TechStuff Rerun: Could we make a sarcastic supercomputer?

other stuff. So great things right around the corner. I did not want to leave you without an episode at all, So we're going to listen to this one that originally published on October eighteenth, two thousand eighteen, and it kind of goes in line with some other stuff we've been covering in recent episodes of Tech Stuff. This episode was

titled Could We Make a Sarcastic Supercomputer? And yeah, it really dives into the whole concept of artificial intelligence, natural lane, which and just kind of understanding the quirks of what it is to be human and the whole concept of sarcasm. I hope you guys enjoy it. I mean that without even a hint of sarcasm. And I'll chat with you

after the episode. Today. I want to talk to you about an interesting topic that I got to explore a couple of years ago with Joe McCormick and Lauren fogobaum As we debated the possibilities of computers learning how to understand sarcasm. We did it for a podcast called Forward Thinking,

which was around for a couple of years. It was a lot of fun to work on that that show is over, but I thought I would revisit the topic and talk about it for you guys and kind of go over what would it take to have a computer that could actually understand when someone's being sarcastic. Now to understand why this is a big deal, it helps to

have a refresher course on how computers process information. And I know I talked about this a lot, but I still think it's important to cover the basics when you want to talk about something as advanced as being able to detect and understand sarcasm. So computers understand machine code or assembly language. This is a language that corresponds with the actual physical architecture of the computers. So the way

the computer is built, that's how this language interacts. It's it's essentially how the physical components of the computer are able to handle electric current or voltage differences in order to process information, and computers can interpret this and execute upon this language very quickly. It is the basic language

of those physical components. However, it is almost impossible for humans to work with this, at least on a way that is at all of shion, because it ultimately for most computers boils down to binary language, right, zeros and ones. So you see a huge block of zeros and ones, and unless you are neo from the matrix, it means nothing to you. So we speak in natural language to

one another. Natural language, however, is filled with a lot of components that make it very very challenging for machines to interpret, like ambiguity, or there might be double meanings in a phrase and you may mean both meanings at the same time, and that is too complicated for most machines to be able to process. They just can't deal with that. So to bridge the gap between the way we humans communicate and the way that computers process language,

we have created programming languages and compilers. Now, programming languages fall into two broad category worries. It's more like a spectrum, and you could be further on one end than the other, and we typically call them high level programming languages and low level programming languages. The lower the level of programming language, the closer it is to machine code, and the easier it is for a computer to understand, but the harder it is to work with. If you happen to be,

you know, a human being. High level programming languages are easier for humans to understand. Now, if you have never taken any courses in programming and you're looking at a page of code, it could seem indecipherable to you. It is just meaningless strings of characters. But once you learn the rules of that programming language, how you construct an instruction, and a series of instructions, how you go from one

instruction to the next. Once you understand the rules, it actually becomes quite easy to use in the grand scheme of things, much more easy than machine which would be. But again, the problem here is that computers don't understand programming languages, not natively. Even though this is not exactly the same as human natural language, it's also not the same as machine language. That's why you need compilers. A

compiler is essentially a translator. It takes this high level programming language or higher level anyway and then converts it into a machine readable language for the computer to actually execute upon. And this is all in the design of the programming languages and the compilers. So this is the way that for decades we have interacted with computers, when you're talking about it on a on a direct level, not just executing a program, but creating code, creating programs

for computers to run. Over the last few decades, we've had some very very smart people working on natural language systems for machines, which would allow a computer to interpret natural language in a way that would make some sort of sense, and for the computer to be able to act upon that language. And we've seen this in plenty of examples recently. Most smartphones have some sort of smart assistant.

You have standalone products like Amazon's Echo, you have Google Home, You've got tons of devices that can interact with people. It can be activated by typically an alert phrase, which I'm not going to say because I don't want any of you guys to have to deal with that. I know how irritating it is when I'm watching a video and someone activates their specific system and then mine begins to respond, and all my lights started going on and

off because the people on YouTube we're talking funny. I know how irritating that is, but use that it activates and then you can speak. And typically you can say the same thing several different ways and the device appears to understand you no matter how you word it. And this is a real challenge because we human beings can find lots of different ways to say the same thing. For example, if I say what is the weather today, it could be very similar to if I if I

ask a question is it going to rain today? Both of those are asking for information about the weather, but are very different ways of saying that. A good natural language recognition program will be able to parse that information and then return the appropriate response. This is not an easy thing to do. Typically it involves creating a neural network structure, and I've talked about artificial neural networks recently.

That's a typically a network that can accept multiple binary inputs, so either a zero or a one input that represents some thing uh, some sort of yes, no or on off kind of feature. It can accept multiple multiple inputs of that nature, so multiple zeros or ones that all factor into making a decision, and then it has a waiting for each of those components, and then it produces a single output that's also binary in nature, either is zero one, and it passes that on to other artificial

neurons further down the chain. Sometimes that will come back around and you have a recursive artificial neural network. The goal here is for this process two ultimately result in a response that is reasonably certain to meet the requirements of the person asking the question. This tends to be

talked about in the realm of probabilities. We we talked about how certain the machine is that the response is the appropriate one, and if it falls below a certain toushold, then the machine would typically respond with I'm sorry, I don't know what you're asking for, or something similar to that. There are cases where you just get misinterpreted and you'll get a response that does not reflect whatever you ask.

That's a little different. That's where the machine has drawn a conclusion, has been reasonably certain that it came to the right conclusion. It turns out it was wrong the whole way, but that's the process. Now, when it comes to sarcasm, that adds yet another layer of difficulty, because now a machine isn't just parsing what you are saying. It has to understand what you mean, the meaning of your words and the meaning of the way you deliver them.

It could be different. So if I were to just write out a phrase with no tone, nobody language, uh, not emphasizing any one word over another, it might be very difficult to detect what my intent was. It may seem like I'm being sincere, when in fact I'm being insincere. For example, Uh, if I were to say that guy is super tall, but I'm being sarcastic, then just in that phrase the way I write it out, you would think, oh, well, that person he's looking at must be super tall. How

do you recognize sarcasm? How can you detect that this is in place and then understand what the meaning underneath it is. One of the approaches that has been put forward relates to IBM's Watson platform. Now. Watson first made headlines back when it was a contestant on Jeopardy. It went up against two former champions, including Ken Jennings, who shows up on a house Stuff Works podcast. Anyway, Watson went up against these two former champions, and it is

able to interpret natural language. It had to in order to play the game of Jeopardy, And for those who do not know what jeopardy is or they're not familiar with the game show, Jeopardy is a game where you are presented with categories of trivia and each category has multiple uh questions or multiple entries in it, and they range in dollar value, and the lower dollar value ones are easier to answer than the higher dollar value ones, and UH, you're Typically the way jeopardy works is that

you're you're given quote unquote the answer and you have to provide the question. So, uh, if the answer were this film that detailed the adventures of a young playwright in sixteenth century England, one best picture, you would say, what was Shakespeare in Love? So this computer is playing against these two former champions. This was sort of an exhibition series of games. It wasn't meant for, uh, a competition in the way the typical Jeopardy games were there

was money on the line. Was an exhibition and Watson won. It beat both of the champions, and it did what I was telling you. It It would analyze the clue that was given, the answer that was given, It would try and generate a question to correspond with that answer, and only if the question met a certain threshold of confidence with Watson buzz in. If it did not meet that level of confidence, Watson would remain quiet. And most importantly,

Watson was not at all connected to the Internet. All the information was contained within a massive series of servers more than gosh I can't even remember. There's a ton of processors attached to it. Um so a very powerful machine, but it still wasn't exactly able to detect sarcasm. It could work with word play and it could work with riddles,

so that was really impressive. But what it really did was it gave IBM the opportunity to say, we have this platform here and we're welcoming developers to create applications that tap into this platform and make use of this in order to do interesting stuff with it. And IBM was largely working with the medical industry at that point to try and help doctors treat and diagnose patients, and

it was sort of computer guidance. It wasn't that you had an automatic doctor, but rather the doctor had what equates to a medical expert to confer with when trying to determine why's the best course of action for a patient. IBM put up an application Program Interface or API, and let developers create their own cognitive computing applications built on top of Watson. One of those was called the tone analyzer. It still exists back when we were doing this episode

for Forward Thinking. It was in the form of analyzing some text and telling you whether or not that text would come across as agreeable or argumentative, or positive or negative, and it would assign tone to those pieces. I'll explain more about how it did and what it did in just a minute, but first let's take a quick break to thank our sponsor. So how did this tone analyzer work.

It would search for cues in any written text, social cues, written cues, emotional cues in order to determine the overall tone of a piece, which actually meant that the analy lizer would tag individual words within a text, words that it recognized and had already pre labeled as falling into various categories. So words that might have a positive meaning like happy, glad, joy, things like that, those would get tagged as cheerful. But then it would then assign all

the individual words tags and then tally everything up. So let's say you've got a bunch of sentences and it starts individually labeling certain words as being cheerful or sad, or angry or helpful, and then it adds it all up and then would give you a percentage. So a message might be agreeable or thirty conscientious, you would actually get multiples of these, and that would just really indicate

the density of those types of words within the message itself. Now, in an ideal world, if language were very simple to understand and interpret by machines, this would help you gauge how people would respond to your work. Right, So, you could write a message. Before you send it, you put it through the tone analyzer and it tells you what

sort of a tone you are setting. So if you wanted to create a business letter, you could send it through this tone analyzer and if it came back as saying it's coming across as as a indecisive, you might want to go back in and edit that message so that you can make a more straightforward and uh decisive message and not give the wrong impression before you send the message out to your actual human recipient and come up with alternate word choices in order to make sure

that your message is received the way you intended it and anyone who has communicated over the Internet can think of ways that this might have been helpful in the past, because again, language depends on so many different elements to get your meaning across, and when you reduce it to the written form, especially the written form online, where we tend to be very short with our our communication, it comes in very quick bursts, a couple of sentences here

or there. We lack all that body language, we lack that tone. It's very easy to misinterpret. I'm sure there's been an example in your life where either you got offended from receiving something that was meant in a way that was different from the way you you interpreted it, or the reverse happened where you sent a message and somebody had a reaction you did not anticipate because they could not tell what tone you were using just from the words you were using. Machines have that same problem.

In the future, an analyzer like this tone analyzer, it could be incorporated into word processors, or email servers, or email services, i should say, or social media platform. So you start typing in your message and before you hit published or post or send, you could analyze that text.

It could tell you what the tone is and then you could say, oh, no, that's gonna come across totally the wrong way, and you could actually fix it before you posted it or sent it, and then you wouldn't have that awkward decision of whether or not to edit something, or, in the case of Twitter, which continues to refuse to

allow you to edit tweets, to delete a tweet. I deleted a tweet the other day when I posted a link to a news story, and I had done a rookie mistake, one that I try to avoid, but I did it this pastime, which is that I didn't think to look at the date when the news item had been published, and had been published a full year earlier, so it was not new news, it was old news. And uh then deleted the tweet and it wasn't up

for long, but I still felt dumb about it. It would have been nice to have been able to check that. Although that's not tone obviously, that's but similar in the and the idea that you want to check before you end up offending someone, unless you're one of those jerk faces that just sets out to offend people, in which case rethink your strategy. There are better things to do. It's just as you can make just as big an impact being a positive person as you can being a

jerk face. I know it can seem like it's more work, but it's also more rewarding in the long run. Okay, soapbox done. So. There is a demo of the tone analyzer that's available online, and back when we were recording Forward Thinking, the demo worked in a way where it would tell you about emotional tone and break it down by percentage. It's a little different now, but I want to tell you the what words and the results we got in the past because they were so much fun.

Granted you would get a different result now because the tone analyzer has been tweaked since we recorded that episode. So, when we recorded that episode, one of my co hosts decided to put a a sentence that is somewhat known in literary circles into this tone analyzer and find out what it said. And the sentence used was it is a truth universally acknowledged that a single man in possession

of a good fortune must be in want of a wife. Now, the analyzer said that this emotional tone was cheerful, the social tone was seventy six percent open and fifty one agreeable, and the writing tone was analytical. You can also view the sentence in terms of word count as opposed to the weighted value of individual words, and using that view, five percent of the sentence sentences were in an emotional tone, eighty nine percent in a social tone, and five percent

in a writing tone. Now, the analyzer highlights each word according to how it classifies them, so emotional words would be highlighted in red or pink in that older version of the Tone Analyzer, social words would show up in blue, and writing tones would be in green. And you could click on any word and the analyzer would offer alternative words that you might want to use and classify those words in the tones that they are associated with, so that you could shape your message to meet the tone

you wish to convey. Also, the Tone Analyzer demo used the business letter format as the means of comparison, so, in other words, we compared Jane Austen to a business letter. Presumably, if you were to use a full version of the analyzer, not just the demo version, you would have other options so you could compare it with other models, not just a business letter. Joe McCormick. He included an excerpt from

Dostoyevsky's Notes from Underground. That excerpt was, I could not become anything, neither good nor bad, neither a scoundrel nor an honest man, neither a hero nor an insect. And now I am eking out my days in my corner, taunting myself with the bitter and entirely useless constellation that an intelligent man cannot seriously become anything, that only a fool can become something. The feedback was that the emotional tone had anger at cheerfulness at so happy anger negative at.

The social tone was agreeable zero percent conscientious, zero percent open. The writing tone was analytical, zero percent confident and tentative. Joe would actually end up highlighting some of the words to find out which words were the ones that ended up giving that cheerfulness result. Those four words were good, honest, hero, and intelligent and that kind of are that That's important because those words, the way they are used, uh in

that passage are not used in a positive sense. They are positive words, but they're meant to show kind of a negation there not and not an assertion. So that really highlights a big problem in this tone analyzer, which is that it's tagging these words individually without context. So if I wrote the phrase I am not glad, it would tag the word glad and say that's a cheerful word.

But I said I am not glad. You if I told you I am not glad, you would not think, oh, well, that's a cheerful thing to say or a positive thing to say. But according to the tone analyzer, it would come across as a cheerful statement because it had tagged that word as as being cheerful. In the other words are not that strong, They don't They don't warrant being

tagged in a way like that. Now, over time, we might have a tone analyzer that can actually take context into account, and then you would learn a lot more about the actual meaning behind a phrase. It would be

more than just tone. So if you were trying to get across tone by using more complicated and subtle word choice where you're sort of being kind of uh poetic in your expression, you're trying to get across a feeling by using irony or sarcasm, then a tone analyzer like this would totally miss it because it would just be counting the hits and not understanding the usage. There the hidden meeting the word play, so that is going to be a real challenge. So it's kind of another interesting

use of IBMS Watson. There are a lot of other ones that we could talk about, like Chef Watson, which was my favorite. Chef Watson would generate new recipes based upon ingredients that you would tell it that you had on hand, and it wouldn't it wouldn't go and reference old recipes and pull one up for you. Instead, it would make flavor profiles based upon all the different combinations of food that were found in various recipe books and generate a brand new recipe for you right there on

the spot. And sometimes they were whacka doodle crazy, y'all. So in a way, you could say that Chef Watson was another another way of seeing how I b M S Watson has a lot of promise, but it requires a ton of work on the app level in order to leverage it and make actual practical use out of it. I have more to say about computers detecting sarcasm, but

first let's take a quick word from our sponsor. So back in two there were some researchers at the Hebrew University in Israel who designed a system called the Semi Supervised Algorithm for Sarcasm Identification or SAZI, and they used SAZI to analyze collections of nearly six million tweets and also around sixty six thousand product reviews from Amazon. They wanted to find rich treasure troves of sarcasm that turns out reviews and tweets. They fit the bill sarcasm is.

Really it's typically conveyed in some vocal tone, right and nonverbal cues. So you have to first go someplace where sarcasm is is rampant in text form to be able to really fine tune how you can identify sarcasm versus something that's meant exactly the way it's written on the surface level. So they started to map out the various

features that were common in sarcastic comments online. So they were looking for things like hyperbolic words and if you're using a lot of exaggeration, that could be a key. Excessive punctuation was another one, especially ellipses, which I tend to use a lot, though I don't know if I use it so much for sarcasm as I do for just timing purposes. To indicate this is the beat I would take if I were saying this out loud, I guess that's just as irritating. Though, also how straightforward is

the Senate structure? And they gave it examples of sarcasm. They fed it tweets that were tagged hashtag sarcasm, so that the machine quote unquote knew that that was already a sarcastic tweet and could start to analyze it and

build out a model for what sarcasm is. They also fed at a bunch of one star Amazon reviews that had been judged to be sarcastic by a panel consisting of fifteen human beings, and the system was told it had to rate sentences on a scale of one to five, One being not sarcastic, they mean exactly what the Senate says, five being holy cow, this person should write for the Onion. This is incredibly sarcastic. SAZI could identify sarcastic Amazon reviews

with precision. Not bad, But when it came to Twitter it did even better. I think, probably because there had to be very short messages on Twitter. This was before Twitter had even expanded to characters, so it was still back in the one character days. The precision rate for SAZI for Twitter was so it was really good at detecting straightforward sarcasm, the kind that a lot of people on Twitter use because you have limited space so you can't really set it up in a more complex way.

But it was also uh more prone to judging things as false negative evaluations rather than false positives. In other words, it was more likely to look at a negative sarcastic message and say that's not sarcastic than it was to look at a straightforward message and say, no, that is sarcastic.

So that was kind of interesting back to Watson. Another use of Watson came out of the Milk and Institute Global Conference at IBM showed off some research that it had been working on internally, and it was calling this

research debating Technologies. This was a project in which IBM was trying to see if they could feed a computer raw information, have the computer synthesize the information, understand that information at least on a computational level, and then create a a debating strategy for both pros and cons they on that information. So it would take a huge amount of content like all of Wikipedia, for example, and then on any given subject that would be covered in Wikipedia.

It would be asked form an argument that is in favor of or is against a concept, whatever that concept might be. John Kelly of IBM showed off in a demo how the tool could be used to predict pro or con arguments about a subject based on a body of information. So you might be able to use this technology in order to anticipate what an opposing person might say on any given subject. Let's say that you are getting ready to debate a topic. You might feed that

information to a computer system using this Watson platform. You might feed in a ton of information, and then you might say, who is a man and someone who is against this particular topic, whatever it might be. Uh. Let's say it's it's it's renewable energy, and the uh the efficiency of solar panels, whether or not it makes sense to invest in solar panels. Let's say that your stance is that you have to argue for solar panels. You might say, what would someone who wants to argue against

solar panels say? And then Watson would analyze this information and return to you what it thinks would be an argument someone would use to support that that stance, and then you could prepare for that, which would be an incredible tool. I mean, you could think of this as

for political debates. It would be amazing. You could think of how you might want to prepare so that you can argue intelligently against an opponent, and you can already anticipate what that opponent is going to say, because you know their general stance on a topic, but you might not know what tactic they might use to support that stance. Maybe politics isn't a great choice because that's not always in the realm of rationality. That often falls into a

call toward emotional response rather than rational response. That's more of a a commentary on politics in general, regardless of what side you might be on, all sides do this anyway. He actually showed at this demo a different example. He said, what if you were to take the sale of violent video games to minors should be banned. That's the topic, and that the computer would then go through all the

information and had access to. It would end up sorting out all the parts that were relevant to the discussion, so it just put those aside and that would become the core of the data it would reference. I would then go through and identify basic statements as either being a pro stance of banning violent video games to my nes or a constance for that saying no, we should

be able to sell violent video games to minors. The tools scanned four million articles, it returned the top ten articles that were determined to be the most relevant to that particular debate, and it scanned approximately three thousand sentences from from top to bottom, and it then identified sentences that contained candidate claims that would be statements that would either be interpreted as being pro or con for the stance.

Then it identified the parameters of those claims. Then it assessed the claims for the pro and con polarity, then constructed a sample pro or con statement. And the statements in the demo were kind of interesting. And since the computer is constructing arguments based upon what people have already written, it would reflect a lot of vague statements that aren't

a firm stance. So, in other words, like it couldn't take a bunch of stuff that was written that it sell did not take either a pro or constance, and then transformed that magically into the perfect pro stance or the perfect constance. Uh, it's dependent upon the words that human beings have already written, so it could not magically come up with a killer argument if the data that had been written about this subject didn't come down on

a firm stance one way or the other. Um, the point of the demonstration wasn't to create a tool that could either troll people or counter trolls. It was to show that a computer could be useful to aid in

the reasoning process when you're making a critical decision. Again, to go back to that medical example, it could be used to help a doctor determine which diagnosis is the most likely to be accurate for a patient, what what course of treatment might be the most helpful for that patient, and thus it could have real practic coal use outside

of this more esoteric, interesting uh debate news. Now, will we see computers in the future able to detect sarcasm just as easily as your typical human being can when given the right circumstances. And I use the word typical reluctantly, but you get what I mean. I don't know. It's gonna take some time. It takes an awful lot of processing power too. You have to remember that for these neural networks systems, the ones that are running these these

various platforms and programs and strategies. They take up a lot of processing power because our brains have billion neurons in them, so we have a very sophisticated supercomputer sitting in our heads. Moreover, our brains are insanely energy efficient. They require about the equivalent of twenty watts of power.

A supercomputer needs a lot more power than that. So while we're seeing advances in this, it requires so much processing power, so much energy, it is not a practical approach to most forms of computing, at least from a consumer standpoint. You might see a future where the sort of stuff is all in the cloud and then we can access it through an app or a program or whatever.

That way, you don't have to have a supercomputer sitting on your desk in order to tap into those, uh, those capabilities, but you have to have an Internet connection, which most of us these days tend to have fairly frequently. I mean, there are a lot of people out there who at this point have had a persistent Internet connection for pretty much their whole lives, which blows my mind. But that's the kind of world we'd have to live in in order to really take advantage of this at

least in the near term. I don't know if we're ever going to see a computer that can analyze, say, an article from the Onion and not only point out that it's being sarcastic or ironic, but also point out why it's funny. I think at one point, when you start analyzing comedy, there gets to be a level where nothing is ever funny ever again, but it is a really interesting problem. So that's whether that's that's this look

back on if AI is ever going to understand sarcasm. Well, guys, I hope you enjoyed that classic episode of tech stuff. I guess I guess two years old isn't old enough to be classic. That uh that that only somewhat less than fresh episode of text stuff about artificial intelligence and sarcasm and things of that nature. I am constantly impressed

with how artificial intelligence is advancing year over year. But when you look at what it means to be human and the ways that we humans interact with one another, and the ways that we can communicate complicated three things, sometimes just through you know, subtle methods that are not overt or or you know, directly spoken, it reminds us that machines have got a long way to go in order to really grasp what it is to be human, So unless you're Commander Data, you're probably struggling a bit.

So I hope you guys enjoyed this. If you have suggestions for future episodes of tech Stuff, I've got a few episodes based on listener suggestions coming up soon. But if you want to get your suggestions in tweet me. The Twitter handle is text stuff H s W and I'll talk to you again really soon. Text Stuff is an I Heart Radio production. For more podcasts from my Heart Radio, visit the i Heart Radio app, Apple Podcasts, or wherever you listen to your favorite shows.