Computers Know If You're Sarcastic? Yeah, Right.

That's true, among many other things. True. In fact, they also don't understand our music, right, they don't. They don't get my clothes, my style. Uh. Now, we're we're talking about how computers, you know, trying to teach computers how to understand what we mean when we say things. And

we've talked about this before. We've talked about computers in natural language and how that's a really tough problem getting computers to understand the way we humans communicating naturally yea, and especially trying to get them to mimic it or or feed it back to us and participate in a conversation that's convincingly human. That's the standard problem of the

touring test. Yeah, even if it's not convincingly human, at least useful, right like we you know, I would settle for a computer that would understand what I mean when I say things a certain way and give me the stuff that I'm requesting, even if it can't hold a conversation with me. Right right, We don't always need Siri to to respond back if if she could just find

what we're bloody looking for. Cortana would be another great example when those ten has just been released the day we're recording this, and I'm seeing a lot of reports about how Cortana is almost but not quite awesome. You know. One of the things that's interesting to me about Siri is that Siri is playful Siri. Siri will sometimes be koy and sometimes like play along when you're when you're being I don't know, suggestive or weird at her. I

don't say this from personal experience. I've read about this, but I get the impression that that is not original like synthetic behavior, that it has been assembled by series logical engines. That is more of a hard coded behavior. I think that's more of their sort of jokes put

in directly by the programmer. The programmers anticipate the kind of like they just say, Well, if I found out that this audio personal assistant could answer questions that I ask of it, what are some of the ridiculous questions I might ask? And they probably made a list and and that's probably the basis for a lot of those funny responses, things like where can I hide a body? Yeah? Yeah,

but but that's all coded by your word choice. It's not like it's not like, you know, Siri is gonna listen to you say something like, h Siri, where can I find another pizza place? Because I'm so interested in pizza today? Like she She's not going to come back with with with like, well, maybe you should try some chicken nuggets this time, fatty, Like I mean, like I was thinking, it's Taco Tuesday, chicken nuggets. The cure to

facts that would be a revolutionary episode forward thinking. However, we wanted to talk today about I learned it from Laura. We wanted to talk today more about how computers might soon or at least down the road, learned to recognize not just what we say, but what we mean when we say it. Right, So could Siri participate in a sarcastic sort of caustic back and forth humorous exchange with you without that being hard coded by the programmers, could

could Siri detected and even and even synthesize it herself. Right, So to start this, we thought it might be useful to kind of give a quick overview of why there's this us connect between natural language and what computers understand and basically computers machines work in uh in machine code or assembly language. Assembly language is generally defined as a very low level programming language, sometimes a one to one correspondence with the architecture of the actual machine you are

using it on. So the thing about this, the defining characteristic, is that computers understand it and humans have real trouble with it because it's it's so far removed from any sort of language that we use. It would be like looking at a just enormous page, just a block of texts that are all zeros and ones, and trying to make any sense of it. Um. I assume that most human beings, the vast majority, would be unable to do so. So we humans communicate natural language. That's what I'm doing

right now. And natural language isn't just agree and agreed upon syntax and grammar. It also has all these other elements to it, implications where you know, you don't say something outright, but it is implied by the way you say it, or the tone of how you deliver something, including sarcasms. Sure, even the gestures that you're making or

the facial expression. Yeah. I think we talked pretty recently on this program about how one major difference is that humans learn language by induction rather than by you know, we don't have an explicit list of rules and uh and definitions, Like we don't learn how to speak our language by reading a dictionary in a grammar book. You just sort of gradually into it the rules based on experiencing them. Yeah. Yeah, and we free associate a lot of stuff with a lot of other stuff. That's true. Yeah.

So one thing that we have to do is find a way to bridge that gap between the way we humans communicate and the way machines understand commands. And we do that through programming languages and compilers. A programming language, depending upon whether you call it low level or high level, is something that may be easier for a human to grasp. In general, if we say it's a high level programming language.

That means it's more like the kind of languages we use, although if you're unfamiliar with programming language, it still looks like gibberish exactly. Yeah, this is a funny fact about programming languages. People often think of them as something that

helps the computer understand. That's not what it is. It's something that helps the human understanding its programming language is a tool for you, right, it's so that the human can say, uh, this is this is the input I'm going to give you, this is the desired output, make it go. And so on the machine side they have there are compilers and compilers. The job of a compiler is to essentially translate, to switch from that programming language

into machine language. So it's this combination that allows machines to understand what we want them to do. But more recent work, really this has been going on for decades, but truly in the last decade we've seen a lot of work done in trying to come up with ways where machines can deal with natural language and the and if it is obvious, because it removes a barrier between use, you know, a human trying to use a machine, and

the machine doing what what the human wants it to do. So, for a very simple example, if you go on your computer and you wanted to find a particular piece of information, you could just type something in and it would understand what you meant by that, so it wouldn't return a page of search results that are in some way related to what you wanted. It would return exactly what you wanted. Uh.

So that that's a simple example. There are lots of other examples of this, but we're still just at the very early stages of getting a good grasp on natural language. We're getting better at computers parsing sentences to at least make an educated guess as to what we want, but we're not all the way there yet. But we wanted to talk about some interesting tools that are getting us closer, and one was one that was reported on in July two thousands of team that I really wanted to talk about,

which was IBMS tone Analyzer. Yeah. I had some fun playing with this earlier. Today. We can talk about the results I came up with later, but I think a common theme in our analysis of this will be that it's interesting, but maybe more for the reasons that it fails than for the reasons it succeeds. Yeah, I would agree with that. It's kind of a very advanced version of Clippy almost in that way I'm like, oh, it's really cute. The way that you have no idea what's

going on. I almost think of it as as like and of evolution of just word count. Yeah, like it's still counting words, it's just now classifying what word is, what like or what general categories words fall into. But it's part of the Watson development cloud. Uh. And that's that if you are familiar with IBM S Watson, you know, you know that that refers to the machine that played

on Jeopardy and beat to Jeopardy Champions. Yeah, which makes it hilarious that we're using words like cute to describe something is so ceated with Watson yea one of the most amazing computers ever put together. It's really a function of how difficult the task is. Yeah, more so than than how dumb this that's not that's not what I'm saying, No offense, wats no, no, totally that I'm on the

same page. Because Watson was obviously amazing. It was able to take Jeopardy clues which are in the form of an answer and come up with the appropriate question more frequently than not. And we've talked a little bit about how it did this, and that it was able to come up with potential answers or rather questions if you prefer, and then judge how probable that particular response was to being the correct one, and if it met a certain threshold, Watson would buzz in and offer that up as the

response to the clue. Yeah. And so what made Watson interesting was different than what makes most Jeopardy contestants interesting. It was because most Jeopardy contestants are competing based on their essentially their volume of trivia that they have contained in their brain. They're not having trouble discerning what the clue is trying to say, right, yeah, yeah, like like they get the puns in it, and and they get the sarcasm or whatever it is. I guess Alex Trebeck

isn't sarcastic very often. But you know, references as well, they cultural references contest. Yeah, right, So for them what's impressive is, Wow, I can't believe that lady knew all that stuff about ancient Rome and about you know, records and about video games whatever they talk about. Yeah, but you wouldn't be impressed at all that she, you know, makes sense of what the clue is saying. And that's

the deal with Watson. What it's not interesting that a computer has all that knowledge because it can just you know, have terabytes of memory that it runs through. What's interesting is that it knew what the question was referring to, right, and that that was one of those things that really opened up a lot of eyes and said, wow, this is exciting. A computer is, at least on some level

understanding what it's supposed to be looking for. Whether that you know, that understanding doesn't go on to the same level as it would with a human, but it's still really impressive because generally, you would have a computer and you'd give it some sort of word play response, and chances are it would not come up with an appropriate answer. It would either not answer at all because it wouldn't hit that threshold of certainty, or it would give them

an incorrect answer. And you know, once and once or twice Watson gave funny wrong answers. It's not like it was infallible, but it was. It worked more than it didn't work and in fact beat the two champions, so pretty exciting. Well, the Watson Developer Cloud is UH sort

of it's it's kind of an umbrella. It's a it's an application program interface also known as an a p I, and developers can use it to leverage this cognitive computing approach, this language wrecking mission approach natural language recognition UH, and then leverage that into other applications. So Watson itself has gone on to do lots of work in other fields, and most notably medicine, being able to help doctors end up looking at various means of treatment, even personalizing treatment

for patients with certain illnesses or conditions. But this was something where you know, if you were a developer and you wanted to try and leverage this technology that had already been built, you could then build your own thing and make something really special. And one of those things

was the Tone Analyzer UM. So it it searches for social, written and emotional cues and a body of text in order to analyze the tone of the overall text and to tell you how is that coming across in an analytical way, Like you know, I I have tested the block that you have written and it comes across as seventy three percent cheerful, which sounds really weird, but that's

essentially what you're getting. Yeah, yeah, And it's important to note that that this is harder in written text hypothetically than it is in spoken text because of all of those clues that we get from each other when we're talking out loud. Sure, I mean, it's much easier to detect sarcasm in person. I mean, you've probably had this experience, even though you're not a computer. You are a real human I assume, Yeah, we're guessing at least half of

you are. Assuming you are a real human, you have probably had instances before where you can't read sarcasm in an email Why why is this person so angry at me? And then you need to include a winky face right there, like I was just joking, and then you feel foolish. But I mean, the lack of all this context that we get from body language and tone and stuff can be difficult, and in fact, that's part of the application of this, but I'll get into that in a second.

So essentially, what this tone and lizer is doing is it's it's examining all the words within that block of text and tagging those words based upon how a predetermined categorization has been set up for the tone analyzer. So certain words fall under the general category of cheerful. That's a great example because it pops up a lot and uh. Then it ends up giving you a percentage of the overall tone of the piece. You can actually do it in two ways. You can get a word count or

you can get a percentage approach. The word count just tells you how many of the words within a given block of text fall into each category, So whether it's social, or it's a writing que or it's an emotional cue. And then the percentage gives you kind of a what what is the thrust? Like? What was the impact on the reader? So that suggests that there are waitings to these words, right, that certain words are weighted as being

more powerful. I found the percentage results to be more interesting because the word count results just tell you most of your words or social orientation, which could be simple things like the and but or like that doesn't have that doesn't impact a lot unless you're using them really well. But the words that were or butts can be extremely

extremely uh powerful. Yes, yes, but but Ideally, what this would allow you to do is tell how your work might impact a reader before the reader has a chance to to see this written work, and you might be able to then revise that work if it doesn't seem like it's getting across the message you had intended. So it's it's kind of like a hey, listen, uh, I know you think you might be coming across this way, but this is analytically how you are coming across. Do

you want to option to change up some of this stuff? Yeah, maybe you could change some of these aggressive words to be more friendly, for example, yeah yeah. Or it may be that if it's in a business letter, maybe the aggressive tone is exactly what you wanted. So it gives

you the opportunity to go the other way. And in fact, what will happen is when you use the the app, it highlights each word or most of the words, almost all of them in a block of text, and then by clicking on it, you can have options to change that word to to a synonym, and it even categorizes the synonym according to the various social writing or emotional cues.

So if you wanted to make it a more emotional plea, you could do that by going under the emotional category and choosing a synonym under there, and presumably this would make your presentation or letter or whatever it is more have more of an emotional impact. I didn't I didn't understand some of the options it was giving me with the synonyms, because at one point it's suggested to make my thing more agreeable, I changed the word bad to

the word lousy. Yeah. Yeah, I mean, well it's more jokey like, kind of like like it's got I think that lousy has a slightly softer connotation. Lousy just bad, I would say, comes across as a little more playful, right, Like if I were to say Lauren is a bad person versus Lauren is a lousy person. Yeah. Yeah, you know, if you're just a lousy person, then you know you can pick those louses off of that shampoo. Yeah, much

better than being bad. Yeah. It reminds me of that amazing Michael Jackson song Lousy video for that was amazing. So the idea of being with a tool like this in the future, you could actually build this functionality directly into something else like a word processor or an email program or ta most useful. Right, the idea being that, or you save a document before you send an email, before you send off that text message, or update your

Facebook post. The analyzer could tell you, oh, hey, by the way, you're coming across as a told jerk face, this should be beside the tweet button. Yes, it should be well and I think, you know, making it something that is um like word count or spell check, spell

check or even grammar check these days. Yeah, I think as long as it didn't get in the way of what you were doing, if it were an optional thing for you to check, or it was you know, unobtrusive and be great, underlined it in in fuchia or something like that, you know, whatever color is left over, right, so exactly something that's not going to easily be confused with the colors were already using for everything else with

grammar and spelling errors. But it would be helpful, and uh, it might mean that you would avoid some of those situations where you dash off a message to someone thinking it's perfectly fine and they receive it and are offended or confused or or hurt or whatever that may be. Well, let's talk about some examples. Yeah, so there is a demo available online. Uh, you can go to the tone

analyzer website and actually try the live demo. It allows you to paste in or type in a block of text and then you can analyze it um And so I decided I would throw in. It took me a long time to decide which opening line I would use from all the different novels that I love, but I chose this one. See if you recognize it. It is a truth universally acknowledged that a single man in possession of a good fortune must be in want of a wife. Yes,

is from Black Books. The series actually is used in Black Books, but Black but that's not obviously where it's from. At any rate, it's Jane Austen quote obviously. So the analyzer said that the emotional tone was cheerful, the social tone was open and agreeable, and the writing tone was analytical. Uh. Looking at a word count like Joe was saying, not

as interesting. So I just went with the percentiles. But if you did look at the word count, only five percent the sentence is in an emotional tone, in social tone and five percent in writing tone just by words. But when it looks at the impact, the emotional impact and the UH and the social um or the writing impact rather is much greater than the social impact. So in that case it was almost like a third or

third and a third. So even though there were fewer writing cues and emotional cues in the sentence I picked, they had a greater impact than the social cues did, which I thought was kind of interesting. But it also shows that when you when you do this, you see how how the analyzer is picking out each word and classifying it. Yeah, it breaks down by color. Yeah. Yeah, So if you are looking at the emotional words, those

are highlighted in shades of red or pink. Uh. And you know, the the emotional one is divided up a little bit too, so that way, like cheerful is one of them, and I think that's a very pink color that they used for cheerful, And so all the cheerful words were bright pink um. Then social words were all in shades of blue, and writing tone words well in shades of green. And if you clicked on them, that's where it would give you the option to switch those out, so that you can you know, if you did you

did you really mean in want of a wife? Uh? But also I should point out that the results we get are you know, one thing you have to keep in mind is what is the basis of comparison, because it's not just a universal you know, text analyzer, it's actually analyzing it against a standard. And in this case, the standard they were using was the standard you would use for business letters. So according to you know, compared to your business letters standard, Jane Austen, you know, it's cheerful,

so I'll buy that. But yeah, it's one of the other things is that I think a full tone analyzer would have different comparisons you could use, not just the business letter roach, it would also like compare you to threatening letters from creditors. Yeah, and and maybe maybe we can finally get that direct computer comparison of Jane Austen

to like Hemingway or something like that. It would be interesting. So, you know, I know there was an article in which an author, uh or the writer of the articles said that they had compared themselves to Mark Twain. Oh yeah, I read that, yeah, which was entertaining, and said that according to the analysis, the two wrote in a very similar way, like the percentages came out in a similar way, and actually used that as a means of talking about the limitations of that but Joe, you used an example

that also kind of showed some limitations of the tone analyzer. Yeah. I was like, well, what's it gonna make of something really philosophical? So I put in a quote from Dustevsky's Notes from the Underground. Okay, cheerful stuff that the underground man is talking in his long sort of diary section in the first half, and he says 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 consolation that an intelligent man cannot seriously become anything, that only a fool can become something. All right, So what what did the analyzer have to say about this? Well, again, I'm going with the percentile feedback, not the word counter. This is more interesting. It says the emotional tone was anger

one percent seems accurate. Negativity one okay, yeah, I got that, and cheerfulness cheerfully angry and negative. Actually that's not a bad interpretation that passage. It is kind of manic in

a way. Yeah, yeah, it's it's gonna upbeat about the fact that that the world is bleaking dire and there's absolutely nothing to be done about to make me wonder what the five emotions in the head of the underground man all inside out would be doing at this particular point, right, So I want to say more about that in a minute, but just a little more on the result. Probably I'm guessing you're right. Yeah, but yet he refuses to do

anything about his liver problem out of spite anyway. So it also says among the social tone breakdown, it was fort agreeable, nous, zero percent conscientiousness, and zero percent openness. And then the writing tone was a hundred percent analytical, zero percent confident, and percent tentative. And I was like, well, that's about zero percent confident. Is this is really telling a description of notes from underground? So well, no, it's

actually like simultaneously a hundred percent confident and zero percent confident. Uh, tentative might be a good word. So anyway, I highlighted four words, and that feedback the it are, so it tells you why it's rating in a certain way. So I was looking for the word did that cheerfulness result come from? And it was like, look, you said the words good, honest, hero, and intelligent. Clearly you were being cheerful, right.

I mean, this is hilarious if you consider the passage in context, because the first three of those good, honest, and hero are counterfactual negations. He says, I am neither good nor bad, and I'm not these things. And then intelligent, he's not saying like it's good to be intelligent. He's talking about it being a curse to be intelligent. Yeah, and this actually highlights a problem with the analyzer in general, which is that it's looking at individual words, but it

cannot necessarily understand the context of those words. Yeah. Yeah, it's doing nothing to part the context, right. It's it's it's just isolating each word and then kind of doing a tally at the end and saying, well, here are all the good words, and here are all the negative words, and here are all the you know, the the indifferent words. And when you weigh them all out by there the there how many there are and the actual emotional weight. I don't know what the waiting system is, but it's

clearly not a one to one sort of thing. But once it all figures it out that here's the result, So it would probably give the cheerfulness thumbs up to an email that I sent to my boss if it said like, you are not good, you are not honest,

and you should not go on living. Yes, exactly, if you were to type I am glad and then you typed I am not glad, you would get very similar results in the tone analyzer because it's picking out glad as being a cheerful word, but it's not figuring out that one of those senses essentially is the opposite of

the other. You know, it doesn't know that. Uh So perhaps maybe one day the tone analyzer will actually be able to understand context as well, beyond on just these these you know, recognizing the individual words, but understand what collectively they are trying to get across, so that when you do analyze the tone of a message, it's more accurate.

So before you send that email, before you save that document, before you you know, deliver your presentation, you can get a cold unfeeling robot to tell you how much of a cold unfeeling robot you are. Cold unfeeling robot could say, you know, based upon the analytical uh or based on the data analysis of how what your word choice and the way you put them together, you're going to come across as a real minch. I mean, you know, are

you sure you want to keep comparing your coworkers to insects? Mr? Kafka? Are you are you sold on this being a bug thing? Uh? Yeah? And until until that point, it's really just kind of a interesting footnote in in the overall history of I B M and they're wonderful word processing technologies. They actually coined the term word processing back in nineteen sixty four in the marketing materials for an electric typewriter, the first electric typewriter that had a magnetic tape memory drive. Ye,

my dad had one of those. Uh not that, not that particular generation of word processors, but dad did have one was of that same Before we had a computer, we had a word processor. Um. And it also illustrates how difficult natural language and understanding humans. Uh this how

difficult that problem is for for computers. For artificial intelligence. Uh. We we see a lot of developments in AI that are really really promising, but we have to remind ourselves there's still a long way to go, and there are other things that we can talk about. Two, this isn't the only tool that's ever been built to try and understand the tone or whether or not someone's being sincere in a message. Right. Oh of course. So back in there was a group of researchers out of the Hebrew

University in Israel who designed Sassy. That's the semi supervised algorithm for sarcasm identification, and that's so awesome. It's even better because it's not Sassy with the y, it's s a s I yeah. Actually, really there should be a heart over the eye. I think so, I think so. I think that's probably if Sassy had hands, that's how it would write its own name, right, but it would be a sarcastic herd over the eye, an ironic one.

Hell is sarcastic. So so this so this team set Sassy to I'm gonna giggle a recent goole time to

say that name. Okay. They set Sassy to analyze collections of five point nine million tweets and sixty six thousand product reviews from Amazon and Okay, Since, as we have discussed, sarcasm is most naturally conveyed via vocal tone and nonverbal cues, they first had to map out what sarcasm actually looks like in text and and came up with kind of kind of a matrix of of like hyperbolic words and excessive punctuation or using lots of ellipses in particular, was

something that they earmarked and straightforward sentence structure diagramming sarcasm. Yeah, and I think this was probably the most difficult part part of their research, like everything else after that, Like like creating these patterns for the machine to look for was the most difficult thing. So they gave it examples of sarcasm by by feeding it tweets that were tagged like hashtag sarcasm and also one star Amazon reviews that had been deemed sarcastic by a panel of fifteen humans.

It's funny because they could have just had the computer follow certain Twitter accounts and say, like, you can be any tweet coming from this account is sarcastic. Also, remember this was back in so so Twitter was relatively new at the time. Yeah, knew enough that in their report about it, they spent like a long time describing what

Twitter was. Uh Okay, So they then instructed Sassy to rate sentences from one to five, with one being not sarcastic at all and five being super sarcastic, and they found that Sassy could identify sarcastic Amazon reviews with precision, and it did even better on Twitter, kind of unexpectedly because there's less context to work with in in tweets, which are short and kind of stream of consciousness. A lot of the time it's precision rate over there was seventy.

When you're limited to characters, it is is something of an art to get across sarcasm in a way that people realize it's sarcastic. By the way, not definitely an art, not a science. Oh yeah. I have posted many things where I thought, well, clearly people will understand that this is not an actual sincere statement, and I have been wrong. At least one person proves me wrong. Well, this will be the second time in a couple of weeks that I've had to bring up pose law on a podcast.

I mean, if you're saying something that sounds extreme to parody extremism, people will take you seriously because it's hard to tell. It's hard to tell the difference between a parody of an extreme view and an actual extreme view, right, yeah, yeah, That's why a lot of the kind of rival sites

that are popping up to the to the onion. I think fail really hard sometimes because I'm like, oh, you guys need to take it much further or just stop writing entirely, because satire just comes across as a lie, right, yeah, and slander is different things than satire, Yes, so, but

so so. This research team decided that Sassy was really good at detecting very straightforward sarcasm, the kind that in retrospect, lots of people probably use on Twitter, precisely because you have such limited space and limited context, so you have to be pretty direct. Sassy made a lot more falseness of evaluations than false positives, which indicates that a lot of the more subtle stuff or the more intricate stuff

was slipping by it. Okay, so could you give an example of, like the difference between straightforward sarcasm and non straightforward sarcasm? Uh? Yeah, sure. A simple one might be something like wow, Mondays are my favorite days ever? Exclamation point, exclamation point, exclamation point, and something a little bit more complicated. I can't say whether or not this was actually used in the study, but I cribbed this from um amazon dot com from from that banana slicer. If you guys

have seen that. Yeah, one of the reviews is I tried the banana slicer and found it unacceptable. As shown in the picture, the slicer is curved from left to right. All of my bananas are bent the other way, so, you know, a little bit harder for a computer to pick up on that, right. Yeah. There are other accused as well that they would rely upon, like the hashtag sarcasm. If there's certain phrases that without that hashtag, they're not sarcastic,

like I can't wait to get home tonight. If I just post that, then it seems like I can't wait to get home tonight. But if I do hashtag sarcasm, then you're vague tweeting and it's annoying. Well, no, then you know I'm you know I you know I definitely can wait before I get over tonight. You might not know why, but you know that I'm not looking forward to it, right, which is vague tweeting, which is annoying. It's not as vague as some of the stuff I

see on there. At least with the hashtag you realize what the tone is. It's not as vague at any rate. Yeah, So another example of this going back to IBM and and Watson. Actually, UM is the use of the the that language recognition, that natural language cognitive computing approach toward perhaps like getting computers to uh you come up with some arguments. Yeah, this is an interesting thing that I read about last year and wrote a blog post for

a website about is the Watson debater. Yeah. So this was a new iteration in the development of Watson technologies that was designed to look for arguments like statements in support or in opposition to a proposition. And this is interesting because it's more difficult than you might think to do this. So there was a presentation about it last year at the Milken Institute Global Conference. The Milken Institute is an economic think tank. I didn't know that. I

looked it up. I was curious, but uh yeah. The conference had a whole bunch of different people give a presentation about the UM the near future and it was kind of like you know what's next gonna is gonna blow your mind, kind of sort of the same sort of stuff we like to talk about here on Forward Thinking. And they had several guests. Among them was a representative from IBM, said John Kelly. Yeah, and and he was unveiling the debate of the first time to the public.

It was something that up to that point had only been discussed internally at IBM. Right, and so what he was demonstrating was the debater's ability to not come up with arguments because we're nowhere near there yet that level of processing and synthesis. But it could look at a whole bunch of articles and say, what are some statements

in support of or in opposition to a proposition. So they gave the example and the presentation of the proposition the sale of violent video games to minors should be banned. I love it because it includes banned Yeah, and and and it's it's cool. The process that this this tool

uses is pretty interesting. First, it scans pretty much everything and has access to UH and in this case the demonstration they showed at the conference, it scanned four million articles and this was just to see if the articles that it had access to were relevant to the actual question.

Then it picks some top contenders, yeah, the ten best fits to the topic at hand, and then scanned all the sentences in those ten best articles a yeah, going through three thousand sentences, and then started to classify those sentences as being either in favor of banning violent video games or against the banning of violent video games at

least the sale of violent video games to minors. And UH, they looked for sentences that contained what they called candidate claims that would either be one of those statements for or against the central premise. UH, and then they they it would identify the parameters of those claims and then assessed those claims whether or not they were truly pro or con and then put them into those those camps. So ultimately you would ask the computer, all right, so

what are the arguments for and against this proposition? And it would say, in favor of the motion, violent video games should be banned, it should be noted that violent video games actually cause aggressive behavior. And then you know, some other statements along those lines, and then it would say, in opposition to the proposition violent video games should be banned, it should be noted that violent video games do not

actually cause violent behavior. Right, there's no causation link. Yeah, which is funny because it'll it'll take to completely contradictory statements and cite them both. Right. Yeah, there are certain things that that leap out at you, especially with this particular example. And it's funny because I watched it and I made notes, and then I read the rest of your blog and posting, and you and I have the

exact same reactions. Yeah, one of them being that one of the statements that just rubbed me the wrong way was about how it was essentially said that video games, the playing of video games is a a popular pastime for boys. It says playing violent video games is part of a boy's natural childhood development. And it was just like, Okay, I know the computer is not sexist, but it just

has access to some sexist views. And this this shows the limitations of this approach, right, oh sure, well and and and it shows really the limitations of humans at that point to to a to okay, yeah, well, to to come up with convincing debate or convincing debate statements in in saying something completely unsupported and also in just making making stuff up. Yeah, so yeah, like why, I don't know what the funny thing to me was. It

had plenty of statements it could have chosen from. I like, actually found the Wikipedia article that it was cribbing its arguments from, and I looked at the article and I was like, there were better statements in this exact same paragraph you could have used than that one. Why did

that statement get elected? And ultimately, I don't know. One thing we we don't necessarily know is when it looked at the Wikipedia article, which could have been altered between the time it did its research and when you did yours. But I doubt that it was that significant if it was within that same paragraph, I doubt there was a lot of there were a lot of changes. I mean, if that particular sentence didn't appear at all within the paragraph,

then I would say, oh, something's happened. But at any rate, yeah, it's it's it was problematic with that particular example, but it does show that it's reliant completely upon the It's relying completely upon the content that humans have created undeniably since the beginning of time. It has been a natural part of boys child, since we dwelled in the halls of Castle wolf in Stein. I'll never forget the uh, the story I read about the ancient humans playing Wooly

Mammoth Destroyer and uh, you know games like that. So but no, I mean, yeah, you're exactly right. It's just reflecting the fact that all it has to work with is what humans have said. And it doesn't have a good criteria as far as we can tell for judging the difference between a good supporting argument and a bad supporting argument. Interesting, it can't parsing for is this in support or not? Yeah, it can't. It can't judge is this a good logical argument? Right? It could actually come

up with arguments that contain logical fallacies in them. That's the really interesting thing is that. And it's not because the that's not doing what's supposed to do. It's doing exactly what it was meant to do. It's just that the material it's pulling from itself is flawed. So, yeah, we can't think of it as some sort of magical

oracle that we can consult. And it has access to truth with a capital T. It has access to the same stuff you and I would have access to if we just poured over Wikipedia for a few days when

we were trying to research something and had no judgment criteria. Sure, but but it's you know, hopefully in the future or possibly in the future if they choose to continue developing it, it could turn into, if not an oracle, at the very least a source of of better advice well, and and the thing that they were talking about using it for didn't have anything to do with trying to figure out a debate like a political debate or or a

social debate. They were actually talking about UH using it in the context of UM again, the discipline of medicine, where let's say that you are part of UH a hospital administration staff, and you're trying to determine which policies and procedures you want to put in place in your hospital,

and so you have to do all that research. You have to figure out what are the benefits, what are the drawbacks of all these different alternatives, and potentially you could use UH technology like this that would then come back with the pros and cons of each approach, allowing

you to make a more informed decision. I'm sorry, I'm just loving the idea that the machine that identified playing violent video games as part of a boy's natural childhood development deciding what anesthetic does I get To be fair, it's not relying on Wikipedia in the second instance. Okay, all right, so we can relax on that now when we get the home version there and ask which demonstrable, which transform is demonstrably the most important, and then it

would be able to tell us. Yeah, I mean obviously, I don't think doctors are actually just going to seed all of their authority to the Watson debater and say, well, what do we give them? You know, they were they were specifically using this as the demo of what the technology can do, not as a you know, this is exactly what's going to rely upon. So instead of relying upon Wikipedia, to be relying upon the literature in the

medical field, the research that's been done. And even then I assume it would be in an advisory function nothing decision. It would essentially well, again, if it's getting pros and cons, then ultimately the decision still falls on the humans. It's not able to wait which ones are more important. Like it could say the pros are your hallways will be less cluttered, the cons are seventy of your patients will die, and like, I recommend you do this because you have

cluttered halls. That's not gonna be how it turns out. Well, I can see something like this actually being useful to me in my job, just as a shortcut for digging up leads on a subject. For example, like you know, I read an article about a subject I've never read about before, and the article has one pretty clear view on it, like it's in favor of X, and I don't even know what the arguments against X are. If there are any something like this could be really useful, like, oh,

here are some things to look up. I think it would be really useful for something like, let's say that we wanted to cover a controversial topic in physics, something like because I've seen it pop up yet again. The m drive would be a great example. Something that could scour through the actual scholarly material and come back with the pros and cons of something like that, so that we at least have a starting point. And I think you can go look up the what's behind the exact

kind of like the references in Wikipedia. Right, you go to a Wikipedia article, you scan down to the references, and you go to those sources to see if in fact they do reflect what is or if the article reflects what was in the actual primary sources, which is the proper way to use Wikipedia. Yeah, yeah, I agree. Yeah, And it kind of funny how often that's a dead link and you don't know if it's ever been alive. Yeah, Well, you can always go to archive or dot Oregon and

check it out. I've done that many times. Actually. Uh So, one of the things I wanted to talk about really briefly to kind of wrap this up, is is this, actually the future of computing is cognitive computing. This idea of creating a machine that can, at least on some level, mimic the way we humans think is that the future. And it seems like more and more people are leaning towards Yes, that is the future. But it is the future.

It's not now. Yeah, it's it's one of those I mean, I don't know if you guys can tolerate the theme song today, but but it's it's going to happen in a certain period of years from now, right, so that the neural network software, the hardware that's necessary it it requires a lot of processing power in order for it to even come close to what we humans are capable

of doing just naturally. And in fact, in that same presentation that that Kelly did at the conference, he pointed out that brains are really amazing and said that you know, they require very little power. It's the equivalent of about twenty watts, so less than a lightbulb. Most lightbulbs UM and if you compare that to a supercomputer, it's nothing. And you also look at the intricate connections that our brains have. We have billion neurons and then all the

different connection is between those neurons, trillions of them. It's

it's phenomenal what our brains are able to do. Meanwhile, if you wanted to create a simulation of that, they talked about how they used UM a supercomputer at the Livermore Laboratory that was a simulating a neural network like a human brain, and it required eight megawatts of power, So eight million, what's as opposed to twenty with one point five million processors, so compared to eighty two million neurons, UH capable of processing six point three billion threads simultaneously.

And it ran hundred times slower than the human brain. So just simulating a fraction of what our brains can do requires much more processing power, much more literal power like electricity, and it takes longer. So while this is awesome, and we love the idea of having computers that can actually learn as a post to you have to program it. You know, if you can teach the computer something over time, and we this goes back to the Google Dream stuff

we were talking about two. Um, that's fantastic, but we're still at a point where until there's a breakthrough, either on the logical side or the programming side, they're lagging way behind what we we humans can do, just because we're fundamentally different. But it is exciting that this is a trend, and I expect we'll see that trend continue.

And I love the idea that at some point I'm going to have a little app on my phone that will warn me before I send a text to my wife so that I can reword it and she won't be mad at me for for good reason. But you know, it might be a misinterpretation but accidental. But it falls on my shoulders that I did not word things in a way that misinterpretation. Did you? Did you really want to say that you're a lousy wife? Yeah, maybe you want to say bad. No, I don't want to say that, thanks,

But yeah, this was this was fun to look into. Uh. I recommend you guys, if you haven't played with the tone analyzer going go and play with that. Either take a paragraph from someplace or type something up. Or I even when I did the video episode about this. I took the first paragraph I wrote for the episode and fed it back through the analyzer, and it said I was very open and conscientious and cheerful, So I got

it right. Um, but yeah, you should go check that out because that demo is available for everybody, gold Star. You know what's going to happen when you run your secret underground diaries through it. It's probably going to say, like, don't ever show this to anyone else. So you, guys, if you have any suggestions for future episodes of forward Thinking, you should let us know to analyzer. Yeah, yeah, that'd

be great. Write up an email to us, put it through the tone analyzer, change of the words are bitrarily and then send it along to us and we'll see what do you think of it? Make it more cheerful? U. No, but seriously, if you have a suggestion, you should write us our email addresses f W Thinking at how Stuff Works dot com, or drop us a line on Twitter, Google Plus or Facebook. At Twitter and Google Plus, we are f W Thinking. Just search fw Thinking in that

handy dandy search bar on Facebook. We will pop right up. You can leave us a message and we'll talk to you again. Really, soon. For more on this topic in the future of technology, I'll visit forward thinking dot Com, brought to you by Toyota. Let's Go Places