Thank you for keeping us company. This is Y254 News Highlights. Now we're moving to our discussion Monday, where we are talking about the Nairobi Summit, the 25th anniversary on ICPD, and the theme immediately is accelerating their promise. That will be the driving agenda, but for us tonight here we'll be looking at what will be addressed in the summit. I'll be speaking to Marie Gathwethi, the communication consultant and the CEO of Violate Bitty Foundation, and just to bring you up to speed with what the summit is all about and where it began, something has been put down or noticed to just to make us to understand and this is in regards to what happened in Cairo. Now in 1994, 179 governments and other stakeholders held an international conference on population and development that is ICPD in Cairo. They adopted a landmark after a faceted plan to advance human wellbeing by placing the rights of individuals and the center of the global development agenda through a consensus member states developed the ICPD program of action that lays out a plan for advancing human wellbeing, positioning the human rights of individuals rather than numerical population targets at the center of the global development agenda. The plan was to be implemented in 20 years period from 1994 to 2014 which was never to happen, but now today the world is aiming to achieve the United Nation Sustainable Development Goals by 2030 which is the SDG and Universal Sexual and Reproductive Health is essential to much of this agenda. Now ending poverty, security, good health and wellbeing, realizing gender equality and achieving sustainable communities among many other goals, agent and sustained efforts to realize reproductive health and rights are crucial. The summit will offer a platform for a broad range of public and private stakeholders which is something that will get to understand what will be happening there. Now heads of state and government, minister, civil society, youth leaders, parliamentarians, business representatives, media and others to come together and deliberate on the linkages between ICPDs and SDGs. Now the themes of this summit are five and that is what you want to address tonight. We begin with one which is the universal access to sexual and reproductive health as part of universal coverage and I want to begin with you Violet in regards to sexual and reproductive health and rights and inclusion. Would you support sex education and why? Good evening viewers, my name is Violet Mbitiasuvaad and sexual and reproductive health is is very key and it's also it was also recognized by the 179 member states in 1994 and that is why now because of them signing it now it became a program of action and sexual reproductive health is very key and they did this because at the beginning you found that there was high maternal mortality rate you also found that meaning high maternal mortality rate meant that when women were giving birth most of them died or even the kids died but upon now signing this program of action 25 years later now that we're in 2019 you find that 50% of now the maternal deaths have been reduced because of now these what's now the governments from all over the world did and you find that sexual reproductive health is very key because you find even among the youth they need to know this information so that they can know how to carry out family planning because you find like there is a there is you find like there is unintended pregnancy unintended pregnancy means that you with your partner and you engage in we can we can say sex and then you are not using a condom and then by accident now you get pregnant and it wasn't it wasn't it wasn't planned so that is why we are also pushing now even for family planning for them to access contraceptives for them even to access even this family planning services yes mariga we remember last year we had several cases of young girls doing kcp they were pregnant those are small girls i could say but now we having this summit and one of the agendas are to be addressed in regards to sexual reproductive health is about sex education to our young girls in school what's your take in this my take is that sex education is a human right at this pace because one of the most interesting arguments i've heard is that um the sex ed is coming too early for children you guys are joking the average age children having sex right now is 10 and 9 right people want sex ed for children to be at 16 how stupid how because you how do you give sex ed five years after your children are sexually active and that's that's that's what's happening sexual education doesn't tell children to have sex right no one sits in a class and shows them how sexual positions i don't know there's a lot of misconceptions about what it is sexual education involves talking to them honestly about sex about how it works about the actual physical effects doesn't involve things like scare tactics which we were involved in a lot of and statistically and through many countries have carried it out countries have effective sexual education programs tend to have children having sex later in fact if people actually read just a bit beyond their own religious beliefs and propaganda you'd understand that one would improve the livelihoods of all these people number two you'd reduce a lot of criminal activity criminal activity in terms of when you talk about the girls who couldn't do who were doing the kcp pregnant the majority of people doing this to these girls are not children they're adults these are 18 19 20 year old adults so one you'd have children prepared for sex even within their own age number two you'd have them have enough markers to know what sexual abuse is what rape is and it be able to report it and help to deal with some of these things and by three it helped them improve their own health one by reducing the stds and the std rates and two reducing the amount of and plan for pregnancies especially by teenagers i absolutely do not see how people are objecting to this many people will argue in the 21st century and things are not like they were before but now the people doing it are our small sisters our small brothers we could say our parents the current generation is not doing enough in terms of educating the young people of about sex but now if this summit goes through and every policy is passed assumably now with the comprehensive sexual education is it necessary for now and will it help us to achieve the reproductive health and rights as stakeholders in the sexual reproductive health field we have been engaging the ministry of health so that you can see whether now the curriculum can be introduced on on comprehensive sexuality education but you find like there is some resistance because you find like there are some stakeholders who may who are not are not for it but the good thing is that i think the time is ripe now because at the end of the day you find like parents themselves they don't talk to their kids about sex okay and comprehensive sexuality education we want it it is it is we are not saying that they should have sex now no it should be age appropriate when they are ready maybe after maybe they have finished school that is when now they can they can engage but what we want to tell them is that so that at least they can understand their anatomy so that they can know that if i do this i'll get pregnant and if i do that maybe i'll maybe make another lady or a kid pregnant but now when you introduce comprehensive sexuality education they become knowledgeable and once they become knowledgeable they will deter from it and they will do it at the right time because at the end of the day they will be told that if you do it at this moment these are the consequences that are going to happen so it is better you finish school and then now after that maybe now that is when you can you can engage in it but you find like some governments they are against it maybe because of culture okay and then you find that the blame is usually thrown on parents but parents they don't even know now how they can approach their kids and tell them about sex now the religious leaders now the churches they are calling for abstinence but not all not all all persons are equal there are some who can control themselves there are others who can't control themselves so for those who can't control themselves what is the solution and yet they are in school because at the end of the day if you don't inform them they will go to the internet and they can get this information from the internet and they can get it from their peers that is why we we we are engaging now even the government the ministry of health and also the ministry of education to see what can be done and there are many countries that are in Nairobi at the moment so the plan is that after now the conference there's going to be social accountability so that we can look at now the ratifications this what they have signed the commitment that they have made right them coming to fruition and one of them is going to be comprehensive sexuality education all right now mariga to to to wind up on this matter sometimes last year we had a problem with the ministry of education they didn't know there was a group of people going to schools and teaching them about uh these things now with this how do you think should should be the strategy on how to educate this group of young people i think just having real and honest conversations because i feel like there's a lot of piousness involved as a basis for these conversations these people talking about morality them and all they are to set 10 side chicks you know it's so funny how we selectively apply morality to children people talk about you know only having kids within marriage say half of us were not born outside of marriage and you're to begin with and people assume that talking you can prevent kids from having sex you can't that's one of the things i was heading for you can't prevent when kids around my age started having sex around 16 or 15 the age has just gone lower and preaching abstinence will not work not that people won't be abstinent but more kids and not have sex and so what do you do about it in a realistic manner when we were in primary school we had uh the comprehensive sex ed we got was by a video a scare video of STDs um i think yes let rafael tuju i've i've never forgotten that video it showed you that if you ever had sex you're going to die that was the main principle of that but it didn't matter people still had sex afterwards as you cannot scare scare tactics do not work with children when you have honest conversations about these you one would reduce amount of sex that's going on underage sometimes even most times between the children themselves but number two even if you don't you'd enable these children to make safer choices that's my bigger a more than being morally above all of these things all right now let's move to a different matter which is another same thematic area which will be addressed in this summit now it's about the financing to finish the icpd program of action are to sustain the gains made and i would want to ask these uh we are 10 years away from the STDs in our country now how can we have the opportunities uh make us accountable for the services they provide in the same vein have we been able to do this as a nation while every project every employment opportunity that comes there's corruption when will we get to our uh target as 2030 there was the abuja declaration so the in the abuja declaration uh like you found like african governments committed that uh they are going to ensure that 50 15 percent of the health of the budget the the national budget 15 percent of which should go towards health but in kenya at the moment you find that it is not at at at 15 it is it is even less than 10 percent and for us to ensure that we have universal health access that even that even uh budget or money allocated for for adolescent and youth sexual reproductive health and also for for even women women in general governments need to take this matter seriously because at the end of the day uh we have to attain the sustainable development goal especially goal three on gender on on on on on health wealth and health and well-being and also SDG SDG five as well the thing is government need to whatever like kenya is very good at signing policies but when it comes now to implementation now that is where the hurdle is but now we want we want them that to ensure that they increase the health budget so that we can reach people living at the grassroots because you find even at this age at this time and age you find like there is maternal mortality and this maternal mortality we are not saying just women who are over 18 you find like even they are kids who have gotten pregnant and they die while giving birth and even kids also die while giving birth the other thing you find is that you find that there's increase of HIV and AIDS okay among adolescents and youth and because of lack of comprehensive sexuality sexuality education you find that many things arise from it is is it teenage pregnancy and if it is teenage pregnancy if that's kid can't can't can't sustain the child who has been born you find now that they have now engaged into the cycle of poverty and they drop out of school and now that affects now their education and now they become illiterate and at the end of the day you find that this Kenya is so competitive people are looking for jobs even those who have graduated they are looking for those jobs so you find like this particular kid there they have dropped out of school because of pregnancy and they don't have a source of income right so as a policy maker what do you need to do then that that is why it is important to put into consideration the funding for even awareness on their sexual reproductive health and also still pushing for comprehensive sexuality education yeah all right now uh Mariga social uh accountability is a process by which citizens who can hold politicians and policy makers accountable for the services they provide the other day we saw the electries in Kambu uh going around they want their governor out they want to see him returning the money but now as a nation we've had so much about corruption how we moved as a people united to see that we have fought corruption for us to achieve the SDGs I don't think any country at any point moves together as a nation there's we have a bit of an idealistic view about how change or fighting corruption works many countries even the countries which are a couple of countries right now which are protesting very heavily against their own government right and this goes across Africa so this goes all the way from Spain um Spain has its own issues the UK has have has its own issues for a while Chile has its own issues for a while all of these countries but you see all of these countries when you look at the protests that are having their total number of people on the streets and maybe a hundred thousand at most if you look at it hundred thousand there's a percentage of Kenya's population it's a fraction you need only 3.5 percent of a nation to achieve a revolution maximum all we be aiming for is three but there are very many other complications that come towards holding people accountable ones that the context in which people are having is that we expect people on the street and people to protest people to complain in a situation where a lot of these people are struggling to survive we have the economy which in all for all intents and purposes is undergoing a recession people are losing their jobs left right and center and so although people do feel the pinch of the corruption in many situations people feel helpless people are just trying to survive get on day by day people on average people earn less than 10,000 shillings a month in Nairobi this person has a wife and a family you are telling them they're not angry enough they're angry but they're tired they haven't eaten their first thought and every thought needs to be how they're going to survive and so it takes a bit more than anger in many situations to get people calling the government accountable because people are angry let everyone seems to say Kenyans aren't angry enough they are but anger on its own does not provide change very many other aspects that lead to holding people accountable before I let you go on the same area now what's the role of private sector in contribution and investment in the sustainable development goals the private sector I don't know my I do not believe in private sector for public good the private sector is there for profit at best and the points at which the private sector help the public just happen to go with the intersection between what happens for them and their profits and what's happened to our good so I don't I have no reliance on private sector for my own good or for the good of citizens and how then we are speaking of unemployment in our country and so these are the people who will be in the private sector we want to have tenders in the government but I am a private person so how then will I not contribute if I'm given certain tenders there's this lack of accountability what's happened and what I feel is an extension of failed leadership from the top downwards is that there's been a tumble effect down especially when it comes to how the government works and how accountability works we start at the top where we've had scandals all the way up from literally the president and deputy president and people think that's innocent until I tell you this is exactly how it happens so you start from the top you've had scandals across every single ministry you can think of but doesn't end there you've had scandals across past status you've never even heard of existing and so there needs to be and so there needs to be way more accountability especially but you see a lot of the the biggest issue in why the large part of the private sector won't call for accountability is that they benefit from some of these systems all right yes now violet what will take governments to eliminate preventable and prevent maternal and unato deaths what it will take governments to prevent to prevent maternal deaths is first of all they should have the will to ensure that because you know like there are three zeros in the icpd program because you know like there are three zeros in the icpd program of action and one of them is zero maternal mortality so we want to ensure that all countries in the world no one dies out of maternal because of maternal death so so as you can as i've said it should be zero then to ensure that it is zero then first of all we need political will all right so the government in the world they should commit money towards now funding adolescent and youth sexual reproductive health programs especially ones that are going to prevent uh even it is just like malaria and even uh and and and and even hiv and uh and even uh mefan even unintended pregnancies that maybe result that that much result that's why i'm saying that now the government should have the political will they should set aside money for that the citizens themselves they should now engage in health budget advocacy because when they engage in health budget advocacy where you usually found the you find the civil society organizations citizens should hold their government accountable because at the end of the and at the end of the day each country has its own constitution and and like the one in kenya it says that everyone has the highest attainable needs the highest attainable rates of reproductive health and in kenya it is in our constitution so it is by law so it is not like government are citizens are asking the government begging the government please do this please do this no no it is the right as citizens to request for these services and the and and and the government role is to hear their people because at the end of the day all the governments in the world the budgets that they pass they have to be there has to be public participation and which has to go through the citizens and then now once there is public participation now the now the funding can be released for the different health programs including now for preventive yes i won't let you go on that i want to understand we have our own project by the first lady magrid kenyata beyond zero campaign would you see it has worked for us well i can say that it has worked it has worked here in kenya and you find like even in some in in in some counties which health services couldn't be reached you found you found that the beyond zero now enable them to enable the service to be to get there but at the end of the day you find that even though beyond zero has done its part in kenya we haven't yet reached the state where we can say the zero maternal mortality so that means that they still work to be done and the different stakeholders now like the donors the UN agencies and the CSOs the the private sector they should all work hand in hand so as to ensure that we achieve the zero maternal mortality rates all right especially about beyond zero i have a lot of issues with beyond zero particularly because that's not the function health is not the function of the office of the um first lady right that's what the function you should not at one beyond zero is riddled with scandal on its own which people tend to avoid and number two the primary function of reducing this is not beyond zero it's a ministry of health you've lost significantly more in the ministry of health than beyond zero could ever contribute and beyond zero is also a private sector initiative in many ways a lot of the money involved was fundraising right we pay taxes i do not support a parallel program to fundraise when we have the ministry of health losing billions of shillings each year and so even in terms of accountability like beyond zero a lot of reports rely on are from beyond zero they cannot they cannot tell us what their effects are they are still maternal deaths going on and so one i don't hold beyond zero responsible for anything it's not there oh we didn't vote for the we didn't vote for the first lady and whatever these these programs are just parallel the bigger question goes to the ministry of health ministry of health has the audacity to donate money to beyond zero which is supposed to be doing its own work so when i feel like there's a slight problem because beyond zero creates a slight problem because it takes responsibility away from the ministry of health that's why i want to interject on what you have said okay what i'd like to say is that uh you find that you you you find that uh now like the beyond beyond zero now that is the office of the first lady isn't it right and you find like in kenya like we have the 47 governors and there is the office of the first lady there but but one thing you should note is that the government has many priorities but the funding is limited and they and you find that like the government depends on the national revenue that is going to get it's going to get from taxes but that is not enough to cover all the sectors in the country and that is why you find that like even when the first lady started the beyond zero that is why you find that the private sector and other organizations chipping in right because at the end of the day health affects everyone of us because you can't just rely on the government alone the the tax money alone to cater for health i'm saying but the reason why i'm saying is that you find like like now the government you find like yes you can look at the budget yes like we had the three trillion budget isn't it but yeah all of it was not for health you find like it was never enough it was never enough actually to never be enough it will never it will never be enough but it could be true it's a program we lose a third of our budget to corruption correct now when you talk about lack the government lacking capability i disagree we lose a third out of three trillion we're losing one trillion shillings this year to corruption the ministry of health and the scandals involving a lot of these things could more than the quadruple what the beyond zero is producing we don't it's not a lack of funding i disagree the lack of funding corruption loses us way more money than than beyond zero could ever collect the bigger question told me the government accountable the beyond zero came to bring some sanity in a way but that's a conversation that we can never conclude and it's very debatable and our time is up unfortunately many things for coming marigah and violet marigah is a communication consultant and the violet he's a ceo of violet in bt foundation they have been my guest coming up next is why mashawiki keep it tuned to y254 i'll be seeing you again next week on monday until then have yourself a very good night and a wonderful week my name is dereba hilavi goodbye


Jules Caserta and I use they-them pronouns too much. Jay Simpson and I also use they-them pronouns. We're talking about hate inside the LGBTQ plus community, which is a very fun topic. Very fun. Very fun. We love it. We love hate in our community. Favorite sarcasm is important. Sarkasm is obvious. Remember humor is important in children.  Let's get into it. To start, community is hard to define. We might consider things that are part of the LGBTQ community that you might not. So if that comes up, we'll think about it. If I say, oh, pan-fessional, part of the LGBTQ community, and Piper's like, no, we'll go into that. If that doesn't happen, we probably won't. Sorry. Start with the one that I hear most commonly, most frequently, that's a better word for that. Community, which is byerasure, the act of erasing the bisexuals. Yes. It's also, it's a very common thing where take a female person who is bisexual, if they were to be with a man, people would be like, oh, you're just straight.  And if they're not with anyone, people will often just be like, oh, you're confused. You can fuse. You can fuse. You'll figure it out. You'll figure it out. You'll get all of faith. There's the whole thing about how it's like you're attracted to men and women, but bi-means too. It doesn't just happen. It doesn't happen. It doesn't happen just men and women. It could be like women and non-binary, like men and non-binary. Another non-binary group. One or two. Yeah. So it's, yeah, people are often also. Yeah.  And then you find pansexual being confused with bisexual. So people are like, bisexuality isn't a thing, but pansexuality is the same thing as bisexuality. Yeah, people.  Because I'm panromantic. Same. So. Same.  It's like. I did for a while. Fun fact. I said I was bi. I hated it. And I was like, oh, no, I'm not. Sorry, mom. I do like the colors more than the pansexuality. They're much nicer than the pansexual flag. Do you know what they represent? Female. Or is it male on top? Is it blue on top? I think it's blue on top. Yeah, it's blue. Blue for boys. Pink for female, which is not good. And then purple is F. It's yellow. It's, the bisexual flag is not yellow. Oh, I thought you were talking about the pansexual flag. There's no purple in the pansexual flag. The pink. Honey, okay. Look at them two. Because that was it. Okay. That was a great tangent, but we'll move on. Pansexuality is also considered bisexuality. People are saying bisexuality isn't a thing. So as a panromantic, what have you experienced? How is it? What is it? What is it like?  Or a lot of times with me personally, I don't think it's every pansexual person. A lot of people just go, oh, you're just a lesbian because I tend to date more females than I do male. Because females are amazing. Good point, good point. What I was going to say is that people also forget preference is also a thing. You can be bisexual, have a preference towards women, but still be bisexual. I am panromantic with a preference towards feminine people. So it doesn't matter what your gender is, it's just feminine. Yeah, and people, you also see people confusing just pansexual as a whole because they think, oh, there's only two genders, pan means all. You just meant that you must be bi, right? Right. Because if all is only two, then the same thing. Which yes, if that was the case and all was only two, you would be correct. But it's not. You're wrong. You're wrong. Yeah, and then you also see TV characters in representation who are bisexual, being called gay, lesbian. It's just people just completely stop using the term.  The actual character is bisexual, but in all fandom spaces, he gay.  But I know people who are bisexual. I've had a couple bisexual friends where I'd be like, oh yeah, we're all gay here. And I had a bisexual friend pretty much snap at me and like, no, I'm bi. And I'm like, yeah, that's definitely fair. And I think it's getting more and more common that gay is an umbrella term. Yeah. It's like queer.   It's really, words are so difficult, especially in this community. Like it's very hard to be completely politically correct. Yes. You don't know. It's impossible. You don't know. You know what you say is going to, what you say and how it's going to hurt someone. Yeah. Because some people can be affected by words differently than others. Yeah. I mean, and then there's also panromantic, which any romantic orientation is disregarded. Yeah. I mean, it's like just not a thing. Yeah.  Most of the time I say that I'm just pansexual because most of the time. I just say pan. Yeah. I say pan. I'm just pan. You can put whatever ending you want on it. I'm just pan. As long as you get. As long as you get the all parts.  Yes. Kitchen utensils? I came out of the cupboard in the closet. Yes. I make that joke. I do too. So it's a good joke. You guys are hilarious. Thank you. We made it all funny. Stop doing that. But yeah, there's obviously something like being attracted to pans and it's just like, it's so frustrating. It's like, if you're not my friend and you made a joke about me wanting to screw pans, I will actually slap you. With a pan. With a pan. With a cast iron pan. With a cast iron pan. Let's not talk about a song. Yeah, that's probably a good idea. But with the romantic orientation that I was, we went on a little bit of a tangent, but with like panromantic. Yeah. Any romantic orientation is regarded with leads into the ace arranger. Yeah.  And I will. That's what the show is for.  And because of this, people have decided that asexual isn't a part of the LGBTQ plus community. So it did come up in the show. Did you look at that? Well, it's not just that. I have heard a lot of arguments where there'll be straight. And cis people. And cis people. And cis heads. And they're, but they're asexual. Yes. So people have been like, oh, they're not part of the community. But in my opinion, they are. Yeah. In my opinion as well. Because the LGBTQ plus minority, in my opinion, is for anyone who is not the norm. And the norm is. Cis head. And you have a, and you're not, and you're not ace or arrow. Yeah. The, okay. We're going to, we have a rebuttal. Yes. But it's like, I also understand what you're saying. I believe it depends because there are many cases of cis head aces or arrows who are, you know, they are generally accepted into the community and they, or at least they should be, except there are many cases where they are like screaming about how queer they are. And that's not, I don't believe it's, many people disagree that they should be reclaiming that word. Queer. Queer is a word for anyone in the community, in my opinion. Because at its core, queer just means odd. And I'm odd. We're all a little odd. But I mean, it's just, I think it's a word for anyone who isn't considered the norm. Because anyone who isn't considered the norm is then considered odd. Yes. And although we have a really deep history that we can get into and we won't, because it'll take, we'll be here for hours. Yes. It's a tricky word. And asexual people and aromantic people are. LGBTQIA. The A is not for ally, folks. T. The A is for aromantic and asexual. And facts. That is, those are the facts. Those are the facts. Those are the facts. It's just, I hate it. There's a lot of controversy. Say that right. It's a controversial topic. It is. In the community itself. But also, in my opinion, again, anyone who's grown up asexual or aromantic, even if you're cishet, even if you're not. You are growing up and you are feeling broken. And you are feeling like you are not normal. You have to struggle with that your whole life. And I think that everyone in the LGBTQ community can relate to that. I think that's why we banded together, because we can all relate to the feeling of growing up broken. I think that's why we form this community. So if someone grows up feeling like that, shouldn't they be accepted in? And people will, people will rebut that with, oh, tumblr aces and tumblr arrows. Oh, they're horrible. They're just trying to like force their way into this community. And no, we're not trying to force their way into this community. First of all, don't use tumblr as an example. Everyone in tumblr is horrible. Facts. Don't deny it. I'm on tumblr. Yeah, I was about to say you're on tumblr. Yeah, and it's awful. It's not a good place. It's not a good place. But people will use that. And it's like, no, that's not part, that's not who. Those people are aggressive.  But all aces and arrows, all those people. Exactly. That doesn't mean that they should be erased completely from our community. Right. And they should be for everyone. Yes, exactly. But here's the thing. Tumblr should not be where people get their facts about the community. Yeah. Yeah, it does not, like, tumblr may, like, I personally found help through tumblr. But it shouldn't be where everyone is like, oh, because these people on this website said it, that's the truth. I recently read a post that was, dear ace people, please stop forcing your sexuality on children. And I was like, honey, we have no sexuality to force. That's literally the whole point.  Straight people do that. When they make their sons wear the chics on a loaf of meat shirt. Or, like, the fact that... Oh, they're friends. They must be getting married. These two little... It's just like, stop it. Like, from birth most people are like, oh.  Like... And that's outside of the community too. Yeah. Just straight people, just being straight people. No offense, straight people.  That's just how it works. That's how the world works. Some people you love and some you hate. Like the asexual. Not only some people. Some people are amazing. Some people love as much. Right. But that doesn't mean we just outcast that whole damn community.  They're like, you know. There's a time, I mean I'm so sorry. It's okay. But it's another, and the same post. It was like, you don't know what it's true oppression feels like and I read that and I was like, corrective rate. I was, my voice was like, but I was like, I was just so, it's so bizarre to me that people can like think this. Like I'm ace, right?  Right. I'm just like, why? And corrective. Why is there so much hate in the community? Yeah, we don't need. People hate things they don't understand. Isn't this like why the community, like, that's why the LGBT community exists. Exactly.  And now people in the community hate each other because they don't understand. Well, yes, there are some like weird lines that are, there is a line that is being crossed by weird things that we won't get into right now. What's that at the end? Yeah. Yeah. But like. Sidebar that or a little, we'll come back to that later. Yeah.  And it's horrible. And that can be inside or outside the community. Right. Another thing that I've heard is like, a lot of people think that ace people, like the reason people are ace automatically are from like sexual trauma from childhood. And I know plenty of ace people who had no trauma from childhood. I had a wonderful childhood. And I just don't want anything in my genitals. Thank you. Okay. Moving on. If you have anything else to say. This one you can talk about. And I'm very interested. I mean, I have like another thing is. Okay. The ace spectrum. Like people just forget that there is a spectrum. Yeah. It's not just asexual. Like you're Demi, right? Yeah. I fall in the spectrum. But most. Many people will have conflicts over what on that spectrum is valid. And it's insane. I mean, there's also like people. There's asexuals fighting asexuals. Because this one asexual is considered some cells gray sexual. And this other asexual is like that's not a thing. And like people think that asexual just means constant hate. Sex repulsion. Yeah. There are plenty of asexuals that aren't sex repulsed. They just don't frequently have sexual attraction. In my case, I'm Demi, but I take that as I need a lot of time with that person. Explain what Demi means, please. Demi is, well, I was basically explaining it. Do it better. Okay. You're doing great. Demisexual is where you take a very long time with and getting knowing a person before you have sexual relations. Yeah. And in my case, I'll make out with you all day long, you know. But there are other Demis that don't. And then you see Demis and Demis fighting about being Demi. They're all individual experiences. I've had people tell me that I'm not Demi because I'm too sexual. What does that mean? What does that mean? Well, the things that come out of certain people's mouths just boggle my mind sometimes. Especially because I teach sex ed, which I don't get how.  I'm sorry. You're teaching important education to people. You're too sexual. You're too sexual. You can't be Demi. So asexuals are pretty sucky. I have one that I'm very interested to hear you talk about. Yes. What hate have you experienced from the LGBTQs? It's a very long acronym. LGBTQ plus community being Pauly. I've had people tell me that Pauly isn't in the community. Because a lot of the time it could just be like a guy getting off with two girls. So a threesome? Yeah. Like people just think, oh, it's just a threesome. You're the other misconception with bisexuality as well. It's just you all want threesome. Yeah. It's like, no. You can be bi and you can be ace. There's plenty of bi people who don't want multiple people in bed. There are plenty of people who don't want multiple people in bed. Period. Multiple people in bed sounds exhausting. And that's coming from a polyamorous person. But you heard it wasn't part of the community for people. Yes. It's part of the community that it's just you not being faithful to your partner. Okay. That you just want to cheat. That you just want an open relationship. Is polyamory not an open relationship? It's different for each individual. Okay. Like polyamorous couples could be three people dating each other. Like polyamory could be one person dating two people. It could be one person dating three other people. One's dating the other. Like, it can be weird shapes.  It's not like this or this. It's like polyamorous. Diagrams for everyone at home. That's your homework is to make a poly diagram. I'm not doing the homework. You're going to fail our class.  How do you feel? Like a disappointment. Me every day. Okay, so you're not part of the community. You're not. What else? You're just like a... It's a kink. Have you ever been called? I've been called names like whore. I've been called slut. Town bicycle. Freddie Mercury's quaking. My bicycle. Okay. It's a good song. It is a good song, but not in that context. Could be. Okay. Which is very interesting because I'm also Demi.  That doesn't... And I'm like, how does it not? Like what? It's like you're poly.   Like, that's...  Or is it just everything? It's an everything. Like people can... There are... Polyamory is like... Because it doesn't end with like sexual or like romantic. Yeah, polyamory is just like how you are. You don't need to be in a multiple person relationship. You just could be. You could be. You have the capability to do it. And there's plenty of people in this world that don't. I don't. I couldn't do it. Yeah. And that's perfectly fine. I do. And that's perfectly fine. Some... Like some people in certain relationships could be and some could not. Like you could be polyamorous, but in one relationship you're like, you know, I don't want multiple people, I just want to be with one. Or the opposite. Like it's a very broad thing that people get very confused about. And they're afraid and they hate it. People also confuse it with polygamy. Oh. Is that like where you illegally take multiple marriages? It's where one is not made individual. It's illegal in the United States. Typically one man taking multiple wives and the wives only have relationship with him. They can't be with each other? No. They could like form a bond over that. Like we have to date this awful guy, but like we have each other. Yeah. It was... I'm gonna write a book. Typically originally a Mormon thing. Oh my God. I forgot about that. I don't mean that disrespectfully. I'm sorry. I'm baptized Mormon. So that's what a lot of people... I don't practice the faith, but I'm... So a lot of people, that's another thing. People confuse. Another thing with people like that. If people know that I am baptized in that faith, even if I don't follow it. They're like, how are you polyamorous? They're like, oh, you must be... You must want polygamy then. And I'm like, no. I want my partners. Like if they want to be, I want my partners to be with each other as well. And that's completely up to them. It is a complete, consensual, loving... So have you ever... So you might want to answer this. Have you ever been in a poly relationship or has it always been one person? I have been in a poly relationship. Okay. I have a question. Yes. How do you not... Do you get jealous? If you have two partners and then dating each other? I don't get jealous because I'm dating both of them. No. And if I know the person that my partner is dating and I'm not dating them, I'm more likely not going to get jealous. As long as I have communication in the relationship, I'm fine with it. I don't know about... So it's turning you to the poly episode now. I don't know about other poly people. But personally, if I am in a relationship with someone, it doesn't matter as long as I have that communication. Because communication is key in any relationship. Don't point at me like that. Point at my pencil. So this is one that I think we can all talk about if we wrapped up the poly. I think... I'm sure you could go on. I could probably... For a later date, which sidebarring so many things. I love how we've like mixed like the ace episode up. Yeah. It's so good. Plus the ace episode, we were going to a poly episode book. We have all that combined into one episode about... Hey! Yay! Um, something we can all talk about. Non-binary erasure. I believe we all identify as non-binary if I'm correct. I'm non-binary. I am non-binary. You don't have to be. You don't have to tell everyone. I'm pretty sure that's like the closest term that I can... Well, I have seen this a lot where you... This is not coming from me. You can't be non-binary. You're just a trans guy or just a trans girl. Have you guys ever experienced that? Every day. Or you just... Or you just cis. Which is when I get a lot. Oh, you just cis. Every day. I'm like, no, honey. I'm not cis. Don't call me cis. Unless I was an SIS, don't call me cis. Um, I feel like if I was trying to... Because I try to explain things to like my mom or something, it's like she won't get it. And she'll just be like, so you're a man. I'm like... And it's weird when you find people who are accepting trans people or even trans who then don't accept non-binary people. I mean, yeah. There's many, many trans people who don't accept gender fluid or non-binary people. And it's like, why? Or they only accept non-binary people when they are born. Interest next? That. And that's the only case that they would accept it. Huh, really? Yeah. It's so interesting that people have like criteria for who to accept.  This is a great case of what we're talking about. All of the hate. Yeah, it's just like weird. It's like... Gatekeeping? You're not trans. You're not a part of this community. We're keeping, we're closing the gates. We're keeping you out of the community. Ace people have to deal with it. Non-binary people, poly people. It's everyone has to deal with...  Some level of hate. Just bullshit, hate, and gatekeeping in general. Yeah.  You're not trans enough. You're not ace enough. Oh my God, you know how many times I've been told you're not ace enough? I'm not sex repulsed, right? Like I'm open to the idea of having sex. I'm comfortable living in the assumption that I won't. Yeah. But people are like, oh, you're not ace enough because you're open to the idea. I'm open to most ideas, honey. Like I said earlier, people don't think I'm on the spectrum at all. The asexual spectrum? Yeah. Like they don't think...  I have many people that think I'm just a lesbian. I feel like... And that's not a label like you.  I've had that happen so many times and it's the funniest thing to watch is confusion cross over their face. They're like, I thought you were a lesbian. I've had people like, I thought you were a lesbian. And I'm like, honey, you're so far gone. You're the thing, lesbians can appreciate a pretty face. I would love to be a lesbian. You can be like, there's plenty of lesbians that I'm like, that I've met. And they'll be like, that guy? Yeah, people find it so bizarre when I can like appreciate someone's face without wanting to date them. So being aesthetic isn't like... Aesthetic attraction. Yeah, we've talked about this. We're pulling stuff from every episode. Um, what topic? Not mine or anything. That's what we were all in. Yeah. It's so frustrating. Oh, it's not a real thing. That or the... Every...  Yeah, yeah. And it's frustrating. Like, I'm not upset when people mistrend me. And it's not uncomfortable, but I'm not upset because I look female. I'm okay that you can't understand what's going on up here, because you can't see that. You can see this. Like, I'm fine with that. But it's just... The thing that gets me is when people who know me do it, that's just like frustrating. Yeah. And it's just like people don't understand it. As we talk about people don't understand it, and from there they're quick to hate it. And it's like, why? Why don't you just sit down with me? We can have some tea, we can chill the freak out, and we can have the conversation. The opposite of spilling the tea. The opposite of spilling the tea. All the tea will remain in the cup. We're going to sip the tea politely with our pinkies out. What's next on my list? I mean, we can talk about non-buyer anymore. Yep.  It's just like the stereotypes from the line.  It's like, no. It's like small, but it's a razor. Yep. It's like your identity isn't valid because you wear dresses. Also, what clothing and makeup? Not a gender thing. There's gender expression? Yeah. Sorry. Or just expression, if you just want to call it expression. Yeah. But it's not a genderized thing. There are plenty of guys I know who wear skirts because they like them. I mean, look, huh. Okay. I want, I think they're more acceptable for men to wear makeup because men and makeup looks so good. I know the fact that there's hate for it in the first place is so sad. Like, what in the world?   Why? Or if a guy puts on makeup, he's immediately gay. Yeah. You never said a lot. And that can, we can even go into the, so that we can go into the woke card. Which you see. Is that it? That's the next on the list. Okay. I'm trying to, we're trying to organize. It's not working, but we'll try it. So, a classic example of this is Glee. I love that show, but I hate that show. Oh, my God. I have a love-hate relationship. So, Glee, most people know, was a show about high schoolers. Who can sing? In a group, in a singing group. Called the Glee Club. Called the Glee Club. Okay. And one of the main characters was named Kurt.  That's literally his only character. That's his only character trait. Well, he does eventually get- Eventually he evolves to have a boyfriend. Evolves to have a boyfriend? My golden life.  And the show was praised for all of the representation. And all this amazing stuff. I mean, the longer the show went on, it got better. Because it progressed with time. Glee started in 2009, right? Yes, I think so. But you see that with TV shows a lot.  On occasion, butch lesbian. Oh, yeah, that's true. Sorry. They sprinkle in a few butch lesbians every night. Very rarely you see a lipstick lesbian. As they're called. Lipstick lesbians. That sounds so pleasant. Yes.  These terms are so funny to me. I'm a lipstick. There's certain flags for lipstick lesbians. It's the lesbian flag. They're a lipstick on it? There's a kissy mark on it. Aw. That's cute. That is cute. I love lesbians. It's not like a weird straight guy is turning on my lesbians. That's a different topic. They're straight guys who are homophobic. But then watch lesbian porn? Yeah.   Let's continue with other topics. Yes, that's such a great thing for some other time. Let loose. Let loose. Put that on a t-shirt. You would. I would. So you see that all the time which is a razor and even was the razor of all the other identities.  There's only one of them. The token. The token gay guy. Yeah. Or there was this show, I don't know if you two ever saw it. Oh God. It was called Faking It and it was put out by MTV and it was basically these two girls, one of them went, hey, wanna get popular?   The fastest way to not be popular. And they were the first lesbian couple in the school. They were not the first. I guarantee you that. Yeah. They were the first out one. But basically the whole plot of the show is one of the girls actually turns out to be gay and in love with the girl. But she's doing it to get a guy. MTV. It's just, we did a whole episode on representation. So we're pulling from that episode too. Wow. There's no non-binary, have you ever had a non-binary corner show? I have never seen one. I'm thinking. I wanted to just like start, I wanted to go, she raw, but like not yet. Um, Klaus Hargreaves? We don't, no one's confirmed. Has anyone, have you ever confirmed non-binary in any show or any media that you have watched or consumed? That I can not remember. No. Not that I can think of no. There are two pansexuals I can think of that are confirmed which are Klaus and Deadpool. But Deadpool's a whole other issue, we'll talk about it later. So many things we're going to talk about later. Put a pin in it. People are going to like message you like you said put a pin in it so. We have to do a whole episode on Deadpool next. I'll do it. I have a poster that I can bring in. No. This is one, okay. So not woke cards, just automatic in general. Like stop it. Stop putting characters in your show just to be considered woke or cool or progressive. Like actually be progressive maybe. There's a good representation. Maybe let's actually be, let's, what are those weird commercials with the lady that pops up? Progressive? Yeah. Flow. Flow. Her. Let's be her. Flow I hope is a good person. No gay rights activists. If we may, oh my god. Can we hire flow? Can we make that canon? Canon? Canon. Okay. Are there cannons for car insurance commercials now? Just hate topic. Okay. Because we have so many. I can't talk about this. I haven't really experienced it. Aromantic hate. I have heard that they are all predators. Oh. Ooh. This one. This one I've heard a bit. I have a, I don't want to pull this card. I do have a few aromatic friends. They love to rant to me. They have a very fun time writing to me. And again on the aromatic episode we talked about them all being predators. Asexuals, all being broken, aromantics all being predators. Right. Aromantic people. She's just, they're lovely. They're nice people. Like just sometimes. Don't, I mean they're, and you can say that about anything. Yes. You could say that about a tree frog or something. Like it's lovely, but it's poisonous. Like, yeah. That's not, that's not, maybe it was not the best. It was not the best. Anyways, continue. They just don't want romance. And who are we to stop them from not wanting romance? If they want to have, like, they're, it doesn't always have to be asexual, aromantic. It can be aromantic, pansexual or. I am asexual and I am panromantic. Yeah. Because I'm all hopeless romantic. It can work. Somebody please. Both ways. Both ways, but and you, oh my god. They're just people. They're just people. And they're also being kept out of the community. Yeah. This damn gate keeping getting in everyone's way. Whoa. It's just asexual. Aggressive. Asexual. Okay. Be aggressive. Um, asexual people being kept out on binary people, aromantic people, pansexual, bisexual, literally every identity you can think of has probably been hated by the, the community they're meant to be loved by. Yeah. People being kept out, just like all of them. All of them, all of them. Literally asexual ones.  Like, I don't understand star gender. What? They exist. Are we going to put a pin in this too? Put a pin in that. Put a pin in that. Put a pin in that. Shout out to any star genders watching. But then this is the whole thing of like trans-trenders. Oh. People who are doing it for the attention. For the glory. It's trendy. It begins in the show. Oh, yeah. They're doing it because it'll make them popular. You see this all the time. You see them making, you see them using these identities and giving those identities a bad name. Non-binary people, it happens all the time. Or people, like the stereotype, oh, all girls experiment in college. Is that, it's a thing? You go to an all-girls college.  Why do people care so much? Like when I come out of asexual, they're like, oh, no, we never get an experience that. And I'm like, why do you care? Who do you care about asexual? Are you the one laying in my bed? Like why do you care about who the heck I'm going to screw with and all that? Oh. Like why? Why do they care? And then it's like girls hooping up with other girls. Why do you care? How does it affect you in any way? Can people just play? We're not telling you to hook up with other girls. We're telling you to leave us alone.  Not yourself out. But we're just telling you, don't, we're not telling you, we're requesting from you.  I mean, well. Okay. Pin.  Just wait a minute. That's a whole other episode. Yeah, we're going to put that in a safe. That's not a whole other episode. No. I thought that was in the, but. I, okay. Last thing on my list. We can probably go on for longer, but I, I know. We could probably touch back at all. Well, yeah, I want to touch back on what we, we will in a minute. Okay. I have experienced so much damn hate from the community because I considered myself bisexual. I went to pansexual and then I went to panromantic.  And I'm like, yes, the heck I can. Okay. People just, people, first of all, before I even continue with what I'm about to say, though, like your attraction, your romantic, sexual. It changes. It is fluid. It will change. It will change. You know what? And it just, it happens. And people finding those terms and labels that fit them and they're comfortable with, that's okay. Because they're just finding what fits. People change and grow and they evolve into different people. I'm not the same person that I was when I was six. I changed. I mean, when you were six, you expected probably to be straight. I didn't know what straight was when I was six. Okay. I expected to be straight. And then I kept having panic attacks about pretty girls. People get like so upset when you change.  I'm like, that's not possible, Carol.  Susan. Sorry. It's like, jeez, Marie. So many names.  And it's like, why? Again, why do you care? If it's not hurting you or hurting others, unless it's consensual. Jays. Look, continue. Continue.  Thank you. Like why? It's just like, people love to stick their nose in other people's business. And that's where so much of this comes from. It's like, they want to understand. When they don't, they hate it. Maybe you can just go on living with us. Hate ourselves. Okay. Personally, like I, like because I'm trying to figure out what like term for my gender identity is, I like, I'm like going through all these things trying to figure it out. But it's like, I'm not going to like tell my parents. I don't feel like I should tell my parents or try to explain it to them because we're just going to go. You're being filmed right now. You know that. I, they're not going to watch. And you know, I love them. I just, they already know. I've probably gone on this rant before. But like, I'm just saying like, and I don't want to, I feel bad. Like if I was going to be like, hey friends, I figured this out. These pronouns, this, and then I would be like, I don't know. I'd feel bad. I always feel bad changing them. Like I came out to my family's bisexual, right? As we know now, that's incorrect. But I, I never, I never think I actually came out to them out of my other identities. Cause I was like, I already did it once. So we don't need to do that again. So do they know our pan-lomantic? Probably not. Do they know what that means? Probably not. They going to watch the show? Probably not. I'm not. I really appreciate that. No, just the way you kept saying, sorry. I love it. It's upset at all. I'm perfectly calm and collected and fine. You, did you want to touch back on something? What was the last topic we did on that paper? A romantic date. I'm buying her a hate. Woke card. Okay. Pan hate. I'm just going up a list. No, I'm now, I'm not sure I had something to say. Reverse, reverse. Slide to the left. Slide to the right. That's probably Chris. That's what we're doing right now. But, yes. Are you trying to get something saying you can't? My paper. Deal with it. I have an example of changing of labels. Go for it. I, go nuts. Came out originally as gender fluid. I, as we now know, not gender fluid. Yes. But with, first of all, I'm going to talk about gender fluid. When I was labeled gender fluid, a lot more people had a problem with my gender identity than now when I'm labeled non-binary. Because people don't understand. Yes. People have a really hard time now that I'm going to head around gender fluid. People like, what do you care? What do you care? Really have a hard time. And that's in and out of the community. And like, I understand struggling to understand something. I do it all the time. Algebra one, anyone. But it's like hard to understand things. Sure.  Except people and love people and you're going to be fine and they're going to be fine.   You're not ace enough. You see it so much. And we don't need it. This community needs to be a place for love and acceptance. And it's more just this ugly monster that we need to kill. We don't need it. There's so many faces that I've made that could be put in memes. We should have zoomed in on them. We should have zoomed in on them. We don't need any of this. No one needs to feel hated in ever. No one needs to feel outcast or ostracized. Especially in the community. We get enough hate from outside the community. Let alone getting hate from in the community where we're supposed to be safe.  There was hate from the sisters attacking them. So they had banded together and they made a community, right? A community that was meant to be a place for love and acceptance. And over time, this little community grew and grew and grew. And now it's becoming a thing that they were originally formed to fight against. Yeah. I mean, there's people that say, like, statistics show that there's more gay people now. And it's like, no, there's more people comfortable coming out now. But we're the same amount that there's always been. Yes. And progress has been made and that's fantastic. But I feel like progress has been made outside of the community. And it's backtracked inside of the community. Yeah. And progress still needs to happen. We're not done. We still need to fight for and work on and we're not done. It's like yourself. You're always working on things, doing yourself, right? We always notice our flaws and we always hopefully try to work on them. But this community as a whole has these flaws and we need to work on them.  We're trying. By using our voices on the internet. Ah, yes. The internet. The wonderful world, the internet. Tumblr. Oh. No. We already touched on Tumblr. Burn Tumblr. I mean, they're a nice fan art. Let's move on. I think a good way to end this is with we need a community for acceptance. We had that for a little while and it's going away. Yeah. And we need to protect it. Yes. Keep it safe. Hold on to it. Keep it warm and safe. Make happy. Give it water. Give it sunlight. Let it grow. Put it occasionally. A cheeseburger. Two different things. But any last things y'all would like to say before we end this? Let's go around, Piper. Last sentence or phrase.  Walk away within their heads.  It's not your turn. Yeah. Be quiet. I'm sorry. I talk a lot. Don't be afraid to understand yourself and changing your labels.  That was not articulate whatsoever. It's beautiful. Your turn. Don't let people in or out of the community tell you that you are wrong. All my lovely people out there. You are beautiful. You are valid. And it may not feel like it. You are loved. This has been All Things LGBTQ Plus Youth Edition. We'll see you next time. Stop it.


im about the existence of contextual information inside the text that can be utilized and retained using the embedded approach to demonstrate the validity of this claim we created a vector representation of the text using the state of the art sentence transformer model mapping our sentence to 384 dimensional dense vector space after the creation of the embeddings we proceeded with vector clustering and used BERT topic modeling technique to create hierarchy Andrei, is it supposed to be that you still show the title slide only? Oh, no, no, no Yes, you can see on the slide the result in output of the topic clustering In this case you can see the results for the Google Tweets it produced a list of 20 topics denoted by the distinct circles and later grouped out into four main large-crust clusters of the topics So next slide Now we will connect these topics and market data and observe multiple time periods with high and low user activity During low activity period we can see proportionally equal spikes of clusters topics reacting to volatility changes and they are caused by just the number increase. On the other hand high user activity leads to great diversity in topic reactions all of them have their peaks and lows meaning that the larger sample helps us make a distinction of topic trends resulting in higher correlation that is at least twice as large as you can see in the tables below the graphs At least further spikes in favor of our ongoing hypothesis that great amount of extra information is hidden inside the text semantics that can be used to predict stock market volatility. Next slide Now we proceed to an overview of overall scheme of conducted experiment It consists of four main steps First is data retrieval a process where we collected a five-year data set published on Kegel It was containing tweets regarding Tesla, Apple, Amazon, Google and Microsoft companies Then the data preprocessing so we deduplicated some tweets in order to clean the data Then the covariates aggregation depending on the experiment we either used binary sentiment score or created the embedding vector and used this as a feature For the target variable we chose the close price Last step is model prediction After covariates are fed into either TFT or linear models we train it and make a three or five step ahead historical prediction on validation data set depending on the experiment Next slide In order to prevent overfitting and produce better results we introduced a custom loss function DMSE which is one of the most popular loss functions for regression text the mean square error but we added directional component it was done because DMSE just focuses on the difference between true and predicted price In terms of stock price prediction the direction of the price moment is actually a more important factor in the value itself that's why the custom loss was introduced Next slide Depending on the experiment three main groups of task covariates were used containing market, sentiment and embedding features Further they will be denoted by the abbreviations where HLOV is market features S denotes sentiment score and E is for embedding vectors Next slide Two models and one baseline approach were used during experiments to predict the closing stock price the temporal fusion transformer and linear and naive seasonal approach TFT is an encoded-to-coded transform model it makes use of multi-head attention GRNs and LSTM while N linear is a model approach of a simple linear layer introduced in 2022 that outperformed multiple transformer models on time series benchmark data sets that's why we used it as kind of a benchmark for experiment Next slide During elaborate exploration of sentiment score we achieved results confirming that the social network sentiment is a great indicator for stock price moment prediction as you can see on figures on the left we demonstrate stock price moment sentiment score calculated in both ways first as a relation between negative and positive tweets and second as a fraction of negative tweets in the total amount visually we can observe a great amount of resemblance between price and sentiment variables in this case Apple which is above has a lower correlation compared to Amazon below for Apple in this case the correlation was around 20% while Amazon has 40% when we compare stock volatility with sentiment score in figures in the middle we observe the same phenomena correlation is higher for Amazon by 52% and actually for Apple we can clearly see the time lag between public reaction and stock volatility volatility precedes public sentiment shift but for Amazon the situation is slightly different we can see that sentiment changes are synchronized and even precede the volatility shift in some cases making it a greater predictor of price movement using scatter block we can further observe linear dependence between sentiment and volatility for both companies although for Amazon the dependence is more prominent we therefore claim that there is a clear statistical relationship between observed values next slide here we present a table of a resulting error score for symmetric mean absolute percentage error for all companies overall TFT model performed better than in linear for all companies the best performing model for Apple is TFT with embedding vectors as a feature comparing the closest instance we observe at least 30% degrees for SMAPE it is also important to point out that for Apple using sentiment score actually didn't help to improve models accuracy for both of the models on the contrary with Amazon we observe different behavior of models performance given the input features both in linear and TFT received performance boost with usage of sentiment score an embedding vector actually yielded higher error values models with embedding vectors showed the best accuracy only in two cases out of five for Apple and Microsoft that were companies with the lowest correlation between volatility and sentiment as we said in the previous experiments for Amazon, Google and Tesla sentiment score outperformed our embedding vector approach next slide the above results could not lead us to a complete conclusion so we experimented further with a smaller prediction window of three days another approach was a different sentence embedding algorithm created by Microsoft MPNet it has twice the amount of dimensions compared with the approach mentioned previously but the accuracy of closing price prediction dropped significantly for our model in the table we observed that for some metrics embedding approach still shows better performance but the difference actually is insignificant and sentiment approach received better results for four out of five metrics like in the case of Apple another company that had nearly similar results for both feature sets was Google the difference are negligible only 0.04% for MAPE for Amazon and Tesla sentiment again proved to be a better feature scoring higher for all of the metrics both of those additional experiments they further demonstrated that sentiment as a baseline solution still performs better than the proposed embedding vector approach next slide the results of this study provides further evidence in support of sentiment analysis as an effective tool for predicting price movements in financial markets it showed that the binary sentiment polarity extraction approach outperformed sentence embeddings in terms of accuracy and finding time for some cases the embedded approach proved to be useful on five prediction window five day prediction window outperforming sentiment baseline solutions this suggests that the choice between binary sentiment polarity extractions and the sentence embeddings as the preferred approach can possibly depend on the specific task and the prediction horizon as well as the effectiveness of sentiment as a predictor in the given context in the majority of the conducted experiments the sentiment approach outperforms the embedding vectors method this fact might be might be counterintuitive because embeddings seem to encompass more valuable contextual information however sentiments tend to represent information in a more concise way bringing less noise into the prediction model nevertheless the embedding approach still has an advantage that it does not require an additional model for sentiment extraction and the consequent quality verification of that procedure on the other hand sentence embeddings approach that doesn't require us to verify it and can produce similar results to sentiment extraction retaining more of the semantic and contextual information contained in the text nevertheless the model training time for sentences embedding sentence embedding is significantly longer these findings suggests that sentence embedding could be considered as a robust solution after further works due to its similar performance to the sentiment extraction yes thank you for your attention if you have any questions feel free to ask yes thanks a lot so we have time for probably one question any questions in the audience either online or offline please just speak up I have a question thanks for the talk I'm not familiar with stock price prediction field what usually related work do I mean what additional field do they use I guess sentiment polarity score should be somehow known for stock price prediction yes yes so the current approaches most of them use the sentiment polarity extraction from multiple resources like social networks or some of them use the financial reports in order to predict long-term price movements but the usage of the text itself isn't very researched topic but even if we talk about the sentiment polarity extractions most of them perform rather bad in terms of testing on the real data when we try to apply these models to the real stock market movements it only performs good on the historical data but it is actually a very under researched topic because the price movements are rather random and it is very hard to find some robust solutions to bring the prices yes okay thank you thanks I believe we have to move on to the next and last talk thank you again thank you and the next and actually the last talk of this session in fact the last talk of the NLP track of eyes this year is whether large language models learn at the intern stage by about Leon Kulikoff, Ilya Makarov and Rodislav Neyshev and they see Rodislav is here in zoom can you say something just to make sure that yes we can hear you sure definitely so here we go you should be up and running now yes please move on 15 minutes thank you Andrei so hello everyone thank you for hosting me here today unfortunately I was unable to join in person but hope next time it will be so we have a lot of different discussions on recent advances in NLP especially in the generative models including transformer based architectures and with my colleagues we tried to analyze why actually they perform really better in some cases when we provide some additional information during the intern stage or is it so called the learning and reasoning effect during the intern stage so main goal was not actually to provide some new approach to make it better but to understand and explain and maybe provide it to other people the pipeline how to make it work and analyze what are the main reasons of this happening so just in case the main question is how do these models especially transformer based models like GPD or Bloom family learn during the intern stage learn in quotes of course so they incorporate some additional information which wasn't present during the training stage with no changes either to the architecture and to the model premise and in addition we tried to cover these two questions because they are quite widely known but they are not that strictly defined first of all what is Elander effect learning and reasoning effect and do they actually learn in reasoning during the intern stage by them I mean the large language models or they simply intelligent behavior so there are a lot of different papers of course on this topic I've only brought through them that for me seems a little bit most relevant for this particular research but actually in the original paper supplementing these speech we have more than 15 different sources then they all are important so first of all and there was a first paper which provided the chain of prompting in large language modeling in the same approach we do the first one second one is just original paper on GPT-3 and the last one is actually quite old compare it with all the other papers paper on the future learning in machine translation from back 2018 but we have a few relevant ideas from the paper here so let's first of all formulate the hypothesis which will help us move on main hypothesis in this work and we will try to support it with several experimental and literature results is a following large language models and by large language models we assume all the models which have big enough parameter of space space parameters I will define what is big enough a little bit later we assume that they create some inner language spaces that contain not only the language itself like grammatics, semantics and so on but some patterns of rules and which are implicitly not explicitly embedded in this space so a lens instead of learning something new during the inference stage simply adjust their state of mind to the particular already learned behavior trajectories so main idea once again of this hypothesis is that the majority of the information is a learned and embedded into this space during the training stage and during the inference stage the examples of resonance chain of thought and so on only provide some instructions to collaborate the behavior of the model so I will be brief on the problem of you because I understand that everybody on this conference is well aware of all the problems with language models but just in case we assume that we have given a sequence of some tokens from finite dictionary which contain description and maybe one or several examples of the behavior on the desired problem and we assume that these behavior examples help us to solve the problem let it be easy classification regression text generation whatever so first of all let's take a look at the data size and the model size for which we actually observe this learned and risen effect because if you take the GPT provided by a breaker party with 300 lines of code then you definitely will not see any calendar effect and that's fine so according to the data we checked in several sources all the sources list once again are available in the paper itself the corpus itself corpus of data meaning should have at least 300 billions of tokens because otherwise it's kind of too small and also the models should be of size billions not millions of course it should be approximately 33 or 50 billions of parameters however even 7 billions seems to be enough for certain types of tests for example if you are speaking only about the machine inflation and speaking about the paper on unsupervised machine translation and how is it related for the current speech actually translation systems show us that if we are speaking about unsupervised machine translation from the time we can see that if we have two different models trained for language modeling on two different languages we can simply try adjusting the internal spaces together by using only small amount of label data what I mean we can have one, some kind of language model or just word embedded model like word2vec, fastex and so on and the other one in here on the slides I just adjusted some kind of strange clouds and the idea in there is the following we assume that all the languages are actually based on the real world real world scenarios outside outside there and we know that actually word sun and sky are usually much more aligned and they usually come together then words sun and I don't know dimtile or hedgehog or something else so when we provide some examples of words that directly translate one into another for example word gato in Spanish I guess or adelen sorry I forgot it and cat in English we simply adjust these clouds of points and then we can get the time of course almost state of that approach with almost no data which is aligned between two spaces so the assumption is the following these models already have created some internal states and then we simply align them with several examples which are labeled for first and second spaces so the spaces are aligned as the markd and now we get the aligned spaces and the translating can be performed rather well okay that was the idea from the machine translation and later we tried to adjust this idea to the chain of thought reasoning because when we provide some chain of thought reasoning for the model or provide just an example to make the model work better we can see that the model starts working better but the reason might be the same we might simply adjust the internal state internal language space of reasoning of the model because the model is overparameterized because it contains billions of parameters and it might cause the better behavior there is even an important note because which follows our observations and experiments and it somehow proves our results because when we provide some examples for the model how to work in the upcoming problems like classification or tech generation in specific form or some arithmetic problems even providing examples with wrong reasoning so we provide example that the model should follow some path of reasoning but the example itself is just incorrect for example it has arithmetic errors or just broken logic and so on even such examples help the model to perform better and to achieve better results but if we change the order of reasoning for example we try to just shuffle the sentences with these examples then the whole sequence is kind of broken and the model behavior fails the model quality fails and the model behavior is not as we expected okay to provide some experimental proof of our claims not only to refer to some other external research papers we've run the experimental set up with five different problem scenarios first one was absolutely no prompt so we simply asked the model to answer to our question just like in zero shot learning for example compute something I don't know how many computers are in here and we also had four additional prompt scenarios first of all standard prompting so we have simple example in here like one shot learning for example question there are nine computers in the server room five more computers were installed each day blah blah blah and the answer is 29 after that we assume we have another question for the model and we'll ask the model to continue the sentence to generate the answer as we usually work with this language modeling approach the second scenario is chain of thought prompting so we provide a question and then we provide a chain of thought sequence so we not only provide the answer but the chain of thought as well so scenario is actually invalid reasoning so the same stuff as we did with the chain of thought but now the chain of thought is actually incorrect but in this case we still have the correct sequence of steps so we had original nine computers then we have for every day we have mathematical errors and the answer and finally the irrelevant prompting so we have question on something related for example with computers or with vegetables or with something and the answer is just random coming from another example for example so we had five scenarios and we used couple of open and closed source models because we wanted to check the behavior either on some on some state of the art models like GPT family or some open source models that's in case this work was mostly performed during spring so we don't have the most recent models like second llama and so on so we mostly focus on bloom if I'm speaking about open source models we started using models from a half billion of parameters to 176 billions so including bloom MT0 Excel and Quancao and of course GPT4 because it's kind of state of the art for now so I will provide the example table so it's much easier to see but the main idea was kind of simple models with a small size including bloom up to seven billions could not provide any useful improvement using chain of thought either correct or incorrect so they're not present in the table and MT0 was also kind of inefficient if we try to provide some chain of thought reasoning while bloom 176 Quancao and GPT4 and also including 3.5 and 3 models like 0.2 and 0.3 provided some useful improvements so first two columns in here speaking about the models correspond to open source models while last three correspond to open AI models which are kind of close and we have percent of the correct answers in five scenarios so no Dima no prompting at all then standard prompting chain of thought invalid chain of thought and irrelevant chain of thought and we can see that actually bloom behavior did not improve on the arithmetic reasoning at all using any of the examples except standard reasoning and chain of thought so it improved the behavior a little bit for the Quancao it seemed a little bit surprising so without any Dima it performed much better than with standard prompting but chain of thought and invalid chain of thought provided improvement of the score so once again we prove the hypothesis that actually the order of the prompting is more relevant than the correctness of it itself and speaking about the open AI models we can see that we solved them we did not use them with no prompting at all so we use only with standard prompting and chain of thought we can see that chain of thought the behavior invalid prompting doesn't break it and sometimes it even improves for some reason maybe it's the consequences of not big enough size of the tested dataset but for all the other models this size was quite enough because the results were rather stable and even when we provide some irrelevant prompting we can see that it might improve or not degrade for GPT-like models spoken about open AI and last but not least GPT-4 provides great results out of the box and with prompting we can either achieve the same behavior or even break it despite we are using some useful prompting so it's standard prompting or chain of thought with correct examples same stuff was achieved with question and answer and reasoning on the bamboo bill test we can see exactly the same behavior chain of thought either improves everything a lot or doesn't change it for GPT-4 invalid prompting in this case can break it a little bit but still it's really close to correct prompting but irrelevant breaks it a lot so the conclusion might be the following and I decided to just break it into the couple of questions we formulated in the beginning so first of all what is learning and reasoning effect we assume according to the research we found that learning and reasoning effect is more about finding some similar reasoning patterns in the Latin space the model has created during the training stage and then these examples chain of thought or just several short learning provides the model example how to collaborate itself to find the appropriate maybe a projection of its own parameter space for the desired problem to solve the problem better so it seems like the application of pre-existent knowledge rather acquiring NSNU and speaking about the idea of large language models learning during the inference stage seems like they with okay I cannot say that they're not learned during the inference stage but for now we have not found any exact evidence that they actually learn something new during the inference stage but we can assume that they are kind of exploiting their already existing knowledge and that once again is proved also by the results which we observe when we provide incorrect prompting but preserving the desired sequence so while when the model is just following the path we provided it it achieves better results even if we provide it with wrong examples so that's kind of it and if you have any specific questions thank you just from my sorry you welcome just from my side actually the area is rapidly evolving and I understand that if there are some new results this might become a little bit incorrect just every day because we got into really tricky area of trying to explain why all these models work so I would be glad to hear any of the questions including the questions which just questions the correctness of this paper as well because I want the discussion to be useful for all of us thank you, you're welcome thanks a lot this sort of concludes the ISNLP session but maybe we still have time like for one quick question I guess people on site and online are anticipating the closing of the conference but yes still any questions okay yeah just a brief one for me I'm a bit a bit confused in fact about your final claim or statement that large language models don't learn anything during a few short few short scenarios isn't it sort of trivial and I mean does anyone actually claim that models learn anything in a few short scenarios of course they don't because the weights are not updated okay maybe I can fix a little bit this formulation we're not speaking about the learning in classical paradigm when we just update the model weights or add some another adapter no we merely meant that the models do not acquire any new knowledge so if they are unable to solve some problems at the if they're not trained to solve some problems during the train stage they won't acquire this ability even if we provide them some useful prompts some examples and so so we only can make the model solve the problems they already have seen during the train stage maybe in a little bit different scenario but we cannot generalize them to unseen regions of our fish space or problem space well but we do know that we can I think just this claim sounds to me a bit self-supporting and it's a little bit obvious right yeah okay then it might be a little bit obvious so the main reason we performed actually this research was the curiosity to find out whether they do or do not because when we started it was kind of a little bit at little less than a year ago there were several examples like providing iris dataset to change gpt and it was able to perform the classification on the inference stage providing some examples and improving the quality of the answer a lot so we tried to make a little bit bigger overview including couple of different papers different approaches to actually approve or claim that it's not learning during the inference stage so yeah we're not trying to provide any novel I guess result in terms of it's astonishing result we're trying to actually a little bit more formally prove that no they're not learning yet okay thanks a lot I guess it's time to thank the speaker again and close the session thank you Radislav and now yeah I step down to leave the space for the closing session thank you Andrei for chairing so this is the 11th edition of ice conference first of all we're very glad that many people made it offline and many nice and really high quality talks presented during these two days and the first nomination in the natural language processing area award goes Antoni Lekseev, Sergei Nikolenko Gulnar Kabaev for they work on Kyrgyz language so with this I would like to please Anton to come and to make some short one minute presentation and highlight how they work but first let's well first of all thank you for the honor I guess now I really have to continue the research on the topic to make the data set even more fine grained and more justified the the whole purpose of this work was to create the want only topic classification for Kyrgyz language which would and is essentially the first data set for the applied NLP task in Kyrgyz language and then the overall idea why such a task is that there is an urgent need in some data set to find out whether the multi lingual models work for Kyrgyz language and as we've shown they do to a certain extent and I'll perform the very basic by large margin the work is going to be continued and now for sure and more interesting works I hope are to come because during this year and a year and a half maybe a large community of volunteers in Kyrgyz developed so this is it and I'm pretty sure that there will be a special time for that but may I have some personal remark I would like to thank the organizers who make the conference happen who made this conference happen again and of course our fabulous hosts who manage to do everything perfectly despite the trying times thank you very much the next section is computer vision and awards go to Razan Didoa Andrey Galichin, Pavel Astashev Dmitry Dilov and Alek Rogov and this is work on deep learning based one pathology localization with classification with X-ray images so I'm not sure whether Alek is for first time and arrived just this morning and already got my favor so please this is probably because yesterday I was giving a speech regarding the artificial general intelligence I believe I think here all the credits go mostly to Razan Didoa who is now the PhD student of the Tenzor Networks Lab in Skoltech so we all know that attention is all you need but sometimes you have to look on the ones so we decided to combine these approaches and eventually we found an architectural approach to address very important medical task of Charles risk trauma detection in hospital we eventually got asked the preclinical trials and we developed the approach that combines the state-of-the-art approaches in the object detection gradient attention mechanisms and they shifted window and blocking it so well I think that as once Andrey Kolmogorov said that really new things line between something trivial and something incomprehensible thank you thank you very much I think the next award on social network analysis go to Sergey Siderov Sergey Mironov and Alexei Grigoryov on limited distributions of friendship index in scale free networks please please say a couple of words about your work thank you a big surprise for me thanks organizers friendship index was studied a lot in the social studies but what hasn't been given enough attention in network analysis so here we did some extensive research on friendship index and its continuation of our work well we we've studied a lot of distributions, how it's distributed what its limits are and well actually I think now it's it's time to put friendship index away and move on because it's not a great measure it's not a well all-solving one but and I hope this well I hope you like my talk and all this stuff thank you very much so the next talk is on machine learning goes to Vladimir Berikov on ensemble clustering with heterogeneous transfer learning Vladimir say please a couple of words thank you very much for it's a lot surprising for me yes so this work this work the idea of some additional information which can give some insight to the analysis of data of target data is used and the algorithm is based on finding some useful meta features and data from both domains are quite different the features are different for the domains and we should find some structural properties of data and transfer knowledge from one domain to another domain so thank you very much thank you last but not least last award goes to Dmitry Ignatov on his theoretical work on sub-technical efficiency for the maximum anti-chain of partitions and related counting inequalities and I really hope that Dmitry will decrypt now what his contribution is thank you this is a bit unusual being as a member and receiving this kind of award but I always wanted to contribute with this theoretical section with something worthy and it was a pleasure for me that the committee evaluated my work by its scientific merits and I was also happy to apply data mining and machine learning techniques to the problems that were posed by the relevant mathematicians like Giancarlo Rota known in combinatorics Ron Graham Harper, Canfield and Kledman so here I just added one small brick to our knowledge on the number of maximum anti-chains and anti-chains of partitions in particular reduce uncertainty in some asymptotic coefficients so thank you and let me now say once again great great thanks for our hosts we all heard probably about Armenian hospitality but now we certainly all experienced the best of it and this conference this event would not happen unless Habet and Amalia would do so much during this half a year or more and first of all thanks for just proposing opportunity to host the conference and providing all the resources and all the support and our every need so Habet and Amalia let's thank them and computer science and engineering department of EU so thanks a lot and of course thanks for all our supporters from Skoltec, Iremisi high school economics those who basically contributed time of their people or other resources to the conference so with this I'm glad and I'm being said to conclude this edition of IST and I hope to see you next time at the conference stay tuned when and where it will be and be sure that we working to make it happen next time