It's time! It's time! U.S. Olympic marathon team trials preview! Let's go! And yes, at the end of the vlog I'll give you my top three picks who I think are going to finish in the top three in Atlanta, Georgia, Saturday, February 29th. The men start at 12.08 p.m. and the women start at 12.20 p.m. Eastern time. It will be live on NBC. So in the United States the selection process is the top three women and the top three men in one race, on one day, in the same location, punch their ticket to the Olympics. It's amazing. Plus there are, there's one alternate, so fourth place just in case somebody gets injured. So I just love the battle. So some countries have a selection committee and they choose the runners they think will perform best in the Olympics. Well, I love that the fact that the runners show up in Atlanta, Georgia fit and ready to rock and roll, all battling together in one spot, one location, same conditions. Ah, it's going to be epic. Now here we go. I'm going to do something a little different for Rio de Janeiro in 2016. I'm going to throw out the splits that, whether they negative split or positive split, in the marathon team trial race. Okay, so here we go. For the men, for Rio de Janeiro, number one, Galen Rupp, he negative split by one minute and 50 seconds for 2016. Wow. Second place, Medkofleski, he negative split by 44 seconds. So meaning his second half marathon was 44 seconds faster. And same with Galen Rupp. It was a minute and 50 seconds faster for his second half in the team trial. And then last, third place was Jared Ward, who negative split by two seconds. Okay, there you go. On the ladies side, on the women's side for Rio de Janeiro, first place, Amy Craig, negative split by 38 seconds. Wow. Deslinden, she negative split by a minute and 14 seconds for her second half marathon. And then Shalene Flanigan was third place and she positive split by 19 seconds. All right, so that's how the race unfolded for the 2016 Rio de Janeiro Olympics. I just wanted to jog your memory as to what happened just the last Olympic go round. Now Flanigan and Kofleski have since retired since the 2016 Olympics, so they will not be racing in Atlanta, but Craig, Linden and Rupp and Ward, so four of the top six from 2016 are back. They will be back towing the line in Atlanta. Now, I know I'm throwing a lot of numbers at you, but here's some more. This is crazy. Listen to this. So how many men and women will tow the line in Atlanta on February 29th for the Olympic trials? Here we go. The final list includes 511 women and 260 men, totaling 711 qualifiers. This is the largest number of qualifiers in trials history for the marathon. And in comparison to 2016, when the marathon team trials were hosted in Los Angeles, listen to how many runners qualify that year. 211 men and 246 women qualify. So here's the difference for Los Angeles and here's Atlanta. Wow, it's amazing. Just look at those numbers. What does that tell me? It tells me the depth of American distance running continues, continues to improve. And it's exciting. I must say like for world championships, for Olympic years, I think the U.S. presence toward the top of the podium is going to continue to improve, not just for the 10K and the 5K, which we've actually done better at recently in the Olympics, but also for the marathon. Here we go. We're diving into the Atlanta team trials. I'm going to give you my picks for the women first and the men's second. And we're going to go with the top contenders, some dark courses, and then my picks. All right. So top contenders, dark courses, and my picks. And okay, I have to say right now there are so many names.  So if the name is not stated, it's not because I don't like the person. It's because like the list is so, we're just so deep. Like I said, that 511 women and 260 men. So here are my top contenders to look for in the streets of Atlanta for the marathon team trials on the women's side. All right. Here we go. In no particular order. All right. So Amy Craig, who runs for Nike, she made the team in 2016. She has a 221 marathon to her name, the fifth fastest American of all time. Amy Craig. Okay, Sarah Hall, who runs for ASICs. Jordan Hesse, who runs for Nike. And I believe she has a 221 to her name as well. I'll try and I will put stats and numbers on the screen right here just to confirm some numbers. Molly Huddle, who runs for Soccany. And yes, she is the 10k American record holder. And there's her time in the 10k. Blazing fast. Kellen Taylor. She was the second American at the New York City Marathon in 2019. All right, Kellen Taylor. And then as far as top contenders, the last one, Emily Sisson. Emily Sisson. And again, if I'm not saying names right, forgive me, Emily Sisson, who ran 223 in London last year in 2019. A solid, solid time in London. All right. There you go. All right. Moving on to the dark courses for the women's side. We're going Laura Tweet. Shout out to the buffs. Laura and I were teammates back at the University of Colorado. So Laura Tweet, Molly Seidel, JC Smith, Shannon Malone, Callie McCumber, Stephanie Bruce, Alexis Zeiss and Marie Tuxbury and Sally Kip Yago. All right. And listen, again, I could have, I could have listed 30 more names, but those are the dark courses that I am going to be watching for in Atlanta, especially. Okay. Now drum roll for my top three picks for the women who will qualify to go race and represent the USA in Tokyo at the Olympics. All right. In order. Okay. And I'm going to ask the question of the day here in a minute. I think you know what it is. It is going to be. So first place, who's going to win this race? I should also mention the course in Atlanta, Georgia, has 1,300, 1,300 feet of vertical gain and loss. That is, well, roughly 350 feet more than the New York City Marathon course, which is considered to be a very, very difficult course, New York City. So at least this is a tough course. And that factored into my choices for the top three on the men and women side. All right. So I factored that in absolutely and for the placing that it's going to be. Okay. So number one, who's going to win the women's team trials? Dez. Dez Linden. She is going to win in Atlanta. That's my prediction. Why experience grit? I think she's tough. I think her stride lends itself to doing a little bit better in the hills. Okay. Dez Linden. There you go is my top pick for Atlanta. Second place, Jordan Hesse. Jordan Hesse. I think she's been through a lot lately. You know, she has been through a lot outside of running. And so, but I think that's fuel to the fire. I think her talent level is off the charts. And so I'm going with Jordan Hesse for second place at the Atlanta team trials. And third place, oh, this was a tough one. Very tough, very tough. I was going between two runners. Okay. Oh, but I decide. Okay. Sarah Hall or Molly Huddle. I went with Molly Huddle only because I think Molly, now she hasn't put together a really fast marathon yet. But I think, I think this might be her moment. Okay. I think that's my, that's what my guts telling me. So I went with Molly Huddle for third place, but it was a really difficult decision between her and Sarah Hall. All right. So there's my top three. Dez, Jordan and Molly for making the team, punching their ticket to the Tokyo Olympics. It's going to, it's going to, oh my gosh, that last two miles, last three miles in Atlanta is going to be insane. Oh my goodness. All right. Men's side. Here we go. Top contenders on the men's side in no particular order. We've got Reed Fisher of Tin Man Elite, who has a 101, 37 to his name. Leonard Carrere. I don't know who Leonard actually runs for. I couldn't find it, but Luke Puskettra, Galen Rupp, Scott Faubel, Jared Ward. Those are my top contenders for Atlanta. All right. And in no particular order. And then dark horse is Jacob Riley, Gerald Mock, who both ran 210 in Chicago last fall, Parker Stinson, who also ran 210 a little slower than the two than Riley and Mock, but he was, I believe he just dipped under 210 in Chicago. Chris Derrick, Andrew Bumbolo, Jim Walmsley, Nathan Ritzenheim. At real quick, I just have to mention Nathan Ritzenheim, Abdi Abdurrahman and Bernard Legat, like some guys that are getting up there in age. I'll just put it that way. Like they are veterans and I would love to see Nathan have a great day. And listen, Nathan is still a 1256 5k guy. Like you can't take away his talent level. I don't know. He's been injured a little, you know, he's been struggling with injuries for a while. And anyway, I'm a buff. So I would love to see Nathan at least have a great day. And if Bernard Legat is like anywhere close at the end, like he's going to out kick some people. I don't know. I don't think he's going to be toward the front, but it's just going to be so fun to see Abdi Legat and Ritz kind of just like kind of the elders of the group leading the way on the starting line. It's just going to be amazing. Okay. And lastly, oh, yeah, also a couple more names. Harvey Nelson, Joe Raikow, Grant Fisher and Sid Vaughn also ran really great qualifying times for Atlanta. All right. There you go. So my picks. This is, uh, this was, this was tough. This was very tough. I actually don't know if it was as tough as on the ladies as the lady side, but for first place, um, I, this, oh man. Oh, okay. This one was tough. Jared Ward, Jared Ward for first place. Now the only reason is because I think he's been more consistent over the past couple of years compared to, yes, my second place pick of Galen Rupp. Rupp has been through a lot with his injury that he had. I believe it was his Achilles tendon and then also everything with Nike and his coaching situation. Now he did just run a 101 low, I believe in a half marathon in Mesa, Arizona. I think it was 10 days ago. So it's tune-up race. Remember we talked about tune-up races. So he is, Rupp is ready. All right. And Rupp is, he's, he's fast. He's the American record holder for the 10K. So, um, Rupp and Ward, but the only reason I went with Ward is because of consistency as of late. Um, I think he's a little more, maybe a little more, uh, primed and ready. I think he may be a little more ready for, let's say the Hills. Um, he had a solid race in New York City last fall. I think he chose New York City to prepare for the Hills of Atlanta. So anyway, Jared Ward, Galen Rupp and third place to make his first Olympic team. And yes, to punch his ticket, this is a little bit of an outlier, but I went with Scott Fawble, a Colorado native, Scott Fawble who runs now for Hoka in, uh, he trains in Flagstaff, Arizona. So those are my top three, Jared Ward, Galen Rupp and Scott Fawble. Now for that third place position, here were a couple names that almost snuck in and beat out Scott Fawble. So, uh, Leonard Carrere, um, Reed Fisher of Tin Man Elite. And I'm just going to say it, Jim Walmsley. Listen, there's a lot of buzz right now around Jim Walmsley. For those that don't know, he's an ultra runner who's coming back in distance to the marathon. And here's the deal. Why I didn't go with Jim or maybe some of the other guys is that, um, I think they will all have strong races, especially Jim in the hills of Atlanta. Like I think Jim is going to be primed and ready to do well in the hills because of all of his experience in ultra running. But at the end of the day, it's all about turnover and speed in the last four miles, three miles, you know, two and a half mile. Like that is where the race will be won. I think they're all going to be in the conversation at the 20 mile mark, so 10k to go. But it's that turnover that I think these other guys are more primed and ready for. For example, Rup running that 101 low in Mesa, Arizona, like he's obviously ready to rock and roll. Uh, so it's really interesting. But yeah, that is, those are my top three picks. And yes, here we go. Question of the day list in order men and women, your top three picks to punch their tickets to the 2020 Tokyo Olympics. And if you want to give a little, um, it would be great if you could give a little synopsis as to why you chose those particular runners, men and women. That would be amazing if you could take the time to do that. And yes, I'm going to, I don't, I don't usually do this. If you could spread this blog to your running friends, do that because I'm going to Atlanta to film the race for all of you. And I would love to spread the word to as many runners as possible that I will be there and I will be there filming to see what unfolds and see if my predictions are anywhere close to correct as well as the predictions of everyone down in the comments. So spread this blog around to your running groups, your running friends all over social media. That would, I would appreciate it. That would be amazing. And there's my picks. Oh my goodness. And I know I left names out. If I forgot someone, I do apologize, forgive me, forgive me, but I am beyond excited. Oh, it's going to be insane. It's going to be insane. All right, everyone. That is today's vlog. We're going to toss it back on the right to the running race vlogs. And that is what we are prepping for running race blog playlist. I should say that'll be on the right. And then on the left, I will pick a race that I filmed in 2019. You guys rock. All right, seek beauty, work hard and love each other. See you tomorrow.


Hello everyone welcome back to another Adobe Premiere Pro 2021 tutorial and this one I'm going to show you how to make changes to an effect so that if you keep reusing the same effect on different pieces of footage you can save it as a preset. Why would I do that? Well one of my people that watched my video suggested that they're trying to make changes to some 90s films and they want to keep making it brighter and every time they drop the effect on it well they got to go through a whole bunch of changes to keep doing it over and over so I'm going to show you how to fix that by making your own preset. I've got some stock footage here as you can see I'm going to drag and drop it into the timeline presto we're on our way this footage is stock footage and it's dark so it's just basically you know a dark forest and it's deliberately dark because that's the that's the effect that we're going to work on we want to brighten this up and then we want to make it so that once you have all of the way the effect you want it dialed in well you don't have to keep doing it over and over so the first step here is we're going to go to the effects workspace so we want you might be in editing go to effects just like this you're going to see the effects box over here type in lighting if you're following along with me taking lighting you'll see lighting effects I'm going to drag and drop it onto the timeline right onto the footage and it's going to load up a very weird looking flashlighty type look and when we go down under effect controls you're going to see that's got a weird shape here what one of my people that watch the video asked is they want this to go from instead of spotlight they want it to be directional which is what the is the effect that they're looking for but every time they keep adding this effect they don't want to keep having to go through all these steps every time so here we go so I'm just going to go ahead and increase the intensity to about 50 40 just for demonstration purposes so you guys can see like the big effect here off on okay now the next step and this is all it is guys is you basically want to left click on make sure you're on your effect right click now and go save to preset and I'm going to call this 90s film lighting or something like that make it something unique so that you know exactly what it is and it's just going to take when you're 90s film 90s yeah film and then I'm going to hit okay and here's what happens so the next time like if I was to bring in some other footage like let's say this footage here I'm going to drag and drop that here and I'm going to create a new sequence with this footage uh where are we did the new sequence from clip let's say I wanted to apply that same effect to this clip but I don't want to go through all the process of you know dialing in all the lighting and stuff like that look right here you'll see 90s film lighting that's the preset we just made I'm going to drag and drop that onto the footage bang it does it in one click one drop and just to make sure that I'm not just spouting off here it has the same intensity and effect as you can see here right here under effect controls so this way if you're doing a repeatable task and you don't want to have to keep dialing the effect that's all there is to it guys please please let me know if you want to see anything in particular I've got tons more stuff coming up I take requests I'll be back soon thanks for watching be back soon


I felt like we came into the game with a great game plan. We committed a lot to, you know, trying to really cover their targets down the field. And when we had those guys cover, he scrambled around, made plays with his feet. I mean, we knew especially from the last game, he wasn't really using his feet like that because of the knee injury. But he came back, man, and he did what he needed to do to go win the ball game. So hats off to them. They're a great team. But at the end of the day, man, I'm just, my mind is so much on the guys in this locker room because we understand the way the NFL is. This group would never be together again. So just trying to cherish that, cherish these guys for the last few moments that I can and understand that, hey, man, we'll just get away from the game for a minute. But we got to come back next year and be motivated to get to this opportunity again because we got a taste of it. But we just didn't finish. Looking to control our group of relievers. What does that mean to you? We faced a lot of adversities this year from coming into the season, projecting to go three and 13, to go two and four, to have all the changes that we had, the ups and downs. I mean, everybody in this locker room still believe that we can make it to this point. We believe that we can win the Super Bowl. So just to understand what we accomplished this year is nothing short of incredible. I don't think anybody, any guy in this locker room should have a reason to walk out of here with their heads down. Everybody's going to walk here with their heads up. And I'm just telling all the guys how much I love them, how much I appreciate the work. Getting up here early in the morning, it takes a lot of work to get to this point. It takes a lot of mental strain. I mean, people don't really understand that, but it takes a lot to get to this point. It takes a lot to play in NFL in these high pressure moments and coming to these games and trying to go out there and win a ball game. So I'm proud of the guys, the effort. I'm just proud of everybody in this locker room. So, I mean, guys just take a moment to get away from them, kind of rest up and recharge, man. Come back motivated for next year.


All right, here we go everybody 2022 top scrum sonic baseball light eight box number 17 pick your team style Bill H. Phillies chris d angels don l reds tigers eric a rangers jeremy k guardians joe d orials pirates john r cardinals yankees jordan w metz joshua b red Sox justin o d backs white Sox a's kevin w cubs lin s nationals mark k with the giants roger m braves padres blue jays ryan d rockies marlins brewers shon m with the royals steve f has the mariners big swick dodgers little swick twins tangy rays and tyler r astros good luck everybody let's go ahead and get started all right matt pulled an auto today we gotta pull an auto now or one of one rick you refractor guardians ernie clements prism george springer blue jays black and white ray wave tim anderson white Sox brice harper refractor for the Phillies youth quake rwanzigan treyress pirates prism yellich brewers black and white ray wave adam frazier mariners refractor lance lin purple yellow sonic pulse guardians rikki elie morgan number 253 out of 299 rikki refractor zack pop marlins prism refractor kbert awees nationals and a black and 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base refractor some dollars right there for that buyback program again march 10th we're close to being done on that thing now you can do it through us you get to the buyback program through us uh for break credits youth quake blotty jr blue jays black and white ray wave jash chisel marlins presale refractor uh tops are just buying back gold schmidt and judge are probably making some mvp centered product oh aqua ray wave erin judge 153 of 199 yankies there you go yeah numbered parallels go for a good bit more you know and the buyback program again march 10th march 2nd right now frack the rookie ryan valade rockies black and white ray wave jared calinick mariners just send us an email latin sports cards gmail.com with your order number if you're interested if you have like the cardinals or yankies in one of these wander franco rookie for the rays prism like your shremski giants if you're interested that is goes through lane told me march 10th today that's what lane told me did he set me 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sam are live at 10 a.m eastern black and white ray wave guardians hosea ramirez matt's got p y t's sam has um random styles both started 10 a.m eastern like i said inception y'all last one of uh this break again baseball is all sold out tonight so bowman inception breaks tomorrow that's what you're looking forward to for baseball matt and sam will fly up at 10 see some good amount of orders coming in so that's good i'll check spots after this but again again three breaks in mosaic basketball all four box p y t's teams are on sale prism kepler twins black and white ray wave rookie joe ryan for the twins refractor ricky zack louther orials refractor swanson prism hosea ramirez guardians i did not see those brian i did not personals are after group breaks tonight will probably start around midnight eastern as long as everything fills on time black and white ray wave rookie 20 son see on reds rookie refractor joe ryan's wins good box for the twins here green ray wave trevor story rockies 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Great, thank you. So the title of my talk is Do You Know Nothing When You See It? And when I say nothing, I don't mean nothing like television static because that's actually not nothing, that's something, that's the image, it just got garbled as it was going through the air. I mean something more like this, but really not even this because this is random noise that I generated with my computer and so it's pseudo random and probably your awesome human brain is finding patterns like deep dream already. You can see the bunnies and hearts and clouds in my random data. So our goal is to be able to identify when something that we've created is nothing. And in order to do that, I'm gonna try statistics. So I am a statistics professor and that means whenever I get in front of a group of people, I actually try and sneakily teach them some statistics. So the question is what is statistics about? And it's about many things. It's about variation which is one of the main themes that we're gonna talk about here. Sometimes there's modeling. I'm gonna ignore modeling almost completely. And when you take an intro stat class or if you're doing basic science, you're often asking the question, are these two numbers different? Which is another way of saying, is this one number the difference different than zero? And then sometimes you wanna know this number, what are some other reasonable numbers that we could have seen if we hadn't seen this one? And in order to answer those questions, we need context about the variation. So the first example that came to mind when I was preparing this talk was ants. I don't know why.  I got five big looking ants and five little looking ants but really they're both draws from the same population and overall the groups don't have any difference in size. But you found, you observed a difference in the group sizes of three quarters of an inch. You and I know something about ant size variation and so that sounds very large and we'll see that it actually was, okay? In another context, imagine you had some tug of war game set up, you had two teams, there's men and women, they're split into the blue team and the pink team and we mapped their heights, we found the average height and we found that the average height between those two groups was different. And again, we observed a difference that was about three quarters of an inch. And we wanna know, is that significant? Are those two teams really different or did we just observe a difference by nature of the selection process, the random generation that we're assuming is happening behind the scenes? So if you've taken a standard statistics class, you're thinking confidence intervals, you're thinking point estimate plus or minus the standard error times something that has to do with some distribution and maybe you're thinking about some pages in your textbook that had some standard error calculations, there's some square roots, there's some fractions. Okay, so we're thinking about difference of means so maybe it's just that first one, but really it's worse than that because there's all these different proofs about if you have different group sizes and if the variances are different then you have to pick those things out and then it actually is worse than that because once you have your standard error then you have to know the degrees of freedom that you're gonna look at in your distribution. And even I, I have a PhD in statistics, I get kind of the heebie-jeebies if you ask me to sort of think of the right standard error computation. Okay, so let's say that you are better at statistics than me and so you were able to come up with the right standard error calculation, you found the degrees of freedom, now you're gonna go look at some idealized distribution and we call this a sampling distribution. So it's not a distribution of data, it's a distribution of statistics which are numbers that were computed from other numbers. In this case, we had the average heights of the two tug-of-war teams and then we were looking at their difference. And then you could come up with some confidence interval and you could see if there was really no relationship between the team and the height, how often would we observe a difference of three quarters of an inch. I'm gonna argue that's not the way that you should do that problem solving. Instead, you should use randomization. And randomization is just what it sounds. You have two things that you think they might have a relationship.  And so what you're gonna do is you're going to take the values, the labels here and you're gonna mix them up and you're gonna compute the group height means for those different groups in your mix-up data and you're gonna compute the difference in the heights. So you can see already sometimes you're getting a positive difference, sometimes you're getting a negative difference. I think one of those turned out to be zero. We're gonna do this like a thousand times and then we can look at the distribution of that statistic. This distribution is centered around zero because it's a null distribution. And then we can calculate where is 95% of the data? What's the middle 95%? And sort of say if there really was no difference between the heights of these two tug-of-war teams, what sorts of differences might we observe? And so in the case of the tug-of-war teams, if there was really no difference, we could observe height differences that were negative four inches to positive four inches different. Our observed difference of three quarters of an inch is tiny and so we think that that really is nothing. Even though we saw a difference of three quarters of an inch, it's nothing. For the ants, we can do the same thing. That distribution looks different. Randomization distributions are not always symmetric and they're not always smooth. They can have these lumps. But again, we can compute where the middle 95% of the data is. So for the ants, it goes from negative 0.5 to 0.5 and that means that our observed difference of three quarters of an inch, that would actually be pretty weird to see if there was really no difference between my two different ant size groups. Okay, I'm a statistician. The open source programming language of my choice is R. And so if you wanted to do this yourself, this is the code. So if I wanted to compute one difference in means, it's that first piece of code. And if I wanted to do 1,000 of them, I could use the second chunk. But there's another technique that we might want to use for assessing whether the number that we got is sort of reasonable or what some other possible numbers we could have observed would be. And that's bootstrapping. So with bootstrapping, you take the data that you already have. It's kind of like pulling yourself up by your bootstraps. You're making something from nothing only that's not really possible. So in this case, we're gonna make data from our old data. We're gonna sample with replacement. So we're gonna pull out data points. Here, we're not breaking the relationship between the two variables. We're just pulling them out directly. And you can see sometimes I get the same data point more than once in my bootstrapped sample. But now I have new data and I can treat that as my current data and compute the mean heights and then the possible difference in heights. And so if that observed tug of war game that we saw was really representative of the world, other than 0.75, I might have observed 0.667. And so then I can come up with bootstrap distributions much the same way with randomization distributions. They might not be symmetric. These are always sort of centered around the estimate that we got from the real data. So in this case, centered around that 0.75. And then it shows us some possible numbers that we could have observed. So again, if that was our data and we were generating bootstrap samples, we might have seen differences from negative three to positive four. And again, we think that ours is sort of not different than nothing or from zero. And with the ants, all of the possible observed values that we think we could have seen, they're all negative. So we think that there really is some relationship. One of those groups is bigger than the other. If you wanna know more about randomization and the bootstrap, you could look at this open source textbook, what? It was written by some really cool statisticians. The source is all on GitHub. You can download the PDF for free. And then if you like physical books, you can buy this textbook, statistics textbook that I used in my class for $9 on Amazon. So it's really cool and it's really good. If you want some other resources, Jonathan Stray has a five minute lightning talk from Nikar, which is called solve every statistics problem with one weird trick. And that one weird trick is randomization. So you kind of know about it. And Tim Hesterberg at Google has a paper called what teacher should know about the bootstrap. I think it really should be called what everyone should know about the bootstrap. So you could learn more there. Then the question is, what does this have to do with visualization? So the idea is we're gonna try and do the same thing with visualization that we did with numbers. We're gonna try and say, is this different than nothing? And what are some other things that we could have observed? And this is gonna help us try not to make visualizations that don't show anything.  And I don't even mean showing nothing in the way that Daryl Hough is talking about and how to lie with statistics in the 1950s. Instead, I'm talking about using techniques like randomization. So there's an awesome paper by Hadley Wickham, Andreas Buia, Dye Cook, and Heike Hoffman. And it's called Graphical Inference for Info Viz. So you could find the link here. I think it's really worth checking out. But one of the techniques that they suggest in this paper is called the lineup. And the idea of the lineup is that you put your data, your plot that you think shows something real, in a lineup with a bunch of innocent plots. And if you can pick your accused plot out of the lineup of innocent plots, that means that it's somehow different than nothing. So again, to illustrate this, let's see if the... Okay, the video's not working. But I would do the same thing. I would break the relationship between the X and Y and look at what other plots I could have gotten from the same data with that relationship broken. Instead of looking at them all in sequence, I wanna look at them all together. So let's take an example. This is some data about loans. And I'm plotting the balance of the loan against the income and I'm coloring by whether or not the person defaulted on their loan.  I say it looks like people who default on loans are the ones that have really high loan balances. It doesn't matter if they're rich or poor, it's just those high loan balances. But is it possible that I just sort of made that up? So this is what it would look like if you used the protocol that they're suggesting in this graphical inference paper and you look at the lineup of plots. There's 20 of them because in classical statistics you use a p-value cutoff of 0.05. That's the same as one out of 20. So if you just guessed randomly, you'd get the right one right one out of 20 times even if you were just guessing at random. So with this one, I think that you can probably identify which is the real plot. Part of it is I showed you the real one before so you might have recognized it. In practice, you shouldn't look at the accused plot alone before you look at it in the lineup. So either you should build this into your workflow where you're looking at a lineup of plots every time you make a visualization or maybe more realistically than that. If you make a plot and you think it might sort of show nothing, make a lineup and then show it to someone who hasn't seen the original plot. Again, if you're an R person, this code probably looks pretty familiar. The top code is using the ggplot2 library to make a scatter plot. And then the bottom piece of code is just showing how you could randomize the data to make those null plots. So I'm doing a null permute on the label of default versus not default. And then I'm wrapping those facets into an array of plots. And when you run this code, they've done this sort of clever thing where it'll make the plot in your plotting window, but then it also prints this thing in the console.  And that's where they hid the answer. So if you were doing this just on your computer and you wanted to not know ahead of time which one is the real plot, but you have to figure it out afterward, this is how you do it.  So in this case, we got it right. You can use the same approach to do a visual t-test. So this is the data from those tug-of-war teams again. Somewhere in there, I've hidden the real plot of the different heights. The crosses represent the mean heights. And then the question is if you could pick out the real data. So in your head, make your guess. It says decrypt. So if you're gonna decrypting things quickly, you could see what it is. But the true data is in position five. So I'll show it to you again. I don't know, that wasn't the one that I had picked out. It's certainly not the most extreme of the possible plots. And so what this is telling me is the same thing as the permutation test that I did with the statistics and the distributions at the beginning of the talk that there really isn't a difference between these two groups. So these examples are sort of, there was like a positive one and a negative one, but this one I don't think that you really needed visualization to tell you that answer. You could have used standard statistics. The power of the graphical inference technique is that it's very generalizable. You can use it for anything. So one thing where I think humans are great at finding patterns in noise is in time series analysis. So I took some data. This is about the steps that I take. So it's from my Fitbit. And sometimes I like to make up stories about like, ah, like the variance is going up or the mean seems like it's changing or like there was something that happened in December. And I'm really good at making up those stories about the distributions. So this is, again, I hid the accused plot, the real data somewhere in there. Again, look at it and try and see if you can guess which one it is. Oh, maybe I have another one where I made the scale a little bit nicer. And now we're going to try and decrypt. So there's the string again. It says the true data is in position four. And I'll show it to you again. Again, that wasn't the one that I had really picked out. But if I had just shown you the one time series plot, you probably would have believed my stories about what was interesting about that plot. Okay, so with the statistics analog, we've kind of been doing the, is this different than zero task? But then I wanna switch to the, is what are some other reasonable values that we could have gotten for this? So with the numbers, that was what are some other reasonable values that we could have gotten. And then I'm gonna talk about that in terms of plots. So some of this comes from joint work with a colleague of mine named Aaron Lunzer. We work together at the communications design group. And this is the highest quality image of Aaron that I could find. I guess it's almost life-size in this theater. So maybe that's fine. But we worked on this tool, which was a prototype tool. And we call it lively arm. So this is something that is real in the sense that if you go on GitHub and download the code, you could run it yourself. I don't recommend it. It's very buggy. But it really has R on the back end and then it has Lidly Web and JavaScript on the front end. And what it lets you do is play with the bin width and bin offset of a histogram. Many things let you do this, but this tool also lets you overlay a sweep of parameter values so that you can see a variety of histograms all with the same bin width but slightly different bin offsets. And then they form what I like to call a histogram cloud, which is kind of like a kernel density estimate, but it's easier for people to understand. There's one more feature that we've built into this, which is that you could call out the individual histograms if you wanted to. And so instead of just seeing them overlaid as a cloud, you can do small multiples. And it's actually a two-hand interaction here. There's like an iPad and you're controlling the one screen with your right hand and the other with your left hand. So it's a little bit buggy, but you can see the small multiples of the different possibilities of histograms that you could have seen. Again, I think it's really easy with histograms to just use the default algorithm, like the Sturges algorithm or whatever it is in your favorite tool, which will make the bin widths for you. If you're using GG Plot 2, it will give you a warning. Like, I chose a default but you should come up with something better. But many people don't. I know that you all do because you're visualization professionals, but I think that it's not always obvious to non-professionals that those defaults make a huge difference. So you might use the defaults and find what looks like a pattern, but it's really just the result of the parameter values that you chose. So giving people the opportunity to play with the parameters is really powerful. So after Aaron and I worked on this work, which is essentially in one dimension, we're looking at histograms, I started thinking how could we do something like this in two dimensions, which made me think about maps. So the modifiable aerial unit problem says that if you aggregate spatial data in different polygonal shapes, you're gonna get different spatial patterns. And so this is like something that can happen with gerrymandering. It can happen with the different county data and zip code data, these different levels of polygons. And there's a lot of statistical problems that go along with that. So Aaron Luncer and I worked together on this next tool, which doesn't really have a name, but what it does is it takes point data, it's about earthquakes in Southern California, and then it's aggregating them into these polygons. But instead of just making fixed polygons, you can scale and rotate and move the polygons to see some other possible values of the visualization that you could have gotten. So you start with your default values and maybe you think that there's some very obvious trend that everyone should be aware of. And you can tell some great story about why there's a hotspot of earthquakes in this one place. But then when you start manipulating the polygons, you can see that there's many other possible patterns that you could have created. So the takeaway that I want you to take from this talk is that there are statistical tasks and there are very analogous visualization tasks. So in statistics, we want to know if two numbers are different, which is like knowing if one number is different than zero. And we could use tools like randomization to answer that question. In the visualization world, we can use randomization in a different way to answer the question, is this plot different than nothing? In statistics, we might want to ask what are some reasonable other possible values for this number? So what other differences in heights could we have observed? And you could use something like the bootstrap to show you other possible reasonable values. And in the visualization space, I think that using parameter manipulation or giving users the ability to see how your parameter choices impact the visual story that they are seeing can be really powerful. And so I, thank you. I think we might have a minute or two for questions if anybody has any. All right, cool. Oh, there's one. I'll talk to him. Yeah, so I think that's a great question. The question was, could we have a computer make those assessments? And the answer is not really. It's hard to generalize a technique that would allow a computer to make those assessments. The paper that I was referencing, the graphical inference for InfoViz, they do try and come up with some distance metric between the null plot and then the interesting plot, and then use some cutoff value to say, like, this is different enough that we think that it's different. One thing that I didn't say is that these authors also were sort of using computer tools as an intermediary.  But I think asking a computer if a number is different than zero, it's much easier for a computer to assess that than for a computer to assess. Is this picture different than nothing? All right, thank you so much. Thank you.


Good afternoon everyone. My name is Sudarshan. So this session is going to be about the UID project and what we are doing in terms of reporting analytics and some of the AKS that we are planning to launch. The original presenter is Pramod who is the chief architect. He is unwell so he has not been able to make it. And Sanjay Jain who is the product manager is on the way, he is slightly delayed. So there are two broad sections to this. One is in terms of the whole BI and reporting analytics framework that we have and the kind of data and how we use data within UID. And the second session is about the authentication and whatever kind of applications that we see, ARTHAR, data and ARTHAR services. That's something that Sanjay will cover. In case he gets late then we can always take that in the launch session. So let's start off with reporting analytics specifically in terms of UIDI. I've seen a lot of people talking about what will be going on. They understand reports. Reports is something they understand but that's pretty much a very ad hoc system in terms of if they have anything with any of the senior officials that's when they look at the data. But from a UIDI perspective what we wanted to look at was make this a very systemic process. And when I talk about data here there are two types of data. One is typically the data that you would get published as part of census or the other data that you know then you can do a lot of statistical analysis of. But what as an organization we also wanted to do was we wanted to use a lot of data literally on a real time basis for operational processes. Typically as you see in corporates you have data that comes with your time and then people and all the managers and all the whole field is working on that. We wanted to bring about that system within UIDI to be able to drive a lot of the operational work. And the reason for that was because as an ecosystem that UIDI has built, UIDI is a very lean organization. It's hardly about 150 people from a UIDI perspective. But from a project size we are obviously looking at a very, very large project here. And what we do is we work with a lot of partners. So one is what we call as registrars. These are the people like the state governments in the case of Karnataka. It's a Karnataka state government. Or you have SPIs, LICs and the other non-state registrars as well. So they are the people who actually take, enroll the people, enroll the agencies that enroll you on the field. So if somebody has got an Aadhaar number, they would typically go to an enrollment station and they would enroll themselves. And the state government are the ones the registrars are the ones who typically would hire these agencies. So the UIDI is not directly going on the field and enrolling. Now this provides, this scenario brings in a lot of complexities because what we have is, you know, we have the overall UIDI objectives. But then we have the, we have a big layer in the middle of the registrars. And then we have the agencies on the field.  It's all working and it's all being monitored in a consistent manner. And that's where we saw the need that, you know, we can't have a system which was a stringy post-mortem system that, you know, you're getting data and then you're figuring out that things are not working or working. What we wanted was to be able to drive a lot of the operations on the field very, very quickly. So here are some of the ways in which we are using analytics and reporting. And this is, again, specifically from our very operations, running the data field operations perspective. So, you know, we have an analytics portal, a reporting analytics portal that's the house portal that actually is open to all the ecosystem partners. And it's all log-in based, so when you log in, you'll see data that is specific to you. And it covers all the key performance parameters, key operational parameters, and you'll try it and you can know the status on a daily basis of where things are, what, I mean, right from enrollment status to the kind of quality you have, productivity. You name the metric and you will be able to provide you the metric. And separately, we also have what we call the NOC, which is a Network Operations Center. This is typically like what you would see, you know, it's a big room with a lot of screen panels and you have several people who are continuously looking at it. It runs the data center and a lot of the operations because what we have is about a million enrollments that have data for more than a million enrollments coming in on a daily basis. And what we need to do is make sure this ship is running smoothly and there are no bottlenecks. So it provides a real-time tracking and it's literally all screens, panels with which show where the bottlenecks, what stage, I mean, how many packets are at what stage, at what processing level. And it typically offers you any breakdowns and deep dives so that you know what's happening. So one is from monitoring operations on the field, so that's what the analytics portal does. It provides the whole ecosystem of registrars. So for example, a state, a Karnataka state government can log in and they can see each of their enrollment agencies, how many enrollments have they done and what's, and that too down at the operator level and at the station level. So they're not exactly there what is happening.  You have state, non-state players and a lot of the private sector players. So we can't run this whole ecosystem purely based on traditional methods and information and data is the only source that we saw as a way of running this organization. We provide end-to-end visibility all through. So that's a part of our, you know, driving the whole transparency piece. So anybody who logs in, they can see right from the point where the packet started its journey and who came in and who enrolled at what point of time right to the time it's processed and given to the time it is delivered. In fact, if you go on the website and there's a link called Checker Autostatus that actually at a packet level you're in a resident or anybody and go and enter the basic details and they can look at the full details of where the packet is in real time. So it will tell you what is at this stage of processing or it has already been printed on the way or it's been delivered. So you have a complete end-to-end visibility within the ecosystem as well as outside the ecosystem. And it's important because what we wanted to make sure was it's very easy to confuse things in such a large ecosystem because each of them has a different incentive or a different reason why they are part of this program. And, you know, there is a big chance that there is going to be, you know, there could be a lot of conflicts right from the actual contractual to operational conflicts. And hence you need a single source of truth, a single data typically is the most objective form of truth, but then there's something which nobody can question. This is also interesting. We actually use real-time feedback. So data is updated pretty much on a real-time basis. Midnight, the data is updated. So you can pretty much look at today what happened in last night. So whatever data we have, everything is published available on a real-time map. Typically, a lot of the government programs, you know, you would have seen data that comes in and there's a lot of gap between when the data actually comes and then is used by the organization themselves. And that is what we wanted to cut down the time and try operations using literally real-time data fed back to the whole ecosystem. We have fraud detection modules also because there is obviously going to be a natural behavior towards trying to beat the system. So we have fraud detection modules for multiple... So one is from an enrollment perspective. So people who enroll in the field, we have, based on the number of tools that we have created, we have the fraud detection module and that scans each packet and then takes a call whether this could be a potential fraud or not. And if so, then it goes through a separate set of processes. And then from a transparency perspective, one is we spoke about all the data, at least from an operational perspective, we try to share it with the ecosystem. And similarly from sharing it outside the ecosystem as well, we provide a lot of the data as data sets and outside. In fact, it's all anonymized and ruled of data sets aggregated at certain levels depending on what the data set is. And this is soon to be available as live data feeds. You can subscribe to the analysis data feed and you'll come to know when the data has been updated. There's also an analytics white paper we have online and that speaks in much more detail in terms of specifically what we've been doing, what's the kind of architecture we've been taking. I do not want to cover that in too much detail, but we'll just cover a couple of slides after this. But that white paper talks in much more detail in terms of what we do from a data analytics perspective, what kind of structure we have. And it also recommends from a government, as a template, how can this be used by other government programs as well for setting up their own BI modules and reporting analytics modules. But this was shared by a lot of the ministries and departments and we brought a lot of interest back from them so soon we'll be presenting to some ministries in terms of how they can use these systems for their own operations. This is just a small snapshot. Again, I wouldn't say we'll do any high-end modeling and analytics as yet. What we started off is to make sure the base system is set from having a proper reporting system in place. We have basic visualization in place and a lot of the data. One also needs to understand that the audience here is people who have not been exposed to this kind of data or are not exposed to just the use of data in the first place, which is why it was very important that we... We can do as complicated as we want, but what we wanted to do was to inculcate the habit of looking at data on a regular basis, which is why it was very important to stick to very, very basic data, very basic visualization, and we also provide a lot of canned reports. We don't expect people to go over real-time and download data sets, create custom data sets. We just provide a very simple thing which they can take a printout of and put up on a file and then review that. All of this is login-based, so when you log in, then the data and downloads are specific to what dimensions you have. At a very high level... One question is, is the data available directly and later for... Like I said, we are publishing data sets, anonymized and rolled up data sets for public as... So for the ecosystem partners, they don't get access to the raw data. Even from a reporting infrastructure perspective, we have certain parts of data which we use for reporting only. So we don't have access to the raw individual data that we can then use. It's typically data sets that are aggregated at certain levels which are to be used for specific reports only. Are these data sets already online or are you planning to do it online? Okay. So when do you intend to... Unfortunately, I will only say soon right now. Okay. But a lot of work has already begun on the space so soon as... The data sets are ready, working on the processor. So the processes are not easy to quantify. At a very high level, the whole... So one good thing about the design at the initial stages was the whole BI modules and the reporting modules was there from day one in the whole overall architecture design. And which is why today we have all the modules in place and it obviously spans the standard data capture. In fact, we capture a lot of operation metadata. So I don't know how many of you have done enrollments on the field, but data like somebody's enrolling, the operator for example, how much time did he spend on each screen? So when he moves to the next screen, so he's taking your demographic information and he moves to the biometrics where he's taking the right slab, left slab. So we have able to capture time stands on each of that. And the reason we do that is because we want to help improve the operation of the productivity. So when we go back to people with insights in terms of where they are typically slower, how they can improve their productivity, that is an incentive for somebody to look at data because the faster they do and the smarter they do, the more enrollments they do on the field. Similarly, then we have data acquisition. So that's the module in which all the source systems that feed data into the organization. So like enrollment, authentication, other sources. Then we have the whole data storage or data warehouse modules. So we have of course, what is the production system so that all the servers and all the live processing happens. But there's a copy that is created. So from a reporting databases perspective, we obviously do not have access to the production data. So there is a separate set of data sets, data data also available for reporting specifically and that's what we work off. Data distribution is then done by multiple ways. So we have data marks on data sets and some of the raw data that is used to publish this information. And then data access is how we then finally share this information ahead and which is through the delivery platform the important that we spoke about. Signered dashboards, we also offer a lot of cell service capability. And the reason this is important is because it's like a scope that we have a very lean team. So we can't do the analysis for each and every partner of ours. So what we try to build is a sense of system so people can actually go and look at all the data and figure out how they want to slice and dice the data and download data sets for themselves based on that. And then a lot of ad hoc reports also can get generated and then there are the canned reports because most of our people prefer to just take a spoonful of it to them. At a very high level key principles in terms of the whole BI reporting modules, we are completely open source and we like our file system to give the BI modules that we use we use pent-up open source software and we stick to that all through. So it's scalable in here and given that we need to move to a very the size of the data is going to run and it's going to be a huge data set. So the system has a bit so that it can scale it's very modular in nature and it can scale very quickly. What we also do is since we spoke a lot about operational data so we have access to not just processed data in terms of how many people have got input but also a lot of that operational data real-time data that is provided and both of these modules are handled separately. Big data of the file handling and storage systems we use pretty much most of the cutting-edge technologies. Data security and privacy is extremely important for us so we have security to standard physical and electronic security and from a privacy perspective I mean within UID of course there's a lot of systems but from a reporting and analytics perspective also we have a data council that monitors all of the data that is shared and tried no personally identifiable information is available to any of the reporting data sets and then there is a huge amount of anonymization and aggregation that is done so that only a specific piece of data comes out to RT. This is about how I started this early part. Want to take a look? About other, I guess most of you are aware of this but what we're trying to do is bring the largest biometric system in the world. The key point here is we're trying to provide online identity to all the residents and this is based on one identity which is already complicated so you get one number and that's the number that will stay with you for a lifetime and from an open data perspective all of our data is accessible via the online authentication service that we cover in the next slides and what I just spoke about which is the bi data that's accessed internally and extremely through our data portal. Other authentication is about the question of when a person enrolls with us they tell us who he is we ensure they're unique and then authentication is about confirming that identity so when they claim that identity somebody you can validate it. Now one of the key principles is that we only respond to the yes or no we actually don't give out any personal information during this process and the other part is that it's online. We support multi-factor authentication using biometrics, PEN, OTP and of course any combinations of these and all sorts of protocols and devices. The API spec is again public we encourage you to go look at it and if you have any feedback or use it. Actually it's all on our website and I think this PPP is a little bit too much. Yeah, in any case it's a simple search for your idea of authentication and Cydec Lance this is what it is you've given your other number so you're not actually searching with biometrics against the entire system so once to unmatch so when you provide us the other number you go pull up your appropriate data and then you send us additional data biometrics or demographic we match it and we respond to the yes or no. It's a very simple system it's eminently parallelizable and scalable we expect to support about 100 million odds a day so in over a 10 hour period that's the kind of volume we're scaling up for and a lot of the applications are actually quite obvious a lot of the government welfare programs would use it the financial inclusion, the banking systems we're using it there's a KYC requirement for a lot of telecom providers for banks, for other service providers and we expect to be participating in that and no, I'm a customer, sorry I'm so used to these acronyms but yes, this is a no-yah customer requirement where service providers are required to know who they're servicing and in particular this is used potentially from a law perspective if they want to go back and trace somebody but essentially this requirement has been put into various service providers and we expect that eventually the other number will end up unifying various systems and providing the interface there's a white paper that we have published on authentication and we expect to actually come out in the document shortly and we'll talk about the effectiveness of biometrics in authentication that's it so at this point I'll take questions and the solution this is a lot of sensitive data yes so you must obviously be having security distance and all so are there any laws that prevent misuse of this data so overall we don't have in India significant data protection in privacy laws some which exist but from the UIDI perspective we actually put in a lot of protection on to this data so basically resident data is not visible to anybody other than people inside UIDI for the purpose of doing other assurance or any other investigation and from the external perspective the only data we give out is anonymized and there's no personal information available so the other API is only a yes or no nothing else that's accessible no other APIs you're building on nothing that could cause there are APIs for different purposes but the authentication API only provides a yes or no and in any case none of the APIs provides any data outside the system and anybody can query this they'll use APIs for the authentication API they'll come up with an authentication framework where you have to be a registered user agency to actually access the API so can you give us an update on what is the legal framework protected in this data what is the status of it obviously UIDI is not responsible for the law in terms of the privacy laws that exist in India but you must be aware of it because it does affect your work can you give us an update on that what do you know of the status basically there is no overarching data privacy law everything comes under the IT Act and the provisions that come from there where as a service provider this this is not my form so it comes under the IT Act and that's pretty much the only act that covers data issues right now there is a privacy bill which is in parliament which I don't think I've moved for a while and this UIDI also had proposed a bill which would then give protection to UIDI data alone and again that's still in parliament it's not being either approved or whatever because there are two consents with this one is leakage from within the UIDI database a rogue employee with access to data could leak it and I work with government data I know how transparent this whole system is when it comes to pulling data out of the system because there is a framework in place that says that data can only be accessed in a certain manner but that framework also prevents day to day operations and therefore lower level employees find various ways to get around it for their own convenience and I work with the Karnataka government I've seen how this happens there's one public directly at one particular IP address which is slash.com logs it's got data for everything and you're supposed to just not acknowledge this in public that's how it works you want data from the data center you call them then put it in this public folder you download it from there this happens in reality because the rules that govern access are so tight that they prevent data operations and I'm concerned that this may or may not be the case with UIDI so that's one problem the second thing is the public API is only a yes or no answer and that's an extremely powerful tool because it lets me now correlate private databases I have a private database which has a UIDI number somebody else has one so merge our databases because the UIDI number is guaranteed to be accurate and that is a problem that's not your problem it's a privacy problem which there should be privacy law for it to prevent or to regulate this kind of database mergers I actually completely agree with you on the second point where you talk about the need for overarching privacy law and it's a requirement UIDI or not and in some sense in the UIDI becomes a sort of lightning rod for all criticism related to the overarching privacy law so we are a little sensitive about this but the fact is that yes we do need a privacy law which protects data for residents in general but on the other side a lot of our systems are actually not hooked up and that leads to a lot of inconvenience to residents and so we end up having to play this balancing act where you don't want to prohibit people from merging to databases because that actually can be used to find for example fraud in the PDS when you talk about the fact that the number of ration cards in the state is more than the number of residents now someone is going to have to go look through databases to figure out who's real and who's not and so somewhere in there is a balancing act which has to be worked out and it's not just about private databases so and also there's a lot of issues about maturity in the society in regards to privacy and today you could go to a street corner and stand and fill out hand out forms without telling who you are and people will fill in these information all kinds of information so we have to balance the two extremes where people are completely free with the information and where we are concerned about people being cracked and so on so somewhere in there is a balancing act UIDA is a player in it but we don't control it a non privacy question to move away from that so one important part of UID data is address and since there is no standard way of actually filling out addresses anywhere in India how do you manage that so one thing which is standardized is PINCO but is there a list like so it has UIDA I actually come out with a way of actually doing identification based on PINCO how does that work and would that be available for public since you've already taken the trouble okay so there are two databases which exist in the country today one of them is with the Registrar General of India they actually have an entire administrative hierarchy of India in a codified form so you get you have ports for the states districts, sub districts and down up to the villages and cities nothing below the level of city would show up in that system and the second database is the postal data so they have this PINCO hierarchy which doesn't exactly merge with the they have postal circles and not states and so state could actually be divided across both or PINCO can go across the state border it just happens by the way so and they don't clearly map to each other but we have worked with the postal department to try and get this mapping and in fact the client enforces that so you type in your postal your PINCO and it only shows you the villages, towns and cities which that maps to that at least so at all codified levels we have a separate field which ensures that this data stays consistent anything under a city in the for example the depending on where you live it could be a ward it could be a neighborhood or in Bangalore you have layouts those are not codified and those are free form entry right now that's basically the best you could do at least to the public we I think we will try and get that out because this is really public data it's just that it comes from two sources neither of which is UIDI one of them is the RGI and one of them is the postal department and you have worked with them to try and get together but I don't see why we couldn't release it the developer portal of the UIDI is it is it open to only registered users I think it's a you have to register at the portal but I don't know so what does it take to get registered access to the API right now actually the developer portal you can get the API itself there is a developer access to the API which is not the same as the public so it's not the UIDI number is less and then you have to go through the process and become an authorized user agency for that if I were to say develop APIs for accessing the UIDI database for various languages and frameworks now at what level of access do I need to go build this obviously I need to test it with live data to know that my library works but I'm not actually interested in the data I'm only interested in making sure that this intermediate library works so what do I need to do just go ahead register let us know what you need we will work with you to help you out there is some data but he wants languages in some ways which if it's not there in the API there seems to be no concept of the person who's being authenticated approving who can authenticate that is true is that a thought or debated is that a valid idea because that solves a lot of the privacy I'm not going to say that but it's an interesting thought because finally end of the day you're going to there are two issues with that let's just think of it from a scale perspective it means that when you come in and specify your authentication preferences I have to have a sort of a photo which is you know for every person in the country which is becomes a very large scale exercise in itself and second a lot of times you don't necessarily know who's authenticated you go swipe a credit card somewhere at the shop and it shows up on a different name because there's a lot of federation etc going on or do you want to go by a class of providers like you only want banks to authenticate you but not telecom companies there may be ways to look at it but it's an interesting idea I'm not sure of its practicality but the technology is going to improve so it's an idea worth looking at Is there some applications that are already enabled and using so far we have only one field application which is actually for financial inclusion so you might have read about these narrator payments being made on the basis of duality so we have enabled business correspondents who can work with certain banks who withdraw funds from those bank accounts using duality applications which states are doing the other one that's happening is in Mysore, Indian oil is doing a two questions so the factor of the data comes in is very interesting I would like to know what kind of cases have come out and the second was the data what kind of servers are they stored on in the data company servers were there are tools governing how servers can be used so if the data is non-modifiable for 10 years so do you have those kind of servers so the data council specifically is about approving which data sets can be made public so really there's no cases per se that would come up before that the second is that basically once we decide that this data is public it will eventually be stored in a place where you can access it to the board so really the actual storage and servers are different but it's the rules that govern the data set that are more important as to how long the data lives and I don't know right now it's been at a high level in terms of making sure we spoke about a lot of the reporting and what comes out and is accessed by the ecosystem this comes in and makes sure that the data at one level is sufficiently minimized and the basic rules are made and also the final what of these metrics is published to the ecosystem from a measurement and operation that's the front end, the back end part is what I was asking about, it still was very storing and you haven't hit that lock right now you haven't hit that lock right now one last question I don't have a question I have to add to what he said so in case anybody has a question can ask okay one last question if a citizen has some problem in the way you are codified because like in a credit card company sometimes a mistake is made all the head it belongs to you now suppose something goes wrong how can an Indian citizen rectify what is the process that is going to be used so we have an update process which is not yet rolled out to the field which will be rolled out within the next refresh cycle in a couple of months during that cycle we will roll out a process by which any resident or citizen president can come and update the data so that would take care of that