Speaker 1: Brought to you by Toyota. Let's go places. Welcome to
Speaker 1: Forward Thinking. Hey there, everyone, and welcome to Forward Thinking,
Speaker 1: the podcast that looks at the future and says eat
Speaker 1: up or. I'm Jonathan Strickland, I'm Laura, and I'm Joe McCormick.
Speaker 1: Hey guys, Hey Joe. I want you to imagine something. Okay, Okay, Lauren,
Speaker 1: imagine you're human. Check. Okay, Jonathan, you imagine you're a computer.
Speaker 1: Already there, and you got you a big microphone, and
Speaker 1: you happen to be sitting right between Lauren and I
Speaker 1: as we discuss uh recent movies that have come into
Speaker 1: the theaters. We frequently do that. Okay, And I say
Speaker 1: to Lauren, Hey, Lauren, did you see her? Interesting? Okay,
Speaker 1: I'm with you so far. Do you understand what the
Speaker 1: heck I am talking about? Jonathan? Know, I never understand
Speaker 1: what you and Lauren are talking about. Ever, you know
Speaker 1: I'm saying in your in your computer imagination world, Oh God,
Speaker 1: got in this scenario? No, No, I don't understand what
Speaker 1: you're saying. That's perfectly simple to anybody who speaks English
Speaker 1: and in this sort of modern English lingo are familiar.
Speaker 1: What with what movies are recently came out. There's ambiguity
Speaker 1: in that sentence already, even if I weren't a computer,
Speaker 1: because unless I know for a fact you're speaking about
Speaker 1: a movie ahead of time, I just think you're using
Speaker 1: a a pronoun in a way where you could be
Speaker 1: literally talking about anyone, Yeah, anyone who's female. But when
Speaker 1: we have everyday conversations like that, asking if somebody saw
Speaker 1: a movie or anything, that's simple, you don't think about
Speaker 1: how much incredibly complex figuring out you're doing in real
Speaker 1: time to make sense of their words shared so much context,
Speaker 1: the actual literal data is an entirely separate issue. Yeah,
Speaker 1: even if even if we were all to agree upon
Speaker 1: what we were talking about ahead of time, there's still
Speaker 1: so many idiomatic ways of putting information in various languages. Uh,
Speaker 1: there are things like figures of speech, there's metaphors, all
Speaker 1: these other sarcasms. Sarcasm is a great example. There are
Speaker 1: all these ways that we can say sarcastically. Sarcasm is
Speaker 1: a great example. But at any rate, there are all
Speaker 1: these different techniques we can use to communicate with one
Speaker 1: another that as you grow up in and around a language,
Speaker 1: you start to get a grasp on it naturally, right,
Speaker 1: you have natural affinity for that language, and you understand
Speaker 1: what the intent is, even if on the surface level
Speaker 1: it's different than what's the true underlying information. Right, so
Speaker 1: even if you say, great job at opening the pod
Speaker 1: bay doors, how, he's probably not gonna get you. No,
Speaker 1: How's just gonna think, Wow, I thought I did a
Speaker 1: terrible job, but every one else didn't notice. Well, how
Speaker 1: how would probably understand? Because how is really clever like that? Yeah, well,
Speaker 1: there are a lot of these clever uh speech understanding
Speaker 1: robots and computers in science fiction. You've got you've got
Speaker 1: all the cartoon ones like Rosie from the Jetsons, and
Speaker 1: you've got Hall nine thousand, who people just talked to
Speaker 1: and he responds in this calm voice and explains your
Speaker 1: doom sings Daisy right, uh, star Trek Star trek. Yeah,
Speaker 1: the computer's voice, which was voiced by Gene Roddenberry's wife, right, um,
Speaker 1: Margel Barrett yep so uh. She also played as I
Speaker 1: recalled Diana Troy's mother. Yes, in the series. I'm still
Speaker 1: not sure whether it was sweet or creepy that that
Speaker 1: he had her playing. Yeah, let's not get into that.
Speaker 1: That's that's that's a podcast all on its own. I
Speaker 1: don't know, I mean more more the ship's computer. But
Speaker 1: but but anyway, um yeah, and so we have all
Speaker 1: of these terrific examples in science fiction, but it's not
Speaker 1: quite living up to that. Reality is not quite living
Speaker 1: up to that. Yeah, Sirie isn't quite the inter prize computer. No,
Speaker 1: it's it's pretty good series, pretty good at voice recognition
Speaker 1: and responding. Google has its own Google Search that you
Speaker 1: can use with voice. Also can do things like transcribe
Speaker 1: a message you are speaking into a microphone as a
Speaker 1: text message or or whatever, or instant message. I mean,
Speaker 1: if you have an Android phone, you can pretty much
Speaker 1: do everything through voice. Then you've got uh the Google Glass.
Speaker 1: Google Glass has voice control in it, although it also
Speaker 1: is very limited in what you can do. You can't
Speaker 1: just say anything. You have very specific command words. Wouldn't
Speaker 1: it be great if like to talk to people like
Speaker 1: you could not get their attention without saying okay Lauren. Yeah,
Speaker 1: that that's my idea of the perfect feature. Let's enact that, right.
Speaker 1: I actually know some people that that's pretty much true anyway,
Speaker 1: but um yeah, and and I even had a joking
Speaker 1: little reference in here, like even even if you look
Speaker 1: back to Ferbie, Ferbie could respond to to voice commands,
Speaker 1: although Furbie spoken Furbish and would have gradually learned English,
Speaker 1: and it wasn't really responding in the way we're talking
Speaker 1: about here. Yeah, but one thing that I think is
Speaker 1: really important to make the distinction between is natural language
Speaker 1: and voice commands. Right, being able to interpret sound as
Speaker 1: language is one thing, and and interpreting the context of
Speaker 1: words is something else entirely. Let's back up. Let's so
Speaker 1: we're going to talk about computers and robots understanding natural language.
Speaker 1: Let's step back and look at what is the essential
Speaker 1: difference between the language that computers understand and like to
Speaker 1: use in the language that real that human beings used. Okay,
Speaker 1: so human language or natural language as we will refer
Speaker 1: to it in this podcast, and that's you know, generally
Speaker 1: accepted term natural language processing would mean using machines that
Speaker 1: could actually process uh commands are written out in whatever
Speaker 1: native language you happen to be working in human language.
Speaker 1: They include things like syntax, grammar. They have these rules
Speaker 1: that structure that give you the framework you need in
Speaker 1: order to communicate an idea to someone else who speaks
Speaker 1: that language, and then a vocabulary of words for conveying
Speaker 1: specific ideas. Right, So, if you didn't have a vocabulary,
Speaker 1: you literally have nothing to say. If you didn't have
Speaker 1: the grammar and syntax, you wouldn't have a way of
Speaker 1: putting it together that would make sense to someone else.
Speaker 1: It would be a jumble of words with no no
Speaker 1: real way of being sure what the underlying meaning is. Yeah,
Speaker 1: I think things like grammar and syntax give you a
Speaker 1: more precise grasp on meaning. So you could probably communicate
Speaker 1: some meaning with just words but no syntax, But syntax
Speaker 1: helps you put together complex site. We could all turn
Speaker 1: into Yoda and more or less understand what the other
Speaker 1: person is saying. But it gets increasingly difficult to uh
Speaker 1: to express more complex thoughts like you were saying. It
Speaker 1: gets challenging to do that if you're not following a
Speaker 1: essentially a previously agreed upon framework. That's really what language is.
Speaker 1: But how or even though it's previously agreed upon, that
Speaker 1: does not mean it's static. Right, It changes all the time,
Speaker 1: which I think is one of the amazing and beautiful
Speaker 1: things about language that that new vocabulary enters our our
Speaker 1: syntax all the time. Yeah, and the new structures, new
Speaker 1: structures enter enter our our understanding of language all the
Speaker 1: time because internet, right and text messaging. Uh. You know
Speaker 1: it's it's funny because you'll hear a lot of of
Speaker 1: linguistic purists kind of uh dismiss or or look down
Speaker 1: upon any kind of change to the languages. If language
Speaker 1: itself is a sacred thing that should never change. But
Speaker 1: that's if. If it doesn't change, it it stagnates. Well,
Speaker 1: it becomes more difficult to communicate. I can understand the feeling,
Speaker 1: but it's just pure personal stubbornness. I mean, the language
Speaker 1: changed huge amounts before those people got to it in
Speaker 1: the form that they wanted to stick. Sure, we didn't
Speaker 1: even have grammatical rules or spelling rules before the printing
Speaker 1: press was invent to it was all kind of willy nilly,
Speaker 1: and it changes all the time. And the point we're
Speaker 1: getting at is that language is very complex. It's something
Speaker 1: that as you grow up and as you are exposed
Speaker 1: to language and as you understand it better from either
Speaker 1: a practical point of view just because you're using it,
Speaker 1: or from a formally educated, you know, perspective. It's one
Speaker 1: of those things that you get more adept at over time.
Speaker 1: Machines have a very different way of communicating. It doesn't
Speaker 1: have this this kind of level of linguistic complexity. It's
Speaker 1: really when you boil it down to the very basic level,
Speaker 1: a series of zeros and ones. It's binary code. It's
Speaker 1: essentially saying off or on, and these these little zeros
Speaker 1: and ones are instructions more or less. I mean, you
Speaker 1: can have it represent something. Those zeros and ones could
Speaker 1: represent a once translated a a letter in a natural language,
Speaker 1: or it could represent a numeric figure, and then you
Speaker 1: would have other zeros and ones that essentially give instructions
Speaker 1: to a processor on how to push that other information through,
Speaker 1: and then you get an operation performed. Or you can
Speaker 1: use them to represent a photograph or a piece of music. Yeah. Yeah,
Speaker 1: it really doesn't matter what the data is. It just
Speaker 1: means that you have to be able to represent it
Speaker 1: somehow in a quantifiable sense, to reduce it to that
Speaker 1: numerical component. I think one way that's interesting to think
Speaker 1: about the difference between natural language and machine languages. That
Speaker 1: machine language is very good at getting it right. Natural
Speaker 1: language is very good at making it work. If you
Speaker 1: think about that, so machine language. Um, it's going to
Speaker 1: be very precise, but if a single thing is wrong
Speaker 1: in a long string, it's likely to come up the
Speaker 1: whole well, so that you know your entire meaning is lost.
Speaker 1: You can misspeak a sentence and people can still understand
Speaker 1: what you're saying. Luckily for us, because we do that
Speaker 1: all time. Of course, if you're pedantic like I am,
Speaker 1: you cannot help but point out when other people make
Speaker 1: these little misteps and say I think you mean blah
Speaker 1: blah blah, which, by the way, it makes you sound
Speaker 1: just as obnoxious as I indicated, and I do it
Speaker 1: all the time. Well, it makes you as obnoxious as
Speaker 1: a computer because it seems like you're suggesting that you
Speaker 1: have a perfect grasp of meaning, which nobody who speaks
Speaker 1: language does. Language is sort of natural. Language is necessarily approximate,
Speaker 1: and it's sort of adaptive and elastic as you use it.
Speaker 1: I might be as obnoxious as a computer, Joe, but
Speaker 1: I have feelings and words can hurt. Uh No, alright,
Speaker 1: So this brings us to programming language. So programming language
Speaker 1: is what a lot of maybe not a lot of
Speaker 1: some people who aren't really familiar with computers, they know
Speaker 1: about computers. They know in general what computers do, that
Speaker 1: kind of thing. They've used computers, but they haven't ever
Speaker 1: gotten into any kind of programming or anything along those lines.
Speaker 1: Nothing and computer science a programming language to a lay
Speaker 1: person may seem like, oh, this is the stuff that
Speaker 1: computers communicate in, and it's not. A programming language. Is
Speaker 1: really just a set of rules that exist for our
Speaker 1: benefit for humans, benefits for for human computer scientists and
Speaker 1: computer programmers, so that they can follow these rules and
Speaker 1: create a program that a computer can then execute in
Speaker 1: some future operations. Compromise. Yeah, it's it's really just like,
Speaker 1: this is the set of rules you follow, and as
Speaker 1: long as you follow these rules and you're really careful
Speaker 1: and you do them, do the steps properly, then the
Speaker 1: computer is gonna understand what you want it to do
Speaker 1: whenever whatever input comes into that computer. This is all
Speaker 1: dependent on something called a compiler, which is basically a
Speaker 1: translation mechanism from from something that humans can process easily,
Speaker 1: a language that humans can process easily, to the code
Speaker 1: that lets the computer know what it's supposed to do exactly.
Speaker 1: So a compiler is that that intermediary step. It's what
Speaker 1: takes that that code, which to to a lay person
Speaker 1: again doesn't look simple. Right someone who is never coded ever,
Speaker 1: if they were to look at just a page of
Speaker 1: raw code that someone, some programmer has created, to them,
Speaker 1: it looks like gobbledygook. It's just incomprehensible. But or you know,
Speaker 1: you might recognize some words here and there, but there's
Speaker 1: gonna be a lot of stuff where you're like, I
Speaker 1: don't know what that's a string of symbols. Yeah, but
Speaker 1: but that's poetry. It's it's still it's still a format
Speaker 1: that humans find much easier to understand than just a
Speaker 1: bunch of zeros and ones, which if you were to
Speaker 1: look at a full page of zeros and ones, that
Speaker 1: would seem to be meaningless to to most people. I
Speaker 1: would say, there might be some people out there who
Speaker 1: could look at a page of zeros and ones and
Speaker 1: because their brains are wired a different way than mine,
Speaker 1: is it totally makes sense. But for most of us,
Speaker 1: beyond like Neo, it's it's pretty difficult to so in
Speaker 1: that that sense, this compiler changes that programming languages into
Speaker 1: that that object code, those zeros and ones for the
Speaker 1: computer to understand what. Then it goes and runs whatever
Speaker 1: the operations are in the program. This technology, by the way,
Speaker 1: was pioneered in the nineteen fifties by someone that you
Speaker 1: might have heard about, a Rear Admiral Dr. Grace Hopper,
Speaker 1: who was a mathematician and computer scientist who who was
Speaker 1: working for a number of decades, like the forties through
Speaker 1: the nineties, and largely on compilers and computer languages. One
Speaker 1: of her nicknames, in fact, was Grandma Coble because she
Speaker 1: she helped create that particular language, not the planet and
Speaker 1: not the character in Mortal Kombat, but the common business
Speaker 1: oriented language. And and one one little piece of trivia
Speaker 1: before we move on. She's also known for creating the
Speaker 1: term computer bug because, as the story goes, a moth
Speaker 1: flew into a circuit and short circuited the machine, and
Speaker 1: she referred to it as a bug in the system,
Speaker 1: and thus we have computer bug. There you go. Before
Speaker 1: this kind of work was was being done with compilers,
Speaker 1: computers were very different, and she she happened to work
Speaker 1: on one of those as well. She was part of
Speaker 1: the original team working on the Harvard IBM Mark one. Uh.
Speaker 1: The kind of computer that that was was it was
Speaker 1: a punch tape computer, right and even even the later
Speaker 1: punch card computers used compilers. Here's the steps. I mean,
Speaker 1: it's crazy if you think about what what computer programs
Speaker 1: had to go through. If you think that those lines
Speaker 1: of code are difficult, if you think that's laborious, just
Speaker 1: imagine what would happen if you were programming computers in
Speaker 1: the fifties and sixties. So you would get a probably
Speaker 1: you'd start with with some sheets of paper that are
Speaker 1: lined up so that you can actually write your program
Speaker 1: by hand using a pencil, and these sheets of paper
Speaker 1: you might even have to erase stuff over and over again.
Speaker 1: You then would either hand those that stack of paper
Speaker 1: that you've created that has your program on it to
Speaker 1: someone who is an expert using a key punch machine,
Speaker 1: or you do it yourself. But you would then uh
Speaker 1: coordinate with the key punch machine to key in your program.
Speaker 1: It would punch holes into a series of cards. A
Speaker 1: program might be uh, you know, a few dozen cards,
Speaker 1: amount several hundred, it could be several thousand physical punch cards.
Speaker 1: Generally speaking, programs were about as heavy bell as long
Speaker 1: as the number of punch cards that could fit into
Speaker 1: the hopper of a computer. So if you made a
Speaker 1: program that was longer than that, you pretty much gave
Speaker 1: up because it just it was too hard to feed
Speaker 1: it into the computer. Now that you don't just take
Speaker 1: your program straight to the computer. That's actually not the
Speaker 1: way it works. It's kind of similar to what we're
Speaker 1: talking about with that programming language. This is your programming language,
Speaker 1: your punch cards. You would take that to a compiler.
Speaker 1: So the punch cards you had created are your source deck.
Speaker 1: Starts to sound like I'm playing Magic Big Gatherers. You
Speaker 1: then run it through the compiler. The compiler then compiles
Speaker 1: your program and creates an object deck, which is a
Speaker 1: new deck of cards with holes punched in it, but
Speaker 1: it really kind of compresses down the number of cards
Speaker 1: you need that compiled deck. That object deck is your program,
Speaker 1: which you then can take to the appropriate computer and
Speaker 1: run through to see if you did it correctly or
Speaker 1: if the computer gives you an error. Therefore, you can
Speaker 1: write much longer programs than you know. If it all
Speaker 1: has to fit into that one little stack, well, you
Speaker 1: could definitely, yeah, much you would have. You would have to.
Speaker 1: You would have to break it up into uh into sections,
Speaker 1: which might be somewhat challenging, but uh yeah, And I mean,
Speaker 1: and here's the thing. You need to make sure you
Speaker 1: number those punch cards, y'all, because there's nothing like watching
Speaker 1: a computer engineer just weep uncontrollably after someone accidentally spills
Speaker 1: over a deck of cards that were not numbered and
Speaker 1: you have no idea what order they're supposed to go in. Yeah,
Speaker 1: it still happens a lot today, just kidding. Actually it's
Speaker 1: a lot better today. Today we have these programming languages
Speaker 1: like like See and Java, you know, and and it's
Speaker 1: all and we have monitors and we have story you know,
Speaker 1: like before we're talking about you're you're creating these physical
Speaker 1: programs on physical pieces of paper because there was no
Speaker 1: other way of doing it, right, So these programming languages
Speaker 1: we have today and all the stuff like monitors and
Speaker 1: and everything that makes it a lot easier to write
Speaker 1: programs like this, But it's still a lot of work
Speaker 1: on the front end. Is there a way that we
Speaker 1: could make computers as easy to talk to as our
Speaker 1: coworkers so that when we have an idea of what
Speaker 1: we wanted to do, we can just explain it to
Speaker 1: the computer, right, So this this is sort of the
Speaker 1: idea also behind things like the semantic web, where we're
Speaker 1: able to have the web not only understand exactly what
Speaker 1: we want, but begin to anticipate it based upon that.
Speaker 1: That obviously has other elements of artificial intelligence on top
Speaker 1: of natural language processing, but it's natural language processing as
Speaker 1: a as an integral part of that. So this is
Speaker 1: a really difficult problem, and it's a problem to the
Speaker 1: problem on multiple levels. We probably started just the absolute
Speaker 1: simplest thing, like just getting it to understand regular words
Speaker 1: and stuff. I mean, it's you know, the components. You
Speaker 1: can break it down to include things like understanding sentences,
Speaker 1: so actually understanding the structure and meaning of a sentence,
Speaker 1: which is not a trivial task. It's really complicated. Uh.
Speaker 1: There's also the machine translation, which is the whole idea
Speaker 1: of translating one language into another language, which is really
Speaker 1: what we talked about in the video, is that idea
Speaker 1: of real time translation. Again, very difficult to do if
Speaker 1: you want to preserve the intended meaning of the original message.
Speaker 1: There's parsing and tagging, which is all about examining the
Speaker 1: parts of a statement to determine the relationship to one another.
Speaker 1: So being able to identify what part of a sentence
Speaker 1: is what and how they interact. So if I say
Speaker 1: a simple sentence like Joe, go get me a cup
Speaker 1: of coffee, that it's able to figure out all of
Speaker 1: those things, like what what all of those things mean,
Speaker 1: and what what I'm actually what the command actually is well,
Speaker 1: and even that it's a command, I mean, it needs
Speaker 1: to understand that you are giving an imperative sentence, Joe,
Speaker 1: you do this, not a descriptive sentence some subject called
Speaker 1: Joe should got went and got me a cup of corps,
Speaker 1: right right, some something that already happened. It clearly has
Speaker 1: not happened because I sit here with no cup of
Speaker 1: coffee in front of me. Joe, you just don't take
Speaker 1: a hint anyway. There's also a grammar induction. So this
Speaker 1: is the idea of mapping out the hierarchical structure of
Speaker 1: a natural language statement. Um. And this is the way
Speaker 1: I try and explain to other people is it's like
Speaker 1: your diagramming a sentence, where you're identifying what each part
Speaker 1: of the sentence actually is, what it what its purpose
Speaker 1: is within a sentence, and these as we know, like
Speaker 1: these things can change depending upon the structure of the sentence.
Speaker 1: An object can become the subject in the next sentence, right,
Speaker 1: But a computer doesn't know that. So if you were
Speaker 1: to say, take one sentence and you define all the
Speaker 1: different parts of that sentence, for for the the the
Speaker 1: what they're doing within that sentence, and then you create
Speaker 1: a brand new sentence using those same words, the computer
Speaker 1: wouldn't necessarily be able to tell you what was what.
Speaker 1: It may be basing its decisions on what the previous
Speaker 1: s and said, and then everything's wrong and nothing makes sense. Um.
Speaker 1: On top of that, there's the word sense, disambigulation, ambiguation
Speaker 1: rather and it's um, I know, I know, I like
Speaker 1: to add ls into words where they don't belong. Disambiguation,
Speaker 1: I was being ambiguous. Uh yeah, so yeah, these are
Speaker 1: words that can have multiple meetings, sometimes contradictory meetings. If
Speaker 1: I say that something is cool, I could mean one
Speaker 1: of two things, you know, at least one of two things.
Speaker 1: If I'm being really ironic, I could be meaning multiple things.
Speaker 1: So uh, it's it's one of those deals where it's
Speaker 1: again something that we humans can figure out, usually through
Speaker 1: context and tone, but a computer may have real problems
Speaker 1: with that. And none of these are trivial right. All
Speaker 1: of these are are challenging issues and U and it's
Speaker 1: interesting to me how different people have gone about trying
Speaker 1: to address these oallenges. Uh. For example, just trying to
Speaker 1: teach a computer all these pieces, even if you're working
Speaker 1: with a very limited vocabulary. It's exhaustive to try and
Speaker 1: get all the different variations on there of all the
Speaker 1: ways a human could use those uh, those words and
Speaker 1: those commands to mean different things. And just teaching a
Speaker 1: computer that just using brute strength, it requires a lot
Speaker 1: of processing power and it it just takes a lot
Speaker 1: of time. Sure, there's a lot of parallel processing that
Speaker 1: goes on in the human brain and this is this
Speaker 1: is what allows babies and foreign language learners to pick
Speaker 1: up on this kind of contextual contextual stuff that's that's
Speaker 1: going on simultaneously and sorting out all of those different parts.
Speaker 1: But that's much harder to convince computer to do, or
Speaker 1: to allow a computer to right. Yeah, yeah, just programming
Speaker 1: computer to have that same kind of capability is you know,
Speaker 1: a pretty hefty task. So in the sixties, the approach
Speaker 1: was really to create a semantic based understanding to resolve
Speaker 1: lane which ambiguity. This was kind of the brute force
Speaker 1: approach teach a computer everything you can about language so
Speaker 1: it understands all the rules and can follow along and
Speaker 1: be able to interpret commands. But that's pretty tough to do.
Speaker 1: The example I read was was great, talking about using
Speaker 1: using a language uh interpreter where you could type in
Speaker 1: a phrase translated into a different language, and then translated
Speaker 1: back to see how well it did. Um. My dad
Speaker 1: loves to do this every Christmas. He'll take Christmas carols
Speaker 1: and run it through the Google Translator and running through
Speaker 1: about five or six languages until it gets back to English,
Speaker 1: and then you have to try and figure out what
Speaker 1: the carol is. I used to do this in high
Speaker 1: school with Metallica lyrics. It was great. Um. But yeah,
Speaker 1: the the example they gave and the presentation I was
Speaker 1: reading about this was about translating English to Russian back
Speaker 1: to English and the phrase they used was time flies
Speaker 1: like an arrow, followed of course by the phrase fruit
Speaker 1: flies like a banana. You know, those are two different
Speaker 1: phrases that mean two different things. It's obviously a joke,
Speaker 1: but a computer would have really a lot of difficulty
Speaker 1: with both of those. So they fed time flies like
Speaker 1: an arrow English to Russian Russian English, and it got back.
Speaker 1: Time flies enjoy arrows. So time flies, which is like
Speaker 1: the flies of artists, like, yeah, they travel throughout the eons,
Speaker 1: Ye bring us wisdom from They also enjoy arrows. They
Speaker 1: don't enjoy arrow which would mean that they like a
Speaker 1: show on the c W, but they enjoy arrows. I
Speaker 1: think that there should be a semi colon in there.
Speaker 1: It is it is a imperative. It's saying time flies,
Speaker 1: so therefore enjoy arrows, right, and what little time we
Speaker 1: have left exactly, don't eat and drink for tomorrow week.
Speaker 1: I guess it's like, make sure, make sure you stop
Speaker 1: to smell the roses and enjoy arrows. Uh yeah, so
Speaker 1: I didn't he rate again. This shows that this this
Speaker 1: is you know, it's hard. Those words are ambiguous, like
Speaker 1: is ambiguos was right? Um? So, as computers became more
Speaker 1: powerful in the nineteen eighties, you got to a point
Speaker 1: where your desktop computer had the same amount of power
Speaker 1: as those the most sophisticated computers of the fifties or sixties.
Speaker 1: So now we've got desktop PCs that can do this.
Speaker 1: The idea originally back in the fifties and sixties was
Speaker 1: with enough power, we can totally do this natural language
Speaker 1: processing just by removing all ambiguity as much as possible.
Speaker 1: But at this point, once that power was really getting
Speaker 1: within our grasp, computer engineers said, it's actually the problem
Speaker 1: is bigger than that. Part of it is that these
Speaker 1: this kind of brute force approach isn't scalable. In other words,
Speaker 1: if you want to have a working vocabulary of more
Speaker 1: than two words, if you want to have opened that
Speaker 1: up and have as as many different variations as you
Speaker 1: can imagine, you cannot take this approach because it would
Speaker 1: just it would it would be Yeah, it's not just
Speaker 1: the computer power or the storage, it's the actual man
Speaker 1: hours of programming a computer to be able to do this.
Speaker 1: So they started looking at different approaches, including using a
Speaker 1: shift to probabilistic approaches. So we're talking about statistical approaches
Speaker 1: where no longer are we uh, speaking in definitive terms,
Speaker 1: We're looking at statistical probabilities. So it starts to break
Speaker 1: down sentences into a different you know, like kind of
Speaker 1: like tears of possible meanings and then goes with the
Speaker 1: highest ranking out of all of those. Okay, so is
Speaker 1: this sort of how Watson works at least in the
Speaker 1: last stage of what. I don't know if this is
Speaker 1: how it interprets UH language to begin with. But when
Speaker 1: you see Watson competing on Jeopardy, the way it compares
Speaker 1: answers is it doesn't have a definitive sense of meaning.
Speaker 1: It gets probabilistic answer saying this was the closest match,
Speaker 1: it's right, and if it fell below a certain threshold,
Speaker 1: it wouldn't It wouldn't hazard a guess in the first place.
Speaker 1: Right there was that was the rule was that if
Speaker 1: it didn't have over I can't remember what the number was,
Speaker 1: UH certainty, it wouldn't buzz in because it wasn't certain
Speaker 1: enough that that could be the right answer. And yes,
Speaker 1: Watson would come up with multiple answers to the questions
Speaker 1: or or multiple questions to the answer, if you want
Speaker 1: to be technical with the way Jeopardy works, UH, and
Speaker 1: then would assign that that percentage of probability that that
Speaker 1: each one was the right answer, and go with the highest.
Speaker 1: But you're saying here, that's sort of the same principle
Speaker 1: applied to assigning meaning to a unit of lane exactly.
Speaker 1: So they found that this approach was scalable they didn't have.
Speaker 1: It wasn't as monumental as trying to to just teach
Speaker 1: a computer all the different ways to put these words together.
Speaker 1: And according to some of the research I was looking at,
Speaker 1: they said that the most successful approaches were what they
Speaker 1: called supervised learning, which means that the data has been
Speaker 1: labeled or tagged by humans to give it context so
Speaker 1: that the machines can start to interpret what words mean.
Speaker 1: This is also very similar to what we talked about
Speaker 1: when we when we talk about semantic web and tagging
Speaker 1: the heck out of everything that we can think of,
Speaker 1: so that computers have multiple ways of of classifying words.
Speaker 1: It's ontologies right there. There this ability to organize language
Speaker 1: in a way that a machine can have a better
Speaker 1: grasp at meaning, and to create enough cross references within
Speaker 1: any body of work or multiple bodies of work so
Speaker 1: that machines can start to figure out how they relate
Speaker 1: to one another. Yeah, yeah, and I like that. Uh.
Speaker 1: One of Google's approaches for this translation um application, which
Speaker 1: you know, if you've never gone on Google Translate and
Speaker 1: used it it can be. It's first of all, it's
Speaker 1: really useful. I mean, there are there if you're using
Speaker 1: something like Chrome, you often will have the option to
Speaker 1: translate a web page written in a different language, whatever
Speaker 1: your native language you've set for Chrome. If it's a
Speaker 1: different language that you visit, it will give you the
Speaker 1: option to translate the page into English or whatever one
Speaker 1: of your native language happens to be. Um, that's really useful.
Speaker 1: It's it's pretty good. It's not, you know, perfect, it's
Speaker 1: it's never going to be as good as a human
Speaker 1: translator who is skilled in translation. In fact, Google has
Speaker 1: said like, this is a good start for people to
Speaker 1: help you get the general meaning of something, but if
Speaker 1: you want to be really precise, you really need, at
Speaker 1: least for now, a human translator. Sure, some people would
Speaker 1: definitely argue with your use of the word never there though,
Speaker 1: UM and uh. One one of them is a relatively
Speaker 1: recent addition to the Google team, and he's one of
Speaker 1: the people who's who's work in natural language in general
Speaker 1: has basically changed the entire field. And I'm talking about
Speaker 1: Rake Hurtzwil, also known for his UH. I'd say I
Speaker 1: was going to say specifically the singularity, but yeah, he's
Speaker 1: he's one of the the guys who often talks about
Speaker 1: this idea of the singularity where we we reached the
Speaker 1: point of no return, and his version of the singularity
Speaker 1: is kind of super happy, everyone live forever version of
Speaker 1: singularity a much less terminator than some other people talk
Speaker 1: about it being yeah, funny you always hear the super
Speaker 1: happy everybody live forever or the dystopian hellscape work. And
Speaker 1: I want to I just want to point out that
Speaker 1: to the robots, that second one is everybody is happy
Speaker 1: and less forever. I'm just saying it's all a matter
Speaker 1: of perspective. I, for one, welcome our robot over lords.
Speaker 1: But you were actually Lauren talking about Kurtswild himself. Well,
Speaker 1: I fair enough that that that is absolutely a part
Speaker 1: of the discussion to consider whenever you're talking about kurtswhile
Speaker 1: and and I'm I'm very fond of him. I say
Speaker 1: this in utmost fondest, But the dude is a little
Speaker 1: bit wacky, do he He? He can come across as
Speaker 1: a very enthusiastic and sometimes in ways that you wonder
Speaker 1: if they reflect reality. Well, however, a lot of his
Speaker 1: predictions have come completely true, um, which which is not
Speaker 1: how a whole lot of futurists work. So so i'd
Speaker 1: say that that's pretty impressive and he's done a lot
Speaker 1: of work in this specific field. Oh yeah, and well,
Speaker 1: I mean he's he's a terrific inventor. He he invented
Speaker 1: the first flatbed scanner, the first software that could optically
Speaker 1: recognize any typeface rather than a typeface that had been
Speaker 1: specifically designed for the software. UM, also the first text
Speaker 1: to speech synthesizer UM and then combined them together to
Speaker 1: create the Kurtswhile Reading Machine, which was the first device
Speaker 1: that could read text allowed for the visually impaired. And
Speaker 1: that was like in nine seventy six. Yeah, he was
Speaker 1: like what by then twenty seven years old? Smart dude. Yeah. UM.
Speaker 1: He also one of the companies that he worked for,
Speaker 1: I mean, he was leading the team, but one of
Speaker 1: the companies that he created also invented the first commercial
Speaker 1: speech recognition system and that was in seven UM. As
Speaker 1: of today, he's working for Google, is their director of Engineering,
Speaker 1: though in a in a practical sense, he considers his
Speaker 1: job to be bringing natural language understanding to Google. And
Speaker 1: speaking of that, Google actually has the at their research
Speaker 1: at Google website, they have the natural Language Processing page
Speaker 1: and uh that includes lots of research done at Google
Speaker 1: and by people associated with Google on the subjects of
Speaker 1: natural language. So if you want to get really technical,
Speaker 1: I mean I'm talking about we're talking we're talking about
Speaker 1: technical both on the level of computer science and just
Speaker 1: the way linguistics work, the way language works. Go check
Speaker 1: out some of these articles and uh, you know, not
Speaker 1: all of them are meant for the layman, right if
Speaker 1: you're not into this. For example, a discriminative latent variable
Speaker 1: model for online clustering might be a little beyond someone
Speaker 1: who's just curious about natural language processing in general. Uh.
Speaker 1: And or a data set of syntactic ingrams over time
Speaker 1: from a very large corpus of English books like these
Speaker 1: are These are the types of things that you'd have
Speaker 1: access to if you want to go and look at
Speaker 1: them on this research page, and it will tell you
Speaker 1: a little bit more about the different challenges that engineers
Speaker 1: are facing when coming at this problem. And also we
Speaker 1: should make it clear not everyone is approaching this exactly
Speaker 1: the same way, and there are lots of people working
Speaker 1: on it, so we may see different implementations at different
Speaker 1: levels of sophistication in various fields before it becomes a universal.
Speaker 1: Oh absolutely, which is one of the cool things about
Speaker 1: the field, really, and I mean especially because this this
Speaker 1: technology has application beyond being able to yell at machines
Speaker 1: or or I mean you can yell at machines now,
Speaker 1: but being able to yell at machines and have them
Speaker 1: respond appropriately. I want my toaster to feel ashamed when
Speaker 1: it has burnt my text. Yeah, that's that's true. You
Speaker 1: can yell at a machine now, like you can tell
Speaker 1: a machine, probably in some way or other, that it
Speaker 1: did something wrong in a very rudimentary way, with some
Speaker 1: pre programming. What's really important is to be able to
Speaker 1: subtly shame that and and through with through a very
Speaker 1: nuanced way, belittle it and make it feel insecure, and
Speaker 1: then be able to take a picture of it and
Speaker 1: upload it and make a meme out of it. Important. Okay, well,
Speaker 1: well you guys. You guys are joking. I hope, um
Speaker 1: otherwise I'm kind of upset. But but but this does
Speaker 1: tie heavily into artificial intelligence. I mean, if we're gonna
Speaker 1: have convincing robot buddies, they have to be able to
Speaker 1: understand us without us having to to feed them punch cards. Yeah.
Speaker 1: In fact, artificial intelligence that you know, you see, you
Speaker 1: would see a lot of improvements as we improve computer's
Speaker 1: ability to handle natural language processing. You'd see improvements in
Speaker 1: artificial intelligence skyrocket as well. We've also seen this in
Speaker 1: other areas that are related to but are not directly
Speaker 1: connected to natural language processing. For example, you mentioned that
Speaker 1: Kurtzwild had created that scanner that could that could recognize
Speaker 1: different type sets, different fonts. Uh. You know there's that
Speaker 1: security uh measure that was around for a really long
Speaker 1: time but has officially ended capture. You know what I'm
Speaker 1: talking about. Capture has officially ended because we've gotten to
Speaker 1: the point now where machines are able to recognize characters
Speaker 1: even after being distorted about as well as humans are.
Speaker 1: I'd imagine this is thwarting way more humans. Yeah, I
Speaker 1: was about to say, I'm sure that they can do
Speaker 1: it better than I can, because I am terrible at
Speaker 1: those things. Dear a B. I don't know. The interesting
Speaker 1: thing to me is that the relationship between making captures
Speaker 1: harder and harder to recognize, because if you remember, you know,
Speaker 1: maybe six or seven years ago, these captures were a
Speaker 1: lot easier to read back then, and they've just gotten
Speaker 1: increasingly difficult. The reason they've gotten increasingly difficult. Is people
Speaker 1: started building programs that were better and better at figuring
Speaker 1: out what those words were. And once you get to
Speaker 1: a point where it's very hard for the average person
Speaker 1: to figure out what the word is, much less a machine,
Speaker 1: it's no longer a useful system in the first place.
Speaker 1: Just as you were pointing out, if we can't figure
Speaker 1: it out, then it's not good for us. But the
Speaker 1: but it was an example of how artificial intelligence was
Speaker 1: improving in the field, and even the people behind capture
Speaker 1: we're saying, I'm not upset because it means that we're
Speaker 1: getting better computers it Now, granted, it means that we
Speaker 1: have to design better and better security systems, but that's
Speaker 1: a small price to pay for the fact that we
Speaker 1: are advancing artificial intelligence through this response. And then you know,
Speaker 1: it's it's this constant response. It's response from the people
Speaker 1: who are trying to break that security system to the
Speaker 1: people who want to make it more secure. Now, granted,
Speaker 1: for a long time, the easiest way to break the
Speaker 1: capture system was to pay a bunch of people a
Speaker 1: tiny amount of money to just do it for you,
Speaker 1: like to just physically look at the screen and type
Speaker 1: it in for you, which, sadly that's the way most
Speaker 1: of the capture systems were broken. So another application is
Speaker 1: and this is a really important one, big data, which
Speaker 1: we've talked about before. Yeah. That that's the kind of
Speaker 1: convenient catch phrase for the fact that every two days
Speaker 1: we're creating as much data as as it took humanity
Speaker 1: to generate from the don of history up until two
Speaker 1: thousand three, Right, So we are generating and and normal.
Speaker 1: It's impossible to exaggerate how bunch of data we are
Speaker 1: generating on a daily basis. It's crazy. I mean, you
Speaker 1: just just take a look at these statistics or the
Speaker 1: little fact from YouTube about how more than a hundred
Speaker 1: hours of footage gets upload YouTube every minute. So a
Speaker 1: lot of this data probably wouldn't be useful to most
Speaker 1: people for any reason at all, not a single parts,
Speaker 1: not on the individual right, but an aggregate. It can
Speaker 1: be really really fascinated. And what's crazy is you can
Speaker 1: see in a chaotic system a rise of patterns that
Speaker 1: at least computers could see this. We couldn't. It's just
Speaker 1: too much information for us to be able to process well.
Speaker 1: But the thing is that computers can't either because they
Speaker 1: are unable to contextualize all of this stuff that's written
Speaker 1: in natural language, right, so they would need to have
Speaker 1: an ability to understand natural language. Not even if you're
Speaker 1: talking about a system that just collects data from various
Speaker 1: like app that have been optimized so that computers can
Speaker 1: at least sort through stuff so that a human can
Speaker 1: later come in and take a look at the data
Speaker 1: and make meaning out of it. Even then you can
Speaker 1: start to see patterns. But just imagine what's possible once
Speaker 1: the computers themselves understand what is they're looking at, They're
Speaker 1: not just classifying it based upon tags that we've put in,
Speaker 1: but can actually have this natural language processing that allow
Speaker 1: it to make those draw those conclusions itself. Oh right.
Speaker 1: And and that kind of combination of the understanding of
Speaker 1: big data with artificial intelligence is is what people like
Speaker 1: Kurt's while talk about being the next step towards this
Speaker 1: singularity um, which which is, you know, computer is becoming
Speaker 1: as colloquially intelligent as people and humanity therefore progressing to
Speaker 1: a new technological state of evolution. Mm hmm, yeah, yeah,
Speaker 1: I mean it's it's exciting to me because I like
Speaker 1: this idea that we're going to learn more and more
Speaker 1: about ourselves in ways that we had not anticipated. Maybe
Speaker 1: we eventually learned that, uh, ultimately, when you really dig down,
Speaker 1: we're no more complex than your typical ant colony. Maybe
Speaker 1: that's what we learn eventually when you when you really
Speaker 1: look at the big, big picture. Or maybe we learned
Speaker 1: that there's something really complex going on and that we
Speaker 1: can actually use the information to help people in a
Speaker 1: measurable way. That would be fantastic. Yeah, and you know that.
Speaker 1: I think the idea is that if we if we
Speaker 1: can teach you know, high processing capacity computers to really
Speaker 1: understand us, then we can we can create better versions
Speaker 1: of ourselves. Speak for yourself, Lauren Jonathan three point oh,
Speaker 1: which is the current version I'm running, is pretty darn awesome.
Speaker 1: Now that now you had that whole vision thing corrected,
Speaker 1: it's pretty you know, I'm happy Joe and I are
Speaker 1: programmed to not argue. Yeah, alright, Well, now that we've
Speaker 1: well know, we established that when the bottom line is,
Speaker 1: it's still very much a challenge for compute its to
Speaker 1: understand natural language. Is a challenge for us to be
Speaker 1: able to teach them how to do that. But it's
Speaker 1: it's a process that's seen a lot of progress over
Speaker 1: the last few decades, and that progress is increasing. It
Speaker 1: kind of a I don't know if you'd say an
Speaker 1: exponential rate, but it's really amazing the developments that are
Speaker 1: coming out of all these different companies. And again, the
Speaker 1: implementations we see today may seem pretty simple on the surface,
Speaker 1: but it took a lot of work to get there,
Speaker 1: and it just it's just kind of a stepping stone.
Speaker 1: It's just a stepping stone for for more incredible applications
Speaker 1: in the future. So that kind of wraps up this discussion,
Speaker 1: but we wanted to remind our listeners that you can
Speaker 1: get in touch with us. Let's know, what sort of
Speaker 1: futuristic topics you really want to hear about. You can
Speaker 1: email us our addresses f W Thinking at Discovery dot com,
Speaker 1: or drop us a line on the numerous social networks
Speaker 1: we frequent, which include Twitter, Facebook, and Google Plus. With
Speaker 1: the handle f W Thinking and don't forget go to
Speaker 1: FW thinking dot com to see all the video, to
Speaker 1: read the blog posts, to listen together podcasts, check out
Speaker 1: all the other information we have up there. It's fantastic
Speaker 1: and we look forward to talking to you again really sick.
Speaker 1: For more on this topic and the future of technology,
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