Speaker 1: Get in touch with technology with tech Stuff from how
Speaker 1: stuff Works dot com. Hey there, and welcome to tech Stuff.
Speaker 1: I'm your host, Jonathan Strickland. I'm an executive producer with
Speaker 1: How Stuff Works and I love all things tech. Today,
Speaker 1: I want to talk to you about an interesting topic
Speaker 1: that I got to explore a couple of years ago
Speaker 1: with Joe McCormick and Lauren fogobaum As we debated the
Speaker 1: possibilities of computers learning how to understand sarcasm. We did
Speaker 1: it for a podcast called Forward Thinking, which was around
Speaker 1: for a couple of years. It was a lot of
Speaker 1: fun to work on that that show is over, but
Speaker 1: I thought I would revisit the topic and talk about
Speaker 1: it for you guys and kind of go over what
Speaker 1: would it take to have a computer that could actually
Speaker 1: understand when someone's being sarcastic. Now to understand why this
Speaker 1: is a big d it helps to have a refresher
Speaker 1: course on how computers process information. And I know I
Speaker 1: talked about this a lot, but I still think it's
Speaker 1: important to cover the basics when you want to talk
Speaker 1: about something as advanced as being able to detect and
Speaker 1: understand sarcasm. So computers understand machine code or assembly language.
Speaker 1: This is a language that corresponds with the actual physical
Speaker 1: architecture of the computers, so the way the computer is built,
Speaker 1: that's how this language interacts. It's it's essentially how the
Speaker 1: physical components of the computer are able to handle electric
Speaker 1: current or voltage differences in order to process information, and
Speaker 1: computers can interpret this and execute upon this language very quickly.
Speaker 1: It is the basic language of those physical components. However,
Speaker 1: it is almost impossible for human to work with this,
Speaker 1: at least on a way that is at all efficient,
Speaker 1: because it ultimately for most computers boils down to binary language, right,
Speaker 1: zeros and ones, So you see a huge block of
Speaker 1: zeros and ones, and unless you are neo from the matrix,
Speaker 1: it means nothing to you. So we speak in natural
Speaker 1: language to one another. Natural language, however, is filled with
Speaker 1: a lot of components that make it very very challenging
Speaker 1: for machines to interpret, like ambiguity, or there might be
Speaker 1: double meanings in a phrase and you may mean both
Speaker 1: meanings at the same time, and that is too complicated
Speaker 1: for most machines to be able to process. They just
Speaker 1: can't deal with that. So to bridge the gap between
Speaker 1: the way we humans communicate and the way that computers
Speaker 1: process language. We have created programming languages and compilers. Now,
Speaker 1: programming languages fall into two broad categories. It's more like
Speaker 1: a spectrum, and you could be further on one end
Speaker 1: than the other, and we typically call them high level
Speaker 1: programming languages and low level programming languages. The lower the
Speaker 1: level of programming language, the closer it is to machine code,
Speaker 1: and the easier it is for a computer to understand,
Speaker 1: but the harder it is to work with if you
Speaker 1: happen to be, you know, a human being. High level
Speaker 1: programming languages are easier for humans to understand. Now, if
Speaker 1: you have never taken any courses in programming and you're
Speaker 1: looking at a page of code, it can seem indecipherable
Speaker 1: to you. It is just meaningless strings of characters. But
Speaker 1: once you learn the rules of that programming language, how
Speaker 1: you construct an instruction and a series of instructions, how
Speaker 1: you go from one instruction to the next. Once you
Speaker 1: understand the rules, it actually becomes quite easy to use
Speaker 1: in the grand scheme of things, much more easy than
Speaker 1: machine language would be. But again, the problem here is
Speaker 1: that computers don't understand programming languages, not natively. Even though
Speaker 1: this is not exactly the same as human natural language,
Speaker 1: it's also not the same as machine language. That's why
Speaker 1: you need compilers. A compiler is essentially a translator. It
Speaker 1: takes this high level programming language or higher level anyway,
Speaker 1: and then converts it into a machine readable language for
Speaker 1: the computer to actually execute upon. And this is all
Speaker 1: in the design of the programming languages and the compilers.
Speaker 1: So this is the way that for decades we have
Speaker 1: interacted with computers, when you're talking about it on a
Speaker 1: on a direct level, not just executing a program, but
Speaker 1: creating code, creating programs for computers to run. Over the
Speaker 1: last few decades, we've had some very very smart people
Speaker 1: working on natural language systems for machines which would allow
Speaker 1: a computer to interpret natural language in a way that
Speaker 1: would make some sort of sense and for the computer
Speaker 1: to be able to act upon that language. And we've
Speaker 1: seen this in plenty of examples recently. Most smartphones have
Speaker 1: some sort of smart assistant. You have standalone products like
Speaker 1: Amazon's Echo, you have Google Home, You've got tons of
Speaker 1: devices that can interact with people. It can be activated
Speaker 1: by typically an alert phrase, which I'm not going to
Speaker 1: say because I don't want any of you guys to
Speaker 1: have to deal with that. I know how irritating it
Speaker 1: is when I'm watching a video and someone activates their
Speaker 1: specific system and then mine begins to respond and all
Speaker 1: my lights started going on and off because the people
Speaker 1: on YouTube we're talking funny. I know how irritating that is.
Speaker 1: But use that at debates and then you can speak
Speaker 1: and typically you can say the same thing several different
Speaker 1: ways and the device appears to understand you no matter
Speaker 1: how you word it. And this is a real challenge
Speaker 1: because we human beings can find lots of different ways
Speaker 1: to say the same thing. For example, if I say
Speaker 1: what is the weather today, it could be very similar
Speaker 1: to if I if I ask a question, is it
Speaker 1: going to rain today? Both of those are asking for
Speaker 1: information about the weather, but are very different ways of
Speaker 1: saying that. A good natural language recognition program will be
Speaker 1: able to parse that information and then return the appropriate response.
Speaker 1: This is not an easy thing to do. Typically it
Speaker 1: involves creating a neural network structure, and I've talked about
Speaker 1: artificial neural networks recently. That's a typically a network that
Speaker 1: can accept multiple binary inputs, so either a zero or
Speaker 1: a one input that represents something uh, some sort of yes,
Speaker 1: no or on off kind of feature. It can accept
Speaker 1: multiple multiple inputs of that nature, so multiple zeros or
Speaker 1: ones that all factor into making a decision, and then
Speaker 1: it has a waiting for each of those components, and
Speaker 1: then it produces a single output that's also binary in nature,
Speaker 1: either a zero one, and it passes that on to
Speaker 1: other artificial neurons further down the chain. Sometimes that will
Speaker 1: come back around and you have a recursive artificial neural network.
Speaker 1: The goal here is for this process two ultimately result
Speaker 1: in a response that is reasonably certain to meet the
Speaker 1: requirements of the person asking the question. This tends to
Speaker 1: be talked about in the realm of probabilities. We we
Speaker 1: talked about how certain the machine is that the respons
Speaker 1: is the appropriate one, and if it falls below a
Speaker 1: certain threshold, then the machine would typically respond with I'm sorry,
Speaker 1: I don't know what you're asking for, or something similar
Speaker 1: to that. There are cases where you just get misinterpreted
Speaker 1: and you'll get a response that does not reflect whatever
Speaker 1: you ask. That's a little different. That's where the machine
Speaker 1: has drawn a conclusion, has been reasonably certain that it
Speaker 1: came to the right conclusion, it turns out it was
Speaker 1: wrong the whole way. But that's the process. Now, when
Speaker 1: it comes to sarcasm, that adds yet another layer of difficulty,
Speaker 1: because 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, no body 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, Utson
Speaker 1: went up against these two former champions and it was
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 picture, you would say, what
Speaker 1: was Shakespeare in Love? So this computer is playing against
Speaker 1: these two former champions. This was sort of an exhibition
Speaker 1: series of games. It wasn't meant for uh, a competition
Speaker 1: in the way the typical Jeopardy games were. There was
Speaker 1: money on the line. It was an exhibition and Watson
Speaker 1: won it beat both of the champions, and it did
Speaker 1: what I was telling you. It it would analyze the
Speaker 1: clue that was given, the answer that was given, it
Speaker 1: would 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 wordplay, 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 m interface or API
Speaker 1: and let developers create their own cognitive computing applications built
Speaker 1: on top of Watson. One of those was called the
Speaker 1: tone analyzer. It still exists back when we were doing
Speaker 1: this episode for forward Thinking. It was in the form
Speaker 1: of analyzing some text and telling you whether or not
Speaker 1: that text would come across as agreeable or argumentative, or
Speaker 1: positive or negative, and it would assign tone to those pieces.
Speaker 1: I'll explain more about how it did and what it
Speaker 1: did in just a minute, but first let's take a
Speaker 1: quick break to thank our sponsor. So how did this
Speaker 1: tone analyzer work. It would search for cues in any
Speaker 1: written text, social cues, written cues, emotional cues in order
Speaker 1: to determine in the overall tone of a piece, which
Speaker 1: actually meant that The analyzer would tag individual words within
Speaker 1: a text, words that it recognized and had already pre
Speaker 1: labeled as falling into various categories. So words that might
Speaker 1: have a positive meaning like happy, glad, joy, things like that.
Speaker 1: Those would get tagged as cheerful. But then it would
Speaker 1: then assign all the individual words tags and then tally
Speaker 1: everything up. So let's say you've got a bunch of
Speaker 1: sentences and it starts individually labeling certain words as being
Speaker 1: cheerful or sad or angry or helpful, and then it
Speaker 1: adds it all up and then would give you a percentage.
Speaker 1: So a message might be agreeable or thirty conscientious, you
Speaker 1: would actually get multiples of these, and that would just
Speaker 1: really indicate the density of those types of words within
Speaker 1: the mess itage itself. Now, in an ideal world, if
Speaker 1: language were very simple to understand and interpret by machines,
Speaker 1: this would help you gauge how people would respond to
Speaker 1: your work. Right, So, you could write a message. Before
Speaker 1: you send it, you put it through the tone analyzer
Speaker 1: and it tells you what sort of a tone you
Speaker 1: are setting. So if you wanted to create a business letter,
Speaker 1: you could send it through this tone analyzer, and if
Speaker 1: it came back as saying it's coming across as as
Speaker 1: a indecisive, you might want to go back in and
Speaker 1: edit that message so that you can make a more
Speaker 1: straightforward and decisive message and not give the wrong impression
Speaker 1: before you send the message out to your actual human recipient,
Speaker 1: and come up with alternate word choices in order to
Speaker 1: make sure that your message is received the way you
Speaker 1: intended it. And anyone who has communicated over the internet
Speaker 1: can think of ways that this might have been helpful
Speaker 1: in the past, because again, language depends on so many
Speaker 1: different elements to get your meaning across, and when you
Speaker 1: reduce it to the written form, especially the written form online,
Speaker 1: where we tend to be very short with our our communication,
Speaker 1: it comes in very quick bursts, a couple of sentences
Speaker 1: here or there. We lack all that body language, we
Speaker 1: lack that tone. It's very easy to misinterpret. I'm sure
Speaker 1: there's been an example in your life where either you
Speaker 1: got offended from receiving something that was meant in a
Speaker 1: way 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 sir verse,
Speaker 1: or email services, I should say, or social media platforms.
Speaker 1: So you start typing in your message, and before you
Speaker 1: hit 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 case,
Speaker 1: rethink your strategy. There are better things to do. It's
Speaker 1: just as you can make just as big an impact
Speaker 1: being a positive person as you can being a jerk face.
Speaker 1: I know it can seem like it's more work, but
Speaker 1: it's also more rewarding in the long run. Okay, soapbox done. So.
Speaker 1: There is a demo of the tone analyzer that's available online,
Speaker 1: and back when we were recording Forward Thinking, the demo
Speaker 1: worked in a way where it would tell you about
Speaker 1: emotional tone and break it down by percentage. It's a
Speaker 1: little different now, but I want to tell you the
Speaker 1: what words and the results we got in the past
Speaker 1: because they were so much fun. Granted you would get
Speaker 1: a different result now because the tone analyzer has been
Speaker 1: tweaked since we recorded that episode. So when we recorded
Speaker 1: that episode, one of my co hosts decided to put
Speaker 1: a sentence that is somewhat known in literary circles into
Speaker 1: this tone analyzer and find out what it said. And
Speaker 1: the sentence used was it is a truth universally acknowledged
Speaker 1: that a single man in possession of a good fortune
Speaker 1: must be in want of a wife. Now, the analyzer
Speaker 1: said that this emotional tone was cheerful, the social tone
Speaker 1: was seventy six percent open and fifty agreeable, and the
Speaker 1: writing tone was analytical. You can also view the sentence
Speaker 1: in terms of word count as opposed to the weighted
Speaker 1: value of individual words, and using that view, five percent
Speaker 1: of the sentence sentences were in an emotional tone, in
Speaker 1: a social tone, and five percent in a writing tone. Now,
Speaker 1: the analyzer highlights each word according to how it classifies them,
Speaker 1: So emotional words would be highlighted in red or pink
Speaker 1: in that older version of the tone analyzer, social words
Speaker 1: would show up in blue, and writing tones would be
Speaker 1: in green. And you could click on any word and
Speaker 1: the analyzer would offer alternative words that you might want
Speaker 1: to use and classify those words in the tones that
Speaker 1: they are associated with. Such you could shape your message
Speaker 1: to meet the tone you wish to convey. Also, the
Speaker 1: tone analyzer demo used the business letter format as the
Speaker 1: means of comparison, So, in other words, we compared Jane
Speaker 1: Austen to a business letter. Presumably if you were to
Speaker 1: use a full version of the analyzer, not just the
Speaker 1: demo version. You would have other options so you could
Speaker 1: compare it with other models, not just a business letter
Speaker 1: Joe McCormick. He included an excerpt from Dostoyevsky's Notes from Underground.
Speaker 1: That excerpt was, I could not become anything, neither good
Speaker 1: nor bad, neither a scoundrel nor an honest man, neither
Speaker 1: a hero nor an insect. And now I eking out
Speaker 1: my days in my corner, taunting myself with the bitter
Speaker 1: and entirely useless constellation that an intelligent man cannot seriously
Speaker 1: become anything, that only a fool can become something. The
Speaker 1: feedback was that the emotional tone had anger at cheerfulness
Speaker 1: at so happy anger negative at. The social tone was
Speaker 1: agreeable zero percent conscientious, zero percent open. The writing tone
Speaker 1: was analytical, zero percent confident and tentative. Joe would actually
Speaker 1: end up highlighting some of the words to find out
Speaker 1: which words were the ones that ended up giving that
Speaker 1: cheerfulness result. Those four words were a 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 choice,
Speaker 1: where you're sort of being kind of uh poetic in
Speaker 1: your expression, you're trying to get across a feeling by
Speaker 1: using irony or sarcasm. Then a tone analyzer like this
Speaker 1: would totally miss it because it would just be counting
Speaker 1: the hits and not understanding the usage there the hidden
Speaker 1: meeting the word play. So that is going to be
Speaker 1: a real challenge. So it's kind of another interesting use
Speaker 1: of IBMS Watson. There are a lot of other ones
Speaker 1: that we could talk about, like Chef Watson, which was
Speaker 1: my favorite. Chef Watson would generate new recipes based upon
Speaker 1: ingredients that you would tell it that you had on hand,
Speaker 1: and it wouldn't it wouldn't go and reference old recipes
Speaker 1: and pull one up for you. Instead, it would make
Speaker 1: flavor profiles based upon all the different combinations of food
Speaker 1: that were found in various recipe books and generate a
Speaker 1: brand new recipe for you, right there on the spot.
Speaker 1: And sometimes they were whacka doodle crazy, y'all. So in
Speaker 1: a way you could say that Chef Watson was another
Speaker 1: another way of seeing how IBM S Watson has a
Speaker 1: lot of promise, but it requires a ton of work
Speaker 1: on the app level in order to leverage it and
Speaker 1: make actual practical use out of it. I have more
Speaker 1: to say about computers detecting sarcasm. But first let's take
Speaker 1: a quick word from our sponsor. So back in twent
Speaker 1: there were some researchers at the Hebrew University in Israel
Speaker 1: who designed a system called the Semi Supervised Algorithm for
Speaker 1: Sarcasm Identification or SAZI, and they used SAZI to analyze
Speaker 1: collections of nearly six million tweets and also around sixty
Speaker 1: six thousand product reviews from Amazon. They wanted to find
Speaker 1: rich treasure troves of sarcasm that turns out reviews and
Speaker 1: tweets they fit the bill sarcasm is. Really it's typically
Speaker 1: conveyed in in some vocal tone right and nonverbal cues.
Speaker 1: So you have to first go someplace where sarcasm is
Speaker 1: is rampant in text form to be able to really
Speaker 1: fine tune how you can identify sarcasm versus something that's
Speaker 1: meant exactly the way it's written on the surface level.
Speaker 1: So they started to map out the various features that
Speaker 1: were common in sarcastic comments online. So they were looking
Speaker 1: for things like hyperbolic words and if you're using a
Speaker 1: lot of exaggeration, that could be a key. Excessive punctuation
Speaker 1: was another one, especially ellipses, which I tend to use
Speaker 1: a lot, though I don't know if I use it
Speaker 1: so much for sarcasm as I do for just timing purposes.
Speaker 1: To indicate this is the beat I would take if
Speaker 1: I were saying this out loud. I guess that's just
Speaker 1: as irritating, though, also how straightforward is the Senate structure?
Speaker 1: And they gave it examples of sarcasm. They fed it
Speaker 1: tweets that were tagged hashtag sarcasm, so that the machine
Speaker 1: quote unquote knew that that was already a sarcastic tweet
Speaker 1: and could start to analyze it and build out a
Speaker 1: model for what sarcasm is. They also fed at a
Speaker 1: bunch of one star Amazon reviews that had been judged
Speaker 1: to be sarcastic by a panel consisting of fifteen human beings,
Speaker 1: and the system was told it had to rate sentences
Speaker 1: on a scale of one to five, One being not sarcastic.
Speaker 1: They mean exactly what the Senate says, five being holy cow,
Speaker 1: this person should write for the Onion, this is incredibly sarcastic.
Speaker 1: SAZI could identify sarcastic Amazon reviews with precision, not bad,
Speaker 1: but when it came to Twitter, it did even better,
Speaker 1: I think, probably because there had to be very short
Speaker 1: messages on Twitter. This was before Twitter had even expanded
Speaker 1: to characters, so it's still back in the one character days.
Speaker 1: The precision rate for SAZI for Twitter was so it
Speaker 1: was really good at detecting straightforward sarcasm, the kind that
Speaker 1: a lot of people on Twitter use, because you have
Speaker 1: limited space so you can't really set it up in
Speaker 1: a more complex way, but it was all so uh
Speaker 1: more prone to judging things as false negative evaluations rather
Speaker 1: than false positives. In other words, it was more likely
Speaker 1: to look at a negative sarcastic message and say that's
Speaker 1: not sarcastic than it was to look at a straightforward
Speaker 1: message and say, no, that is sarcastic. So that was
Speaker 1: kind of interesting. Back to Watson. Another use of Watson
Speaker 1: came out of the Milk and Institute Global Conference at
Speaker 1: IBM showed off some research that it had been working
Speaker 1: on internally, and it was calling this research debating Technologies.
Speaker 1: This was a project in which IBM was trying to
Speaker 1: see if they could feed a computer raw information, have
Speaker 1: the computer synthesize the information, understand that information, at least
Speaker 1: on a computational level and then create a a debating
Speaker 1: strategy for both pros and cons based on that information.
Speaker 1: So it would take a huge amount of content like
Speaker 1: all of Wikipedia, for example, and then on any given
Speaker 1: subject that would be covered in Wikipedia, it would be
Speaker 1: asked form an argument that is in favor of or
Speaker 1: is against a concept, whatever that concept might be. John
Speaker 1: Kelly of IBM showed off in a demo how the
Speaker 1: tool could be used to predict pro or con arguments
Speaker 1: about a subject based on a body of information. So
Speaker 1: you might be able to use this technology in order
Speaker 1: to anticipate what an opposing person might say on any
Speaker 1: given subject. Let's say that you are getting ready to
Speaker 1: debate a topic. You might feed that information to a
Speaker 1: computer system using this Watson platform. You might feed in
Speaker 1: a ton of information, and then you might say, who
Speaker 1: imagine someone who is against this particular topic, whatever it
Speaker 1: might be. Uh. Let's say it's it's it's renewable energy
Speaker 1: and the uh the efficiency of solar panels, whether or
Speaker 1: not it makes sense to invest in solar panels. Let's
Speaker 1: say that your stance is that you have to argue
Speaker 1: for solar panels. You might say, what would someone who
Speaker 1: wants to argue against solar panels, say, and then Watson
Speaker 1: would analyze this information and return to you what it
Speaker 1: thinks would be an argument someone would use to support
Speaker 1: that that stance, and then you could prepare for that,
Speaker 1: which would be an incredible tool. I mean, you could
Speaker 1: think of this as for political debates. It would be amazing.
Speaker 1: You could think of how you might want to prepare
Speaker 1: so that you can argue intelligently against an opponent, and
Speaker 1: you can already anticipate what that opponent is going to
Speaker 1: say because you oh their general stance on a topic,
Speaker 1: but you might not know what tactics they might use
Speaker 1: to support that stance. Maybe politics isn't a great choice
Speaker 1: because that's not always in the realm of rationality. That
Speaker 1: often falls into a call toward emotional response rather than
Speaker 1: rational response. That's more of a a commentary on politics
Speaker 1: in general, regardless of what side you might be on,
Speaker 1: all sides do this anyway. He actually showed at this
Speaker 1: demo a different example. He said, what if you were
Speaker 1: to take the sale of violent video games to minors
Speaker 1: should be banned, that's the topic, and that the computer
Speaker 1: would then go through all the information and had access
Speaker 1: to it would end up sorting out all the parts
Speaker 1: that were relevant to the discussion, so it just put
Speaker 1: those aside and that would become the core of the
Speaker 1: data it would reference. I would then go through and
Speaker 1: identify basic statements is either being a pro stance of
Speaker 1: banning violent video games to miners or a constance for
Speaker 1: that saying no, we should be able to sell violent
Speaker 1: video games to miners. The tools scanned four million articles.
Speaker 1: It returned the top ten articles that were determined to
Speaker 1: be the most relevant to that particular debate, and it
Speaker 1: scanned approximately three thousand sentences, come from from top to bottom,
Speaker 1: and it then identified sentences that contained candidate claims that
Speaker 1: would be statements that would either be interpreted as being
Speaker 1: pro or con for the stance. Then it identified the
Speaker 1: parameters of those claims. Then it assessed the claims for
Speaker 1: the pro and con polarity, then constructed a sample pro
Speaker 1: or con statement. And the statements in the demo were
Speaker 1: kind of interesting. And since the computer is constructing arguments
Speaker 1: based upon what people have already written, it would reflect
Speaker 1: a lot of vague statements that aren't a firm stance. So,
Speaker 1: in other words, like it it and take a bunch
Speaker 1: of stuff that was written that itself did not take
Speaker 1: either a pro or constance and then transform that magically
Speaker 1: into the perfect pro stance or the perfect constance. Uh.
Speaker 1: It's dependent upon the words that human beings have already written,
Speaker 1: So it could not magically come up with a killer
Speaker 1: argument if the data that had been written about this
Speaker 1: subject didn't come down on a firm stance one way
Speaker 1: or the other. Um. The point of the demonstration wasn't
Speaker 1: to create a tool that could either troll people or
Speaker 1: counter trolls. It was to show that a computer could
Speaker 1: be useful to aid in the reasoning process when you're
Speaker 1: making a critical decision. Again, to go back to that
Speaker 1: medical example, it could be used to help a doctor
Speaker 1: determine which diagnosis is the most likely to be accurate
Speaker 1: for a patient, what what course of treatment might be
Speaker 1: the most helpful for that patient, and thus it could
Speaker 1: have real practical use outside of this more esoteric, interesting
Speaker 1: UH debate. Us. Now, will we see computers in the
Speaker 1: future able to detect sarcasm just as easily as your
Speaker 1: typical human being can when given the right circumstances. And
Speaker 1: I use the word typical reluctantly, but you get what
Speaker 1: I mean, I don't know. It's gonna take some time.
Speaker 1: It takes an awful lot of processing power too. You
Speaker 1: have to remember that for these neural networks systems, the
Speaker 1: ones that are running these these various platforms and programs
Speaker 1: and strategies, they take up a lot of processing power.
Speaker 1: Because our brains have billion neurons in them, so we
Speaker 1: have a very sophisticated supercomputer sitting in our heads. Moreover,
Speaker 1: our brains are insanely energy efficient. They require about the
Speaker 1: equivalent of twenty watts of power. A supercomputer needs a
Speaker 1: lot more power than that. So while we're seeing advances
Speaker 1: in this, it requires so much processing power, so much energy.
Speaker 1: It is not a practical approach to most forms of computing,
Speaker 1: at least from a consumer standpoint. You might see a
Speaker 1: future where the sort of stuff is all in the
Speaker 1: cloud and then we can access it through an app
Speaker 1: or a program or whatever. That way, you don't have
Speaker 1: to have a supercomputer sitting on your desk in order
Speaker 1: to tap into those uh, those capabilities, but you have
Speaker 1: to have an Internet connection, which most of us these
Speaker 1: days tend to have fairly frequently. I mean, there are
Speaker 1: a lot of people out there who at this point
Speaker 1: have had a persistent Internet connection for pretty much their
Speaker 1: whole lives, which blows my mind. But that's the kind
Speaker 1: of world we'd have to live in in order to
Speaker 1: really take advantage of this, at least in the near term.
Speaker 1: I don't know if we're are going to see a
Speaker 1: computer that can analyze, say, an article from the Onion
Speaker 1: and not only point out that it's being sarcastic or ironic,
Speaker 1: but also point out why it's funny. I think at
Speaker 1: one point, when you start analyzing comedy, that gets to
Speaker 1: be a level where nothing is ever funny ever again.
Speaker 1: But it is a really interesting problem. So that's whether
Speaker 1: that's that's this look back on if AI is ever
Speaker 1: going to understand sarcasm. I'm curious to hear what you
Speaker 1: guys think. Do you think we're closer than I am suggesting? Uh? Maybe, well,
Speaker 1: I mean, we're definitely closer than we were when we
Speaker 1: did this episode on Forward Thinking, because that was a
Speaker 1: few years ago. But I don't know that we're you know,
Speaker 1: significantly closer. It's a it's a real tough problem. Or
Speaker 1: do you think that sarcasm is one of those things
Speaker 1: that's just innately human and machines are never really going
Speaker 1: to be able to handle it. We've got a lot
Speaker 1: of programs out there that appear to be sarcastic, but
Speaker 1: that's because they're they're acting on preprogrammed respond says two
Speaker 1: things that we ask them. It's not exactly the same.
Speaker 1: It's kind of cheating, but I'm curious to hear what
Speaker 1: you guys think. Also, make sure you go to our
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