My name is Michael, I'm from here, and it's what I'm making today, it's about making sense to use your quality content to remember myself, what I'm teaching you how to make. So, let me just go straight into what I want to show you about moving, that for all of you, it's something you do not maybe sing or maybe not possible in Microsoft Excel, predictive analysis. So beyond that, there are how many types of analysis in the level of where the analysis comes from, you're going to do a focus on different types of analysis, how many types of analysis, predictive analysis, predictive analysis, any other one. So, predictive analysis, that's in tactics, let me just show you how that, any other one. Don't care about that, doing this might be preventable, but when I explain why the quality certificate is also exploratory. So, let me just explain what all those forecasts are. You can, they are not too many, so I don't need to write them again, I'm used to writing. So let's start with the number one one, one that we are all familiar with, this is a descriptive that we do a lot in business, right? So, management and the business folks, they tell you, if you're one of them, you really want to tell you that this is what you want to see on a daily or weekly or monthly basis, right? And typically, you want it to describe what is happening. So you want to see maybe our sales, how your sales are going to be by day, by some different kind of, maybe by food or by region, by sales person, right? So, there is a lot of descriptive analysis, in fact, some predictive things descriptive is like the role of what you can do in business. So that descriptive analysis, you are basically looking into the detail you have, going from download data to insightful reports, something that you can look at and see the kind of patterns you want to see, and begin to be one of the things that you want to be able to figure out, to do all the data here, which are being presented. Then there is the predictive analysis. So what most of us do in business is we do much of normal, you know how we call much of normal, then we do much of normal. So, maybe I'm a sales person, and then I get to work today, and my manager calls us or all these things and says, oh, we have an emergency meeting. So you know what, we had a meeting with manager last week, and a whole lot of things that have come up, your targets are going to be changed. And so maybe you used to do, say, 100 million per month, and they say, now your target is 150 million. And when you try and ask, how did they come up with that figure? It becomes the case of digital. You don't want to, you don't want to, you don't want to start working towards achieving, right? Magic number, you don't want to have to fall from somewhere. You can't even say, this was a calculation they need to come up with that this is the number you should do, right? So it's very common. MDC is going to do 20% here. Then everybody starts working. 20% came from, I don't know, maybe Tony Shaw. And then everybody else starts, you know, getting some other magic number that the few who have got the magic number, like Michael is an Indian, Michael, that's what he does, and I understand he wants to do his magic number. So that is one secret thing that he uses to do, like that's all he does that I do. Then the second one, we do moving average people. So we look at how you've been doing for like the last couple of years, periods, and if you do it 20% here or 20% month on month, you'll say, okay, if we ordinarily do this, I want to get to work harder. So if you're not going to get to 20%, maybe you do 30% on what basically comes down moving average, right? So those ones are very easy to do in nature. The magic number, you don't need to do any work, just ask the person giving the magic number, the right thing to do, right? And then when it comes to moving average, the last couple of periods, what about the much more intense ones? The ones that people, while you're in a zero data analysis, you can't do it in a zero, this come and then fight on, you learn to have. What about those ones? Are you now, let it help you when it comes to that and you're like, okay, if it's that, I'm not going to answer, I hope I'm going to do that. No, if you're an Excel person, maybe you don't have to go there, where do you learn something for, why are you learning those things? Also, you need to do that. There's something already very important that you can take advantage of in the Excel, right? So that's prescriptive. And someone said prescriptive. So prescriptive, that's why everything works, because it's when you combine with it. So maybe now, I put in a predictive analysis that can predict when, say, a particular product is going to go up to stop. So maybe we have to wear out, we have to have some logistics. And so that includes prediction. And then you want it so that anytime the product is going to go up to stop soon, it makes generating requests for output and sends to all of your mentors, right? It's just the information part. There's going to be more information outside. And there may be a feedback if you want to. Once you put it in, it makes it easier to understand. And then there's exploration. The exploratory is much more like what do you do to make a line, do what you want to do. And then most of the time I have a friend that does what he's into, when he starts talking about what he does in medicine. Like, are you doing medicine or are you doing, like, programming?  Aha, those kind of things are... What they are doing, their mind is black. When you're doing this, please, when you're doing this script, if you have an idea of what it includes, right? Exploiting everything, you don't have anything in mind. Just look at all the details, look at the patterns. So it's not really, it's not in flow stream. So the kind of things that you remember in the script are really like flow stream. Start with a narrative about some things that do market, market, basket, analysis, and segmentation. And it begins to look at that as a bit of kind of exploration of trying to make sense of things. Okay, so that's the thing that we're going to talk about. We're going to do things like... We're going to predict the whole situation, to keep people going, and then you want to run things through integration. All those things you mentioned when you talk about details and you have a lot of serious things that can't find in common, that's in Excel. Then the second aspect is the Excel JavaScript hat. How many of you are not familiar with it? You're very familiar with it. So we know that the people who are who, who's ready to make things inside most of the things, like automated communication, from scratch, from A, starts to make. And so you do the program, the whole thing, what you create, it's already transferred to the program and then why do you write the program? So I do this a lot to automate your photo, where it takes hours. But then there's something new, JavaScript. So this one is the whole point, when we get there, I'll explain it. So let's start with the first one. So, if you can see what I'm talking about on screen, let me explain what's going on. Say you work for a micro-finance, or you're giving back, right? You are interested in giving those regular types, right? That's why I said micro-finance, not feedbacks. So, if you now want to put some level of automation, do more with your data, right? So you have people who are taking notes from you before, and then there are basic information about them and it feels like useful. So their names, their gender, their age, their marital status, their employment status, their position, where they live, then their monthly salary rate, and then the loan amount it to, and then whether the pensioner needs to be paid for it also. This is information that is valid, that if you have someone who can do predictive analysis, you can look through it and find the pattern. And there are some people who, when you prefer them, so when you prefer them, when you look at their own parameters, they kind of fit into the kind of people that are involved, or the people that you are interested in, right? So how can I distinguish them? Well, I'm going to do an algorithm that I'm going to use a classification because it's two of the kind of specifications that I created before. So that's the binary thing to do. So we use something like a binary classification algorithm, and then you want to come up with the ability for materials to approach you, ask for your details, and I'm putting into the Excel stuff we're going to view, and it's a predict whether you are likely to be involved. I'm going to put down a vote, except for me. But the look of the idea would be to, first of all, define what factors, in the whole factor of determining something to go left or right. And for one, the way that I saw the list, how can I say unemployed people? But then, if you look at the multi-siders, how do people not take any above the middle? So there are things like investments, TVs, all that way that they make money. So if you can't just say no reason. So you have to, sometimes, maybe you have to pick some factors in between the two of them. And also, besides doing it fully, yes or no, can you complicate it a little bit more by saying certain things? Like, if somebody is unemployed, you, yeah, certainly this is like that. You can have a total, and they can complicate it. So the total comes... You are really the algorithm yourself. No, no, that's not my problem. Because, yes, it's supposed to use something that you're not the one who has no bias from you. You are the one who knows that I'm not the person of knowledge who's not the one who has no bias. But then, no, you don't have bias. You're the one who's exploring it. Okay, I'll finish first. Yes, sir. Maybe if you had units, if you had bombards somewhere behind the engine, attached to any of these components, you can do, you can go see, you can see where that number is and go for the new person's knowledge. So if I say, anybody, if I do this, who finds it, anybody who has less than 50 minutes will be found. So I'll probably just now set a goal-seek program at the end and I'll say if you just look for it, guys, who has more than 50 minutes. Good. Good. Good. Good. Good. Good. Good. Good. Good. Good. Good. Good. Good. Good. Good. Good. Good. Good. Good. Good. Very good. Good. Good. Good. Good. Good. Good. Good. Good. Thank you. Good. If you know statistics, you can always do this from post-printing. And you know post-printing means it doesn't matter what you have, right?  You have to clean out. We have to use the example you used something like this for. What we need to do is we need a regression, it's about regression. But then regression is not the best for a binary outcome. So we say, okay, let's try our example to be aggressive. Small problems are always going to be there, but then there's a lot to it. So we are trying to stretch ourselves. And we're writing the formulas and we're like using that here. It was a whole lot of work that we wanted to do. When I did it, because at the point we switched to hard, when we did it in hard, we were like, we wanted to do nothing because we were stressing it out. Because it's just the line code you put in the data, the data frame, and just like one. All of us spent like one day doing it ourselves. In like five minutes. And that's what I wanted to show you. How you two can leverage this new thing around the country. So I'm saying it's impossible to do this well. I also think you will ask me, am I already sure about that? You're in exile. Who stayed in the state of immigration or random threats? People are in exile. Some of us are in exile for doing some of these things. It's quite a lot of things. So we are going to find out. Are you ready? I'm going to do it there. Here I am. I'm working with this one. Because I need to have some... Okay. So I'm going to come back from now. I'm going to say it goes over there. So this is the raw data. It's about 5,000 records. What we want to do is... So what I want to do is... I want to do something that... I think it's the effects of data science. If you give someone who is a statistician or a data scientist what is they supposed to do? To build up something that we learn from the data we look at. So can you give them the activity or what's the difference? Are you saying 3,000 bits? Those that we pay, what are those unique characteristics, right? And then we waste them. So there might be some characteristics that are kind of like non-significant. You put the code and that's where last you put the code. Then you might be lucky that out of the 6, 7 characteristics there might be 3 or 2 that kind of you see it by actually you put it in the bit that you pay. And people maybe like to beat up the code, right? So you want your algorithm to do that. And you want to be... You want an algorithm that can keep relating to the code. You get more data maybe every 3 months because it's changed. Like there's no power names, right? So that's all we want to achieve. But then how do we achieve it? Are you ready? So there will be a small gtp. At the end of the... How's yours? That is the body or it's an embodiment of all Microsoft cloud solutions. So meaning that anything we want to do that we want to be able to leverage instead of building things ourselves. We want to leverage existing tools and in the minutes or later communicate with every single one of them which is what we're going to do. That's what we're going to be now used to achieve what I want to show you right now. So what I want to show you right now is... So this is what we want to do. So what I have is... So I have some random... I don't ask anyone to volunteer even though they're doing most of it before I sign in to volunteer. So I have some random... The issue that I have here now I've got all of their names. So maybe you might get that out of the phone. Or if you even integrate these with a web platform or a Microsoft platform. So if you integrate with that so that you can perform and that the back end begins to uplift and exert. We are named gender age. All this information that you can ask them that. But then, because you understand that we are trying to do your first command, but they don't know that if you are going to use it to take something, you know, more meaningful decision modules are being required. So I now have the amount you want to request for. I need something to predict. At least the express level verification. So almost can't take an intervention at all. But let's not run it through the machine, right? Let it tell us if these guys are doing things or not. So I'm going to select the data that I have about them. I'm going to... I'll show you how it's going to be. Let me just demo for you how it works. So I pick that and say yes. And then I need you to put the outcome here for me. And so I pick where I want the outcome to be. It's edited, right? You can still see it, I guess. That was that one. We'll make a note of the situation we have. So I wanted to be there so I can dance. Let me know. And what I will do next is I'll take you to make the pitch. And you're going to see some vocals, I think, right? And it's time for you to go dance. You'll see that it's good to tell me that these people are liking to be both. And this is the probability score, right? So I know that this person's probability of defaulting is a lot higher than that's how binary specifications work. The work on the A is the probability scale. So two things will be at the extreme. Probably if you're emailing and I want to ask some questions. So I could say it may be zero. It may be one. The probability is closed up to zero. And it's closed up to zero. So you see that everything is 0.5 and a boom will be tuned into another point on the scale. And then so that's why I know that this one that is really low is not closed up to that slide. So in case you... I mentioned that you look like a probability So it's the same thing that all of them do. And so how do you make it happen? You see that it's possible to have your central approach from the raw data to the final result. So we have to go back to the other move. Our zoom, since I will use the web in lock points, I'll be able to zoom in. You'll never wear this in contact with that zoom whatever you want. So it's product we are going to use. I thought it was Azure, right? But if you want to try it out, it's azurestudio.azureml.net. It's a big product. Studio.azureml.net. When you have done, it gives you the... So you give the algorithm in the end, it gives you any type of internet that you can use. So that's why I put that feature in there. I'm going to increase the zoom size. So the way to try it out, it's this studio.azureml.net. It's pre-account, everything is free. I don't think there's anything to do with this. Studio.azureml.net.  It doesn't mean you have to, like, create a new image. And in the same way, when you buy a new tool, then you have to go and use any of it. So this one is in the way. And for me, because I'm logged in, I'll just go straight to my experimental best practices. So once you've logged in, this is what you will also see in your own case. So once you sign up and you log in, because I'm already used to... You know how close you are, if you want to experience it, this is where you see the menu on your left and you log out on your right. So it's not useful for us, because we have to carry it. So when you log in, you can just see if I can make the screen clear. This looks better. This looks better, right? So I can go ahead and insert this. So this is how it works. The data that I want you to make the prediction, so you wrote data that I want you to do the algorithm, because you must build up the algorithm first, right? I already have the data. This is it. So you would have given me your data and then I would have it already like this. This is the way you see it before. Whatever format you need, right? So this is what I will use to train the model, to build the algorithm. And then this is how, once I have that, I go to this place. I say I want to create an experiment. So I want to create an experiment. I do new. Can you see the new? This is that new. So once I click on that new, it will say I want to create, they have a lot of samples that you can use to get from it. So you need to see something like, oh, okay. You can analyze it, you can do all kinds of predictions. So, I'm not going to say about the experiment, but we'll do a blank experiment. I'm going to click a blank. So that way I create some structure. So I click on the blank. It gives it a generic name, right? So I'm going to give it a name that I feel is very much what I want to do. I want to do a slow prediction. So once you give it a name, you don't have to do anything of the same, right? And the first thing that we want to do is, I want to bring in the data that I have that I want to use to. You can do analysis without data. So all the data you say, you can't do this, you can't do that. And then you forget about it, you can't do that, you can't do that. So, I already have something in data. It's very easy to bring in just the important thing. So I have it as my data set. Let me see if I can show you that state. It doesn't look like that. On this left side, experiment, there's projects, there's where they are, there's some things. The one that I want to talk about is the data set, because that's where you need to start. So I want to bring in the data set. The new is here. There is a new lower left. You see the new here, right? So that's where I'm going. And then I see that I have it in my computer and I go, it's here to the file. It's like that. And I say, yes, bring it in. So that's what we're doing. So I've already done that. So that's what I'm supposed to do. So once you do that, you will see it's here. So I'm just going to take what I have. To see if it's a drag and drop. So, you're not going to be typing anything to your... Everybody knows how to do that. They don't have to go like that. So, I'm driving the data set, right? So I think at this point I can go back for everything I need to do. So you're going to find some things that are similar. Why not bring in data? When you want to do a 3D algorithm, you want to separate the data into two sets, a 16 set, right? Doesn't make sense to ignore your data with an algorithm. How do you not test the algorithm? So you split it maybe like maybe 64. Use 63. I'm going to use 42. So that's what I'm going to do. So I'm going to say I want to split it. I want to split the data. So I need to split it. I drag this there too. And then I connect it to... I connect this to this. Then I say I should split it in there. I'll rather... Instead of doing that, maybe you want to use 64. So it's always the left. That's your control. So that left, whatever you put here, you want to make sure of the data around your left side. And then I am going to now say this data... By the way, there's something I would like to do here. You know we have your names. I don't know if you remember. We don't have names in the prediction, right? So I'm going to tell the model that I don't need any names in the prediction. I'll read your name. Whether you feel or not, please. So I want to select some of the... I'm going to say I don't want to use all of the data. I want to select... It's not faster if I just write what I want to do. It has to... it has to be something. So once you say this is what you want to do, type something that you don't have, and I select columns. So I'm going to do that. In fact, I prefer to do that before you ask me. So when I select a column, I get this column, column selector, pick it and say the columns I want to include. So you see, I've probably got the columns. Because I want to do it faster. I'm not letting me do it really fast. Let me just briefly show you what it will look like on my notes tonight. Because the doing it is just dragging. And the first time I showed you what to be dragged. But I will focus more on the aspect because I still have like just... I'll show you what to do, all the things that I am planning to drag there and then the end results. So this is... This is where we... the data, right? The select, where I'm going to select the columns that I need to use. So this is what I was trying to show you. So I split the columns I want. I want them to be back home by one minute. Yeah, I want them to be back home by one minute. So yeah, I take out the name. Right? And then I split the data now. So you only call them two columns. You don't want to take it in the name. That's all I say. I don't need your names. All the other columns are there. All the other columns in the data set are there. Yeah, except for me. So I don't think I need a split. So I do this split. Which is... I'm going to use the answer for the descent there, right? Then it splits into... I split it into some... some more... Yeah, except for that one. So I split it into the left. It's going to be my trading set. And it's going to be right. It's going to be my trading set. And then I really... I need to... I don't know if you can see it. I think it's going to be the trading set. It's going to be more there. And it's going to be the trading set. Where I connect the output. I need to... I was very fast on the back. So that's what I'm going to do. So I'm starting from... I'm dragging the data set. I want to select... the columns I need. So I click... I click for the select columns. I connect the two. Then I want the column selects. Then... So you see, it displays everything inside. So you see, it displays everything inside. I want everything except... the name. So you see I pick everything except the name. I move them to the other side, right? Then that's what I want to do it on. Then I'm going to... bring in the split. So on this time, you will see that I have 5 minutes to come. That's why I click this column. I connect the split. Right? And then I say... I want it to be less than 16 columns. Right? Then I bring in the trade model. Right? So it's everything I type in. It comes up with the... So I'm bringing the trade model. I drag that right in. So as we are dragging it in, it needs to start... to make it... So the trade model I'm going to... do the... connect the data here to this. Then I need to... use an algorithm for the trade. So I do that... to find out... that I like a classification... a... some cloud can classify... to one of the... So this one... I don't type... so I can see all the options that I have. And you know... So if this is a machine... and then... a different anomaly... classification... is what I want. You... I always have... this thing called regression. So one of the things... it... it does it very well with this kind of data. So I'm going to drag it into this regression. It's like regression... but... so I drag that in. Then I'm going to... evaluate the model. So... evaluate the model. I bring that in. I connect the... output of the model... to the evaluate model. I connect the output of my... spring data to the other side... that I will have to test it, right? And then... So once you... this is what I'm drawing. You can see it like this. So once you do this... once you run it... it will allow you to deploy it. So is this possible to expand... the model? Okay, so like this. I'm using the idea... when you know it's let go of... and it's going to run... and then it's evaluated... and then it's run... and then it's out. So once that is done... I run it... this is run... and after I run it... I will deploy it. So... I will deploy it... after I run it... I'll show you. So... So if you connect this together and blah blah... then you run the model. You run the model... then you deploy it. So if you run the model... then by running it... you... at what point do you run it? At what time do you connect? All of the time. So once I run it... once it runs successfully... it may now be the option of... set of... I need to set up web service. I need to... now... So once I did the set up... set up web service... and I launch it... launch it in the other part... which you will now be able to exert... as you will see... a deployment... Once you run it... you will get... I need to deploy... after I run it... that is what completely might... once you can deploy... once you can deploy... once you can deploy... deploy... So it's trying to deploy... I can quickly switch... when I run it... I can switch to the complete part of mine. Once that's the last bit... you will see an example... that you can... So that's the predictive side... and everything... has all the different types of predictive... are going in that way. So that's the predictive side. I think you know in Portugal... I'm sure you know how to do it... not as... I just want to also make sure that... I show you the next part too. The next part of the presentation... which is... Excel, JavaScript... I asked us that... we are familiar with DBA, right? And then... So this is the one... I don't know... The other one... is the new hardy experience. So this one... those of us that are... who might be enjoying this one... because... one of the problems you have with... you have with the... existing DBA kind of adding... is the fact that... you... it can be exploited... I think some companies are... they disagree with something like this. So if you try to put in any program... that has a DBA... it's not going to be wrong, right? So what are the ways you can deploy things... and use without you having to... run the DBA? You can use in Office 36 Friday. Thank you. If you use Office 36 Friday... if you go to INSAN... you will see hardys... you use Office... Office 2013 and 2016... because... if you use Office 365... you get a lot more... and so forth... you can explore. So if I do this get hardys... you will see it just can be right in store... using the old versions... or if you use the old version... you see get hardys... it's going to show you... the whole model of the tools... that you can use... beyond what is needed in the Excel, right?  Can you see something in Nigerian market data? I think that I need to put it up... so you can do something too... if it's not just your organization... if you want to do something... anybody can install, right? And so like in my case... what I built is something that I can allow people to use... and in some other cases... you solve the problem of distributing you all... aha! they can even pay you... you can only price three minutes. If you have the problem of distributing without something... then you have to start doing... paying for marketing. You can't just see it without me having to... give them the need to... and then if you have... it's time to add it to your Excel, right? And then... so what I've done with this... what I've done with this... I made it possible for you to... do... information that we all like to get from Nigeria... like you want to know what the effects are... and you also want to know what the parameters are... and what the effects are... one of the commonest things you keep telling me about... and that's the aspect you want to know. So I go there... I say I want to see the... exchange rate... and it's time to... I want to see... maybe the stock market... I want to see the stock market prices... and it's time to... stop and choose me. And so today... it should be equal to 29... because that's the last market... right? What if I want to know what it was... this same year or last year... right? If I type in... if I type in this same year or last year... 2018... I think 6...  I like to see... and if you can... so this can be for those who have investment portfolios... and they can go back in time... and the same thing... for the exchange rates... you know... you guys forgot to put the closing... exchange rates... I can't predict... what about 2019... 2003... end of the month... whatever it is... I want to get what the exchange rate was... last year... and I think you have particular things... some of the elected things... and you get it out for you... and if you like to get... this is the one I put there before... you say... exchange rates... so you have a significant rate... and I raise it... so all those things are there... but the thing I want to mention... is how do you use something like this... because the key thing is... you want to make it... see, I didn't have to install the MP... you can use it on your phone... because the same functionality is on your phone... and then even when... IT puts the password... to give you all these... and give you that right time... and so on and so forth... because you actually know it's going to be... there are only 18 or something like that... so how do you do this... is... it's going to be part way... some people might be like... okay... and now I know it... but I will give it to some other guys... you will need some help... yeah... you will need to have bigger skills... and then you will build it inside it... what means you know it's impossible... so if you have a very large organization... and you work in IT side... this is something you can learn with that... so how do you do it... if you go here... so now we are getting to the question time... if you go here... the same place I want to do the... get add-ins, right? you will see my organization... so there is a place where... if I could... there is a place called My Harding... there is a new manager... a new manager is... an organization you can publish... things that are just where you are... in your company... so now we are on our way to deploy things... things like that exchange rate... so I have come to the end of my presentation... unfortunately do you want that... I was really wanting to know... how you are taking... okay it's finally done... deploy... so you say if I deploy... and then I want to deploy... any questions... so I can keep the question on there... so when the deployment is done... can you see something like... let me just close all this... can you see that I can download... the actual part? this is magical... this is what I now can do... that now makes this... like we open it... you now see the same experience... you saw me use to answer... and then I can type in any questions... oh I need to... so this was the thing... so you now see... that you saw me... using the demonstration... that's the only bit that is outside of it... the moment you do that... everything you see... right here... this is how you make it... any questions? I saw you scroll... scroll more... more positive... the... in picking the correct model... or whenever you go in the model... you want to know how it performs... and you got the... testing... that means right... so that's what that's for model stores... for those of us... I'm actually not showing you the results... so this is how we have... we have... we have... so this one we now give you... there's a technical way of doing it... you call it... true positive... or future measures... so it's very easy to see... true positive, false positive... and it's called the accuracy and saving... it's like you're waiting to use the testing... so that's what I think I want to be showing you... so it's a long question... you're a pressure model... sorry pressure model... how did you... how did you... how did you... how did you... very good question... remember I said that everything can be done from first place... this question is the... the regression model I gave... what did I... did I set the picture... did I... good thing with... of the shell... product is... they can... I think... find the best setting... I don't know if you have used forecasting... if you go to data... maybe... you will see something called forecasting... how many of you have used it? forecasting says that... it's a time series tool... but if you... if you don't know the strategy behind you... you cannot take it as trivial... what it does is what we call... people... explanations... like... find seasonality... find strength... find the error factor... that thing I've said... if you try to do it... you know, outside of Excel... you can... but they put it there, right? and the thing is... because... they don't expect you to restart the shell... you can automatically determine some analysis... which is the one you can do all those things... you can find the best setting by itself... because it can also score itself... and it won't be... so that's... I don't... I don't... I'm doing a competition... and I want to do it like that... so... so if you... if you want to... if you want to... if you want to... if you want to... so... if you use this thing we have done... it's just the guitar set... to replace... and those we call... so that you can have a drink... enjoy... you will know... you don't even need to re-download this game... you just put it down... replace the guitar mode there... play it for a bit... and then have it... you can keep the game... so... I just want to confirm that you don't have a tax code... right? around this big data set... because there are many expectations... about how common you are... so... I don't know if you are... if there is already a significant population... it's a significant sum... if you use this formula... it's almost like a rule of thumb... the formula to use to that... it's easy to find out... the population of... 2 million... what sum to size can I take as representation? so that's what people have even told... the direction and government... and all these other rules... so they know that this is the target nobody needs... for the customer... so... the first thing is... once I know that... amount... I don't know what my data set is... but I have a data set of... based on... if all our customers... if the... what you can Google... but you can Google it... something like... if people have few tools around you... say... so if they can say... something like 2000... if you give me... if I have the computer that I want... I will have done that... and say... because people can talk now... you're like... you just give me my computer that's good... so I will now use that... back to how it comes to your question... so now when I know I have 5000... but if this is I need 2000... then in that case... I can play... I don't have a lot I put on the testing side... it's more than 2000... so there's no more... or if I have 3000... I'm most... start to... start to size requirement... so that's where it's more... just you come to the step... so you select data set... select from the split data... bringing data set... I select... because sometimes it's very common... that there would be some... maybe a confidential... or things that... that's what you select... split data... then... you start to do two parts... the logistic... you change on frame... but... is that even what you use? the train... it's called... so the way it works is... that one goes to the train... the train goes to the score... so that's called... the train goes to the score... and the second side of the other... goes to the other side of the score... and now... the scores will go down... so that one goes to the evaluation... so that... so how do I evaluate to run? and so on... how do I evaluate to run? that evaluation... what part do you run? can I click on evaluation and view... without the accuracy? so I'm going to click on that... show me that... I'm very confident... but you cannot read it... 60% actually... so sometimes I think there are cases... I'm going to change... I'm going to do my evaluation... but after that... I'm going to do my evaluation... so find out... how do you... so how do you find... how do you find... how do you find... how do you find... how do you... read it or say it... how does that work? is it something you can't read... or it's already set up... or it's sort of thinking that... and I think I have found it better... with the evaluation... you know I wrote it in detail... I'm not sure if you can... but that would be better... so you can let it adjust... but you don't want it to be... wanting to... take the point... the only thing is... you might need to... you know I'm good about detail... maybe you should bring the last 5,000... so you know how you need to... select the top... one column... so without... what is that you don't like asking questions? I like... okay I think that's enough... let's talk from... how much of... how much of the... theory of satanic... you know... which is that... say... like now you know your name... binary... so when do you know... how do you know when to use the... what component are you using... and how do you... you know I'm just saying... how do you know... how do you know... how much of the theory do you need to know... before you go... and my time is up... so I'll just take the steps... so I don't know either... the geography... how does that make it... so if you have a data set... a lot of data sets probably explains... the americans set for example... if you bring it to... the migrant sector... if there is anything you need to do... to account for behavior... that what you get as religion... kind of matches what happens... you don't just take a test... it's quite good... so if you... can I just go on and pick a model... and say this is how it is... and then three months down the line... you know... let me answer the questions again... so question number one is... how do you know which app are you going to use... how do you know how you feel about those... do you know how to include these types of things... to things... if this is a part of your life... you want to be a professional in that part... then you can buy a part of it... on that account... or if this is going to be a one of things... maybe something... not like maybe you want to switch career... then you can just read... actually three bits of what you want to do... I can do that... in a packet packet... packet packet analysis... or something like that... just move on... just focus on the things I want to do... and use that language... so that's what I think... when I have things that... I know what I want to do... then question number two is... can you take something... that someone has used outside of the middle... to read... okay... the good thing is data is not... we are going to know that this is injected... data doesn't know that... so the only thing is that... this time I will be different... so it's more about data itself... so it's always good that... algorithm might be similar... but it's good to run your... from scratch... you might use that as a guide... use that to do your first run... then start fine tuning... but it's always never... correct your thinking about... about human life... you will always have to do your fine tuning... even in the same manner... to defend confidence... thank you very very much...


There are three manufacturers, which consistently put out great workstation devices and Dell is one of them. Their precision series are built with focus on stability, durability, and performance. The price tag of such notebooks is always hefty and the model we're testing today is not an exclusion but it offers a lot of important features to justify it. This is the Dell Precision 153570, and it has an ISV certification ensuring your most critical applications will run reliably. It also has an optional IR camera with AI based proximity sensors, privacy shutter and intelligent noise cancellation. The device is enveloped in environmentally conscious materials, such as recycled plastics and reclaimed carbon fiber. The latter is an interesting addition and does offer improved durability. It has mill STD certification for while its weight is only 1.59 kilograms and has a profile of around 22 millimeters. The design is simple and elegant, with only a small Dell logo in the center of the lid. The keyboard offers decent key travel and OK feedback. The touchpad is the better of the two, using a smooth surface and almost no input lag. On the left side, you can see two Thunderbolt 4 ports and a smart card reader. As for the right, it is the more populated side, with two USB type A 3.2 generation 1 ports, which cap at 5 gigabits, an HDMI 2.0 port, a LAN connector, an audio jack, and a micro SD card reader. There's an optional 4G LTE slot. On the inside, you find two RAM slots and two M.2 PCIe slots. The laptop supports DDR5 RAM, while the SSD slot to the right can fit generation 4 drives. Watch our disassembly video to see how to open and upgrade the device. Only 2 per 100 people watching this video are subscribers. If you decide to just start following us, we'll be able to reinvest more in our laboratory thus making even more helpful videos for you. Thank you, you're awesome! Most people will go for the 1080p display option, which is why we choose to test it. While it is far away from being ready for design work, it shows zero PWM usage and has even brightness that peaks at 291 nits. The 58 watt-hours battery pack on the inside lasts for 12 hours and 14 minutes of web browsing, or 9 hours and 43 minutes of video playback when paired with the 10-core Core i7-1265U, which is the best Alder Lake UCPU that you can get. If the U-series isn't enough, you can get the device with the Alder Lake P-series, including the Core i7-1280P, which is currently number 4 in our top laptop CPU ranking. Compared to the older Tiger Lake U-series chips, the new 12th generation is significantly ahead. There are two pro-grade GPU options in addition to the integrated Iris graphics. Both the Quadro T550 and the RTX A500 from Nvidia are quite more powerful than the integrated solutions, but can also work in tandem with them, something Intel made possible with QuickSync.  Both the CPU and GPU hover around 70 degrees Celsius, even during full load, so the laptop can definitely handle a P-series chip. What is more, we don't see a reason why the Core i7-1265U isn't pushed further, as the chip still has a long way to before reaching its 4.80 gigahertz boost clock. External temperatures are really low, with the hot spot reaching 39.8 degrees Celsius. While the laptop, in its most popular configuration, will be unusable for professional design work, unless you have a separate monitor lying around, it can still tackle other heavy workloads with ease, while lasting through a whole day without a problem. Of course, we've done a lot more tests, and you can check out all the results in our in-depth review on laptopmedia.com. You'll find the link in the description below the video.


unless we increase the consumer confidence. OK, House Minority Leader Nancy Pelosi, again scoffing at economic growth and job growth and getting it wrong again, notably on consumer confidence. Now let's go through the numbers. Wage is growing nearly 3% year over year. Joblessness at an 18-year low. And look at this, consumer confidence at an 18-year high. America's wealth surpassing $100 trillion. There's more, 300,000 manufacturing jobs, created since Trump was elected. You may see 4% growth the next time that number is reported July 27. Let's bring in Kadina Group President, Gary B. Smith. And look, who else is here? Owner of the company, the stock swoosh, Melissa Armo. What do you think, Gary B? Nancy Pelosi's path to getting the House speakership again is permanent outrage at everything Trump. I respect her in one regard. She sticks the message. Like, she just, Trump cannot do anything right. And she sticks to it. But you know what, here's the funny thing. You talked about the metrics. People evaluate their own economic goodwill based on three prongs. Do they have a job? Are they making money? Is their house worth anything? Yes, yes, and yes, right now, more so a lot than when Obama was in office. I don't know what metrics he would like at this point. Yeah, you know, Melissa, it's been said that the Democrats' policy has been like a Seinfeld episode where nothing is happening. I mean, these are really opportunistic attacks and off base.  They want it under Obama, right? And look at the growth there. It just makes no sense.  I mean, she says things that don't even make sense, even if you don't follow politics. Even if you're just a regular person, get up every day and go to your job and maybe watch the news once a week on a weekend. You say, this lady doesn't make any sense. How can she take something that's so positive and make it negative? And just don't forget this. People are saving money in their taxes. Even if they're not making more, even if wages haven't risen, they're seeing more money in their paycheck because they're paying less in taxes, which has been since January. So people, wages, it'll take time for wages to go up. That's a process.   Now we know Nancy Pelosi has been pushing aside the up and coming Democrat moderates as the Democrat party has been yanked to the left by Bernie Sanders, who now says, Gary B, his socialist ideas are winning. Let's listen. The truth of the matter is, what we have been focusing on is grassroots activism. And that is doing extremely well. We have been focusing on the need for, oh my God, they're winning all over the country. But you have a lot of guys who lose too. I don't even think you're at 500. Our ideas are winning. And at the grassroots level, more and more people are getting involved. All right, he didn't talk about job growth. He noticed that there. He didn't say anything about job growth policies. It's almost like Bernie Sanders' stubbornness has drained him and the Democrats a common sense about what works. Well, it's some delusional fantasy. It's like people now are saying, yes, the economy's good. They're willing to say that. But Obama set the table. Trump has done everything opposite of Obama. Well, Obama did get us out of the downturn, right? With a lot of government spending at Fed Health. Fed Health. I view those eight years, Liz, as the great malaise. Remember, what was the GDP growing? Barely 2% a year, 1% a year. It was really just horrible, not horrible times. He got us out of the worst of it. But for Sanders' point, again, it's like Pelosi. What socialist ideas exactly are you talking about? That work. That work. I'm not seeing any better. The message doesn't work. Their message is essentially, take from the rich and give to the poor. That's it. That's all they got. That's the only message that they have. And it doesn't get both. And, Liz, can you explain to the viewer how socialist aspects are already in this capitalist economy? Medicare, Medicaid, Social Security, right, Gary? Yeah, exactly. All the programs that are broke are going to be broke at some point or certainly actually. Well, originally, those programs were set up way back in FDR. But I don't think those programs were meant to be permanent. They were meant to be temporary. But now people have become to rely on them. And that's what Trump's trying to do. Trump's trying to say, go out. Get a job, because you're going to be better in the long run if you work, rather than getting the help from the programs. Even though some people need them, some people do still really need them. Can we put you on Bernie Sanders and Nancy Pelosi? Watch you, too, because you did a great job there. Can you come back? Well, we'd have to be on it. We'd have to be on it every day, though. Maybe every hour. Maybe every hour, right. Hell, we wouldn't mind if you do it again.  We hope you have a good weekend, Gary. You guys have a great time. We love having you on. Let's get to the story. Liberal Silicon Valley tech company Google, again,


So I took a couple weeks off 2K, said I wanted to regain my sanity. Man, I said I had to bounce back! I could have probably the worst person I could have in the situation. But hey man, the last time you saw him, this was going on. We playing PS4, what the f***, Xbox controller, you know. Dum-dum. But hey ladies and gentlemen, I'm doing a giveaway. Over the course of the video, I'm gonna have letters on the screen that once put together make a work. Your job is to watch the video, find the letters, and make that word. Once you do, go to my Instagram and Benny's Instagram and comment that word. Drop a follow, of course. And on both our Instagrams, we're gonna choose five people each and give away a bunch of VC. Well, how about we get started then? I just thought about you let another man dress you. He's my tailor, he wasn't dressing me man. Jesus, get off me bro. Jump! Hello, what are you doing bro? Y'all we gotta shoot me, kill it. ISO up top? Well, it's not ISO at all, there's a screen. Let me ISO, let me ISO. I promise I can ISO with this player. A few moments later. She promised. What are you doing? What are you? You saw that? Did you see the ISO? Oh god. Oh yeah, good take. If you beam, yo, you see me, right? Boom, bops, bitty boop. Bow, let's get it. Oh, sorry, sorry, sorry. Then why are you sending a screen for me? Move out the way. Okay, all right, shoot. Ah, there we go. That's that top tier player if I know. No way. And no such thing and nobody shoots over agent beam star. Okay, good move. You gotta shoot with the... Yo, I take too much time off of this game, bro. And then I have to come back and then, you know, you know the usual drill, make like 16 excuses for why I missed my open three, that kind of stuff. Whoa, what are you doing? We didn't play the other day. What are you talking about? Yes, we did.   I don't miss, baby. That's a false story. Fake news media. Yo, you know what's crazy? I was playing with Dip and Dip invited D-time and I've never played worse in my whole life.  You got no more, what's up? I can cut, I can cut. Watch it. Okay. There we go. Good pass. Listen, man, yelling pass with the ball in his hand. Oh my God. Yo, man, calm down. Let me, Iso, Iso, Iso, Iso, Iso, Iso, Iso. Yo, I put on Steezo's dribble moves.   What a swag. Iso, Iso game is crazy for me, man. You know how it is, bro. Number one dribbler in the game, baby.   All right, share. I have three pairs of pants for every weight class. You know what I'm talking about? So I have a lot of pants. No, I don't. It's for different weight classes. As I lose weight, I'll have to throw away to three and get another three, you know? But I have it, it's all in my closet. I was talking to this female, yo, she was just dying. I mean, she was telling me agent. Nothing better than a guy in ball shorts in the middle of winter. So I was like, damn, I didn't even know ball shorts were that attractive. So I just started to wear them everywhere, you know? Yo, shoot, I'm not wearing no short shorts, bro. Unclick that. Why? Look at them.  What? What, dude? She's quite a sexy man.  You know what's crazy? I went to Mandarin the other day and they were like, hey, how you do? We're celebrating Chinese New Year. I was like, oh snap. Yo, they pulled up with a red envelope and it had this coin in it. And I sort of got it. I thought it was going to be a chocolate looney. How do they say it again? Huh? How do they say it again? We're saying it again? How do they say it again?  They say it, they say it. They say it just like that. No, that's how they say it. No, you had the accent. How do they say it? No, I don't know, man. I'm not really good with accents and all that. No, you had it. You had it just a few seconds ago. How do they say it again? You say it, bro. Because you said it. How do they say it? They say it. Look, you guys don't even have one watch on. I got two. I always know what time it is. Let's get it. Hey. What is it called? It's a bus down. Yeah. Face size. Good block, good block, good block. Yeah, I got a question for y'all, though. What do you think about the fit right now? You need to wear that a little better than what you used to wear, bro. Didn't you wear like the red shirt? So this is better than that? I'm not going to lie. I look, you just feel what you were talking about. I look, you just feel the way for like three seconds. Yeah. No, I feel OD delayed right now. I don't know what it is. Did we join West? Yeah. I didn't say you said it. All right. They thought it was nice. Oh, look at that. And he dragged me down. Get off me. I'm pretty sure like I live my whole life for other people's approval. So I want to make sure they think I look good.  Drive, low in the high post. Pass the ball back to agent. Agent drives left. Goes in post position, spins back out. There's a heavy delay, even though agents trying as hard as not to talk about late and see inside cut, pass. He better not ever yell at my ear again like that. Good rebound. Good rebound. To shoot. Shoot's going to kick it. Dot. No, he's not. All right, cool. Look at it. It's pure sharp. Pure sharp. What? Shit. What? Wow. That's animation I haven't seen in a minute. Don't ever in your life shove me like that, bro. We're going to have problems. Okay. That was the most disrespectful stuff I've seen in my entire life. Oh, yeah. You see this. You see this with the two rollies on. Okay, all right. I see that shirt. I'm going to shirt. Yeah. Pans. Okay. I can see the color of pants. All right. I'll see what you did there. See what I didn't see. I see. The two rollers are just two. Two, it's just two. It's not a rollie, bro. It's a bus down. I learned about it in the A Boogie song. You, do you know what it means? What is bus down? Yeah. Yeah. He goes like, I got a bus down, a bus down. You know? Do you know what that means? Yeah. Yeah.  I got a bus down, a bus. You know? Okay.  Let's do it, baby. But let me go last because I have the clout. So, you know, people follow me. There is one of them following you. It's like two. One of them is you. I didn't know I had to try that hard. I'm playing with agent. You feel me? Like, what does that mean? Oh, you're right. Because I usually just carry. I feel you on that. Okay. Brick. Oh, here I'm wide open, by the way.  Just open. Not anymore.  Oh, yeah. Kill him. Go ahead. Kill him. I so shoot in the building. Okay. All right. Get in your bag, man.  Hold on. We up top. No energy. No energy. No energy.  Hey, look at me. Look at me. Look at me.  I'm the captain now. Bro, I just want to kill this kid. Yeah, but you're not though, right? You're not doing it. Didn't I just kill him? Didn't I just throw his brain to the floor or something? Like, whatever. No. He did it. He didn't do any of that. What? You're not that guy, man.  Oh, you did it. Oh, yeah. Oh, yeah. That's him right there. Oh, yeah. I'm not going to lie, bro. I'm going to cut that out the video. All right. Can we win a game versus? Can you green this shot? My next shot, please.  Yo, I got a frame drop. What do I do when I get frame drops? When I get frame drops, what do I do usually? I like how you had the excuse ready, though. He was like, frame drop. That's a frame drop right there. But you don't even worry about that. Like, he was no hesitation. He already knew that. Wow. He already knew. Great job. They have me. They have me fade. That's another one. Yo, I don't know what are you. Do I just suck? Why are they making me fade, man?  Oh, he faded to look. I am, what's the word I'm looking for? Differente. I'm so different. They can't even use the word different. Differente. Oh my.  Look at this. Bro, are you crazy? Look at his inability. He can't dribble. He cannot dribble. Bro, what do you think? What do you think? Hold on. What do you think? He's just zigzag. Bro, have you ever heard of Thomas the Train? I think I can. I think I can. Oh, stop. That's not Thomas. What? I know you didn't just. Oh, whoa, whoa, whoa. I know you didn't just. Yo, that's not Thomas, my guy. That's not. That's just a little engine that could. All right, we got to shoot with the ball. Rhythm dribble. Hold on. Okay, hold on. Reset, reset. Momentum. Oh, oh, oh. Frame drop. All right. All right. Six, zigzag, zigzag. Spin, spin. Oop, two, oop. That's our point guard. That is our point guard, guys. Legend. Go for the oop. Can you open this time? Can you dunk it? He has no offensive capability whatsoever. Oh, as I said, he's. I'm proud of you, love. I'm proud of you, buddy.


theCUBE presents UiPath Forward 5, brought to you by UiPath. We're back with theCUBE's coverage of UiPath Forward 5, and we're live, Dave Vellante with Dave Nicholson. Ajay Gupta is here. He's the Chief Digital Transformation Officer at the Motor Vehicles of California, DMV. Welcome, Ajay, good to see you. Thank you. Good to be here. Wow, you have an interesting job. I would just say, you know, I've been to go into conferences for a long time. I remember early last decade, Frank Slutman put up a slide, people hangin' out, waitin' outside the California DMV. You were the butt of many jokes, but we have a happy customer here, so we're going to get into your case study. Yeah, yeah, very happy customer. Obviously, you've transformed the organization. I think it's pretty clear from our conversations that automation has played a role in that, but first of all, tell us about yourself, your role, and what's going on at the DMV. Sure, myself, Ajay Gupta, I am the Chief Digital Transformation Officer at the DMV. Somewhat of, I would say, a made-up title, but Governor's Office asked me, okay, we need help, and what do you want to do? It's a cool title, though. Yeah, so I'm like, well, we are doing business and technology transformation, so that's what I've been doing for the last three years at the DMV. Before that, I was in private sector for 25 years, decided it was time to give back, because I was mostly doing public sector consulting, so here I am.  You're smart, you're very cogent, and a lot of times, people in the private sector don't want to go work in the public sector, unless they're power crazy, you know? All right, anyway, so speaking with David Nicholson, the experience has gone from really crappy to really great, I mean, take it from here. Yeah, why am I gonna be, because I'm from California, I was just, you know, waxing eloquently about the change that's happened, just in terms of simple things like a registration renewal. It used to be go online and pray and weed through things, and now it's very simple, very, very fast. Tell us more about some of the things that you've done in the area of automation that have increased the percentage of things that could be done online without visiting a field office, just as an example. Yeah, what's the story? Yeah, so first of all, thank you for saying nice things about DMV, you as a customer, it means a lot, because we have been very deliberately working towards solving our customer pain points, whether it's in-person experiences, online, call centers, kiosks, so all across the channels. So we started our journey myself and Director Steve Gordon about three years ago, almost at the same time, with the goal of making Department of Motor Vehicles in California as the best retail experience in the nation across industry. So that's our goal, right? Not there yet, but you are working towards it. So for our in-person channels, which is what you may be familiar with, first of all, we want to make sure brick and click and call all the customer journeys can be done across the channels. You can decide to start journey at one place, finish at another place, all that is very deliberate. We are also trying to make sure you don't have to come to field office at all. We would welcome you to come, we love you, but we don't want you to be there. You have better things to do for the economy. We want you to do that instead of showing up in the field office, being in the wait line. So that's number one, creating more digital channels has been the key. We have created virtual field office. That's something that you would become familiar with if you're not as a DMV customer during COVID. The goal was we provide almost all the services. We connect our technicians to the customer who are in need of a live conversation or an email or a text or a SMS conversation or chat conversation in multiple languages or a video call, right? So we were able to accomplish that while COVID was going on, while the riots were going on, those of you know about that, our offices were shut down. We created this channel, which we are continuing because it's a great disaster recovery, business continuity channel, but also it can help keep people away from field office during peak hours. So that's been very deliberate. We have also added additional online services using bots. So we have created these web and process bots that actually let you do the intake, right? We could set up a new service in less than four weeks, a brand new service online. We have set up a brand new IVR service on call centers in less than a month for our seniors who didn't want to come to the field office and they were required certain pieces of information and we were able to provide that for our customers by creating this channel in less than four weeks. And the pandemic was an accelerant to this? Was it the catalyst really? And then you guys compressed it or had you already started on the journey? Well, we were ready.  Yeah, so I came on in 2019, pandemic started in 2020 early. So we got lucky a little bit because we had a head start. I was already working with UI path and we had come up with design patterns that we're going to take this journey for all DMV channels with using UI path. So it was about timing that when it happened, it accelerated the need and it accelerated the actual work. I was thinking I'll have a one year plan. I executed all of the one year plan items in less than two months out of necessity. So it accelerated definitely the execution of my plan. So when you talk about the chat channel, is that bots, is that humans or a combination? Yeah, it's a combination of it. I would say more AI than bots. Bots do the service fulfillment work. So there is the user interaction where you have, you're saying something, the chat answers those questions, but then if you want something, hey, I want my registration renewed, right? It would take you to the right channel. And this is something we do today on our IVR channel if you call in the DMV number in California. You'll see that your registration renewal is all automatic. You also have a AI listening to it, but also when you're saying, yep, I want to do it, then bot triggers certain aspects of the service fulfillment because our legacy is still sitting about 60 years old and we are able to still provide this modern facade for our customers with no gap and as quickly as possible within a month's time. This is that. How many DMVs are in the state? Okay, so we have 230 different field locations out of which 180 are available for general public services. Okay, so, and then you're creating a digital overlay to all that. Yeah, it's digital and virtual overlay, right? Digital is fully self-service. Bots can do all your processing. Automation can do all the processing. AI can do all the processing. But then you have virtual channels where you have customer interacting with the technicians, our technicians, virtually. But once a technician is done solving the problem, they click a button and bot does rest of the work for the technician, so that's where we are able to get some back office efficiency and transaction reduction. When was the last time you walked into a bank? Oh, man. I mean, is that where we're going here? Well, you just don't have to go into the branch and into it anymore. And that is the goal. In fact, we already have a starting point. I mean, just like you have ATM machines, we have kiosks already that do some of this automation work for us today. The goal is to not have to, unless you really want to, we actually set up these personas. One of them was High Touch Henry. He likes to go to the field office and talk to people. We are there for them. But for the millennials, for the people who are like, I don't have time, I want to quickly finish this work off hours, 24 by seven, which is where bots come in. They do not have an HR complaint. They don't have overtime. They're able to solve these problems for me 24 by seven. What's the scope of your, like, how many automations, how many bots, can you give us a sense? Sure. So right now we're sitting at 36 different use cases. We have collected six point of eight point. Well, we have saved 8.8 million dollars just using the bots. Overall savings, if you were to look at virtual field office, which bots are part of, we have collected 388 million dollars so far in that particular channel. Bots, I've also saved paper. I've saved a million sheets of paper through the bot, which I'm trying to remember how many trees it equates to, but it's a whole lot of trees that I've saved. And how many bots are we talking about? So it's 36 different use cases. So 36 bots? Well, no, there's more bots. I want to say, so we are running at 85% efficiency, 50 bots. Oh wow. Wow, okay. So you asked the question about who loves last time someone was in a bank. The last time I was in a bank, it was to deposit more than $10,000 in cash because of a cash transaction. Someone bought a car for me. It was more of a nuisance. I felt like I was being treated like a criminal. I was very clear what I was doing. I had just paid off alone with that bank and I was giving them the cash for that transaction, as opposed to the DMV transaction transferring title. That was easy. The DMV part was easier than the bank. And you're trying to make it even easier. It shouldn't be that way. But I have a question for you on that bot implementation. Can you give us, you've sort of given us examples of how they interact. Yeah. But as your kind of prototypical California driver's license holder, how has that improved a specific transaction that I would be involved with? So, wow. You as a Californian and you as a taxpayer. You as a Californian getting services and you as a taxpayer getting the most out of the money that the DMV spending on providing services. Both are benefits to you. So, bots have benefited in both of those areas. If you were used to the DMV three years ago, there was a whole lot of paper involved. You got to fill this form out. You got to fill this other form out. And you got to go to DMV. Oh, by the way, your form, you didn't bring this thing with you. Your form has issues. We have calculated that about 30% of paper workloads are wasted because they just have bad data. There is no control. There's nobody telling you, hey, do this right. Even dates could be wrong. Names could be wrong. Fields may be incomplete and such. So, we were able to automate a whole lot of that by creating self-service channels which are accelerated by bots. So, we have these web acceleration platforms that collect the data. Bots do the validation. They also verify the information, give you real-time feedback or near real-time feedback that, hey, this is what you need to change. This is what you need to verify. So, all the business rules are in the bot. And then, once you're done, it'll commit the information to our legacy systems which wouldn't have been possible unless a technician was punching it in manually. So, there is a third cohort of Californians which is our employees. We have 10,000 of those. They, I don't want them to get carpal tunnel. I want them to make sure they're spending more time thinking and helping our customers, looking at the customers rather than typing things. And that's what we are able to accomplish with the bots where you press that one button which would have required maybe 50 more keystrokes and that's gone. And now, you're saving time. You're also saving the effort and the attention loss serving the customers. Jay, what does it take to get a new process on board? So, I'm thinking about Real ID. I just went through that in Massachusetts. I took, it was going to be months to get to the DMV so I ended up going through a AAA. Had to get all these documents. I uploaded all the documents. Of course, when I showed up, none were there. Thankfully, I had backup copies but it was really a pleasant experience. Are you, describe what you're doing with Real ID and what robots play? Yeah, sure. So, with Real ID, what we are doing today and what we'll be doing in the future. So, I can talk about both. What we are doing today is that we are allowing most of the work to be done up front by the customer because Real ID is a complex transaction. You've got to have four different pieces of documentation. You need to provide your information. It needs to match our records and then you show up to the field office and by the way, oh man, I did not upload this information. We are getting about 15 to 17% returns customers and that's a whole lot of time. Every single mile, our customer travels to the DMV office which averages to about 13 miles in my calculation for average customer. It's a dollar spent in carbon footprint, in the time lost, in the technician time trying to triad some other things. So, you're talking $26 per visit to the economy. And amazing frustration that has to come back. And our customer satisfaction scores which we really like to track goes down right away. So, in general for Real ID, what we have been, what we have done is created a bunch of self-service channels which are accelerated by workflow engines, by AI and by bots to collect the documentation, verify the documentation against external systems because we actually connect with Department of Homeland Security, verify what's your passport about. We look at your picture and we verify that, yep, it is truly a passport and yours and not your wife's, right? Or not a picture of a dog and it's actually truly you, right? I mean, people do all kind of fun stuff by mistake or intentionally. So, we want to make sure we save time for our customer, we save time for our employees and we have zero returns required when employee, customer shows up which by the way is requirement right now. But the Department of Homeland Security is in a rule making process and we are hopeful, very hopeful at this point in time that we'll be able to take the entire experience and get it done from home. And that'll give us a whole lot more efficiency as you can imagine and bots are at the tail end of it committing all the data and transactions into our systems faster and with more accuracy. That's a great story, I mean really congratulations and I guess I'll leave it, last question is where do you want to take this? What's your roadmap look like? What's your runway look like? Is there endless opportunities to automate at the state or do you see a sort of light at the end of the tunnel? Sure, so there is a thing I shared in the previous session that I was in which is be modern while we modernize. So that's been the goal with the bot. They are integral part of my transition architecture as I modernize the entire DMV bring them from 1960, bring us from 1960 to 2022 or even 2025 and do it now, right? So bots are able to get me to a place where customers expectations are managed. They are getting their online, they're getting their mobile experience, they are avoiding making field of his trips and avoiding any kind of paper based processing right for our employees and customers as well. So bots are serving that need today as part of the transition strategy going from 1960 to 2022. In the future, they're going to continue to serve us. I think it's one of the things that was talked about by the previous sessions today that they are looking at empowering the employees to do their own work, back office work also in a full automation way and self empower them to automate their own processes. So that's one of the strategies we're going to look for but also we'll continue to have a strategy where we need to remain nimble with upcoming needs and have a faster go to market plan using the bots. Outstanding, well thanks so much for sharing your story and thanks for helping Dave. Real life testimony. Never thought I'd be coming on to praise the California DMV but here I am and it's legit. Yeah, well done. Can I make an introduction to our Massachusetts colleagues? Good to, well actually we have been working with State of New York, Massachusetts, Nevada, Arizona, so our goal is to share but also learn from that. Help us out, help us out. But nice to be here. Great to have you. And looking for feedback, next time you visit DMV. All right, yeah, fill out that NPS score. All right, thank you for watching. This is Dave Vellante for Dave Nicholson, Forward Five UI Pass Customer Conference from the Venetian in Las Vegas. We'll be right back.