Speaker 1: Brought to you by Toyota. Let's go places. Welcome to
Speaker 1: Forward Thinking. Welcome everyone to Forward Thinking, the podcast where
Speaker 1: we think about the future. I'm Jonathan Strickland, I'm Lauren
Speaker 1: Vogel Dan, and I'm Joe McCormick. And today we want
Speaker 1: to talk about the evolution of speech recognition software and hardware,
Speaker 1: and to talk about how did we get to where
Speaker 1: we are and where are we going from here? Because
Speaker 1: clearly this is a thing. I mean, we're seeing speech
Speaker 1: recognition in lots of different devices and including computers, mobile devices.
Speaker 1: I know that my phone allows me to talk to
Speaker 1: it and ask questions and occasionally I get the correct response.
Speaker 1: I also have programs that will create an automatic transcript
Speaker 1: of voicemails. So how did we get to this point?
Speaker 1: How did we get to a time where computers can,
Speaker 1: at least on the surface level, appear to understand speech.
Speaker 1: And to really understand this, we have to go back
Speaker 1: aways and by a ways, I mean seventeen seventy three, what, yeah,
Speaker 1: all right, So seventeen seventy three, shortly before the Atari
Speaker 1: twenty six hundred came out by a couple of centuries,
Speaker 1: there was a Russian scientist named Christian Kratzenstein, and he
Speaker 1: actually Kratzenstein, Kratzenstein, Kratzenstein. That's a great name, it's an
Speaker 1: awesome name. Right. Well, during this time people were starting
Speaker 1: to get really interested in the nature of sound and
Speaker 1: ways of producing sound, and Kratzenstein actually created something very interesting.
Speaker 1: He created a machine that was capable of producing vowel
Speaker 1: like sounds using organ pipes and resonance tubes. Wow. So
Speaker 1: totally synthetic. Totally synthetic. And in this case we're talking
Speaker 1: about a machine producing sounds, not a machine taking in
Speaker 1: sounds and analyst exactly. This is a machine. But the
Speaker 1: history of speech recognition is also a history of designing
Speaker 1: machines that talk back to us. They don't just listen
Speaker 1: to what we have to say, but they can communicate
Speaker 1: back to us. So this is one of those earliest
Speaker 1: versions of that. And he wasn't alone. In seventeen ninety
Speaker 1: one Wuff Gong von Kimpellen in Vienna he built the
Speaker 1: Acoustic Mechanical Speech Machine or two for two. Yeah, yeah,
Speaker 1: and then let's just skip the entire nineteenth century. But
Speaker 1: wait a minute, I actually it wasn't I think that
Speaker 1: Alexander Graham Bell. His wife was deaf correct, and he
Speaker 1: originally when he was starting to play around with sound,
Speaker 1: created was trying to transform, trying to create the device
Speaker 1: that would transform audible words into a visual output that
Speaker 1: a deaf person could interpret. And I mean he wound
Speaker 1: up creating pictures with sound, but his wife never really
Speaker 1: managed to interpret them. However, that research started going into
Speaker 1: things like the telephone. Well, and also there were early
Speaker 1: attempts when the gramophone first became a thing, when they
Speaker 1: started to use wax cylinders to record sound in a
Speaker 1: physical medium. Was that at a cent or somebody before him?
Speaker 1: Bell did some of this as well, So we're talking
Speaker 1: about there are actually quite a few inventors who were
Speaker 1: working on this sort of technology. But there were people
Speaker 1: who had created these different devices to record sound on
Speaker 1: a physical medium, and there were already people thinking, well,
Speaker 1: if we can do this, is there some way where
Speaker 1: we can reverse the process, where we take the physical
Speaker 1: medium and make that into an input of some type.
Speaker 1: And some people were even thinking maybe we can make
Speaker 1: an automatic typewriter. Once the mechanical typewriter came into being,
Speaker 1: there were thoughts of if there's some way to make
Speaker 1: this same process where we're recording sound onto a physical
Speaker 1: medium then turn into a way of actually transmitting this
Speaker 1: into text. That would be amazing. No one quite figured
Speaker 1: it out at that point, but it got a lot
Speaker 1: of people thinking, and in fact, Bell Laboratories was one
Speaker 1: of the leading companies or leading research firms that was
Speaker 1: really concentrated on this speech recognition problem. And in the
Speaker 1: nineteen thirties there was a guy named Homer Dudley. I
Speaker 1: guess probably not quite as colorful as Kratzenstein or Wolfgang.
Speaker 1: It's okay, it's a more uh, what would you say,
Speaker 1: it's a more Americana kind of name. Homer Dudley, Yeah,
Speaker 1: Homer Dudley. He was at Bell Labs. He proposed a
Speaker 1: system model for speech analysis and synthesis, and he also
Speaker 1: designed the Voice Operating Demonstrator also known as the VOTER,
Speaker 1: which was a speech synthesizer, And this was essentially building
Speaker 1: on that scene work that the other guys had done
Speaker 1: centuries before, but in a electronic capacity as opposed to mechanical.
Speaker 1: Then we get into this era where the researchers were
Speaker 1: starting to try and figure out how to make machines
Speaker 1: actually understand speech. At least on a surface level. And
Speaker 1: the early emphasis was on phonetics, which is the sounds
Speaker 1: we make in our language. You know, the age language
Speaker 1: has its own list of phonemes that we generate in
Speaker 1: order to make the words. These are, yeah, the building
Speaker 1: blocks kind of speech. It's the individual sounds, and they're
Speaker 1: similar across languages. But yeah, for example, English has how
Speaker 1: about forty. Linguists are actually kind of in a disagreement
Speaker 1: about the exact number. It all depends on where you are,
Speaker 1: like in the South. Next, we produce sounds in the South,
Speaker 1: like we can make a one syllable word into at
Speaker 1: least three or four syllables, y'all. So we have the
Speaker 1: ability to insert sounds where no sound was before, which
Speaker 1: is why things like hooked on phonics does not necessarily
Speaker 1: work as well as advertised, not necessarily unless you create
Speaker 1: a regionalized version, in which case that would be interesting.
Speaker 1: But you've got to understand how hard this is for
Speaker 1: computers to hear, right, So we're used to it. I mean,
Speaker 1: we talk to people all the time. But just think
Speaker 1: about like when you're on the phone and say somebody
Speaker 1: is reading something to you, like spelling out a word
Speaker 1: or something, and you did you say P or B?
Speaker 1: Did you say P? I? Could you know what? That's
Speaker 1: why you need the Yankee Hotel fox trot kind of sure,
Speaker 1: sure stuff. So when you it's so easy for computers
Speaker 1: to mess this up, well yeah, and beyond that, even
Speaker 1: if you are enunciating clearly, the speed at which you
Speaker 1: say a word can completely make a computer misunderstand you, absolutely,
Speaker 1: because if you've programmed the computer program in such a way,
Speaker 1: you've built the computer pro in such a way that
Speaker 1: it analyzes a word based on a sequence of sounds,
Speaker 1: and it expects each part of that sequence to be
Speaker 1: a certain length. If you pronounce that word at a
Speaker 1: different speed than someone else, then the computer might have
Speaker 1: trouble figuring out that the two versions of that was
Speaker 1: the same word. And this can vary within an individual speech.
Speaker 1: I could say the same word twice in one paragraph,
Speaker 1: and the way I say it each time might be
Speaker 1: different enough to cause problems. Right, So these are non
Speaker 1: trivial problems, and in these early days they were mostly
Speaker 1: focused on just trying to figure out how to teach
Speaker 1: a computer to recognize those basic sounds. In nineteen fifty two,
Speaker 1: Bell Labs introduced the Audrey system and that could recognize
Speaker 1: spoken digits, which made it a little easier because you
Speaker 1: eliminate everything that's not a digit, right, you're just going
Speaker 1: through a series of what ten, twenty? It was probably
Speaker 1: only nine actually, because you usually do one at a time.
Speaker 1: Maybe maybe ten. If you include zero there as well,
Speaker 1: I mean you might not. It all depends. They discovered
Speaker 1: the number zero in the nineteen fifty They did, but
Speaker 1: they lost it for a while. Oh yeah, so, but
Speaker 1: I think by nineteen fifty two they re found it.
Speaker 1: The Mayans had it at least. Yeah, it was you know,
Speaker 1: twenty thirteen, we had to have it. I mean yeah.
Speaker 1: Bell Labs ended up having this Audrey system, and by
Speaker 1: limiting it to just digits, it meant that they could
Speaker 1: work very hard on a drastically simplified version of speech recognition,
Speaker 1: because again, you just throw out anything that's non digit,
Speaker 1: and it means the computer it can concentrate on which
Speaker 1: digit did that sound like the most, based upon the
Speaker 1: phonemes that are needed to say whatever that digit is.
Speaker 1: In nineteen sixty two, so this is a decade later,
Speaker 1: IBM demonstrated the shoe box machine at the World's Fair
Speaker 1: and it could understand sixteen words spoken in English. Another
Speaker 1: good point is that speech recognition, some of these systems
Speaker 1: are language specific. It's not that it can adapt to
Speaker 1: any language. It is most of the program's programmed specifically
Speaker 1: for a certain one, right exactly. So, Again, if the
Speaker 1: phonemes that we produce here are different from ones and
Speaker 1: say China, then it's not going to give you like
Speaker 1: whatever it produces is not going to be the response
Speaker 1: that someone who's speaking Chinese would want, right So generally,
Speaker 1: in the nineteen sixties, Japanese labs began to work on
Speaker 1: vowel recognition phonemes, and they also did some early work
Speaker 1: and continuous speech recognition. Now this is important because again
Speaker 1: those early speech recognition programs, even when they got to
Speaker 1: the point where they could recognize full words, you had
Speaker 1: to put long pauses between each word or else it
Speaker 1: never would And unless you're William Shatner, that's not really
Speaker 1: a natural way of speaking or Christopher Walkin. Yeah. Either way.
Speaker 1: Also in the sixties, Fry and Dean's, two researchers at
Speaker 1: the University College in England, designed a phone name recognizer
Speaker 1: that could recognize four vowel sounds and nine consonant sounds,
Speaker 1: and they use statistical data on phoneme sequences found in
Speaker 1: English to help the system recognize more words than it
Speaker 1: normally would. And this is kind of interesting. What you
Speaker 1: do is you say, all right, there are a certain
Speaker 1: limited number of sounds typically found in the spoken English language.
Speaker 1: But those sounds are not you know, it's not that
Speaker 1: those are completely interchangeable and that you're going to find
Speaker 1: every single combination of those sounds in an English word.
Speaker 1: There's certain sounds that are rarely, if ever, going to
Speaker 1: go together. So if you start to take those sounds
Speaker 1: out and then concentrate on the words that do use
Speaker 1: the sounds that are left, you have reduced the number
Speaker 1: of possibilities and thus made the system more efficient and reliable.
Speaker 1: So now are we starting to get into an era
Speaker 1: of what you're talking about here where the machines are
Speaker 1: doing some analysis, Yes, to uh, to figure out what
Speaker 1: the language means, right, well, really what it means even
Speaker 1: just to figure out what the word is exactly. Yes,
Speaker 1: that's what I meant. To interpret the sounds into words.
Speaker 1: It's not just drawing on things that have been directly
Speaker 1: programmed into it, you know, the hard coded, right, understanding
Speaker 1: that it's using statistical analysis. Yes, and and I mean
Speaker 1: clearly this would be important if you're talking about any
Speaker 1: sort of dictation software, right, because with dictation software, to
Speaker 1: program every single word in the English language into a
Speaker 1: vocabulary for this program and to do every variation of
Speaker 1: the pronunciation of that word would be pretty that'd be
Speaker 1: a lot of work. Yeah. So if you can create
Speaker 1: a system that can analyze the phonemes and then, based
Speaker 1: upon the certain statistical analysis, figure out or make a
Speaker 1: best guess at what that word is, you've fixed a
Speaker 1: lot of the problems. And in fact best guest becomes
Speaker 1: really important in just a few decades. So in nineteen
Speaker 1: seventy one, oh wait, I'm sorry, let me back up.
Speaker 1: Late sixties early seventies, researchers start to look into non
Speaker 1: uniform timescale approaches to speech recognition, which is what I
Speaker 1: was talking about earlier. The fact that not everyone speaks
Speaker 1: the same words at the same speed or uses the
Speaker 1: same emphasis. So you have to figure out a way
Speaker 1: of analyzing that and accounting for that. And it's called
Speaker 1: the it's called dynamic time warping, which is not a
Speaker 1: jump to the left and a step to the right.
Speaker 1: I'm disappointed, Jonathan, I'm sorry. Dynamic to me, H, well,
Speaker 1: you know, I'll take you to a movie. On Friday
Speaker 1: nineteen seventy one, the United States Department of Defense Advance
Speaker 1: Research Project Agency, also known as DARPA initiates a program
Speaker 1: called Speech Understanding Research or su R, and it funded
Speaker 1: several projects, including one by Carnegie Mellon University called Harpie,
Speaker 1: which is just charming. But yes, there's a speech understanding
Speaker 1: system which could understand one thousand and eleven words, which
Speaker 1: I said was about the same as a vocabulary of
Speaker 1: a three year old. And it used something called beam
Speaker 1: search to narrow down the possibilities of what a spoken
Speaker 1: sound could be by comparing it to the statistical data
Speaker 1: and going with the most likely results. So it's going
Speaker 1: with probabilities. And so this is really interesting to me
Speaker 1: because it doesn't necessarily mean it's going to produce the
Speaker 1: correct result. It's making a best guess based upon the
Speaker 1: input that it got what it was you said. So
Speaker 1: in this case, if I were having the conversation with you, Joe,
Speaker 1: and I said a letter and you weren't sure if
Speaker 1: it was p or b you. Instead of you asking me,
Speaker 1: you just say, well, I think it was probac was
Speaker 1: a P. I'm just gonna write, well, I mean, if
Speaker 1: your computer is smart enough and it has a large
Speaker 1: enough dictionary, it might understand that that say, the words
Speaker 1: starting with a P sound makes sense here, But the
Speaker 1: words starting with a B sound is not. So like
Speaker 1: I ate a pair or I ate a bear, and
Speaker 1: now some days, some days the pair eats you exactly.
Speaker 1: But of course i'd imagine the machine at that time
Speaker 1: didn't have the resources to say, go figure out if
Speaker 1: I ate a pair or I ate a bear made
Speaker 1: more sense, right right? We need to remember that this
Speaker 1: is you said, early seventies, so this is when you know,
Speaker 1: computers were the size of like three of my car
Speaker 1: at least, you know, right well, and there were I mean,
Speaker 1: there was no Internet yet, and that'll come in a
Speaker 1: big way in a little bit here. By the seventies
Speaker 1: they had Arpanet, but that was very limited and that
Speaker 1: they had have anything to do with it. They had
Speaker 1: no web to draw on, no no web at all
Speaker 1: for massive sampling of of data. So nineteen seventy six,
Speaker 1: the Serve program that DARPA had concludes there were a
Speaker 1: couple of other agencies that had tried to create speech
Speaker 1: understanding algorithms and hardware, but had not quite met the
Speaker 1: requirements by the end of the program to really count
Speaker 1: as a success, but they did end up contributing quite
Speaker 1: a bit to future endeavors. So then we've got the
Speaker 1: nineteen eighties that typically follows the seventies, and that's when
Speaker 1: they introduced a statistical method that was based on the
Speaker 1: hidden Markov model. Have you guys heard of this the hmm,
Speaker 1: all right, it's a little complicated and it's difficult to
Speaker 1: really explain without the benefit of complicated graphics behind me,
Speaker 1: but I will try. So it's a probability model. And
Speaker 1: let's say that you've got let's say you've got three
Speaker 1: earns in front of you. Okay, three three vases are
Speaker 1: in front of you. They're solid, you can't see through them,
Speaker 1: but you see that you've put a certain number of
Speaker 1: orange ping pong balls in each. The first one has
Speaker 1: the most. You put a certain number of white ping
Speaker 1: pong balls in each, The middle one has the most
Speaker 1: of those, and you put a certain number of yellow
Speaker 1: ping pong balls in each. The third one has the
Speaker 1: most of those, and then you already know the states
Speaker 1: of the you're actually watching as you draw these ping
Speaker 1: pong balls out, and then you're combining them to get
Speaker 1: some sort of response at the end. It doesn't matter
Speaker 1: what the response is, but you're drawing a ping pong
Speaker 1: ball out from each combining them together, and that you
Speaker 1: see the whole process. Now, that's a normal Markov model
Speaker 1: because you know the state of each of those draws
Speaker 1: from the vases, all right, so you observe the state. Now,
Speaker 1: let's say those vases are in a one room and
Speaker 1: you're in another room, and you cannot see into the
Speaker 1: other room. You just get to see the output of
Speaker 1: the three ping pong balls as they come out of
Speaker 1: this process. So you don't see which one's drawn from
Speaker 1: which earned, but you know that one is drawn from
Speaker 1: each one, and you see what the result is. Now,
Speaker 1: you don't know the state of those individual urns, but
Speaker 1: you do see the result, which gives you enough information
Speaker 1: to draw some conclusions about the state of the urns
Speaker 1: inside the room. Not enough for you to know for certain,
Speaker 1: but you can get sort of a probability of what
Speaker 1: happened in there to get the result that you have
Speaker 1: that's a hidden Markov model, and that is an oversimplification
Speaker 1: of the hidden Markov model. So anyone out there who
Speaker 1: actually works with systems that use this is screaming that's
Speaker 1: way too simplistic, I know, but this is the easiest
Speaker 1: way for me to explain it, Okay. But so basically
Speaker 1: what you're saying is that it uses it looks at
Speaker 1: the statistical prevalence of these three different colors appearing into
Speaker 1: the room, and by that it makes judgments about how
Speaker 1: common they probably are in the vases more or less.
Speaker 1: And so these models are used a lot in things
Speaker 1: that require a lot of interpretation on machines. Part voice
Speaker 1: recognition is a big part of that, but it's not
Speaker 1: just voice recognition, gesture recognition, handwriting recognition, anything where you know,
Speaker 1: two people could try and make the same result, but
Speaker 1: because we are individuals and because we do think slightly differently,
Speaker 1: even though we're both creating the same result, we're doing
Speaker 1: it in a different way. The computer has to be
Speaker 1: able to interpret that, right. So it's because it's taking
Speaker 1: sort of ambiguous analog data from the world, sure, yeah,
Speaker 1: and it has to be able to react to that
Speaker 1: and create a meaningful result. So once people started to
Speaker 1: concentrate on this form of statistical analysis, voice recognition pretty
Speaker 1: much hit its peak as far as recognizing individual words,
Speaker 1: not necessarily knowing what the context is or what the
Speaker 1: meaning is, but it meant that if you were speaking
Speaker 1: into a mischie that had this kind of software in it,
Speaker 1: it could determine with relative ease what it was you
Speaker 1: were saying, not what it meant, but what the actual
Speaker 1: words were. So if, for example, if it's a simple
Speaker 1: speech to text program, it be fairly accurate, and it
Speaker 1: got more accurate as time went on. In nineteen eighty two,
Speaker 1: that's when a certain Ray Kurtzweil got involved. Our old
Speaker 1: Kurtzweil's a well known futurist, one of those evangelists for
Speaker 1: the oncoming singularity, a fellow who I think is hoping
Speaker 1: to achieve immortality through technology in some method or another personally, yes, definitely.
Speaker 1: So he created in nineteen eighty two the Kurtzweil Applied
Speaker 1: Intelligence Division Company, really and it was all about creating
Speaker 1: computer based speech recognition, And in nineteen eighty seven it
Speaker 1: introduced a commercial speech recognition system. And Kurtzweil was really
Speaker 1: applying his expertise in two areas, computer science and pattern recognition.
Speaker 1: He was really interested in the way that computers can
Speaker 1: identify patterns and respond to them, and speech was certainly
Speaker 1: part of that. So he applied that knowledge and that
Speaker 1: expertise and really made some big contributions in the speech
Speaker 1: recognition field. Skipping over to the nineteen nineties, I mean,
Speaker 1: essentially we're having this field evolve over time. But in
Speaker 1: the nineties we started seeing the development of real speech
Speaker 1: enabled applications. So this is when we started getting those
Speaker 1: telephone systems where you would call in and get an
Speaker 1: automated response saying say say or press one, which is
Speaker 1: again going all the way back to the Audrey system
Speaker 1: in nineteen fifty two that no labs DI it Yeah,
Speaker 1: You've only got ten responses, and so it just has
Speaker 1: to figure out which one right, and then eventually it
Speaker 1: would get to things like you know, say yes, or
Speaker 1: like I can help you with that. What is your
Speaker 1: problem he's a keyword? Yeah, not that keyword. Sorry, I
Speaker 1: don't understand. Can you restate that? Yeah. So by twenty
Speaker 1: ten we get the we get Google's English Voice search system,
Speaker 1: which incorporates around two hundred and thirty billion words from
Speaker 1: actual user queries. Wow, have you all tried this thing? Oh? Yeah,
Speaker 1: I use it all the time. No, I do, because
Speaker 1: I've got an Android phone, so I actually do use
Speaker 1: voice search all the time. Sometimes I think it's really
Speaker 1: hilarious how accurate it is, Like, you know, it shouldn't
Speaker 1: recognize that term, but it does. I use it mostly
Speaker 1: for navigation purposes, So I'll pull up a map application
Speaker 1: and it's a Google one. So then I, you know,
Speaker 1: speak destination and I can say an address, or I
Speaker 1: can say a business name, or you know, if I
Speaker 1: have someone in my contact list, I can say their
Speaker 1: name and it pulls up the information, which leads us
Speaker 1: kind of into a second part of this speech recognition discussion.
Speaker 1: We've got the idea that speech and search are really
Speaker 1: tightly connected, actually to the point where advances in one
Speaker 1: field often mean that the other field benefits as a result.
Speaker 1: But now we're talking about not just recognizing words, but
Speaker 1: pulling some sort of meaning from them. Right, Well, what
Speaker 1: is the goal of input of an interface that takes
Speaker 1: input from a human and turns it into data. I mean,
Speaker 1: I don't know. We'll say I would argue that the
Speaker 1: ultimate goal of an input interface is to become invisible,
Speaker 1: to make things as easy and as natural and as
Speaker 1: intuitive for you as it possibly could be, so that
Speaker 1: you don't even recognize the tools you're using, right, Right,
Speaker 1: to give the computer the ability to answer your questions
Speaker 1: almost before you ask them, exactly and right now, you know,
Speaker 1: we're still using tools that we have to learn how
Speaker 1: to use. Right. So when you when you want to
Speaker 1: talk to the voice recognition program on your smartphone, you
Speaker 1: do have to be aware that it's only going to
Speaker 1: be listening to certain keywords, right. You have to you
Speaker 1: have to give it keywords and sort of specific commands
Speaker 1: that it can understand in order for it to help you. Sure,
Speaker 1: And in that sense, it's kind of like a program
Speaker 1: where you know, you have a certain number of buttons
Speaker 1: you can click on, or commands you can enter on
Speaker 1: a command line that are chosen from a list of
Speaker 1: pre selected commands, but you're just doing it with your voice, right.
Speaker 1: Anything outside of that would just be interpreted as an error. Sure, yeah, Yeah,
Speaker 1: you can say open and close, but if you say
Speaker 1: French fries, it goes qua. Yeah. Yeah. So let's say
Speaker 1: you had this and you're looking for something on Google, right,
Speaker 1: you're looking at Google Maps and you're using your voice,
Speaker 1: you could probably say French fries, though, right, should say
Speaker 1: like French fries near my house. Well, even there it
Speaker 1: might be able to understand those keywords. Right, You've given
Speaker 1: it something that it knows how to work with. But
Speaker 1: what if you've got a problem, like I'm trying to
Speaker 1: remember this meal I had that was real good in
Speaker 1: town and I don't know, and you're kind of describing it,
Speaker 1: but right, it can't do anything with that, right, Right,
Speaker 1: you'd have to talk to a person at that point.
Speaker 1: You would either that or you would have to have
Speaker 1: every single restaurant give every single possible explanation of what
Speaker 1: its meals would be like exactly. Yeah. But so this
Speaker 1: leads me to a question about the future of voice
Speaker 1: and speech recognition. And here's a question. Why do we
Speaker 1: call tech support? I mean, if you've I mean, most
Speaker 1: people are working, most people have called tech support at
Speaker 1: some point. But I will venture that almost any problem
Speaker 1: that can be solved by tech support, there's already a
Speaker 1: written out solution to the exact problem you have somewhere online. Sure, right,
Speaker 1: somebody has already solved this problem, and they've probably typed
Speaker 1: up instructions on how to fix it right, and they
Speaker 1: may even be easy to follow instructions. But the challenge
Speaker 1: there is for the person who's experiencing the problem, how
Speaker 1: do they frame their problem in such a way that
Speaker 1: they get the exactly the right response. How do they
Speaker 1: connect the problem they're experiencing to the solution that exists
Speaker 1: somewhere out there If they don't know what the correct
Speaker 1: keywords are, If they don't they don't know what the
Speaker 1: problem is itself, They're just going my screen won't turn
Speaker 1: on exactly. And that's why we call tech support. I
Speaker 1: think you call tech support because you need something that
Speaker 1: can process natural language, which is right now a person.
Speaker 1: A person can listen to you describe your problem, and
Speaker 1: whatever terms you come up with, can take that information,
Speaker 1: get the gist of it, and connect that to a
Speaker 1: piece of knowledge, right, and then in return, that person
Speaker 1: can respond with language that the person who called in
Speaker 1: for tech support can understand. So, for instance, if I'm
Speaker 1: experiencing a problem and I call up Joe and Joe
Speaker 1: tells me how to fix it, but I don't understand
Speaker 1: his explanation, I can say I am sorry, I just
Speaker 1: I don't get that Joe can actually then take the
Speaker 1: time to reframe what it was he said in a
Speaker 1: way that my puny brain can comprehend, right, And then
Speaker 1: I can turn off my computer and turn it back
Speaker 1: on again and suddenly works. So yeah, it totally works
Speaker 1: both ways. I mean, but it's so especially important in
Speaker 1: identifying what the problem is to begin with, because a
Speaker 1: lot of times we just don't know the right way
Speaker 1: to explain it to a computer in terms of commands
Speaker 1: and keywords. And so I think this is sort of
Speaker 1: the future of where voice recognition is going from here.
Speaker 1: And there are a couple of things we need to
Speaker 1: explore about voice and speech recognition. One of them is
Speaker 1: how does the computer understand whole speech like sentences that
Speaker 1: you're speaking to it, as opposed to just little words
Speaker 1: at a time, and make sense of those in a
Speaker 1: grammatical way and actually make sense of them instead of
Speaker 1: instead of yeah, picking up on those keywords, because you know,
Speaker 1: right now, the technology doesn't know what you're saying, right,
Speaker 1: So yeah, well, I mean, and in our way, it
Speaker 1: will probably never know what you're saying. Oh, we can
Speaker 1: have a debate what will computers achieve consciousness? You know,
Speaker 1: will the terminator learn to love. But whether or not
Speaker 1: the terminator, terminator will understand the meaning of love. The
Speaker 1: terminator will at least be able to make sense of
Speaker 1: my grammar, even if it's spontaneous and kind of manful.
Speaker 1: So the terminator may not love, but it may be
Speaker 1: able to mark up your paper. Yeah, it may be
Speaker 1: able to help me figure out what restaurant I went
Speaker 1: to when I was in town last year, just by
Speaker 1: me describing some dish. If the terminator doesn't love you,
Speaker 1: I don't see it taking the opportunity to actually help
Speaker 1: you with that problem. And I'd like to put in
Speaker 1: that I do not want the terminator to be my
Speaker 1: English teacher ever. Thanks, I'm pretty sure I had the
Speaker 1: terminator as my English teacher. You'll fail English. So so
Speaker 1: before well, no, I want to want to introduce a
Speaker 1: possible way of viewing the progress of our input through voice.
Speaker 1: And that's a way of looking at the computer helper
Speaker 1: as something that's that's got an obedient orientation versus a
Speaker 1: sympathetic orientation. All right, and what do you mean by that?
Speaker 1: And so I would say that right now, computers have
Speaker 1: an obedient orientation, meaning they they solve directly problems that
Speaker 1: you give them. Right, they do what you tell them
Speaker 1: to do, and that's it. Yeah yeah. And when they're
Speaker 1: not doing that, it's because you haven't told them how
Speaker 1: to do it exactly right, Yeah yeah. And so you
Speaker 1: enter a command and it follows the command exactly, It
Speaker 1: performs the calculation, it searches for the search term. However,
Speaker 1: it goes like that. Now, what makes that person on
Speaker 1: tech support different. That person has a sympathetic orientation as
Speaker 1: opposed to an obedient orientation. What that person does is
Speaker 1: listens to your whole problem, gets the gist of it,
Speaker 1: figures out what's important, and then helps you solve it. Right,
Speaker 1: They see the end. They see not just each of
Speaker 1: the individual commands you're giving, but they understand what you're
Speaker 1: trying to do overall. Right, and we're already making some
Speaker 1: pretty big strides in natural language recognition. For instance, IBM's Watson,
Speaker 1: which was famous for going on Jeopardy up against two
Speaker 1: former Jeopardy champions and beating them, winning in a game
Speaker 1: of Jeopardy. But what it had to do was it
Speaker 1: essentially had a huge amount of information stored in its
Speaker 1: in its data banks. Yeah, but it had no connection
Speaker 1: to the Internet while it was playing the game, So
Speaker 1: it was it had much of the Internet on it.
Speaker 1: You know, it was self but it was self contained. Yeah,
Speaker 1: all the YouTube comments were left off, but otherwise, yeah,
Speaker 1: it was Why didn't it need those worry about what
Speaker 1: happened when when it learned Urban Dictionary? Right, Yeah, they
Speaker 1: taught it Urban Dictionary and then they basically had to
Speaker 1: nuke Urban Dictionary from orbit from from its data banks
Speaker 1: because it started off. It's true. That's completely true. Okay,
Speaker 1: so I understand why it doesn't need YouTube comedy turn
Speaker 1: Watson into a vicious sociopath. Yeah, yeah, which I will
Speaker 1: kill you. It was essentially becoming the the Sean Connery
Speaker 1: from the Saturday Night Live skits. So anyway I had
Speaker 1: it was it was closed off, so it didn't have
Speaker 1: an outside. It didn't have an outlet to uh to
Speaker 1: to go out and do a search on the internet
Speaker 1: for everything. So when a Jeopardy clue came up, it
Speaker 1: had to analyze the clue, go through its database and
Speaker 1: then determine which bits of information were most likely to
Speaker 1: be the relevant ones to answer or to form the
Speaker 1: question in the case of Jeopardy for that clue, and
Speaker 1: uh and the way it did this was that it
Speaker 1: would assign probabilities to answers based upon parsing out the clue.
Speaker 1: And the thing about Jeopardy is that it's not just
Speaker 1: really straightforward answers. You know, things like you know, this
Speaker 1: is the is Beethoven's symphony that contains Ode to Joy?
Speaker 1: What is the ninth Symphony? You know it's not that's
Speaker 1: there's word play in there, right exactly, there are punsure
Speaker 1: and yeah, yes, So they had to create programs that
Speaker 1: could that could parse that language and determine what is
Speaker 1: the underlying meaning of this phrase, not just what do
Speaker 1: these words? What are you know? Not just using those
Speaker 1: words as search terms, because if it did that, it
Speaker 1: never would have won. It had to figure out the relevance.
Speaker 1: And so what it would do is it would pull
Speaker 1: up all these different answers and a sign that probabilities
Speaker 1: for being the correct one. And if the probability was
Speaker 1: higher than a threshold and I can't remember what the
Speaker 1: threshold it was, like seventy or eighty percent or whatever,
Speaker 1: but if it was higher than that threshold, then then
Speaker 1: and only then would Watson guess and guests. Yeah. Otherwise
Speaker 1: Watson would be quiet and allow one of the other
Speaker 1: two people to answer, which is really interesting to me
Speaker 1: because that's a it's a step towards that natural language recognition,
Speaker 1: the idea that it's not just looking at the words
Speaker 1: as search terms, but as these are things units of meaning,
Speaker 1: they have meaning, and therefore you need to find the
Speaker 1: data that corresponds with that meaning. And that is incredible. Well,
Speaker 1: it was searching for if I'm correct, it wasn't it.
Speaker 1: It had something to do with like keywords would be
Speaker 1: searched based on when they were in proximity to other
Speaker 1: important terms, right as far as I can understand it, yes,
Speaker 1: but I mean it gets really pretty complex. And then
Speaker 1: beyond that, you know, we're starting to see Watson being
Speaker 1: used in medical facilities yea. You know they're using to
Speaker 1: describe what's wrong, you know, kind of a diagnostic although
Speaker 1: a lot of doctors will tell you that while it's
Speaker 1: a useful tool, it's certainly not a replacement for a
Speaker 1: doctor because so many cases can like two people with
Speaker 1: the exact same condition can come in and nonsent present
Speaker 1: different symptoms and even explain the same symptoms in very
Speaker 1: different terms, and so it becomes increasingly difficult for a
Speaker 1: machine to be able to interpret that and come up
Speaker 1: with the right response, as opposed to a doctor who
Speaker 1: has that experience and has the ability to be much
Speaker 1: more dynamic and even proactive and asking the right questions
Speaker 1: to get the right information. Well. Also, I mean, a doctor,
Speaker 1: much like the tech support person is, though with much
Speaker 1: higher stakes obviously, is able to identify what's important. I
Speaker 1: mean a lot of most of the time, when you
Speaker 1: come describing a problem, you're giving too much information. All
Speaker 1: the time, You're giving all this information, and a huge
Speaker 1: amount of it is probably not actually relevant to what's
Speaker 1: really the problem. And that's when the human who's experienced
Speaker 1: this before knows what to zero in on. Computers have
Speaker 1: more trouble with that, right, Like, they have a hard
Speaker 1: time figuring out what's important when you've given it a
Speaker 1: list of sure, yeah, yeah, no, it without without giving
Speaker 1: it some form of a way of recognizing context and
Speaker 1: way of recognizing the importance of particular words and particular phrases. Yeah,
Speaker 1: I mean, it's just how does a computer determine that
Speaker 1: the third word in a sentence is more or less
Speaker 1: important than the fifth word, right, It's all statistical probability
Speaker 1: and a certain point you're going to plateau on that
Speaker 1: because the more the more input that you give to
Speaker 1: these kinds of programs, you know, they'll analyze it and
Speaker 1: analyze it, and it gets really accurate, and then kind
Speaker 1: of stops getting more accurate. Yeah. In fact, that's been
Speaker 1: a real issue with voice recognition in general, and a
Speaker 1: very interesting thing that I think. Maybe it's interesting to
Speaker 1: me because Kurtzweil worked on voice recognition, and I know
Speaker 1: the man must be aware that the technology increased at
Speaker 1: a pretty rapid pace but then began to plateau off.
Speaker 1: You know, really it even began to plateau off in
Speaker 1: the eighties. We made improvements and we learned how to
Speaker 1: use the technology we had created better in better ways.
Speaker 1: But it's not like the advances we made are exponentially
Speaker 1: better than the previous generations. So in a way, the
Speaker 1: curve is starting to plateau off and level off. We're
Speaker 1: still we're still making advancements, but not at an accelerated rate,
Speaker 1: whereas with Moore's law, every two years, essentially we're seeing
Speaker 1: computers get twice as powerful, and so I think that
Speaker 1: that makes some futuristic predictions less likely because we recognized
Speaker 1: not all elements of technology accelerate at this same rate. Yeah,
Speaker 1: I think for the future of speech recognition, natural language
Speaker 1: processing is key, and natural language processing, you know, Watson
Speaker 1: seems amazing, but compared to what's probably going to come
Speaker 1: in the future, Watson is actually very primitive, right And
Speaker 1: also we got to in mind that right now, even
Speaker 1: though Watson does did do this amazing thing by beating
Speaker 1: humans at their own game, it had thousands of processors
Speaker 1: and tens of thousands of cores. So you're talking about
Speaker 1: an incredibly powerful, energy hungry machine that was able to
Speaker 1: do something that a human can do. Right, well, that
Speaker 1: a tiny little meat thing. It was. It was able
Speaker 1: to do what the tech support operator can do. And ultimately,
Speaker 1: I think that's the endgame here. What we're talking about
Speaker 1: in the far future. What we dream about is when
Speaker 1: your computer is as sympathetic in its orientation as a
Speaker 1: human helper, is that you can describe in spontaneous human
Speaker 1: language what you're trying to do and it can actually
Speaker 1: help you with that as opposed to just operating off
Speaker 1: of set commands. And it's we're getting there. I mean,
Speaker 1: if you talk to people who have used Apple's Siri
Speaker 1: or the Google Voice search stuff. You know, you can
Speaker 1: use some pretty you know, colloquial sayings to get what
Speaker 1: you want. And it's it's getting better and better at
Speaker 1: interpreting those and giving you the response that would be appropriate.
Speaker 1: And and granted this is all again still on a
Speaker 1: surface level, but it's it's seemingly deep, you know, to
Speaker 1: the user experience. It seems like the machine understands what
Speaker 1: you're saying, even though that's not really yes. And and
Speaker 1: you know, maybe in the future we have the semantic
Speaker 1: web that responds exactly to what we want even if
Speaker 1: we were to you know, I know that it's really
Speaker 1: hard to get tone across and text messages, but maybe
Speaker 1: computers will be better than people are. By the way,
Speaker 1: I'm always I'm always j slash k if you if
Speaker 1: you're wondering, all right, well, you know we should wrap
Speaker 1: this up. We've gone on quite a bit about voice
Speaker 1: and speech recognition. It's a fascinating topic and it is
Speaker 1: one that I am eager to see more advances in
Speaker 1: the field. We've seen stuff not just in smartphones and tablets,
Speaker 1: but also game consoles, things like Microsoft's Connect and other
Speaker 1: devices as well incorporate voice recognition, and I expect we're
Speaker 1: going to see even more of that. I can't wait
Speaker 1: for my thermostat to have it. It's too darn on
Speaker 1: here and then it just immediately just cranks down five degrees.
Speaker 1: That'd be fantastic because I don't have one that's connected
Speaker 1: to the internet, so I can't just use my smartphone.
Speaker 1: I actually have to. I can't believe it, get up
Speaker 1: and walk to it. I know it's a My life
Speaker 1: is a drama waiting to be filmed. So guys, that's
Speaker 1: our episode about voice recognition. We hope you enjoyed it.
Speaker 1: We highly recommend that if you have any topics that
Speaker 1: you think board Thinking should cover stuff about the future
Speaker 1: that really has you excited, that you get in touch
Speaker 1: with us. We have an email address now, it's fw
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Speaker 1: and we will talk to you again really soon. For
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