Speaker 1: Brought to you by Toyota. Let's go places. Welcome to
Speaker 1: Forward Thinking. Hey there, and welcome to Forward Thinking, the
Speaker 1: podcast that looks at the future and says no dark
Speaker 1: sarcasm in the classroom. I'm Jonathan Strickland and I'm Joe McCormick. So, guys,
Speaker 1: I thought today we could talk a little bit more
Speaker 1: about how, you know, computers just don't get me, man,
Speaker 1: they just don't understand. They're like parents. You know, parents
Speaker 1: never understand the importance of the flavor of apple Jack's.
Speaker 1: That's true, among many other things. True. In fact, they
Speaker 1: also don't understand our music, right, they don't. They don't
Speaker 1: get my clothes, my style. Uh. Now, we're we're talking
Speaker 1: about how computers, you know, trying to teach computers how
Speaker 1: to understand what we mean when we say things. And
Speaker 1: we've talked about this before. We've talked about computers in
Speaker 1: natural language and how that's a really tough problem getting
Speaker 1: computers to understand the way we humans communicating naturally yea,
Speaker 1: and especially trying to get them to mimic it or
Speaker 1: or feed it back to us and participate in a
Speaker 1: conversation that's convincingly human. That's the standard problem of the
Speaker 1: touring test. Yeah, even if it's not convincingly human, at
Speaker 1: least useful, right like we you know, I would settle
Speaker 1: for a computer that would understand what I mean when
Speaker 1: I say things a certain way and give me the
Speaker 1: stuff that I'm requesting, even if it can't hold a
Speaker 1: conversation with me. Right right, We don't always need Siri
Speaker 1: to to respond back if if she could just find
Speaker 1: what we're bloody looking for. Cortana would be another great
Speaker 1: example when those ten has just been released the day
Speaker 1: we're recording this, and I'm seeing a lot of reports
Speaker 1: about how Cortana is almost but not quite awesome. You know.
Speaker 1: One of the things that's interesting to me about Siri
Speaker 1: is that Siri is playful Siri. Siri will sometimes be
Speaker 1: koy and sometimes like play along when you're when you're
Speaker 1: being I don't know, suggestive or weird at her. I
Speaker 1: don't say this from personal experience. I've read about this,
Speaker 1: but I get the impression that that is not original
Speaker 1: like synthetic behavior, that it has been assembled by series
Speaker 1: logical engines. That is more of a hard coded behavior.
Speaker 1: I think that's more of their sort of jokes put
Speaker 1: in directly by the programmer. The programmers anticipate the kind
Speaker 1: of like they just say, Well, if I found out
Speaker 1: that this audio personal assistant could answer questions that I
Speaker 1: ask of it, what are some of the ridiculous questions
Speaker 1: I might ask? And they probably made a list and
Speaker 1: and that's probably the basis for a lot of those
Speaker 1: funny responses, things like where can I hide a body? Yeah? Yeah,
Speaker 1: but but that's all coded by your word choice. It's
Speaker 1: not like it's not like, you know, Siri is gonna
Speaker 1: listen to you say something like, h Siri, where can
Speaker 1: I find another pizza place? Because I'm so interested in
Speaker 1: pizza today? Like she She's not going to come back
Speaker 1: with with with like, well, maybe you should try some
Speaker 1: chicken nuggets this time, fatty, Like I mean, like I
Speaker 1: was thinking, it's Taco Tuesday, chicken nuggets. The cure to
Speaker 1: facts that would be a revolutionary episode forward thinking. However,
Speaker 1: we wanted to talk today about I learned it from Laura.
Speaker 1: We wanted to talk today more about how computers might
Speaker 1: soon or at least down the road, learned to recognize
Speaker 1: not just what we say, but what we mean when
Speaker 1: we say it. Right, So could Siri participate in a
Speaker 1: sarcastic sort of caustic back and forth humorous exchange with
Speaker 1: you without that being hard coded by the programmers, could
Speaker 1: could Siri detected and even and even synthesize it herself. Right,
Speaker 1: So to start this, we thought it might be useful
Speaker 1: to kind of give a quick overview of why there's
Speaker 1: this us connect between natural language and what computers understand
Speaker 1: and basically computers machines work in uh in machine code
Speaker 1: or assembly language. Assembly language is generally defined as a
Speaker 1: very low level programming language, sometimes a one to one
Speaker 1: correspondence with the architecture of the actual machine you are
Speaker 1: using it on. So the thing about this, the defining characteristic,
Speaker 1: is that computers understand it and humans have real trouble
Speaker 1: with it because it's it's so far removed from any
Speaker 1: sort of language that we use. It would be like
Speaker 1: looking at a just enormous page, just a block of
Speaker 1: texts that are all zeros and ones, and trying to
Speaker 1: make any sense of it. Um. I assume that most
Speaker 1: human beings, the vast majority, would be unable to do so.
Speaker 1: So we humans communicate natural language. That's what I'm doing
Speaker 1: right now. And natural language isn't just agree and agreed
Speaker 1: upon syntax and grammar. It also has all these other
Speaker 1: elements to it, implications where you know, you don't say
Speaker 1: something outright, but it is implied by the way you
Speaker 1: say it, or the tone of how you deliver something,
Speaker 1: including sarcasms. Sure, even the gestures that you're making or
Speaker 1: the facial expression. Yeah. I think we talked pretty recently
Speaker 1: on this program about how one major difference is that
Speaker 1: humans learn language by induction rather than by you know,
Speaker 1: we don't have an explicit list of rules and uh
Speaker 1: and definitions, Like we don't learn how to speak our
Speaker 1: language by reading a dictionary in a grammar book. You
Speaker 1: just sort of gradually into it the rules based on
Speaker 1: experiencing them. Yeah. Yeah, and we free associate a lot
Speaker 1: of stuff with a lot of other stuff. That's true. Yeah.
Speaker 1: So one thing that we have to do is find
Speaker 1: a way to bridge that gap between the way we
Speaker 1: humans communicate and the way machines understand commands. And we
Speaker 1: do that through programming languages and compilers. A programming language,
Speaker 1: depending upon whether you call it low level or high level,
Speaker 1: is something that may be easier for a human to grasp.
Speaker 1: In general, if we say it's a high level programming language.
Speaker 1: That means it's more like the kind of languages we use,
Speaker 1: although if you're unfamiliar with programming language, it still looks
Speaker 1: like gibberish exactly. Yeah, this is a funny fact about
Speaker 1: programming languages. People often think of them as something that
Speaker 1: helps the computer understand. That's not what it is. It's
Speaker 1: something that helps the human understanding its programming language is
Speaker 1: a tool for you, right, it's so that the human
Speaker 1: can say, uh, this is this is the input I'm
Speaker 1: going to give you, this is the desired output, make
Speaker 1: it go. And so on the machine side they have
Speaker 1: there are compilers and compilers. The job of a compiler
Speaker 1: is to essentially translate, to switch from that programming language
Speaker 1: into machine language. So it's this combination that allows machines
Speaker 1: to understand what we want them to do. But more
Speaker 1: recent work, really this has been going on for decades,
Speaker 1: but truly in the last decade we've seen a lot
Speaker 1: of work done in trying to come up with ways
Speaker 1: where machines can deal with natural language and the and
Speaker 1: if it is obvious, because it removes a barrier between use,
Speaker 1: you know, a human trying to use a machine, and
Speaker 1: the machine doing what what the human wants it to do. So,
Speaker 1: for a very simple example, if you go on your
Speaker 1: computer and you wanted to find a particular piece of information,
Speaker 1: you could just type something in and it would understand
Speaker 1: what you meant by that, so it wouldn't return a
Speaker 1: page of search results that are in some way related
Speaker 1: to what you wanted. It would return exactly what you wanted. Uh.
Speaker 1: So that that's a simple example. There are lots of
Speaker 1: other examples of this, but we're still just at the
Speaker 1: very early stages of getting a good grasp on natural language.
Speaker 1: We're getting better at computers parsing sentences to at least
Speaker 1: make an educated guess as to what we want, but
Speaker 1: we're not all the way there yet. But we wanted
Speaker 1: to talk about some interesting tools that are getting us closer,
Speaker 1: and one was one that was reported on in July
Speaker 1: two thousands of team that I really wanted to talk about,
Speaker 1: which was IBMS tone Analyzer. Yeah. I had some fun
Speaker 1: playing with this earlier. Today. We can talk about the
Speaker 1: results I came up with later, but I think a
Speaker 1: common theme in our analysis of this will be that
Speaker 1: it's interesting, but maybe more for the reasons that it
Speaker 1: fails than for the reasons it succeeds. Yeah, I would
Speaker 1: agree with that. It's kind of a very advanced version
Speaker 1: of Clippy almost in that way I'm like, oh, it's
Speaker 1: really cute. The way that you have no idea what's
Speaker 1: going on. I almost think of it as as like
Speaker 1: and of evolution of just word count. Yeah, like it's
Speaker 1: still counting words, it's just now classifying what word is,
Speaker 1: what like or what general categories words fall into. But
Speaker 1: it's part of the Watson development cloud. Uh. And that's
Speaker 1: that if you are familiar with IBM S Watson, you know,
Speaker 1: you know that that refers to the machine that played
Speaker 1: on Jeopardy and beat to Jeopardy Champions. Yeah, which makes
Speaker 1: it hilarious that we're using words like cute to describe
Speaker 1: something is so ceated with Watson yea one of the
Speaker 1: most amazing computers ever put together. It's really a function
Speaker 1: of how difficult the task is. Yeah, more so than
Speaker 1: than how dumb this that's not that's not what I'm saying,
Speaker 1: No offense, wats no, no, totally that I'm on the
Speaker 1: same page. Because Watson was obviously amazing. It was able
Speaker 1: to take Jeopardy clues which are in the form of
Speaker 1: an answer and come up with the appropriate question more
Speaker 1: frequently than not. And we've talked a little bit about
Speaker 1: how it did this, and that it was able to
Speaker 1: come up with potential answers or rather questions if you prefer,
Speaker 1: and then judge how probable that particular response was to
Speaker 1: being the correct one, and if it met a certain threshold,
Speaker 1: Watson would buzz in and offer that up as the
Speaker 1: response to the clue. Yeah. And so what made Watson
Speaker 1: interesting was different than what makes most Jeopardy contestants interesting.
Speaker 1: It was because most Jeopardy contestants are competing based on
Speaker 1: their essentially their volume of trivia that they have contained
Speaker 1: in their brain. They're not having trouble discerning what the
Speaker 1: clue is trying to say, right, yeah, yeah, like like
Speaker 1: they get the puns in it, and and they get
Speaker 1: the sarcasm or whatever it is. I guess Alex Trebeck
Speaker 1: isn't sarcastic very often. But you know, references as well,
Speaker 1: they cultural references contest. Yeah, right, So for them what's
Speaker 1: impressive is, Wow, I can't believe that lady knew all
Speaker 1: that stuff about ancient Rome and about you know, records
Speaker 1: and about video games whatever they talk about. Yeah, but
Speaker 1: you wouldn't be impressed at all that she, you know,
Speaker 1: makes sense of what the clue is saying. And that's
Speaker 1: the deal with Watson. What it's not interesting that a
Speaker 1: computer has all that knowledge because it can just you know,
Speaker 1: have terabytes of memory that it runs through. What's interesting
Speaker 1: is that it knew what the question was referring to, right,
Speaker 1: and that that was one of those things that really
Speaker 1: opened up a lot of eyes and said, wow, this
Speaker 1: is exciting. A computer is, at least on some level
Speaker 1: understanding what it's supposed to be looking for. Whether that
Speaker 1: you know, that understanding doesn't go on to the same
Speaker 1: level as it would with a human, but it's still
Speaker 1: really impressive because generally, you would have a computer and
Speaker 1: you'd give it some sort of word play response, and
Speaker 1: chances are it would not come up with an appropriate answer.
Speaker 1: It would either not answer at all because it wouldn't
Speaker 1: hit that threshold of certainty, or it would give them
Speaker 1: an incorrect answer. And you know, once and once or
Speaker 1: twice Watson gave funny wrong answers. It's not like it
Speaker 1: was infallible, but it was. It worked more than it
Speaker 1: didn't work and in fact beat the two champions, so
Speaker 1: pretty exciting. Well, the Watson Developer Cloud is UH sort
Speaker 1: of it's it's kind of an umbrella. It's a it's
Speaker 1: an application program interface also known as an a p I,
Speaker 1: and developers can use it to leverage this cognitive computing approach,
Speaker 1: this language wrecking mission approach natural language recognition UH, and
Speaker 1: then leverage that into other applications. So Watson itself has
Speaker 1: gone on to do lots of work in other fields,
Speaker 1: and most notably medicine, being able to help doctors end
Speaker 1: up looking at various means of treatment, even personalizing treatment
Speaker 1: for patients with certain illnesses or conditions. But this was
Speaker 1: something where you know, if you were a developer and
Speaker 1: you wanted to try and leverage this technology that had
Speaker 1: already been built, you could then build your own thing
Speaker 1: and make something really special. And one of those things
Speaker 1: was the Tone Analyzer UM. So it it searches for social,
Speaker 1: written and emotional cues and a body of text in
Speaker 1: order to analyze the tone of the overall text and
Speaker 1: to tell you how is that coming across in an
Speaker 1: analytical way, Like you know, I I have tested the
Speaker 1: block that you have written and it comes across as
Speaker 1: seventy three percent cheerful, which sounds really weird, but that's
Speaker 1: essentially what you're getting. Yeah, yeah, And it's important to
Speaker 1: note that that this is harder in written text hypothetically
Speaker 1: than it is in spoken text because of all of
Speaker 1: those clues that we get from each other when we're
Speaker 1: talking out loud. Sure, I mean, it's much easier to
Speaker 1: detect sarcasm in person. I mean, you've probably had this experience,
Speaker 1: even though you're not a computer. You are a real
Speaker 1: human I assume, Yeah, we're guessing at least half of
Speaker 1: you are. Assuming you are a real human, you have
Speaker 1: probably had instances before where you can't read sarcasm in
Speaker 1: an email Why why is this person so angry at me?
Speaker 1: And then you need to include a winky face right there,
Speaker 1: like I was just joking, and then you feel foolish.
Speaker 1: But I mean, the lack of all this context that
Speaker 1: we get from body language and tone and stuff can
Speaker 1: be difficult, and in fact, that's part of the application
Speaker 1: of this, but I'll get into that in a second.
Speaker 1: So essentially, what this tone and lizer is doing is
Speaker 1: it's it's examining all the words within that block of
Speaker 1: text and tagging those words based upon how a predetermined
Speaker 1: categorization has been set up for the tone analyzer. So
Speaker 1: certain words fall under the general category of cheerful. That's
Speaker 1: a great example because it pops up a lot and uh.
Speaker 1: Then it ends up giving you a percentage of the
Speaker 1: overall tone of the piece. You can actually do it
Speaker 1: in two ways. You can get a word count or
Speaker 1: you can get a percentage approach. The word count just
Speaker 1: tells you how many of the words within a given
Speaker 1: block of text fall into each category, So whether it's social,
Speaker 1: or it's a writing que or it's an emotional cue.
Speaker 1: And then the percentage gives you kind of a what
Speaker 1: what is the thrust? Like? What was the impact on
Speaker 1: the reader? So that suggests that there are waitings to
Speaker 1: these words, right, that certain words are weighted as being
Speaker 1: more powerful. I found the percentage results to be more
Speaker 1: interesting because the word count results just tell you most
Speaker 1: of your words or social orientation, which could be simple
Speaker 1: things like the and but or like that doesn't have
Speaker 1: that doesn't impact a lot unless you're using them really well.
Speaker 1: But the words that were or butts can be extremely
Speaker 1: extremely uh powerful. Yes, yes, but but Ideally, what this
Speaker 1: would allow you to do is tell how your work
Speaker 1: might impact a reader before the reader has a chance
Speaker 1: to to see this written work, and you might be
Speaker 1: able to then revise that work if it doesn't seem
Speaker 1: like it's getting across the message you had intended. So
Speaker 1: it's it's kind of like a hey, listen, uh, I
Speaker 1: know you think you might be coming across this way,
Speaker 1: but this is analytically how you are coming across. Do
Speaker 1: you want to option to change up some of this stuff? Yeah,
Speaker 1: maybe you could change some of these aggressive words to
Speaker 1: be more friendly, for example, yeah yeah. Or it may
Speaker 1: be that if it's in a business letter, maybe the
Speaker 1: aggressive tone is exactly what you wanted. So it gives
Speaker 1: you the opportunity to go the other way. And in fact,
Speaker 1: what will happen is when you use the the app,
Speaker 1: it highlights each word or most of the words, almost
Speaker 1: all of them in a block of text, and then
Speaker 1: by clicking on it, you can have options to change
Speaker 1: that word to to a synonym, and it even categorizes
Speaker 1: the synonym according to the various social writing or emotional cues.
Speaker 1: So if you wanted to make it a more emotional plea,
Speaker 1: you could do that by going under the emotional category
Speaker 1: and choosing a synonym under there, and presumably this would
Speaker 1: make your presentation or letter or whatever it is more
Speaker 1: have more of an emotional impact. I didn't I didn't
Speaker 1: understand some of the options it was giving me with
Speaker 1: the synonyms, because at one point it's suggested to make
Speaker 1: my thing more agreeable, I changed the word bad to
Speaker 1: the word lousy. Yeah. Yeah, I mean, well it's more
Speaker 1: jokey like, kind of like like it's got I think
Speaker 1: that lousy has a slightly softer connotation. Lousy just bad,
Speaker 1: I would say, comes across as a little more playful, right,
Speaker 1: Like if I were to say Lauren is a bad
Speaker 1: person versus Lauren is a lousy person. Yeah. Yeah, you know,
Speaker 1: if you're just a lousy person, then you know you
Speaker 1: can pick those louses off of that shampoo. Yeah, much
Speaker 1: better than being bad. Yeah. It reminds me of that
Speaker 1: amazing Michael Jackson song Lousy video for that was amazing.
Speaker 1: So the idea of being with a tool like this
Speaker 1: in the future, you could actually build this functionality directly
Speaker 1: into something else like a word processor or an email
Speaker 1: program or ta most useful. Right, the idea being that,
Speaker 1: or you save a document before you send an email,
Speaker 1: before you send off that text message, or update your
Speaker 1: Facebook post. The analyzer could tell you, oh, hey, by
Speaker 1: the way, you're coming across as a told jerk face,
Speaker 1: this should be beside the tweet button. Yes, it should
Speaker 1: be well and I think, you know, making it something
Speaker 1: that is um like word count or spell check, spell
Speaker 1: check or even grammar check these days. Yeah, I think
Speaker 1: as long as it didn't get in the way of
Speaker 1: what you were doing, if it were an optional thing
Speaker 1: for you to check, or it was you know, unobtrusive
Speaker 1: and be great, underlined it in in fuchia or something
Speaker 1: like that, you know, whatever color is left over, right,
Speaker 1: so exactly something that's not going to easily be confused
Speaker 1: with the colors were already using for everything else with
Speaker 1: grammar and spelling errors. But it would be helpful, and uh,
Speaker 1: it might mean that you would avoid some of those
Speaker 1: situations where you dash off a message to someone thinking
Speaker 1: it's perfectly fine and they receive it and are offended
Speaker 1: or confused or or hurt or whatever that may be. Well,
Speaker 1: let's talk about some examples. Yeah, so there is a
Speaker 1: demo available online. Uh, you can go to the tone
Speaker 1: analyzer website and actually try the live demo. It allows
Speaker 1: you to paste in or type in a block of
Speaker 1: text and then you can analyze it um And so
Speaker 1: I decided I would throw in. It took me a
Speaker 1: long time to decide which opening line I would use
Speaker 1: from all the different novels that I love, but I
Speaker 1: chose this one. See if you recognize it. It is
Speaker 1: a truth universally acknowledged that a single man in possession
Speaker 1: of a good fortune must be in want of a wife. Yes,
Speaker 1: is from Black Books. The series actually is used in
Speaker 1: Black Books, but Black but that's not obviously where it's from.
Speaker 1: At any rate, it's Jane Austen quote obviously. So the
Speaker 1: analyzer said that the emotional tone was cheerful, the social
Speaker 1: tone was open and agreeable, and the writing tone was analytical. Uh.
Speaker 1: Looking at a word count like Joe was saying, not
Speaker 1: as interesting. So I just went with the percentiles. But
Speaker 1: if you did look at the word count, only five
Speaker 1: percent the sentence is in an emotional tone, in social
Speaker 1: tone and five percent in writing tone just by words.
Speaker 1: But when it looks at the impact, the emotional impact
Speaker 1: and the UH and the social um or the writing
Speaker 1: impact rather is much greater than the social impact. So
Speaker 1: in that case it was almost like a third or
Speaker 1: third and a third. So even though there were fewer
Speaker 1: writing cues and emotional cues in the sentence I picked,
Speaker 1: they had a greater impact than the social cues did,
Speaker 1: which I thought was kind of interesting. But it also
Speaker 1: shows that when you when you do this, you see
Speaker 1: how how the analyzer is picking out each word and
Speaker 1: classifying it. Yeah, it breaks down by color. Yeah. Yeah,
Speaker 1: So if you are looking at the emotional words, those
Speaker 1: are highlighted in shades of red or pink. Uh. And
Speaker 1: you know, the the emotional one is divided up a
Speaker 1: little bit too, so that way, like cheerful is one
Speaker 1: of them, and I think that's a very pink color
Speaker 1: that they used for cheerful, And so all the cheerful
Speaker 1: words were bright pink um. Then social words were all
Speaker 1: in shades of blue, and writing tone words well in
Speaker 1: shades of green. And if you clicked on them, that's
Speaker 1: where it would give you the option to switch those out,
Speaker 1: so that you can you know, if you did you
Speaker 1: did you really mean in want of a wife? Uh?
Speaker 1: But also I should point out that the results we
Speaker 1: get are you know, one thing you have to keep
Speaker 1: in mind is what is the basis of comparison, because
Speaker 1: it's not just a universal you know, text analyzer, it's
Speaker 1: actually analyzing it against a standard. And in this case,
Speaker 1: the standard they were using was the standard you would
Speaker 1: use for business letters. So according to you know, compared
Speaker 1: to your business letters standard, Jane Austen, you know, it's cheerful,
Speaker 1: so I'll buy that. But yeah, it's one of the
Speaker 1: other things is that I think a full tone analyzer
Speaker 1: would have different comparisons you could use, not just the
Speaker 1: business letter roach, it would also like compare you to
Speaker 1: threatening letters from creditors. Yeah, and and maybe maybe we
Speaker 1: can finally get that direct computer comparison of Jane Austen
Speaker 1: to like Hemingway or something like that. It would be interesting. So,
Speaker 1: you know, I know there was an article in which
Speaker 1: an author, uh or the writer of the articles said
Speaker 1: that they had compared themselves to Mark Twain. Oh yeah,
Speaker 1: I read that, yeah, which was entertaining, and said that
Speaker 1: according to the analysis, the two wrote in a very
Speaker 1: similar way, like the percentages came out in a similar way,
Speaker 1: and actually used that as a means of talking about
Speaker 1: the limitations of that but Joe, you used an example
Speaker 1: that also kind of showed some limitations of the tone analyzer. Yeah.
Speaker 1: I was like, well, what's it gonna make of something
Speaker 1: really philosophical? So I put in a quote from Dustevsky's
Speaker 1: Notes from the Underground. Okay, cheerful stuff that the underground
Speaker 1: man is talking in his long sort of diary section
Speaker 1: in the first half, and he says I could not
Speaker 1: become anything, neither good nor bad, neither a scoundrel nor
Speaker 1: an honest man, neither a hero nor an insect. And
Speaker 1: now I am eking out my days in my corner,
Speaker 1: taunting myself with the bitter and entirely useless consolation that
Speaker 1: an intelligent man cannot seriously become anything, that only a
Speaker 1: fool can become something. All right, So what what did
Speaker 1: the analyzer have to say about this? Well, again, I'm
Speaker 1: going with the percentile feedback, not the word counter. This
Speaker 1: is more interesting. It says the emotional tone was anger
Speaker 1: one percent seems accurate. Negativity one okay, yeah, I got that,
Speaker 1: and cheerfulness cheerfully angry and negative. Actually that's not a
Speaker 1: bad interpretation that passage. It is kind of manic in
Speaker 1: a way. Yeah, yeah, it's it's gonna upbeat about the
Speaker 1: fact that that the world is bleaking dire and there's
Speaker 1: absolutely nothing to be done about to make me wonder
Speaker 1: what the five emotions in the head of the underground
Speaker 1: man all inside out would be doing at this particular point, right,
Speaker 1: So I want to say more about that in a minute,
Speaker 1: but just a little more on the result. Probably I'm
Speaker 1: guessing you're right. Yeah, but yet he refuses to do
Speaker 1: anything about his liver problem out of spite anyway. So
Speaker 1: it also says among the social tone breakdown, it was
Speaker 1: fort agreeable, nous, zero percent conscientiousness, and zero percent openness.
Speaker 1: And then the writing tone was a hundred percent analytical,
Speaker 1: zero percent confident, and percent tentative. And I was like, well,
Speaker 1: that's about zero percent confident. Is this is really telling
Speaker 1: a description of notes from underground? So well, no, it's
Speaker 1: actually like simultaneously a hundred percent confident and zero percent confident. Uh,
Speaker 1: tentative might be a good word. So anyway, I highlighted
Speaker 1: four words, and that feedback the it are, so it
Speaker 1: tells you why it's rating in a certain way. So
Speaker 1: I was looking for the word did that cheerfulness result
Speaker 1: come from? And it was like, look, you said the
Speaker 1: words good, honest, hero, and intelligent. Clearly you were being cheerful, right.
Speaker 1: I mean, this is hilarious if you consider the passage
Speaker 1: in context, because the first three of those good, honest,
Speaker 1: and hero are counterfactual negations. He says, I am neither
Speaker 1: good nor bad, and I'm not these things. And then intelligent,
Speaker 1: he's not saying like it's good to be intelligent. He's
Speaker 1: talking about it being a curse to be intelligent. Yeah,
Speaker 1: and this actually highlights a problem with the analyzer in general,
Speaker 1: which is that it's looking at individual words, but it
Speaker 1: cannot necessarily understand the context of those words. Yeah. Yeah,
Speaker 1: it's doing nothing to part the context, right. It's it's
Speaker 1: it's just isolating each word and then kind of doing
Speaker 1: a tally at the end and saying, well, here are
Speaker 1: all the good words, and here are all the negative words,
Speaker 1: and here are all the you know, the the indifferent words.
Speaker 1: And when you weigh them all out by there the
Speaker 1: there how many there are and the actual emotional weight.
Speaker 1: I don't know what the waiting system is, but it's
Speaker 1: clearly not a one to one sort of thing. But
Speaker 1: once it all figures it out that here's the result,
Speaker 1: So it would probably give the cheerfulness thumbs up to
Speaker 1: an email that I sent to my boss if it
Speaker 1: said like, you are not good, you are not honest,
Speaker 1: and you should not go on living. Yes, exactly, if
Speaker 1: you were to type I am glad and then you
Speaker 1: typed I am not glad, you would get very similar
Speaker 1: results in the tone analyzer because it's picking out glad
Speaker 1: as being a cheerful word, but it's not figuring out
Speaker 1: that one of those senses essentially is the opposite of
Speaker 1: the other. You know, it doesn't know that. Uh So
Speaker 1: perhaps maybe one day the tone analyzer will actually be
Speaker 1: able to understand context as well, beyond on just these
Speaker 1: these you know, recognizing the individual words, but understand what
Speaker 1: collectively they are trying to get across, so that when
Speaker 1: you do analyze the tone of a message, it's more accurate.
Speaker 1: So before you send that email, before you save that document,
Speaker 1: before you you know, deliver your presentation, you can get
Speaker 1: a cold unfeeling robot to tell you how much of
Speaker 1: a cold unfeeling robot you are. Cold unfeeling robot could say,
Speaker 1: you know, based upon the analytical uh or based on
Speaker 1: the data analysis of how what your word choice and
Speaker 1: the way you put them together, you're going to come
Speaker 1: across as a real minch. I mean, you know, are
Speaker 1: you sure you want to keep comparing your coworkers to insects? Mr? Kafka?
Speaker 1: Are you are you sold on this being a bug thing? Uh? Yeah?
Speaker 1: And until until that point, it's really just kind of
Speaker 1: a interesting footnote in in the overall history of I
Speaker 1: B M and they're wonderful word processing technologies. They actually
Speaker 1: coined the term word processing back in nineteen sixty four
Speaker 1: in the marketing materials for an electric typewriter, the first
Speaker 1: electric typewriter that had a magnetic tape memory drive. Ye,
Speaker 1: my dad had one of those. Uh not that, not
Speaker 1: that particular generation of word processors, but dad did have
Speaker 1: one was of that same Before we had a computer,
Speaker 1: we had a word processor. Um. And it also illustrates
Speaker 1: how difficult natural language and understanding humans. Uh this how
Speaker 1: difficult that problem is for for computers. For artificial intelligence. Uh.
Speaker 1: We we see a lot of developments in AI that
Speaker 1: are really really promising, but we have to remind ourselves
Speaker 1: there's still a long way to go, and there are
Speaker 1: other things that we can talk about. Two, this isn't
Speaker 1: the only tool that's ever been built to try and
Speaker 1: understand the tone or whether or not someone's being sincere
Speaker 1: in a message. Right. Oh of course. So back in
Speaker 1: there was a group of researchers out of the Hebrew
Speaker 1: University in Israel who designed Sassy. That's the semi supervised
Speaker 1: algorithm for sarcasm identification, and that's so awesome. It's even
Speaker 1: better because it's not Sassy with the y, it's s
Speaker 1: a s I yeah. Actually, really there should be a
Speaker 1: heart over the eye. I think so, I think so.
Speaker 1: I think that's probably if Sassy had hands, that's how
Speaker 1: it would write its own name, right, but it would
Speaker 1: be a sarcastic herd over the eye, an ironic one.
Speaker 1: Hell is sarcastic. So so this so this team set
Speaker 1: Sassy to I'm gonna giggle a recent goole time to
Speaker 1: say that name. Okay. They set Sassy to analyze collections
Speaker 1: of five point nine million tweets and sixty six thousand
Speaker 1: product reviews from Amazon and Okay, Since, as we have discussed,
Speaker 1: sarcasm is most naturally conveyed via vocal tone and nonverbal cues,
Speaker 1: they first had to map out what sarcasm actually looks
Speaker 1: like in text and and came up with kind of
Speaker 1: kind of a matrix of of like hyperbolic words and
Speaker 1: excessive punctuation or using lots of ellipses in particular, was
Speaker 1: something that they earmarked and straightforward sentence structure diagramming sarcasm. Yeah,
Speaker 1: and I think this was probably the most difficult part
Speaker 1: part of their research, like everything else after that, Like
Speaker 1: like creating these patterns for the machine to look for
Speaker 1: was the most difficult thing. So they gave it examples
Speaker 1: of sarcasm by by feeding it tweets that were tagged
Speaker 1: like hashtag sarcasm and also one star Amazon reviews that
Speaker 1: had been deemed sarcastic by a panel of fifteen humans.
Speaker 1: It's funny because they could have just had the computer
Speaker 1: follow certain Twitter accounts and say, like, you can be
Speaker 1: any tweet coming from this account is sarcastic. Also, remember
Speaker 1: this was back in so so Twitter was relatively new
Speaker 1: at the time. Yeah, knew enough that in their report
Speaker 1: about it, they spent like a long time describing what
Speaker 1: Twitter was. Uh Okay, So they then instructed Sassy to
Speaker 1: rate sentences from one to five, with one being not
Speaker 1: sarcastic at all and five being super sarcastic, and they
Speaker 1: found that Sassy could identify sarcastic Amazon reviews with precision,
Speaker 1: and it did even better on Twitter, kind of unexpectedly
Speaker 1: because there's less context to work with in in tweets,
Speaker 1: which are short and kind of stream of consciousness. A
Speaker 1: lot of the time it's precision rate over there was seventy.
Speaker 1: When you're limited to characters, it is is something of
Speaker 1: an art to get across sarcasm in a way that
Speaker 1: people realize it's sarcastic. By the way, not definitely an art,
Speaker 1: not a science. Oh yeah. I have posted many things
Speaker 1: where I thought, well, clearly people will understand that this
Speaker 1: is not an actual sincere statement, and I have been wrong.
Speaker 1: At least one person proves me wrong. Well, this will
Speaker 1: be the second time in a couple of weeks that
Speaker 1: I've had to bring up pose law on a podcast.
Speaker 1: I mean, if you're saying something that sounds extreme to
Speaker 1: parody extremism, people will take you seriously because it's hard
Speaker 1: to tell. It's hard to tell the difference between a
Speaker 1: parody of an extreme view and an actual extreme view, right, yeah, yeah,
Speaker 1: That's why a lot of the kind of rival sites
Speaker 1: that are popping up to the to the onion. I
Speaker 1: think fail really hard sometimes because I'm like, oh, you
Speaker 1: guys need to take it much further or just stop
Speaker 1: writing entirely, because satire just comes across as a lie, right, yeah,
Speaker 1: and slander is different things than satire, Yes, so, but
Speaker 1: so so. This research team decided that Sassy was really
Speaker 1: good at detecting very straightforward sarcasm, the kind that in retrospect,
Speaker 1: lots of people probably use on Twitter, precisely because you
Speaker 1: have such limited space and limited context, so you have
Speaker 1: to be pretty direct. Sassy made a lot more falseness
Speaker 1: of evaluations than false positives, which indicates that a lot
Speaker 1: of the more subtle stuff or the more intricate stuff
Speaker 1: was slipping by it. Okay, so could you give an
Speaker 1: example of, like the difference between straightforward sarcasm and non
Speaker 1: straightforward sarcasm? Uh? Yeah, sure. A simple one might be
Speaker 1: something like wow, Mondays are my favorite days ever? Exclamation point,
Speaker 1: exclamation point, exclamation point, and something a little bit more complicated.
Speaker 1: I can't say whether or not this was actually used
Speaker 1: in the study, but I cribbed this from um amazon
Speaker 1: dot com from from that banana slicer. If you guys
Speaker 1: have seen that. Yeah, one of the reviews is I
Speaker 1: tried the banana slicer and found it unacceptable. As shown
Speaker 1: in the picture, the slicer is curved from left to right.
Speaker 1: All of my bananas are bent the other way, so,
Speaker 1: you know, a little bit harder for a computer to
Speaker 1: pick up on that, right. Yeah. There are other accused
Speaker 1: as well that they would rely upon, like the hashtag sarcasm.
Speaker 1: If there's certain phrases that without that hashtag, they're not sarcastic,
Speaker 1: like I can't wait to get home tonight. If I
Speaker 1: just post that, then it seems like I can't wait
Speaker 1: to get home tonight. But if I do hashtag sarcasm,
Speaker 1: then you're vague tweeting and it's annoying. Well, no, then
Speaker 1: you know I'm you know I you know I definitely
Speaker 1: can wait before I get over tonight. You might not
Speaker 1: know why, but you know that I'm not looking forward
Speaker 1: to it, right, which is vague tweeting, which is annoying.
Speaker 1: It's not as vague as some of the stuff I
Speaker 1: see on there. At least with the hashtag you realize
Speaker 1: what the tone is. It's not as vague at any rate. Yeah,
Speaker 1: So another example of this going back to IBM and
Speaker 1: and Watson. Actually, UM is the use of the the
Speaker 1: that language recognition, that natural language cognitive computing approach toward
Speaker 1: perhaps like getting computers to uh you come up with
Speaker 1: some arguments. Yeah, this is an interesting thing that I
Speaker 1: read about last year and wrote a blog post for
Speaker 1: a website about is the Watson debater. Yeah. So this
Speaker 1: was a new iteration in the development of Watson technologies
Speaker 1: that was designed to look for arguments like statements in
Speaker 1: support or in opposition to a proposition. And this is
Speaker 1: interesting because it's more difficult than you might think to
Speaker 1: do this. So there was a presentation about it last
Speaker 1: year at the Milken Institute Global Conference. The Milken Institute
Speaker 1: is an economic think tank. I didn't know that. I
Speaker 1: looked it up. I was curious, but uh yeah. The
Speaker 1: conference had a whole bunch of different people give a
Speaker 1: presentation about the UM the near future and it was
Speaker 1: kind of like you know what's next gonna is gonna
Speaker 1: blow your mind, kind of sort of the same sort
Speaker 1: of stuff we like to talk about here on Forward Thinking.
Speaker 1: And they had several guests. Among them was a representative
Speaker 1: from IBM, said John Kelly. Yeah, and and he was
Speaker 1: unveiling the debate of the first time to the public.
Speaker 1: It was something that up to that point had only
Speaker 1: been discussed internally at IBM. Right, and so what he
Speaker 1: was demonstrating was the debater's ability to not come up
Speaker 1: with arguments because we're nowhere near there yet that level
Speaker 1: of processing and synthesis. But it could look at a
Speaker 1: whole bunch of articles and say, what are some statements
Speaker 1: in support of or in opposition to a proposition. So
Speaker 1: they gave the example and the presentation of the proposition
Speaker 1: the sale of violent video games to minors should be banned.
Speaker 1: I love it because it includes banned Yeah, and and
Speaker 1: and it's it's cool. The process that this this tool
Speaker 1: uses is pretty interesting. First, it scans pretty much everything
Speaker 1: and has access to UH and in this case the
Speaker 1: demonstration they showed at the conference, it scanned four million
Speaker 1: articles and this was just to see if the articles
Speaker 1: that it had access to were relevant to the actual question.
Speaker 1: Then it picks some top contenders, yeah, the ten best
Speaker 1: fits to the topic at hand, and then scanned all
Speaker 1: the sentences in those ten best articles a yeah, going
Speaker 1: through three thousand sentences, and then started to classify those
Speaker 1: sentences as being either in favor of banning violent video
Speaker 1: games or against the banning of violent video games at
Speaker 1: least the sale of violent video games to minors. And UH,
Speaker 1: they looked for sentences that contained what they called candidate
Speaker 1: claims that would either be one of those statements for
Speaker 1: or against the central premise. UH, and then they they
Speaker 1: it would identify the parameters of those claims and then
Speaker 1: assessed those claims whether or not they were truly pro
Speaker 1: or con and then put them into those those camps.
Speaker 1: So ultimately you would ask the computer, all right, so
Speaker 1: what are the arguments for and against this proposition? And
Speaker 1: it would say, in favor of the motion, violent video
Speaker 1: games should be banned, it should be noted that violent
Speaker 1: video games actually cause aggressive behavior. And then you know,
Speaker 1: some other statements along those lines, and then it would say,
Speaker 1: in opposition to the proposition violent video games should be banned,
Speaker 1: it should be noted that violent video games do not
Speaker 1: actually cause violent behavior. Right, there's no causation link. Yeah,
Speaker 1: which is funny because it'll it'll take to completely contradictory
Speaker 1: statements and cite them both. Right. Yeah, there are certain
Speaker 1: things that that leap out at you, especially with this
Speaker 1: particular example. And it's funny because I watched it and
Speaker 1: I made notes, and then I read the rest of
Speaker 1: your blog and posting, and you and I have the
Speaker 1: exact same reactions. Yeah, one of them being that one
Speaker 1: of the statements that just rubbed me the wrong way
Speaker 1: was about how it was essentially said that video games,
Speaker 1: the playing of video games is a a popular pastime
Speaker 1: for boys. It says playing violent video games is part
Speaker 1: of a boy's natural childhood development. And it was just like, Okay,
Speaker 1: I know the computer is not sexist, but it just
Speaker 1: has access to some sexist views. And this this shows
Speaker 1: the limitations of this approach, right, oh sure, well and
Speaker 1: and and it shows really the limitations of humans at
Speaker 1: that point to to a to okay, yeah, well, to
Speaker 1: to come up with convincing debate or convincing debate statements
Speaker 1: in in saying something completely unsupported and also in just
Speaker 1: making making stuff up. Yeah, so yeah, like why, I
Speaker 1: don't know what the funny thing to me was. It
Speaker 1: had plenty of statements it could have chosen from. I like,
Speaker 1: actually found the Wikipedia article that it was cribbing its
Speaker 1: arguments from, and I looked at the article and I
Speaker 1: was like, there were better statements in this exact same
Speaker 1: paragraph you could have used than that one. Why did
Speaker 1: that statement get elected? And ultimately, I don't know. One
Speaker 1: thing we we don't necessarily know is when it looked
Speaker 1: at the Wikipedia article, which could have been altered between
Speaker 1: the time it did its research and when you did yours.
Speaker 1: But I doubt that it was that significant if it
Speaker 1: was within that same paragraph, I doubt there was a
Speaker 1: lot of there were a lot of changes. I mean,
Speaker 1: if that particular sentence didn't appear at all within the paragraph,
Speaker 1: then I would say, oh, something's happened. But at any rate, yeah,
Speaker 1: it's it's it was problematic with that particular example, but
Speaker 1: it does show that it's reliant completely upon the It's
Speaker 1: relying completely upon the content that humans have created undeniably
Speaker 1: since the beginning of time. It has been a natural
Speaker 1: part of boys child, since we dwelled in the halls
Speaker 1: of Castle wolf in Stein. I'll never forget the uh,
Speaker 1: the story I read about the ancient humans playing Wooly
Speaker 1: Mammoth Destroyer and uh, you know games like that. So
Speaker 1: but no, I mean, yeah, you're exactly right. It's just
Speaker 1: reflecting the fact that all it has to work with
Speaker 1: is what humans have said. And it doesn't have a
Speaker 1: good criteria as far as we can tell for judging
Speaker 1: the difference between a good supporting argument and a bad
Speaker 1: supporting argument. Interesting, it can't parsing for is this in
Speaker 1: support or not? Yeah, it can't. It can't judge is
Speaker 1: this a good logical argument? Right? It could actually come
Speaker 1: up with arguments that contain logical fallacies in them. That's
Speaker 1: the really interesting thing is that. And it's not because
Speaker 1: the that's not doing what's supposed to do. It's doing
Speaker 1: exactly what it was meant to do. It's just that
Speaker 1: the material it's pulling from itself is flawed. So, yeah,
Speaker 1: we can't think of it as some sort of magical
Speaker 1: oracle that we can consult. And it has access to
Speaker 1: truth with a capital T. It has access to the
Speaker 1: same stuff you and I would have access to if
Speaker 1: we just poured over Wikipedia for a few days when
Speaker 1: we were trying to research something and had no judgment criteria. Sure,
Speaker 1: but but it's you know, hopefully in the future or
Speaker 1: possibly in the future if they choose to continue developing it,
Speaker 1: it could turn into, if not an oracle, at the
Speaker 1: very least a source of of better advice well, and
Speaker 1: and the thing that they were talking about using it
Speaker 1: for didn't have anything to do with trying to figure
Speaker 1: out a debate like a political debate or or a
Speaker 1: social debate. They were actually talking about UH using it
Speaker 1: in the context of UM again, the discipline of medicine,
Speaker 1: where let's say that you are part of UH a
Speaker 1: hospital administration staff, and you're trying to determine which policies
Speaker 1: and procedures you want to put in place in your hospital,
Speaker 1: and so you have to do all that research. You
Speaker 1: have to figure out what are the benefits, what are
Speaker 1: the drawbacks of all these different alternatives, and potentially you
Speaker 1: could use UH technology like this that would then come
Speaker 1: back with the pros and cons of each approach, allowing
Speaker 1: you to make a more informed decision. I'm sorry, I'm
Speaker 1: just loving the idea that the machine that identified playing
Speaker 1: violent video games as part of a boy's natural childhood
Speaker 1: development deciding what anesthetic does I get To be fair,
Speaker 1: it's not relying on Wikipedia in the second instance. Okay,
Speaker 1: all right, so we can relax on that now when
Speaker 1: we get the home version there and ask which demonstrable,
Speaker 1: which transform is demonstrably the most important, and then it
Speaker 1: would be able to tell us. Yeah, I mean obviously,
Speaker 1: I don't think doctors are actually just going to seed
Speaker 1: all of their authority to the Watson debater and say, well,
Speaker 1: what do we give them? You know, they were they
Speaker 1: were specifically using this as the demo of what the
Speaker 1: technology can do, not as a you know, this is
Speaker 1: exactly what's going to rely upon. So instead of relying
Speaker 1: upon Wikipedia, to be relying upon the literature in the
Speaker 1: medical field, the research that's been done. And even then
Speaker 1: I assume it would be in an advisory function nothing decision.
Speaker 1: It would essentially well, again, if it's getting pros and cons,
Speaker 1: then ultimately the decision still falls on the humans. It's
Speaker 1: not able to wait which ones are more important. Like
Speaker 1: it could say the pros are your hallways will be
Speaker 1: less cluttered, the cons are seventy of your patients will die,
Speaker 1: and like, I recommend you do this because you have
Speaker 1: cluttered halls. That's not gonna be how it turns out. Well,
Speaker 1: I can see something like this actually being useful to
Speaker 1: me in my job, just as a shortcut for digging
Speaker 1: up leads on a subject. For example, like you know,
Speaker 1: I read an article about a subject I've never read
Speaker 1: about before, and the article has one pretty clear view
Speaker 1: on it, like it's in favor of X, and I
Speaker 1: don't even know what the arguments against X are. If
Speaker 1: there are any something like this could be really useful, like, oh,
Speaker 1: here are some things to look up. I think it
Speaker 1: would be really useful for something like, let's say that
Speaker 1: we wanted to cover a controversial topic in physics, something
Speaker 1: like because I've seen it pop up yet again. The
Speaker 1: m drive would be a great example. Something that could
Speaker 1: scour through the actual scholarly material and come back with
Speaker 1: the pros and cons of something like that, so that
Speaker 1: we at least have a starting point. And I think
Speaker 1: you can go look up the what's behind the exact
Speaker 1: kind of like the references in Wikipedia. Right, you go
Speaker 1: to a Wikipedia article, you scan down to the references,
Speaker 1: and you go to those sources to see if in
Speaker 1: fact they do reflect what is or if the article
Speaker 1: reflects what was in the actual primary sources, which is
Speaker 1: the proper way to use Wikipedia. Yeah, yeah, I agree. Yeah,
Speaker 1: And it kind of funny how often that's a dead
Speaker 1: link and you don't know if it's ever been alive. Yeah, Well,
Speaker 1: you can always go to archive or dot Oregon and
Speaker 1: check it out. I've done that many times. Actually. Uh So,
Speaker 1: one of the things I wanted to talk about really
Speaker 1: briefly to kind of wrap this up, is is this,
Speaker 1: actually the future of computing is cognitive computing. This idea
Speaker 1: of creating a machine that can, at least on some level,
Speaker 1: mimic the way we humans think is that the future.
Speaker 1: And it seems like more and more people are leaning
Speaker 1: towards Yes, that is the future. But it is the future.
Speaker 1: It's not now. Yeah, it's it's one of those I mean,
Speaker 1: I don't know if you guys can tolerate the theme
Speaker 1: song today, but but it's it's going to happen in
Speaker 1: a certain period of years from now, right, so that
Speaker 1: the neural network software, the hardware that's necessary it it
Speaker 1: requires a lot of processing power in order for it
Speaker 1: to even come close to what we humans are capable
Speaker 1: of doing just naturally. And in fact, in that same
Speaker 1: presentation that that Kelly did at the conference, he pointed
Speaker 1: out that brains are really amazing and said that you know,
Speaker 1: they require very little power. It's the equivalent of about
Speaker 1: twenty watts, so less than a lightbulb. Most lightbulbs UM
Speaker 1: and if you compare that to a supercomputer, it's nothing.
Speaker 1: And you also look at the intricate connections that our
Speaker 1: brains have. We have billion neurons and then all the
Speaker 1: different connection is between those neurons, trillions of them. It's
Speaker 1: it's phenomenal what our brains are able to do. Meanwhile,
Speaker 1: if you wanted to create a simulation of that, they
Speaker 1: talked about how they used UM a supercomputer at the
Speaker 1: Livermore Laboratory that was a simulating a neural network like
Speaker 1: a human brain, and it required eight megawatts of power,
Speaker 1: So eight million, what's as opposed to twenty with one
Speaker 1: point five million processors, so compared to eighty two million neurons,
Speaker 1: UH capable of processing six point three billion threads simultaneously.
Speaker 1: And it ran hundred times slower than the human brain.
Speaker 1: So just simulating a fraction of what our brains can
Speaker 1: do requires much more processing power, much more literal power
Speaker 1: like electricity, and it takes longer. So while this is awesome,
Speaker 1: and we love the idea of having computers that can
Speaker 1: actually learn as a post to you have to program it.
Speaker 1: You know, if you can teach the computer something over time,
Speaker 1: and we this goes back to the Google Dream stuff
Speaker 1: we were talking about two. Um, that's fantastic, but we're
Speaker 1: still at a point where until there's a breakthrough, either
Speaker 1: on the logical side or the programming side, they're lagging
Speaker 1: way behind what we we humans can do, just because
Speaker 1: we're fundamentally different. But it is exciting that this is
Speaker 1: a trend, and I expect we'll see that trend continue.
Speaker 1: And I love the idea that at some point I'm
Speaker 1: going to have a little app on my phone that
Speaker 1: will warn me before I send a text to my
Speaker 1: wife so that I can reword it and she won't
Speaker 1: be mad at me for for good reason. But you know,
Speaker 1: it might be a misinterpretation but accidental. But it falls
Speaker 1: on my shoulders that I did not word things in
Speaker 1: a way that misinterpretation. Did you? Did you really want
Speaker 1: to say that you're a lousy wife? Yeah, maybe you
Speaker 1: want to say bad. No, I don't want to say that, thanks,
Speaker 1: But yeah, this was this was fun to look into. Uh.
Speaker 1: I recommend you guys, if you haven't played with the
Speaker 1: tone analyzer going go and play with that. Either take
Speaker 1: a paragraph from someplace or type something up. Or I
Speaker 1: even when I did the video episode about this. I
Speaker 1: took the first paragraph I wrote for the episode and
Speaker 1: fed it back through the analyzer, and it said I
Speaker 1: was very open and conscientious and cheerful, So I got
Speaker 1: it right. Um, but yeah, you should go check that
Speaker 1: out because that demo is available for everybody, gold Star.
Speaker 1: You know what's going to happen when you run your
Speaker 1: secret underground diaries through it. It's probably going to say, like,
Speaker 1: don't ever show this to anyone else. So you, guys,
Speaker 1: if you have any suggestions for future episodes of forward Thinking,
Speaker 1: you should let us know to analyzer. Yeah, yeah, that'd
Speaker 1: be great. Write up an email to us, put it
Speaker 1: through the tone analyzer, change of the words are bitrarily
Speaker 1: and then send it along to us and we'll see
Speaker 1: what do you think of it? Make it more cheerful? U. No,
Speaker 1: but seriously, if you have a suggestion, you should write
Speaker 1: us our email addresses f W Thinking at how Stuff
Speaker 1: Works dot com, or drop us a line on Twitter,
Speaker 1: Google Plus or Facebook. At Twitter and Google Plus, we
Speaker 1: are f W Thinking. Just search fw Thinking in that
Speaker 1: handy dandy search bar on Facebook. We will pop right up.
Speaker 1: You can leave us a message and we'll talk to
Speaker 1: you again. Really, soon. For more on this topic in
Speaker 1: the future of technology, I'll visit forward thinking dot Com,
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