Welcome to Dealing with Materials Data Course. In this session, we would consider the case of sampling distributions. Let us first review what we have done in the past. We introduced certain special random variables with some discrete random variables, we call them discrete distribution functions. Continuous random variables can also be called a continuous random, continuous distribution functions. With each one of them, we introduce what is their mean, what is their standard deviation. We once again introduced central limit theorem. And in just the previous session, we looked into how to generally check the correctness of assumed distribution graphically through probability plots. What we want to do in this sessions or coming up all the sessions from now onwards is make a relationship between the observed sample and the population. Let us try to understand. Why are we doing all this? The whole purpose of learning statistics is that we all are aware that there is a general population, there is a larger information in the world, there is an entirety of certain kind of data in the world. But we cannot look into each and every data points that is available in the population. This entirety I call a population. But what we do is we take a small sample out of it, we observe it, we do certain calculations on it and we try to make the judgment on what the population would be like. This is the whole game of statistics. So here now onwards in this course, we are trying to do something to set up our understanding of population through a random sample. So we are going to introduce what is called a random sample once again. We have already done it once in the past, we are going to do it again. We will introduce something called a sample statistic. Then we will talk about the most common way to understand any population or any sample for that matter. As we studied in the descriptive statistics, the most common numerical method is the central tendency or we can call here sample mean and then dispersion of the data through sample variance. In this particular session, we are going to concentrate only on the sample mean. We will find what is the expected value of sample mean because remember sample mean is also a random variable. So it has a expected value, it has a distribution and therefore we would like to find out what is its expected value, what is its standard deviation of the sample mean. We will revisit central limit theorem because it tells us more about the nature of distribution of sample mean when the sample size is very large. We will also give an example, we will also show that how a binomial distribution can be approximated as a normal distribution. Remember in the past, we have approximated binomial by Poisson. So we will clarify as to when it is to be distributed, approximated as a normal distribution. We will give some examples. So let us understand as I said the population and the random sample. As I said statistics is a science, the whole purpose of statistics is to understand a population through a small observed sample value, small compared to the population itself. So here I have given an example, this example concerns that you imagine that in a production line in an industry certain alloy is being produced and the customer is requesting an alloy with a certain value of strength property of the alloy. Now it is the industry's responsibility, marketing department's responsibility to tell them that our alloy has a certain strength properties. It has a mean strength properties of this. How will it do it? Well, it cannot start testing each and every alloys that ingots that are produced. And what it may do it, it may take a lot of it, the randomly chosen ingots and then from each ingot it will randomly choose a sample and it will put through a test and find its strength property and then it will say that our lot of production will have this much of strength property. So here the complete production lot is what I call or what we call in statistics population and whatever that small sample you take is called a random sample. So formally speaking capital X1, capital X2 up to capital Xn be an independent random sample from a common distribution function f. This common distribution function f refers to the population distribution function then it is said that X1, X2, X3, Xn constitutes a random sample or just a sample from a population with a distribution f. Here the randomness indicates that there is no obvious order in which X1, X2, Xn are selected. There should not be it should not happen that you take always the first ingot from a lot or you take every 10 ingot from a lot you must choose it in a random fashion so that the sample becomes a representative of the population. A sample statistic or simply a statistic is a quantity that is computed using only the data. You have come across such quantities a lot in this course for example average standard deviation, maximum of a data, minimum of a data all these quantities are calculated without making any assumptions on what the distribution of the population is. It is purely from the data such a quantity is called statistic please remember it is singular it is statistic. Now as I said our whole interest is in finding out f the population distribution f capital f. There are two cases here if you know the form of the distribution f as in the past I said that if you are dealing with the strength property of a material there is a possibility that it follows a log normal distribution. So your f has a form of a log normal distribution. If you are looking at the differences suppose you already know that this whole alloy is designed to have a particular value of strength property say yield strength and in that case what you are going to do is you are going to actually measure the yield strength and take the difference. If you take such difference between the yield strength you are sort of trying to calculate an error and therefore it may follow a normal distribution with a mean 0 and some variance sigma square. So it means that you are able to decide a priory right from the beginning you are able to decide what is the form of the distribution f. But what you do not know you know the form of the distribution up to a point up to a level where you know you do not know the parametric values of that distribution. So if you have assumed normal distribution for an error you say that the mean value is 0 or you can say that you do not know what is mean value the two unknown parameters are mu and sigma square. If you are looking at a Weibull distribution and you want to look at only two parameter Weibull distribution then you are worried about the scale and shape parameter. If you introduce you think that no no it may be a three parameter Weibull distribution then you have the form of the distribution which is Weibull but what you do not have is the three parameters values. So the psi which is the location, scale and shape. So such situation it is called a process of parametric estimation. You know the form of the distribution but you do not know the parametric value. So then it falls in the first case which is called a parametric estimation and inference. Suppose you do not even know the form of the distribution that happens or you are not able to justify it properly that it should fall in this particular form of distribution then it is called a non-parametric inference and estimation. In this present course we are going to consider only the case of parametric estimation and therefore the common distribution will be referred as a population distribution we know its form but we do not know its parameter and we are going to go for parametric estimation case. The first and foremost estimation that we would like to learn about is as I said in descriptive statistics also mean plays a central role and so does the standard deviation or a variance. So we will talk about two sample statistics. One statistic is sample mean and another is sample variance which we will follow in the next session. In this session we will consider sample mean. So consider the case of a population with distribution function f with mean value mu and a variance sigma square. Remember we are not saying that this is a normal distribution this is a Weibull distribution we are just saying that it is some distribution which has a mean mu and variance sigma square. Mu and sigma square are also called population mean and population variance respectively which you do not know these are now your two unknown parameters. So we are looking at a very generalized case right now. Let x1, x2, x3, xn be a random sample from the same population with mean mu and variance sigma square and then we define sample mean as an average of the random sample. So x bar, x bar is an average of random sample value. So when you actually want to find out you are going to replace x capital Xi by its real realisation small xi. But here we would like to show that you see I take a sample and I will find an x bar someone else takes a sample and finds an x bar there are many values of x bar. Therefore, we are talking here random sample as a random sample as a you know random variables arising out of the same distribution f with mean mu and sigma square and therefore x bar is a function of random variables and therefore x bar is also a random variable. So then we can take an expectation of x bar, so if you take expectation of x bar follow the algebra of expectations it is 1 over n summation expectation of xi and therefore it is 1 over n summation of mu because it is coming from a common mean mu for every xi and therefore it is mu. It means that for the random variable x1, x2, xn is an observed values then the x bar is a x bar has an expected value of mean mu or it is an estimate of mean mu. You can say that mu can be estimated using x bar. In the next further session we will call this a point estimation. What is the variance of x bar? Let us calculate variance of x bar is 1 over n square summation variance of xi and therefore it is n sigma square divided by n square and therefore it is sigma square by n. I hope you have remembered all our past thing that if variance of x is given to you then variance of a x is equal to a square variance of x. This is the relationship I have used here in order to show that variance of x bar is n sigma square divided by n square. This n square comes in this form, it comes from this identity and therefore it is sigma square by n or the standard deviation is sigma divided by square root of n. Let us try to understand this. This says that the expected value of x bar is mu, it does not depend on the sample size. It is always mu, the population mean value. But the variance of x bar is going to decrease as n will increase. So if n increases then the whole value actually decreases. It means that the spread of the distribution becomes smaller and smaller and therefore the distribution mean value x bar value comes closer and closer to the actual population mean. This is shown in the next slide. Here you see that we have plotted, I have plotted standard normal distribution function, standard normal pdf not distribution function, probability density function. Blue line is when n is equal to 1, the green line is when n is equal to 2. You see that the spread is drastically decreased when you come to n is equal to 2. If you take n is equal to 5, it is further decreased and if you go to n is equal to 10, it is even further decreased. It means that the as your n value becomes larger and larger, the spread, the distribution becomes closer and closer to the mean value, population mean because we have taken standard normal distribution. It is a population mean value mu and it is coming closer and closer to that. Now let us revisit central limit theorem. What does central limit theorem say? Let us recall, let x1, x2, xn be independently and identically distributed random variables. So it is a random sample with a common mean and a finite common variance sigma square. Then for large value of n, probability of summation of xi minus n mu divided by sigma square root n is less than t is approximately probability of z less than t where z is a standard normal variate. What it tries to say is that this quantity, what it tries to say is that this quantity comes closer and closer approximately to z which is distributed as normal random variable with 0 mean and 1 standard deviation, it means that it is a standard normal variable. If you divide the numerator and denominator by n, then you find that x bar is minus mu divided by sigma square root n is less than t also has behaves like a standard normal variate. Now let us look at this quantity very carefully. As n tends to infinity the sigma over square root n becomes smaller and smaller and therefore it actually means that x bar comes closer and closer to mu. So this is now with reference to the present understanding of random variable and population. We can say that a population mean comes closer and closer to the, sorry the sample mean comes closer and closer to the population mean and if you take the standardized variable, this is another term that I would like to introduce. Any random variable like here it is x bar, if you subtract it from its expected value x bar and divided by its variance square root of variance x bar then this is called normalizing normalization of random variable x bar. Why it is called normalizing? Because it starts behaving like a standard normal variate. It behaves like a standard normal variable. So this is called a normalization. So now in future when you do when you hear anywhere the when the values are normalized what is happening this is what it really means. So now let us worry about what is the approximate distribution of sample mean. Again we say that x1, x2, xn is a random sample with a common mean and a common finite variance sigma square. Then the central limit theorem tells us that the normalized value of x bar follows a standard normal distribution, sorry. So then it follows a standard normal distribution. This is equal to z where z follows standard normal. This is what exactly we talked about. The question again comes how large should be n when you can consider that it is tending to infinity. Well the thumb rule is that it should be at least 30. Please recall the discussions we had in the previous sessions when we discussed the central limit theorem. The same thing applies here also. So let us now see another approximation. Applying the normal that is the central limit theorem we would like to study the the another approximation of binomial distribution when n tends to infinity that is when the number of independent Bernoulli trials tend to infinity or then when the number of Bernoulli trials are very large. Let us consider the case. You remember the previous question we had a production unit has a probability of producing defective unit as 0.01 and a randomly chosen batch of 100 units. So random this is there is a random variable with 100 units n is equal to 100. What is the probability that it has at the most 50 defective units? Maximum number of defective units it can have is 50. It does not have more than 50 units is what defective units is what it says. You recall we did the same thing by calculating the exact value in order to show the binomial approximation to the Poisson distribution. Now we are going to the normal approximation. So x1, x2, x3, xn 100 denote the 100 units and we say that xi is 1 if the unit is defective and xi is 0 if it is not defective. And we define x as a summation of 1 i summation of xi i is equal to 1 to 100 and we want to find probability that this x the summation is less than 50. So xi is a Bernoulli trial with probability of defective as 0.1. So if you look at it if you apply the central limit theorem it says that summation of xi minus n times the population mean divided by population standard deviation multiplied by square root n behaves like a normal standard normal variate z. Here the mu in Bernoulli trial is p which is 0.1 and sigma square in Bernoulli trial is p times 1 minus p which comes to 0.564. You can calculate it out. And therefore probability that x is less than or equal to 50 is probability that x minus np divided by square root of n multiplied by p times 1 minus p is this and this says that this comes to less than minus 1.33 which is approximately on standard normal variate less than this and this probability turns out to be this. This probability you can calculate either using normal tables, there are normal tables available I will discuss about it in the next slide or you can use R and find out what is the probability value or you can use even excel table excel spreadsheet there also they have a function to find out this particular value. But in all the cases I say that please remember to look at the help. So, let us look at here, here I have a standard normal distribution plotted, this is the standard normal distribution red line and this is minus 1.33 value as the Z is going to be less than or equal to that. So, we are looking for this probability. The question is how much is this? General normal tables that are available tend to give you the probability of any value t less than or equal to this. So, it gives you integration of minus infinity to t 1 over square root 2 pi e to the power minus 1 half x square dx. It gives the area under this curve, this is the curve of 1 over square root 2 pi exponential to the power minus 1 half x square, this is the curve. So, this is the value which is generally tabulated. But remember that in the normal distribution it is a symmetric bell shape curve. So, if you come exactly at the 0 value, this you already know is a 0.5, it is half and so is this is also, this is also 0.5. So, number of times here it takes a value of t between minus infinity and plus infinity. Sometimes it takes the value of t greater than 0 less than infinity and it will give you values of this nature. How do you tackle this? It says that the t is here. Well, remember that whether you take it here or you take 1.33 here, this area and this area are exactly the same.  And therefore, instead of finding the probability on this side, you can find a probability here. So, what I am trying to say which must have been said in your R session, I just want to repeat it that when you apply a normal distribution table or you use a standard routine to find out the normal probability, please check the help and make sure whether it considers this kind of probability or it considers this kind of probability. In our present case, this value turns out to be 0.092. So, here I have. So, if you calculate it out, this turns out to be 0.092. If you look at this, if you have to put this t value, so when you put mu is equal to 0.1 and sigma square is equal to 0.564, you want to have your x less than or equal to 50. So, x minus np divided by square root of np times 1 minus p turns out to be 50 minus 60 because it is 100 with possibility, probability of failure probability of a defective is 0.1. So, probability of its point where does this came from, wait a minute, wait a minute. I think I have made a mistake, we must correct it. I think it is time I correct it. Probability, so n is this, so this will be actually 10. So, we are looking for this denominator is correct, this value will be different. We are looking for 10, 50 minus 10, so 40 divided by square root of 100 times 0.564 and therefore, it may not be 1.33, you please check what is the value and what should be the distribution. Maybe in the further section I will clarify, but please make sure that there is a mistake here. Mu is when you find an np, n is 100 and p is 0.01. I do not know where did I get 60 from. So, actually it is 50 minus 10. So, this is wrong, it should be this value should be this much, 40 divided by square root of 100 times 0.564. You can work out this is, this will come to 40 divided by square root of 56.4 and you can work out put that value and as shown in the previous case, now it is a positive value, 0 is here. So, your T value will be somewhere here, your 1.33 will be some positive value.  So, it will be more than a half. Please check it out, there is some error here, please correct it. So, what we have done? So, we have approximated binomial distribution by normal distribution. Now there is a confusion. Once we have approximated it by exponential and now we are approximating it by the normal distribution. So, which one to use that is clarified in here by stating that if n tends to infinity and p tends to 0 such that np remains constant, then it binomial can be approximated by exponential distribution. But if only n is very large and you have no restriction over p, then n can be approximated by a standard normal distribution under a central limit theorem. So, with this, let us summarize what we learnt today. We learnt the concept of random sample and sample statistic with the common distribution function f, which is a population distribution function. The whole purpose of this session and the sessions coming up is to get information about the population through the random sample. If you know the form of the distribution function f, population distribution function f, in that case, you know what is the, it will be called, I am sorry, it will be called a parametric distribution function, parametric estimation. If you do not know the form of f, then it is called a non-parametric estimation. In this course, we are considering only parametric estimation. We showed that the sample mean estimates the population mean and the variance of sample mean, here there is also a correction to be added. Let us do that. And variance of sample mean is population variance divided by sample size n. We showed that as sample size n increases, the variance of sample mean decreases. If we also introduced a distribution of sample mean as a using a central limit theorem and we showed that using central limit theorem, binomial distribution can be approximated as a normal distribution when the only sample size is large and you have to make no assumption on the probability of success p being small. Thank you.


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Yeah, well dolo rookie to 99 and L central He knew he wasn't gonna be there long read Ricky Henderson four of six AL West He's having a good time and Sultan say a Suzuki to 150 NL central the crossing. What are you doing a John McGraw full count swatches. That's gonna be Giants NL West. That's New York Giants four of six John McGraw I'm gonna make sure that that that is NL West Giants Is that a Tim's dad? It is Giants NL West Dude comes in wearing Oreos and thinks he just Dual patch on a read Detmer's rookie AL West number two one of five Yeah, I get it Matt. Yeah, he doesn't oh Argentina just just ended the game pretty much. I Think they still both move on though Creating yeah, unless you just create and dude. He's a part of like every goal Look, he didn't touch the ball did he I thought I thought I was him in the middle Curran burns in 99 It's like the same haircut. That's why three NL central a Brennan Davis prospect four of six NL central and a Miguel Cabrera AL central 63 of 150 future first ballots Jumbo bat relic Martin McGuire AL West four of six and Dual patch auto one. Yep has rookie for the NL central Number one of six. I think they might still be able to advance over the Mexico win if they Yeah, see I Don't know though Mexico scores again Then they're and then they're in bad shape Saudi Arabia They're up 1-0. Oh wow, this is good. This is getting dicey. It's getting dicey So would Poland stay? Have a loss Bryce Harper three of five for the NL East Mexico by two. Oh, oh, it's getting real dicey now Prospects Adley six of six. 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That's for sure on side. Yeah, they're flying. Oh Got a hand on it, right? No missed it dude if he puts that in the back of the net Should go low I think oh that was Alvarez the rookie It's a big name in these prospecting these products or prison. I should say Yuri Perez to 99 NL East Pull name out of a hat. Could you go? Dude, I don't even know what I would do if I was Like oh, we're picking him out of hat. Sorry Lucio rookie AL East 2 of 6 and a Kyle Tucker to 150 for the AL West that's like the coin flip for draft picks in the NFL If there's like a tiebreaker for draft picks, I think they just flip a coin That's the decision-making God Full count swatches Steve Carlton number three out of six for the NL East and no NL central Steve Carlton st. Louis NL central And prospect patch auto Jeremy Pena AL West one of five big heart on the back and That's gonna do it for the break. Thanks again everybody beautiful three-color patch on that as well this player worn Thanks everybody more football up next


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And we are live here at Orlando, Sapphire now. This is theCUBE, SiliconANGLE.tv's continuous coverage of Sapphire, where we bring you all the information, the smartest guests. We extract knowledge and share it with you, our community. We're here with Sean Blevins, who's with Opera Solutions. Opera's a company doing some very interesting innovations around data, around big data. One of the hottest trends. You don't hear a lot of that from SAP. You know, last year we were talking about this on theCUBE. Maybe Bill McDermott mentioned it once in his keynote today, or this year about 10 or 15 times, so they're really starting to get it. Sean, welcome to theCUBE. Glad to be here. First time guest, and we were talking off camera a little bit, Opera. I know a little bit from our man, Jeff Kelly. He talks about you guys a lot, pretty hot company, doing some pretty innovative things. You are very familiar with Sapphire, very familiar with SAP, my co-host John Furrier. So, talk a little bit about Opera. Tell us, set it up, set up the discussion here. Who are you guys, and what are you all about? Alright, so Opera is an eight year old company. It has about 650 employees. What's interesting about it is it has 230 data scientists, so compare that to Google with about 300 scientists. Ours are all pointed to the commercial side of the business, and they do these very targeted industries and lines of business. So the whole premise of Opera is to industrialize big data predictive analytics in a way that can be used by the C-suite. As things become more complex, when you add more layers, more data warehouses, the CEO, the CFO, they've had to, at the CMO, they've had to seed all of that complexity down to IT. So they can't actually get just the core agenda items that they have met. And so what we've done in this company is we've built a series of signal hubs that take big data sources outside the four walls of the business, unstructured social information, Yelp, Facebook, Twitter, comments on the websites, government information, and we play that out by industry in a set of signal applications. So if you want to do customer attrition and fading, and you're a CMO, and you want to see exactly how to treat your customers, what products to treat that fading, how do you get a curriculum built where they buy and cross buy? There's one example of that where we used a customer, they deliver food to homes, and they come in with their hand held. So in the span of a year, we took two control groups, one who didn't get that treatment, and that big signal information coming in, and one who did. We drove $100 million of new business to the bottom line in recommendations. When you buy X, you haven't bought this in a while, you should buy Y, and have you thought about this. The other group churned out minus 16%. So that'll give you some idea when you're able to look at beyond just the nice little neat rows of data inside your business, but you're able to get to the outside and see how customers buy, what they prefer. If it's healthcare, it might be revenue leakage and things that are falling out of your business. You look at the line item invoice from a big data perspective, and you see this should have been for Tylenol and not three, so you missed that one, and Tylenol's in a hospital, or what, $1,000, right? There's a lot of revenue that leaks out, and so maybe they're not that much, right? But these solutions allow you to have an industrialized, repeatable play out of the box for big data and predictive analytics. And we announced yesterday, and you may want to talk more about this, we announced our partnership with SAP HANA so that, you know, if you're crunching all this information, before we'd have to batch it, we'd have to stage it across a bunch of servers, now we can literally put both the signal information, we can play more signals, more types of information, and the data science that our scientists produce, the algorithmic models, the blending of those models, all of that goes into a simple environment in HANA, and now we can collapse the time window from what used to take an overnight batch, and maybe even multiple days, to do that into moments, into minutes, or even seconds, depending on what signals and what kind of information you're trying to drive. Okay, so, I do want to talk about HANA, but before we get there, so you're taking this, so today, unless you get the wall to garden approach, right, very complex, and relatively inflexible, you're bringing in all this external data. There's a lot of exhaust, John calls it exhaust, there's a lot of noise in there, so you're saying you filter that down, and get to the golden nuggets, the needles in the haystack, is that right, is that the promise that you're delivering to the customers? That's exactly right, if you look at the business today, I'll give you an example, we were talking to a customer, and for the last several years, their revenue, based on demand forecasting, was stuck, right, 80%, and they have process optimization, they have all, they've done everything right internally, there's nothing that they could do any better from a best practice point of view. The challenge was, they can only see inside their four walls, right, so the options for that are, you buy a tool, you try and find a data scientist, and, or maybe you back a busload of consultants up, and you either train up your person on a tool, and you get this much lift, because the tool does statistical analysis of one kind or another, or you wait two years and you hire 20 people, and at some point after $30 million, then you get the lift you want, but, you know, you've had to wait for that. We're talking about weeks and months here, maybe 16 weeks, until that company can go from 80% accuracy to 90% accuracy, because we bring in all those sources outside the business. So it's very real, it's immediate, and I think the other point is that it's exponential, it's not some incremental 2% thing, it's 30, 40, 50 times what you would expect, because it's just the results are as big as the data. Sean, so last year we covered deeply with big data, and you're hitting a big business benefit, so it's not just like, oh, social media campaigns, this is real business model stuff. That's right. And you're talking about weeks versus, you know, years in ROI in terms of deployment of an actual solution. So it's pretty obvious. Your doors open up pretty fast, I imagine, when you make your pitch to the businesses, but how do you go to the market there? I mean, obviously, on the business model side, I mean, because you have to go right to the top, I imagine it's business line. That's exactly right. Take us through how you engage with a prospect, for example, and what's the value of the pitch. Okay, so in a nutshell, the first thing is it's non-disruptive, right?  None of those approaches are our approach.  You're running $100 billion a year of spend. We will guarantee that we got $30 to $50 million worth of spend we can put back on your bottom line. And the way we do that is we're very focused on non-disruptive as a service coming in. We know where those sources of information are. We know where those categories of spend are. So we focus not on putting the data in nice little refrigerators, right, but streaming it, right? So looking at it as panning for that gold that you mentioned, I think that's exactly right. And so we come in and talk to those C-suite about just those core agenda items that they need. And in the case of some of our customers, you know, we're six months into it, nine months into it, we've done it for someone else, particularly in the areas of spend, fraud, risk, marketing, all those kinds of areas, you know, we find that organizations need to get their media spend right. They need to deal with the friction.  That's exactly right.  It's exactly right. And the external sources are acquired through deals or just open APIs or how do you acquire the data? Oh, that's a great question. So obviously the first step is a privacy and NDA agreement, right? And then for the internal data, and then the external data, we just know where it is. And our scientists really come into two teams, the ones who build the predictive models and the data science machine learning piece, and those who know how to extract the signals. And so extracting the signals is like, you take 50 terabytes across 30 data sources, and that becomes a DNA strand of a terabyte of data. So we're looking for time series over history, behavior, data, and then derived data, basically. Precisely. You got it. That's exactly it. Awesome. I'll give you an example of how HANA drives the change around. And we're showing this in the demo floor over here at 228. If you want to go see it, we had a wealth management customer. This customer had a very specific need. When an analyst makes a recommendation, they had over, you know, almost $2 trillion in assets in inventory. They had 30,000 financial advisors, you know, millions of portfolios with all these different assets managed, unmanaged. Every one of those customers had different risk tolerance levels. They had different propensity to take action. They had the success rate of that analyst over time. So when he says buy this asset, and he makes that recommendation, our solution would go in, and it took about two and a half days. But that advisor would come in on that on that next Monday morning, and there would be five, not 500, but five recommendations that said, get out of stock A, and the machine is writing this language. There are no people involved, right? The machine language says get out of asset A, get into asset B, and you can go from a minus 3% in this portfolio to plus 5%. So the customer obviously loved that. The problem was, we could only do that every two, two and a half days. Honest lets us do it every 30 minutes. 40 minutes. That's huge. Maybe maybe I'm being, you know, maybe I'm being a sales guy. Maybe it's 60 minutes. But it sure is. It's multiple times per day. Yeah, so we're less than a day.  Now it's an intraday thing. Let's talk about revenue. So, okay, growth side, how you guys self-funded. I'll say you must be throwing off a lot of cash flow on your end. You've ventured back. How old is the company and how do you guys grow? Companies eight years old. It is cash neutral at this point. We have received a first round series A investment, a minority investment from Silver Lake Sumeru and Excel KKR. They own, you know, they own a minority stake. And the I believe the company was valued somewhere around half a billion dollars. And so much is just taken. Oh, well, I mean, the private company. But did you announce the funding? We did announce the funding. Yes. 84 million. 84 million. No, I think we're about 100 mil this year. No, no, did you announce the did you announce the funding? We announced the 84 million funding. So they brought in 84 million funding and you're about 100 million in revenue. Exactly. Yeah, which is okay. So I gave you some of the idea of the valuation. I don't know why it's so so low. I mean, look at the what's going on. I think it's exposure innovation bubble. It's exposure. And I think the the the linchpin, the watershed event is having Hannah marriage made in heaven. They need that IP to let them go sell the C suite. We need the speed and performance. We and the collapsing of all the complexity. I mean, when you think about having to do this and staging terabytes of data in the cloud and doing it on premise, the ability to collapse that in a single environment and and take a scientific model. So if you go to the floor over here, our demo literally has k-means clustering and algorithms running in memory in Hannah. So we, you know, this is we have a built set of solutions. And then we can pick those up using the open standards in Hannah and just plug them right in. Well, we're big fans of what you're doing because one, we have our own little small scale analytics for our media. We have one data scientist. We use it. I do, but it's free. But what you're doing represents really the future around instrumentation of business and using data to drive real business value. So congratulations. And I love want to follow you guys for forever at this point. I think you'll be very, very successful as you roll out these verticals. The question I have is going forward, okay, in the customer environment, what's the preferred user interface for the customer? Is it dashboard driven? Is it obviously it's a great question? Are you platform as a service? What's your, how does it, how does you look there? You've put your finger on probably the secret to our success, right? Because we do that. You need a new class of interface where it's not, here's really the key. The current paradigm is I have a human being doing some unit of work and I'm going to let a machine do that work. What we've found is it's both, it's man and machine. The analogy that I've heard before in our company is you take a grand chess master, you put him up against Watson, it's 50-50, it could go either way. But you take an average chess player, just someone that knows how to play chess pretty well. You put them with a computer, he beats the grand wizard and the machine every time, right? Because it's human intuition plus what the machine sees. It's getting past our bias, getting past the way we want to see the data, the way we want it to happen. AI has always been grounded in training computers with some reasoning, meta-reasoning, if you will, in this case. And humans are the last mile, as I said. That's it, you got it. So the front end has to be different to say, for example, in our spin intelligence solution, you don't do that data munging or anything else. You walk in, right, and it has like a Facebook post that says, while you were asleep last night, I identified these six areas of spin. Let's go explore them together, because I think there's big money to be made here. So what's the UI? Is it dashboard based? Is it HTML5? Sometimes it's dashboard. It's HTML5. It's those kind of things. Sometimes it's been, you know, it's usually just a runs on iPad, ubiquitous dashboard, but can just as easily be embedded into other solutions, ran on mobile devices. But the API systems are... Yeah. So you're at a self-service? Exactly. We did partner with DataViz, the data visualization folks, so we got that library. And then we've continued to, our focus really going forward is to have front ends that allow you to have these kind of man-plus-machine conversations. I think this is something. Yeah. Is it something... I'll give you an example. Yeah, it's an Apple computer, Apple Fruit. You need to have someone do that. That's it, right, exactly. So at... Have you heard the story about the AIDS virus? That they took a machine and they tried to crack the AIDS virus, the actual gene for it? And they couldn't. And then they gave it to people and they couldn't. Well, they turned it into a game.  They used to fold it and six weeks later they'd crack the AIDS virus. And so it was the machine plus the people that made that happen. So how many customers do you guys have? Because that's really the core adoption, a bottleneck at this point. Just not enough people get it. So I'm sure that's a... That's a great question. I guess it's a sales inhibitor. But so how many customers are you currently working with? Well, the heritage of this company is interesting. Right now it's Fortune 50, very large wealth management, credit card issuers, retailers, and as you can imagine, this is a, you know, you walk into the C-suite, this is a competitive advantage. Right? So they're like, okay, well we, for the next six months we don't want you to talk to our competition. Right? So yeah, you got it, exactly. Add a couple zeros to the back of that one. Exactly. And so, but I guess the key here is we're starting with HANA, we believe that there's an opportunity to bring this to market at a certain volume. And do these, do these signal-based applications. And you're like 10 customers, 100,000? Oh, I would say at this point for different solutions we have 15 wealth management customers for our Mobius solution. We have about 130 for what we call our BIQ product, which is the spend analysis piece that SIP is now the executive dashboard for. You know, but it's, I would say it's somewhere around that. We're definitely at the stage right now where I think it's going to explode as people understand the paradigm shift. We're talking hundreds of customers, right? Yeah, about less than 200.  It's going to explode. You kind of, you're on that line and all of a sudden it kicks up and goes vertical when the growth comes in, when people kind of rock this. When you go in these conversations, what's the critical success, what's the critical, what is the biggest sales inhibitor for you? Put aside that they don't get it, but like when you go in and you say, okay, I can do X for you. Right. I can do kinds of instrumentation provide this kind of real value. Do they think like almost too good to be true? Is it like I want to do a proof of concept? Where do you kind of, great question, get that point there? Well, if it's, if it's an executive, if it's somebody who has walked into a boardroom and said I'll deliver this result, they're already drowning in information. They've got report after report, but they can't make decisions based on fact beyond what's in those reports. Right. And so a lot of times it's about, we'll take those information and the first step was we'll bring back that set of signals and we'll say, here's 45 ways of looking at your business you've never been able to see before.  And usually they have a very discrete set of, these are the goals for the company and we want to go that way. But it starts usually with getting their information and extracting that signal so that they can feel comfortable that we can do what we say we can do. Sean, we're getting the break signal here. We could also go on and do a whole day with you. We love your business. We love what you're doing. We think you're way ahead of the curve right now. Thank you. Congratulations with the HANA deal. In terms of performance, I think Dave, we should dig into that a little bit later, but breakthrough days. Sean, I love the enthusiasm. You were with SAP. I was. And you've popped up for this opportunity. SAP is a great company, a great place to be right now. Seven years of SAP running competitive sales and going up against our competitors. You heard lots of comments from Haas Saturday so there was a lot of fun times there. And I just think that this is a new class of impact to customers. And it was worth it was, you know, I think coming back here and seeing HANA working with our solutions is the perfect is the perfect marriage, the perfect way to go to market together. Well, congratulations. Good luck on your new entrepreneurial venture.  Great customer base to build off of and great revenue run rate. Congratulations. Thank you, sir. Thanks for coming inside the Cube. Thank you, Sean. This is a special Cube coverage. A big day of meets business value. We'll be right back with more coverage from Sapphire now inside the Cube right after this short break.


నినితి నినినిన౿షి మామిఌటిదం నినోత్లి పనినివరిమం సనిత్త్త్నినిం నిమరఱలమపినినీ టాత్మానిమి నిమాటి వతైలినీ లాపనావరికూ మమ� నా solve of points system է simplify ని ని 0, 1, 2, 3, 4, 5, 6, 7, 8. 9 మి మి మా R .. మరallTu మ powiedz మ ట ఠ ఠaut � Strike current on the maximum There are other systems also which we are going to discuss later. Now we come to the categories of the number system. What are the different categories? The first one which I showed you, it was decimal, the most popular one. Then it comes most important one in computer system, this binary. Then octal and hexadecimal. So these are the four categories of number system. So let us discuss one by one, what are they? As we know decimal number system has 10 unique or distinct digits represented by 0, 1, 2, 3, 4, 5, 6, 7, 8 and 9. So the base or radius of the system is 10. For better understanding, let us take an example, suppose it is a decimal number 453. So what we know, this first one is known as unit position. Second one is the 10th position and this third one is the 100th position. How this come? Because the base of this decimal system is 10, this is the base. So from when we come to towards left, it increases by a proportion of 10. So how we write this 450t, actually this is represented as 4 into, as it is a second place, 10 square 5 into 10 to the power 1 plus 3 into 10 to the power 0. So this is the breakup of the system and when we specifically define the decimal number system, so we have to write the base. Now we come to the binary number system. In binary number system, there are only 2 digits that is 0 and 1. Whole system is, number system is defined using 0 and 1 and in this system the base or the radius is only 2. So next we come to the octal number system. It is also a most commonly used positional number system and in octal number system, it uses only 8 digits. It seems similar to the decimal number system but in decimal number system, we had the number from the ranging from 0 to 9. But here only it, it considers only 8 digits and it is up to 7. So what is its base? Its base is 8. If we consider any number and I write here base as 8, this represents an octal number and how we break them. This way we can break them. If we solve this, this will not remain 253. The result will come as decimal number. Now one important thing, in this number 253, it is applicable to the odd number system. In this number 253, here the 2 is phase value and this one that means h square or 8 to the power 1 or 8 to the power 0, whatever it is, this is the known as phase value. This is common to all. So we have discussed till now decimal binary and octal. Now comes the most interesting one that is the hexadecimal number system. Why it is interesting? Because it uses 6th, its number system consists of alphanumeric characters 8 and 8, 9. After 9, it uses the alphabets A, B, C, D, E and up to F. So it consists of total 16 alphanumeric characters to represent a hexadecimal number system. So its base is 16. In other way, as the previous number system, we can break any hexadecimal number to decimal ones. So if we can, if you find a number 2, F, E, such type of number, you should definitely understand that this is a hexadecimal number system and for better precise, we write the base here in this way. So till now, we have discussed about the 4 basic number systems. In the next video, we are going to discuss about the conversion of this number system. That means, how we can convert a decimal to binary or binary to decimal or binary to octal, everything will be discussed in the next video. Thank you.


Hello everyone. Thank you for having us in this session. Maybe during our presentation you can have your tea or coffee. First, I would like to introduce myself. My name is Gita Purnama and this is my wife, Charma Chitravati. We are a volunteer of Denpasar Wikimedia, which is part of Indonesian Wikipedia. We also lecture at Balinese Language Department at Udayana University. So in this session we would like to present to you our presentation. It is about the challenges and rescue the Balinese manuscript or long time on the Balinese Wikisource by creating the metadata, cataloging and digitizing. So I have divided my presentation into five parts. And the first part I'd just like to just giving you a brief information about what is long time, what is Balinese long time actually. Maybe some of you have heard about long time before this is your first time heard about long time. So this is just like a brief information about long time. And then we would like to continue to explain deeper about the background of this project and then move to the advantages of this project and then the challenges of the project and the last and the activities to retrieve the manuscript. So first, okay Balinese long time. So what is Balinese long time? Long time is one of the writing material in Bali. So actually long time is not known in Bali but also all around in Indonesia, maybe in Cambodia, India and some part of South Asia. But in Bali we have a different type of long time. And then long time are made from the palm leaf. So in Balinese term we have at least three names for the palm leaf. So long time or long time is a local language, Balinese language means leaves, and time is the type of the palm leaf. So the specific palm leaf or I said the Latin name of the palm leaf is Porasus Flabeli-Lin. So another name in Balinese is Siwalan and also Antal. So Javanese also known this kind of family as Siwalan. So we have same term of long time in Java and Bali, Siwalan. And the last term, Antal, Antal is a specific name only known in Bali. So each part of Bali the people call this palm leaf Antal. So we have three different names in Bali. And then to make long time. There are some stages to make long time from raw material from the palm leaf into writing material. There are some stages. So we started from harvesting the leaves. You can see clearly maybe from the image. The first image is how people in Bali are harvesting the palm leaf. And then the second part of this process is clean or wash the palm leaves, the second image. And then after we clean it and then we boiled the palm leaf with specific ingredients. So there are some specific secret ingredients. Okay, this is a very secret ingredient because every single family, they have their different secret ingredients to make these kind of writing materials. And then after we boiled the palm leaf, then we dry the leaves under the sun. It's like, okay, the fifth image, we dry the palm leaf under the sun. And then the last step is pressing the leaves with specific tools. So the last image is, we called it palm lag bag. So these tools make the leaves flat. So we pressed the leaf. It takes a very long time. So it takes from six months to one years. Press the leaves to get the best result of writing material or long time. Then we jump to the next four points. So this is the tools of writing on long time. So as you can see this one, okay, I would like to do this one. This one is a specific or special knife to writing on palm leaf. We call it Peng Rupa. So it's totally different with another knife. We made this very specific knife only for writing on palm leaf. So besides image, you can see how we handle the knife. It's like that. And then start to scratching the script on the palm leaf. And then the next step, we inked the script with candle nut. So we don't use ink, actually. We burnt the candle nut until it burned and then used the charcoal to ink the script. And the final result of this activity is the last image. So the script will be shown. And in long time it's not only script but also image or illustration. And then, yeah, it's just like a brief information for all of you about long time. So next is the background of this project. Balinese actually kept a lot of long time, especially in their houses. Recently data showed that there are more than 25,000 long time around Balinese which is kept in Balinese houses. So unfortunately the condition of this long time is very bad and all of the long time need to be preserved. And as this movement background is because we believe we Balinese we are proud about our culture and long time kept a lot of valuable things about our culture. Long time kept valuable things such as literary works and then traditional medicine, astrology, such beautiful illustration art just like what I've shown, mantras, everything. So that's why we think it's really important to save our culture or indigenous. And then until we've made this project there are no digital resources for Balinese long time which is integrate long time data into people's houses in Bali. So that's the background of this project. And then the advantages about this project maybe we divided into four, at least four advantages of this project. So we've done to preserving the indigenous heritage. We've done about 602 long time in this project. So this project run start back in 2020 and then finish in 2021. And then because of this project we believe that we're saving the local knowledge and wisdom which are contained in the long time. So the creating integrated metadata for Balinese long time and also this project is the first integrated online data for Balinese long time. And then the most important for us in this project we can increasing the digital resources for Balinese local knowledge. It is useful for researchers who have concern for Balinese language and culture. Maybe in the future it will be useful for the next generation of course of Balinese next generation. And then of course this project has a lot of challenges that we face in this project. As you can see on slide convincing the owner of the manuscript, the bad condition of the long time making the metadata because we've got problem about our internet connection of course. And then how to encourage the young generation, the last challenges. I would like to explain deeply one by one. So convincing the owner of the long time. This is the most important things to do at the first time before we do this project. So convincing the manuscript long time in considered confidential and private. So maybe this is the first time you know about long time. And long time is a secret things for Balinese people. It's a very, very secret things. And this is a very private collection. So in Balinese actually there are two type of long time. Yeah I can say government long time which is kept in the government library. So this kind of long time is not secret. You can access free for free access. But when you want to access the long time from private collection, Balinese people collection, it's really hard to get access. Therefore to access the long time is impossible without any permission from the owner or the priest. Some of Balinese still believe that the owner can be cursed by their ancestor if they open their collection to stranger. So this is a challenging part to convince that you will not curse by your ancestor actually when you open your collection to us. When you join, when you get involved in this project. So this is the most challenging thing that we convince the, when we convince the manuscript owner. And then most of the long time which is kept by the people in Balinese houses, they are owned by a big family member. So if you want to get access, you have to get agreement from all the family member to get approval from all the family member. You have to wait, they have to make a meeting for all the family member. And commonly family's meeting is held every six months or when they held a big ceremony. So it's really challenging with time. As you know when we get grant from Wikimedia for example, there is a limit time. It is impossible to work more than one year for one, repeat grant maybe for example. It's really hard, it's really challenging to get permission, to get access to open this collection. In this project actually we had planned to access 12 private collections yet when we convince the owner half of them rejected our proposal and just a half of them, yeah we got permission from the half of 12. So this is the condition of Balinese long time. Is it clear? Maybe? Yeah. Okay, so the condition of the Balinese long time. This is the collection of the private collection, I mean private collection of Balinese people. This is not a government collection. So the condition, the situation is just like this. The condition long time itself mostly in a very bad condition. As you know long time is a biological material, so it's very easy. They are very vulnerable to rodent by insect, fungi and weather. And the rescue of the mages long time cannot be done. Those restraining the jitising process. So approximately 40% of long time we encountered in this project are the mages. So this is the other challenges of this project, how you can read in complete manuscript. We just try to imagine what is the text before this text. And we need to compare with another manuscript. So we are lucky because we have much, a lot of manuscript with the same title, the same content. And then we can compare it. When we found just like a half page of manuscript, then we can compare it with another that have same content or have same title. So move to the other challenges of this project is creating metadata. So actually we run this movement since 2016 before we joined in Wikipedia. So in 2016 to 2019 we did this movement preserving or creating metadata manually. We just type it on our computer or on laptop and then we have no digital resources to save our data actually. Then 2019 we met with some of Indonesia Wikipedia members and then we start established the Wikimedia then PASAR community and we start to learning how to create a metadata from Wikipedia. And then to 2021 or 2022 actually we proposed the proposal and 2021 we continue starting this project. So the project named Wikilontar project which is focused on creating metadata on Wikipedia platform. So Wikidata, Wikikamons and Wikisource. The other challenging things in this creating metadata is the internet connection at the project site. Some of us manuscript owner lives in a very remote village. So there are no internet connection. So can you imagine it how we input the data on site where there are no connection. A bit challenging thing we have to find a higher place to find a signal and internet and then insert the data and then back again to the site. Because we cannot bring the long time out of the site. We only get permission to input the data on site or you take the photo on site. So the other challenging on creating the metadata. And then the last challenging is how to encourage young generation in Bali to get involved on this movement actually. So actually young generation, we are young generation, we are still young of course. We are still young. And then how to get involved in another young generation to get involved in this project. This is another challenging thing for us in Bali Wikipedia. So in the young generation mindset, long time are old relics. So there are only four older generation. So long time is not for young generation. So we have to change this mindset actually. And then the other mindset of young generation cannot make a living from long time. So long time doesn't have any economic value except you sell it to the collectors. Of course, but we didn't did that things because this is our ancestor cultural heritage. So we want to sell it to the collector even though it has a high value of economic value. So the last is how to the rescue activities. So there are five steps of preserving, digitizing and creating metadata. So the first one is checking the long time condition. So before we preserving the long time or manuscript, we have to check condition one by one, pages by pages. So when we finish check the condition and then we start to preserving the long time. So that image when I was checking the condition of the manuscript and then the other image is how we preserve the long time. We cleaned the surface of the long time from the dust, insect or fungi. We use citronella oil and alcohol. We mix it and also we use candle not cut charcoal to make it dark again. And then the next step is digitizing the long time. Actually in this project we only got permission to digitize the first page and the last page of the long time. Actually the manuscript or long time it has hundreds of pages but we only got permission for the first pages and the last pages. Why? Because in philology maybe you know the first pages is usually contain the identity of the long time. The title who is the writer of the long time and the last pages usually contain the age, the date when the long time was written. And we are using digital camera for digitizing. We don't use scanner machine, no. We only use digital camera. So the next, creating metadata. So this is how we create the metadata of the manuscript. We input the identity of the long time on Wikipedia, all of the identity, the kind of long time and then the site of the long time, the location of the long time and then the title of the long time, the language of the long time and the type of the long time. Everything, the identity about the long time and then we also input the location of the long time as you can see the red dot here. This is Bali's island map and then there are a lot of red dots. So that are the site of the manuscript and the title of the manuscript. Also we upload the images on Wikicamons. So the final result is the map and the data on the right. Next. Okay. Sites we creating metadata, we also retype the text on long time. As you can see, this is a Balinese Wikisource, Wikipustaka. The name of Balinese Wikisource is Wikipustaka. So Pustaka means literary works actually. So the long time image, we retype again with Balinese script and then they are automatically translated into Latin or Romance. So yeah, this is another site advantages of this project. So we've made this script, become easy to read to others. So, okay, I think the next step of this project is cataloging. As in my presentation title, the final result of this project is catalog for Balinese manuscript long time. So working on the design and layout of the long time catalog. And then we uploaded the vinyl catalog on Wikicamons. You can find that link free or download of course. And then the final step is handing over the catalog and some souvenirs from Wikipedia to the owners. Okay. And this is my last page. So if you want to know more about our project, please join and visit our website on banwikisource.org. Actually, we've done more than 602 long time in this project. And this is ongoing project. Next, we hope this can be kind of inspiration movement to others community in Indonesia. Maybe for another community that has a local manuscript. It's like what we've in Bali. So the last page of my presentation is Suksma. Thank you very much for your attention.


Good afternoon. You're all very welcome. My name is Peter Gunning. I'm a member of the Institute and of the future of Europe group here. It's a great pleasure for me personally and an honor as well to introduce a personal friend and an Irishman who has had a terrific stellar career in the administration of the European Union over the years. Ambassador David O'Sullivan, currently the EU's ambassador in Washington. David is a one-time colleague of mine in the Department of Foreign Affairs. In the EU, which he joined back in I think the late 70s, he has fulfilled a very wide range of posts, including in a number of cabinets, notably that of President Prody. He has been director general for trade in the European Commission. He has been secretary general of the Commission, and he has been the chief operating officer of the European External Action Service. And is now, since 2016, the EU's ambassador in Washington, D.C. I'm not going to go through the rest of David's illustrious CV. Many of you will know some of that already. But and his time is relatively short here today. But I will say that he's been a consistent friend and supporter of this Institute for International and European Affairs and has spoken here on a number of occasions. Most recently in I think 2015 and 2016 on both occasions on the state of EU-US relations as they were then. And in light of the circumstances that pertained then, he has a rather different set of circumstances to address now. I see that his talk is entitled Crisis or Opportunity in Transatlantic Relations. I'm not sure whether that's out of a concern for even-handedness or a sense of irrepressible optimism. But it is hard to see the opportunity. But without further ado, I'll hand over to Ambassador Sullivan to give his take on this transatlantic turmoil and what it will become. The usual rules apply. David Sullivan's address is on the record. And Chatham House Rules will apply to the question and answer, which will follow in about 20 minutes time. Thank you. Well, thank you very much, Peter. And ladies and gentlemen, great pleasure to be back here. Indeed, Peter is an old friend and colleague. We live next door to each other in Rue Langeveld. Used to take each other's kids to school, which is the ultimate bonding experience. And I'm delighted to be back with the Institute. I do indeed think this is a remarkable institution in Ireland, performs a remarkable job and I know you're under my cross. You're doing this reflection on EU 27, the future of the EU. I'm always very grateful when people say they're studying the future of the EU because it means they think we have one. So it's deeply, deeply reassuring. The title, I suppose, is the last refuge of all speakers is to say crisis or opportunity. I think it comes from the notion that in Chinese there are two characters, one which is crisis, the other which is opportunity. I recently discovered this isn't true. They're both, they're both crisis. So I think the opportunity perhaps comes more from the optimism of the will. I saw an interview today with Madeleine Albright who said, was asked, was she an optimist or a pessimist? She said, I'm an optimist who worries a lot. So I think that kind of describes where I am these days. I remain optimistic, but I do, I do worry a lot. I think at my last times here, you know, I was able to present a very positive picture of transatlantic relations. We had had a great achievement in the Paris climate change deal. We in 2015, we had done the Iran deal through strong cooperation between the European Union and the United States. And then we were still, you know, sort of optimistic that I think I was relatively cautious that we might conclude a landmark trade deal between the European Union and the United States' T-Tip, perhaps towards the end of the Obama administration. Well, what a difference two years makes. The 2016 election changed all of that. And Katrina Perry is here, and I always give Katrina for more to give her the credit, which you can read about in her book. But she was one of the first people who said to me in the run-up to that election, I think Donald Trump will win. And that was very much against the run of opinion at that time. It was very perspicacious of her to have spotted that from her travels around. And this was indeed a very big shock to the establishment in Washington, certainly, frankly, to most of the diplomatic corps who had confidently predicted the alternative outcome. And of course, since then, the big question has been, what does what will this mean in terms of a change of administration? Is this just another change of administration from Democrat to Republican? Or is this something more dramatic or a more radical shift in policy? The question was, would President Trump be the same as candidate Trump? And for, I think, the last year or so, you know, there's been backwards and forwards on the extent to which there would be a fairly consistent message from the president. But now I think we see very clearly that, in fact, the president has been remarkably consistent with what he says as a candidate. He has withdrawn from the Trans-Pacific Partnership. That was one of the first things he did, signalling already his sort of skepticism about multilateral trade deals. He withdrew from the Paris climate change agreement. He moved the embassy to Jerusalem. He has withdrawn from the Iran nuclear deal. And we are now engaged in a fairly robust conflict over trade. I wouldn't yet call it a trade war, but it's getting there. It's a skirmish, I would say, for the moment, but it has the potential, unfortunately, to escalate. And I think what is perhaps slightly more worrying from a European perspective is that the president has also developed in the last few weeks a fairly consistent narrative of which is fairly critical of Europe. Basically drawing on the standard U.S. criticism, which is that we don't do enough to shoulder our responsibilities for our own security, specifically the defense spending, the 2 percent commitment of NATO members, but more generally the sense that somehow America is being taken advantage of by Europe in this context. And he now links this also to the trade narrative because he sort of says, well, actually, the Europeans had the benefit of the American security umbrella. This enabled them to develop social welfare systems, healthcare systems, and develop a big market behind barriers which were unfair for American exports. I mean, I'm slightly summarizing it, but that's basically the gist of the message. And I think this is, of course, deeply worrying from a European perspective. I mean, as always, some of this criticism is justified. I think on the security spending, we would say, yes, indeed, there is an issue there. I mean, it's a bit more complicated than the way it's sometimes put. But yes, we do not spend as much, or at least we don't get as much value. And Michel Barnier, before he became Mr. Brexit, when he was working as an advisor to Jean-Claude Juncker on defense matters, he produced an excellent paper on European defense, which showed that in fact we are the second largest defense, if you put the 28 budgets together, we're the second largest defense budget in the world. And we've something like 1.4 million people in uniform. The problem is that the output from that expenditure is a fraction of what it is in the United States, about 10 or 15 percent. Our ability to actually put boots on the ground in any given situation is much less than the expenditure would imply, which by the way is, I think, an important response what we're doing through the European Union initiatives in defense and security. So I think there's more sympathy for what is said about the defense expenditure, rather less, I think, on the trade and economic side, because frankly, I don't think that it can be said that the trade deficit, which undoubtedly America has in goods with the European Union, is the result of unfair trading practices. The European Union is one of the most open, one of the low tariff, rule of law economies in the world, as is the United States. There are certainly things which can be criticized about our trading policy, and equally, we find things that we could criticize about the United States. But none of this criticism would lead me to conclude that we're anything other than open and fair traders who benefit a lot from the exchanges we have. So I think that's a rather more complex narrative to discuss, but we have to accept that this is the narrative that's there. This is what the president feels is deeply the case, and this is what's led him to address the issue of steel and aluminium tariffs, and we are now facing something which could be a potentially major escalation, which would be using the national security provision to put tariffs on car and car part imports. Now there will be a very lively debate in the United States about these issues, particularly I think about the auto parts. If you look, for those of you interested, the submission of the U.S. Chamber of General Motors and others, many of them have introduced submissions which are highly critical, saying this would actually be counterproductive, this would be damaging to the U.S. economy. I don't know how this is gonna play out, we will see in the next months. President Yonker will be in Washington on the 25th of July as a follow-up to a first discussion which was held at the G7. I'm sure he will come with Cecilia Mellstrom in a constructive mood, trying to see if there are solutions we can find. We've proposed various options at various stages, a limited trade deal on industrial products only and government procurement. There may be other ideas there about some plurilateral negotiations, but I don't know whether this is going to, at this point, encourage the president to take a different view. I think we certainly engage as constructively as we can, but I do observe also that it's the factual debate. This is not a debate always driven by economic facts and figures, it's also driven by perceptions and by sentiment which we have to, I think, take into account. And I think one of the things that we need to think about in the implications for the transatlantic relationship is whether this is a temporary phenomenon, this is something, you know, President Trump has been elected, he has these views, maybe his views will evolve, maybe he will be a one-term or a two-term president, we don't know. I think it would be a mistake on the European side to think this is only about President Trump and his personality. It is also, there are changing opinions in the United States, a changing view of America's role in the world. Now, this is not necessarily that new, we've had this before if you go back to the post-World War I period when America went through a period of relative isolationism which only changed dramatically with the Second World War and then the embracing of the multilateral trading and finance system which America helped to create. So it's not necessarily something which we haven't seen before but I think there is some shifting of the tectonic plates and I think those of us who are committed to this relationship and who care about the relationship between Europe and the United States, we have to make an effort to try and understand that, not to the point of making concessions that we think are not justified or that are not domestically acceptable to us but I think we have to try and understand that there is a shifting perception that maybe the international system has not always worked to America's benefit. Now, many people in this room I think would say, no, that's clearly not the case. But we have to understand that this is felt by many Americans and they feel that somehow the globalization has left them behind, that the system has not worked very well to their benefit and they appreciate a president who says, I'm going to take a more robust position and I'm going to stand up for America first. Of course, I would argue that even the most forensic assessment of America's national self-interest would show you that Europe is an important partner, an important partner in security and an important partner economically and I think when you look at the numbers and Mark Redmond from the US Chamber is here, I mean, the numbers are mind blowing. The 70% of foreign direct investment in America comes from the European Union. The European Union is the biggest export destination for 45 out of 50 US States and American companies invested in Europe make 100 billion more than the equivalent European companies invested in the United States. So we have a very vibrant and I think actually relatively balanced economic relationship which it would be a shame to put in jeopardy. But having said that, we have to understand that something is happening. There's a shift. We don't yet know and it's far too soon to tell whether this will be a permanent shift in the US perception of the relationship with Europe. The ultimate test will be electoral. It will be the midterm elections. It'll be 2020 when the American people and they're the ones who count in this discussion will make their judgment as to whether they think the policies pursued by President Trump have delivered what they hoped for or whether they take a contrary view and it's probably too soon to tell. What is clear is the president is benefiting from a hugely positive economic situation in the United States and this is, if you like, cushioning to a certain extent people's maybe fear of a trade war because the economy is booming. We'll have to see how this plays out over time and whether that changes. So I think, frankly, we are in for a bumpy year or two in transatlantic relations. I think we have to accept that to be frank as I go through this sort of agenda. I see, as Peter says, not the pessimism but I see more of the problems than I see the solutions and I'm sure we will get to solutions at some point. On the climate change, I'm relatively optimistic because I think America Inc. has bought into the climate change agenda. When I travel around, I talk to governors, I talk to mayors in cities, I talk to business, they've got it. I think the kind of climate change agenda is almost irreversible. The business case is almost irreversible and I think that will continue. America could even exceed its Paris objectives, notwithstanding the fact that they have withdrawn from it. On Iran, I think we're heading into a much more difficult situation and I think particularly the issue of secondary sanctions and how this impacts on Europe's ability to live up to what it believes to be. Its commitments under the Iran deal will be something for further discussion and of course the trade issue is hugely complex and difficult. So I think we need a degree of strategic patience in this. Above all, I think we need to try and maintain a united European front. This is not always easy as we know in the European Union, the best of times and I say that not in any sense of oh we have to be united against but I just think where we are sure of our position and the rightness of what we're doing, we need to stick to it, we need to keep explaining and where we can find compromises, where we can find ways forward, we should be willing to do that and we should be creative about that as I'm sure President Junker and Cecilia Maelstrom will be. But above all else we have to understand that our greatest strength in this discussion is our unity and the fact that we are actually the largest single market in the world. We're a very important trading partner and our views need to be taken into account in a fair way. I nonetheless remain optimistic even if I worry a lot because I do think we have to go back to basics. The transatlantic relationship is absolutely fundamental. I've mentioned the economic and commercial side. I think you can say the same about the security and I know the President feels that Europe is not doing enough but frankly, Europe's security is America's security. America's security is Europe's security and that's just a fact. The only time Article 5 of the NATO Treaty has been invoked was when the Europeans invoked it and the Canadians invoked it after 9-11 and that sense of solidarity was shown then I believe would be shown in other circumstances. European soldiers have fought in Afghanistan and fought in Iraq so it is not as if we have not been a good ally of the United States and for most of our member states, the NATO Alliance is absolutely the cornerstone of their territorial defense strategy and I don't think that's gonna change but I think America also understands that the security of Europe is a vital element in their own security. And we still have the values agenda, democracy, human rights, okay, human rights, America's just left the human rights council in the UN so yeah, maybe we don't quite agree on some human rights issues at the moment but I still think when you travel around America and you talk with the good American people, we do share a huge amount in common in terms of values. So I think and I remain convinced that what unites us is much more important than what divides us but I do think we have to prepare for a fairly difficult period in which that thesis is gonna be tested and tested severely in coming weeks and months over the issues I've described. I think however my overall conclusion is that America is a very diverse country, very divided at the moment. This is part of the issue, I think, very polarized. We need to understand that and above all else, my last word, Peter, has got nothing to do with any of the things I've been talking about but I hope that we can keep the people to people channel of communication open. I worry a little about the fact that also with visas and everything else, people in Europe travel a bit less to the United States, people in Americans come a bit less to Europe. We need always to reinvent this relationship for each successive generation and that requires an effort of will, if I may say so. I mean, this doesn't happen, the Fulbright scholarship, the other achievements which move people across the Atlantic, we need to redouble and increase those efforts to avoid that all of these tensions which are there turn into a greater divide including on the values agenda which I think then we would be in deep trouble. So that's all I want to say by way of introductory remarks but thank you all very much for coming here and this warm indeed sultry afternoon and as I say, like Madeleine Albright, I worry a lot but I remain optimistic. Thank you. Thank you.