football taping. maybe in portugal to one for all right, next meryl, thanks very much. they're watching day w news. now can the information you share on the internet help computers predict how you will vote coming up next. documentary series film looks at the role of artificial intelligence elections. i'll have more headlines for you at the top of the hour. don't go ah, with the green. do you feel worried about the meals of the on the green fence remains to change. join me for the green transformations for me to use for the plan.

ah, for almost a century pastors have been interpreting public opinion like just coming their method straightforward. ask a small group of people question and then use that sample group as a representation of the opinions of the entire population. nice meeting where you're welcome, good night. thank you for coming by. but in recent decades, the public has soured on answering questions, calls or door bells. and instead of being available at a single point of contact, people have become increasingly mobile, which makes traditional pulling more challenging me today. people are expressing their opinions and habits through mobile and internet technologies like smartphones and social media. that avalanche of information is creating possibilities for new

methods to explore what the public is thinking, the ah me today. data is available everywhere. credit card information, loyalty card information, demographic information, and community organization information. think about the things you sign up for. think about the events you go to. and when you click that little box that says i have read and agreed to the terms of service that is contractual. several screens of stuff that you didn't read, nobody ever reads, it wouldn't really matter if you read it anyway. so that means we've got information from all over the place, and that's actually what makes it quite valuable. many people give google

their email data by a g mail, their video data by youtube, their physical location, by google maps. so when you connect all that data together, it's remarkable. the intimate picture it paints with the individual, which i actually think eclipse is our own memory. and that we forget all the places we go to the the challenge is knowing how to extract meaningful information from what looks like random noise. that's where applied artificial intelligence, known as a i presents both promise and risk. the wildest dopey and science fiction dreams up worlds where machines rule over humans is more commonly understood by its engineers as the ability of a machine to learn and process information. the they consider it a human guided tool that learns by digesting massive volumes of data. like social media for example,

and then identifying patterns and the noise. what if they were in a high that could use those patterns to predict how humans would behave in the sure . like say, predicting the outcome of an election. ready campaign for the next canadian federal election has just kicked off. ready while prime minister jackson trudeau with the liberal party cops with a justice scandal in the ouster of to ministers, the conservatives, new democrats and black cubic law all have new and untested leaders. despite a tight 6 week campaign, the parties are slow to outline their platforms. it's unclear of voters will make their choices based on the issues the party or the leader in kansas. capital ottawa, a small startup, is using ai to predict the upcoming election seinfeld election? yeah, yeah. yeah,

this is do i feel like people with people that means this is like the 1st one where they're actually looking at the policy not the leaders, the 2 front runners. they mean nothing to me. it's really about who i think is going to invest for the economy for the environment. and i had heard a lot of people and obviously economy is always the top for everybody. but who has the best policies? me? physicist kenton, white and accountant. aaron kelly co founded advanced symbolic ce, or a s i in 2015. the market research company uses public data and a i to forecast consumer attitudes. you know, in the era of cambridge analytics and all that, people think it's some sort of video science like we have a shame and making things happen. i want people to understand that nobody is, this is just math and science, but anything else and it's probably, it's probably just like, it's always better except it's being done by poly and that is by phone operators.

they can use a similar approach to predict political opinions for be kind of hard to get information about millions and millions of people and apply statistical physics to it. for now, i've got facebook. now i've got twitter. how do i use that though, they don't work for either political parties or news outlets, kelly and white? so the electron prediction game is a way to promote their research method. think about when we do traditional market research, how many times we interfere with the subjects with people as we're studying them, we interfere by asking them to participate. we interfere in the results by asking a question and how we phrased that question. so our whole idea, we saw that with social media was a great opportunity to observe people in their natural environment. and aren't you going to get much better policy? and a much more utopian society if you know exactly what the people in that society

want. and i was inspired by science fiction growing up like a most foundation series where you can use physics and physical physics on people and population and map out the whole course of the galaxy. i was quite disappointed when i learned that that's not really possible. the explosion of social media offered white a potential treasure trove of data. 2 he could apply statistical methods from physics to crunch what might seem to others like messy and unusable sources of data. it was the birth of the a r s i cause cali. polly, it was sure for politics. so that's we just happen to call or we're going to car polly. we didn't put any thought into it except politics. you know, poly is made up of a number of different part little programs that are crawling the web and

collecting information. these programs contain complex algorithms which help them process the huge amounts of data. you've got an algorithm to say, when do you pick someone? when you don't little algorithms that analyze that person and come up with the person's male or female or richer poor young or older, black or white algorithms that allow us to find people that are talking about the topic we want to measure. if i showed you any one of these algorithms and how it work, cases are, you'd say, that's pretty simple. it's when you take dozens and dozens of put all these things together, that something more complex emerges. me, she's using more data than me or you are pretty much anyone here could understand and lifetime but what is it that makes poly intelligent?

and notice that if they sent, they said intelligence. so she is intelligent because she can grasp the concepts, mathematical concepts, and see patterns in people that we can't see ourselves. the but that description makes critics of a are uneasy. the method comes across as a black box outside which leaves not experts at a disadvantage to challenge the inner workings or its conclusions. in the face of skeptics, as i saw the breakfast referendum in june 2016 as a way to test their algorithms. citizens of the united kingdom were voting to remain in or leave the european union. traditional polls conducted almost daily revealed. the country was split down the middle of the

voting day approached. it became clear that undecided voters would likely determine the country's fate. meanwhile, polly was sifting through new k twitter data to predict users opinions without contacting them directly. polly was she remained right up until 3 days before the actual referendum had 50 percent voting for remain 40 percent voting for just like everyone else in the world. then there was a fascination of jo cox labor party, n p. john cox, who had been campaigning for remain, was shot and killed by a foreign extremist on june 16th 2016. 3 days before the referendum. we wake up, be checking with polly, and suddenly britain is going to exit the that it was different from what she'd

been saying every other day this week. and throughout this campaign, i was worried because an assassination of the black swan event lacks one. that is a rare event, one that my a i have never seen in none of the elections that i had trained her on, had there been fascination? assassinations are rare. so we told her ignore the assassination of jo cox. what is the result remain? success again, joe cox's assassinated exit. what had changed when she seeing that everyone else in the world was missing? the british pound claims to a 30 year low and world stock markets convulsed on the news that the majority of u. k. voters, one out of the european union, the 2nd largest economy in the world. the tale absurd things that we wouldn't even think to observe. we didn't ask her to observe the space. well, we started going through, david cameron had done

a many press conferences during that time. and there was one line is something he said it's right that we're suspending campaigning activity in this referendum. everyone's thoughts will be with joe's family, with her constituents. has this terrible time. she noticed that throughout the year that she was observing people, if people came online and they said, i'm thinking of voting for an exit. if they had a network of people around them, friends and family, who calmly talks to them about why we should have a remain and what the consequences would be over time. that network was effective in winning these people over to remain if they kept the dialogue going. but once people weren't talking anymore, a certain percentage of those undecided people would then go exit based on what she had noticed in the past. so she applied that algorithm and she got it bang on me to better understand polly's method. it's helpful to look at what she has in common

with traditional polling. ah, one way of measuring public opinion is by interviews, brief interviews are called polls. people are carefully selected as samples of the important groups of the general public. for decades, posters successfully reached people by landlines, direct mail, or knocking on doors, get 80 percent response rate, so all all calls were landlines. we didn't really have any cell phones. and people love to participate in surveys and you know, getting it wrong, getting an election wrong would be like, you know, falling out of a boat and missing the water. that's the 3rd. now knock in the house. looks dark. yeah. they're not home. but in the last 30 years, landline use and poll response rates have plummeted. you phone a landline in any house, you're much more likely to get an older woman than you're to get anybody else.

millennials, if you want to get millennials, you're probably not going to get them using landlines. you're probably going to get them using cell phone, but as, as likely or you're likely to get them online. traditional posters need to diversify their methods to reach different audiences. what you're trying to do is represent the political marketplace, the important political marketplace than voting population as accurately as you can . so what you need to have is good coverage of all of these different groups of the population. and then what you do is you try to look at them in aggregate to cover the canadian population in 2019 poster daryl bricker. combined landline polls with cell phone polls and online surveys. but pollies, a, i takes a different tech as our population gets more diverse, we actually need to have a bigger sample. i mean, canada is very different now than it was in 1970 or 1959 when we 1st started doing phone pulling. but we haven't really evolve the science since the 1950s. so we're still getting the same sample sizes. we're still approaching the science from the

same way, even though the population is changed a lot. now with social media, finally, we have the opportunity to involve this science for the 1st time in 50 years. ah, traditional polling has struck out on several recent election predictions. we're going to win the lighthouse, we're going to take it back. perhaps most famously in the 2016 us presidential election posters overwhelmingly predicted hillary clinton to be donald trump. if the lines are long tomorrow, please wait me trumps. when demonstrated the posters had missed the boat, they predicted the popular vote but missed the tight local races in swing states which delivered trump an electoral college victory. clearly, their models were just completely off. the actual percentage of the vote predicted

by the polls is actually what happened. but we weren't measuring the left for college. obviously, you go through a, you know, a dark night of the soul. whenever these kinds of things happen. it's too expensive and inefficient to pull regularly, by traditional methods in each electoral district known in canada as a writing. but social media is huge, scale makes that possible with a i talking about politics on social media or all your opinions on social media is now kind of the norm like that's become a normal way to communicate in the same way that people use stuff. i think long conversations on the phone frequently to now, of course, back in the day you couldn't just tap people's phones. you had to call up and ask them, but opinions, but, but now the thing about social media, it's interesting is when people are communicating their opinions on social media, often they're doing it in a form that is public. and that allows us to see what they're saying and to then derive their, their opinions from that. but not all social media platforms have data that are

relevant to white. we use a sample for data source, depending on the problem that we want to look at. we have found that for elections, we get the strongest signal from many posters and social scientists question the value of data from twitter platforms like quarter. and facebook is basically a group of self selected people. because not every canadians are on social media. and either they're on social media, got all of them, use it to discuss politics. so get it. and basically you're getting a slice of smaller slice of the public. i think it's like 12 or 15 percent. use twitter for news 20 percent. use twitter overall in my own lay understanding of that. i tend to think about journalists and like high engaged, super participants that are on twitter. so not everyone is on twitter. let's take the loan number, 17 percent. that is still a whole lot bigger than the current 9 percent or less. you get on diets. 17 percent

of a country of 36000000 is about 7000000 people that we had access to white thinks that a i can fit through the large, but typically young and urban user base of twitter and canada. and nonetheless, make forecasts that are representative of the entire population less than 3 weeks to go until election day. and i know at some point, you're artificial intelligence pulling system comes to a conclusion about what you think is going to happen on the 21st of october to early still, i presume it's too early. what both well for mr. trudeau is that the liberals have consistently started this campaign. been in 1st place, 1st place in the popular vote, or total vote, i guess, as we call linda or c count, speak count the just for okay, good. we're doing each individual writing so and that's as we saw on the ontario election. it makes a big difference just because you're leading in the total vocab in the polls

doesn't mean you're going to win. exactly. vote. efficiency is what with whereas, that in a 1st post passable system, that's right. talk to most people that work with data from lots of people. they will say i want to collect everything. everything everyone is saying on facebook and twitter and read it and look at it all. most critics assume air sy withdrawn what is called the twitter firehouse. if so, why would need a way to filter the huge volume of real time data and collect only the data useful to predict elections? given the magnitude of the challenge, critics dispute whites optimistic claims. now if i all, i did capital that fire pull in that stream of data, you're going to get a very skewed distorted view. that's why we don't count. treat people

are big idea. is what the posters have been doing. works actually secret it make really, really good sam, the people in other words, rather than trying to clean up the twitter fire hose white claims that he has found a way to bypass it before holly collects any actual data. poly 1st builds a representative sample, just like posters, those who say that you can look at social media and get to representation and what's happening at the writing level. it's an interesting concept. we're certainly exploring that ourselves. what i bet the farm to morrow and being able to do an election in which i predict every single right incorrectly based on social media. no. the 1st of all person people back to the writings difficult. it's difficult to figure out where that account tweeting from,

you know that, oh, job the on the internet. nobody knows your dog on twitter. nobody knows whether you are in china, russia, or right here in toronto. it's very different to say yes for sure. that accounts come canada, a representative sample of canadians must consist of actual canadians, white things he can confirm his twitter users are legitimate. we use the information that people put on their account. so we see that they're in canada. great. they say, i mean canada. so we verify that 1st test is your friends also in canada. look at your history. look at what you're talking about. if you're canadian, probably talking a little bit about maybe hockey or other canadian thinks you're talking about the cold weather. for posters. a sample is representative and random only when every member has an equal chance of appearing in the sample. imagine that

you had a whole big bowl of jelly beans and there was a red ones and blue ones and green ones. in yellow ones. you don't need to count every single jelly being to figure out that there's red ones or whatever the colors were. i said, if you, if all of them are equally distributed across the entire big ball of jelly beans. if you stick in your hand and you pull it out and you put down those jelly bands, you should have some representation of what's inside the jar. if you do it 10 times, you'll have a pretty good representation of what's in that or not because the jar is getting smaller, but because the samples become consistent. so that's what sampling is really all about. the historically, poster sampled responded by randomly selecting names from a phone book in the digital world. random and representative sampling requires a different paradigm. is a game called 6 degrees of kevin bacon and says,

i'll pick him movie act and try to link them back to kevin bacon in 6 art. and you can play the same game on facebook and twitter can pick any person, any celebrity, and track link yourself back to them in an average about 33, and 4 connections. anyone in the world? holly's algorithm using mathematical computation to guarantee an average minimum of hops between the twitter users that collects. that's how it defines virtual distance and randomness between users. the critics argue that still doesn't address the lack of diversity among twitter users. i can account for that, binds twitter and do it by saying this is what the census is, how my distribution should be. and then the algorithm, you say it differs too much for what you expect, and you have to wait until balances to, for example,

when i see that my sample starting a little bit lopsidedly younger than males, just stop collecting younger than males. you pause, you add any more into your sample till you start to get older rural female. so instead of looking like the majority of twitter, poly sample is limited to a mix of users that match the actual demographics of each writing. why claims this addresses? what critic see is a tragic flaw in polly's main source of data. and that is the key algorithm behind poly. if ever a bombshell hit a canadian election campaign, it happened this week. photos of justin trudeau in black and brown faced swirled a very complicated broo of opinion in the country. aaron kelly, what is poly telling you about the follows from all of this? it was a big bomb show. so poly saw mr to those numbers. the number of states seek out fall by 25 feet yesterday though he is rebounding this morning. he's 5 in that of

20 seat losses, but it knocked him from majority territory into minority territory. jake, me saying the n d p leader was the biggest winner yesterday. we saw his, his poll numbers go up. so he has really, he really shot through this. so how does an algorithm go from tweet to projecting and dropping feet? what is a tweet actually need? and what does it mean when we're tweeting? we know people are making jokes in there, i rodnick people are sharing more images and that creates challenge. it's harder to interpret means which of these shorthand graphics are often pop culture references . it's not like, i don't know what's going on here, and then i'm like, well, i don't understand this and my computational power is the kind of what living, breathing human. what is this poor computer? you know, he's gonna have to make sense of it. according to white, poly is not trying to interpret the meaning of tweets at all. so my 1st few 4 a's

and 2 elections forecasting. i ran the experiments and consistently, what i found was just the plain old mentions out performed the positive or negative sentiment. every time. ready all the tracks, the percentage of users in the sample who mentioned each federal party. then the a, i compare those results to historical opinion. patterns. after asking, go broke, involving an old photo of the prime minister in blackface mentions of the liberal party spiked in relation to the others without understanding the meaning of these tweets. polly interprets these changes and mentions as a drop in projected seats for the liberals. i think paul's are interesting because they're a flash point in the past, right? they don't predict the future there a moment in time in which you're getting a flavor of what the public is sort of thinking about the a i collect data constantly that makes it possible to identify patterns and user activity over time compared to poles that are held less frequently,

one of the biggest, most difficult things in pulling when you're doing these tracking. you see something happen for good or for bad? was one piece of data. is it going to you then you have to wait another day? is that real or was it just a blip? and you're always sort of in that in that feeling of how many points makes a trend, is it one? probably not. is it 2? maybe 3. yeah. for for sure. but you know, where is that point? in order to identify a trend poly use as public polls as a reference point, she analyzes patterns of user engagement. whenever her selected twitter users mentioned political parties. then she compares engagement with each party standing in the polls. based on those relationships poly predicts that for data indicates a trend to stable or not. people sometimes feel be angry about something about an issue and they might be angry about it in a moment of time. the poly is able, in a very unbiased way to see through that anger and say,

how long will it take the population to get over this anchor? and this hurt and will it carry long enough to election day poly trucks, twitter engagement, constantly, that continuous stream of activity is referred to as longitudinal data by studying data. and this way poly can identify patterns over time and then compare them to present activity. it also means poly can look backwards in time at the data to reexamine unexplored trends for the brown case, black to scandal. i don't think there was a single poster that asked a question if you saw a party leader in a questionable costume in their twenty's. pretty change your opinion? i don't think anyone thought to ask that question to the wonderful thing about poly in the launch 2 studies is that i can go back and i can get an accurate measurement

of exactly how you thought a week ago or a month ago. and that's something you can't do a week later or 2 weeks later by asking a question. i would suggest that perhaps canadians don't really care about blackberries as much as they perhaps profess do. on social media, there was a lot of outrage and i would even call it performative outrage on social media in particular, but being like, oh my god, i can't believe this as i really can't believe those. you can't believe that, like i'm, or, you know, a rich white guy from back would have dawn to black face at a, with the private school that had a party themed and ruby and nights like, i mean the party in and of itself without the blackface was racist in the given how massive that story was when it broke a week and a half ago. it has virtually, i want to say disappeared from the media,

but you see so few references to it. now, is this story essentially over to the best that you can tell through your polling? yeah, i think to keep bringing it up for the other parties. keep bringing it up. would be beating a dead horse at this point. i think she is recovering. and i think the slower recovery might be due to you know, a mix of issues and not just black face. just me saying really impressed a lot of people with his initial statements after the black face scandal broke back, goodwill is done now. has him gone? well, he's up to seats from before a black face, but that's down from having been up 9 before. so it's starting to, peter owed me like artificial intelligence and machine learning or so i use in different ways to mean different sorta magical things about computers. i think the way humans do come out are meeting pretty much is just these very sophisticated and very flexible algorithm which allow us to process very large amounts of data with, with very complicated relationships between them and have very flexible models

which will allow the computer to sort of figure out, unlike the reason why or why not just statistics or collection of coke, the difference is that she's able to do this mostly on her own. i don't have to go in and write a rules and say, now poly, when you see this, do this, she's able to figure things out on her own. polly's ability to learn is the basis for how she predicts future voting outcomes. the key to machine learning is a process known as training a statistical model. talk about training a model. we mean, we have some data where we already know the answers, or maybe it's how people voted in the last election. or maybe you know, how many people did or didn't buy guns, but we already know the answer. and then we use that to dr. francis a. okay. it seems like men who live here are more likely to vote for that person,

or many subscribe to this magazine are more likely to buy a gun. and we learn those relationships. and we say, let's see if we can use our model that was trained on a previous data to try to predict what's gonna happen next. ready election night 21900. ready almost $18000000.00 canadians have cast their ballots, including the advance pulse that represents a turn out of 66 percent, several percent higher than expected. a mental form, the government. what the conservatives win, the popular vote. days later, it's time to compare the actual results with the seat predictions federal election, 2019 wrapped up of course this week with a liberal minority government. this is the national seat projection that you gave us, which showed the liberals in arranged between 116 and 173. but essentially at 145, the tories at 123. the n d p at 48 the block at 46,

and the green somewhere between one and 5, and you have the possibility there of a couple of independence and a people's party. now let's flip that over. and this is what the national seat projection was on sunday. so the last one was saturday. this one is sunday, the day before the election. and you could see that things have changed. you've got the liberals now at $155.00 tories that $118.00. and then let's roll monday. and this is what the actual election results were. 157 liberal seats, 121 conservatives. 32 block, 24 new democrats, 3 greens, one, jody, wilson, re bold and nothing for the people's party. if you ever want to evaluate how well someone did predicting election, i think the fair thing to do is to compare them to other people who try to predict the election. if one company better than all the other companies. and that's probably a pretty good result. the day before the election, some traditional posters were only predicting the popular vote. others were also

willing to predict a liberal minority, but few publicly predicted a narrow range of seats ah, according to pollster $338.00 canada. that correctly predicted the outcome of 299 of a 338 writings according to advanced symbolic poly correctly predicted 308 . was this quote unquote, an easy election to predict. it wasn't an easy elections predict, but it was predictable, right? and i find it, you know, it's interesting because everybody was saying was so close, that's the, that's why being able to look at the seat count and do the writings is it's becoming almost a necessity if you want to do pulling. we saw it at 1st with the trump election in the states, and that's been consistent. it seems that elections are getting closer and closer. and so obviously when it's a minority government, it's a little bit harder to call. but this was consistent for poly that she always

thought as a liberal when you look at our election results, we did really well in the 2019 election. but there were some writings we got off. it comes down to we're making assumptions that all parties are campaigning equally on the last weekend before the election in all writings where they might have a chance. all the political scientists out there are probably laughing at me right now. turns out that's not true. turns out political parties, tick, writing is that strategically, they want to win. and we haven't taken any of that into account. it's why i would love to have a real open dialogue with the people that know the political science. both white and kelly probably claimed that poly spinal c prediction was more accurate than most established posters. although white does admit a lack of expertise and how campaigns work to critics of an a i method this

admission reveal some naive and damaging political assumptions. but to supporters, holly's relative accuracy and projecting seats shows great promise for a whenever i hear people say, i've got to sit back and say ok. * you got the thing well, you know, the big publicly traded research corporations that have more money to do this kind of stuff than anybody else has to do it. not to mention the google's in the, in the face books and the twitters and all these other people who have all this money and all the best data scientists all working on this kind of thing. they don't, they can't predict it based on their platform. better and posters and statisticians still demand more proof. they continue to doubt the value of twitter as a primary data source. and they insist that poly established a much longer track record of predicting elections before they concede this method

is reputable and accurate. so the common saying this uses a defame or when people buy products and on this as past performance is not an indicator of future performance. and i think that applies to predictive algorithms as well. if they've done well in a last couple actions. i think that works in their favor, but still not a guarantee and as they do better and better and more and more elections, i think you can start to trust them more and more according to a s i. polly's prediction model will only get better with more election data and with input from social scientists to refine their algorithms. we've come a long way to get to where a are you that today and a lot of what has helped a i get to where it is that today is the internet then explosion of what the explosion of public data comes with risks. social media is free exchange for users, privacy. personal data is then available for marketing. critics also warning that

platforms are using the data against their own users by making the platforms more addictive. when the car came out, there was no seat belt, there was no airbags. but when we discovered that, oh my gosh, these cars and getting acid and they can run people over. we are demanding better breaks. we start demanding airbags, we start demanding seat belts. the 1st 1015 years of social media, the argument was, let's not put handcuffs on this company because we want to see where and what they can do and how to help society. but we're now discovering all the car accidents. we're discovering all the harms that they can do. so for me and i think the next few years is going to be debating. what does regulation of social media looks like? i think the big problem is we're thinking of campaigning in an analog world. when in fact, it's all automated and digital, and neither our language nor our laws really anticipate the power that facebook has . and the way in which individuals are political parties or foreign governments can

use that power to tip the scales of the election. if not just muddy people's perception of the election, because the whole point we have an election is to legitimize the people believe rightly or wrongly that algorithms have taken over the election. well, then they're not going to see the results as legitimate. they're not going to see the government as legitimate, therefore it's going to make government's ability to actually govern that much more difficult in many ways, using data to get people to vote and pulling people to participate in politics is fairly beneficial. in fact, if you had asked me what could data be good for, it's like getting more engagement in politics. and so i don't think that by definition, political advertising is a problem where i think is that the way that online advertising works in the fact that requires so much surveillance to develop ad profiles. and that the way you're doing targeting is so much ed fraud. those are the kind of relations i think

canadians are feeling crete out about the way in which technology and surveillance is playing a growing role in their society. but i think they feel powerless. so we've had this notion of, if you want privacy, stay at home. if you want privacy only tell, but if you decide to participate in the public square, you put information out there you will be held to have said it's okay for anybody to do anything with that information. and that is what just does not work in the modern world. i think it's really quite pernicious to think that we'll, we'll solve this with consent and we're just going to change the consent in or consent out the fly. and that is that still the individual against this massive organization. and the great divide is they know a lot about you. you don't know a lot about them. we invented all of these structures and we can invent something else. polly's election predictions depend on the ongoing popularity of twitter and

easy access to its data. but if new regulations to protect user privacy or restrict the social media business model came into force, they could profoundly change the way people use social media. i don't believe social media is going to become more private. there will be a need for public networks or people can go to craig content that anyone can view and anyone can engage with kind of making a big debt on it to me. i don't know. i think maybe the traditional pulses are afraid of because it's the future me. there was a famous twitter shot that microsoft name in the chat box, but it was a demonstration of language. and it was a really,

really wonderful why people got wind of this chap on twitter. can you started saying, what can we do to teach this a i to the most offensive repugnant twitter account on the planet. and they succeeded. little a, i became a horribly racist you phobic microsoft had to show why do we as people feel the need to be miserable? mean bad to the art. show that example to the machines. ah, when you see bad children, usually. so when it comes to a i, when it comes to all of this that to me i northstar, i have to be a good parents. have to be the best parent for polly's me.

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