UH, you know, as
Speaker 1: you go on, you either make money when you answer
Speaker 1: questions correctly, get nothing if you don't answer at all,
Speaker 1: but lose money if you And at the final round,
Speaker 1: there are two rounds of regular questioning and once that's done,
Speaker 1: there's what they call Final jeopardy, which is UH a
Speaker 1: last question on which you are shown the category. So
Speaker 1: you have the domain from which this question is being pulled,
Speaker 1: but you don't know what the answer will be for
Speaker 1: you to come up with a question, so you have
Speaker 1: to bet based on what the other two contestants have
Speaker 1: on on their boards versus what you have earned over
Speaker 1: the course of the game. And if if they both
Speaker 1: have fifteen dollars each then and you have ten thousand,
Speaker 1: then you don't have to worry about your betting strategy. Right.
Speaker 1: If your neck and neck you have to figure out, well,
Speaker 1: do I know enough to answer this question or question
Speaker 1: this answer it really is? Or do I do I
Speaker 1: wager that they don't know what it is, and therefore
Speaker 1: I keep my bets small, hoping that they're going to
Speaker 1: bet big and lose enough money so that I win anyway?
Speaker 1: Or am I in the lead? Do I? Am I
Speaker 1: in the lead enough where I can bet a smaller
Speaker 1: amount just so that in case either of them double up,
Speaker 1: they still don't overtake me. Yeah, there's a lot of
Speaker 1: betting strategy involved. Or you could cliff clayvin it and
Speaker 1: just bet the whole thing, even though you are hopelessly
Speaker 1: in the lead. I mean, there's like no way you
Speaker 1: could lose. You bet the whole thing and then you lose.
Speaker 1: Who are seven people who have never been in my kitchen? Uh?
Speaker 1: So Yeah, the strategy team, they were in charge of
Speaker 1: the game playing betting strategies. Then you had the systems team,
Speaker 1: um and uh they were the ones who helped design
Speaker 1: the way that Watson would interpret a question across thousands
Speaker 1: of different cores, you know. So then you've got the
Speaker 1: speech team. So that's the team that actually worked on
Speaker 1: that text to speech capability so that Watson talks too.
Speaker 1: In the game. You don't just see words appear on
Speaker 1: the screen. Watson actually has a voice. It does not
Speaker 1: always pronounce everything correctly, but they worked very hard to
Speaker 1: try and give him a pretty wide range of pronunciations
Speaker 1: because Jeopardy tends to use lots of fancy words. Um.
Speaker 1: There was an annotations team which built the taxonomy for
Speaker 1: the search databases. That's interesting to all our librarians out there. Yes,
Speaker 1: taxonomies are important. I mean, that's how you find information,
Speaker 1: and of course you have to design in such a
Speaker 1: way so that the computer can hit the most likely
Speaker 1: sources first so you can come up with the answer
Speaker 1: as quickly as possible. Uh. There are also teams in China,
Speaker 1: Tokyo and Haifa. Uh. There was a project management team
Speaker 1: which was sort of the liaison between Jeopardy and IBM.
Speaker 1: And then there was an applications team, and that's the
Speaker 1: one that I think is really the most interesting moving forward,
Speaker 1: no matter whether Watson wins on the fourteenth or not.
Speaker 1: The applications team, that's the group that's looking at ways
Speaker 1: to use this kind of capability. Be yawned. The Jeopardy
Speaker 1: scenario so some of the examples I heard were included,
Speaker 1: Like the one that they spent the most time on
Speaker 1: was a diagnostics like medical diagnoses. Yeah, the idea being
Speaker 1: that you could input your doctors could use this when
Speaker 1: seeing patients who are giving, you know, interesting symptoms, something
Speaker 1: that maybe was contradictory, and you would use a computer
Speaker 1: that could could essentially reference the world's information on medical
Speaker 1: knowledge and come up with the most likely of diagnoses,
Speaker 1: which is pretty interesting. But I've also seen other potential
Speaker 1: uses of government and law were two that were mentioned
Speaker 1: as well, which is kind of interesting where you know,
Speaker 1: you start looking for a precedent maybe for a law
Speaker 1: case or something along those lines. So, um, yeah, there's
Speaker 1: there's definitely uses for this beyond just hitting that daily double.
Speaker 1: That's true. That's true. You know, I was just thinking
Speaker 1: about it, uh too. I was reversing in my head
Speaker 1: the betting strategy because when you when you mentioned whether
Speaker 1: Watson wins or not, I started thinking, what if you're
Speaker 1: Brad Rutter or Ken Jennings and you're trying to devise
Speaker 1: a betting strategy and you're like, well, I know he's
Speaker 1: going to do this because I've seen him. I mean,
Speaker 1: both of these guys have played Jeopardy enough times where
Speaker 1: the other one probably knows how they're going to bet.
Speaker 1: But how do you devise a betting strategy against the computer,
Speaker 1: especially a computer that seems to jump all over the board.
Speaker 1: Did you watch any of the things where like there
Speaker 1: was one there was one video in particular where Watson
Speaker 1: got someone went went for like one of the two
Speaker 1: hundred dollar questions, which is the lowest level, right right,
Speaker 1: and uh, and Watson got it right. And then Watson
Speaker 1: went immediately for the thousand or two thousand whatever the
Speaker 1: top level question is now on on that board, it's
Speaker 1: a thousand, okay, So he went right for the like
Speaker 1: in the category had been untouched, so all of the
Speaker 1: all of the versions were available, every single variation of
Speaker 1: however much. I can't even remember how they go anymore
Speaker 1: because I haven't watched it so long. The first round
Speaker 1: of Jeopardy is two hundred four six eight hundred and
Speaker 1: a thousand dollar questions for each kid right, and then
Speaker 1: it doubles four. And I remember when it was one
Speaker 1: d two or three hundred four and oh my god,
Speaker 1: we're old. I think there are people who remember when
Speaker 1: it was um, yeah, Serony San Francisco treat. Uh, I'm
Speaker 1: sorry that was that was I lost on Jeopardy by
Speaker 1: weird al Yankovic. I remember that too. Yeah, I also
Speaker 1: remember when that came out on three D. I think
Speaker 1: I think this is gonna be a fun exper I'm
Speaker 1: sure it's It's been fun for the people who've been
Speaker 1: working on and extremely challenging. Um. I'm interested to see
Speaker 1: how it turns out and whether or not IBM will
Speaker 1: be up for a rematch. Depending on how it goes,
Speaker 1: will they be able to improve it enough, and will
Speaker 1: they convinced the Jeopardy producers to them back on. But
Speaker 1: I think it's gonna be fun. It'll be fun to watch, yeah,
Speaker 1: even if even if it loses. It's such a phenomenal
Speaker 1: achievement to create the algorithms and the database necessary to
Speaker 1: be able to navigate natural language. I mean, that really
Speaker 1: is I did not expect to see it this early,
Speaker 1: you know, I thought that might be a thing, not
Speaker 1: a not a twenty eleven thing. It's it's extremely difficult
Speaker 1: to do. As you can the aforementioned librarians will tell
Speaker 1: you or the catalogs to process natural language questions English English,
Speaker 1: majors will tell you that the language is very difficult
Speaker 1: as well. And you know, so my hat is off
Speaker 1: to to IBM and those those engineers and employees who
Speaker 1: all work together to bring this this technology to life
Speaker 1: because um, like you know, even the applications they were
Speaker 1: talking about, that's just the beginning. We had talked about
Speaker 1: the semantic web before. Um, this is really kind of
Speaker 1: what the semantic web is promising, is as this this
Speaker 1: web experience, uh not grant again. Watson is not a
Speaker 1: web based experience, but a web experience where it can
Speaker 1: understand what you're saying and give you the right response. Oh, yeah,
Speaker 1: I know what you mean. You're looking for this right, right? Yeah, like,
Speaker 1: and I mean it's amazing. You could think in a
Speaker 1: few years you could have a computer that can understand
Speaker 1: a joke. Supposedly it made a joke and yeah. And
Speaker 1: when one of the preliminary games, supposedly it said something
Speaker 1: that caused the entire audience to laugh, and it was
Speaker 1: that it was I think it was Fox News that
Speaker 1: was reporting it, and they did not go into detail
Speaker 1: about what this thing was, but they said that it
Speaker 1: was at the end of one of the like Watson
Speaker 1: got something right and then said something that made people laugh. Now,
Speaker 1: whether or not it was a joke in the sense
Speaker 1: that the computers somehow manifested this desire to make a joke,
Speaker 1: I don't know, because clearly we're not talking about saying
Speaker 1: that's actually alive. If answer is correct and next next
Speaker 1: question has not been asked, say yeah, people on that show, um,
Speaker 1: just follow that logic. So and I'm also looking forward
Speaker 1: to the segment before the second round begins where they
Speaker 1: start asking you about your background. Right, well, Alex, I
Speaker 1: was born four years ago. Right, Well, I don't know
Speaker 1: if you could say born right, And I like computing,
Speaker 1: reading and long walks on the beach. But yeah, the
Speaker 1: other the other side of this that we haven't really
Speaker 1: touched on, and I think it's a good place to
Speaker 1: wrap up. It really shows you how remarkable human beings are. Yeah,
Speaker 1: because look at what has to happen. In order for
Speaker 1: a machine to compete against humans. You have to have
Speaker 1: two thousand, eight h eight cores processors, you have to
Speaker 1: have fifteen terabytes of RAM. You have to have this
Speaker 1: computer that has the equivalent of two million books worth
Speaker 1: of information stored on it. In order to compete with
Speaker 1: humans and in order to even come close right too,
Speaker 1: I mean if if it doesn't win. So that's really
Speaker 1: kind of a testament to how amazing people are, not
Speaker 1: just how amazing the technology is. And I I also
Speaker 1: think it's nice that IBM found a way to do
Speaker 1: this experiment in a way that will actually make people interested,
Speaker 1: right and it building some interesting and I'm glad that
Speaker 1: that Sony Uh Entertainment has found a way to uh,
Speaker 1: you know, use this to their advantage to to show off, um,
Speaker 1: you know, how cool they are essentially, you know, and
Speaker 1: and give IBM an opportunity to play. It's definitely a nice,
Speaker 1: a nice uh event to see. I mean the fact
Speaker 1: that it's going to promote this idea of of the
Speaker 1: semantic computing and artificial intelligence in a way that is
Speaker 1: both entertaining and and really informative. It's it was clever.
Speaker 1: It's a very clever approach. Definitely, So kudos IBM, kudos Jeopardy.
Speaker 1: And with that we're going to wrap this up. You
Speaker 1: have any suggestions for topics or you want to chime
Speaker 1: in on our discussion about Watson, you can let us
Speaker 1: know on Twitter or Facebook. Are handled. There is tech
Speaker 1: stuff hs W or you can write us an email
Speaker 1: and that address is tech stuff at how stuff works
Speaker 1: dot com and Chris and I will talt you again
Speaker 1: really soon. Boop For more on this and thousands of
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