so what's up again everybody it's chris from the rewired soul where we talk about the problem but focus on the solution and again if you're new my channel is dedicated to taking different topics that are going on in the youtube community or in pop culture or society or whatever it is and try to see what we can learn from them all right so yeah like i'm supposed to be finishing my book right now but ever since i posted my last etica video like just something has been eating at me like when i made that video when i made that video and like here's the thing like i understand people and when i made that video i knew i knew it was gonna come and a lot of you are new here and like welcome thank you for subscribing because i'm you know all about mental health awareness and trying to convey a message of hope and everything like that but i knew there was going to be some backlash and it's absolutely ridiculous okay so here's here's why and like the first the first comment that i saw were on twitter right and somebody tweeted a fan of the rewired soul so if you're watching this like no offense towards you it's just i knew this was gonna happen i'm just using this as an example because other people think of it and the reason i'm addressing this too is because a lot of creators are going to be making videos about this all right and it's important that we talk about this when we raise awareness but somebody you know they tweeted at me and they said it's too soon to be talking about this it's too soon to be talking about the loss of etiquette i'm like are you serious right now like are you kidding me all right like that that kind of ridiculousness reminds me of when we have yet another mass shooting in the united states and people are like yo yo yo yo yo yo yo it is way too soon to be talking about gun control like what we need to talk about this stuff now while it's fresh on everybody's mind so we can hopefully do something about it so hopefully people will reach out to a friend and talk to them about it so hopefully if other people are watching this happen and they're in a dark place they know that there's help available all right like this stuff cannot wait we need to talk about it all the time not just now we need to talk about it all the time so it's absolutely ridiculous when people are saying it's too soon to be talking about this we need to be talking about this more all right and clearly clearly every single time we lose a life we need to talk about it more i was actually on twitter earlier and i saw um i think i'm alex made uh a little twitter video about it and he mentioned in there you know like nobody's talking about this stuff right and it's true like this is something that one of the reasons why my channel exists right like nobody talks about these things until something happens when something happens then everybody wants to talk about it when something happens like today was like anthony bourdain day and you know raising awareness and everything like people only want to talk about this stuff when things happen we need to talk about this all the time we need to be there for one another all the time we need to let people know that there are resources available we need to let people know that there is hope all right but to anybody out there like i i empathize greatly like i had another twitter comment again from a loyal subscriber so i love you but this is just not the only one so i'm using this as an example that was saying like oh that you you shouldn't be monetizing this i'm like did you even watch the video like it's not monetized like the video is not monetized all right but there were some other comments and most of the comments were good most of the people thanked me but the goal of that video was to answer some questions because i still see it on twitter people being angry upset like and that's that's normal like whenever there's a loss of life like we get upset we want somebody to blame we want to blame somebody for this right and we want to blame you know the people who were like egging him on on twitter or in his live streams we want to blame you know people who are interviewing him or we want to blame the people who were like making fun of him on twitter we want to blame the people who thought you know this was a joke when he first released that video we want to blame people but like i wanted to make that video to explain like how serious this thing is and we can try to point fingers all we want but at the end of the day here's here's the real issue here is the real problem all right although we have more people talking about mental health and mental illness more than ever before a lot of people don't like talking about it most people who are struggling with a mental illness severe depression anxiety whatever it is they are not getting help you know why in fear that people are going to judge them in fear that people are going to think they're weak right and i'm here to let you know right now if you're struggling you are not weak okay there is nothing to be embarrassed about all right if you got diagnosed with some kind of physical disease you would get help so why not if you have a mental illness but again like one of the other reasons i want to make this video is like if you're somebody reading the ridiculous comments that people are leaving because i guarantee they're not just leaving them on my videos they're gonna be leaving on other people's videos like just just don't let this prevent you from spreading awareness and talking about this like i'm good i'm gonna keep doing my thing baby because i have lost over 70 people all right in the last few years working at a drug and alcohol treatment center many people died after they left treatment from overdoses or suicides i've also had people in my life who weren't just clients who have passed away and i'm going to keep talking about this there's something that we do not talk about enough all right but again like do me a favor and reach out to somebody like be there for other people and the other thing is too like i guess i want to talk about this real quick because i left it out of my last video is don't don't blame yourself all right like i could have never worked in the drug and alcohol treatment center as long as i did if i took personal responsibility for every person that we lost and i i was so passionate about my job i'm so passionate about what i do here and the only thing that we can do is do the best thing that we can do which is help people as best as possible every time we lose somebody it motivates me more to help everybody else out there right but we have to realize like we cannot save everybody the most we can do is be proactive and talk to people and be there for people and try to understand people try to direct them towards getting help like you guys like something that you can do if you have a friend who's struggling like you can't get better for them but if you can help by finding them resources do it okay like the united states mental health system is absolutely screwed up all right it is fucked up okay but there are resources out there okay like man i i don't i don't know really what to say because here's here's the thing here's here's what bothers me as somebody who tries to advocate for mental health and increase awareness about mental health what's going to happen in the next week in the next month right this story is going to you know go away and people are going to stop caring about mental health again until the next one and then until the next one and until the next one this is something that we need to care about every single day you know what i mean we need to be there for one another we need to recognize that mental health is something that we need to take very seriously and i cannot express enough that we need to be proactive about our mental health we need to treat our mental health when it's at like a two so we're not getting to the place where it's at a 10 okay something i try to teach people all the time is you don't practice a fire drill when a fire is happening all right you practice a fire drill so when the fire happens you know what to do there are things that i do on a daily basis for my mental health because when i get in that dark space i need to have tools at my disposal that will help me and this includes my support group first and foremost my therapist i have 12 step meetings i have meditation i have workbooks i have journals i have so many things and we all need that so to anybody out there saying it's too soon to talk about this it's never too soon and it's something that we need to be talking about all the damn time all right and again down in the description below there will be resources if you're struggling please get help please talk to somebody okay i as well as many others are living proof that even if life doesn't seem like it's worth living things can get better okay one of the biggest things that i realized through my mental health struggles is that i struggle with patience i want things to be better now okay and as a recovering drug addict and alcoholic it took me years to get to a better place but i look back at those dark times and i can't even believe that i was almost going to miss out on how amazing my life is today so please never lose hope all right anyways thanks for watching you guys be kind to each other talk about mental health don't be embarrassed don't be ashamed help one another out all right thanks again i'll see you next time


This is International Day, our global village. Students from all around the world, the countries that are here in Christensen, they represent their culture and their universities in their country and also we have partner universities represented here and today is for Norwegian students who want to meet other cultures that go abroad and also for the ones that are here to get to know a little bit more around the world. We did very hard to making this brochures. Yeah you can see like it's very fancy and we're trying to introduce our culture and food and kind of some things. I'm wearing the traditional Austrian clothing like the dandelion as you can see in the video. That's me playing the traditional Austrian music. He's wearing laterhosen. This is our traditional garment closing. It is the traditional patterns. It's not like it's not fancy one, it's the casual one. This is called a tagia. Tagia. I'm here at Marking Rassel and work at the university here. We work with Grundeskolen, which is a collaboration project with many universities in Norway. There is one of them and we want to send innovative students to Boston, Toronto, or Berkeley to work with Grundeskolen. You, Marie, were just at the exchange in Pisa, so you can tell us a little bit about how that was for you. It was a very Czech experience. I recommend everyone to travel at the exchange if they have the opportunity. We traveled a lot. I'm very happy for this opportunity. You get the support from the university in addition to Rasmus de Ben when you travel at the exchange in Europe. I invited Italian girls. I also met people from all over Europe and the whole world in general who have always been very much so. We just tried to encourage them to take the opportunity out. I mean to go really, to go anywhere if they have the opportunity because expanding your horizons and joining a different experience is really a life-changing opportunity. So to really look through it and maybe do that. I hope students will go away from today and think, you know, think about our university but more importantly just really think about an international experience to help them out as they move along.


today is a new day and you know what is also new this thing that's happening in the Cape of Twitter which is called hashtag YG is over party whoop whoop what actually is it hashtag YG is over party hashtag YG oh hashtag YG is going to jail party okay so short story short this man he did some bad things some bad things with tax which is not good not good when we find the tweet we'll go over it but yeah you know like I want to do this new series where we go through the freaking trends through the freaking K-pop community let's go let's get it let's all weep no okay anyways so if we just go in dab on over we will see what just happened oh my god perfect for y'all they don't know what happened he owns a club when you own a restaurant you pay 10% taxes but the club is 20% he registered the club is a restaurant therefore tax evasion he's going to jail jail yeah well we don't know if he's going to jail jail but he probably might potentially go to jail jail but the thing that was happening was the beauty okay we don't care about the that shit anymore we care about the beauty of this entire thing which is well okay well it's all about him going to jail which is like the beauty but you know like we've got to keep it on down low because you know I don't I don't want no YG to delete my channel because he could do that actually he could really potentially do that so YG my god this is all fun and jokes I love you where's my coffee I'm still looking in the lens I was grabbing my coffee picking it up I'm bringing it I'm sorry YG it's all fun and games my guy so like this if you if you ever want if you somehow have chance to come to New Zealand because of like jail time that we can have a coffee together and we can have fun times my guy anyways he should have been sent to jail for breaking him apart oh don't please that to anyone don't know ah I love to anyone man like their songs are freaking good and I kind of really hated how they just yeah that's only the start of this we love these memes okay we freaking love them and I didn't want to watch that clip because it was from Mnet and that means I'll get a copyright strike and I don't want to copyright strike because that means my channel will get deleted if my channel gets deleted then I'll just do a freaking gaming videos of my gaming channel and then maybe now I'm gonna do k-pop stuff on that because you know yeah so if this channel ever gets deleted I'm just gonna be doing gaming videos okay I love you so anyways maybe if you promoted a black pink better and have them it's CB CB CB CB CB oh come back at least twice a year then he wouldn't have to it wouldn't have to that kind of pear ship pear ship pear find a shit no but anyways let's watch this because I mean it's a black pink freaking dance practice oh I forgot this would make it so much easier for me to do this video full screen oh beauty yeah they are oh oh no way someone did this edit with this oh heck no by the way Jesus my bias now oh my god when I have some fun oh shit oh shit oh this is fire bum bum what a bum bum what a bum bum what a bum bum what a bum bum what a bum bum you know when I first heard this fun fact everyone may have potentially not everyone but let's just say everyone just so the story sounds way cooler so when I first heard this song I feel like everyone else just like me put that saying water bomb so when they said that I was like oh they're playing a prank on someone like water bomb bomb water bomb bomb water bomb bomb water bomb bomb you know like throwing water bombs at people I know that's just me next every artist why she has mistreated showing up to hashtag why she is going to jail party what is it Jackson that's good it that's a good it this is what I mean this is what I love this is the beauty behind this behind this is that a meme I guess it could be a meme over this chain why do you add a celebrating why are you getting locked up in GD becoming the new CEO see CEO oh my god I cannot let and speak I cannot speak okay I could I cannot speak I'm loving this GD becoming the new CEO though like yes let's see what this is because right now it's just a link and we need the context behind the link don't we okay so if we click on this link where is it gonna take us oh wow it took me to my YouTube channel hey when you look at there why did you hit the way we subscribe button I guess you can't subscribe to me so just pay money to join the fucking community I mean the freaking community no that's your link sad times it brought me to nowhere to be honest well yeah no actually brought me to nowhere maybe if YG didn't disrespect ace I can't even play that man it's just gonna be a massive blur but I mean I I know what this is about so without even watching the I'll give you the context behind this so ace I mean YG he attacked a saying yo why are you guys wearing my short shorts or whatever they're called hot pants why what are they called hot pants and and then the member from ace I forgot their names I'm sorry but here's like yo you know when it wore hot pants and then why do you it's like and now that's why he's gone to jail dude I'm sorry did not destroy my channel like YG it's all fun and games like y'all like it's all fun and games like look at this guy like this is me and you like high five man see we're good friends my guy I mean I'm good friends with YG see out in black Jack's is the police take away YG even though I somehow got that sentence of Oh yo all the fandoms that hate YG coming to give it a trend hashtag YG is going to jail party we love the theme spirit okay that's too long that's that's too long G-Dragon coming back from the military to see that he's the new CEO please please please GD be the new YG and just changes to GD entertainment or good entertainment because you know the start of good size of G and the end of good ends of D good entertainment you can make it a good entertainment oh did I just check YG yes anyways let's see what this is it's definitely not a clip that we saw just before that was real funny and I like the tweet yeah when I was tough great God Conan Gray screen mirroring we know what your phone is up to nowadays my person and because you just screen recorded I'm saying you screen recorded this person's one yeah you definitely screen recorded this person's one this one's got 6.53 million views how much does your one have you always got 17.5k you screen recorded it you're a monster dude these are actually so very good I love these so much anyways oh we got another one of these is it gonna be like the same one YG is going to jail for tax evasion it's about time that creep gets fucked up oh fuck I didn't say that that person did anyways let's see is it gonna be the same song it's just too long it is like as you made it like 10 seconds beautiful but it's 36 seconds which is not beautiful it's just too long wait what did I just stop on next one okay maybe if YG didn't disband 2 any one and gave Blackpink a full album let several boys debut didn't treat strongly like shit let I come meet Blackpink gave one in Akamu a comeback maybe then he will have to fear for his arse now damn this person snapped YG and jail in the same sentence we love that song I wish I could play this it's a from the looks of it do the do the do from the choreography Lisa fan cam okay that that's all that I can say we don't want copyright strikes on the channel guys you want to strive to survive YG artists and leaving the company building knowing they are free oh I feel like this is the BTS one yep yep I knew it I knew wait wait wait is that John Cook and Jen look at Jen he's like yeah that's just the John Cook just continued but yeah that is what the YG artists would be doing I mean the good artists GD artists would be doing I call it's on the way to get the icons here we go hashtag YG is going to jail party this in this off on a great note here we go let's watch this let's see what it's all about and yeah just have a fun time I have no idea what the fuck that I just watched but those dress were freaking cool that song was alright and her voice is very British boom boom you know what guys now let's just end this video off with an English accent yeah so thank you all for watching this video I hope you guys did enjoy if you guys did show some support by leaving a like and also comment down below what you think about this tea that we have just been witnessing with our eyes because you know we love a good tip of sip of sea of tea a sip of tea we like a good sip of tea when it comes to news like this hashtag YG is going to jail party and yeah if you guys enjoyed this video leave a like and also comment down below I think I just said that before but anyways leave a like and comment down below I think I'm losing my I just lost my accent how the fuck I just lose my accent whatever in vain I'm in me mom's car so yeah thank you all for watching and I yeah other than that I hope you my doofies have a great day and just keep on smiling and adios my doofie should I do a video where I'm just like pure English accent oh my god like yes we are doing that


Hello everyone, I'm Alicia Woodruff and welcome to the Fort Worth Fort. So this week I'm with Shea Patterson Young to talk about Fort Worth's fall gallery night. So Shea, first of all, what is the space that we're in? We are in the Atrium Gallery at the UNT Health Science Center. It's located on the first floor of the Carl E. Everett Building directly across from the Amon Carter Museum. And we've actually been in this space for over 25 years. We're kind of the best kept secret and we'd like to change that a little bit. Cool. So those Fort Worthians who have never participated, what is gallery night? Gallery night is an exhibition that the Fort Worth Art Dealers Association puts on. And the association, one of its missions is to help raise consciousness of the visual arts and awareness in the community. We have university members. We have, we tell independent gallery members, museums, about 25 members. All of those members, including some businesses that are our sponsors and friends, about 45 total will be open for business for gallery night on September 8th at Saturday. Our official hours are from noon to 9, although some of the association members will have different hours. Grocery is available from our website. It's downloadable. It's also in the current edition of the Fort Worth Weekly. That's the September 5th edition that should be out on shelves right now. Our website is www.fda.com for Fort Worth Art Dealers Association. The specifics of each gallery is listed there. They'll have their specific hours and what exhibitions they're presenting. All of the galleries are free. Some of the museums have free exhibition space, some don't, so it's best to check each individual location you might be interested in. Most will have food and beverages and some refreshments, and we hope that it'll just be a great festive environment and that we can promote the arts here in Fort Worth because we're really proud of what we have to offer. Cool. It sounds like it's going to be really awesome this fall. I think it is. I'm really excited about it. I hope everybody will come out and join us. As part of the gallery night, the Arts in the Garden at the Botanic Garden will feature works of juried art for sale by 55 different artists. A special reception will be hosted on Saturday from 5.30 to 7.30 p.m. For more information, call<PHONE_NUMBER> or visit www.fwbg.org.


Meanwhile, the presidential candidate of the Labour Party, Peter Obie, plans to redesign the Nigerian Borg electricity trade in the N-Bet to make the Nigerian electricity market efficient. The party notes that electricity flows in one direction to the consumer, but the cash does not flow back upstream to all those who have participated in creating the electricity. The Labour Party dropped this hint earlier today during a panel session on power on the global funder-raiser Telethon for the Obie Dati campaign tagged the final push. Energy and power expert Jerome Okolo gave a breakdown of this during the session. The manifesto is offering to redesign N-Bet to be the efficient electricity market that takes charge of all the funds coming in, and therefore the distribution of money within the Nigerian electricity system will then be on a non-recost basis. It will not be dependent on the discourse sending money upstream to other participants, but the money will come into a pool and based on the contracts that have already been agreed by NERC, the money will go to the discourse for their work, the transmission company for sending the electricity around the country, and then to the generators, and then to the suppliers of feedstock like gas. And then it also will be able to cope with the new generation coming in from the renewables, the solar power, the wind power, the wave power. When you have a system that ensures that once you put power into the system, you will get money back without having to go to anybody's office, without having to beg anybody. Also Director, Centre for Public Policy and Research, Dr. Sam Amadi gave an insight of what effective regulation can do for the power sector. The effective regulation would mean that the power regulator ensures that money flows back to the power generator and straight to the gas supplier. So misalignment is one aspect of it. The second aspect is that, look, the reason why we have problem with electricity is that unlike telecom, which is plug and play, electricity has so many values, value chains, and each of these value chains must operate at a certain level of efficiency. So if, for example, the discourse goes up to today, I find it difficult to implement change management and therefore maybe meet their customers, recover all the revenue.


Hello everybody, welcome back. This is Keith at 30 seconds sci-fi this week I'm going to talk to you about the Murderbot series by Martha Wells. This was a discovery I made Just last year After getting all these books for free at tour.com if you're not following tour.com You should because they're giving away free science fiction all the time and I'm finding it to be excellent stuff consistently anyway, the Murderbot series for little novellas that Together make an excellent little novel. It is about a security Android that is assigned to a science expedition on a remote world that Encounters problems with Outsiders trying to control its actions and it discovers that That hacking itself is not outside of its Instructions, so it basically becomes a free entity without telling anybody The the Murderbot's adventures Murderbot is what it secretly calls itself and the the stories are about this creatures adventures and how it learns to pass as a human and Discover the secrets about its own past. It's excellent character work. That's what I would say about it I enjoyed it all the way through all four Installments and I recommend them Thumbs up. Thank you for watching. That's it for this week Please give this video a like and subscribe if you haven't already and please follow the links on screen and in the description Below to become a sponsor to help me continue this work. Thank you


What's up everybody? Jayce here for jasperjasecasebreaks.com 2016 Panini Immaculate football one box break two random teams each number one just sold out And again, you get two random teams per spot again. This is 2016 draft class So Zeke, Dak, Wenz, Goff, Michael Thomas There's just the list goes on guys pretty strong draft class and of course Immaculate very nice stuff huge patches Of course, so you get two random teams per spot guys We doubled up your names from Michael down to EA Cardinals down to the Washington football team. Let's roll the dice and we got a four and a two for six times one two three Four and a two six times a down to Diane six times one two three four five Six Packers Cincinnati Bengals Yeah, you have the Packers rig with the Bears Adam with the Washington football team Michael with the Browns. Yeah, you have the Jets Chris wrote the Falcons Rick with the Patriots read with the Vikings Brian Croft with the Buccaneers Steelers Kang with the Bills Chris wrote the Panthers Kang with the Titans rig with the Chiefs Brian with the Colts EA with the Cardinals Brian you have my Eagles Adam with the Chargers willing with the Dolphins Brian with the Lions 49ers Rick with the Texans Read with the Seahawks Diane with the Raiders Christopher with the Cowboys and the Giants Eric With the Ravens willing with the Broncos Adam with the Jaguars Michael the Saints Adam with the Rams and Diane with the Cincinnati Bengals So again quick trade window if you guys want to trade All right, so if anybody wants to trade there you go, and I'm gonna quickly open up the case So this should be a six box case.  Let's close the trip window All right, so there's six boxes one two three four five or six. That's actually number them There you go. Now. Let's use a little dice roll. I roll whatever number rolls is the box we use Six there you go. All right, friends. We got a little Todd girly It's a 99. We got a little Braxton Miller Little new era hat number 13 out of 15. That's Houston Texans Rick Hoffman we have a Earl Thomas for the Seahawks little numbers. It's pretty nice one out of ten What is the Seattle Seahawks? going to read We have a triple relic Teddy bridge or Laquan Treadwell and Morton Bullringer Minnesota Vikings Number 40 out of 49 It's going to read as well We have a eye-blap autograph 97 out of 99 little Jordy Nelson a little white chip on that corner EA with the Packers lost to one. Oh very nice Just got paid this year little Joey Bosa. That is a rookie And that it is a patch auto 59 out of 99 that is the number subs that I believe Chargers and it's going to Adam Kay There you go just like that That was the break guys just a little one box break two round of teams each so I'll post up another one Number two will be posted up soon Jasper's case breaks.com


Good afternoon everyone and welcome. Welcome to the Stocks Will Show play of the day here. This is Juniper. Gee, it's Juniper is going. Juniper is going, going, going and Juniper is going to go to 19. It looks like here. First target was 1950. It got to that and I had to get out of it in the morning period. Again, if you want to trade some of these days past 10 o'clock, 10th of 15, yes, she certainly can. I don't like to do it on the Friday. I did actually trade, I think it was Tuesday afternoon this week and I'm just not trading afternoon on a Friday.  That's the next target and I think it's done when it gets to that. Although it could have been done at 1950. Let's look at it. So here was this. I don't think this was the open. This was the open. Open dropped in here, rallied up, reset dropped. Here is the move down in here into the first number. So this broke after 10 came down in here next your bristle time. I actually barbed by barring the one one that held it. Let's look at the five. I'm all out of this but this was a nice nice trade. I mean this is why you have to have exit rules.  And I know what those are.  You're up and looks good. It looks good to get to 19. Low of the day in there was 1916. Nice bar in here. What would I say this morning when I talked about Juniper? What did I say in the trading room every time I give me analysis? What did I say as soon as it's all the gap? Great gap and no chance of failure and here it is. So nice move in Juniper had an ad and it's working. It's getting there. It's 1130 and it's going to continue down in here and try to get down to 19 or break it. Again I'm all out of this. I play in the morning and I definitely don't want to trade in a Friday afternoon. But for anybody that is still in Juniper it's working and it was a great gap. It was a nice golden gap in it and it was a good short. So Juniper was the play of the day here and the stocks were showed by the trading room and for anyone that wants to do the class this weekend there's still a few spaces left. It's 1130 or almost and the deadline today to sign up is five o'clock Eastern Time. So if you want to take the class this weekend it's October 11th and 12th, 9 a.m. to 5 p.m. Eastern Time all weekend. Good time of the year to learn gaps. There's going to be a million Junipers plays of the days between now and the end of the year. So nice bearish gap in this Juniper. If you want to sign up for class this weekend email me at Melissa at thestockswish.com. You have until five o'clock to sign up today. Have a great weekend everyone. Nice long weekend here with Columbus Day on Monday. Not trading on Monday. Just taking a break. Relax. Enjoy your weekend. Have a good one and I'll see everybody next week. Email me at Melissa at thestockswish.com if you'd like information or sign up for the class this weekend. Thanks everybody. Have a great day.


here with the easy care and who are you Richard Chan and you make a special case for keyboard yes I make a special keyboard case for the smartphone the idea of this is to make it easy accessible for older people and people who can't use the touch screen so there's a big keyboard right here and easier to type in all the yes everything is there it's economically made so that it's easy for people to with trembling fingers to steady the fingers and press the keys all right so it is made for accessibility and there's also the phone it's also the phone for seniors and is it a smartphone no it's a feature phone yes it's a feature phone with GPS and heart rate monitor you can check here oh and it has a pedometer here you can see how many steps I walk today very interesting so this this keyboard is already shipping it'll be shipping in June and it's which model phone it's for Samsung is for Samsung A3 2016 it's DCS for model the Samsung S6 and the Samsung S4 for recycling how about the S7 also the S7 later on it'll be a later model that we will evaluate whether we should make and it's is it Bluetooth or is it connected to the cable Bluetooth connected so it's a Bluetooth connection and the battery has got 8 8 to 12 days lifetime so the outlast is the phone right and the you have a special app what do you do with the app the app makes the phone very easy to use is made for one touch application one touch calling one touch SMS and one touch emailing and how long is the battery life the battery life is 12 days 12 days and then you have a answer we can answer the phone without opening the casing yeah because when the incoming call comes it'll be shown here the identity or caller and by pressing the answer button you answer the call and you can terminate the call by pressing the red button I need to SOS and there's an SOS function for rapid alert for assistance all right okay so when you open the phone you can see the full keypad it's actually equivalent to a PC keypad for ease of use where your headquarter my headquarters is in Norway yeah in the place called Lillistrom okay yeah and factory in factory is in Shenzhen Shenzhen so you can make millions you can make millions as much as you like just start to order now yeah yes please do okay and we are looking for agents we are looking for distributors and we're looking for partners cool it's the only product of his kind in the world nice is the K1


Yn ymgyrchu'r pethau, rydyn ni'ngymrif yn gynhyrch i dynifodu llyfrgellau yn 500,000.  Yn ymgyrchu'r hyn, roeddwn i ymgyrch gŷfynuig lleig arمكن i ddyn nhw anodd a addedres gener stereotyping in machine learning methods. In particular, I'm going to show how the methodology outlined by Rebecca Cook and Simone Cusack in 2010 can be used to identify gender stereotyping in machine learning, and this paper forms part, as David said, of my ESRC funded PhD research as part of the Human Rights Big Data and Technology project. So start by what are gender stereotypes? What are stereotypes? So stereotype is a belief about the characteristics of individuals and Cook and Cusack in 2010 defined gender stereotypes as a structured set of beliefs about the personal attributes of women and men and this definition implies a binary model of gender but I think that it can easily be extended to encompass individuals who identify or are in fact identified by other people outside of a simple binary. These beliefs, these stereotypes, are not the same across cultures but all societies construct i'r cyfnodd o'r rhithledig thatidau ac i'r rhithledigon ar ddau whoalogiaid yn cael fy mhag yrwyddoedd. Diolch, y llifonedd gynnig yw'r cyfinodd yn llaod arllun o'r anodd o'r resurtyn Tyndu Cymru ac o anodd rheswm ond bodهمau na'r rhithledig yn cael ei cyfrifunol hwnnw o'r anodd. Dw i'n credu gan'r rhithledig cyfnodd, oherwydd cyfnodd rhithledig yn hwnnw a'r Okefwc Cyswch, Llywodraeth replyent hwnnw o'r wneud yw'r ysgawdd ar meddwl sydd y Shryf Llywodraeth Llywodraeth yw ddechrau'n yn mynd i wneud bod yw y môn o fofod Fawr? Felly mae'n ddybyr yw'r idea i drafod yma mae'r ystafell ystafell mewn ymlaenu, ymlaenu, ymlaenu, ymlaenu mewn gyfanieg o bwysig yr hyn yn ystafell, a gwaith yn ymlaenu gweithio hyn sy'n bywch chi. Mae rhanwm sydd o fwyloes cydnod oedd yn fawr o mynd i illu. Stereotypes might be used to underpin prejudices, so that's a hostile or avertive attitude towards somebody just because they belong to a particular group or even just because they're perceived to belong to a particular group and therefore they're assumed to have the negative qualities associated with that group. So this kind of prejudice which is underpinned by stereotypes has a clearly harmful effect. It leads to discrimination or hostility to a given individual. But that isn't the only method by which stereotypes can cause harm. Stereotypes may or may not be based on observation, be based on everyday interactions or even on statistics, but even in the situations where they are, where stereotypes arise because of observation of groups, this doesn't necessarily mean when they're used in decision making of any kind, particularly about an individual, that they're legitimate to the situation at hand, that they're a legitimate thing to use in a particular decision. So where does human rights come in? Article 5A of the Convention on the Elimination of Discrimination Against Women states that state parties shall take all appropriate measures to modify the social and cultural patterns of conduct of men and women with a view to achieving the elimination of prejudices and customary and all other practices which are based on the idea of the inferiority or the superiority of either of the sexes, note the binary, there again, or on stereotype roles for men or men and women. So in other words, states parties to CEDAW, which is the majority of countries in the world, have an obligation to end practices which are based on notions of inferiority or superiority, or on stereotypes related to gender. So back to identifying gender stereotyping. How do we know that it's happening? So again, as I mentioned at the beginning, I'm going to be using CUC and CUSAC's methodology that they use to identify gender stereotypes. And the idea that they propose is that if you can name gender stereotyping, that's the first step to eliminating harm caused by prejudices and practices based on gender stereotyping. So they identify a set of indicators that can indicate the presence of gender stereotyping. They identify category-based judgment, so judgments based on membership or not of a particular category, decisions based on tangentially relevant information, selective perception and interpretation of information, and judgment based on extreme interpretation of limited evidence. So we're going to come back to that. I'm going to talk briefly about some feminist legal approaches to stereotyping and discrimination. Feminist legal scholars, as I imagine a lot of you in the room will be aware, have argued that law isn't neutral, that law is a product of social forces. It's constructed by those in power, it's built up over time and it reflects the priorities of the people who have been in power over time. Historically, those have usually been men, usually been powerful men, wealthy men and men of the dominant ethnicity in a particular country. So as a result, law isn't neutral and legislation and judicial decisions can uphold existing power structures and therefore they can reinforce existing discriminatory structures including gender stereotypes. A lot of feminist legal scholars argue that challenging discrimination in a substantive way requires that law as well as policy and practices promotes substantive equality, which requires recognising the substance of inequality, its roots, its causes and its consequences and working to address these in their totality. Which means, from a women's rights perspective, fully recognising women's circumstances, their needs, their abilities in order to have effective measures to end discrimination. As with many other disciplines, feminist legal scholars have called for a more intersectional approach to international human rights law in general in order to truly address the human rights violations experienced by individuals who are marginalised on more than one ground. So I'm going to move slightly on, talk about what is machine learning because I'm going to use these ideas about gender stereotyping to look at machine learning specifically. And Cat is already covered a little bit about this, so I apologise when repeating a little bit. Any of you who've been following a lot of the discussions in the media will have seen the word algorithm being bandied around. An algorithm isn't a new technology, the name comes from a Persian mathematician several centuries ago, I can't remember the exact date. A ninth century, I've got it in my notes, ninth century Persian mathematician Muhammad Ibn Mulsar al-Qurizmi. It's a process, an algorithm is a process, it's a set of instructions for obtaining a result. And the first algorithms and a lot of algorithms I'll show you today were originally a set of discrete intelligible steps. Sort of like a flowchart. So the idea is that you can start with a particular set of information and you can take a number of steps and you get an output. So they're a natural thing to computerise, a set of discrete steps that you can put data into and get an outcome from. But the increase in available data has allowed the development of increasingly complicated algorithms. And this is where machine learning comes in. So machine learning is the practice of using algorithms to pass data to learn from it and then to make a determination or a prediction about something in the world. So it sometimes gets called part of artificial intelligence, particularly in the popular sphere, particularly in the media. I'm going to probably not use the term artificial intelligence because I think artificial intelligence tends to mean kind of what computers can't do yet, but we think they'll be able to do very soon. So I'm not sure it's a very helpful definition to use. But I'm going to give a short overview of some specific ways in which machine learning is used. So in supervised machine learning, we start with a set of label data, a training set, which tells us something about what we're interested in. So basically a set of data points with a number of features and labels for those features. There's lots of different methods that we can use to analyse this data in order to make predictions, but what they haven't come in is they're built on this set of training data, which means data that's already been labelled by humans with the features and the variables that we're interested in. So data, like so many words that are in science, comes from Latin, comes from the Latin diary to give. But in fact it's probably more accurate to say that it's captor. You could come from the Latin capere to take because it's not actually something that is given to us by nature. It's something that somebody somewhere has chosen to collect, that it's the outcome of a conscious decision to choose and record some data but not others. So data isn't a representation of the world as it is. It's a representation of the world as it is seen by the person or the team or the organisation that has collected this data. So the data set is a collection of these observations, each with a set of features for which value of some kind is assigned. But the choice of which items to include in your data set, which features to look at and what values to assign often aren't neutral. They're made often implicitly or explicitly by the data collectors. So, for example, data sets may over or under represent certain types of data. Some of you might be familiar with a particularly well-known data set that's used for facial recognition that's called Labelled Faces in the Wild. And this is a set of photos of famous people and their names. The idea is you can use this data, it's available on the internet, to train facial recognition systems. But this data set was collected from articles put out by Yahoo News in the period I think it's about 2002 to 2004. So it's not representative of people as a whole, it's representative of who Yahoo News thought was important and newsworthy in the years 2002 and 2004. So as a result, the faces are 77% male, more than 80% white, and the most represented face is that of George W. Bush. So it's a data set, but it's not a data set that represents humanity as a whole, it's a data set that represents quite a lot George W. Bush. So any machine learning algorithm trained on this data or similar data sets will have more data to work with for white male faces and American politicians. And facial recognition has been in the news a lot recently. An independent assessment of this tool called recognition that was put together by Amazon, the company by Amazon said that it was trained on a database of one million faces, a different one, not labeled faces in the world. But an independent assessment found that it performed a lot better on white male faces than black female faces. And this can be inconvenient and demeaning if the facial analysis tool that's been developed using this data set is used to identify who is in a photograph or used to give you access to your smartphone or your tablet. But it's dangerous if the tool is being used to identify people who are accused of committing crimes if a tool is more accurate for one set of people than for others. So that's an example of machine learning that uses training data. There's other kinds of machine learning methods as well. So another type of methods called unsupervised learning starts with a data set, but instead of trying to predict something, we're trying to organize the data. So we use machine learning to structure a data set depending on the data itself. So for example, if you have a set of data you might use a type of unsupervised machine learning to cluster data into sets based on how similar observations are to each other. So if you have a set of data points, imagine them sort of put in a graph, you might use a machine learning to divide them into different groups. And in this way, clustering algorithms, which sort of look at points that are similar to each other in the data sets, it's a fairly new technology, but it's got echoes all the way back to Aristotle, all the way back to Aristotle's idea of equality. This idea that equality is when you treat things that are like, alike, and things that are unlike, unalike. And the Aristotelian models of discrimination have rightly so been come under a lot of criticism, again from a lot of feminist legal theorists, for centering what is considered normal and marginalizing what is considered different, who gets to decide what things are alike and who gets to decide what things are unalike. And these criteria are often determined by who is in power. So in law, these are legislators and judges who will sit and decide what things are alike, how alike are two things in order to determine whether somebody's been discriminated against. And in machine learning, these are the people who create the data sets and who decide whether an algorithm and the output of an algorithm are useful enough. So I'm drawing a sort of analogy between the law decision makers and the machine learning decision makers. So without careful screenings, it's very easy for training data, which I mentioned earlier, to contain gender stereotypes. It can be as simple as who is overrepresented in the data, as in labelled faces in the world, which I mentioned earlier. It's clear that decisions about who is considered newsworthy, which were made by editors at Yahoo News, influenced the content of the data set, which as we saw before is 77% male. Another example is a corpus of 3 million words that was taken from Google news articles. And a team from Boston University and Microsoft used a technique called word embedding, which is essentially representing different terms, different words in a geometric way, and then measuring how close they are using this geometric metric to analyse the data. And they found words that could be linked together in a way that demonstrated that they were connected. So for example, you could link the words Tokyo and Japan in the same way as you could link the words Paris and France. Essentially, they found a geometric way to describe how words are connected to each other. But they also found that using this methodology, certain occupational words were much more closely linked to men than they were to women. So for example, certain occupational words that were linked to men or linked to male were architect, broadcaster and boss, while words that were gendered female included homemaker, librarian and receptionist. So it's really clear that socially constructed stereotypes about which roles are appropriate for men and which roles are appropriate for women were mathematically encoded into this data set. And I think this is important because machine learning, although it sometimes gets called artificial intelligence, it isn't intelligent in the way that people are intelligent. It is essentially interpolation. It's making decisions based on information it already has. It's very, very hard to teach a machine learning system to generalise from data. And this means that it relies on what has gone before. It makes decisions based on what it already knows, and in this case it's the training data, whether it's labelled training data as in supervised learning or unlabeled training data as in unsupervised learning. So once you've got this algorithm and you've trained it, when a new piece of information comes in, it's evaluated based on the features that are designed in and the previous training data. In other words, the new piece of information, which might be a picture, it might be details about a person, is evaluated not on its own merits, but on how close it is to previous data. And this is important because going back to Cook and Q-SAC's indicators for gender stereotyping, which I mentioned earlier, it's clear that many of the features that they identify as pointing to the presence of gender stereotypes are present in machine learning systems. So category-based judgment, that's a feature of these systems. By definition, they're using existing variables to make decisions about new pieces of information. It's clearly possible for decisions made using machine learning systems to be based on tangentially relevant information. The nature of mathematical models, the nature of the systems that are being used in these machine learning systems is that they are in perfect views of the world, that they weight certain features over others and they pay attention to certain things about the world and they ignore others. Selective perception and interpretation is a feature, not a bug, of datasets which capture only some of reality, only some of what's actually there. And so, too, is judgment-based on extreme interpretation of limited evidence. And so, by its very nature, therefore, I'm arguing that machine learning can be a form of gender stereotyping. So what are we going to do about it? We've already mentioned how gender stereotyping is a problem under international human rights law, is it a technical necessity for machine learning? Do we need to abandon all machine learning, all of these systems, because they are stereotyping? Is it feasible to use these machine learning systems in a way that doesn't violate human rights? To what extent can we get rid of the harmful impacts while preserving the usefulness of machine learning as a system? Because the computer scientists for sure and the data scientists argue that it's useful. So it is possible to propose technical solutions in specific cases. For example, the example that I mentioned earlier about word embeddings also proposes a solution in the form of another algorithm to de-bias the datasets, essentially to move data points around to get rid of that geometric relationship that puts certain occupations closer to man than women. Another possible approach, which is increasingly called design justice, argues that right from the beginning, right from when we start to think about using these kind of tools, we should be evaluating the values, it's not a good phrase, that we choose to encode when we design. And think not just about avoiding or minimizing harm, but also about transformational possibilities. So not just kind of stripping out the potential problems, but actually thinking about how we can actively use these methods to promote equality. And if we don't do this evaluation work, then it's very, very likely that existing forms of oppression, existing stereotypes are going to be implicitly coded into the systems that we're building. In addition, as I've argued, there are parallels between legal theories and algorithmic processes. So we can borrow these ideas, as I've done in this paper, from feminist legal theories to challenge stereotypes and discrimination in machine learning. And we can use analyses of where power lies in machine learning systems. We can analyze what data is included, how accurate is it, what kind of accuracy is acceptable, because mathematically there are many different ways to measure whether or not something is accurate. And, importantly, what decisions are made on the basis of results obtained through machine learning, which is what Katja was talking about in her presentation. We can identify who is centered in data sets and who is treated as other. And we can situate the decisions made by machine learning in particular context. We don't have to look at them all as one big system that is called machine learning, but we can look specifically in the context. So, while I think there's potentially a lot of issues with machine learning, I'm arguing that it is possible to design systems that maybe don't violate human rights, but they require a lot more effort than is currently being put in. Thank you very much.


We need those family members to employ on our basis. We need more child care providers in our child development centers. So we need the workforce to help with the quality of life. So one of those things we did is we increased the re-licensing from $500 to $1,000. So if you move from state to state and you need to re-license, the Army will reimburse up to $1,000 and we increased that from $500 to $1,000. But we also changed a little bit on how you do it. So at some point it says you had to be licensed at their last duty station and we'd only value that if it came from Tennessee. But what we're finding is if we move a spouse and she was licensed in Kentucky and then she moved to Tennessee, she didn't work and then she moved to Hawaii, but she still just needed to be re-licensed. We should be able to pay for that too and we kind of fixed that too. So I think it's very important that we keep our spouses employed. It's good for the communities. It's good for the Army.


Okay, okay fine. Don't give me a kiss. I'll make you a whole booth where you can get paid in treats for kisses How about that? Hey guys, I'm 33 Life's getting exhausting. Anyways, this idea already exists on YouTube where people make a kissing booth for their dogs Some of my favorite videos are when they do this for dogs that are up for adoption Which is probably the cutest thing in the entire world So I got materials since Valentine's Day is this Friday and although Julie and I do enjoy kissing each other Except we don't because we're friends So that would really be crossing a line if we kiss a lot, you know, it's like we try to keep it to a minimum We're gonna make a kissing booth for the dogs because if you don't have a kiss for Valentine's Day And you would like one for the low low price of a treat I don't know maybe some money if you're peach because she keeps taking it from me You can get a kiss some of our dogs are better at kissing than others I would say Kermit is the best at kissing on command. I don't know if Bunny will participate in this although we will try Peach never gives kisses. She also takes money from me. Like I said marble very rarely gives kisses But he only kisses noses no no mouth kissing from Marbles only nose kisses We're gonna make a kissing booth and hopefully maybe I can convince Kermit to give me some kisses I'm gonna do this video a little differently instead of sitting here and crafting for hours I think we're just we're gonna do it off-camera, you know, maybe listen to some music enjoy ourselves friendship Am I right? No, no, no, we're friends friends with no benefits for insurance All right, you ready to get crafting when the kiss it's don't So for this project we used big pieces of foam board that I had Julian Help me cut out with an exacto knife because I'm afraid to use it.  One of them doesn't have any glitter on it. I don't know why Very angry about that kidding It's just super cute. We drew hearts on it. We put little doilies on it and Julian helped me draw some of those Lips and hearts that you saw because I'm not gonna try we don't need to go there. We already know that I'm not gonna try Please don't rub it in don't make fun of me. Anyways, uh, yeah, I'm an adult and I made a kissing booth for my dogs. I think it's great Well, what do you think? I think it came out pretty great. Who wants to give me a kiss? Oh Can you sit yeah, but I just want to see if they would check it out first. I'm wondering if I can get any for free Actually, I don't really want to kiss her Pg your mommy money I'm kidding.  You never give out kisses. Can you say it? Oh, that's so pretty Yeah, why are you so good? Can I have a kiss? Can I have a kiss? Let me see your cheek. Wow Okay, Peach thinks this is I sit here.  Can I have a kid? Well, can I have a kid? please I have a feeling it's just gonna be the eggy's working the booth this year Marble it up. Where did he go? Marble you working today or what? Is it possible for me to get a kiss please? How do you like being at work? Is the first job you've ever had Okay, so here's the other thing I know will happen If one dog that likes to kiss other dogs is in the booth, maybe instead of kissing us We'll kiss each other like watch this All right Marble's here to visit. Wow Look at that. She's such a good girl. Yes. Okay now give me my money Those are the only peachy kisses that we're gonna get probably. Oh karma. You jealous that peach is at work You guys like this?  Do not drool near the kissing booth. Is he drooling? He's drooling. Look at it.  Good girl Hey bunny Yeah, you're above the kissing Yeah, honey.  This song. I should have them at like petted option centers. Like you get in the kissing booth Can you kiss? Buddy, that's the camera What did he get here? I had no idea he was right here. What a punch Give me a kiss.  This is the worst kissing booth I keep paying her with treats and she keeps not giving me one of these This is how she ends up in so much debt. Get on in there kerm Get in there. Oh good boy. Kermi. Hey. Oh peachy. Wow. Oh, wow. Oh peach Peach She's stone cold stun at the kissing booth bunny. You're taking up the entire frame Thank you Well, thank you. Okay. You gotta get in the booth guy in the booth. Okay kermit now. It's inappropriate You gotta get back in the booth. You gotta get in the booth kerm. You gotta get in the booth. Wait, don't do that. Oh Hi Can I have a kiss? Can I have the kiss? Does he know where he is? Mama, please. Can I have a kiss, mama? Hi Please mama It's possible, but he just completely forgot where he is. Thank you.  I need a kiss.  Oh, thank you. I got peachy kisses. Thank you Oh now it's a kissing booth It's a fighting booth Does she owe you money too kermit?    Thank you nasty Oh happy valentine's day Is kermit your valentine? Yeah kermit's my valentine get absolutely wrecked friend.  You just go Yeah, I'm not getting any kisses. It's all good Unless my friend wants to give me a friend kiss You get in there.  Marble, please Here peachy someone came to visit you peach. It's marble It's marble peachy Give marble a kiss. Oh There he is. Oh, there the kisses are. I see how it is peach kermit All right, okay, you're not in the booth. All right, we get it. We get it. All right You're like making out That's really sweet God, he's so nasty Hi buddy You love the kissing booth because it's the give me attention booth, isn't it? I stand in here and then everyone says how cute I am. It's my favorite. I know you're so cute That's how you get so much money from people But it's deceptive and it's morally and ethically wrong You know someday I'm gonna report you to the authorities. What peach did you like this video? Yeah, I said I got a couple treats I didn't didn't give out any kisses. I didn't pay anyone back the money that I owe them Um, I got a kiss on the forehead. I kissed marble And that was about it. Did you have a good time? Do you want to be my valentine? Thank you for being in the kissing booth To be honest with you, I paid you almost nothing and I got 7 000 kisses like open mouth kisses And it was a lot, but I adore you. So it was worth it. Did you sign up to work the kissing booth just to creep people out? What do you think Julie? You did? I think it came out so cute and we can keep this for valentine's day and we can shake hands through it What? It's a good idea. I'll just rate shaking hands booth friendship booth And then if we are if we get in a fight, we'll just change it to yelling booth. Okay, we'll take turns standing in it Sounds healthy I'm the kissing booth now.  All right, I'll see you guys next week


Civilians must not look away from this device. Pay close attention to this message to ensure your safety and security against an anomalous threat. This message is from an anonymous source, but has been confirmed as genuine by your national government, and you must follow all instructions within. Under no circumstances may you look at the moon. You should move inside into a room without windows, if possible. If not, shutter any windows in your building. To ensure you do not look at the moon, avoid looking at the sky, even during the day. Only travel to reach a suitable shelter, and only look at the ground while traveling. If you hear screaming or other noises of distress from anyone during this time, assume that they have looked at the moon. They are beyond help. Please disregard them. If you hear any individual attempting to convince you to look at the moon, you should retreat from them immediately. They have become an infection vector. Local, national, and international authorities will be in touch to provide you with advice and support over the coming days. Please shelter in place until then. Thank you for your attention and cooperation. The situation will be secured and contained, and you will be protected.