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