Speaker 1: Welcome to tex Stuff production from I Heart Radio. Hey there,
Speaker 1: and welcome to tech Stuff. I'm your host, Jonathan Strickland.
Speaker 1: I'm an executive producer with I Heart Radio and a
Speaker 1: love of all things tech, and I'm going to bring
Speaker 1: you guys a little bit of a rerun today. I
Speaker 1: am trying to get some stuff put together for a
Speaker 1: special series of episodes as well as prepare for some
Speaker 1: other stuff. So great things right around the corner. I
Speaker 1: did not want to leave you without an episode at all,
Speaker 1: So we're going to listen to this one that originally
Speaker 1: published on October eighteenth, two thousand eighteen, and it kind
Speaker 1: of goes in line with some other stuff we've been
Speaker 1: covering in recent episodes of Tech Stuff. This episode was
Speaker 1: titled Could We Make a Sarcastic Supercomputer? And yeah, it
Speaker 1: really dives into the whole concept of artificial intelligence, natural lane,
Speaker 1: which and just kind of understanding the quirks of what
Speaker 1: it is to be human and the whole concept of sarcasm.
Speaker 1: I hope you guys enjoy it. I mean that without
Speaker 1: even a hint of sarcasm. And I'll chat with you
Speaker 1: after the episode. Today. I want to talk to you
Speaker 1: about an interesting topic that I got to explore a
Speaker 1: couple of years ago with Joe McCormick and Lauren fogobaum
Speaker 1: As we debated the possibilities of computers learning how to
Speaker 1: understand sarcasm. We did it for a podcast called Forward Thinking,
Speaker 1: which was around for a couple of years. It was
Speaker 1: a lot of fun to work on that that show
Speaker 1: is over, but I thought I would revisit the topic
Speaker 1: and talk about it for you guys and kind of
Speaker 1: go over what would it take to have a computer
Speaker 1: that could actually understand when someone's being sarcastic. Now to
Speaker 1: understand why this is a big deal, it helps to
Speaker 1: have a refresher course on how computers process information. And
Speaker 1: I know I talked about this a lot, but I
Speaker 1: still think it's important to cover the basics when you
Speaker 1: want to talk about something as advanced as being able
Speaker 1: to detect and understand sarcasm. So computers understand machine code
Speaker 1: or assembly language. This is a language that corresponds with
Speaker 1: the actual physical architecture of the computers. So the way
Speaker 1: the computer is built, that's how this language interacts. It's
Speaker 1: it's essentially how the physical components of the computer are
Speaker 1: able to handle electric current or voltage differences in order
Speaker 1: to process information, and computers can interpret this and execute
Speaker 1: upon this language very quickly. It is the basic language
Speaker 1: of those physical components. However, it is almost impossible for
Speaker 1: humans to work with this, at least on a way
Speaker 1: that is at all of shion, because it ultimately for
Speaker 1: most computers boils down to binary language, right, zeros and ones.
Speaker 1: So you see a huge block of zeros and ones,
Speaker 1: and unless you are neo from the matrix, it means
Speaker 1: nothing to you. So we speak in natural language to
Speaker 1: one another. Natural language, however, is filled with a lot
Speaker 1: of components that make it very very challenging for machines
Speaker 1: to interpret, like ambiguity, or there might be double meanings
Speaker 1: in a phrase and you may mean both meanings at
Speaker 1: the same time, and that is too complicated for most
Speaker 1: machines to be able to process. They just can't deal
Speaker 1: with that. So to bridge the gap between the way
Speaker 1: we humans communicate and the way that computers process language,
Speaker 1: we have created programming languages and compilers. Now, programming languages
Speaker 1: fall into two broad category worries. It's more like a spectrum,
Speaker 1: and you could be further on one end than the other,
Speaker 1: and we typically call them high level programming languages and
Speaker 1: low level programming languages. The lower the level of programming language,
Speaker 1: the closer it is to machine code, and the easier
Speaker 1: it is for a computer to understand, but the harder
Speaker 1: it is to work with. If you happen to be,
Speaker 1: you know, a human being. High level programming languages are
Speaker 1: easier for humans to understand. Now, if you have never
Speaker 1: taken any courses in programming and you're looking at a
Speaker 1: page of code, it could seem indecipherable to you. It
Speaker 1: is just meaningless strings of characters. But once you learn
Speaker 1: the rules of that programming language, how you construct an instruction,
Speaker 1: and a series of instructions, how you go from one
Speaker 1: instruction to the next. Once you understand the rules, it
Speaker 1: actually becomes quite easy to use in the grand scheme
Speaker 1: of things, much more easy than machine which would be.
Speaker 1: But again, the problem here is that computers don't understand
Speaker 1: programming languages, not natively. Even though this is not exactly
Speaker 1: the same as human natural language, it's also not the
Speaker 1: same as machine language. That's why you need compilers. A
Speaker 1: compiler is essentially a translator. It takes this high level
Speaker 1: programming language or higher level anyway and then converts it
Speaker 1: into a machine readable language for the computer to actually
Speaker 1: execute upon. And this is all in the design of
Speaker 1: the programming languages and the compilers. So this is the
Speaker 1: way that for decades we have interacted with computers, when
Speaker 1: you're talking about it on a on a direct level,
Speaker 1: not just executing a program, but creating code, creating programs
Speaker 1: for computers to run. Over the last few decades, we've
Speaker 1: had some very very smart people working on natural language
Speaker 1: systems for machines, which would allow a computer to interpret
Speaker 1: natural language in a way that would make some sort
Speaker 1: of sense, and for the computer to be able to
Speaker 1: act upon that language. And we've seen this in plenty
Speaker 1: of examples recently. Most smartphones have some sort of smart assistant.
Speaker 1: You have standalone products like Amazon's Echo, you have Google Home,
Speaker 1: You've got tons of devices that can interact with people.
Speaker 1: It can be activated by typically an alert phrase, which
Speaker 1: I'm not going to say because I don't want any
Speaker 1: of you guys to have to deal with that. I
Speaker 1: know how irritating it is when I'm watching a video
Speaker 1: and someone activates their specific system and then mine begins
Speaker 1: to respond, and all my lights started going on and
Speaker 1: off because the people on YouTube we're talking funny. I
Speaker 1: know how irritating that is, but use that it activates
Speaker 1: and then you can speak. And typically you can say
Speaker 1: the same thing several different ways and the device appears
Speaker 1: to understand you no matter how you word it. And
Speaker 1: this is a real challenge because we human beings can
Speaker 1: find lots of different ways to say the same thing.
Speaker 1: For example, if I say what is the weather today,
Speaker 1: it could be very similar to if I if I
Speaker 1: ask a question is it going to rain today? Both
Speaker 1: of those are asking for information about the weather, but
Speaker 1: are very different ways of saying that. A good natural
Speaker 1: language recognition program will be able to parse that information
Speaker 1: and then return the appropriate response. This is not an
Speaker 1: easy thing to do. Typically it involves creating a neural
Speaker 1: network structure, and I've talked about artificial neural networks recently.
Speaker 1: That's a typically a network that can accept multiple binary inputs,
Speaker 1: so either a zero or a one input that represents
Speaker 1: some thing uh, some sort of yes, no or on
Speaker 1: off kind of feature. It can accept multiple multiple inputs
Speaker 1: of that nature, so multiple zeros or ones that all
Speaker 1: factor into making a decision, and then it has a
Speaker 1: waiting for each of those components, and then it produces
Speaker 1: a single output that's also binary in nature, either is
Speaker 1: zero one, and it passes that on to other artificial
Speaker 1: neurons further down the chain. Sometimes that will come back
Speaker 1: around and you have a recursive artificial neural network. The
Speaker 1: goal here is for this process two ultimately result in
Speaker 1: a response that is reasonably certain to meet the requirements
Speaker 1: of the person asking the question. This tends to be
Speaker 1: talked about in the realm of probabilities. We we talked
Speaker 1: about how certain the machine is that the response is
Speaker 1: the appropriate one, and if it falls below a certain toushold,
Speaker 1: then the machine would typically respond with I'm sorry, I
Speaker 1: don't know what you're asking for, or something similar to that.
Speaker 1: There are cases where you just get misinterpreted and you'll
Speaker 1: get a response that does not reflect whatever you ask.
Speaker 1: That's a little different. That's where the machine has drawn
Speaker 1: a conclusion, has been reasonably certain that it came to
Speaker 1: the right conclusion. It turns out it was wrong the
Speaker 1: whole way, but that's the process. Now, when it comes
Speaker 1: to sarcasm, that adds yet another layer of difficulty, because
Speaker 1: now a machine isn't just parsing what you are saying.
Speaker 1: It has to understand what you mean, the meaning of
Speaker 1: your words and the meaning of the way you deliver them.
Speaker 1: It could be different. So if I were to just
Speaker 1: write out a phrase with no tone, nobody language, uh,
Speaker 1: not emphasizing any one word over another, it might be
Speaker 1: very difficult to detect what my intent was. It may
Speaker 1: seem like I'm being sincere, when in fact I'm being insincere.
Speaker 1: For example, Uh, if I were to say that guy
Speaker 1: is super tall, but I'm being sarcastic, then just in
Speaker 1: that phrase the way I write it out, you would think, oh, well,
Speaker 1: that person he's looking at must be super tall. How
Speaker 1: do you recognize sarcasm? How can you detect that this
Speaker 1: is in place and then understand what the meaning underneath
Speaker 1: it is. One of the approaches that has been put
Speaker 1: forward relates to IBM's Watson platform. Now. Watson first made
Speaker 1: headlines back when it was a contestant on Jeopardy. It
Speaker 1: went up against two former champions, including Ken Jennings, who
Speaker 1: shows up on a house Stuff Works podcast. Anyway, Watson
Speaker 1: went up against these two former champions, and it is
Speaker 1: able to interpret natural language. It had to in order
Speaker 1: to play the game of Jeopardy, And for those who
Speaker 1: do not know what jeopardy is or they're not familiar
Speaker 1: with the game show, Jeopardy is a game where you
Speaker 1: are presented with categories of trivia and each category has
Speaker 1: multiple uh questions or multiple entries in it, and they
Speaker 1: range in dollar value, and the lower dollar value ones
Speaker 1: are easier to answer than the higher dollar value ones,
Speaker 1: and UH, you're Typically the way jeopardy works is that
Speaker 1: you're you're given quote unquote the answer and you have
Speaker 1: to provide the question. So, uh, if the answer were
Speaker 1: this film that detailed the adventures of a young playwright
Speaker 1: in sixteenth century England, one best picture, you would say,
Speaker 1: what was Shakespeare in Love? So this computer is playing
Speaker 1: against these two former champions. This was sort of an
Speaker 1: exhibition series of games. It wasn't meant for, uh, a
Speaker 1: competition in the way the typical Jeopardy games were there
Speaker 1: was money on the line. Was an exhibition and Watson won.
Speaker 1: It beat both of the champions, and it did what
Speaker 1: I was telling you. It It would analyze the clue
Speaker 1: that was given, the answer that was given, It would
Speaker 1: try and generate a question to correspond with that answer,
Speaker 1: and only if the question met a certain threshold of
Speaker 1: confidence with Watson buzz in. If it did not meet
Speaker 1: that level of confidence, Watson would remain quiet. And most importantly,
Speaker 1: Watson was not at all connected to the Internet. All
Speaker 1: the information was contained within a massive series of servers
Speaker 1: more than gosh I can't even remember. There's a ton
Speaker 1: of processors attached to it. Um so a very powerful machine,
Speaker 1: but it still wasn't exactly able to detect sarcasm. It
Speaker 1: could work with word play and it could work with riddles,
Speaker 1: so that was really impressive. But what it really did
Speaker 1: was it gave IBM the opportunity to say, we have
Speaker 1: this platform here and we're welcoming developers to create applications
Speaker 1: that tap into this platform and make use of this
Speaker 1: in order to do interesting stuff with it. And IBM
Speaker 1: was largely working with the medical industry at that point
Speaker 1: to try and help doctors treat and diagnose patients, and
Speaker 1: it was sort of computer guidance. It wasn't that you
Speaker 1: had an automatic doctor, but rather the doctor had what
Speaker 1: equates to a medical expert to confer with when trying
Speaker 1: to determine why's the best course of action for a patient.
Speaker 1: IBM put up an application Program Interface or API, and
Speaker 1: let developers create their own cognitive computing applications built on
Speaker 1: top of Watson. One of those was called the tone analyzer.
Speaker 1: It still exists back when we were doing this episode
Speaker 1: for Forward Thinking. It was in the form of analyzing
Speaker 1: some text and telling you whether or not that text
Speaker 1: would come across as agreeable or argumentative, or positive or negative,
Speaker 1: and it would assign tone to those pieces. I'll explain
Speaker 1: more about how it did and what it did in
Speaker 1: just a minute, but first let's take a quick break
Speaker 1: to thank our sponsor. So how did this tone analyzer work.
Speaker 1: It would search for cues in any written text, social cues,
Speaker 1: written cues, emotional cues in order to determine the overall
Speaker 1: tone of a piece, which actually meant that the analy
Speaker 1: lizer would tag individual words within a text, words that
Speaker 1: it recognized and had already pre labeled as falling into
Speaker 1: various categories. So words that might have a positive meaning
Speaker 1: like happy, glad, joy, things like that, those would get
Speaker 1: tagged as cheerful. But then it would then assign all
Speaker 1: the individual words tags and then tally everything up. So
Speaker 1: let's say you've got a bunch of sentences and it
Speaker 1: starts individually labeling certain words as being cheerful or sad,
Speaker 1: or angry or helpful, and then it adds it all
Speaker 1: up and then would give you a percentage. So a
Speaker 1: message might be agreeable or thirty conscientious, you would actually
Speaker 1: get multiples of these, and that would just really indicate
Speaker 1: the density of those types of words within the message itself. Now,
Speaker 1: in an ideal world, if language were very simple to
Speaker 1: understand and interpret by machines, this would help you gauge
Speaker 1: how people would respond to your work. Right, So, you
Speaker 1: could write a message. Before you send it, you put
Speaker 1: it through the tone analyzer and it tells you what
Speaker 1: sort of a tone you are setting. So if you
Speaker 1: wanted to create a business letter, you could send it
Speaker 1: through this tone analyzer and if it came back as
Speaker 1: saying it's coming across as as a indecisive, you might
Speaker 1: want to go back in and edit that message so
Speaker 1: that you can make a more straightforward and uh decisive
Speaker 1: message and not give the wrong impression before you send
Speaker 1: the message out to your actual human recipient and come
Speaker 1: up with alternate word choices in order to make sure
Speaker 1: that your message is received the way you intended it
Speaker 1: and anyone who has communicated over the Internet can think
Speaker 1: of ways that this might have been helpful in the past,
Speaker 1: because again, language depends on so many different elements to
Speaker 1: get your meaning across, and when you reduce it to
Speaker 1: the written form, especially the written form online, where we
Speaker 1: tend to be very short with our our communication, it
Speaker 1: comes in very quick bursts, a couple of sentences here
Speaker 1: or there. We lack all that body language, we lack
Speaker 1: that tone. It's very easy to misinterpret. I'm sure there's
Speaker 1: been an example in your life where either you got
Speaker 1: offended from receiving something that was meant in a way
Speaker 1: that was different from the way you you interpreted it,
Speaker 1: or the reverse happened where you sent a message and
Speaker 1: somebody had a reaction you did not anticipate because they
Speaker 1: could not tell what tone you were using just from
Speaker 1: the words you were using. Machines have that same problem.
Speaker 1: In the future, an analyzer like this tone analyzer, it
Speaker 1: could be incorporated into word processors, or email servers, or
Speaker 1: email services, i should say, or social media platform. So
Speaker 1: you start typing in your message and before you hit
Speaker 1: published or post or send, you could analyze that text.
Speaker 1: It could tell you what the tone is and then
Speaker 1: you could say, oh, no, that's gonna come across totally
Speaker 1: the wrong way, and you could actually fix it before
Speaker 1: you posted it or sent it, and then you wouldn't
Speaker 1: have that awkward decision of whether or not to edit something, or,
Speaker 1: in the case of Twitter, which continues to refuse to
Speaker 1: allow you to edit tweets, to delete a tweet. I
Speaker 1: deleted a tweet the other day when I posted a
Speaker 1: link to a news story, and I had done a
Speaker 1: rookie mistake, one that I try to avoid, but I
Speaker 1: did it this pastime, which is that I didn't think
Speaker 1: to look at the date when the news item had
Speaker 1: been published, and had been published a full year earlier,
Speaker 1: so it was not new news, it was old news.
Speaker 1: And uh then deleted the tweet and it wasn't up
Speaker 1: for long, but I still felt dumb about it. It
Speaker 1: would have been nice to have been able to check that.
Speaker 1: Although that's not tone obviously, that's but similar in the
Speaker 1: and the idea that you want to check before you
Speaker 1: end up offending someone, unless you're one of those jerk
Speaker 1: faces that just sets out to offend people, in which
Speaker 1: case rethink your strategy. There are better things to do.
Speaker 1: It's just as you can make just as big an
Speaker 1: impact being a positive person as you can being a
Speaker 1: jerk face. I know it can seem like it's more work,
Speaker 1: but it's also more rewarding in the long run. Okay,
Speaker 1: soapbox done. So. There is a demo of the tone
Speaker 1: analyzer that's available online, and back when we were recording
Speaker 1: Forward Thinking, the demo worked in a way where it
Speaker 1: would tell you about emotional tone and break it down
Speaker 1: by percentage. It's a little different now, but I want
Speaker 1: to tell you the what words and the results we
Speaker 1: got in the past because they were so much fun.
Speaker 1: Granted you would get a different result now because the
Speaker 1: tone analyzer has been tweaked since we recorded that episode. So,
Speaker 1: when we recorded that episode, one of my co hosts
Speaker 1: decided to put a a sentence that is somewhat known
Speaker 1: in literary circles into this tone analyzer and find out
Speaker 1: what it said. And the sentence used was 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. Now,
Speaker 1: the analyzer said that this emotional tone was cheerful, the
Speaker 1: social tone was seventy six percent open and fifty one agreeable,
Speaker 1: and the writing tone was analytical. You can also view
Speaker 1: the sentence in terms of word count as opposed to
Speaker 1: the weighted value of individual words, and using that view,
Speaker 1: five percent of the sentence sentences were in an emotional tone,
Speaker 1: eighty nine percent in a social tone, and five percent
Speaker 1: in a writing tone. Now, the analyzer highlights each word
Speaker 1: according to how it classifies them, so emotional words would
Speaker 1: be highlighted in red or pink in that older version
Speaker 1: of the Tone Analyzer, social words would show up in blue,
Speaker 1: and writing tones would be in green. And you could
Speaker 1: click on any word and the analyzer would offer alternative
Speaker 1: words that you might want to use and classify those
Speaker 1: words in the tones that they are associated with, so
Speaker 1: that you could shape your message to meet the tone
Speaker 1: you wish to convey. Also, the Tone Analyzer demo used
Speaker 1: the business letter format as the means of comparison, so,
Speaker 1: in other words, we compared Jane Austen to a business letter. Presumably,
Speaker 1: if you were to use a full version of the analyzer,
Speaker 1: not just the demo version, you would have other options
Speaker 1: so you could compare it with other models, not just
Speaker 1: a business letter. Joe McCormick. He included an excerpt from
Speaker 1: Dostoyevsky's Notes from Underground. That excerpt was, 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 constellation that
Speaker 1: an intelligent man cannot seriously become anything, that only a
Speaker 1: fool can become something. The feedback was that the emotional
Speaker 1: tone had anger at cheerfulness at so happy anger negative at.
Speaker 1: The social tone was agreeable zero percent conscientious, zero percent open.
Speaker 1: The writing tone was analytical, zero percent confident and tentative.
Speaker 1: Joe would actually end up highlighting some of the words
Speaker 1: to find out which words were the ones that ended
Speaker 1: up giving that cheerfulness result. Those four words were good, honest, hero,
Speaker 1: and intelligent and that kind of are that That's important
Speaker 1: because those words, the way they are used, uh in
Speaker 1: that passage are not used in a positive sense. They
Speaker 1: are positive words, but they're meant to show kind of
Speaker 1: a negation there not and not an assertion. So that
Speaker 1: really highlights a big problem in this tone analyzer, which
Speaker 1: is that it's tagging these words individually without context. So
Speaker 1: if I wrote the phrase I am not glad, it
Speaker 1: would tag the word glad and say that's a cheerful word.
Speaker 1: But I said I am not glad. You if I
Speaker 1: told you I am not glad, you would not think, oh, well,
Speaker 1: that's a cheerful thing to say or a positive thing
Speaker 1: to say. But according to the tone analyzer, it would
Speaker 1: come across as a cheerful statement because it had tagged
Speaker 1: that word as as being cheerful. In the other words
Speaker 1: are not that strong, They don't They don't warrant being
Speaker 1: tagged in a way like that. Now, over time, we
Speaker 1: might have a tone analyzer that can actually take context
Speaker 1: into account, and then you would learn a lot more
Speaker 1: about the actual meaning behind a phrase. It would be
Speaker 1: more than just tone. So if you were trying to
Speaker 1: get across tone by using more complicated and subtle word
Speaker 1: choice where you're sort of being kind of uh poetic
Speaker 1: in your expression, you're trying to get across a feeling
Speaker 1: by using irony or sarcasm, then a tone analyzer like
Speaker 1: this would totally miss it because it would just be
Speaker 1: counting the hits and not understanding the usage. There the
Speaker 1: hidden meeting the word play, so that is going to
Speaker 1: be a real challenge. So it's kind of another interesting
Speaker 1: use of IBMS Watson. There are a lot of other
Speaker 1: ones that we could talk about, like Chef Watson, which
Speaker 1: was my favorite. Chef Watson would generate new recipes based
Speaker 1: upon ingredients that you would tell it that you had
Speaker 1: on hand, and it wouldn't it wouldn't go and reference
Speaker 1: old recipes and pull one up for you. Instead, it
Speaker 1: would make flavor profiles based upon all the different combinations
Speaker 1: of food that were found in various recipe books and
Speaker 1: generate a brand new recipe for you right there on
Speaker 1: the spot. And sometimes they were whacka doodle crazy, y'all.
Speaker 1: So in a way, you could say that Chef Watson
Speaker 1: was another another way of seeing how I b M
Speaker 1: S Watson has a lot of promise, but it requires
Speaker 1: a ton of work on the app level in order
Speaker 1: to leverage it and make actual practical use out of it.
Speaker 1: I have more to say about computers detecting sarcasm, but
Speaker 1: first let's take a quick word from our sponsor. So
Speaker 1: back in two there were some researchers at the Hebrew
Speaker 1: University in Israel who designed a system called the Semi
Speaker 1: Supervised Algorithm for Sarcasm Identification or SAZI, and they used
Speaker 1: SAZI to analyze collections of nearly six million tweets and
Speaker 1: also around sixty six thousand product reviews from Amazon. They
Speaker 1: wanted to find rich treasure troves of sarcasm that turns
Speaker 1: out reviews and tweets. They fit the bill sarcasm is.
Speaker 1: Really it's typically conveyed in some vocal tone, right and
Speaker 1: nonverbal cues. So you have to first go someplace where
Speaker 1: sarcasm is is rampant in text form to be able
Speaker 1: to really fine tune how you can identify sarcasm versus
Speaker 1: something that's meant exactly the way it's written on the
Speaker 1: surface level. So they started to map out the various
Speaker 1: features that were common in sarcastic comments online. So they
Speaker 1: were looking for things like hyperbolic words and if you're
Speaker 1: using a lot of exaggeration, that could be a key.
Speaker 1: Excessive punctuation was another one, especially ellipses, which I tend
Speaker 1: to use a lot, though I don't know if I
Speaker 1: use it so much for sarcasm as I do for
Speaker 1: just timing purposes. To indicate this is the beat I
Speaker 1: would take if I were saying this out loud, I
Speaker 1: guess that's just as irritating. Though, also how straightforward is
Speaker 1: the Senate structure? And they gave it examples of sarcasm.
Speaker 1: They fed it tweets that were tagged hashtag sarcasm, so
Speaker 1: that the machine quote unquote knew that that was already
Speaker 1: a sarcastic tweet and could start to analyze it and
Speaker 1: build out a model for what sarcasm is. They also
Speaker 1: fed at a bunch of one star Amazon reviews that
Speaker 1: had been judged to be sarcastic by a panel consisting
Speaker 1: of fifteen human beings, and the system was told it
Speaker 1: had to rate sentences on a scale of one to five,
Speaker 1: One being not sarcastic, they mean exactly what the Senate says,
Speaker 1: five being holy cow, this person should write for the Onion.
Speaker 1: This is incredibly sarcastic. SAZI could identify sarcastic Amazon reviews
Speaker 1: with precision. Not bad, But when it came to Twitter
Speaker 1: it did even better. I think, probably because there had
Speaker 1: to be very short messages on Twitter. This was before
Speaker 1: Twitter had even expanded to characters, so it was still
Speaker 1: back in the one character days. The precision rate for
Speaker 1: SAZI for Twitter was so it was really good at
Speaker 1: detecting straightforward sarcasm, the kind that a lot of people
Speaker 1: on Twitter use because you have limited space so you
Speaker 1: can't really set it up in a more complex way.
Speaker 1: But it was also uh more prone to judging things
Speaker 1: as false negative evaluations rather than false positives. In other words,
Speaker 1: it was more likely to look at a negative sarcastic
Speaker 1: message and say that's not sarcastic than it was to
Speaker 1: look at a straightforward message and say, no, that is sarcastic.
Speaker 1: So that was kind of interesting back to Watson. Another
Speaker 1: use of Watson came out of the Milk and Institute
Speaker 1: Global Conference at IBM showed off some research that it
Speaker 1: had been working on internally, and it was calling this
Speaker 1: research debating Technologies. This was a project in which IBM
Speaker 1: was trying to see if they could feed a computer
Speaker 1: raw information, have the computer synthesize the information, understand that
Speaker 1: information at least on a computational level, and then create
Speaker 1: a a debating strategy for both pros and cons they
Speaker 1: on that information. So it would take a huge amount
Speaker 1: of content like all of Wikipedia, for example, and then
Speaker 1: on any given subject that would be covered in Wikipedia.
Speaker 1: It would be asked form an argument that is in
Speaker 1: favor of or is against a concept, whatever that concept
Speaker 1: might be. John Kelly of IBM showed off in a
Speaker 1: demo how the tool could be used to predict pro
Speaker 1: or con arguments about a subject based on a body
Speaker 1: of information. So you might be able to use this
Speaker 1: technology in order to anticipate what an opposing person might
Speaker 1: say on any given subject. Let's say that you are
Speaker 1: getting ready to debate a topic. You might feed that
Speaker 1: information to a computer system using this Watson platform. You
Speaker 1: might feed in a ton of information, and then you
Speaker 1: might say, who is a man and someone who is
Speaker 1: against this particular topic, whatever it might be. Uh. Let's
Speaker 1: say it's it's it's renewable energy, and the uh the
Speaker 1: efficiency of solar panels, whether or not it makes sense
Speaker 1: to invest in solar panels. Let's say that your stance
Speaker 1: is that you have to argue for solar panels. You
Speaker 1: might say, what would someone who wants to argue against
Speaker 1: solar panels say? And then Watson would analyze this information
Speaker 1: and return to you what it thinks would be an
Speaker 1: argument someone would use to support that that stance, and
Speaker 1: then you could prepare for that, which would be an
Speaker 1: incredible tool. I mean, you could think of this as
Speaker 1: for political debates. It would be amazing. You could think
Speaker 1: of how you might want to prepare so that you
Speaker 1: can argue intelligently against an opponent, and you can already
Speaker 1: anticipate what that opponent is going to say, because you
Speaker 1: know their general stance on a topic, but you might
Speaker 1: not know what tactic they might use to support that stance.
Speaker 1: Maybe politics isn't a great choice because that's not always
Speaker 1: in the realm of rationality. That often falls into a
Speaker 1: call toward emotional response rather than rational response. That's more
Speaker 1: of a a commentary on politics in general, regardless of
Speaker 1: what side you might be on, all sides do this anyway.
Speaker 1: He actually showed at this demo a different example. He said,
Speaker 1: what if you were to take the sale of violent
Speaker 1: video games to minors should be banned. That's the topic,
Speaker 1: and that the computer would then go through all the
Speaker 1: information and had access to. It would end up sorting
Speaker 1: out all the parts that were relevant to the discussion,
Speaker 1: so it just put those aside and that would become
Speaker 1: the core of the data it would reference. I would
Speaker 1: then go through and identify basic statements as either being
Speaker 1: a pro stance of banning violent video games to my
Speaker 1: nes or a constance for that saying no, we should
Speaker 1: be able to sell violent video games to minors. The
Speaker 1: tools scanned four million articles, it returned the top ten
Speaker 1: articles that were determined to be the most relevant to
Speaker 1: that particular debate, and it scanned approximately three thousand sentences
Speaker 1: from from top to bottom, and it then identified sentences
Speaker 1: that contained candidate claims that would be statements that would
Speaker 1: either be interpreted as being pro or con for the stance.
Speaker 1: Then it identified the parameters of those claims. Then it
Speaker 1: assessed the claims for the pro and con polarity, then
Speaker 1: constructed a sample pro or con statement. And the statements
Speaker 1: in the demo were kind of interesting. And since the
Speaker 1: computer is constructing arguments based upon what people have already written,
Speaker 1: it would reflect a lot of vague statements that aren't
Speaker 1: a firm stance. So, in other words, like it couldn't
Speaker 1: take a bunch of stuff that was written that it
Speaker 1: sell did not take either a pro or constance, and
Speaker 1: then transformed that magically into the perfect pro stance or
Speaker 1: the perfect constance. Uh, it's dependent upon the words that
Speaker 1: human beings have already written, so it could not magically
Speaker 1: come up with a killer argument if the data that
Speaker 1: had been written about this subject didn't come down on
Speaker 1: a firm stance one way or the other. Um, the
Speaker 1: point of the demonstration wasn't to create a tool that
Speaker 1: could either troll people or counter trolls. It was to
Speaker 1: show that a computer could be useful to aid in
Speaker 1: the reasoning process when you're making a critical decision. Again,
Speaker 1: to go back to that medical example, it could be
Speaker 1: used to help a doctor determine which diagnosis is the
Speaker 1: most likely to be accurate for a patient, what what
Speaker 1: course of treatment might be the most helpful for that patient,
Speaker 1: and thus it could have real practic coal use outside
Speaker 1: of this more esoteric, interesting uh debate news. Now, will
Speaker 1: we see computers in the future able to detect sarcasm
Speaker 1: just as easily as your typical human being can when
Speaker 1: given the right circumstances. And I use the word typical reluctantly,
Speaker 1: but you get what I mean. I don't know. It's
Speaker 1: gonna take some time. It takes an awful lot of
Speaker 1: processing power too. You have to remember that for these
Speaker 1: neural networks systems, the ones that are running these these
Speaker 1: various platforms and programs and strategies. They take up a
Speaker 1: lot of processing power because our brains have billion neurons
Speaker 1: in them, so we have a very sophisticated supercomputer sitting
Speaker 1: in our heads. Moreover, our brains are insanely energy efficient.
Speaker 1: They require about the equivalent of twenty watts of power.
Speaker 1: A supercomputer needs a lot more power than that. So
Speaker 1: while we're seeing advances in this, it requires so much
Speaker 1: processing power, so much energy, it is not a practical
Speaker 1: approach to most forms of computing, at least from a
Speaker 1: consumer standpoint. You might see a future where the sort
Speaker 1: of stuff is all in the cloud and then we
Speaker 1: can access it through an app or a program or whatever.
Speaker 1: That way, you don't have to have a supercomputer sitting
Speaker 1: on your desk in order to tap into those, uh,
Speaker 1: those capabilities, but you have to have an Internet connection,
Speaker 1: which most of us these days tend to have fairly frequently.
Speaker 1: I mean, there are a lot of people out there
Speaker 1: who at this point have had a persistent Internet connection
Speaker 1: for pretty much their whole lives, which blows my mind.
Speaker 1: But that's the kind of world we'd have to live
Speaker 1: in in order to really take advantage of this at
Speaker 1: least in the near term. I don't know if we're
Speaker 1: ever going to see a computer that can analyze, say,
Speaker 1: an article from the Onion and not only point out
Speaker 1: that it's being sarcastic or ironic, but also point out
Speaker 1: why it's funny. I think at one point, when you
Speaker 1: start analyzing comedy, there gets to be a level where
Speaker 1: nothing is ever funny ever again, but it is a
Speaker 1: really interesting problem. So that's whether that's that's this look
Speaker 1: back on if AI is ever going to understand sarcasm. Well, guys,
Speaker 1: I hope you enjoyed that classic episode of tech stuff.
Speaker 1: I guess I guess two years old isn't old enough
Speaker 1: to be classic. That uh that that only somewhat less
Speaker 1: than fresh episode of text stuff about artificial intelligence and
Speaker 1: sarcasm and things of that nature. I am constantly impressed
Speaker 1: with how artificial intelligence is advancing year over year. But
Speaker 1: when you look at what it means to be human
Speaker 1: and the ways that we humans interact with one another,
Speaker 1: and the ways that we can communicate complicated three things,
Speaker 1: sometimes just through you know, subtle methods that are not
Speaker 1: overt or or you know, directly spoken, it reminds us
Speaker 1: that machines have got a long way to go in
Speaker 1: order to really grasp what it is to be human,
Speaker 1: So unless you're Commander Data, you're probably struggling a bit.
Speaker 1: So I hope you guys enjoyed this. If you have
Speaker 1: suggestions for future episodes of tech Stuff, I've got a
Speaker 1: few episodes based on listener suggestions coming up soon. But
Speaker 1: if you want to get your suggestions in tweet me.
Speaker 1: The Twitter handle is text stuff H s W and
Speaker 1: I'll talk to you again really soon. Text Stuff is
Speaker 1: an I Heart Radio production. For more podcasts from my
Speaker 1: Heart Radio, visit the i Heart Radio app, Apple Podcasts,
Speaker 1: or wherever you listen to your favorite shows.