The first item of business is Time for Reflection. Our Time for Reflection leader today is Fiona Stewart, creative director, foolproof creative arts. Fiona Stewart Thank you for giving me the opportunity to address you this afternoon. John F. Kennedy said that we must never forget that art is not a form of propaganda, it is a form of truth. I'm a performer, I'm a writer and a director, but above all else I aim to be a truth-teller. Actors in training are often reminded that truth is in the body. The heart and soul of a good performance is about taking a piece of text or an idea and bringing it to life, making it believable to an audience. As a performer I strive to communicate as specifically as possible. As a writer I try to create characters and situations that enable the audience to understand the deeper story running beneath the action. As a director I want to help actors to find ways of portraying character truthfully. The longer I do my job, the more I notice the disconnection that occurs when an actor plays a role untruthfully or glibly and everything within me is shouting, I don't believe you. Uta Hagan describes the process of performing truthfully, thus, thoughts and feelings are suspended in a vacuum unless they instigate and feed the selected actions. It is the character's actions that reveal the character in a play. In other words, there is no point in feeling an emotion unless it is used to prompt the physical action of the performer. People understand who a character is by what they observe them doing and in real life the same is true. I may speak certain words but you will read what I really think and believe by my posture, my gestures, my facial expressions and you will draw your own conclusion as to where truth lies. We are in the season of Advent, a time of waiting and in John's Gospel Jesus is described as Emmanuel, God with us, the divine made real with human skin and bones, full of grace and truth. People understand who a character is by what they observe them doing. Incarnation, the coming of God in human form reminds us that God's character is revealed in his actions. In him we find truth and grace in equal measure demonstrated in his life, in his death and his resurrection. Truth is in the body. In this advent, may you find time to reflect on incarnation and in so doing may you live, speak and act with grace and truth.


Hello everyone, this is the presentation of the talk's paper titled Moi, Multiplication, Operated Encryption with Trudge and Resilience. It's a joint work between Olivier Bronchain, Sébastien Faust, Virginie Lallement, Gregor Leander, Leo Perrin, which also happens to be me, and François Xavier Standard. Cryptographic primitives are not the only one, many many of the modern operations that we rely on are implemented using integrated circuits. These are at the heart of all electronic hardware. These are then manufactured in cutting edge foundries, but these are extremely expensive, extremely expensive. We are talking about billions of dollars, so that most nations do not have any. As a consequence, production is outsourced. So the way it works is that you have the designers of the integrated circuits, which can be any company in the world, that will design their specific circuit that they need, send the specification of these to one of these foundries, and in return get the physical circuits that they need. More than 90% of all integrated circuits are produced by only 13 foundries, so saying that there is some concentration here would be an understatement. Can we mitigate the security risks associated to this concentration at the primitive level? That's the question I'm going to try to answer in this talk. First I'm going to introduce what security risks I'm even talking about, as well as our general approach to solving this problem. Then I'm going to introduce our solution, MOI, giving its specification. Then I will give a bit more details about the cryptographic properties of the components of MOI and why we chose those. And then of course give a quick security analysis of the cypher itself. So why do we care to put it simply? Why is it potentially a problem that we outsourced the production of these integrated circuits? There are several risks that are associated to this. The counterfeiting is one of them. The design of the circuit could be reverse engineered to steal some intellectual property. And there could also be some malicious modifications of the circuit. And we are going to focus on the third one here. It corresponds to what we call hardware atrogyns. So it means that if someone embeds hardware atrogyns in an integrated circuit, for a while it's going to behave normally, but then there's going to be some trigger. And that will cause this circuit to get some damage either in a physical or in a logical way. So what do we mean by triggers? It could be a physical trigger, like some temperature that changes. It could be a specific input. There is a special keyword that you send to the integrated circuit and it changes its behavior. Or it could be just a counter. So after, I don't know, 110, exactly 110 calls to the integrated circuits, it starts misbehaving. And what do we talk about damage or misbehavior? It could simply be that it stops working. Or that it starts revealing secrets via some subliminal channel or some specific outputs. Of course, neither are desirable as designers. And we then need to implement some countermeasures. So that even if there is such a hardware atrogyne, we can mitigate what it can do. So obviously we can try to detect them. So we can just try to spot the presence of a hardware atrogyne in an integrated circuit before putting it in our product. We can use some logic testing. We can do some section analysis. We can do some optical inspection of the device using special microscopes. We can also try to prevent their insertion or their exploitation using split manufacturing, input scrambling. But the problem we have is that none of these methods is foolproof. And furthermore, that all of them are very expensive and time consuming. In this context, in 2016, there was a paper that was accepted at CCS by Zimbowski, Faust and Standard, which presents a method to protect at least the cryptography part of an IC against hardware atrogyne. It relies on multi-party computation techniques. So the idea is that it will not be possible to activate the atrogyne. The trigger will not work because of these MPC techniques. But also we can still get some strong guarantees under reasonable condition that the number of times the device is used is bounded. So we know, for instance, if the circuit has a throughput that is what it is, we know that it will not be used to encrypt the size of the universe in terms of volumes of data. So under such bounds, we can make sure that an adversary will not be able to use a hardware atrogyne. So even if they manage to put one in the chips, they will not be able to use them. The downside of the approach presented in this paper is that it requires a testing phase. So once when you receive your circuits, integrated circuits from the manufacturer, you need to do some testing on them. And there is a huge increase in the overall circuit size and on the computational overheads associated to the evaluation of the encryption functionality. So our idea is instead to try to find the best way to adapt existing ciphers to this model, we're instead going to design a custom tailor-made cipher, which is intended specifically to be used in this context. So some more details about the model that we're considering. What they consider in that paper from 2016 is that there is... A circuit you want to implement, say the AES, later our cipher. And this circuit is transformed into a set of sub-devices. And the idea is that all these sub-devices, when assembled together by a master circuit, do the same as the initial circuit. So you're kind of splitting the computation into several sub-components, sub-devices. We can ask a single manufacturer, potentially malicious, to produce all these sub-devices. So we do not need to have different manufacturers for the different sub-devices. And that's one of the strengths of this model. We do not need a complicated supply chain. We can just have everything come from the same spot. Once we have received these sub-devices, we test them. And then we need to assemble them using a master circuit in which we have some trust. Of course, the idea is that the trusted master circuit is going to be extremely expensive to manufacture because it has to be done basically by you. So it has to be as small as possible to limit the cost. So how does this model help? We can prevent time bomb triggering by testing the sub-devices. So we can ourselves want to receive the IC circuits. So integrated circuits decide that we're going to test one of them 900 times and the other 937, etc. Time bombs are not going to work. We can prevent the use of cheat code activation or any of these special keywords by using techniques from secret sharing and multi-party computation. So the idea is that the input of each sub-devices is going to be scrambled somehow using secret sharing. So each of them is going to receive an input which is statistically independent from the correct one. And then we can avoid leaks by recombining the outputs from each sub-device. And again, it's a trusted master which will do the combination. So we still assume that there is a part of the circuit that we can trust. The game is to make it as small as possible. How then do we build a cipher specifically for this? So the idea, the main bottleneck that we need to overcome is the use of secret sharing. So what we will do is rely only on linear operations. Which sounds weird, but bear with me. So one round of encryption will consist of two operations. L and M, and the key addition. And both L and M are linear. So we can do secret sharing each time very simply. Of course, if your cipher is linear, you have a bit of a problem. So we're going to have that L and M are linear according to different algebraic structures. So in our case, M will be over binary field and L over modular ring. Once we have that, we can have different sub-circuits that will each implement a full encryption. So that's what you have in these boxes here with the dots. Each of the boxes is a sub-circuit. And then each sub-circuit will have several mini-circuits. So these mini-circuits are done via outsourcing.  I'm going to use them to build a first sub-circuit Gamma 1, which will implement a full run of the block cipher. And I will also build another sub-circuit Gamma 2, which will also implement a full run, etc., etc. And then I will use the majority function to actually get my output from this. So again, L and M are implemented using potentially entrusted chips. But there is a master circuit that handles the secret sharing and the recombination in the end. This simplifies greatly the testing phase from this Zimbofsky paper. So in our case, we can test the input-output behavior of the cipher itself and not of each component individually, which is a great simplification. We also reduce the communication complexity by reducing the number of communication rounds between the trusted master and the mini-circuit. And finally, since we rely on linear operation, the secret sharing is going to be very easy. And in fact, we can reuse only two shares instead of three in the original paper, which again reduces the hardware cost. So basically, each mini-circuit will rely on fewer L and M applied in parallel. Ok, so now how do we do that in practice? And that's Moe. So we have decided to build a 128-bit block cipher, where L is going to be a modular multiplication by 3, which we denote A3. M is going to be a multiplication by a big invertible binary matrix, from a random generator. And then a step consists in the following operations. So first you have a key addition, the inverse of the multiplication by 3, big matrix multiplication, key addition, multiplication by 3, big matrix multiplication. You see that we have an extremely simple key schedule with just some round constants. And we claim 127 bits of security, as long as the amount of data that is queried is less than 2 to the 64. Why did we choose these two main operations, multiplication by 3, and multiplication by a big binary matrix? I will let you read the content of the slide. But basically, if we want to get an abelian group, we can use the Cartesian products of rings of dimension P to the power EI. And if we want to have an endorphism over such a group, we are going to have basically matrix multiplications. So in our case, we decide to use two extreme cases. We set P equal to 2. And then we have that the first group we consider is when all the exponents are set to 1. So we only have z over 2z times z over 2z n times. So then we just get the group of invertible matrices over the field f2 to the n. And at the other extreme, we set the case where we don't really have matrix multiplications because we only have one ring in this instance z over 2 to the nz. And that's the two operations we're going to use. And that's why we use these two operations. But we still need to study these operations because we want to have some strong guarantees in terms of security, of course. Using modular multiplication in symmetric ciphers is not our idea. This was already done before, including, in fact, in a cipher called IDEA in 1991. And here on this slide, you can see an overview of all the modular multiplications that have been used to the best of our knowledge in the literature. And you can see that there is some differences in the modular use. So in IDEA, for instance, it's 2 to 16 plus 1. But we also have some who did what we did, which is to have 2 to the n. So you have that in Mars, you have that in Nimbus, in multiswap, in Chabal, also in Insoce Manuk. It's an operation which is not linear in the field. It has good properties of confusion and diffusion for a low cost in software because one multiplication is going to be one instruction. And it will have, as we will see, a high algebraic degree, which is a good property to have in order to prevent integral and algebraic attacks. Why is that? Why is it that the algebraic degree is high? Let's look at alpha equal to 3, because anyway, it's what we're going to use here in Moe. Multiplication by 3, you can say that it's x plus 2x. And 2x in z over 2 to the nz, it's just a shift. So you get this operation. Then you have that yi is obtained using the xor of xi, xi minus 1, and the carry, which we denote mui. The carry then is given by this induction. So the first 2 bits are set to 0. And then it's the majority function of the previous 2 values and the previous carry as well. So this is quadratic function. And as you can see, then, here, this bit is a linear in the sense of f2, function of the input, same for this one. This one will be a quadratic function. But then the next one, the carry will depend also on the previous carry, which is already quadratic, so it will be of degree 3, etc., etc. So overall, the algebraic degree of a3 is equal to n minus 1, which is the maximum for a permutation actually. And since it's the maximum, the inverse will also have the same degree. I hand-waved the proof here. We have a proper one in the full paper if you're interested. Then we turned our sites on the differential properties. So along the way, we have found that if you look at the ddt of multiplication by 3, xor the identity, you get Sierpinski triangles. Which looks cool and which we were quite surprised by. Again, if you're curious about it, I would urge you to read the paper. In terms of security, what's important is this bound. So it's possible to bound all the coefficients in a given line on the ddt using this formula here. So if you take any coefficient, the coefficient at line A and column B, it's smaller than 2 to the n plus 1 minus c of A. What is c of A? I hear you ask. It's the number of changes. So it's a notion we have introduced. It's the quantity defined by this formula. So basically, it's the Hamming weight of this vector c, where c1 of A is the xor of the first two bits and ci is the xor of the bits i and i minus 1. But it's set to 0 if ci minus 1 was already set. So concretely, if you have A which is equal to this value, you have as first two bits 0, 1 here, so that's one change. Then you still have 1, then you still have 1, then you have a 0, so that's another change. And then you have 0, 1, which would be another change. But since you had a change in the previous position, it doesn't count. So for this value of A, cA is equal to 2. So that's the number of changes. What is very nice is that this bound is independent from the output difference B. So if we can say something on the input difference, then we can bound the probability of a differential transition. And that's why we use the inverse of A3, followed by A3, because then this bound from the theorem tells us something in this direction and in this direction. So what we need is an equivalent of the branching number we have for matrices but for changes. As soon as we have that, we will have some bounds. So that's exactly what we did. We introduced the change branch number, which is the equivalent of the branching number before this number of changes. And we have some theoretical and some experimental arguments to compute the change branch number of a random non-linear permutation. First a random non-linear permutation and then we also push this analysis further to look at linear permutation. And in particular, we expect that the random matrix on 128 bits has a change branch number equal to 24. It means that we can reuse the similar style of arguments as we would use with the regular branch number and the differential uniformity and then we can show that any characteristic covering the three operations A3-1M, A3, will have a probability at most of 2 to the minus 22, meaning that four steps will be safe against an attacker with 2 to the 64 data. As for the security analysis for other attacks, there is the simple related key property with probability 1, which explains why we claim 127 bits of security instead of 128. I won't go over the details, but it's a consequence of the key schedule. And for linear attacks as well as for differential attacks, so I won't get into it, we have some very strong experimental arguments. So MOI is defined for 128 bits, but it's general structure Big multiplication by 3 and binary matrix multiplication can be defined for any block size. So what we did is we looked at much smaller versions for 8 bits to 16 bits. And then we plotted what you can see here. So essentially when the quantity that is plotted is under 1, you are safe from linear attacks. I would refer you to the paper if you want the details, I really don't have time for them in this video. But what's important is that as you can see starting from 4 rounds, we are consistently at most at 1. And in fact as n increases we get much further from 1, meaning that in our case since n is equal to 128, we are already very safe. These experiments are very strong in the sense that we actually computed the LAT of the cipher. So there is no assumption about linear trails in particular. This is not the probability of a complete fully specified trade. It's really linear approximation of the cipher. So it's very strong in that sense. For other attacks we of course have results but again I won't have time to go over them. En conclusion, we have proposed a cipher which is tailor made to the CCS model to prevent hardware atrogens from being exploitable and it has better performances than existing ones at the time of publication. Along the way, we have made a comprehensive study of the cryptographic properties of modular multiplication by a constant in particular 3 and we even found some fractals along the way which is always fun. But obviously this cipher has a very simple structure which while it was intended for this specific use case with hardware atrogens could have other applications which we would be very curious to hear about. And with that I will conclude this talk. Thank you for your attention.


Statistics and Excel. Getting a picture. Data and distribution. Got data? Let's get stuck into it with statistics and Excel. First, a word from our sponsor. Well, actually these are just items that we picked from the YouTube Shopping Affiliate Program, but that's actually good for you because these aren't things that were just given to us from some large corporation which we don't even use in exchange for us selling them to you. These are things that we actually researched, purchased and used ourselves. Ugg slippers. I usually walk around my home in just my socks, but I wanted a high quality pair of slippers that didn't have a heel on them so I can slip them on easily, give me a little bit more warmth than just my socks provide and which has a sole on them so I can deal with messes in the home such as spilled liquid or broken glass without getting my socks wet or my feet cut up and the Ugg slippers do a great job with that. I like the quality of the slippers. They feel like they're going to last a long time. They will probably outlast me so I recommend the Ugg slippers. If you would like a commercial free experience, consider subscribing to our website at accountinginstruction.com or accountinginstruction.thinkific.com where we have many different courses. You can purchase one at a time or have a subscription model giving you access to all the courses, courses which are well organized, have other resources like Excel files and PDF files to download and no commercials. Understanding and interpreting data from tables to graphical representation at the heart of statistics is, of course, data. Various entities like governments, businesses, universities and sports enthusiasts collect a wealth of data on a range of subjects. So in other words, no matter who we are, whatever our interests are in, we're usually busy collecting a whole bunch of data about those particular topics in the hope that we can use that data to give us some more information, a better understanding about the topic we're interested in. So this data is often organized into extensive tables and the key challenge lies in making sense of this sea of numbers. So all that data that we're compiling together and these days we have more access to data than ever before, we want to basically put that information into such a format that it can give us some understanding about what we're interested in. So the role of visual representation, here comes the pictures again that we want to be putting in place when we're thinking about our data. So the first rule of statistics, draw a picture. So we want a picture of the data. Why? Visual representations like graphs can reveal patterns, relationships and other important features within data. So we often think that with a statistical analysis, we're just going to have the numbers, the formulas. No, we want to basically see the pictures because that's going to give us an understanding, a more intuitive sense. The picture giving us those thousand plus words about not only the middle point of the data, but also the spread of the data, the shape of the data, the patterns of the data. So for example, they can indicate the distribution of a variable, highlight unexpected outliers or describe an association between two variables. These are often things that we're looking for within the data. We want to know the midpoint of the data. We want to know the distribution of the data. We want to know the general shape of the data. And we would like to possibly know if that data may have a correlation to other points of interest that can help us make a better story or predictions about things. So graphical representations can also serve as an effective communication tool to share the stories embedded within data. And this is the one I know I pointed this out in the prior presentation, but I want to point it out again here because we often have this idea that the people that are actually understanding this fully are understanding it in pure abstract mathematical terms.  So we imagine like an Einstein imagining things in just numbers and just being able to intuit things in numbers and then we take that information and we put it into a pictorial format for us normal people. But that's not generally the case because even Einstein again is quite famous for being able to try to visualize things like falling next to a beam of light and what would that look like, which helped him to kind of then gear where he wanted to go with the math. Now there's a question as to whether the math is driving the visualization or the visualization is driving the math. But I don't believe there's any question that visualization is an important part of the analysis even for the smartest people out there that are trying to glean insights from information. However, of course, we also do have the skill and this gets more into like presenting the information to like management or into a marketing situation or something like that, the skill of taking data and making it into a pictorial format that best expresses the truth about the data. And I want to emphasize here that I want to best express the truth about the data. We also want to be able to know how people would lie about the data not so not so that we can hopefully lie with the data but so that we can see how data can be manipulated in a pictorial fashion as well. Because as we will see, when we start to group the data into things like histograms, it will make a big difference on how large the boxes are in the histograms, for example, or are we going to add the outliers or remove the outliers and things like that as to the shape of the histogram which could lead to different representations of the data. So if someone was trying to mislead, you have a marketing campaign or people have an angle that they're using the data for, which is usually the case, people are often using data to argue their point as opposed to looking at the data objectively in order to find the truth about a particular thing. That's just the way things usually happen, right? So we have to basically be able to look at data and see those areas where they might be using it not properly. Alright, so characterizing distribution. A crucial aspect of understanding data involves characterizing its distribution. So typically we describe a data distribution by one, identifying the general shape, e.g. bell shape by modal, for example, we'll get into more shape types later, but usually we're thinking about a histogram here which looks kind of like a bar chart. We'll show a lot of examples of HIPPS histograms and future presentations, but once we look at that data we can say does it look kind of like the standard bell curve? We'll talk about bell curves in future presentations. Is it skewed? Is there a lot more data? Is there like a tail to the data? So we'll take a look at examples of that. Is it bimodal? Does it have like two humps like a camel? That's kind of a strange phenomenon happening there. That would give us some idea that there's something happening within the data that would help us to explore deeper into it. Now note when we look at like a histogram or just a shape of the data, that's not actually going to tie in exactly to something like a bell shaped curve, for example, but it might approximate a bell shaped curve. So nothing in real life usually is going to be like exactly on a bell shaped curve, but a lot of things will kind of approximate as you do more samples and stuff closer to a bell curve or some curve that we can represent with a function. If we have something that we can represent with a function and we won't always have something we can easily represent with a function, but if we can represent something with a mathematical curve, that could be a great tool because that mathematical curve can help us to do more mathematical analysis about particular items. And the bell shaped is of course the most famous one for a reason because many things tend to fall into that kind of bell shape. So we'll talk more about the bell shape and other types of distributions that we might be able to apply into a curve, a smooth curve to approximate the shape of the histogram and future presentations. So finding the center of the data, so that's one of the key things that we want to do is find the center point of the data, measuring how spread out or concentrated the data is from the center. So once we know what that center point is, that's not all we want to know. One of the big things that being able to pictorially represent data does is help us to get a sense of the distribution around the center point. Are all the data points close to the middle or are they spread out quite largely as there's a lot of spread from the center point? So organizing and summarizing data, statistics aim to effectively organize, describe and summarize data. So obviously that's the point of statistics. We want to take this wealth of data, put it into an organized fashion so that we can glean information from it. This process involves ordering data usefully. In other words, if we just take a look at a data set, if we just measured things every hour or something like that or every day, the data set might be ordered by day, but that's not usually going to be the most useful formatting of the data. One of the things we might do is try to sort the data from lowest to highest results of whatever the data is. That's something that we can easily do in something like Excel. That's why Excel is going to be quite useful for these tools and we'll do that in future presentation. Grouping data efficiently, so if we have a whole lot of data, then we might want to start to compile that data into certain groups so that we can then be able to handle the data in such a way that it's going to give us some information. And sometimes people say, well, we have computers now. The computers can handle massive amounts of data and that's true. The computers can do that, but just like with Einstein pointing his brain to the right direction, we have to tell the computer, we have to have an insight as to what we want to know. We have to be able to query, ask a question to the computer about the data. So to do that, we have to be able to kind of understand what the data is saying so that we can then tell the computer to look for whatever we're looking for within it. Otherwise the computer doesn't know what we're interested in. So summarizing data with single numbers like the mean or median. That's not the only thing we want to do, but clearly that's going to be a key component to be able to use these key numbers, the mean and the median, and then understand to spread around, say the mean. Identify quartiles, so that's another kind of tool that we can use. We can break the number set out into the mean, the median quartiles, creating graphical representations like histograms and box plots. So we'll make some of these in future presentations. Both of these are things that we can do in Excel, Excel being a great tool. So we'll show them in Excel as well as some other outside of Excel so we can see examples of box plots and histograms. The histogram is probably the big one that gives us a better just pictorial representation of the data. So a histogram, for example, is created by dividing the data into disjoint groups and counting the frequency of data items within each group. So this looks similar to a histogram, it looks kind of like a bar graph, but it's going to be a grouping of the data. We'll see a lot of examples of histograms and different shapes of histograms and how they give us a sense of the different shapes in future presentations and then we'll make histograms. So this gives us a sense of the shape of the data, revealing whether it's skewed, bimodal, or symmetrical among other characteristics. So in other words, these are terms that we're going to be using to describe the shape of the data. So once we see the data, we can say is it skewed, meaning is there going to be more data like it doesn't have a tail to it. There seems to tail off on one side of the data. Is it bimodal? Are there two humps like a camel in the data? As it's symmetrical around the center, it looks more similar to like a bell curve type of shape. So these are kind of characteristic terms that we can use to describe the data. So examining relationships, a fundamental part of extracting meaning from data is examining the relationship between two or more variables. For instance, one might look at the correlation between a student's SAT score and their GPA in college. So this is that example where we can say, hey look, I know all the SAT scores, test scores taken before college, and we can say is there a relationship between the SAT scores, and we're not talking about the SAT scores and the people that get into college. We're talking about the SAT scores and the people that are in college, right? So they're in college. They had the SAT scores before going into college. So now we can plot if we wanted to, the relationship between the SAT scores they had and their GPA, the performance they had in college, to see if the SAT scores had an impact. Now you would think that the SAT scores, if they're a measure of intelligence, for example, and there's arguments and debates on this, right, you can do stats on that and whatnot, but you would think that if it was that they would be more likely to do well and have a higher GPA. Now if you plot that out on a, if you plot that out, you're not going to necessarily find a straight connection in that. So then the question is that is that actually true is of course the question. So such relationship can be visualized using scatter plots. So in a scatter plot with each dot representing an individual specific combination of variables. So we can plot basically the GPA against the SAT scores and see if there's a trend. Now, and then we can really see, and statistics is really looking for this kind of statistics is to see if these two things have a correlation. Do they happen kind of in uniform and unison to each other? In this case, we're usually not going to find like a direct correlation, but they seem to move in the same direction with each other is kind of what we're looking for in statistics. Now, notice that the statistics is not going to tell us the question that is the common question we have to ask then is, well, you often hear that correlation does not mean causation. So then the question is, well, if there's a correlation, there could be causation. So then the question is one causing the other, and then we can also get mixed up in terms of which one caused the other one. So we might say that this thing caused the other thing when really the other thing is causing this thing, or it might be a third thing that's causing. So we get into those kind of questions as to why the relationship seems to be there, but the statistics, the scatter plot, and we can get into technical measures of a correlation. And we can see that correlation, and then we have to resist our human instincts of saying anytime there's a correlation, there's got to be some kind of causation, and we might get the causation backwards. And these, again, are things that people often use to mislead.  There seem to be this weird correlation and they assign causation, or they reverse the things that the cause and the effect as if they get it backwards and possibly they may do that on purpose sometimes it seems like. So those are the things that we have to be careful of. We'll talk more about future presentation. So in conclusion, the ultimate goal is to organize, describe, and summarize data sets. To understand data, we often look at its distribution, shape, center, and spread. Those are the things you want to keep in mind. We're trying to understand the data. What are we trying to do with it in statistics? We want to find the distribution, shape, center, and spread. So graphical tools like histograms and box plots, typically we're going to be leaning towards the histogram. But box plots we'll take a look at them as well along with statistical measures like mean, median, quartiles can help summarize data effectively. However, they do not represent all the information and understanding data often falls analyzing various shapes and visualizing associated data via scatter plots or other graphical representations. So remember one of the things that we the pitfalls we need to avoid or be very careful of mindful of is the human tendency to put everything into very tight boxes. We try to categorize people in boxes. We try to categorize books in boxes, philosophies in boxes, religions in these tight little boxes, right? And obviously the boxes cannot possibly be big enough to really capture all the meaning of a person or a philosophy oftentimes. So we need to be careful that we're not honing down too much. And we also need to be careful when people are purposely honing down too much or focusing in on one particular thing, excluding other things. When we're analyzing other people's work and remember statistics is usually used in practice in the real world to try to prove a point or enforce an action that people already want to take. So they're kind of they have a bias in the statistics, which isn't ideal. We have to look for the for where the biases in statistics when the statistics are not being used, you know, properly. So we have to understand how they can be misused to do that. So all of these concepts are quantified quantified and explored in depth in statistical studies. So we'll dive into more of them in future presentations.


Hi everyone, Joe for jasperjasecasebreaks.com, coming at you with the filler for recon basketball. Pick your team three. You're guaranteed a team in this and you're thinking, what's that giveaway all about? Guess what? We took out, so it's a 17 spot filler, but we took out eight spots and we're giving them away to the people who bought spots. That's not too shabby, right? So there will be two different dice rolls. The dice roll for the giveaway spots and the second dice roll will be matching up with the team. And everyone's guaranteed a team. Everyone's guaranteed something. So big thanks to this group right here for getting in on the action. Here's the first dice roll. Let's roll it, randomize it five and a one six times. So I think there's only nine names on this list. So top eight after nine, sorry, top eight of the nine, we're gonna randomize it six times, five and a one. One, one, one, there's one, two, three, four, four. We had troubles with rammed.org last night. Is it the same things happening here? Ah, come on rammed.org. All right, sorry about this. We're gonna have to use the same dice roll, but can we retry now? We'll have to do it again, sorry. Once again, five and a one. One, two, three, four, five and a one, six in final time. All right. So apologies to Ken. Sorry, no extra spot for you, but you're still part of the action. All right, now let's gather everybody's names and then let's attach you to a team. You know what, just to be on the safe side, we're gonna use psychic science, which we used yesterday, it's just a different random number, a different list randomizer that is. Will this dice still work for rammed.org? No, it's not, so let's use live dice. So we'll randomize that list three and a four, seven times. So let's slip back over here, let's gather those names again. So again, seven times, one, two, three, four, five, six and seven in final time. Good luck. After seven, there we go, we got David all the way down to David. There we go. So congrats to David, he bought a lot of spots. He took that gamble, it took the risk and it's paid off this time. Sometimes it doesn't. Hornets, Lakers, Kings, Celtics for you, David. Joshua Pistons-Nix, David with thunder, Pelicans Hawks, Anthony with the Sixers, Kenneth with the Heat, Martin with the Nuggets and the Nets, David with the Rockets and the Blazers, Anthony with the Clippers and David with the Pacers. So there you go gang, thanks for watching, thanks for breaking with us. Coming up in the next video will be the full case break itself. We'll see you for that, jazzpiececasebreaks.com.


Have you hit a wall working with Scratch Clones?  Well, check out this tutorial for advanced Scratch Clones that uses local variables. We'll use a basic gun thread example to help build our understanding. Stick around till the end too, and I'll share with you a basic game that you can make that'll help take your clones to the next level. All this coming your way. Hello world, Surfing Scratcher here, teacher-surfer programmer, and on this channel, I make how-to coding videos for curious learners and educators just like you. If you dig in this content, be consider hitting that subscribe button to stay in the loop. And hey, if you're an educator and you want to join the mailing list, that's where I'll send news and resources straight to your inbox. Link below in the description. All right, let's make some clones. All right, let's make some clones. All right, let's make some clones. I have to do that. All right, for real, let's do it. All right, I'm just in a new Scratch project here, and I've just got three sprites to ret, to ret gun, and a bullet. And the only code that I've got here is a forever loop. And forever, we're pointing the Tourette gun in the direction of the mouse pointer. We're going to get some bullets firing from this Tourette gun. We're going to use these bullets to demonstrate some things that we can do with clones. I'll have a link to this startup project down in the description if you want to start with it. All right, let's start making some clones. What we're going to do is we're going to grab a, when the space key is pressed, when the space key is pressed, go down to the control and we'll grab out a creator clone. Now if I press the space key, you can see it executes, but nothing happens. The reason for that is I've currently got it hidden. So we need to grab the block when we start as a clone. We want to show that clone, and let's test that out. You can see the clones are forming down here at the bottom of the Tourette. That's not exactly what we want. We want to send the clone to the position of the Tourette gun, and then we want to point it in the direction of the mouse. So let's go do that. In the motion category, grab out the go to random position block and change random position to Tourette gun. Then we want to point in the direction of the mouse pointer. If we head over to the stage and we press the space bar, and you can see it's kind of printing some clones here, which is kind of neat. I've created a sun effect here. I just had a little bit of fun and trying to make a sun. Okay, this is no good to just have static balls here. We want them to move across the stage. So to do that, let's head over and grab a repeat until block. We're going to repeat until we're touching not the mouse pointer, but the edge of the stage. And we're going to repeat moving, let's say, five steps. Let's test that out. Now it won't work at the moment because currently these clones here are touching the stage. So if we stop the project and restart it, and we hit some bullets, there we go. And bullets stop. What we can do is delete the clone once it hits there. So if we head back over to the control category, scroll down and click delete this clone. Fire it, and there it goes. So this is a basic implementation of cloning. There's some more advanced stuff that we can do. And that includes sensing. What do I mean by that? That means sensing or looking at all the attributes of the individual clones. To do that, we need to use a combination of global variables and local variables. To help us understand these more visually, let's jump over to the variables category. We're going to click make a variable. You'll notice that we've got the options for all sprites and for this sprite only. We want to link variables specifically to clones and have clones have their own identical attributes. We want to go for this sprite only. This creates what's called a local variable, local to the sprite. I'm going to call this bullet ID and click OK. You'll see here on the stage, we've got a bullet and a colon sign and then bullet ID. You'll see over here in the left code block pane that we've got the bullet ID variable. If I click to write gun, that bullet variable goes. So that just shows you that it belongs specifically to the bullet. You can see here that the value of bullet ID is set to zero. What we're going to do is when we start as a clone, we're going to set the bullet ID to a random number between let's say 10 and 20. Usually you just probably change the ID by one, but to demonstrate this, we're just going to pick a random number between 10 and 20. Okay, then to see this in action, I've just got to say block from the looks category. I've got a join block that's going to join the bullet ID and the value of bullet ID. And I'm just going to grab that block and going to squish it between the show and the set block. Now, if I fire off a bullet, you'll see that the ID is 14.  And then there's 13. But if you look up here in the top corner, the bullet ID is set to zero. So what's going on there? Would you expect this to change? Well, it doesn't because this bullet ID refers to this single sprite here. And all the IDs that you're seeing belong specifically to that clone. Here's our mum or dad, and all of our bullets are like the children. Just to really underscore this, I'm going to make a new variable.  And this time it's going to be for all sprites. I'm going to click okay. Now, when we start as a clone, we're going to set the global ID to that random number. And then we're going to set the bullet ID to that same random number. Now, watch what happens as we shoot some bullets. I'll aim up here at the global ID.  So the ID of the clone was 19, and the ID of global ID is 19. So because it's a global variable, there's only one instance of it. And it is the same instance regardless of which clone we are operating in. Notice that the bullet ID of the sort of mum or dad sprite is still remaining at zero. So what I've gone ahead and done now is drag out a when the green flag is clicked block, and we're going to set the global ID to zero. And rather than setting the global ID to a random number, we're going to change the global ID by one. This is going to help us keep a record of all the clone numbers and their IDs. The reason I'm doing this is to demonstrate how we can access sprites when we click them. So I'm going to disconnect this, delete this clone block, and I'm going to drag out a when the sprite is clicked. And instead of saying the bullet ID when we start as a clone, we're going to say the bullet ID when this sprite is clicked. I'll just scroll across so you can see that. Okay, let's just demo this now. So I'm going to press the green flag. We set global ID to zero, and we will fire a bullet. Bullet hits the stage, we click it, and bullet ID is set to one. Going to fire it again. You'd expect this to be two, and that is cool. If we look up here, global ID is set to three, which is what happens when I fire another bullet. Fire a bullet, and global ID is now seven. So really, this sprite here should be seven. But it's four? Huh, what's going on here? One, two, four. What happens to this sprite? Eight. It looks like it's doubling each time. So what's going on here? Drop a comment down below if you already know what's going on. So the global ID tells us the number of clones that we've made, but we can only see four here. Yeah, we can only see four, but this number here tells us how many are stacked there, so there's actually eight here. There's four there, there's two there, and there's one single clone here. To prove that to you, I'm just going to click these clones here, and you can see I'm dragging them off, and it's getting a little bit unwieldy. The reason for that is every time we hit the space key, we create clones of clones. So there's an issue. We're going to jump over to our variables category. We're going to create a new variable, and we're going to call it isClone with a question mark, and we're going to create it for this sprite only. When the green flag is clicked, we want to set isClone to no. Then when the space key is pressed, we only want to create a clone of ourselves if isClone is set to no. So in our control blocks, if isClone is equal to no. The last thing that we need to do is when we start as a clone, we need to set isClone to anything but no. So we'll just say yes for consistency here. Why does this work? Well, when we start as a clone, the value of isClone for all the clones will be set to yes, which means when we press the space bar, it will not execute the clone code. Let's go get our full bullets back on the stage. And we'll go through and click them now. So there is bullet one, there is bullet two, there is bullet three, and there is bullet four. You see our global ID is set to four as well. So all is well in clone land. So that's great. But you still may have some skepticism.  Well, the reason is that you may want to do something specific on each individual clone. And now that we have an ID, we can refer to it. So let's do something kind of novel. Let's just go into our control category here. We'll go if the bullet ID is equal one. So that's going to be this bullet over here. I'm going to go over to the looks category. And I'm just going to get it changed the color effect by 25. So now when I click this bullet here, it'll change its color. But if I click the other bullets, their colors won't change. So we can specifically refer to a clone. And that's why this is so handy. Using IDs in this way can be particularly useful when we use lists to store the values. So we can store lists for all the expositions. And we can extract an exposition based on the ID of the clone. But I'm going to save that for another video, which you can see the card up in the top corner for right now. All right, it's time for a scratchy question. And I want to check your understanding this week. Share in the comment section down below one situation where you might want to use local variables with clones. Thanks for checking out this tutorial on scratch clones and local variables. If you found it useful, hit that like button. And if you're really digging this content, consider joining the surfing scratcher mailing list. Link below in the description. Down there, you'll also find a link to this funky tea on Redbubble. But hey, until then, I'm off to go find a wave. I'll catch you in the next one.


Hey what's up guys we're here outside of a grocery store here in Los Angeles we're gonna head in and basically go through my weekly shopping list to pick out everything I need for a week's worth of food prep and I'm gonna take you back to my house show you a typical kind of meal prep for the day including breakfast a couple of main meals dinner post workout all of that good stuff from the macros proteins carbs and fats and kind of give you an insight into what I eat on a typical day so let's get in there so here we have it pretty much all the food I need including what I've got at home now we're gonna head back to my kitchen and basically with a typical daily breakdown of all of my meals and kind of show you a little bit about how I make the food and how you can also follow this and turn some pretty ordinary food into some really tasty good food I'll show you the house hi guys so here we are in the kitchen we have all of the food out on the table that I got today and from what I already had my fridge so now I'm basically gonna show what I use and how I prepare it to kind of get my five main meals for the day plus a shake or pre-workout meal so let's get to it there we have it pretty much a full day's worth of my food obviously I'm not gonna eat all of the turkey and all of the fish and everything you see here but what I wanted to do today was kind of show you a general scope of what I eat how I prepare it and really just how easy it is to make something like this look I get that not everyone might have the freedom of time to be able to go and shop for everything and then cook and prep like this but it comes down to this everybody needs to eat and even if you're gonna eat out at a restaurant or cook something as simple as mac and cheese it's still time this whole kind of days worth of eating took about 45 minutes from coming into the kitchens now sat here with all of this food and it really is quite as simple as that you can see with these turkey burgers and mixed in some mushrooms some onion and some black beans as well basically just mashed it all together into a ball and then use some ground up oatmeal with some herbs and spices to then just coat and lightly shallow fry on the pan using coconut oil this is a high protein low carbohydrate great sort of dish that I can add with the yams which something as simple as I just chop them up boil them for 10 minutes and there they are delicious with either a little bit of cinnamon on or just something like salt and pepper vegetables I always want to have vegetables with my main meals with the exception of breakfast and post-workout plenty of fibrous green leafy vegetables as well as salad stuff like kale lettuce spinach celery bark joint anything green and leafy get in keeping your fridge and just try and have at least a serving or two with your main meal breakfast probably my favorite meal of the day you can see why oats good source of complex carbohydrates I have some fruit on there as well with some almond butter some healthy fats with that and then the egg as well the omelette here some cottage cheese egg white still with one yolk in still want to get some of those healthy fats and just improves the taste of the omelette chopped up some onions and put some mixed greens on there really easy and simple to make it taste great and as you've seen I've only been adding some simple ingredients salt pepper cayenne pepper and something like this sort of low sodium Cajun barbecue so sources aren't really bad you certainly pick up some ketchup some mustard they're all natural they have no high fructose corn syrup those are really the no-nos that you need to be looking out for when it comes to food everything class clean simple natural I didn't really buy much organic stuff and don't get into that notion where you think what to eat healthy you only have to buy organic you don't look out for natural look out for no high fructose corn syrup or low sodium you're gonna do just fine the final part of this sort of daily puzzle post work out couple scoops of white protein in there I might have some simple fruit banana even an apple some some nuts as well just some healthy fats but this is about five meals for me breakfast two or three combinations of this the fish as well I coated in the same way that I did the turkey burgers I'm really getting two of these for one meal or two of those for one meal so it's easy to mix and match if I add some rice into the mix I have a lot of different combinations my main meals breakfast post-workout really what it comes down to is I prep for all my meal for the most part of the day and that night I'll cook my my dinner from scratch and it's during that time that it really doesn't take too much extra to cook a few extra ingredients and have your meals already cooked and ready to go that night so that the next morning you've already got your food done so really only cooking at night and if you can afford 45 minutes for an hour at night to cook your dinner as well as prep a lot of your food to eat the next day not only is it a time saver but you're gonna be eating copier with the right nutrition that is gonna fuel your fitness needs I guess so there we have it to find out more about my full daily eating my approach to diet and nutrition as well as supplementation and training head over to supplements world you can see my full athlete profile everything is on there I'm gonna pack this up we're gonna head back to the gym and move on to a bicep and tricep workout so I'll see you guys there


Next question find the point of intersection of a b and cd where a b cd are these given four points so basically these are the two points and Let's say a B C and d You have to find the point of intersection. This is 6 minus 7 0 This is 16 minus 19 minus 4 0 3 minus 6 2 minus 5 10 How do you find the point of intersection when you're dealing with 3d points? Could we find the point of coincident of the unit vectors? How will you find that? Writing one as lambda times another and then finding the unit vectors Here I would tell you one simple thing use the concept of section formula See, let's say the point P which is a point of intersection. Let me name this point as P Let's say this P divides this in the ratio of Lambda is to 1 Okay, and it divides this in the ratio of let's say Beta is to 1 Okay Now if you take the lambda is to one fact the point P will have a position vector of What will be the position vector? the position vector would be 6i minus 7j times lambda and 16i minus 19j minus 4k by lambda plus 1 my lambda looks like 11. So okay, and The same point P will if you take Cd into your picture in the same point P will have a position vector of beta times or 3j minus 6k beta plus 2i minus 5j plus 10k By a beta plus one Okay Since both represent the same thing. Can I just compare their I coordinates j coordinates and k coordinates? So if you compare their I coordinates, it's 6 lambda plus 16 by lambda plus 1 equal to This side you'll have 2 by beta plus 1. Okay, if you compare the j coordinates then Minus 19 sorry Minus 7 lambda minus 19 by lambda plus 1 is equal to j coordinates here would be 3 beta Minus 5 by beta plus 1 if you compare the k coordinates then minus 4 by lambda plus 1 will be equal to 10 beta minus 6 Or what am I doing 6 beta? minus 6 beta plus 10 by beta plus 1. Okay Now one thing that you would have noticed that There's a stock resemblance between this term and this term. Isn't it? This is just this term multiplied with a minus 2 isn't it? correct So can I say minus 2 times minus 7 lambda minus 19 by lambda plus 1 is actually minus 4 by lambda plus 1 What I did I multiplied both side with minus 2 and equated their left-hand sides Can I do that? So lambda plus 1 lambda plus 1 gone. In fact, this and this will go by a factor of 2 Correct.  Let's simplify this So minus 18 and plus 16 will be minus 2 minus 2 by this will be i Plus 21 minus 19 is plus 2 plus 2 by minus 2 is minus j and This will be plus 2k that means 1 comma minus 1 comma 2 would be the coordinates of this point. Is that fine? clear how it works Okay The next concept which we are going to talk about is another operation on vector which we call as scalar Multiplication so when you multiply a vector a with a scalar quantity lambda what happens to this vector now if Lambda is positive There's no change in the direction of it. Okay, but if lambda is negative then the direction of a reverses if mod lambda is greater than 1 that means There is a scale up Scale up in the size and if mod lambda is less than 1 In fact, less than 1 greater than 0 then there's a scale down in the magnitude of lambda a by Factor of mod lambda. Okay. So what is the role of lambda? It just changes the direction if lambda is negative and It gets and the magnitude of it gets multiplied with the magnitude of lambda So if let's say a is this then what is minus half a So let's say a is this vector minus half a would be the same vector half its length But in a negative that in an opposite direction. Okay Because minus sign reverses the direction half means magnitude is half.  Okay, but before I start talk about co-linearity of vectors There's something which I would like to talk about is linear Combination, this is a very important concept which is very challenging to understand in the first go because Many students are not able to relate easily with this concept. So, please all of you I'll listen to this concept very very carefully when you write a vector R as M1 a1 m2 a2 M3 a3 Etc till m and an Okay, then you say where a1 a2 are vectors a1 a2 etc R vectors of course, and I didn't mention that because I've already shown them with a arrow sign on top and M1 m2 etc. These are scalar quantities Okay Then we say that R has been expressed as a linear combination R has been expressed as R is a linear combination of Is a linear combination of A1 a2 till an is this statement clear any any doubt this stage Are you happy with this statement? Yes, sir. Okay. No doubt regarding. What do we call as a linear combination of a certain system of vectors? clear clear, okay fine now Something which we call as linear dependent and linear independent That evolves from the same concept Linearly independent and linearly dependent. What is linearly dependent a set of vectors a1 a2 a3 till an is said to be linearly dependent if If a linear combination of these vectors expressed as a null vector Implies M1 m2 or if you express this as a linear vector Then your m1 m2 till your mn all not zero. Okay, I'll give you an example let's say I Plus j and let's say 3i minus 2j Okay My question is are these vectors linearly dependent? Are these vectors? Linearly dependent. Okay, so my question is are these vectors? Linearly dependent Now in order to know that We'll take this approach will say m1 m2 m3 Let's say this is a null vector. Now if I'm somehow able to show that At least one of them is non-zero. That means all are not zero not all are zero means I Just have to show a non-zero case of m1 m2 m3 at least one or two of them. Okay Then my job will be done that they are linearly dependent If all of them come out to be zero then that is what we say. They are linearly independent Which I'm going to take up next case.  Let's say I call it as J If you're equating it to zero vector means it is saying zero i zero j correct Okay, so if you compare if you compare the coefficients m1 plus m2 plus 3m3 is zero and M2 minus 2m3 is also zero Correct. So if you take any value of m3, let's say if I take m3 value as k M2 will be 2k Okay, and here m1 will be minus m2, which is minus 2k minus 3m3 Which is minus 3k, which is actually minus 5k now if I don't choose k value as zero Okay, of course 00 is a solution, but there can be non-zero values also So if I don't choose k as zero neither my m1 nor my m2 nor my m3 will be zero correct so There exists not not all zero values of m1 m2 m3 says that this can the linear Combination of these three vectors can be expressed as a null vector if 00 is the only possibility then that is what we call as linearly Independent which I am going to take up next But before I go to the next case. Is this clear enough? Could you explain that part again see in plain and simple language if One vector could be expressed in terms of the other two. They are linearly dependent simple as that For example, if I say 3i minus 2j I Can actually create 3i minus 2j by doing this three times i Or let's say two times i Let me take Let me take minus two times i plus j plus 5i So this vector is generated by Some scalar times this vector and some scalar times this vector that means other two vectors some scalar you multiply and Add them to generate of this vector that means they're dependent on each other Okay, and this is how they are dependent Are you getting my point so we say these three vectors become linearly dependent I didn't understand how you found that too and How I found this minus two and this So, yeah, I would have taken x and y and solve for x and y but that would have just taken a bit of time That's why I see what I did was this x i plus j y into i Then I made a two equations three is equal to x plus y and Minus two is equal to x so x is minus two And x is minus two means y is five. That's why I came with minus two and five. Okay, this was an easy case That's why I did not do all these things, but otherwise I would have done it okay, so the meaning of three vectors being linearly dependent is one of them can be expressed as scalar times the other two Scalar times let's say this one and scalar times the other one okay, so If c vector is linearly dependent on a vector and b vector Actually, we don't say linearly dependent on we say all of them are linearly dependent on each other Let me just correct my this this this and this are Linearly dependent then c could be expressed as some scalar times a plus some scalar times b Are you getting it? because if you do this Then at least you have ensured that One of them is a non-zero for example here m3c this m3 clearly here is minus one isn't it If you compare this m3 is clearly your minus one So not all of them are zero Even if these two become zero at least c component will not be zero Okay, unless until you know of course two of them have to be non-zero in this case If a bc are non-zero vectors are you getting my point? So just remember the simple statement If you want to find out Whether three vectors are more than three vectors. That's the n vectors are linearly dependent Then a linear combination of this this is called the linear combination If it is equated to a null vector Then at least some of this m1 m2 m3 should not be zero That is the meaning of all not zero. I think this you will understand is better when I talk about linearly Linearly dependent sorry linearly independent. Let's not talk about linearly independent Now again a set of vectors a1 a2 Till a n are said to be linearly independent a linear combination of these vectors a Linear combination of these vectors if compared to a null vector Will only result into these scalar quantities being all zero Are you getting this point a typical example of a linearly independent set of vectors is ijk These three guys are linearly independent Okay, that means if you do some m1 i M2 j m3 k and equate it to a null vector This can only be possible when your m1 is zero m2 is zero and m3 is zero There's no other way that this can be equated to a null vector Which clearly indicates the fact that these three vectors are independent of each other, right? In other words, you cannot express one in terms of the other two That means you want to express k as let's say some m1 i plus m2 j You will not be able to m1 m2 doesn't exist in this case This is not possible because they are linearly independent of each other K doesn't depend on i and j i doesn't depend on j and k j doesn't depend on i and k are you getting my point What is the meaning of linearly independent and linearly dependent? Yes, sir


Good afternoon. Welcome to this special mark tech session. I have a very special guest today. We'll talk to the exchange for media readers, the pitch readers, the impact readers, or shall I say viewers, the line between a reader and viewers as much. We have become video first, digital first, exchangeformedia.com was started 20 years back. So we started in the digital arena, but moved to print. We have a very, very special guest. Possibly he was into mark tech before it was called mark tech or ad tech. He's been an author, he's been an investor. He was one of the blue-eyed boys in the first dot-com era. He sold his business to another entity, made lots of money, and since then has done many ads. He's advised political leaders. He's worked with them. He's written a book. He continues to use his passion for technology to help businesses. Let me welcome Rajesh Jain, the founder and CEO of Netcorp to this exchange for media mark tech talk. Thank you Rajesh for joining us. I am pleasure Anurag. Thank you very much for inviting me. You know Rajesh, you have reinvented yourself couple of times in the last 30 years. So is there a playbook to reinvention? I think the only playbook is that you've got to build profitable businesses. Otherwise you fail. Sometimes reinvention gets necessitated. But actually when I started Indiaworld, it was because I failed with my first couple of ventures after I returned from the US. Then Indiaworld got sold. So I had to get acquired by Siffy. I had to look for something new and that's how we started Netcore. And Netcore itself has morphed three or four times because that's the only way you survive and thrive. And when you say Netcore has morphed three or four times, can you take the viewers through that? Because every time you would have chosen something that the market needs, so take us through that journey. Sure. So when Netcore when we started, it was basically a mail infrastructure company. So we did mail servers, Linux based mail servers for companies that was about 20 years ago. And then now we didn't grow much for the first seven or eight years. We are very small. I kept creating new things but I could not sell it. And that's when I decided that I have to get a CEO and a CEO to help build the business out. And under their leadership, we've taken, we've sort of grown leaps and bounds. We've had now three CEOs. Kalpit Jain is our third CEO. And the first we started then with essentially enterprise SMS services. This was somewhere around 2007. Then we added email marketing around 2008, 2009. Then we had email API based services like what SendGrid has been offering for the last many years. And then 2014, 15, we added marketing automation, Martek onto the fold. And as we've been growing in India over the last four or five years, he's also expanded outside of India. So in Southeast Asia, Middle East, Africa and the US. So the growth has been both in the product side and in the geography side. Fantastic. Today, businesses are struggling. Big businesses, mid-sized businesses, small businesses. Some of them have been taken down by COVID for no fault of theirs. The sectors have disappeared because there were high contact businesses like hospitality, like airlines, like restaurants. Other businesses are not doing well because the sentiment is weak and everyone is conserving cash. Businesses are looking at performance. If they're deploying one rupee, they possibly want at least one rupee to come out if not two, five, ten. So tell us why is velvet rope marketing? We are so important. And Ken, can you for our viewers define what is velvet rope marketing? Absolutely. So Anurag, essentially the idea comes from the fact that in every business, there are certain set of customers who are disproportionately more valuable than the others. So to paraphrase a quote from Animal Farm says all customers are equal, but some customers are more equal than others. So what you really need to do is to take care of these customers very well. But before you can take care, you need to identify these customers. Who in your entire customer base really is the largest contributors to your revenues and they'll probably be even higher contributors to your profitability. So first is the identification of your quote, best customers. Then how do you create an experience which ensures they don't churn away? Because when a best customer goes, it's a big loss for any business, B2C or B2B. So how do you create what we call the velvet rope experience? Well, use velvet rope marketing to essentially make sure that they stay on. Then comes the long tail of customers. Some among them who have great potential to be best customers. So how do you identify them early? What are the signals that you can read from the data that customers today leave for us, for brands? How can you identify who will be the future best customers? And then apply the same ideas by taking data from the attributes of the best customers and then ensuring that the acquisition of new customers is also optimized. How do you acquire more profitable customers by looking at the look-alikes of the best customers? So in doing all of this, I think what businesses can really do is to do two things which both impact profitability in a big way. First is grow revenue from their best customers. They are the best bets. So you want to be able to grow revenue from them. And second is they can actually cut down advertising costs, marketing costs on the acquisition side. So you're not acquiring the whole universe. You're filtering the types of customers that you want to acquire and then onboard them faster. By doing both of these things, by ensuring that the good customers don't go and future good customers are acquired, future best customers are acquired, I think they can make a huge impact on profitability for the business. Fantastic for laying out that. You know, earlier we in the media, we in the entrepreneurial space sometimes got carried by how much money does one raise. That was the headline. Oh, it's a unicorn because it raised money at a billion dollar valuation, but it was losing 100 million dollars. Tell us how big startups, big e-commerce companies have suddenly moved to a path to profitability. Instead of being unicorns, they want to be profit cons. They want to be profitable unicorns. So tell us how has that mindset happened and how do you see things going forward? See, I think for every business, like you said in the beginning, there is a, there was the pre-COVID world which was there and now there's the post-COVID world. This is the reality. It's not going to go away anytime soon. Consumer behavior is going to be changed quite significantly. You've covered a lot of that in your previous sessions, interactions. Now, I think what really CEOs and CMOs need to look at is that in a world where profits are under pressure, where sort of capital, there are constraints on raising capital. How is it that you can actually create a new path to profitability? And that's where I sort of coined this word profit con. There's a different mindset that a profit con has with a unicorn. And I'll give you sort of the definition what I came up with and the characteristics of what I see as a profit con. The definition in my mind is a company which is profitable, which is private. So they're sort of not public. They are promoter funded, so they are bootstrapped. So they have actually built the business with the profits of the business reinvested. And they have a reasonable valuation. And it's actually possible to build businesses like these. I've done it twice in my life and successfully. Now, there is a mindset difference because the way you approach it.  Yeah, cost per acquisition mattered, but it didn't matter because you had unlimited supply of money. Absolutely. So I think in this scenario, what businesses need to do? I think there are sort of three key differences, which I look at in the mindset between a profit con and a unicorn. I think the first is that in times like these, and we have seen it happen now, a lot of unicorns tend to fire people. Profit cons tend to hire because you got to go against the conventional wisdom. I think this is a great time to build businesses and we'll see this probably a few years down the line. That if you could, if you had the capital to invest right now to actually make the right decisions on growing your team, you'll come out very strong and maybe two, three years time. The second is the thinking about the long term rather than the short term. So, you know, Simon Sinek's new book, The Infinite Game talks about this that business has no finish line. That business is, it's an infinite game that is there, the infinite mindset.  And that's the easiest way to cut costs, staff and marketing costs. There's no one doing that. Instead, we have cash in the bank. We are willing to make the investments. We are saying, okay, what is the type of company we want to be in two to three years time. Now let's work towards building that company by making the right investments today. And the third is what I call anti-fragile versus fragile. So a lot of unicorns, we see, or even a lot of startups who are not profitable are very fragile because once the capital gets turned off, then it's a very difficult option that they face. There's a difficult set of options that they face. Instead, net core through our 20 years plus years in existence, we faced a number of shocks. Okay, sometimes regulatory changes. And most of the times regulatory changes. Technology advancements. Technology advancements, et cetera. We are to borrow the phrase from Nasim Taleb's book, anti-fragile. So the things basically, every shock has made us stronger. And that's what ProfiCons basically benefit from because the top team has a longer term view is willing to hire, is willing to make the right investments in growth. I think that's the foundation on which you build profitable businesses. Thank you, Rajesh. At this point, I want to talk to you about the cost of customer acquisition. How do you think that marketing technology can bring down the cost of customer acquisition? We know that, but can you give some example and a case study where the impact was so huge that, you know, it wasn't imagined before? Yeah. Essentially, if you think about what type of customers to acquire. Okay, I think that is the key. Now, how do you decide what type of customers? All customers are not equal. And this is where the idea of velvet rope marketing that I talked about earlier comes in. Now, one of the core pillars of velvet rope marketing is the ability to identify customer lifetime value. So essentially, you are going to spend a certain cost for acquisition, customer acquisition cost. But what is the customer worth? If the cake is going to be more than the lifetime value of the customer, then you're going to lose on that acquisition. So figuring out CLV becomes very important. And what we've actually done is used very cutting edge marketing models to get a forward looking predictive value on what the CLV for every customer is going to be. Now, the advantage of the CLV is it does two things for you. It lets you segment all your customers. So you can find, okay, these are my best customers. These are the rest of the customers. Second is it lets you take attributes of the best customers and say, but how can I generate more such customers? So the first pool available is the rest customer. So you have volume. How do you create value from the volume that you already have? So these are your customers who have acquired. They are not doing too many transactions. What are the methods by which you can actually get them to do additional transactions? And that's where another interesting idea comes in. That of what we call the best customer genome or the customer genome. So I can look at all the attributes of different customers and then say, okay, these are the attributes that are shared among the best customers. So how do I take my other customers and get them towards becoming like the best customers? Then when you look at acquisition, now what you're doing is again the same idea. The high CLV customers, what are the attributes of those customers? And now when I'm looking at acquisition, how do I use that data from the MarTech world for at tech? How do I use the data which is sitting in my CRM or CDP system and use it for acquisition? Because traditionally these two worlds have been largely independent. They work in silos, but if we can cross pollinate data between them, I think companies can reduce their cost of acquisition, which I think is going to become very, very important because like we talked earlier, budgets are going to shrink going forward. And second is they will acquire better customers. They will acquire customers who are going to be more profitable for them. So you have a double benefit by using this at tech MarTech bridge as it were. You know what you're saying is there used to be a Pareto's law, I don't know if you know. Yes. It says that 20% of your customers give you 80% of your revenues of value. Absolutely. In some way, you're also eluding to that in the way you're talking about that there are some customers who are more valuable and how to identify and keep them and how to identify such from the secondary set of customers that you may have. Absolutely. And see that for most businesses and now you talked about case studies, we've done analytics for a large number of companies. And in most companies, we find that 20% customers will have, let's say 50, 60, 70% of revenue. Okay, now, so these customers, now you can use a MarTech platform to really create the right journeys for them, the right campaigns for them. That becomes very critical. So it could be, well, I'll give you a simple example. We all love to watch movies, you know, multiplexes, Inox, PBR, it'll be sometime probably before we go back again there. But many of the multiplexes today still we find the experience is that it's like, you know, that movie 51st dates, you are a new customer for them every time. So the result is that when a good new movie is releasing on a Thursday morning. Okay, you are in the queue with everyone else, the digital queue, trying to book tickets. And if you forget, then you'll end up with probably seeds which you don't get together or you won't get to the right show. Imagine a different world where the brand experience is very different, where the multiplex basically sends you a mail on Thursday morning, which says, Anurag, you are part of our velvet rope marketing program. We know that you like to watch your movies on Saturday evening at 530. These are the seeds that you like. We have reserved X number of seeds for you. We will hold these seeds for the next two hours. This is only exclusively available for you. One click to pay and the seeds are yours. What a different world we are living in. Now, but to make this, they have to collect all the data. They have to act on it. They have to automate their systems. This is the real opportunity for brands today. And when we so it's not just about creating an online presence. How do you collect data at every touch point? How do you now calculate the lifetime value so I can treat Anurag differently from Rajesh? Because Anurag has probably a five times higher CLB than what Rajesh has. That is the opportunity which brands have today, which is absolutely doable. And that's the key for driving greater profitability. I will come to this point. How do you develop the skill set in your colleagues to be able to understand data? How do you re-skill them? How do you make them relevant for this new worldwide marketing scenario? Because the tools may be new. If our base, how we think, how we look at it, are appreciation for technology. So how do you build that culture? Very good question. I think what companies now need to think of is this new emerging world of data engineering, data analytics, data science and AI. So a lot of this does not have to be done by people. There is some role, of course, for then the data cleanup. So let's say you're collecting customer data. First step is how do I collect data at every touch point? Okay.  In those cases, you will not get data about your end customers. But can you put something in the packaging? Maybe there is a QR code. Maybe there is a link which someone can get additional warranty on where the customers have an incentive to tell you who they are. That's the first step. That does not require too much of re-engineering. It's just a mindset change that if I can get data about my customers for the next purchase and for additional upgrade, upsell, cross-sell, I can actually reach out to them directly. Number one. The skill sets which become required here are you need to do some amount of data enrichment. You need to ensure that the data is processed, data is cleaned. So that's sort of one world of data engineering. If you can club it broadly there. Then you need a team which can now, as you collect all this data, it's very hard for you to sift through all of this data, figure out the patterns that is there. This is where you now move to the AI world. There's a lot of machine learning techniques which you can apply. If you have a lot of data, you have a training set that you can give, you train the data, and then it'll start making predictions for you on the forward-looking side. That then gives you ideas on what to expect going forward. That is where you start sort of predicting the future as it were. So these are two skill sets. And the third one I think is just creativity. What is it that you can do with your best customers? We are all customers of different brands. If we just take for a moment and think where we think we are best customers of, what are the experiences that we would like which would delight us? That is where it needs creativity. It needs people, say in a customer success team of a brand who can use marketing technology platforms, who can use ad tech, who can work with agencies and companies to do this. But it's a new skill set which has to be added to the layer of creating the products, selling them and so on. That was the earlier world. Every brand has to go digital, has to think of a direct-to-consumer strategy, and has to collect data. And once you start collecting data, then this whole pipeline of data engineering, velvet rope marketing, et cetera, starts. Thank you Rajesh. As an entrepreneur, as a media platform owner, when you're talking, I'm thinking of how it applies to my own business. So I'll possibly talk to you about it some other day. I want to come to another angle. Today, when we talk of marketing, dealing with three more ways. Well, now you added the velvet in it, but there's voice, there's video, and there's vernacular, which is Indian language. Tell us, how can market take these three realities of Indian language content, because content marketing is actually a voice and a video. To a large extent, to a large extent most, sorry, to a large extent most market platforms are sort of agnostic. See, what they are building is basically, okay, so you think of it as like this, there's a content factory, and then there is a pipe to the customer. The content factory is the one which has to worry about what is the language. So they need to know what is the preferred language that a customer sort of interacts in. I'll give you my own example. GEO for some reason has figured out that I like Hindi, much more than I like English. So most of the messages, I have no idea how, most of the messages that I actually get in Hindi, okay, rather than English. And if they had just analyzed their data saying that Rajesh has never, ever clicked on a Hindi SMS, okay, then they would probably stop sending the messages, but they are probably not doing that. This is where brands need to start looking, start listening to the customer, and that's the job of the content factory team. So I can create some customers more comfortable on voice, some video will become a very important method of actually doing selling. Not many of us will be comfortable going into stores probably for some more time. So let's say you wanted to buy furniture, okay, you'll probably be very comfortable if you do it a video call on Zoom and see what is the furniture that is there. It becomes an interactive call with the store owner because you're hesitant to walk into a store. Now, these are circumstances which the brand owners are not prepared for today. So I think voice to video, sort of the gap that is there, is now getting eliminated. What you earlier did on voice, pre-COVID, lot of our calls in our own company were voice conference calls. Very few of us actually did video calls. If we could not do in person, there were voice bridges. Okay, today it's a Zoom link for everyone. We don't even think twice. Even one-to-one calls are becoming Zoom conversations because we like to see each other as an alternative to that. So the vernacular voice and video really become part of the content factory that is there. And then what brands are setting up through the Mantec platforms, the multiple delivery channels, the journeys, et cetera, is the pipe to reach to the customer. And in there, what you want to be able to do is to do personalization. You want to ensure that the right message goes to the right person at the right time to the right channel. That is where what brands also need to do is to figure out what is the channel my customer, each customer likes to interact on. And therefore, if you are an email person, if I am an SMS person, I will not get an email and you will not get an SMS. Or now WhatsApp is emerging as a channel. There are multiple options which are opening up for brands. Fantastic, Ajay. At this point, I want to come to one key question is that if you had to predict trends for the next 12, 18 months, which means that whatever we are doing right now becomes a habit, opening post three months, corona will be under control. So tell us three trends that you forecast for the next 18 to 36 months. In technology forecasting beyond that, can we have it? That's what I am not asking you about. Yeah, I think if I sort of narrow the question a little bit, so it's in the context of say for marketers and for CEOs as business owners really, or as marketers driving the businesses, what is it that they'd like? I think the number one is going to be a very sharp focus on profitability. I think the first thing to realize is that you said the next 12, 18 months, I think the corona virus backdrop is going to stay with us probably for the next couple of years. Okay, till we have a virus which gives lasting immunity. What do you mean? Pardon? You mean a vaccine. A vaccine, yes. We get a vaccine which gives us immunity for a longer period of time. I think that is, so for businesses, it's very important that the focus on profitability becomes very, very critical, which may not have been there earlier because there was always a way by which capital could be raised. Okay, in an uncertain world, investors are also less likely to invest. So number one, I think is is profitability or focus on profitability. Number two, I think is health becomes very, very paramount. So whether it is health of our own health, employees health, customers health, all of this, I mean, if you are seeing those, all the trends are pointing towards a big shift really in the customer's perception because that is paramount. Things which you always took for granted. Okay, we now hesitate. Should I, the risk level has gone up. Should I, I may want to go to that restaurant, but should I go or not? Should I take that flight or not? Okay, when I am going shopping, what is the distance that I now need to keep between the person in front of me and behind? If that person is not wearing a mask, then what do I do? So all of these things which we never thought about are all coming to the fore in our own customer experiences, which also means that the third trend sort of derives from the second one is that because there is going to be some amount of hesitations in the real world experiences that we are all going to have the requirement to have a digital presence, even for brands who have traditionally not been digital become very, very important. So I'll give you a simple example. There is a store, there is a shop outside my building, which basically is a textile shop, small shop, but fantastic collection of saris and women's garments. Now the way the shop is, well, not more than three people could be in the shop at the same time. Now the challenge for them becomes a first is of course reopening. Second is that small pace I cannot probably accommodate more than one customer. Now, optionally for them also now becomes that can I create a presence digitally, whether it's on Amazon Flipkart and the marketplaces or even mystore.com and therefore reach out to customers whom I otherwise would never have been able to tap into because I was limited by the geographical footprint of the store. So profits, health and digital, I think are going digital sort of direct to customer become that I think are going to be the three big trends and I think they are all going to be sort of irreversible for the near future. Thank you Rajesh, I have enjoyed this conversation. Just before I close this, I'll take one of the audience questions. Kushboo Priya is asking, do you think brands are ready to have marketing professionals in the next six to 12 months across sectors, given brands are facing issues of profitability and revenue in COVID times? I think they will hire the people with the right skill sets. We spoke about that earlier, see a lot of customers. A lot of those skill sets just to reiterate that for the viewers, what are those skill sets? I think the three skill sets were data engineering and understanding of machine learning, AI and ML. And third is creativity, thinking, putting yourself in the shoes of the customer, of the new customer, of the digitally savvy customer and then what is the experience that they would like to have. So I think that type of professionals that companies need to hire are ones who can make them go digital. How do I create a brand presence online? If I have a brand presence, how do I strengthen it? How can you help me collect data at every touch point that I'm having with the customer? And third is that based on all of this data that's coming in, how do I do that pipeline of data engineering, of analytics, of figuring out how to use ideas like velvet rope marketing to improve the entire experience on getting the best customers, identifying the best customers and then acquiring better ones like them. So it's really a new world of data that's coming about. I think marketing professionals who can understand this new emerging world of data analytics, data science, I think will definitely have a big demand and advantage going forward because that's what brands are going to have to do. Thank you, Rajesh. I look forward to reading your book and I hope I interact more and acquire those skills that you talk about. Thank you for talking to Exchange for Media at the Mark Tech Bridge. I hope technology can be the bridge that gets you to get the customers and the profitability that you need in COVID time. Thank you, Rajesh Jain, for being a pioneer in this space and continuing to be in mind. Thank you. Thank you very much. Anurad, great talking with you. Thank you.