When the organizers asked me to give a talk in this seminar, I told them that I have no new results or maybe no old results even in number theory. And I can talk about some general topics in my field of interest in complexity theory. And they said that it would be great or good. So I'll talk about randomness, which is one of my favorite topics. I want to talk about this randomness. It's something that I guess mankind was always interested in. And it's a topic you can talk to almost everyone on the street about lack and chance and unpredictability and informally. But you can, you know, mathematicians studied randomness in variety of forms in probability and statistics and so on. And then physicists are extremely curious about randomness and the meaning of randomness and a lot of scientific interest in randomness. So it's a very, very broad topic. And people have sometimes very strong opinions about randomness and its meaning and so on. So what I want to tell you about today is this computational complexity point of view of randomness. And of course, this is a very general, high-level, non-technical talk. It will be a lot of things you'll see you would have known. Or I would like to stress it's a new conceptual element. So there'll be lots of examples. And I'll tell you when something new is coming of importance. OK, so now it's OK. The plan is, first of all, I'm going to talk only about discrete events. For number theory, this is usually OK. So what's perfect randomness for me? The perfect random event will be a coin toss, something that has the probability of heads and tails equal to half. And if there are many of them, they are independent. So I always show this slide. Which of these two sequences of 20 letters is more probable? I know the answer. They're equally probable. So that is what we would call the uniform distribution or the distribution with full entropy. This would be like an ideal model of randomness, perfect randomness. And for the first 15 minutes or so, I'll just go through examples of different applications, you can call them, or ways in which randomness is used, places it is used. And the main thing in the back of our mind should be, can we really do that? In what do we do if we don't have perfect independence? Can any of them be done without it? And in order to understand this, we'll move to define pseudo-randomness, which roughly speaking, is just a, you can think of collectively deterministic structures that share some properties with random ones, some properties. And we'll talk about different such properties. And that will help us understand that. We'll see many examples. And then I will go back to the basic question of what can we do in a world in which there is no perfect randomness, maybe there is weak randomness or no randomness, and we'll roughly discuss the meaning of this, all pretty informal. Of course, underlying these main messages are formal theorems that prove them, that you can quiz me about that. OK, so let me start with the utility of randomness. And as I said, the back of our mind, you'll see this icon in every slide. You can ask yourself, or maybe you have asked yourself, where are the random bits for this coming from? And we'll discuss that later. OK, so the most basic example that everybody gives to the power of randomness is just sampling. You want to know what will be the fraction of people who will vote red in a particular country. You just, even if it's huge, it's millions of people. If you sample 2,000 at random, but completely at random, a perfect subset of 2,000 at random, then you know that with probability at least 99% of this choice, the percentage of red votes in the sample and the red votes in the population will be within 2% of the actual percentage. And we know it's hugely powerful. First of all, we know that these numbers, 2 and 1%, don't depend on the size of the population. That's basically the law of large numbers. It's a very powerful way of using randomness. And of course, the main point is that if we didn't have randomness here, it's not clear how we would get even an estimate up to 2% without asking 98% of the population what they think. So that's a very, very strong demonstration. And I want to move to less obvious ones. Here's one. I give you this portion of the plane, the proportion of the grid. And I ask you, in how many ways can you tile it with dominoes? You don't have to look at the colors here. Just in how many ways can you tile this region with dominoes? Can be this rectangular region. It could be any other region. And this is a basic question, the basic counting question. Of course, it doesn't come out of nowhere. I mean, it's picturesque, but it's also important. It's got a dimer problem in physics. And for diatomic molecules, it captures thermodynamic properties of that matter. You can understand from it free energy, phase transitions, and stuff like that. So it's also an important problem. But the main problem is that there can be, even if the region is small, it's 1,000 by 1,000, the number of such configurations can be used. And here's a very important result from about 20 years ago, Jerome Cichlial Vigoda. They found a way to approximately count the number of dominoes tiling in any region. In fact, it's more general. It's the number of matchings in any graph. It doesn't have to be planar. The type of algorithm, I'm not going to describe algorithm, but the nature of it is, some people know, it's what's called the Monte Carlo method. The number of matchings can be exponential in the size of the region, so the size of the graph. So you cannot just write it down or look at all of them. There's a sort of Markov chain analyzing this. And anyway, figuring out that this Markov chain converges quickly is highly non-trivial. And that gives a probabilistic algorithm for this problem, for approximately counting the number. We know that counting exactly the number is a difficult problem, and I will not go into this. So this approximation problem can be done fast probabilistically, but the best deterministic algorithm known requires exponential timing. We can do really better than just trying all possibilities and adding them up. So I want to stress that this is the best known deterministic algorithm. There may be faster ones that we haven't discovered yet, and actually we'll see at the end that maybe there is the faster one. Okay, so there are many, many such examples, and this is really the key application that I want to eventually discuss, what can we do of randomness, namely to probabilistic algorithm, understanding the power of randomness in algorithm, in computation. Many examples, many of them, they are all over mathematics in every field of mathematics. In number theory, the problem of finding large primes, just give me a thousand big prime, thousand digits prime. We don't know any deterministic way to do that. We can certify primality quickly by now, but we don't know how to just find a large number, which is prime deterministically. Factoring multivariate polynomials over finite fields, it's another example approximating the volume of convex sets in high dimensions, computing large free eco-efficient of multivariate functions that are given by programs, say. That's okay. In all of these things, in all these cases, and there are many more examples, there are fast probabilistic algorithms, in many cases, they are highly nontrivial. And on the other hand, the best known deterministic algorithm for each of them requires exponential time. So we'd like to know whether there are deterministic ones or not. Sorry, Avi, can I ask, for finding a large prime, why don't you just start, if you wanna find a prime above n, you start at n and then you use, we know the prime is in P, so you check if n is a prime, then check if n plus one is a prime, and by the prime number theorem, you should take your log n steps before you find one. If you believe the prime, no, no, it will not take your log n. I mean, the best we know is root n or something like that. We don't know that there is a prime. Prime number theorem does not guarantee that every interval of log n is a prime. I mean, the gaps between the primes. Okay, so that part of it is still probabilistic, even if I can check the numbers. Yeah, yeah, we don't know. I mean, any advance on any of the expressions would be extremely interesting.  Sorry? PolyMath. Yeah, PolyMath, the projects that Tim Garz and Terry Tau organized some 10 years ago, just focused on this particular problem, finding large primes, and yeah, this was one of the PolyMath failures. I mean, I think it was really interesting what went on there, but anyway, yeah. So, but it's good to just stress that, you know, it's when you say n, log n, we mean like a thousand digits, or a number which starts with a one and then has some a thousand digits after it, and it's prime. Find one deterministically. Yeah. I'll just interrupt for a second. I mean, in practice, you can find large primes very quickly because we know that you don't have to go very far to find a prime. It's... There's no privacy, I want the theorem. I want the theorem. Yeah. Joe, but just as long as... Yeah, there are a lot of heuristics for lots of problems and, you know, but yeah. But anyway, these are just examples. It's just... I mean, of course, every time... There's no surprise that you are interested in this one, but you know, I think it's a good challenge for numbers here. It's a really interesting one. Of course, we have an algorithm that is deterministic, that is conjectured to work very rapidly, but we cannot prove that. Yeah, but this is the only question. I want something provable. I want the theorem. So in all these cases, you know, the probabilistic algorithm will guarantee to find with the anti-unprobability of prime, and it will certify that, and we want to do it without randomness. Can we do it? And let's stop the discussion of this. Sorry, I made progress, but yeah, it's a great problem, this particular one. Okay, good. So these were examples of probabilistic algorithms. I want to give you examples of just some other things where randomness is used of a different nature, several one, several such examples. The submissive computation, randomness can make not just an exponential improvement in time, but an infinite one in that they can make some problems that are provably impossible, become possible with randomness. These are two picturesque examples of fundamental problems in distributed computing. One is a dining philosophers problem. The second is a Byzantine generals problem. As you can tell these guys know how to pick names for their problems. You know, so in both cases, I will not explain this. It's a bit beside the main course of the lecture, but I want to stress that these are fundamental problems in distributed computation, in asynchronous distributed computation. One is about consensus and one is about coordination. And the theorem that there is no deterministic solution, whatever that means, I didn't define it. But if the actors in this distributed system have randomness, then it's possible to solve them.  So here the gap can be arbitrary between randomness and determinism. And that's provable. So we will not talk about the applications here, but another demonstration. Here's another one that goes even beyond the, you can think that randomness helps to do things. It also helps to define things. In game theory, you know, which is supposed to model rational behavior, we try to understand in strategic situation what would actors, players, agents do. Here's a very simple example from a game of Auman called the chicken game, which is often done in game theory represented by a matrix of payoffs for two players. In this, you can imagine that this game represents two sort of a match of drivers driving towards each other on a narrow road. And they can either, you know, each of them can either sway to the side or continue to drive. And like being cautious or aggressive. And the payoffs to each of them are written here where you can see in the bottom right that if they are both aggressive and no one deviates, they both basically die. So, but anyway, you asked what would players do in such a situation? What would be a rational behavior?  You want a strategy for each pair of strategies such that given the other one, you would not change yours. So it's kind of a stability requirement on a solution concept, okay? And it's a very natural one to choose. And of course, Nash didn't get the Nobel Prize just for the definition he also had the theorem is that every game, every strategic game of this nature and no matter how many players, no matter how many strategies, there's always such a solution. This solution concept is universal, it always exists. What is interesting and important for us is that, again, as many of you know, this depends on the ability of players to toss coins. So the strategies they will choose, the strategies that exist are mixed strategies, namely the random strategies. In this particular game, there's a unique such equilibrium, with these numbers, it happens to me to be cautious with probability three quarters and aggressive with probability one quarter. If you know that the other driver behaves like this, you will stay with your choice. Anyway, the main point here is that in, if you require or you don't allow them randomness if they are deterministic, there's no equilibrium. So here's another place, so there are several points here. This is something that exists with randomness and does not exist without randomness. Another is that you can ask yourself, here it's really important, I mean, are people in life, in economic situation, speaking strategies like this and are they using randomness and so on? Anyway, but that's just another example. Example closer to computer science and to life of everybody is cryptography, where randomness is basically, you can't live home without it. Everything depends on it. The very definition of a secret needs randomness. I mean, if you, Shannon already pointed out that formalized using his notion of entropy and so on. A secret is basically as good as the entropy in it. I mean, if you pick your 90s password randomly, then my chances of guessing it is exactly one in 10 to the nine. If on the other hand you pick it as the phone number of one of your friends, then I probably have a better chance of guessing it. It will have much less sense of it. But then of course, randomness is absolutely essential to any notion we define in cryptography, basically everything. And also there, and this is something that's practically used all the time. You can ask yourself what randomness is used there and is it perfect as the definitions, all assume perfect randomness like the published cargo, all assume perfect randomness. Where is it coming from? Yeah, randomness is used in lots of other places, of course, and also there you may want the random bits coming from in the casino and so on. So that's about the set of examples I wanted to talk about. And it's clear that randomness is an essential part of lots of our lives. So now I want to ask the question we asked before. Where are these random bits coming from? Let's say in particular applications that we have seen. And I don't know about you, but when I don't know the answer to a question, and I asked Google, I'm sure you'll do many times. So you feed Google through random bits. What do you get? Well, 10 years ago, when this slide is from, I got four million answers, probably today there will be 400 million answers. But you start at the answer more or less is basically you buy them. You want good random bits, you buy them. At least there are lots of companies that will sell you random bits. And then you can ask yourself, well, where do they take their random bits from? And okay, well, you can read what they say. I mean, some take it from various physical phenomena, which is interesting because they seem somewhat unpredictable. Some take them from other physical phenomena, which are maybe more unpredictable, like quantum behavior. If you wonder about casinos, at least some years ago, they were all using this chip of this company. And you can wonder whether it works or not. I mean, whether what they are doing does generate random bits or random in what sense. Anyway, we would like to understand this question. What is randomness? In fact, what is randomness to any particular purpose that randomness is good? And so now I really want to come to a central definition. And yeah, this time to, that's the next slide is maybe the most important slide of the talk. We are trying to define or understand how to define the randomness. So this is taken from a fundamental paper of Blam and Michali from the early 80s. And here I invite Alina to unmute herself and join me. It's okay, Alina. Alina, are you there? Yes, I'm here. All right, yes. Enthusiastic, as I can see. Okay, so yeah, I'm going to sort of do an experiment. We are trying to understand, we said that the, you know, the post-typical random event is a coin toss, half tails, half heads. So suppose I'm holding a coin and I'm going to toss it in the air. I'll toss a coin and, you know, you should guess just as it leaves my finger. So maybe it's, yeah, we assume we are in the same room, let's say. As in the picture, you're watching me. And, you know, maybe it spends two seconds in the air and you should guess just as it leaves my finger, whether it will be heads on tails when it lands on the floor. Okay, what do you think the probability that you'll predict it correctly will be? Uh-huh. Good, I told you not to worry. Good, okay, so I agree and I think most people will give this answer. Okay, okay, let me ask you another question. Suppose, you know, you're holding a laptop. In fact, I know you have a laptop that you are watching. You can use it. So, you know, as I currently have my finger, you know, you can do whatever you want. What do you think is the probability that you will predict it correctly? Still, huh? Okay, we are in agreement. And again, I think everybody will have the same view. Okay, and so now let's change it again. And suppose I give you, you know, any number of video cameras that are trained on my fingers and they are all connected to a great computer and the supercomputer and it is connected to your laptop. And they are all ready to go just as I toss the coin. What do you think will be the chances that you predict the outcome correctly? Well, depends on how well positioned. It is powerful as you want. It is powerful. I guess closer to one, right? Yeah, wow, I think the right person. Thank you. Yeah, so what's the point about this? Again, I think that, yeah, you can imagine that, you know, with sufficient machinery, the one can calculate, you know, the angular momentum of the coin and the, you know, the air pressure and the humidity in the air and the distance to the floor with perfect accuracy. So that's almost surely you'll get the right answer certainly in far less than two seconds. Okay, so what is the point of this? There's a major point in which deviates from all previous views of randomness. I want to stress that the experiment, me tossing the coin, the random event, suppose the random event, the event anyway we are using didn't change. My behavior didn't change here. I'm tossing the coin, I'm the random generator. I didn't change at all, right? This is the point. So it's what is happening is we are seeing a property of randomness that distinguishes between different observers of the random phenomena, right? Depends, you know, randomness somehow, I mean the ability to predict how much entropy is in the coin depends on the, maybe not the eye of the beholder but the computational power of the beholder, okay? So this is very different than, you know, basically every prior point of view till the 80s. It's an objective definition. Sorry, so it's unlike the previous objective definition of randomness, this is a subjective one. It depends on the observer and it's operative. You know, we look at what the observer would say or what's the probability that the observer will say something about what they see. Okay, so we are going to take now this, you know, point of view and I'm going to study pseudo randomness or notions, various notions of pseudo randomness because they too will depend on observers or on properties of events that are being looked at. So I'm switching now to the second part and if there's an important question or somebody in the chat that Philip saw that is, you know, I need to answer now, I'm happy to. So I just repeat the message is that we are now considering randomness as a property of the observer. How much randomness, how much entropy or what we will call random depends on observers or on properties of what we are seeing. Okay, so let me just start giving examples. So now I'm moving to the second part and this is about pseudo randomness. And this slide is a bit abstract and then there'll be many examples. So don't worry if this is a bit abstract but anyway, it should be appealing still. We are looking now at deterministic structure. So imagine that the universe of objects is objects can be, you know, of any type that can be numbers, graphs, sequences, tables, works and quotes and so on, lots of possibilities but discrete ones. And in it, we want to understand what is the random like behavior or a typical behavior. And it will, I'll call it a property and its property will be just a subset of this universe. And the definition will be really simple. A pseudo random property is any large property, any property, any subset that occupies most of the universe. So think of a hay in a, you know, in a haystack. So property that's held by most elements of the universe. Equivalently, it's like the hay in a haystack or, you know, another way to think about it is if you pick a random element from you, it satisfies this property. So this I will call and for the, you know this example that will follow, this will be for me a pseudo random property. I want to stress that both math and CS have lots of questions about pseudo randomness and we'll see them. The typical task, such question in mathematics is whether a particular object has a pseudo random property which is defined. So we study, you know, nice random like properties of natural, this X zero maybe natural, we'll see examples. In computer science is a different angle. Mostly we look, you know, we want an algorithm to find any element of this, with this property. So basically we are looking for finding objects that have random like properties. We know that most of them are like that. So it's like sort of searching for hay in a haystack. And, you know, as we'll see, I mean this is not an easy task. Despite, you know, the popular culture. We are not looking for needles. We are looking for hay. Okay, so let's see examples and, you know we will get familiar to my terminology here. First I want to say just why, you know, yeah. The popular culture does say that we should easily find hay in a haystack. Why is this not true in the problems we are really interested in? First of all that, you know, we are talking about huge objects and typically we see very, very tiny parts of them. So we may see closer to us maybe needles. And we are also often tend to search, you know sort of using the tools we have but they may not be sufficient. I just want to give two sort of popular examples, you know we may look at this and say, oh, this is green everywhere even though we know most of it is red but we have to cut it first. And another example is that I like a lot is this issue of exoplanets. I mean for up to about 30 years ago, 25 years ago I think people had no evidence that other stars which are of course suns in the universe have any planets. There was no way to see them. You need the particular new techniques. So you could have predicted that, you know no suns have planets except maybe ours but it's not true. Okay, so sometimes we don't, these are huge objects and we may not have the right tools. So let's see, you know, more mathematical example. Let me start with codes because most people are familiar with them. Of course codes are extremely, air quality codes are extremely important to, you know lots of things we use in our daily lives and embedded in all of this storage and communication devices are good error correcting codes.  The basically is that Shannon, you know opened the door for this amazing theory, coding theory was there, first of all, the observation that a random code is a good code. Almost all codes are good codes. I'm not defining formally the space and so on but some of you know and some of you can imagine but regardless, a random code is a good code. And the first thing I want to take from this is to, you know, instilling you that, you know my notation, that means that good codes is a, being a good code is a pseudo random property. Why? Because it's, you know, almost every code is good. Okay, that's one point. And then what do you want to do? If you want to put it in various devices you need to be also useful. You want to be able to describe it, decode and code it decode it and so on. And this is a hard problem. I mean that it took, you know, some 20 years and we are still looking for other even better ones but to find explicit codes, you know efficiently usable codes, this is a hard problem. So this is an example of problem, you know of finding an element of a pseudo random property. Here's a very different element, a very different example. I'm sure you're all familiar with this number. So here's a, you know, a property that you would like maybe this random to satisfy or is believed to be a property of pi. If you look at the number of occurrences of the digit seven in the expansion of pi, it looks like it appears about one-tenth of the time. If you look at any pair of digits, let's say five four point 65 four occurs one-hundredths of the time. If you look at any triple one-thousandths of the time et cetera. So let's demand that. Let's say we are looking at real numbers maybe between zero and one and we want all these properties. It's a countable number of properties. We want them to hold about this real number. And moreover, we want them to hold not only in base 10, but in base seven, in base 15, in base million and one and so on. Still a countable number of properties. And what do we know about this? Well, these are called normal numbers. Properties and numbers satisfying all these properties are called normal. And basically an observation that just because they're only countable in many conditions that random real number, let's say between zero and one is normal with high probability, except with probability zero. So again, this means that normality is a pseudo-random property. It is held by almost every number in the universe we picked. And the mathematician studied this question is, for example, particular natural elements like pi, like root two or E or whatever are normal. And I should stress, these problems are still open. Maybe some of you are working on them. I don't know. Anyway, so the open problems that are naturally formulated in this language of randomness. Give me an object that looks like a random object. Here in the pseudo-random property is normality. So this may be not a central example in mathematics, but I want to say that the problems that can be expressed in exactly this form, is a natural object, pseudo-random, are absolutely fundamental in both math and computer science. And I want to give you another example which you particularly will know number of theories. But in general, there are major problems that concern or can be phrased questions about pseudo-randomness. And usually if I have a room, I'm talking to and can see people like us, young ones, what are major problems? How do you know that the problem is major? Well, one answer is that there's a lot of bounty money offered for it. So here's one set of such problems, the Clay Millenium problems. And one is gone. There's still other ways to make a million dollars. What I want to say is that at least two of these problems are really naturally expressed as questions about pseudo-randomness, our main problem, computer science, and maybe your main problem, the Riemann hypothesis. I will not spend much time about this, first one, Piva Sempi, but say briefly, why is it a pseudo-randomness question? Well, what's a pseudo-random property? Being hard to compute is a pseudo-random property. Random functions are hard to compute. And we ask whether a particular function, let's say the traveling settlement problem or solving systems of quadratic equations over finite fields, your favorite, whether a particular problem, natural problem is also hard to compute. That's basically the Piva Sempi question. So it's a question about pseudo-randomness, whether a natural element has a pseudo-random property. And I won't talk about the second one, I'll elaborate on this, I don't need to read it, but again, we'll see a pseudo-random property and many of you, of course, will know this result. So let's express the Riemann hypothesis as a pseudo-random property of something. And it's something will be, the universe will be a collection of works. So here's a canonical description of the drunkard work. You think that there is a pub in location zero in this street, if you want, and somebody walks into it and after a few beers come out and they start walking up and down the streets, maybe a bit drunk. So takes each step up with probability half and down with probability half. And then you can just run an experiment, just toss random coins and see where the person gets to after a hundred steps. And even if you didn't know math, you would observe immediately that something weird happened that despite the fact that the person took a hundred steps, he or she would be only about 10 paces away from the pub. So something happens, which we know, and of course it's an exercise to prove that after n steps, almost surely, it was very high probability, the distance from the origin will be about root n steps. So it's an exercise, of course. And more important for me, it says something we see as pseudo-random property here. So the property of a work staying close to the origin about root n from the origin is a pseudo-random property because almost every work has this property. Okay, good. So now let's talk about the particular work which you all like, the Mabuse work. So here's the definition, for an integer p of x, the number of distinct prime divisors. And we just defined mu of x, a Mabuse function. It will be zero if the x has a square divisor and otherwise it's, oops, there's a minus one hidden. So it's minus one if it's odd. I wonder if I can fix this so that it will be visible. Here is a minus, okay, sorry about that. Okay, anyway, that's a Mabuse function. And Mabuse function can think of it as described in your work. And it just tells you for every time step, first step, second step, third step, what to do, whether to go up the street or down the street. And let's suppose we allow also that sometimes you stay where you are. So I'm changing a bit the definition of a work but that doesn't matter much. And now what are we wondering about is this particular work is just a description of a work like before, but only it's deterministic, not random. And we ask whether it has this pseudo random property. Does it stay close to the origin? And as probably most of you in this audience know, that's basically the Riemann hypothesis, right? If for every, and this work state around the origin, then the Riemann hypothesis is true and vice versa. So it's another formulation, a very convenient formulation and one that you can tell your family and friends about without zeta functions about what the Riemann hypothesis really is. So, but anyway, very conveniently stated as the question about the particular pseudo random property. Okay, so these are basically the examples. We see one more soon, but I want to get now to the point I was talking about in the beginning. How does this point of view, what did it lead us to understand about applications of randomness, especially to probabilistic algorithms and some major results, both technically and conceptually understanding some fundamental tools about the power limitation of randomness, at least in computational settings. Okay, and actually quite beyond, but I will probably not have time to talk about this. Okay, so let me summarize this on one slide and I'll elaborate a bit on one. So the three possible worlds I want to consider are the following. One is the world in which we have perfect randomness in which we could do everything we want. We just are guaranteed a stream of unbiased, independent quantos. The second world is a world that was maybe alluded to by some of the companies that use physical devices to our physical phenomena to create random bits, also the random bits is a world where we have some unpredictable phenomena. You can think of sunspots or stock market behavior or radioactive decay or quantum phenomena, your favorite. In each of them, we feel there's some entropy. We feel there's some unpredictability if we want to know, you know, of course they are not independent events. If you want to know whether tomorrow it's probably a good guess to say that it will be like today. So they are correlated. And they are not sometimes biased and so on. Of course we can manipulate them and try to use them then in one of these applications. Anyway, this is a world in which there's some entropy in the sequence we see, but it's not necessarily independent unbiased, you know, uniform distribution. And the third world is, you know, the world is deterministic. There is no randomness at all. It's all in our imagination. Everything's predictable. What can we do then? We can just, everything is deterministic. So, you know, we solve these applications in case we have perfect randomness, especially what survives otherwise. And in the next two boxes are really major, these major understanding. So in the first, it turns out that all probabilistic algorithms can be made to function as they did, even in the presence of very weak randomness. This is the subject of what is called Extractor Theory, Randomness Extraction. Long, long sequence of works. And it is all about purifying randomness, taking, you know, weak random sources, sources that are biased and dependent and somehow extracting audience or extracting anyway, their entropy in pure form. They may make to look maybe shorter sequences, but they look like they were perfectly random. So that's one, you know, one theory, one major result, sequence of results. The second one is what happens if the world is deterministic. And in this case, we just don't know how to move an inch without an assumption, but it's okay if the assumption is something that we live with and believe. So I want to assume, I put it in quotes because I don't want to formally define it. Well, you can ask me, but something like P is different than MP. You can satisfy yourself at least in the whole electronic commerce world you live in assumes far less than that, right? It's assumed that factoring or discrete logarithm or some other computational problem in MP is difficult. It's exponentially difficult. And that's the nature of the assumption that we need. Okay, so we assume something like there exists a house function. That's what we assume. And now under this assumption, again, you know, we don't need randomness. All fast algorithms for every practical purposes are all algorithms. Fast, I mean, those we can run in our lifetimes for all efficient algorithms that are probabilistic can be replaced, say every fast probabilistic algorithms has a deterministic counterpart. There's another algorithm, which is deterministic does exactly the same thing, has no error and is not much slower than the original. This is a consequence of another theory which sort of the paradigm underlying it is the hardness versus the randomness paradigm. Namely, you can use house function to generate good enough randomness deterministically. Again, long sequence of works, several decades span and the consequences is this understanding. So in other words, it seems like randomness for the purpose of algorithms is far weaker than may have been suspected initially with their power or the seeming success in the absence of deterministic algorithms for lots of problems we want to solve. Okay, I want to say something about let's see what I'm doing with time. Just give a hint. Okay, good, I started the seven minutes late. So that's good. Take five more minutes of your time. I want to tell you something about this extractor theory and randomness verification. And yeah, we saw that perfect randomness has lots of applications. And supposedly in reality, we have this sort of weak random sources, this bias dependent sources. And I want to assume for this, this is only part of the story. I want to assume that there are several of them and they are independent of each other. So let's say we have three or five or 10 such phenomena that we can tap. They are all weak, they all contain maybe an n-bit sequence will contain only n over 100 bits of entropy or maybe square root n of bits of entropy. But I have several of them. So I want to assume that, you know, sunspots don't affect stock market behavior. Some people believe that it does actually, but let's assume it's not. So I have several sources. And the question is if I have such a, you know, collection of outputs of such phenomena, can I transform it into maybe a shorter sequence of something that looks, you know, exactly or almost exactly, statistically exactly like the uniform distribution. That's a subject of extractor theory. And I want to give you a hint into one result that, you know, uses a pseudorandom property. So we'll connect the two parts of the talk. So I want to talk about one more pseudorandom property. It's a property of tables or matrices of numbers. Okay. So think of a random and by a matrix with entries in them, think you're working more than elements between one element. Okay. And when do I call, what's the property I'm after? The property is that if I look at any sub-matrix, small sub-matrix, okay, then I see lots of numbers. So let's quantify this. Yeah, this is certainly that, you know, what you will see in, you know, in such random examples.  Let's say, you know, if I pick a K by K window, the K rows and K columns, and I look at this K squared entries, I see significantly more than K distinct entries. I mean, you would expect actually it will be close to K squared if K is small enough. And K is small enough, K is maybe up to the point one. So in a random table, you know, surely you'll have this property for every window. I want to stress, I want for every window. It's a sort of random property. It's an easy theorem counting argument to prove that a random matrix will have this property. And here's a question of the same nature. Can such matrices be constructed explicitly? Is there, you know, just give me a matrix of numbers in one to n by n matrix where, of course, the family of matrices, so that every K by K window has at least K to the 1.1 distinct entries, say. Okay, I mean, we mathematicians have lots of examples for the matrices we like to look random in various ways. But I mean, let's start from, you know, every K could give you a matrix, right? I mean, second grader can give you the addition table. So here's the matrix. Let's say we wrap more than the numbers. I didn't do it here, but we wrapped the numbers more than. Is it good? Is this to go random in this sense? Does every small window have few entries? Well, of course not. I mean, we've seen in, if we look along, an arithmetic progression in rows and columns, we will see just a linear number in K by K matrix. We'll see just a linear number of distinct entries. So it's certainly bad. Okay, well, we can go to third grade and think about the multiplication table. And we can ask whether this is good. And of course, the answer is no for the same reason. If we look at the, well, this rex tangle doesn't show it. But if you look at the geometric progression along rows and columns, again, you'll see only a linear number of entries. And this somehow reminds me of a lesson that von Neumann was saying, or I'll tell you that too, von Neumann, that anyone who considers a mathematical method to produce random digits is of course in a state of sin. But anybody who knows the history knows that von Neumann ignored his own advice and of course used to the random generator's base on mathematical means. And in fact, you should do it in this case too. And what I'm going to say is the result that probably many of you are familiar with is that while each of these tables, each of these matrices, the addition one, the multiplication one is bad, their combination is good. Starting with other similarity in the case of integers and then we'll get passed out in the final field case, let's say more than, more p. If you do it more the prime, then every window will be good in one of these tables. So if you look at the union of numbers you see in the window above and window below, there'll be plenty of entries as many as you need. So what does it mean in terms of, so first of all, we found by cheating a bit, by taking two tables, we found elements that have this to the random property which are explicit. In fact, they are very simple and easy to compute. And this was used in one of the early solutions to this problem of extractor randomness. Yeah. Basically, some and products are independent. That's the message from this very important theorem and mixing them increases entropy. So one way to view this extractor is that if you have three such sources, you can add the first two and multiply by the third. You know, they think of them as integers more p. The entropy, total entropy will have increased. The entropy rate will have increased. And therefore you can repeat it several times and approach perfect randomness. So that's one example of an extractor using this particular pseudo random property of tables of matrices. Okay, so let me summarize. Some things we have seen, some important messages. I want to go back to this really important point that randomness is in the eye of the beholder or in the computational power of the beholder. This is a subjective definition and this very important point of view that leads to this understanding. It's also pragmatic in the sense that you can study the use of randomness or the intended use of randomness and maybe by understanding it, you can remove the use of randomness in it. And the impact of this kind of point of view is that you can remove randomness from probabilistic algorithms even in situations where you have very weak random sources or even assuming hardness in a world which is just purely deterministic. I should maybe add here that because you are familiar with this example but may not be familiar with how related it is to the lecture. So you all know that there is a deterministic polynomial time algorithm to certify prime to determine whether a number and integer is prime or not. You also probably know that there are lots of probabilistic algorithms for this before the deterministic one was known and of course the question of doing this sufficiently is extremely odd. Gauss talks about it quite explicitly. Andrew Granville has a beautiful survey in which he sites in the beginning this excerpt from Gauss's discussion on this arithmetic arithmetic where he basically calls to the community. He basically says it's a disgrace. We don't get an efficient algorithm for either primality or factoring. Anyway, the way, I think less people know is that the way that the deterministic algorithm was obtained by Agarwal Kajal and Saxena was the following. They've designed a new probabilistic algorithm for primality testing and then, in fact this was earlier work of Agarwal and other students. And the work of Agarwal Kajal and Saxena is a de-randomization of this algorithm. It's a way of understanding the way the algorithm uses randomness and constructing a specific pseudo-random generator which fools it and therefore makes it deterministic. Okay, so anyway, there are many other probabilistic algorithms for which we know it only under some assumptions like P different than M. Next pseudo-randomness, so what was used this general very high level definition of pseudo-randomness which connects basically, like in the example of primality, connects the property, the pseudo-random property with the application in mind. Maybe the algorithm you want to the randomness. The study of pseudo-randomness captures many basic problems and areas both in math and in computer science. I could have talked another hour on the structure versus randomness paradigm which is very much part of a pseudo-randomness study. It captures things like some, this name I think was given by Tao who gave surveys of examples like this and you know many like some other regularity lemma or it's used in high mass progressions in the primes. It's used in boosting in machine learning and it's used basically the other applications of it in analysis and PDEs and definitely in number theory. And yeah, so it stems from a very pseudo-randomness like approach to a very wide array of problems. And the last thing is that pseudo-random objects that you build for particular purposes in particular extractor for extractor theory or expander graphs for various codes or something turn out to actually there were initially designed for fault tolerant circuits or that's at least what Pinsker had in mind as an application. Often they find many, many, many more applications they used to there, they somehow fundamental turn out many of them are fundamental in other contexts the new thing you must have heard of is high dimensional expander and other example of a pseudo-random object of a mysterious nature that we are trying to understand. And yeah, there are many more. So let me end with this. Thank you.


And in Sports President of the Confederation of African Football Cav Patrice says an inexplicable decision to keep an entry gate closed was responsible for the deadly crush which killed eight people before the African Cup of Nation match in Cameroon capital Yawande in Monday. He therefore called for probing to who closed the gate that led to the tragic incident. News released by the country's health ministry says eight people died including one child. Thirty-eight were injured as fans attempted to enter the stadium where the host Cameroons were playing the Camaros.


exchange for media. A name synonymous with the latest news about the advertising industry in India. The exchange for media group set up in 2000 has the most credible media platforms covering the entire advertising, media and marketing domain with its highly acclaimed digital, print and on-ground assets. The group's flagship news portal exchangeformedia.com reaches over 6 lakh subscribers who are the first to receive breaking news in the industry. The buzzing website not only covers the news but goes beyond the obvious to bring in a fresh point of view. Impact, the weekly news magazine from the group is the most widely read business magazine in the advertising trade within depth analysis and news based features providing perspective to key happenings in the industry. The monthly pitch magazine provides a ringside view of events unfolding in the marketing landscape along with media and advertising. Another monthly magazine Realty Plus is a market leader in repertoire on the real estate industry. Today exchange for media is not only a leading publisher in the domain but owns the IP of more than 50 events spread across Mumbai, Delhi and Bangalore making it a powerhouse of information and knowledge sharing. Exchange for media has curated and launched some of the most successful IPs across marketing, digital, TV, print, radio, mobile, OOH and PR. The impact person of the year, exchange for media conclave, Indian digital marketing awards, tech munch, rich CMO summit, India marketing awards, prime time awards, Indian content marketing awards, golden mics and bar are some of the group's top notch events in addition to niche bespoke events and round tables curated especially for discerning clients. Exchange for media events attract stalwarts as speakers along with the loyal audience comprising of leaders, trendsetters and opinion makers. They are the perfect networking platform for the entire media and advertising industry. No wonder exchange for media group publications and events have high credibility and reach and are the destination of choice for agency, brand and media professionals across the industry. Exchange for media Covering a range of topics, BW Business World events look to create a strong narrative around smart cities, digital India, healthcare, Swachh Bharat, human resource issues, education, banking and finance among others. The world is first changing, best practices are available now on the net because development has become a focus in the whole of politics. BW Business World events provide a speaking platform to the voices that matter. You don't have to be a technologist, you need to understand how technology influences the world. Mahatma Gandhi was a great man, he was the leader of the freedom struggle. We believe that e-governance and IoT will play a very, very important role. BW Business World is an excellent exhibition platform that helps you showcase your services to the right audience. To be a part of our legacy, write into us at partnerattheraidbusinessworld.in. Good evening. Well, let me start again. Ladies and gentlemen, these are the biggest awards, the inaugural edition. So let me once again welcome all of you. Good evening, very warm welcome to all of you here to the inaugural edition of Exchange for Media's Mark Tech India Awards 2021. These awards aim to recognize companies transforming the landscape and possibilities of marketing through technology. The nomination is open for technology companies, technology providers, solution providers, vendors, agencies, brands, consultants, client-side marketers, as well as working towards making more effective. So it is that time.  And now I would request, we have with us Mr. Nawal Ahuja and I would like him to come forward for the inaugural edition of Exchange for Media, Martech Awards. As some of you who are part of the industry might know, this initiative of engaging with the marketing technology industry through marketing martech practices was started by us pre-COVID in 2019. I remember 2019, August was our first initiative on martech. And, you know, we all talk about digital, we all talk about digital advertising technology and how the world around us has become so digital and tech focused. What has also increasingly happened is that over the last three, four years, the roles of marketing brand manager, a CMO and the CTO have kind of become very joined together. I recall in 2019, when we did the first martech conference, a large number of brands said to us that their role these days incomplete without significant intervention from a CTO or a CIO. And many presentations at the conference were joint initiatives of a marketer and a CTO because both of them complete the entire sort of approach towards data analytics and marketing and technology. Marketing has become so dependent upon data and analytics and technology. Today, I was reading a story about how Droga 5, the world famous advertising agency started by David Droga many years back, was acquired by Accenture. And recently, Droga was, David Droga was appointed the CEO of Accenture Interactive, which is many times bigger than Droga 5. And the fact remains that a lot of advertising agency which have been working with brands, with marketers are now also finding themselves being completely sucked into this technology ecosystem of data analytics and the associated tools. So here we are with our first edition of martech awards. Last year, while you know, COVID was in full force, we started our, this is the only initiative which we sort of did regularly under a banner called martech Fridays. We have our colleague and our president Mr. Sunil Kumar to thank for this. It was his idea to create a series of initiatives. And I think that's initiative ran for, if I'm not mistaken, four and a half months, we did almost 20 or 22 martech Friday initiatives where we covered almost every topic under the sun. And as I said, martech has become so synonymous with the, you know, what's happening in the tech space. This year, as you will know, from the awards, we have broadly three categories. One is technology providers, the other is for industry and people and third is for user martech. So pretty much they cover large number of initiatives that are happening in this domain, or the companies and people who are behind these. Some of the categories which got a lot of traction naturally are related to AI, a best use of artificial intelligence, best use of martech or social or influencer marketing, influencer marketing, as we've all been seen, all seen in the last year and a half has really taken off. In fact, as exchange for media, we've launched a fresh event on influencer marketing, which is taking place on the 17th of this month, virtually again, we have a day long conference followed by awards. And in the first year that initiatives managed to get almost 600 plus entries. So that tells you the kind of, kind of money attention that influencer marketing is gaining. The third category that we've seen a lot of traction this year is the, is best use of martech for consumer engagement. So these three broad buckets in which we've got a lot of traction naturally, a lot of work is going in those categories. And as we move into the next year, we will look to expand these categories, cover more work, martech is a area where significant and very rapid expansion is happening in terms of the work that is being done. So every few months you need, you see new kind of work coming in. And our aim is to naturally cover a lot of those initiatives, look at and recognize the best work and best practices that are happening in the domain. So with that, thank you again for joining us. I also like to thank all the jury members who gave us their time to judge the entries, starting especially with Abhay Singhal, the chair of the jury and co-founder of InMobi. He's right now in the US, so he's not joining here live, but I'm told there's a recorded video from him. We kept him awake whole night, almost the entire night on the jury day. Thank you to all the other jury members who spent time and some of whom we are who are here. And my congratulations to all the winners. And I look forward to enjoying the evening and felicitating the winners. Thank you so much, Kyaati, back to you. Thank you so much, Mr. Ahuja. So you've just heard from the co-founder of Exchange for Media Group, Mr. Ahuja, taking you through the first inaugural martech awards, the categories, what we are expecting, and of course, the awards. Ladies and gentlemen, before we move forward, we will take you through a short audio version of the awards and doing process. Can you have the audio version, please? Welcome to the inaugural edition of the Exchange for Media, Martech India Awards 2021, and one of its kind virtual commemorations. The awards aim to recognize companies and people who are transforming the landscape and possibilities of marketing through technology. The nominations were open for technology companies, technology platforms, solution providers, vendors, agencies, brands, consultants, client-side marketers, individuals working towards making Martech more effective. The nominations were received under three broad categories, namely technology providers, industry and people, and use of Martech. The grand jury went through all the shortlisted entries and chose the winner of the gold, silver, and bronze awards in each sub category. The Exchange for Media Martech India Awards 2021 probably presents the jury. The jury was led by Mr. Abhay Singal, co-founder, in-mobi group and CEO, in-mobi marketing club, and it consisted of some of the biggest names of the industry who are respected and have in-depth knowledge and understand the role technology plays to make marketing efforts more effective. Abhishek Joshi, CMO, Emmitt's player, Adrian Watkins, growth and marketing director, ad colony, Amit Rella, director and co-founder, M-Filteret, Ashwin Belode, partner, Deloitte Digital, Netindhapra, India lead, growth markets, Oracle, Swati Bhadacharya, chief communication and brand officer, Bajaj Group, Sai Narayana, CMO, Ayesha Bazar, Tarun Jha, head of marketing, Shkoda, Anil Chilla, chief digital and data officer, Laurel India, Atin Jhabra, VP, marketing, Schneider Electric, Gaurav Mehta, CMPO, Zupin, Nachike Deon, head of sales, India, double verifying, and the amazing Oberoi, director, agency partnership and sales, Google. Exchange for media, Matic India Awards and the duty board congratulates all the winners. Now, ladies and gentlemen, we would like you to address your Mr. Abhishek, the co-founder in Mobi to share his thoughts on and his perspective. We begin by welcoming you all and congratulating all the participants and the winners. Unfortunately, I am unable to be with you live due to time zone challenges. However, I hope to be able to share my excitement and communicate my appreciation through this message. From the chair of the jury, I had the opportunity to witness some really remarkable work all around. Thank you all very much for your efforts in pushing the boundaries of what is possible. Having created in Mobi over the last 14 years, I have had an incredible opportunity to witness and contribute to the phenomenal growth of Matic in the world. Marketing and advertising today sits at a perfect confluence of creativity, technology, and analytics. Matic is truly democratizing what is possible and it is powering the new abilities for the brand to connect to its consumers. In an extremely cluttered and noisy world, it has become even more important to be extremely precise and effective in the way we communicate. Thanks to all the platforms and the solutions that are available to us, marketers can be the true entrepreneurs in reimagining the present and planning the future and bringing the meaningful impact to their businesses. It was an incredible chance for me to witness and see some of these examples up close. Members of the jury and I had an incredibly tough time to select the winners amongst equal and to ensure that every innovation, every effort, and every outcome is duly considered and rightly represented. I would like to thank all members of the jury for supporting me in this extremely difficult process and would again like to congratulate all the winners. In the end, I would like to conclude by congratulating Anurag and the entire Exchange for Media team for organizing this much needed event to highlight the creativity and impactful work in this space. The more we celebrate the winners and their achievement, the better it is for our industry. As a country that is growing at its seams, we need to find solutions to our own problems and celebrating the unique Indianness in our solutions is a great beginning. So with that, thank you very much to all of you and congratulations to all the winners and carry forward with the great work. Thank you. Thank you. Thank you so much, Mr. Singhal. Firstly, for sharing your thoughts while sitting away. And now, ladies and gentlemen, the moment you all have been waiting for. But before starting with the awards night, of course, we would like to request all of you to create the buzz as this is the inaugural edition of the Martek India Awards. So use the hashtag E4M Martek India Awards, tweet to us, put out your winning shots on Instagram or any other social media platform, but join us in the celebration. The jury of course had the cruelling task of crowning the winners from a marketplace so vibrant, relatively new and full of innovations and evolving with each passing day. The jury as we mentioned was an independent panel of professionals from across industry categories. We are thankful of course to all our jury members for giving their precious time and valuable inputs in the judging process. And here, ladies and gentlemen, we have with us one of our jury members who's joined us live. And it is a pleasure to welcome Mr. Nithin Thaburiya. Request you to please switch on your camera and mic, sir. Hi. Hi, Cathy. How are you doing? Very well, sir. Very warm welcome. Thanks. Thanks a lot. I really appreciated the time that we spent on the jury. The jury process was very involved, involving in complex because the contestant really went above and beyond. And as Abhay said, Martek is the casper where it is giving people that flexibility and the options to go above and beyond and really make a difference to the world. While we deliberated on a lot of cases and decision making was very difficult, but we looked at all pros and cons and it was definitely a very involving and invigorating discussions with fellow jury members. Finally, congratulations to all the winners and whoever did not make the cut in no way their entries were any lesser. We just had to make a decision. Thank you. Thank you so much, Mr. Thaburiya. Firstly, for being part of this independent jury panel and for being here and giving your encouraging words. Thank you so much. Yes, ladies and gentlemen, to start with our first Martek India Awards 2021, we're going to start with the first broad category, which is technology providers. We're going to start with our first category under it, which is India founded Martek Wender of the Year. I hope all of you are ready and excited. Like I've mentioned, do not forget to create the buzz online. We are talking about Martek, of course. We need to create the buzz online with the hashtag E4M Martek India Awards. Let's see who is taking away the India founded Martek Wender of the Year Award. Can we have the A.V. please? Our first broad category of the evening is technology providers. The first category in the segment is India founded Martek Wender of the Year. The special jury mention of what goes to and the gold of what goes to. Thank you, exchange for media. On behalf of the entire living age team, it's a pity we're not able to walk up to the stage to hold up the trophy. We're really excited about the impact we're making with all of our clients and partners. The Martek Awards are a great platform to bring these ideas to the mainstream. I'm sure we'll together drive a lot of action for more brands to engage better with their customers, retain them and grow profitably. Cheers to the entire E4M team for having pulled this off. Thank you. Many, many congratulations to the winners. And yes, we do miss the thrill of being around each other and walking up on the stage. But the circumstances are such that we stay in the safety of our homes and still create the impact. So with that, ladies and gentlemen, we're going to move forward to our next category, which is marketing automation vendor of the year. Let's have a look at who the winner is in this category. Next in line is marketing automation vendor of the year. And the special jury mention award in this category goes to and the gold award goes to. Hi, everyone. I just want to express my heartfelt gratitude from Team Web Engage to the E4M team for organizing such a spectacular award and event, which is the E4M Martek 2020. It also gives me immense pleasure to see that marketing technology is finally coming of age in India. And brands are realizing the importance of retention marketing. So we'll continue to help scale brands and educate them more on the retention marketing space. And thanks once again to the entire team for recognizing Web Engage's efforts in building out a company to help brands grow faster. Thank you. Thank you and many congratulations to the winners here. With that, ladies and gentlemen, I hope all of you are already tweeting using the hashtag E4M Martek India Awards. If you haven't yet, then start it right away. While I welcome one of our jury members here, Mr. Anil Cheela, Chief Digital and Data Officer, L'Oreal, who will talk about the jury process and advice to the industry. Can we have the audio visual, please. It's very interesting.  And I mean, what's what's really, for me, good to see as a marketer is the evolution that we are seeing in in this space, right, in the Martek space. So the quality of entries is really, really good. So I really, you know, enjoyed going through some of the, you know, the case studies that were entered in here. And I think as a part of the jury had quite a bit of discussion around which ones should be the ones that should that sort of are a notch above and which ones aren't. So really interesting. Yeah, I'll say on on an average, you know, I think really, really good in terms of, you know, the depth and the quality and the effort put in by a lot of the entrants in here. Having said that, you know, I think we did see close contests in a couple of categories. But then, you know, in others, you know, there was a very clear difference between the winner and the rest, right. So yeah, I'll say it depended on the category. And yeah, so some of these actually had, you know, very, very close, let's say contest. Well done. I think keep up the good work. You're showing why, you know, India is, you know, sort of going or evolving so fast and maturing so fast within the Martek space, right. And I think you are the examples of that. For the people who have not won, I think I'll say, you know, you made a good effort. Having said that, I think, you know, the difference between you and the winners was not a lot.  And then, you know, I'm sure they will be on the podium next year. Thank you. Thank you so much, Mr. Chulal for those kind words. And with that, ladies and gentlemen, we are moving to our next award, which is Enterprise Martek Wender of the Year. Let's take a look at who the winner is in this category. Our next award is for Enterprise Martek Wender of the Year, and the gold award goes to everyone. My name is Aditya Bhamati Party. I'm the founder CEO of First Hype. First Hype is a full-stack customer data platform that helps consumer marketing companies take control of their customer data to drive delightful customer experiences. I would like to thank the jury for nominating us for the Enterprise Martek Wender of the Year award. You know, thank you Exchange for Media for organizing this wonderful event. And I'd like to thank our customers, our entire First Hype team for making this happen. And it means a lot to us coming at this juncture as a young platform company that's in our early days of global growth. So thanks again. Thank you and many congratulations to you. I hope all of you are sending out your best wishes to the winners and the winners are taking those victory shots and tweeting using the hashtag E4M Martek India Awards. Moving forward, we'd like to call in another jury member who is Mr. Gaurav Mehta, CMO Zupi, to join us and tell us his views on the charging process. Martek, like people's understanding of Martek, I'm like, you know, there's a lot of fancy terms flying around. I'm like, you know, that can go from like, you know, from an expert level to a novice level. And there's a lot of like space in between. There's a lot of gray area. So till the time somebody doesn't come up with a platform where the right view of Martek is being discussed and appreciated and honored, people will have like, you know, 10 different views of what we are discussing about. So I think what Exchange for Media has done in order to bring a uniform understanding of Martek is admirable and much needed. So that's why I liked this particular award. I think it's much needed. I think in a couple of categories, it was very unanimous. I think the winners kind of just outshone everyone else. I mean, their understanding and deployment of Martek or what they're building was way superior to others. And I think, as a jury, all of us were on the same page almost instantly. Or a lot of others, I think there was a lot of subjectivity because of our own individual experiences of how we looked at the case studies. And that's where I think we needed a little bit of cajoling from each other. But I think it is quite interesting. There's a lot of places we were absolutely, absolutely unanimous. And there were a couple of places where we were not in line with each other. But then that's the fun. I mean, we were looking at things from our own point of view. And he had tried to understand what the other person's point of view on the same point was. So it is quite interesting, especially the one where we were just getting into a discussion with each other where we didn't agree with each other. I think that taught us much more than the unanimous choices. Just the fact that people are trying their hand at becoming a better growth professional, better marketer by using what Martek has given us is a win-win for me. So the color of the medal or the positions really don't sift the winners versus the also rants for me. The fact that they are starting to experiment with this is like the single biggest takeout for me. Because the interesting bit wasn't that was that we didn't only see entries from digital businesses, there were a lot of offline businesses also that were coming in. And that is so heartening for us to see that people who are not necessarily born into the whole technology era are also trying to embrace technology. So I think I'm like, there's a lot of inspiration to take from people who are participated, more from people who are not digital natives. Thank you so much, Mr. Mehta, for those very encouraging words and putting the context of the judging process as well. Moving forward, we are going to our next category, which is marketing analytics or reporting vendor of the year. Let's take a look at who the winner is in this category. We have marketing analytics or reporting vendor of the year. The gold award in this category goes to... Hi, I'm Sanjay Dressel, general manager for India, Southeast Asia and ANZ at Epspire. First of all, I'd like to congratulate Exchange for Media for doing an amazing job over the years for this ecosystem around this region. I would like to thank the entire jury for choosing Epspire and awarding us as the marketing analytics and the tech stack provider for the year. This is really a proud for me and my team. I express my sincere gratitude to the jury and all the decision makers. Last but not the least, I would also like to express my gratitude to the entire team at Angel One, especially Prabhakar Sir, Dinesh Bhai and the entire team for showing amazing amount of faith and trust on Epspire, our products and services. This wouldn't have been possible without the patronage and support that we have had from you. Thank you for choosing us and partnering with us over the years. Thanks a lot, Exchange for Media once again. Thank you. Many congratulations to the winners. With that, we're moving to our next subcategory. They say content is the star. And why not? These days, content is the star of every marketing strategy. Well, which is why our next subcategory is content marketing tech vendor of the year. Let's take a look at who the winner is in this category. Next up is the important award for content marketing tech vendor of the year and the gold award goes to Many congratulations to the winners Zirka here. With that, we're moving forward to our next category. Ladies and gentlemen, do not forget it is the inaugural edition. So we'd love for you to help us create the buzz online as it is the Martek Awards. And of course, if you're winning it, do not forget to share the winning shots and let us join in the celebration by using the hashtag E4M Martek India Awards. With that, we're moving on to the next set of awards. Coming up next is the category emerging vendor of the year. Let's take a look at who the winner is. Next up is the emerging vendor of the year. Less than three years old on July 31st, 2020. And the gold award goes to Again, many congratulations to the emerging vendor of the year. Moving forward to the next category, which is MSME Martek vendor of the year. Let's take a look at who the winner is. Our next award is for MSME Martek vendor of the year. The gold award in this category goes to Thank you, exchange for media. On behalf of the entire living age team, it's a pity we're not able to walk up to the stage to hold up the trophy. We're really excited about the impact we're making with all of our clients and partners. The Martek Awards are a great platform to bring these ideas to the mainstream. I'm sure we'll together drive a lot of action for more brands to engage better with their customers, retain them and grow profitably. Cheers to the entire E4M team for having pulled this off. Thank you. Thank you and many congratulations to the entire team of web engage. Many awards taken already and let's see if they're going to win more. But till then we're moving forward to our next category. Of course, is the biggest one. We're talking about social media. We are on social media all the time. Here it is, the social media tech vendor of the year. Let's take a look at who the winner is. Our final category in this segment is social media tech vendor of the year and the gold award goes to I'm delighted to hear that affinity answers has won the award for MSME Martek vendor of the year. Thanks to exchange for media and the jury for this recognition, particularly at this moment when businesses and industries look to recover lost traffic while identifying new prospects. Thanks to our client, great learning for giving us the opportunity to boost their campaigns and congratulations to the entire affinity answers team on this great achievement. Thank you. All right, moving on. Let's find out the next category and the winner with this audio visual. Can we have the AV please? Martek is the future and AI and VR and AR are really all the things that we are now depending on to make sure that our marketing campaigns are successful. So this is a great move, much needed in today's day and time. So actually it's a great thing that you've done. Yes, absolutely some really, really great entries. And if you've been in the room, you've seen that most of the jury kind of confused about what to do on some of the categories. So some really great entries, great quality, especially for the first time, I think you've got some really, really great entries. So I'm really enjoying doing that. I'm learning a lot in the process. So thank you. It's actually a great session that we're having. Well, not everybody can win. And that doesn't in any way mean that the person who didn't win is any less, because you know, it's a process, there are analytics around how we do these things. But I think every single entry is fabulous. And I would encourage them to try again. I would encourage them to carry on doing the wonderful work they're doing and congratulations and all that they've achieved. Thank you so much, Ms. Bhattacharya. You've just heard from one of our jury members, Ms. Swati Bhattacharya, Chief Communications and Brand Officer, the charge group. Thank you so much for those kind words to the winners and to those who couldn't make it this year. But don't worry, this is just the inaugural edition of the Martek India Awards 2021. While I say that do not forget to tweet to us using the hashtag. Moving forward to our second broad category, ladies and gentlemen, this is the use of Martek. In this category, the first subcategory we're going to award is the best use of analytics. Can we have the AV please? Moving forward to our next broad category of the evening, which is use of Martek. The first category in this segment is best use of analytics, best use of productive analytics or intent technology. The goal award in this category goes to exchange for media and Martek awards for this recognition. A chance of approval is basically solving the matchmaking problem, matching the right customer to the right bank. This model is built on the backbone of credit bureau and demographic information, which allows us to accurately assess the credit risk of each customer. This award is again a validation of our customer centric ethos, where we are trying to solve real-life customer problems using data. Thank you again for this recognition. Thank you and many congratulations to Pesha Bazar. Moving forward to our next category, which is best use of Martek for customer engagement. Really important one right here. Let's see who takes away the award. Our next award is for best use of Martek for customer engagement. The silver award goes to gold award goes to big hearty congratulations to both the silver and gold award winner. Many congratulations to you. Moving forward ladies and gentlemen to our next category, which is best use of Martek for demand generation. Let's take a look at who takes away this award. Next we have the best use of Martek for demand generation and the gold award goes to exchange for media and the jury members for awarding us gold for the best use of Martek for demand generation for sport India. The problem statement given to us by Ford was to digitize their dealer network so that the demand which is there on digital channels can be channelized and sent these dealers directly. Now to deploy this at scale is very very challenging because they have 300 dealers all spread throughout the country so through our product spotlight we were able to build thousands of micro target campaigns which were able to capture this demand at a local level and channelize this demand to dealers directly without any delay or lag in the capture of intent to actual conversation with the dealership. When we deployed this at scale we were able to bring efficiencies which is the best award. Thank you and many congratulations to the winners. Guys do not forget to keep tweeting using the hashtag eforummartekindia awards and congratulate all our winners. Now this platform in fact this event itself is a testimony to the way things are moving forward how we are creating impact with different marketing strategies through events and rather with the best use of Martek for live events online or real world and that is exactly what our next category is. Let's see who takes away the award in the best use of Martek for live events online or real world. Can we have baby please? Please. Felicitating the category of best use of Martek for live events online or real world. The bronze award in this category goes to and the silver award goes to. Congratulations to both the winners. Yes moving forward we are going to our next category which is the best use of virtual reality or artificial reality the VR AR. Let's take a look at who takes away this award. Can we have baby please? Next up is the best use of VR or AR. The bronze award goes to and the silver award goes to. Many congratulations to both the winners right here. Moving forward to our next category which is the best use of artificial intelligence. Let's take a look at who's done this the best in the last year. Moving forward to our next category best use of AR. Let's see who the winner is. Our next award is for best use of AI. The bronze award goes to. There are two silvers in this category. The first silver award goes to and the second silver award goes to. Gold award goes to. On behalf of the team at Hansa Security, I would like to thank Extreme for Media and the esteemed jury members of the E4M Martek India Awards 2021 for the gold and the best use of AI category for Mahindra and Mahindra Auto. We at Hansa Security are committed to provide Martek enable connected customer experiences for brands and organizations. Thank you once again E4M for the honor. Thank you. Hi I'm Shailesh Putran from Mahindra and Mahindra. I would like to take a moment here to thank Exchange for Media Martek Awards 2021 for this prestigious award. At Mahindra we are committed to leverage the power of Martek for improving customer experience. My congratulations to both the teams at Mahindra and at Hansa Security for winning gold for best use of AI. Thank you once again. Stay safe. Once again our biggest congratulations to all the winners till now. What an amazing time that we are living in. We are using AI. We are and what not during these times. We are revolutionizing the marketing landscape. Moving forward we are going to invite another jury member here. We're going to hear from Mr. Tanveer Singh Ubroy, Director, Agency Partnership and Sales Google. Can we have the AB please? Firstly I think thank you for the opportunity to be part of the jury here. Extremely innovative in terms of the award categories that have been set up and clearly give an opportunity for all the nominees to actually put up their best foot forward with respect to the entries. We had some very very innovative entries that really came in and I'm glad that we have a platform of this nature that Exchange for Media is providing for us to showcase some of the work that brands as well as partners are doing in the front of Martek. Well for Google obviously both marketing and technology pretty much formed the DNA of our organization and it was extremely heartening to see the diversity of entries that we actually saw today. Some of the categories were very very closely contested primarily on the back of the fact that they were very innovative. So there was some out-of-the-box thinking both when it came to communication as well as the usage of technology and what we really appreciated or I definitely did appreciate is the blend that that most of the entrants really tried to form during the course of their nominations right and highlighting the fact that they were not thinking in the reams of what they see day to day but actually thinking beyond was definitely a very encouraging to the entries that we saw today. Well I think participation is the key. We had a reasonably high number of entries which is great for the award even this was the first phase of the award.  Keep innovating and we're hoping for even stronger entries in the future for those who didn't win I would say don't get disappointed. The fact is that you were in the final shortlist of the entries and I'm glad that you got an opportunity to actually contest an award of this nature. I'm sure you will develop technologies and communications which will be stronger in the years ahead so keep innovating and success will follow. Absolutely thank you Mr. Tanveer Singh Oberoi and of course take his word for it keep innovating and success will follow. Moving forward to the next category for the night which is best use of marketing automation. Let's take a look at who takes away the silverware. Next up is the important award for best use of marketing automation and the silver award goes to the gold award goes to Hi everyone I just want to express my heartfelt gratitude from team web engage to the E4M team for organizing such a spectacular award and event which is the E4M Motec 2020. It also gives me immense pleasure to see that marketing technology is finally coming of age in India and brands are realizing the importance of retention marketing so we'll continue to help scale brands and educate them more on the retention marketing space and thanks once again to the entire team for recognizing web engages efforts in building out a company to help brands grow faster. Thank you. Thank you and many congratulations to Randstad and web engage. Moving forward ladies and gentlemen do not forget to send out your best wishes and congratulatory messages using the hashtag E4M Motec India Awards. Moving forward the next category is the best use of Motec for customer experience. Customer experience becomes the heart of anything that we do and let's see who takes away this award for best use of Motec for customer experience. Our next award of the evening is for best use of Motec for CX and the silver award goes to the gold award goes to Hi everyone Lakmi has been at the forefront of creating the color cosmetics category in this country making great products and giving the greatest experience to consumers at every touch point. As more and more consumers started shopping online we realized it was critical for us to translate that offline experience in the online world as well and that's why we created beauty tech as a way to give consumers the same experience as we've always given in the offline stores and also personalizing the experience at each and every touch point. We as team Lakmi are super happy to get this award and we would continue on this journey to enhance every single consumer experience on Lakmi in future as well. From a technology standpoint what's worked really well for us has been a deep curation and a judicious selection of which interventions to apply and once deployed a focused drive towards ensuring maximum impact and an effective scale up. Once again many thanks to Motec India and to exchange for media for this award. Many congratulations to our winners and I hope all of you are celebrating wherever you are moving forward to our next category which is the best use of Motec for sales enablement. Sales has to be done to let the business grow so here it is the best use of Motec for sales enablement. Let's take a look at who the winner is. Next we have the best use of Motec for sales enablement where there are two silver winners as well. The first silver award winner is and the second silver award goes to Motec. We are totally business services always believe in customer centricity and transforming customer experience. With customer experience platform we have virtualized product demo experience of customer in seamless manner. We are delighted to receive this award which recognizes our efforts towards creating best in class customer experience. Thank you very much. In today's world experience before you buy a mindset is reshaping in customer for purchase of tech products. There is nothing more powerful than allowing a customer to understand, configure it by itself and then experience the benefit of the product in their own setup and build the confidence that how it is going to work if they purchase this particular product or solution. Customer experience portal does just that. It allows the customer to understand the product thoroughly, configure it by themselves in their own setup then experience it and build the confidence that how it is going to solve their daily business challenges or the issues they are facing. We are happy to be recognized with this award and it only reinforces our commitment to deliver transformational customer experiences. Thank you. Thank you and many congratulations once again to all our winners still now. Moving forward I'd like to welcome another jury member here from whom we'll hear about the jury process. So let's welcome Nachi K. Deol head of sales India Double Verify. I think with pandemic the way we do business has changed and market is no longer an option but it's a necessity for businesses to not take it and I'm really happy that Exchange for Media has taken this initiative to recognize the best of the work in market space which can literally become a benchmark for others in the industry to follow and will encourage everyone to adopt the technology to build better businesses in these times. It's impressive to see the kind of work that's been produced here in India and across so many different categories. It's not one technology which has been followed but a use of multiple technologies right from consumer engagement to demand generation to virtual events. It's a brilliant piece of work that's been produced and I'm really glad that India has really stood up to the global brands and producing such an exemplary work. Yes there is a lot to be done but I think we are on the right path and with all the bright minds in the industry that we have we should see more and more superior work coming in in coming months. I think everyone who has entered is a part of their lead list. It's not a easy space to crack. It takes a lot and it takes a village to get more going. So my super congrats to all the winners winning or and as we discussed within the jury also right winning and losing doesn't matter. All that matters is the intent and the efforts that has gone behind developing something which is so new and so unique for each brand and each organization. So I wish the very best for everyone who is planning to deploy market solutions. Take a clue from all these entries this is really really significant piece of work that's been done and my congrats to all the winners. Thank you. Thank you so much Mr. Dior for sharing those thoughts with us. Moving forward to our next category which is the best use of marketing for social or influencer marketing. The big market that is the influencer marketing let's see who takes away this award. The best use of marketing for social or influencer marketing. Our next award is for best use of marketing for social or influencer marketing. The bronze award goes to again we have two silver winners. The first silver award goes to and the second silver award goes to and the gold award goes to. Thank you for the gold at the Martek India 2021 by exchange for media. I think Aage Ki Sooj was one of the first marketing campaigns post lockdown where we remotely coordinated, shot and executed the entire campaign. Little bit we know that this is one of your norms so it to some extent work like a trendsetter. Big congratulations to all the winners in the category of best use of Martek for social or influencer marketing. With that we're moving forward to the final award in the best use of Martek. Here we have Martek transformation or acceleration project of the year. Let's take a look at who the winner is. Our final award in the segment is for Martek transformation, acceleration project of the year and the silver award goes to and the gold award goes to. Thank you exchange for media and the jury for awarding as the marketing transformation project of the year award in the gold category. I humbly accept this on behalf of the marketing automation team. Providing best in class customer experience has always been a key focus area for us at Tata Daily Services and this project is an important milestone for us in that journey. A great and consistent customer experience across various touch points and various channels will differentiate us from the competition and make us the partner of choice for our customers. Many congratulations to the winners and while we move on to our next I want to once again urge all of you to create the much deserved buzz for all our winners of the Martek awards 2021. Do not forget to use the hashtag E4M Martek India awards. Moving forward, I'd like to welcome another jury member here. Please welcome Mr. Amit Raylan, director and co-founder and filter it. Well, I think it's a I would say in a primal face it's a good initiator from a marketing tech standpoint and with digital bound to grow many fold. I think right now it is about 22, 25,000 crores and essentially with a 20, 25% CAGR, I think it's a great initiator with tech definitely will play a very, very major role and navigating the tech for marketing as a making it simple is something what most of the industry should look for and navigating this challenge and putting up the right stack is the key what I feel and this would definitely help building up talent and also help building up the right companies in this space. I think that's how it should be and one of your colleague was saying almost about 29% of the media spends go to tech vendors. So I mean it's a it's a great opportunity for all the entire ecosystem. So I think the message is I think I think it's a marketing tech is is the future and it is important that optimizations across the marketing tech is done and this would help us to help them to increase their reach towards the consumers and also help them in more effective communication and elements and then more things will come as we go along. Thank you, thank you so much Mr. Rilan and with that ladies and gentlemen we're moving forward to the last category of awards. Before we do that I just want to quote Dr. Goldsmith who said that you don't have to be a technologist. All you need to do is understand how technology influences the world and this is what the next category is all about. This is the last leg of the awards moving on to the next set of awards which is the third broad category which is industry and people. Recognizing some outstanding individuals in this space look out for these talents as we start with our first one in-house marketing technologist of the year. Let's see who takes away this award. Our last and the prestigious segment of this evening is industry and people. Now, felicitating excellence in the category of in-house marketing technologist of the year. The gold award in this category goes to Bhuvna Subramanya, chief marketing officer, Rant Stad India. Thank you so much E4 team for the gold award in the market category. Marketing and technology are like peace in a pod they go together without saying. As a CMO one key part of the job is to help pick the right market stack. It is fundamental to building a robust marketing strategy. In an increasingly complex environment putting together the right set of tools and technology is very challenging. However, once that is done it can help optimize the campaigns, improve the experience, multi-fold and therefore deliver the ROI. I'm thankful to my team at Rant Stad for supporting my digital forward concept that aligns with the to the take and touch vision of the organization. I'm thankful to my team in marketing for supporting that vision as well. Thank you once again E4 team for this recognition in the market category. Congratulations to all the other winners as well. Thank you. Thank you and many congratulations to you Bhuvna. Moving forward ladies and gentlemen we are going to the final award for the evening. So here once again urging all of you if you haven't yet go ahead and tweet using the hashtag E4 MRTEC India Awards and congratulate all the winners. If you are one of the winners then do celebrate. Put out those winning shots and join us in the celebration. Moving forward to the final award for the evening which is the MRTEC Visionary of the Year. Let's take a look at who is the MRTEC Visionary of the Year. Can we have Devi please? And finally honouring individual achievement is the next award MRTEC Visionary of the Year and the gold award goes to Projol Banerjee Founder and Chief Product Officer Zeota. Hi I'm Projol Banerjee Founder and Chief Product Officer of Zeota. I'm grateful to the judges and organizers of the MRTEC India Awards 2021 for this incredible honour of MRTEC Visionary of the Year. It's been a difficult 18 months and we are one of the fortunate few to have landed on our feet. I'd like to thank all my wonderful colleagues around the world who have been simply incredible. Team Zeota for the win. Thank you and congratulations Mr. Banerjee. Moving forward ladies and gentlemen I want all of you to go ahead and congratulate all the winners. This is from all of us at E4M. We'd like to once again congratulate all the winners out there and those who couldn't make it as well thank you for your participation and for all the great work that you're doing in MRTEC. Moving forward this brings us to the end of the inaugural edition of Exchange for Media. India MRTEC Awards 2021. See you next year. Do not forget to stay safe, stay secure and stay happy. Thank you so much. This is Khyatika Vairu signing off till we see you next year.


We are extremely happy for the team that the hard work has basically been recognized in the industry for like overfitting and I think it's a very prestigious award for us and unfortunately we lost the business but this is basically the great show and the hard work team has put in the last three years. Both these campaigns are very unique you know one is basically a brand taking a stance on helping and trying to basically help the people who are facing this score and the second is basically a great example of how technology can help in basically coming out of the brilliant solutions and helping the brands with positive outcomes.