Okay, we're back inside theCUBE's SiliconANGLE.tv and SiliconANGLE.com's flagship telecast. We go out to the events and we extract a signal from the noise and we want to find the smartest, people most entertaining, people we can find to share knowledge with you and extract the signal from the noise and share that with you. I'm John Furrier, the founder of SiliconANGLE.com. I'm Joe with my coos. I'm Dave Vellante at Wikibon.org. We're here with Kim Stevenson, who's the CIO of Intel. She gave a keynote this morning just after Dave, Dave Donatelli. We're here live at HP Discover. Kim, welcome to theCUBE. Thanks. So, we're talking off camera, Northeastern Alums. Yeah. I'm having a very social morning here at HP Discover. My history is HP. Now, at Northeastern alumni, we're at the school. I actually did get a computer science degree and it's on my resume, so it's true. I'm turning on the folks who want to check that. Next job interview, I'll make sure it's on there. Well, Kim, welcome. I didn't, funny enough. I had an undergrad in accounting. So, what do you think of your keynote was fantastic? Big data, it was a big theme. Tell us about the reaction and what you thought about the keynote. Yeah, so I tried to bring to the keynote the business angle of what's happening with the amount of data being created and how that can translate into business opportunities for basically everybody with a real emphasis on moving quickly. The first movers are going to have a real advantage and it is complex, but the reality is we've got the ability to bring in all the data, synthesize what's important, and then move out. And I think the next couple of years are going to be huge learning years, but I wouldn't wait. Yeah, it's absolutely, we're big data fans and we're all over that, we love it. We're pro-big data, so the first question, I want to get your definition. From yours, everyone's got a different definition. How do you look at big data? Tech keeps looking a certain way, business people look another way. You're in kind of both sides of the fence. You're on the infrastructure side and you're also looking at the apps. I mean data's coming from applications, users, machines, systems, it's a tsunami. So what is your definition of big data for the folks out there? So I think it's basically all information created. And so whether it's machine generated or it's human generated, all of that information fits into that big data envelope. A lot of the important parts of big data though are the things, the pieces of information that we've ignored or haven't been able to contextualize in a systematic way up until now, up until now. And that's where you're going to get the real value. We've been working in IT on corporate decision making and workflow for a long time, business intelligence, but bringing in that contextualized human information is going to make it very powerful. We've had Pauline Nist on the queue, she's going to be on later today and she's going to give us probably that systems perspective. And she's- Do you know Pauline? No, I don't know. She's a character. She's phenomenal.  But she'll give us the geeky version, but I'd like to get your perspective on something that we've been talking about at SiliconANGLE Wikibon and inside theCUBE is an observation that for the first time in history, a business can actually instrument their business end to end. Yes. How they acquire it from employees to product development, to marketing, sales, to service, everything, closed loop, full instrumentation, that's data. So as someone who's in that CIO role today, you've got to think about that. What would you share with folks kind of where we're at and if that is a premise, do you agree with it and how would you start putting that together? Yeah, so I do agree with the premise. And I sort of look at it in terms of there are 10, 15, somewhere in that range, corporate business processes. They might be ordered a cash or it might be demand generation through customer life cycle support, whatever the case may be. And that's how IT really has to think about the entire system, that end to end system and how we've been siloed over the years. But those interaction points, the seams of every handoff is where I think the value comes from in passing that information. So I'll give you our manufacturing example. We have evolved over the last 10 years to fully automated factories. And so as a wafer is moving through the factory and it completes a process step, all of that data around every die on that wafer and what happened in that previous process step is transferred to the next tool so that that information becomes the baseline information of exactly what happened, not what was supposed to happen to the next processing. That has improved our yields in manufacturing but in today's wafers, we're at nearly 750 terabytes of data on every wafer when it's end of production. And you talked about how you gave a number of examples of how Intel is using data inside of the organization and it's changing the metrics that you're actually using. You gave an example of MTBF, you said we still measure MTBF but that's not the primary decision making point anymore, is it? Right, right. We're trying to prevent, move into that whole proactive, the predictive analytics part of that data so that mean time between failures isn't the key metrics, it's when do you start to see degrading and then you repair before you ever have an incident, make it totally transparent to the business. It changes the whole game on the execution. With that in mind, I wanted to ask you about, you said big data is all information. Storage has been a hot sector mainly because it's moving from spinning disk to, you got flash, all kinds of IO and real-time analytics so it's like a perfect storm around this whole data movement, kind of transformative. But there's data warehouse and business intelligence systems out there that were kind of built the old way. And so as you talk about the premise that you guys are talking about on the keynote, is it a data, do I start with the databases? The collection is obviously important, right? If it's all data, you got to collect it. How do you get your arms around that? Yeah, I think deciding what you collect, because it can be daunting. It is overwhelming the amount of data. So making, that you have to make a decision on what you're going to collect based on what business decisions you're trying to drive. And you're looking for things that will create inflections in the business. The traditional business intelligence, et cetera, they're all structured data. And it's not that that isn't useful, it is useful. But layering on that unstructured, contextualizing that information is where you get the incremental value. And it could be, so we do stuff with supply allocation, to make revenue forecasting. So look at, I would say, look at the big corporate business processes. And where do you need an inflection point in the level of accuracy or intelligence around those decisions? And that's where you need to start. And that's hard, because a lot of times I don't know what I don't know. But you said something in your keynote, you said social and big data will completely change businesses. And then the other thing you said was that most organizations really aren't using social. You know, that much. And then you gave some examples of Intel. And your social media examples were good. They weren't earth shattering necessarily, but they were practical. And some examples of big data. Why do you think that, so big data I understand, because it's so hard, but social, you know, it was kind of in the Facebook era. You know, we got a number of IPOs and you can debate whether or not they're good or not.  Why do you think that most enterprises are so slow to hop onto social? They're still skeptics. You know, so I think history always teaches us something. And if you look at brick and mortar stores at the end of the 90s into the early 2000s versus the e-commerce stores, what prompted brick and mortars to put up websites and do e-commerce? And they got disrupted. They got disrupted, right? And economics were good too. Yeah, and so, and I think that that same phenomenon that's happening with social, it's an investment upfront. So you have to justify the investment and the ROI calculations are soft. Brand awareness is going to go up. And so, it's hard for successful traditional companies to take the leap. My contention is if they don't take that leap and use the technology and innovation that's available, they will be disrupted. Yeah, and I think that's a good point. I mean, the ROI is soft because there's no real data analytics yet. Right. So what was that loop on like a click on a search engine, right? Delivers a result and that. So that's a big data opportunity. It is a big data opportunity. Yeah, especially with mobility. How does mobility affect the big data vision around from your seat? Obviously you have workers out there and you have devices at the edge. Well, and I started saying the simplest forms, it's just another data source and data fee. When you have to start analyzing the data at the point of creation though, and they're on multiple different kinds of device types, we have it into a concept called the compute continuum, where no difference between the experience delivered on whatever type of device that you happen to be on. And we would tell you we're, we would call the strategy port of choice for the operating system layer. But that compute, that continuum of experience on device consistency is a really important aspect to taking all that data in. We're here with Kim Stevenson, Intel CIO, giving a perspective on big data and just IT. My question that we've been debating and we're trying to understand this is unknown data for us in this whole real time analytics is a big, huge innovative. The iPad has shown C level executives what they could actually see and that we've heard direct anecdotal comments. CIO is going to IT, give me stuff on this. So that brings up the whole dashboard conversation, business metrics, real time analytics. So batch processing is moving to high availability and real time. That's hard, right? With all this data that you're storing, you got to have, get a move in active data and there's all kinds of tiering solutions. What's your view on that in terms of where that's coming from? Is it coming from IT or is it coming from the business line managers? We're seeing a mixed bag on that, some saying I wanted the business driver to be X, IT gets the request, or is IT enabling that kind of dashboards? Because it's still early, so I'm not sure. So I'll give you my perfect MBA answer, which is it depends. You should be a consultant. Yeah, yeah. So no, you know, I believe IT can take real leadership by helping the business to understand how you could create these dashboards. We called our social media dashboard for marketing our social cockpit, but it's a dashboard. Is it perfect? Probably not. Will it change in the next couple of years? Maybe the next six months? Probably so, as we learn more about. But waiting for that perfect answer is probably a bad idea. But I see a lot of businesses pushing their IT shop. I think the single biggest problem that IT organizations have is the velocity at which IT moves is much, much slower than the pace of business and that is something that we have to address and fix. What would you share with other IT executives and leaders out there around how to position themselves and prepare themselves for the big data movement? Like you said, it's inevitable. It's coming. Yeah, yeah. What steps do you take for the folks out there who are kicking the tires, knowing they got to jump into the big data paradigm and start rethinking the value chain of their business? Well, and maybe you could answer in the context of that last statement that you made about the misalignment of the velocity of business versus IT. Yeah, so the first thing I tell people, so cloud is your opportunity to, people talk about ROI for cloud and asset utilization and all of that's true. But the real opportunity for cloud is the velocity at which you can operate. So you'll have a provisioned infrastructure with the storage, the network, the compute available and then you're just layering on the applications. That'll cut out an enormous amount of time in what IT can deliver to the business. So I do think that there's opportunities to close that gap between the business and the way IT moves. But I think back to the data question, I would pick a few areas that the company needs better information and I would focus on bringing in that contextualized human information from non-traditional sources. People call it unstructured data and adding that context is probably the single biggest thing to get you a seat at the table. So I talk a lot about the IT labor problem. If you look at the amount that's spent on labor over the last 10 or 15 years, it's escalating. It's probably two thirds of the spend around IT and everybody talks about 70% goes toward running the business, 30% goes to managing the business. So you've got these decades of complex processes built out that have been hardened. So you remember we went through the business process redesign phase. We have to go through an IT process redesign, maybe around data, to actually attack that problem and solve that agility alignment issue. Well, some companies over the last few years they've gone through data center consolidation, apps rationalization, so if you're already there, we've made some investments to modernize our core enterprise applications, as an example. That gives us a lot of flexibility. If you haven't made those decisions, you're going to have to make those decisions.  In your view, do modernizing applications through the use of new tools and new practices, does that cut away at that sort of legacy process base or are we going to see similar types of calluses built up around those processes? I think it's going to cut away at it, but you have to keep looking out. Are you continuously moving forward? It isn't a one step, so it's not like a stair step. You do this once and boom, you're there. It's a continuous, we have a pretty good appetite at Intel for innovative startup firms and we bring a lot of companies in to look at their technology. Some we use, some we don't use, but part of that is cultural to build, keep pushing ourselves. And that's one thing I think is never done in an IT organization. Pat Gelsinger said in the Cube, Pat Gelsinger wrote the icon, he said that Intel marches at the cadence of Moore's Law. And so does Intel IT march at the cadence of Moore's Law and has that helped with your alignment? Yeah, it does. I mean, we not only march at the cadence of Moore's Law now but with different products and core silicon products as well as system on chip products, we now have shortened cycle times and so we're moving twice as fast and I wouldn't say that we've conquered, they keep up the pace of the business in IT yet, but I think that we've made great improvement and it's recognized by our business. That has really the point of my question. I mean, I think Intel is inherently more aligned because of that. Now, you've got other complexities, size and velocity of your business, but I think that you've compensated a lot of those just through your mission and your focus. Well, we're really excited to speak with you again. We're fans of Big Data. We built our media business from the ground up on Big Data. We actually use predictive analytics as a way to identify trend data within our target verticals to write our stories and because of that, we don't have to do any better advertising and funds are the data acquisition funds, the operation. So we're completely built in the ground up so we can tell you, for example, in our business, we can say, we can look at what's being consumed and what the target audience in our community are actually talking about in real time and it's changed our business because it reduces our risk for accuracy of stories. So one of the things that I said this morning and believe is that we're heading the place where corporate decision making is going to change the way it happens. So if I asked you a question about when you see that your readers and followers don't agree with something you print out, what decisions do you take with that data? Well, for us, we've looked at the social crowd sourcing, one we're instrumenting the crowd that we're serving. Something you talked about in your keynote. So first we have to do is instrument the crowd that we're serving. At the same time, we recognize that the crowd is part of the production process. Unlike the New York Times, which feeds them an opinion and a story, news, they don't really take them into account in the production process. So for us, we actually write stories, we'll reiterate, we'll follow up, we'll write multiple follow-ups and so we look at the crowd as a production element in our operating class. That's what they think. And so. I said we're experimenting with crowd sourcing and we're finding that I'll say the critical few are really powerful to listen to and take action based on. So we've found, our IT organization has found through these crowd sourcing applications that we're rolling out, how to isolate the true, highly skilled individuals from the herd. And that's a really powerful technique. The blended average of sentiment is, it makes your revert to the meaning. I'll share this dashboard just to give you a little taste. So we have an audience of 2.5 million unique users that we've identified as our audience. And that's now basically a panel. So in real time, we can see what's trending within our audience. And on this side is a clustered community classification. And so I can pull up at any given time, you want to talk about Hadoop, because we'll be at Hadoop Summit next week. I can pull up everyone who's in the Hadoop classification. These are people, these are real people. In our 2.5 million, this is the Hadoop sample. I randomly just picked this random guy, see what he is. You can see that these are like real people. So these are like a panel, but we're not out there trying to grab them and sell them something. This guy's a data engineer for JYR employed, employing Hadoop technologies. So he's a data guy. So we don't yet figure out how to, we don't want to go out there and just grab them and try to, but we monitor them. We harvest their conversations, look at their sentiment, figure out what's going on, and we can ping them once in a while. But again, this helps us data. So I'm a big believer that the instrumentation is hard right now. So that's why I think the database angle and this whole flash technology is interesting because the storage seems to be the bottleneck, the storage and the network. And you talk about social plus data transforming businesses. You know, we live that, so we resonated really well with us. So how about HP? I mean, why are you here? What draws you to HP Discover? So Intel and HP, we have, we're a very strategic close relationship, have for years and years and years. They're our biggest customer, but as the CIO, right, I'm here because we partner really closely with HP to help improve what we do with Intel and their infrastructure is key. It's key to my operations. So I wanted to be able to share some of the things that we've done and help them and, you know. Learn and return. So what cutting edge stuff are you deploying that you can share with the folks out there? Because that's the Intel you guys have more as law, but you also have to push the envelope and demonstrate the leading technologies. What do you have in production that's what would be quote, bleeding edge and that's exciting you right now. We have two environments that you would call cloud. One is for our office and enterprise applications and we've got 70, almost 75% of that environment virtualized and all in the cloud and we are provisioning services, new applications in that environment in under an hour. So that impacts all Intel employees because it's the office and enterprise application suite. And we had to instrument that. We've been at this since 2009, middle of 2009 and we had to instrument and automate the entire path to production and then we extended that into our DMZ. So all of our external feeds coming in are also operating in that same cloud environment, same architecture, but it's a different instance in the DMZ. Very proud of that and has really dramatically changed the way we operate. So I'll give you an example. We run an application store at Intel. It's called AppUp and we launched Angry Birds. So we knew marketing was going to launch Angry Birds. We'd worked closely with them.  We provisioned in our cloud the capacity to go up and however, when you give out Angry Birds for free, you get a lot more downloads than you can expect. So we came to this screeching, hauled long queues and because we were in that cloud environment, we were able to provision up that capacity. Even though it was unpredictable how much it was, we were able to provision that up. It took about 24 hours to clear the entire queue because it happened so fast and we've been learning from that experience ever since. So now we're taking that office and enterprise environment and we're bursting it out into the public cloud to create even more of that burst capacity so that that won't happen again. Our second cloud environment is really for our product development, our design engineers and that is a massive compute infrastructure, almost 50,000 servers, hung together, what I would call a grid in old parlance but basically a clustered type of cloud that allows us to improve the throughput time of every engineering job that happens in Intel. Okay, Kim, Stevenson, we've got to get in the hook. Intel CIO, thanks for sharing inside theCUBE. Great to have you. We love the conversation. We can go for another half hour. It's so exciting. Thanks so much for sharing your knowledge and your perspective. We'll be right back with our next guest after this break. Thank you.


What we'll do now is solve an example problem that enables us to use some of the tables that are in the back of the textbook, and specifically we'll look at the steam table. So what we're going to consider is a piston-cylinder device. So this is an example problem. So we have a piston-cylinder device. So we have a piston-cylinder device that contains 0.1 meters cubed of liquid water and 0.9 meters cubed of water vapor in equilibrium at 800 kPa. We're then told that heat is transferred at constant pressure until the temperature reaches 350 degrees C. And so a schematic of what we're looking at, it's a piston-cylinder device whereby the piston is free to move up or down depending upon the pressure within the system. And we have water, and it's both in the vapor and in the liquid state. Water is 800 kPa, and so we have a little bit in the liquid. Some of it is in the liquid state, and the rest of it is as a vapor. So that describes the problem, and what it wants us to do is find a number of things. It wants us to find the initial temperature of the water, determine the total mass of the water, calculate the final volume, and show the process on a PV diagram with respect to the saturation line. So writing those out, find, okay, so that's the problem that we are going to solve. So when you have a problem like this, the best thing to do is to begin by writing out the information that you know, and the unknowns are the things that we have to solve for there. So let's begin by writing out the information that we know. So we know that we have a piston cylinder device. We know that the volume of the liquid is 0.1 meters cubed, and the volume of the gas phase, or the vapor phase, is 0.9 meters cubed. We know that the pressure, we're told it's a constant pressure process, and consequently P1 and P2 will be the same. So it is 800 kPa, and we also know that we have some process. We go from state 1 to state 2, and that consists of heat transfer until we get to state 2, where we know the temperature. We're told it is 350 degrees C, and P1 equals P2 equals a constant in this process. So it's a constant pressure process. So what we need to do is find the four variables, being initial temperature, total mass, final volume, and showing the process on a PV diagram. Now whatever you're faced with these problems, another thing that is very handy to do is to write out a schematic of the process in terms of what you understand, what is going on. And I will write it in terms of a TV, although they want a PV diagram. In the end, I prefer to look at things in terms of the TV diagram. So there's our two phase region, a constant pressure line coming along. So this would be P equals 800 kPa. So we start off with a mixture of both vapor and liquid, meaning that we're somewhere in the two phase region where we don't know. So I'll just denote that with one, and it's someplace in the middle of the two phase region there. And then with our process, what we're doing is we're adding heat, and we're heating up the fluid, or the system, and eventually we're going to move up to some point here on our superheated region, and that would be at a temperature of 350 degrees C. So that's what we know about the process in terms of the schematic. So let's go into the analysis of this problem. So for analysis, what we know is the pressure one is 800 kPa, which is also 0.8 mPa. Now what we can do, remember I said that we were in the two phase region at state one as shown on the schematic. So we can go into our tables at the back of the book, and in this particular case what we want to do is look at the saturated water pressure table. And the reason for that is we know the pressure. We don't know the temperature, we know the pressure. And so if we look there, and we look for a pressure of 800 kPa, we will find a value for that, and it turns out there's a saturation temperature. So from the table we can determine that the saturation temperature is 170.41 degrees C. So that's one piece of information we have. What that also tells us is that, and it's as I sketched on the figure earlier, at T2 equals 350 degrees C, that's higher than 170, and that means that we are superheated. Which makes sense because we start off in the two phase region, we add heat, there's only one place we're going to go and that's into superheated vapor. Okay, so the first question asked us to determine the initial temperature T1. Well we just obtained that by getting the saturation temperature. So we can write T1 equals 170.41 degrees Celsius. Now the second thing they want us to determine is the total mass of the system. So again what we'll do is state 1, we go back to our saturated water pressure table and we look up the values for the specific volume of the liquid. And we also know the total volume of the liquid, it was 0.1 meters cubed. And we know of the vapor phase, the specific volume. And we also know that from the problem statement itself is 0.9 meters cubed. Therefore we can determine the mass of the liquid. So doing this, we obtain that the total mass is equal to 93.4298 kilograms. So that answers the second part of the problem. So there we've solved part one and part two. The next thing they ask us to do is to determine the final volume when we're at state two. So final volume. So what do we know there? We know that the pressure is 800 kPa. And we know that the temperature there is 350 degrees C. Well remember earlier we said that we were in the superheated region. So we go back into our tables and we look under superheated water. And from that we can then determine, we'll find a table at a pressure of 0.8 MPa. And then we look for 350 degrees Celsius. And from there we can obtain the specific volume. So we get the specific volume at state two. And from that, given that we already know the mass, we can determine the total volume at state two. And that's going to be the total mass multiplied by the specific volume at state two. And from that we get 33.111 meters cubed. So that solves the third part of the problem. And finally what they wanted us to do is they wanted us to sketch the process on a PV diagram. So for that what we can do, we read out our pressure, specific volume. And this is what the lines would look like. That constant temperature on a PV diagram. So what I've drawn here are two different isotherms. One is at 170.43 degrees C. And the other one is an isotherm at 350 degrees C. And remember we started off at saturated liquid at 800 kPa. The pressure does not change, but we're somewhere in here, state one. And then with our process we move to the right, to the right, until we hit the 350 degrees C line or isotherm. So that's what our process would look like on a PV diagram. And that concludes the solution to that particular problem.


and it's a lintel you can see what it is it's where the old windows been and that's where the juggles were see where the bricks cut around the juggles Steve writes on since getting them for half two it's so hard on my own i'm not used to doing this on my own good Monday morning Monday the 4th of July happy 4th of July 20th American views here we are in on a new job and it's a lintel i know you all like the lintel jobs so we're all kitted out for the lintel brought these i don't think we're going to need them the reason being it's a bundle Alex just took the tarp off for us the customer took the window out for us which is a big help so that's all been done for us so we've uncovered it you've got this little bit of timber lintel which is too high because it's the same height as this one i think that's like a concrete little in that one but yeah they want it to line through and they want it to line through there as well so we're coming down a bit and we're coming up a bit so three course of brick on the outside i think and course of block on the inside or a few bricks i think we've got plenty of bricks customers their own bricks which is always a big help so yeah um we're going to get the chest set up and have a little see what's up here hopefully there's nothing above that so we're not trying to it'll be all good to build it is we'll have a look and you'll see you'll see what you see a bit windy today Alex isn't feeling 100% he's not he's not been well for a few days so he's managed to drag himself in for me today it's just a big help when i'm up and down on the staff so yeah um usual thing onto time lap so while we get set up right we're up got a bit of timber one course above we'll see that yep tap tap tap this you can see the crack here this is all loose so we're going to take all this out probably get rid of this event because it's been sealed up anyway break this rebook all this i started tapping away at this half came out pretty easy do you mate that out there you go so there you go march out that's sands cement so that's this is an original there you go that lintel is sat on it's not even bedded on well let's get it out get all this taken out and cleaned up and get ready to get it all back in here we go i'm going to lose stuff out yet there's a bit more to come out yet but um yeah it's been worked on here that's where the vent was so obviously that's been bricked up so we can get rid of the vent take this half out get this all bricked back up same again you can see what it is it's where the old window's been and that's where the juggles were see where the bricks cut around the juggles and then just gobbled it up for a really nice to timber in so that's coming out and that's coming out and then we're going to get cracking again there's the wall plate to cause the brick on top of the lintel this is coming down here so this is going to be stunning studding the inside you can just stud this to make it match there's no point messing out with all this when you can just because it's all supported the lintel's okay and then they can just reduce it with the stud Alex is just having a bit of a sit down he's not good but he's um he's dragged himself out to give me a hand which is all good of him so yeah let's get the rest of this loose stuff out and get ready to put it back together right i've had a quick brew Alex still isn't good and yes all tidied up now all cleaned off it's four courses to go in here as we're going level this i've got the window inside to measure off and this is to brick back up that's on your lintel light so we've got a 1.8 to go across there which gives us 185 mil here the side seating and then we're just going to put one course across the top there's no need for the two because it's not doing anything but we'll pass the soffit and then we'll get these four put in later on so i'm going to go i've done all the bricks and i'm going to go put a mixing now so yeah that shouldn't take long put half a mixing and get stuck into it it's chuckling too long unfortunately Alex is too bad so he's coming to get him so i'm just going to keep dragging on get this done so um yeah stick it back over the fence you've got a bit of a bit of my drango and then we'll get this lintel on and get this brick here across the top just a bit um lintels in across as you know these lintels need propping i can't physically prop it off this wall because this is built up today as well and i was i was going to use the lintel the old lintel underneath there but it's quite heavy so it's a bit risky really so i've decided to leave that now head back to it and forwards get some blocks for here get this built up i know it's a pain leaning down like that but get this built up but inside and out put a piece of timber on top and prop off there with the old lintel cut it down again but just prop it straight off there so it's going to be so much easier so much more safer as well so yeah um Alex has gone now he's one picked him up he's still not so good so i'm going to pack up my stuff i don't need and just nip to furrows which is only a couple miles away so won't take me long and then grab a real lunch so we'll see you after lunch Steve writes down so let's get them for half two it's so hard on my own i'm not used to doing this on my own up and down up and down but anyway that's good all the cuts done with this still i hope you can still hear me so you've got the big cuts at the front and it's a very thin cut on the back as an extra course of brick on the back that's all done it's all leveled up insulated ready for the window now i've got that bulk of timber on the top there that's just to rest the prop on for now temporary prop i think you want to try and get the window in tonight we might ask them to leave it tonight and um fit it tomorrow hopefully um yeah because they could do the props day and night and obviously the props in the way the window there's no other way of propping it really so i'm just where i'm with now getting the prop in and then i'm going to get those last few bricks across the top so we're catching a bit we'll skew if but it doesn't matter it's doing its job stopping the sag while i take these two across here right back to time that's nice to get these last few in about half four now just finish packing up that takes a long time on your own that but it's done so there's our new window inside and out done do a little propped up trim popped off just so i can get the last bricks in because i'm going to put that back in tomorrow and put the window in and the wind is going in tomorrow that stays in overnight until it's gone off and then support itself so yeah a bit of extra work all the crap brick work but that's no problem and i've just got to go inside now and tidy that up so as Alex would say if you like today's video give me a thumbs up if you're new to the channel be sure to subscribe and click that bell button and thanks for watching you've been watching britley misty and alex and we'll catch you in the next one


For more videos on people's struggles, please subscribe to our YouTube channel. Now things that you are still going to fight as now and many other possible feelings are the burdens that you must be prepared to win at all costs. Even if it means comrades that we lose our hands and we lose both our legs, we are prepared to fight to the bitter end.


Hello and welcome to the session. In this session we are going to discuss the following question and the question says that Examine which sample is better for making predictions. Alice owns a gift shop. She wants to know what the customers would want for their birthday. Sample A, she surveys first 10 customers at her store. Sample B, she surveys every 10th customer at her store. We know that the sample chosen should be large enough, random and representative of the population. With this key idea, let us proceed to the solution. In this question Alice wants to know what the customers would want for their birthday. If she chooses sample A, that is she asks first 10 customers what would they want for their birthday. She chose the sample according to her convenience and it may or may not represent all the customers because people of different age groups have different lighting and if she chooses sample B it means she asks every 10th customer what would they want for their birthday. So here in sample B she chooses the person after an interval. It is a random sample. The customers are coming throughout the day so she will be able to ask all types of customers and the sample will be large enough to make prediction. So after analyzing the two samples we can say that sample B will give more accurate prediction than sample A or sample B are better representative of the population. So sample B will give valid conclusion. This completes our session. Hope you enjoyed this session.


Yep, looking forward to this. Well, the referee has let it go, and Cook has the lead, and Yanovsky has pulled up. He thought the red lights were going to come on there, and Greg Cook has the lead here, can you believe it? Greg Hancock is in second place, Ty Woolland is third, and Maxi Yanovsky is in the back, and Yanovsky pulled up there thinking that the race was going to be stopped. What on earth happened at the start there?  I'm good to see! Greg Cook makes the semi-finals! Here in Cardiff, nine points on the board!  Three straight, here we see the start, Kel. Well, let's have a look. Yanovsky moves, but penalizes himself, so the referee gets it right. He jumps, you'll get a warning. Quite possibly we will hear that, and for Yanovsky, his chances of being amongst the points were over before he got to the first corner.


Okay, I think most of us are here. I just want to read from Psalm 96. The first few verses before we pray. Okay, Psalm 96 verse 1. Oh, sing to the Lord a new song. Sing to the Lord all the earth. Sing to the Lord, bless His name. Proclaim the good news of His salvation from day to day. Declare His glory among the nations. His wonders among all peoples. For the Lord is great and greatly to be praised. He is to be feared above all gods. For all the gods of the peoples are idols. But the Lord made the heavens. Honor and majesty are before Him. Strength and beauty are in His sanctuary. A wonderful reminder to all of us. To sing His praise. To declare the good news. Proclaim the good news. Declare His glory among all the nations. So that is the responsibility of every believer. That is the privilege of every believer. And each one of us, we are called to this. We are empowered, anointed to do this. So let's pray that the Lord would enable us. The Lord would strengthen us, anoint us fresh to be able to do this. So let's pray. Father, we just want to thank you Lord, that you've created each one of us. Lord, you've redeemed each one of us. Lord, for this special privilege, Lord to sing to you and to proclaim to the nations. Father, God, and to declare your glory, who you are, what you do, Lord, among the peoples, Father, God. And Master, we thank you that you've called us as kings and priests. Lord, you've called us, Lord, as your own special people, as a chosen generation. Lord, to be able to do this, God. And Father, we thank you, Lord, this morning. We ask that you would fill us with your spirit. Lord, that you would, Lord, give us a fresh understanding. Lord, and also that you would give us a fresh impartation and a revelation, Lord, of our privilege and our responsibility in this God. Wherever we are, in whatever realm of under a sphere of influence, Lord, you've placed us in, in whatever environment you have placed us in. Lord, enable us to do this, God. Enable us to sing your praise.  And proclaim your praise to the people of God. We thank you. We give you all the praise and all the glory. In Jesus' matchless name, we pray. Amen. Amen. Awesome. Okay. So we continue with marriage and family. And we, let's just get me from where we stopped last class. So last class, we were looking at some important aspects of marriage, which means, you know, beginning the marriage, or even before we commit to marriage, you know, wedding and marriage. So how one has to prepare oneself, right? And we looked at several areas of preparation, okay, which is sometimes, you know, like we said, we get very excited about the wedding and preparing for the wedding, which in itself is, I mean, it's not bad. One has to prepare. There's a lot of things. It's a special day and so on. But also to understand that it's a, it's a lifetime, right? Wedding is an event and marriage is a journey. And it's a lifelong journey. So it's good if one is prepared like personally. So we looked at several areas, right? We looked at how we can focus on becoming, or we need to focus on becoming the best version of ourselves. And we started by looking at that. And then we looked at how we need to look at our emotional health, see what is our, you know, our attitude, our motive, our behaviors, our thought patterns, you know, and we looked at several things. We looked at, you know, our attitudes. We looked at we negative and pessimistic. Are we burdened with guilt and shame? And because all this is going to affect our relationship, right? With our spouse. So we looked at emotional health and they've also looked at how we need to manage ourselves personally. And we looked at, you know, our work, our ministry, our career, the finances, how we manage our time and how we manage the household, right? We cannot be totally detached from a household no matter, you know, how anointed we are in ministry or, you know, how skilled we are in our workplace or how successful we are, you know, our household skills and how our responsibilities towards the household. So we looked at that. And we also looked at relationship skills, our ability to communicate, right? If you, you know, if you want to express something and you're feeling inhibited to express that or if you're feeling, you're unable to communicate that then there's a, that's a challenge, right? That's a challenge that needs to be worked on and overcome in order to have a good marriage. So communication skills, roles in marriage, relationship with in-laws, because you realize that, yeah, you're just not, you know, it's not marriage and isolation, it's with the extended family in the sense extended the family of the groom, the family of the bride. Everyone is, you know, involved in this is also part of the family. And so there is definitely interactions. There's going to be, you know, there's going to be times when they are involved in certain things in life. And so how do we interact? How do we respond to all that? Because they are family, you know, and they are important to your spouse. And so also your family is important to you. And so how do we deal? How do we interact with each other's extended family? That is also important. So we looked at that. Okay, so then we looked at overcoming the past, which means abuse or trauma or negative experiences, you know, maybe some kind of addictions and what kind of environment we grew up in, which could be influencing us in a very big way. In a big way, when I say big way, I'm saying, you know, it could be in a negative way. So if there's a negative influence because of how we grew up and how we, you know, because of our environment, then we need to obviously break free from that and we also need to work on that. Then we looked at sexual purity. I think this is where we ended last class, right? Where marriage is something that's honorable and sacred in the eyes of God, right?  It is to be between the husband and the wife in the context of marriage. So if there's anything else that is going on in life, you know, in a person, like if there are addiction to, you know, maybe pornography and masturbation and if somebody's, you know, just so addicted to looking at sexually explicit movies and so on, need to break free from that. I need to get help to break free from that and not say, okay, marriage will fix it. Marriage will not fix it. Marriage is just expose that and all those things will come to the surface, right? So the importance of living a sexually pure life, right? And also, the other thing is also true in the sense that to be able to express oneself in the context of marriage to be able to, you know, not have any fears about physical intimacy, about sexual intimacy, right? So maybe because of the past, maybe because of something traumatic which happened and well, if one is saying that I don't want, you know, physical intimacy in marriage then there's a problem, right? Because God has designed marriage to be that way. So in such cases also, one needs to prepare in the sense get help, maybe get counsel and overcome those things, right? Okay, so that's where we stop. So today we'll continue. In your notes, we're looking at chapter, what chapter is it? I think preparing for marriage, right? Chapter two, and we're looking at the seventh topic which is Christian maturity, calling and ministry, okay? So Christian maturity, calling and ministry, right? So in Ephesians four, we see that each one of us like the Lord has placed the five-fold ministry in the church in order to build up the church, which means the church, meaning the body of believers, so that each one of us will come to the fullness of understanding Christ, will come to the maturity of... let me just read that verse, verse 12, for the equipping of the saints for the work of ministry, for the edifying of the body of Christ. Verse 13, this is Ephesians four, verse 13, for till we all come to the unity of the faith, of the knowledge of the Son of God, to a perfect man, to the mature of the stature of the fullness of Christ, that we should no longer be children, tossed to and fro, carried about with every wind of doctrine by the trickery of men in the cunning craftiness of deceitful plotting. So what is God's desire? God's desire is that the body of Christ come to a place of maturity and the body of Christ is like you and I, we are all believers in the body of Christ, we are all part of the spiritual body of Christ. So God's will and God's desire is that every believer come to a place of maturity, every believer come to a place of understanding, not being children, being tossed to and fro by every wind of doctrine, but to come to a place of serving, to come to a place of, you know, to the work of ministry, to do the work of ministry. Now let's say in marriage also, so we need to have a plan and understanding that, okay, this is how I was, this is how I was growing spiritually when I'm single, now how do I continue to do that when I'm married? Okay, the thing is this, because there is, definitely there are additional responsibilities in marriage where you're looking after the home and then maybe when children come, additional responsibilities of parenting and all that. So just need to think, you know, how will I continue? How can I ensure that I continue to grow spiritually along with my spouse, right? And how can we help each other? Maybe, you know, it's ideal if, it will be great if both are in the same level of spiritual maturity and spiritual hunger and growing in Christlikeness, but if not, you know, how can we help one another? Okay, so to think about that and to prepare oneself for that, okay. So what about God's call, right? Maybe God has called you for a specific role and function. So maybe God has called your spouse for a specific role and function in the body of Christ. So how can we help one another, right? How can you help one another? You know, do you understand God's call for your spouse to be or a spouse, right? Have you even talked about it, right? Because one can be very, get very frustrated spiritually saying, hey, you know, this is what I feel God's call is. You know, I've heard people say, you know, if only I didn't get married, I would have been doing great things in the ministry. If I didn't get married, I would have been, you know, doing this and that and the other.  So it's the same God. So which means that marriage is important in God's eyes, family is important in God's eyes. So we can't say, if I was not married, then I'll be doing great things for God. I've been hindered by, you know, here are all these things, right? I've been really greatly this thing, not been encouraged by my spouse, been held with help, probably it's true. So therefore the importance of, you know, preparing oneself for that and even talking about it and planning about it. You know, when we plan for the wedding, you know, there's a lot of things that we talk about. So why not this, right? As believers, as believing, as a believing couple, planning to get married and talk about that. What is God calling you to be and what does God call you to do and see, you know, we're going to look at compatibility in our next chapter and this is one of the things which is very, very important, okay? And also to see that, okay, if, you know, together, what can we do together to further God's kingdom? Is there something that God is calling us to do together as a couple? So, you know, think about that, prepare oneself for that, right? So we looked at seven areas, like broad areas where one can prepare oneself before marriage, okay? So for those of us, again, you know, those of us who are married, well, these are areas to grow in, right? Maybe we look at some of these areas and we see that, you know, there's a problem here, there's a problem here or maybe we're strong in this particular area, but then there's sadly lacking in this because we never address it, we never talk about it and I've never personally, you know, looked at it this way, well, it's an eye-opener and it's a call, you know, it's an invitation for us to grow in these areas and to change in these areas, right? Okay, so, you know, if you see in your notes, you'll see that some very practical things that are given there and this is what we go through, I mean, as in we take couples who are preparing for marriage, we recommend this for them, that they need to spend time studying God's Word, working through these lessons, you know, have open discussions, etc. So it's designed for that, right? So you can just go through, I'm sure you'll find it helpful, okay? Okay, let's move on to, yeah, so I'm just going to leave all that for self-reading, you can just go through the application area and also the, some of these action items that you can go, I'm just going to leave that so that you can go through, right? Okay, just give me a minute, please. Okay, okay, any questions or anything that you might want to share, you could do that, anything at all that, in these areas that we looked at, any questions, any doubts, okay? Pasa, I have a small doubt. Yeah, sure, go ahead, John. Pasa, we spoke about emotional issues in the past, not to be getting carried into the marriage, right? So what if we find an elderly couple, elderly in a sense, six kids, both of them, and they still have that issue, maybe rejection in the past if it's carried into the marriage, causing outburst of wrath and things like that happen in a really bad way. So how can we... Yeah, so that's a very possible scenario, not only for elderly couples and it could also be for any age group and they've done life together for maybe whatever, 20 years, 30 years and then you find that there are certain areas which have not been addressed and because of which there is still, maybe they get back at each other, they get angry and then say, okay, you never do this or you are always been like this and all those kinds of complex things. So yeah, the best way to address that is of course, one thing is if they know that they are lacking in that area and if they feel that, okay, we need help, hey, you know, all these things, this doesn't look good as believers and then they feel that they need help, then we can direct them for help, to a good Christian counselor who can help them address that, who can help walk them through all that, the path of recovery, to see the emotional healing and then address that very main thing. Basically, maybe they're just holding on to certain lies and let a love God's truth to replace those lies and then immediately they will experience freedom or the person will experience freedom and so that is one option. Definitely to do that and also maybe have like a marriage enrichment seminar from time to time or direct them to that, that will also be a great refreshing time where they can celebrate those good things that have happened. That's also important and look at this one area which is causing them a lot of problem and embarrassment and address that and say, okay, it's not too late to change because there's a lot of hope. When we look at scripture, when we look at life examples and we get inspired and say, okay, this has happened to this couple and why not in our lives we can change? So that would be the way to go about. But I think from what you're saying, maybe what would add to the complexity is maybe there's a lot of tradition, there's a lot of stigma. If it's this age group that you're talking about and maybe they're not so open to getting help and talking about marriage. So that would be the first thing, I think. So because that's not, if they are in that age bracket, then obviously they've seen a lot of things and maybe they come from that era where it's not okay to really talk openly about marriage. There's a lot of stigma, shame and all that. So maybe to give them that assurance that it's okay. It's just getting help. It's fine to talk about it. And as a pastor, to give that confidence I think and also that confidentiality. So I think that's the thing, John, first step. Yeah, it's one more just to add to that question. This couple had been in ministry for a long time and they are still in ministry. So there is also that aversion from, it's kind of a built link themselves. They feel like it's too embarrassing for them to get help also. Or they're not willing to talk or counseling or any of that matter. Any gradual way we can... So the thing is they need to have the confidence in the person. Now this also adds to it. They've been in ministry, they are pastors. Obviously it has to be done with a lot of care. I mean this whole process. So from their side also they are very, very hesitant. Whom can we trust? So far we've been examples and leading the... We've been the one who's been giving instructions, but whom can we trust? So it makes it even more complex. But maybe to lead them to someone whom they look up to maybe some ministry couple. So that's why it's important to have that fellowship. That would always help. Gradual way is to just expose them to the truth I think. If there's a forum where... Let's say even a marriage enrichment weekend or marriage enrichment time. We're talking about all these things. And it's a gradual step. We're talking about okay marriage. We're talking this is God's plan. And maybe you could have couples sharing. Young couples, old couples sharing. These are some struggles that we go through. And it won't happen in day one. It might happen after a few months. Where a few months of meeting or exploring God's word, looking at God's truth, allowing God's truth to change. And then saying, okay, we had this challenge, we overcame. Or we still have this challenge. So I think that would be a step in the direction. I think that would help. Yeah, it's possible. Thank you. Okay, so let's move on to the next chapter. I don't have the power point. I hope you got the notes open. So we can look at chapter three. So chapter three is primarily directed to those who are considering marriage. So that's the tone of this entire chapter. But for those of us who are already married. So it's not an encouragement to say, okay, I didn't do this. I made a mistake in this area. So therefore, I need to come out of it. So that's not the intention. The intention is, okay, now what can I do? What can I do to better it? What can I do to strengthen this area? Or maybe, well, I didn't look at it, look at marriage this way. I didn't even consider these possibilities. But despite all that, how can I continue to honor the coming? And how can I strengthen myself and my spouse in these areas so that we can enrich or improve our marriage? So that's how people who are married should look at this whole chapter. So let's look at chapter three. So we're looking at compatibility in the sense, well, when we consider a person, so far we were looking at what we can do to work on ourselves. So now if you're considering someone as a life partner and you are considering several options and looking at certain characteristics and you want to see certain things in them, so what are certain areas to look at? So it's not just that this person is very funny, this person is very handsome or beautiful and has these abilities and skills and talents.  That's a very, I don't know, a very superficial way of looking at it. So here are some things to look at. Well, we can look at four areas, four broad areas of compatibility. So I just want to share some scriptures here. If you look at Amos three and verse three. Amos three and verse three, well, it's not in the context of marriage, but really it says can two walk together unless they are in agreement, unless they are agreed. So I'll just read that verse out. Can two walk together unless they are agreed and the good news Bible says, or the message Bible puts it this way, do two people walk hand in hand if you are not going to the same place. So it means that marriage is a covenant. You're saying, okay, we are agreeing to walk together. So despite all differences, we might have a lot of differences and these differences actually attract us to the other person. I'm a very serious person, but the other person is very funny or I'm a quiet person. The other person is very talkative and I see that, oh, hey, this is very different from me. And then you feel drawn to that person. But the thing is we need to look at other areas where you are agreeing upon. We're looking at things the same way. It's not uniformity, but even with our differences in personalities, even with our differences in likes and dislikes, et cetera, we are agreeing to say, okay, despite the differences, we can agree on this. This is what we can agree on it. So it's a place of agreement. It's an ability to be at a place of agreement to be yoked together. It's a covenant because you yoked together. So we will not be an exact copy of the other person. We will not be, but to come to a place of understanding. We see the differences. We celebrate the differences. We see the common things. We see those things that are the common ground and we say, okay, we are agreeing to walk together. I see the differences, but we are agreeing to walk together. So when it comes to compatibility, the first area that we can look at is spiritual compatibility. So what do we mean by that? First of all, we are actually laying the foundation that we need to be making this marriage covenant to a believer. I think we addressed that in the earlier sessions, earlier classes. We cannot be unequally yoked with someone who does not believe. When you say who does not believe that, yeah, so Sid Kano says, people say that opposers attract. Yeah, very true. But in life, after making the covenant, opposites also, there's a lot of fire and explosions and whatnot because of the differences, like sharp differences. Why are you like this? Why are you so different? Why can't you be like me? That never happens during the courtship. That happens only after you make the covenant and you spend some time. Why are you like this? Why do you always, why is the other thing always messy? Why can't you be orderly like me? Things like that. So the first area of compatibility is spiritual compatibility. So when we say spiritual compatibility, we are saying that one common ground is both our believers. The person whom you are considering has to be a believer. That's a very, very important aspect. So otherwise, there's going to be, we could say that person is a very nice person, kind person, very patient person, very noble person, but you're going to be pulling in different directions. Either you are going to be, they're going to be differences. So that's a given. What is a given? What is the understanding that both have to be believers? Now, even when both are believers, when we say spiritual compatibility, to see, does that person have a similar commitment to Christ? So that's important. Similar commitment, similar passion, similar discipline, these things, does a person have that? So for example, yes, both are believers. Excuse me. So both say, okay, I'm committed. I'm a born again believer. I've received Christ as my Lord and Savior, but if one person is saying, okay, I'm really passionate about Christ, I want to spend time in prayer and worship, and I want to serve in the church where I'm at locally. I want to be involved in ministry. And the other person is saying, it's just one part of my life. I have several other things to do. And for me, my commitment is, okay, I'm in church on Sunday. That's it. Oh yeah, I read the Bible, I pray, but I don't want to go beyond that. So there also, there's incompatibility. So not saying that the marriage will not work, but there are these differences and it's going to create some conflict. So the wife will say, why are you spending time in church all the time? Or the husband will say, why do you want to do this? I don't want to serve. I don't want to relax on a Sunday. Why do you want to do this? So you see all these differences cropping up and so, so spiritual compatibility. So the thing is that the differences can be very sharp and then cause distance between the couple. Now, for those of us who are married, you say, hey, that's my situation. My spouse does not understand the intensity of further or passion that I have for the Lord. I'm here in Bible college and I'm doing this, but where is my spouse? Yeah, she's a good person. He's a good person, believes, but doesn't have the same passion. So what do we do? Of course, we pray. We don't preach at them. The mistake people do is, let me just preach. This is what you need to do. You need to do this. You need to do that. And the person is going further and further away saying, please don't force me. Please don't pressurize me. So the thing is to really be an example first of all. Be an example and also pray. Be an example.  And slowly open the heart. Let the other person be drawn to Christ or drawn more to Christ and let Allah of the Holy Spirit work on their hearts. So this is something that we need to, it's a journey again. I'm talking to, of course, we're talking about couples who are people who are already in a marriage and you see that there is spiritual incompatibility. So what is the second area? Second area is emotional compatibility or intellectual compatibility. So here also, there can be, there can be this potential for incompatibility. In the sense, what is that other person interested in? Now don't get me wrong. Sometimes it's like, maybe you are interested in music, the other person is not. They're interested in something else. So the areas of interest are different. But is there a mutual understanding and respect intellectually? You're able to express something and the person is able to understand that. So when it comes to multi or multi-cultural, multi-ethnic or racial marriages, this is a very important aspect. Because the language is also very important. So the language you use to communicate and the person understanding you and understanding your language or is that a very difficult thing? They're not able to express themselves. They're not able to express themselves in a manner that they want to be understood. So there's incompatibility this way. So emotional incompatibility happens. So it's again, it's a journey. So we need to make that journey. Other things, common areas of interest that you talk about that you can relate to. So what happens is during courtship, the other person just makes an attempt or tries to be interested or just forces themselves and says, oh, yeah, yeah, yeah. And then listens to every conversation and it's like, wow, I'm just holding on to, hanging on to every word. And then it's like, totally different after marriage. And then you see that, okay, I'm actually not interested in this. Please don't bother me with this. I find that boring even. And then there is no, there's no common ground. So is there any activity that you can say, okay, I can do this together? Let's say you can, both of you like to read or both of you like to watch certain kinds of films maybe, documentaries or whatever even movies. There's some common ground. So that's very important. To find out, that's very important to see that, okay, I'm able to connect to this person. So that's important. Okay, for those of us who are married, again, this is an effort that we can probably make, that we can take in order to understand in order to we may not have the same interests or the same level of interest in certain areas, certain activities, but we can make an attempt. We can make an effort. Like for example, me and my wife because I listen to music and, you know, all that. And my wife is interested in gardening. She likes plants. For me, gardening is like, I like trees. I like, you know, like macro level nature. But so when I look at some leaves, you know, I'm not like very excited the way she is. She likes to gift plants to people and all that. So slowly I'm trying to understand, okay, okay, I'm trying to understand, okay, there are different kinds of plants. These leaves are shaped differently. Okay, they are different names. Okay, so those kind of, you know, I'm just trying to learn and also see, okay, maybe I can, if I gift a plant, maybe she'll get, she'll be happy. So, so doing that, right. So I always knew that, you know, the family and she was interested in gardening, but like over a period of time, I realized that she's really, really, really loves plants and et cetera.  So, so that also happens. So, yeah, so the thing is, you know, we have common ground.  So we have all this, so we compare notes, we talk about that, we like watching certain, you know, programs together. So we do that. So, these are things, right.  Maybe, maybe, maybe you're not passionate about it, but these are something you can learn, right. And, and it can be a common ground and it can be a, it can be a thing, you can be a learner, you can ask you know, and then it can be a common ground, it can be a common point of conversation, right. And you're getting to know, understand and so on. Okay, so second one, emotional and intellectual compatibility. Third one is physical compatibility. Okay, the Lord has designed us in a way to be attracted to each other physically, right. So, you know, there's nothing wrong in that, right, when you're considering a person for marriage and you've prayed and asked and all that. So, there's nothing wrong in being physically attracted to that person, okay. So, appreciating the other person's beauty, physical beauty, there's nothing wrong in that. So, physically, you know, if you're saying that, okay, I don't, I'm not even going to consider that area, then we're not being really truthful to ourselves, right. So, that's another area of compatibility if you're saying you know, saying that, okay, that's an area which is which is important as well. So, that is not the only thing but that is one of the things, one of the areas one has to look at compatibility also and be truthful to oneself, right. Okay. Then, the fourth area is compatibility in life's calling. So, this is a big one, right, life's calling and it can it can create a lot of tension, it can create a lot of conflicts if you've not really considered this, right. So, let's say, one of the husband is you know, you know, feels that he's called to be in a city in an urban setting as a working professional, you know, good believer but he feels that he's called to that, you know, that particular area. Urban setting, professional, working professional and wants to serve God. Whereas the wife, or the wife to be, feels that well, I feel that I'm called to, you know, to these villages. You know, I'm called to really minister there, called to be there even and maybe you know, uplift some of those things, break social taboos and I'm called to bring in change there right and these two people are considering marriage. Now, this is a very important area to talk about and say, okay, I feel this is what God has called me to do. So, you know, sometimes the, if you're not being honest and truthful, you might say, okay maybe that will change. You know, let's say the husband, I mean the guy feels that yeah, I know she feels this way, but then once she gets married to me and then we live in the city, maybe she'll change. I mean, that's a big, big gamble right, or maybe the girl feels that, okay, I know that he's a city the thing, but then, you know, once he gets to know, I mean, once he once we once we are married and then I talk about this and once he sees those, you know, my work and ministry and my heart for ministry and maybe he'll change and he'll come. That's again a big, big if that is there, if that happens then it'll be good. But then let's say it doesn't happen at all. Both of them are stuck and what they know, you know, and that is this is God's call for them. But then if it's going to be every day they're going to be, one of them is going to be wilting. One of them is, okay is in their area where God has called them to be and they feel that okay they can do great things and they are thriving whereas the other person feels limited, feels withheld from whatever God has called them. Feels that, you know, I'm supposed to be doing this, I'm not doing this and I feel very frustrated. I don't feel a sense of purpose. Yeah, I know this person loves me, cares for me, etc. and we are living a good comfortable life but then I'm actually called to do this and I'm doing something else. So that's that can be an area of incompatibility. Okay, so one needs to talk about this and find out or consider, you know what is this person's, even before making a commitment, even before courtship time and what is your life's calling, you know what are you called to do and be honest about it and not say things that will please the other person. Okay, well if there is no specific call or you're saying that I'm still considering, I'm still exploring I know I want to serve God but I don't in what way, what form I mean that season of life then then it's perfectly alright but this is another area to consider as an area which needs to be considered for compatibility. Okay, so what if there are some red flags or some signs where you see that okay, these could be potential areas okay, so what are some areas to consider? So we look at these four broad classifications or areas to consider for compatibility and some specifics if you want to look at you know, some things are is that person some questions to really consider is the person mature? Is the person mature enough to handle responsibilities? The responsibility of marriage, okay? So it could be a great person it could be a fun person very lively etc. But is that person mature to handle this? Right? Or is the person still childish and immature to handle responsibilities or does not want to handle any kind of responsibility and so on marriage, family okay second one is is the person serious about preparing for marriage? Okay, so nobody is like fully prepared, fully equipped and perfect so second thing to consider is the person prepared, you know or is the person prepared like some of the things we looked at with regard to work, financially emotionally right? The problem areas to be worked at or working on okay so is the person willing to give time right to do that so preparation or lack of it, okay?  There's a red flag right there and the Bible talks about a man who cannot rule his his own self it's like a city without walls where you just open up your life for any kind of attack, any kind of invasion there's no, you know, the person is going to be in some problem or the other because of this issue of anger and rage okay? So the thing is this, you know are there any addictions emotional issues which is causing this character weakness and is the person doing something about it aware of it, doing something about it being aware itself is the first step and the second step is to do something about it being willing to get help, being willing to change being teachable so that's another area I guess we'll stop here and then we'll continue with, you know, some more red flags or some more things to signs to watch out for and to consider, right? Okay, so we'll take 10 minutes break and then we'll come back


Hello all, in this video we are going to see about p-value. Before calculating p-value for your research, you should understand about p-value clearly. For understanding p-value, firstly we should be clear about our research objective. If your study is about just estimating means or proportion in a specific population, you need not rather cannot calculate p-value. If you are studying relationship between two variables or comparing two groups, then you postulate and test the hypothesis. In any research we have two hypothesis, one is research hypothesis and null hypothesis. Research hypothesis or alternate hypothesis says there is a relationship between two variables or two groups, whereas null hypothesis says there is no relationship or null relationship between two variables or two groups. After framing this hypothesis, the next step is to apply the appropriate statistical test, which is based on the type of variable, type of distribution, sample size and unpaired or paired nature of the sample. So, after applying the statistical test, we get the test value of the statistical test. When this test value is approximated with the p-value tables, we can get the p-value or we can directly use statistical software which will give test value and p-value in its result. We are just one step away in understanding what is p-value. Here we are going to understand the type of errors. The null hypothesis may be actually true or false. In your study, you may accept that null hypothesis or reject that null hypothesis, which will derive four situations. When the null hypothesis is false and you reject it correctly, then it is called as true negative. When the null hypothesis is true and you accept it, that is called as true positive. When the null hypothesis is true and you reject it, that is called as false positive. When the null hypothesis is false and you accept it, that is called as false negative. So, this false positive is called as alpha error or type 1 error. negative is called as beta error or type 2 error. Alpha error is rejecting null hypothesis when it is actually true. Beta error is accepting null hypothesis when it is actually false. Accepting false is least dangerous when compared to rejecting the truth. So we always keep beta error at 10 percentage or 20 percentage before the start of the study and alpha level at 5 percentage or 1 percentage. The inverse of this beta error is called as power of the study. The probability of committing alpha error is called as p-value. As the probability of committing an alpha error is close to zero or the p-value is close to zero, the chance of you rejecting the null hypothesis when it is actually true is going to be nil. But conventionally people have kept it as 0.05 when the p-value is less than 0.05 you call it as statistically significant. When the p-value is less than 0.01 then you call it as highly significant. As equal to the statistical significance, clinical significance is also important. For example, when I mention certain variables like diastolic blood pressure, maximum decibels a person can hear, temperature of the body, all these values revolve around 9200. But when we calculate p-value for these values the statistical test remains same. But the clinical significance differs. So to sum up what is p-value? p-value is a measure of relationship or measure of association between two variables or groups. p-value is the probability of committing alpha error. p-value is the strength of evidence provided by our sample against null hypothesis. So p-value is the strength of evidence provided by our sample against null hypothesis in order to accept our alternate hypothesis. Thanks for watching this video. Please subscribe to think lateral channel. Thank you.


Let me say a little bit about what I'm here to do. So I am Trey Causey. You can find me on Twitter there. You can tweet questions at me if you want about anything I'm talking about tonight, and I'll respond to you. Or you can email me at trey at dato.com. So I work for a company called Dato, which you probably haven't heard of because it's a new name. We used to be known as GraphLab, which you also may not have heard of. But you may have heard of GraphLab before. So GraphLab started as an academic project about seven years ago. It was based out of Carnegie Mellon for quite a few years and became a company about two years ago. And we renamed to Dato in January. So the founder is Carlos Westron, which if you travel the machine learning world is a well-known maestro of machine learning. And so we're now based in Seattle. And you can find out more about us there. So quick show of hands. Who here works broadly defined in the data science slash machine learning world? OK, another quick show of hands. Who would like a quick explanation of what machine learning is? All right, awesome. So there are enough hands. I'm going to give you a quick definition of what machine learning is. And I'm unfortunate because Olivier is actually going after me.  But broadly conceived, machine learning is the kind of subfield of computer science where we try to use data to learn things and try and basically write programs to write programs. So we're trying to use data that we have on some given thing, maybe it's website traffic, maybe it's people rating movies, maybe it's people clicking or not clicking on things, maybe it's genomic data. You can imagine a bunch of different data sets we might be interested in. And we want to use data that we have and make predictions about data that we're going to see in the future. That's a terribly short definition, but that's kind of fundamentally what we're trying to do most of the time in machine learning is make predictions about things. And this predictions might be trying to predict whether you're going to like a movie or not, whether you're going to develop a debilitating disease or not, whether you're going to click on an ad or not. I mean, it ranges from the banal to the kind of very important. So I'm not going to get into the substantive uses of machine learning tonight, but I'm going to talk about how you can do big, big machine learning very fast on your own computer. Is that a sufficient explanation for people? Cool. All right. This is a blank slide. All right. So right now it's kind of undeniable that Python is really kind of coming to the forefront as the language that people are doing machine learning in. So it used to be the case that a lot of machine learning was done in academia. It was kind of caught up in MATLAB and Octave and software like this. Python is becoming kind of the most amazing language ever in the world of machine learning. Thanks in large part to a gentleman sitting here. But it's really a kind of go-to language right now. The problem is that machine learning in Python often does not scale to large data. And everybody knows you've got to have big data these days or not. So this is a problem with a lot of people in Python on machine learning. So one of the problems we're trying to solve here is make it so that you can build machine learning models in Python on your computer and use that same model and ship it off to production. Be that in an app, be that in a web app, be that sitting somewhere making predictions that's not customer facing, anything like that. We want that same model that you build on your computer to be the model that you put in a production. Sounds like it shouldn't be that big a deal, but as it turns out, it actually is kind of a big deal. So typical Python and machine learning workflow typically has about five steps. So the first step is you pull up your favorite Python development environment. Maybe it's an iPython notebook. Maybe it's PyCharm. Maybe you're working in VIM. Whatever, you build your model. You prototype it. You have a sample production data that you've pulled down and you're kind of iterating on that, trying to see how well your model does. And you kind of do this for between hours and months. And so you finally get to a model that you're pretty happy with. If you're diligent and you're a responsible data scientist and or machine learning engineer, you're pulling new data in constantly to make sure you're not overfitting to old training data. Overfitting is when your model just kind of memorizes old data and you kind of connect the dots and it doesn't do well on new data. So that's the first step. The second step typically for scalable machine learning then is you then pull in the C++ or Java or C sharp engineers that work at your company and you have a meeting with them and you say, OK, we're going to spend the next days to months implementing this model and production. So that's the third step, spending those days to months implementing that model and production. And that might mean if you're lucky and you have some engineers to have some machine learning background that they know how the algorithms work and they start working with you and you start implementing it in production. Or it might mean that you're also now giving a crash course in machine learning to people who have no interest in machine learning and they're caring about things that you don't care about, like this corner case here, like what happens if the model returns no prediction or what happens if it returns the same prediction for everything, these are the kinds of things that keep engineers up at night but don't necessarily keep someone who's trying to build models quickly up at night. So that's the third step. The fourth step is crying yourself softly to sleep every night as you realize that there are new bugs introduced by all of this and then you get to the fifth step where you return to the first step and you're screwed. So this is kind of like the process, a lot of enterprise level, like machine learning kind of walks through these days. And so people, there's just so much wasted time and effort doing this. So these switching costs are real, right? Every time you switch between environments, you run the risk of introducing new bugs in your code because let's say you're switching from Python to C++, you're switching from Python to Java or whatever, now you have to remember like, well does Java deal with doubles the same way that Python does or what is a, let's not talk about Unicode and like all these other things that might be problematic as you're switching between languages, right? Those are real costs and there's also like monetary and time costs doing this. When you're handing your work over to someone else, that's a cost, let's say you built your model, it's in production, it's doing great, but you want to add new features to your model. Like let's say now you have new instrumentation in your web logs, I didn't realize that was there, thank you. Let's say you have new instrumentation in your web logs and you want to start incorporating new features in your model that you can make new predictions based on this, well if that model is written in another language, now you have to go back and coordinate with those engineers and it's a royal pain in the butt. So what we're trying to do is trying to streamline this whole process and I'm gonna give you the technical details of how we do that because I think it's pretty interesting, but we kind of see the building of machine learning models I'm putting in my production is taking place in kind of three steps. You have your engineering step which is you get a data set, you start hacking on it, you start kind of looking and seeing, doing running descriptive statistics, visualizing your data, trying to figure out what the data actually contains. The next step is you want to build machine learning models, let's say it's a recommender system, if you're not familiar with those, it's kind of like the classic Netflix or Amazon, you may also like type things. You want to start building those models, making predictions with those models, making them more sophisticated, making them faster and so on and so forth. And then ultimately what you would like to do, especially if you work in data science and industry, is you want to deploy those models into some kind of production setting where they're gonna deal with many requests per second or maybe depending on where you work, like hundreds or thousands of requests a second or maybe they're just peaks in the day when they have to be receiving thousands of requests a second and the rest of the day they don't so it needs to be elastic, it needs to be robust to like big spikes in traffic and so on and so forth. So you need these things to kind of always be on and you need these things to always be ready and responding and when they break you need to have some monitoring on it and so forth. So what we would like to do as Python programmers, we would like to do all of this in the same environment. We don't want to switch those environments, we want to do it all in Python and we want the model that we built to be the one that's in production. So there are kind of two different problems there that I glossed over. So I'm gonna spend a lot of time or not a lot of time because these are short talks, spend the bulk of the time on the technical details of the first part of the problem that I kind of glossed over and that is most existing dataset, processing packages, libraries, et cetera in Python require that you load all of your data into memory and kind of hack on it in memory. There's work going on in the open source home right now. I'll talk about that in a second but really what the problem is you might want to use production data, that production data might be sitting in Spark, it might be sitting on HDFS, it might be sitting in a SQL, some kind of relational database somewhere. You want to deal with the same production data building your model that the model's gonna see when it's in production. You want it to generalize well to that data. So to stay in the same environment, to work kind of all the time in Python, we have this data structure called the S frame, the S stands for scalable, if you've ever used pandas or R or anything like that you'd be familiar with the concept of the data frame, this is just a tabular data structure. So we built this data structure that resides on disk, now that we have SSDs with caches, actually as it turns out, disk processing can be very fast and so we reduce the reliance on a RAM and it's super fast, compressed, binary, then the thing that makes it so fast primarily is that it's columnar. So a lot of other people have kind of independently come to this discovery that columnar data structures actually end up being pretty fast for a number of reasons. I should say before I continue that this is actually all open source as well, so if you want to go check out the implementation of this, you can go to GitHub, our primary repos called data code and then the S frames are implemented here and the repo named data core. So if you want to look at the source code, see how we built this data structure, figure out how we did it, it's all there. In fact, let's see, can I switch? Yes, so here is kind of everything that you find here in our repo. The nice thing is that I haven't even mentioned, we also have a parallel data structure called these S-graph, which is for graph analytics, so if you're familiar with graph theory or like social networks would be a good example of this. GraphLab kind of sets the benchmark for fast graph analytics like in academia and in industry. So if you're familiar with Spark and GraphX, GraphX was created by one of the co-founders of GraphLab, so we're all in kind of a good company here. So you should check that out. There's also a lot of C++ in there, sorry. Let's see here. Okay, so why are columns fast and why are S frames a big deal? S frames are a big deal because it means on your laptop, you can start working on production size data, production scale data. We can ingest just about anything that you want. So we can ingest JSON, CSV, XML, RDDs, ODBC, NumPyRays, and so on, you can read. So we can ingest all that data. In fact, I have to say, I actually haven't been with my company for that long, but it has the best JSON handler that I've ever kind of interacted with. It's pretty awesome, usually in two or three lines of code, you can go from really gnarly JSON to nice tabular data structure that's not painful to read. Sorry to any JSON fans. So the nice thing about this is basically what an S frame is, is it's just a bunch of columns. Each column has a single data type, and this is one of the things that makes it fast. So the columns can be basically any type that you can imagine. There are strong schema types, like ints, doubles, strings, and images. So we can ingest and ingest a directory of images. It'll turn it into a tabular data structure complete with resolution and a thumbnail of the image, as well as weak schema types like lists and dictionaries. So that means that you can have a column of the S frame that's a column of dictionaries which then themselves contain data of multiple types. So pretty flexible. In fact, it's called flexible type. And then the columnar architecture actually ends up being very fast because it allows us to do vectorized operations which we've known for a long time are very fast because it enables use of the cache. Easy feature engineering. I think the thing that's hardest for most people to wrap their head around if they're coming from pandas or R is that columns are actually immutable. So if you're a functional programming fan, that's kind of fun, but you have to get used to the fact that you can't modify values in place.  Now the nice thing about that is you're only gonna be rewriting that column. So you don't rewrite the entire data structure every time you make changes to a column. So this means that you can have extremely wide data sets and you don't suffer any kind of performance breakdown because the data set has gotten wider. Does that make sense? So you're just modifying still in constant time. The columns are lazily evaluated which means that you're only ever loading data into memory as you need it. And it does it in a very smart way. And you can also do extremely fast summary statistics, sketch summaries and visualization of your data. So, sorry about these text dense slides. These are the only two slides that have a lot of text on them. So when we were kind of building this data structure which I think is a super fun way to interact with your data, we kind of had to make some choices to balance between speed and ease of use. So we had to lose some performance in some places to make it easier to use. So some things you might be used to doing in our pandas or something like that, like row-based filtering, ends up being a little slower because it involves loading a lot more data into memory, kind of joints against itself, but it's not bad. But like I said before, we now don't require rewriting all of the data and column operations are fast no matter how big your data set is. So I have like a screencast where I did this kind of test on a data set that was like terabytes in size and it was super fast, even on a laptop you could like sum up columns in a few seconds. So the nice thing is about making things column based, people naturally think in vectors a lot of time, especially if you've been living in the NumPy world or in the R world for a while, you're already used to thinking in vectors and vector operations and you don't have any restrictions in the way you deal with your data, except for that catch on immutability. So we have about a few seconds, let me quickly show you what an S-frame looks like. So if you can just pip install graph lab create, it will ingest the CSV here and create an S-frame out of it. So then the other nice thing about S-frames is once you've created an S-frame out of whatever base data source you have and it's stored on disk, compressed format, loading it is extremely fast after that because it only loads the data in that you need. So if I wanted to load the data from S-frame there, so you can just do all the operations they would normally do on any kind of tabular data structure. This all should look very familiar and there's a good reason for that, like we want it to look very familiar because we want it to just be easy to use on big data sets. So this is like getting both, this is a sketch summary of whatever, two million rows, something like that. I summed up the column there. So I was doing all this on like two million rows, it's very fast. That's actually not a very big data set, but it's just for kind of demonstration purposes. So it'll also do kind of approximate values as your data gets very big, it will approximate those values using probabilistic data structure so that you can get very fast summary statistics on the data that you're using. We can also do kind of Boolean operations here, and we see that returns all songs that have more than 10 listens. And there's also a visualization platform that uses the same sketch summary here. So that's over two million rows kind of pulling up histograms, things like that. Pretty fun, not that impressive on a data set of that size, but I just wanted you to see what it looks like. Okay, so if you're really interested in kind of technical details about building a data structure with this performance and this size, Yucheng Lo, who's one of our co-founders in the Prince Bar Tech, has an awesome technical document in which he lays out like what it's like to build a new data structure that's both easy to use and very fast. So here's the totally intuitive and easy to read bit.ly address here. So 1-A-X-J-K-O-V, that is an O, not a zero. So you can check that out. Okay, so the other problem that I mentioned, I'm about three-fourths of the way through here, the other problem that I mentioned, so that's one problem, you wanna deal with production data, you wanna do it on your laptop, you wanna stay in the same environment. So you also wanna push that to production. So I'm not gonna spend much time on this, but we have another service called Data Predictive Services, which allows you to deploy any machine learning model you built in GraphLab Create as a REST API with like five lines of code. It'll be either hosted on-premise or on AWS. Basically it just creates a service that's on, listening, elastic, scalable, that just waits for a request, then returns JSON objects. And so those might be predictions, those might be recommendations, those might be whatever your model's output is, those just come back as a JSON blob. And the nice thing about it is you can actually ship arbitrary Python code up there. So even if you're not building machine learning models in GraphLab Create, you can ship that code up to AWS as a service. So this is actually start to finish the code it would take to ship a model up to AWS. So this is shipping a recommender system model, like a standard factorization machine model up to AWS. And you can see that the vast majority of that code is actually just arguments to send, like login arguments basically to AWS. So I mean, if we could take out, actually we could just create a dictionary for all those and that would have been cheating, but could have just created a dictionary for all those then it would have looked really short. So that's all it takes. It automatically spins up a load balancer to AWS, gets your nodes running, so you know when they're running it also versions all the models you sent up there automatically. So you can basically call that same visualization platform I showed you, see what version is running on each of your nodes, see how many requests have been made, and so on and so forth. So pretty cool. Also works with scikit-learn models. So this is something we're working on right now to be able to deploy scikit-learn models as that arbitrary code that you ship up and then that turns your scikit-learn model into a service that's sitting on AWS waiting for requests, making predictions. All right. So if you wanna get started, you can download software at Graphite Create. It's free for personal academic use. Feel free to hack on it. The source code for a lot of it is sitting there on GitHub. You can download it at data.com. Things I didn't talk about. There's also a suite of awesome machine learning toolkits in the software. So there's a great neural network. If you're into deep learning, great neural network package in there. I don't know anything about deep learning and I was able to build a really nice image classifier using the ImageNet data set like in 10 lines of code. So it's pretty fun to play with. We're also hiring. So if you're interested in the Pacific Northwest, I was told it's also called the Pacific Northwest in Canada, but I don't know if that's true or not. So the Pacific Northwest of the States in Seattle, we are hiring. Please visit data.com slash jobs and find out more about what we're doing. I think I have a couple of minutes for questions if anybody.


Dr. Manohar, who is an avat-cycler himself is constantly motivating his patients to cycle as he knows how beneficial it is. The health benefits of cycling are innumerable. It improves cardiovascular fitness, it improves our muscle strength and joint mobility, improves flexibility, strengthens our bones, improves our posture. Suddenly you see people who are exercising sit and stand much better than anyone else. It reduces the stress and reduces the total body fat also. More importantly it improves our environment. During this tough COVID times, World Health Organization has recommended cycling and many of the American and European cities have started doing it.