Pleased to introduce a special guest, so Scott Aaronson from the University of Texas at Austin. So Scott is a recipient of the Waterman Prize and a Simons investigator. He's a leading researcher in quantum computation and he's very kindly agreed to come and give us a talk on the subject which seemed appropriate for our general interests. Okay. All right. Well, thank you, Rave, and thank you for inviting me to my first guest PCMI in Park City, surely not the last. So I did tell the organizers when they invited me that I have no particular expertise in QFT or bundles or categories or three manifolds or really any dimension of manifold. I'm a theoretical computer scientist and they said, that's okay. We just want your usual quantum computing talk with the jokes. So I said, all right, well, what's the level of the audience? And they were like, oh, well, it's everything from high school teachers to active researchers in quantum field theory. So I said, okay, I think I got that. So all right, so let's see what we can do. So this here is what you get if you do a Google image search for quantum computer. Well, it's one of the first things that comes up. So I am very far on the theoretical end of this field. I don't actually build them, but even I am pretty sure that that's not what they look like. But we'll be less interested in what they look like than in what they can and can't do. So in order to, and what are the connections to many different issues in physics? Okay, so in order to start talking about that, I first have to sort of explain the basic concepts of theoretical computer science. So I have three slides to do that.  This should not be so bad, okay? So, okay, so what we mean by a problem in computer science is like an infinite set of questions. Okay, like given an integer is it prime or composite? You know, given a list of, you know, locations of stars. You know, can you reach all of the stars, you know, by only moving your telescope this much? You may recognize that as an example of a famous problem called the traveling salesman or sales person problem, okay? So then, you know, any specific number that I give you to say test its primality or not that I give you to compute its crossing number or, you know, manifold to compute its somology groups or whatever, you know, anything like that is going to be an instance of the general problem, okay? And so, you know, we typically, we measure the size of an instance, you know, in, for example, by just how many bits it would take to specify it. And then, you know, we're interested in, first of all, what problems are computable? You know, for which problems is there a general algorithm that will solve any instance and terminate in a finite amount of time? You know, and that can already be a very, very difficult question. For example, you know, it's known that it's decidable whether any two knots are isomorphic, okay? That's a very nontrivial result, okay? But in computer science for the last half century or so, we've wanted to do much better than that and say, you know, which problems actually have efficient algorithms, okay? And roughly what we mean is algorithms that are much, much better than the doofus brute force approach of just trying every possible solution one by one until you find one that works, right? Which is a solution that as the instance size gets larger will rapidly, you know, take much longer than the age of the universe, okay? Even if it's technically computable, okay? So our rough and ready criterion is we want algorithms that are polynomial time, meaning that they use a number of elementary steps that grows in most like n, the size of the instance raised to some fixed power or most as a polynomial in n, okay? Obviously n to the 10,000 is not very efficient in practice. You know, 1.001 to the n would be much better in practice even though it's formally exponential, okay? But, you know, this is sort of the first maybe question you could ask is what is the asymptotic behavior? And it's usually though not always pretty well correlated with is your problem tractable in practice, okay? So then, you know, we sort of have a universe of classes of problems that are solvable with different resources and one of the main tasks of theoretical computer science is to understand these different classes and how they relate to each other, okay? So the most basic of all the classes is called P, polynomial time, and it's just the class of all of the problems for simplicity say decision problems that have polynomial time algorithms, you know, on a standard deterministic digital computer like the one in your pocket, okay? Then the next famous class is called NP, non-deterministic polynomial time. You know, and the physicists, you know, have better names for things, you know, Big Bang, Black Hall, you know, we're stuck with non-deterministic polynomial time and so forth, although, you know, compared to SO3 and so forth, I think these are, these names are just fine. So NP contains all of the problems where if the answer is yes, then there is a short proof that it's yes that can be efficiently checked, okay? That can be checked by a polynomial time algorithm, okay? And a famous example is factoring. So to phrase factoring as a decision problem, I could say, for example, I give you a positive integer like say that one, and I ask some yes or no question, like does it have a factor ending in seven? In this case, I believe the answer is yes. If any of you want to check that in your head, if I made a mistake, you're welcome to do that. But in any case, you know, the point I want to make is we do not today know an efficient algorithm, at least for a classical computer, to self-factoring, the fastest known algorithm takes time that grows like exponentially with the cube root of the number of digits roughly, okay? At the very least, the best publicly known algorithm, okay? And actually from the Snowden revelations, there are some indications that even the NSA does not know much better than that. Or if they did, they would be doing something different than it looks like they are. So we do not know whether factoring is in P. That is a famous unsolved problem, okay? But if the answer to a question like this one were yes, there is such a factor, then it is very easy for some wizard to prove that to you, they just show you the factor. And then, you know, you can just plug that into your computer, factoring might or might not be hard, but division is certainly easy for a computer, and it just checks the factor, okay? So that's NP, and then the next really important concept is NP hard. So imagine that you had like a magic box for solving a given computational problem, okay? Then the next question that we ask is, now using that box, what other problems could you then solve? You know, like by just using, like you could say which problems are easy relative to this box? Like which ones can I do in polynomial time with the ability to make calls to this magic box, which following Alan Turing in the 1930s, we call an oracle, okay? So now if you have a problem where an oracle that solved that problem for free would make all of the NP problems easy, then we call that problem NP hard, okay? So NP hard informally means at least as hard as all the problems in NP, okay? And then NP complete just means the intersection of NP hard with NP itself, okay? So the NP complete problems are just the maximally hard problems in NP, right? They're the NP problems that are at least as hard as any other NP problems, okay? Now just from these abstract definitions, it's not obvious a priori that any NP hard or NP complete problems even exist, okay? So the big discovery in theoretical computer science in the early 1970s that really started this off as its own field in the first place was the discovery that literally thousands of problems of practical importance turn out to be NP complete, okay? So, sort of, given a set of suitcases, can they fit in the trunk of your car? That may be one that you've encountered. Or just given these constraints, can you schedule airline flights that will whatever make this much profit and not have planes crashing into each other? Super Mario and Minesweepers are actually known to be NP complete if you want the really practical ones, okay? But, so lots of problems from chemistry, condensed matter physics, finance, economics, I'm not gonna say every field, I don't know about art history or, but give it time, okay? So, in fact, when you meet like a hard constraints satisfaction problem, like a problem where you have a bunch of possibly conflicting constraints and you wanna find some setting of variables, either discrete or continuous variables that satisfies as many of the constraints as possible, right? The good rule of thumb is that such a problem will be NP hard unless it has a good reason not to be, okay? So, and it's what this means is that all of these, what on their face might look like totally different problems are there, here's another example, I give you the graph of who's friends with whom on Facebook, can you find 500 people who are all friends with each other? That's called the clique problem, okay?  Okay, so all of these problems, although they come from completely different domains, we NP complete and this tells you that in some sense they're all the same problem. An efficient algorithm for any one of them would give efficient algorithms for all the rest, okay? So, by the way, the question of whether two knots are isomorphic, that's one of the ones that no one knows. Okay, so let me, so yeah, okay, so a good example of an NP complete problem, I give you a graph like this, I ask is there a tour that visits each city exactly once? Okay, so here is such a tour, it's easier when it's highlighted in green, to see that it's there, okay? Once it's there, it's easy to see and then it turns out that any other NP problem, like find the factors of this 2000 digit number, it is possible to construct a graph such that if someone found a cycle in that graph, Hamilton cycle that visits each vertex exactly once, then from that you could read out the factors of your number, right? That's what it means for Hamilton cycle to be NP complete. Okay, so here's a little map of where these things all fit together. So P has most of what we would do with our computers on a day-to-day basis. NP has a huge amount of what we would like to be able to do with our computers. And then the NP complete problems is this sort of giant set at the top of NP. You may have heard of the question of whether P and NP are equal, you may have heard of that is that not having been answered, we will come to that, okay? I wanted to point out also that there are many interesting problems that are in NP and that are not known either to be in P or to be NP complete. Okay, factoring is a famous example, graph isomorphism, not isomorphism, problems involving lattices in R to the N, and a bunch of others. These problems, these intermediate problems tend to be extremely important for cryptography, also important for quantum computing, as we'll see. Okay, so then the quantum generalization of the class P, which we'll talk about quantum computing soon, is called BQP, Bounded Error Quantum Polynomial Time. Okay, I drew it with this wavy border because of course everything quantum is spooky and weird. Okay, but this is a class that we know contains P. We don't know its relationship with NP. By, there's one of the biggest discoveries, I think, in the history of computer science came 25 years ago, when Peter Schor discovered that the factoring problem is in BQP. So a quantum computer can efficiently factor numbers and thereby break much of the world's cryptography. As you can see from this picture, we do not currently know if quantum computers can solve NP-complete problems. Okay, and that's one of the questions that will interest us. Okay, so if I had to pick a single scientific question that sort of maximally ties together all the different things that I care about, I think I would pick the question, is there any physical means whatsoever to solve the NP-complete problems or equivalently to solve any NP problem in polynomial time? Okay, or let's say efficiently. Okay, now what I love is that this is a single question that sort of includes within it at least five different sub-questions. I think any one of which could occupy someone for their whole career. So first of all, there's the famous question, does P equal NP? That is, could there just be a polynomial time algorithm running on a conventional computer that just solves all the NP-complete problems? Nobody has proved that that's impossible, so already that. Okay, now that's a purely mathematical question. There's an enormous amount to be said about it, but for me personally, it becomes maybe even more interesting when we bring some physics into the picture as well. And then we have to confront other questions, but you know, well, does nature give us computational resources that might go beyond this mathematical class P, right? This, so P, you know, you could define it in many ways, you could define it using Turing machines, which are these things that Alan Turing invented in 1936, but you could equivalently define the class P in terms of programs and you're written in your favorite programming language, whatever it might be, or cellular automata, okay? Or you know, just about any model of computation that is discreet, digital, deterministic is going to give rise to the same class P, right? So it has that kind of universality property. Okay, but you know, who says that nature has to be discreet and deterministic, in fact, we have some indications to the contrary, okay? So this brings us maybe to the next question, which is, you know, systems in nature like to somehow sit in their ground state, you know, their lowest energy state, but you know, minimizing energy is a perfect example of what's typically an NP-complete problem. So does nature just sort of magically make NP-complete problems easy?  And if not in classical physics, well, then what about quantum mechanics? And you know, you've surely heard or read something about quantum computing. You know, how does that change the picture? Right? And so, you know, I'll try to sort of sort out a fact from fiction, you know, unfortunately, many, many of the popular articles say things about what quantum computers would be able to do that's very, very exciting and appealing and that's wrong. Okay?   And then of course there's the question, well, you know, regardless of what they can do, can they be physically realized? You know, can a scalable quantum computer actually be built in our world? You know, and that is a question for engineering, but you know, maybe even for fundamental physics as well. And I will say a little about that. And then maybe my favorite question, you know, is quantum computing necessarily the end of the line? You know, could nature via quantum field theory, via quantum gravity, via some other, you know, yet, you know, physics that we haven't yet thought to incorporate into our models of computation, could that take us even beyond BQP? Right, and so then we're asking just, is there any, you know, whatever the universe does, call it, you know, universe P? You know, does that class contain the NP? Okay, so the reason why NP completeness is sort of important to this discussion is that if not for NP completeness, you know, you could have imagined a priority that you would just have an enormous zoo of different incomparable hard problems, right? And for each one, you could say, you know, well, can we do it with a classical computer? Can we do it with a quantum computer? But there wouldn't be sort of any interesting general principles, you know, to be found. But what NP completeness says is that, you know, yeah, there are little sort of isolated villages of hardness, but there's also this gigantic metropolis of hardness, right, the NP complete problems.  Can we get all the way up to the NP complete problems? I didn't mention this explicitly before, but there are also many, many computational problems that are not even in the class NP at all, okay? You know, the halting problem, whether a given computer program halts, would be a good example. Three manifold homeomorphism, something that is not known to be an NP, you know, as far as I know, it might or might not be, okay? So, you know, so there are problems where there aren't even efficient witnesses for a yes answer, but we'll be interested in the ones that do have such witnesses. Okay, so let me just, what I'm gonna do in the remaining time is just proceed through these questions and make some remarks on each one. Okay, so let's start with the famous question of just does NP equal NP? Now, you can tell that this is an important question because it's appeared on both the Simpsons, if you squint and a Futurama, many actually several other TV shows that were less good, okay, and it's also one of these clay millennium problems, the seven problems where you get a million dollar prize if you solve them, which includes, you know, the Riemann hypothesis, the Yang-Mills mass gap, the Poncare conjecture, which was solved by Perlman, although he declined the prize, okay, and a few others. My personal opinion is that P versus NP is far and away the most important of the seven. That's just my unbiased opinion, okay, but I can give you a couple of arguments, though. One is a million dollars is chump change, right? Suppose that you would prove P equals NP and via an algorithm that was actually efficient in practice, well then the first thing you could do is make about 200 billion dollars by just hacking Bitcoin, okay? That would be just like step number one in your plan for world domination, okay? Step number two, you could actually solve the other six clay millennium problems and pretty much all mathematical problems by just asking your computer, are you able, is there a proof of the Riemann hypothesis in some formal language like ZF-SET theory that has at most 100 million symbols? And the whole point is that if such a proof exists, it can be easily checked, and so in a world where P equals NP, it could also be found in polynomial time, okay? So of course you'd have to worry about is the algorithm really efficient in practice, but it would certainly be an unbelievable advance towards solving those hard search problems quickly, which includes a lot of what we try to do in mathematics itself, or some formalization thereof, okay? So if you, okay, so I should say that my own belief is that P is not equal to NP. I like to say that if we were physicists rather than computer scientists and mathematicians, we would have just declared that to be a law of nature. We would have given ourselves Nobel Prizes for the discovery of the law. If later it turned out that P equal to NP, we would just give ourselves more Nobel Prizes for the law's overthrow, okay? But okay, in an interdisciplinary subject like quantum information, you learn that people use terms differently, why physicists call laws, we tend to call conjectures, and so forth, okay? So I love many, many of my best friends are physicists. Okay, so, but okay, so there's an enormous amount to say about if P is assuming that indeed P is not equal to NP, which of course hasn't been proven, why is this question so difficult? Well, it's not the only question in math where we're pretty sure we can guess the answer, but proving it is unbelievably difficult, okay? But we can say a lot in this particular case about what are the barriers, why are the known techniques in logic, in combinatorics, and so forth apparently insufficient to resolve this question, why are new ideas gonna be needed? I should mention that some of the most recent ideas for tackling the P versus NP problem come from algebraic geometry and representation theory. It's called the Geometric Complexity Theory Program of Ketan Momoli. Okay, I think that in any case, there's going to be deep insights for many parts of math that are gonna be needed to make progress on these questions. Okay, so if you want the short version, you can read Stephen Cook's clay math description of the problem, or for the more masochistic version, you can read my 122 page survey article on P versus NP. Now, you can see the title has this question mark over the equal sign. In citation indices, the question mark was removed, and so the paper just looks like P equals NP by Scott Aronson, that would be a more exciting one. Okay, so all right, so let me just suppose that P is not equal to NP and move on to, well, if that's so, then are there physical mechanisms that could nevertheless make NP complete problems tractable that could just sort of zero in on the correct solutions without the astronomical amount of time needed for brute force search, right? I don't have to belabor for you guys that if we had even just 1,000 Boolean variables, then trying every possible setting of them by brute force, will take longer than the age of the universe. Okay, so there are lots of ideas about how you might get around that. One idea dating back to the 60s is, well, why not just take two glass plates and put some pegs between them in whatever pattern you want and dip that into a tub of soapy water and take it out and then just look at the soap bubbles that form between the pegs. Right now, we expect bubbles to try to reach a lowest energy configuration and you could hand wave that that means kind of minimizing the total length of bubble connecting these pegs together. Okay, but now there's a paradox or an issue which is that finding the minimum total length of line segment that could connect a finite set of points in the Euclidean plane like in this example here and where those segments could also meet at intermediate vertices. This has a name. This is called the Minimum Steiner Tree Problem and this is one of these famous NP-hard problems known to be. Okay, so are we saying that nature can somehow solve it near instantaneously that just by building some contraption with maybe 100 million pegs dipping it in soapy water, you can just let the soap break Bitcoin for you, let the soap solve industrial optimization problems and you can make a fortune that way. That seems sort of implausible to me on its face but there was a discussion of that online some time ago and someone was saying you're just a bunch of academics following a party line, not one of you know that this doesn't work, none of you have tried it out. That was what led to I guess the one foray into experimental physics of my career. So what I found was that I do recommend to anyone to try it except if you do use plexiglass so that you don't cut your hands, okay? And what I think you'll find is that with three or four or five pegs, the bubbles typically will find the optimal configuration that is the Minimum Steiner Tree. Okay, as you start adding more six pegs, seven pegs, you can get it to suboptimal configurations so the system can get trapped in local minima. Okay, and in fact it can even reach configurations that have like a cycle in them which then proves that they can't be minimal, right? Now I think that a priori this is what we should have expected. After all, if we had a rock and some crevice on a mountain side, such as you may have seen hiking over the weekend, right? Well that rock could reach a lower potential energy by rolling up first and then rolling down but it's rarely observed to do that, okay? So you know, so you know, this sounds like a banal point but like every year or so you will find popular science articles that say, you know, this or that team has found a way to make NP-complete problems easy. Okay, maybe sometimes it's using DNA folding.  Some, you know, recently there was an idea called mem computing that was supposed to do this as well. I think that every single one of these cases is boils down to the example that I showed before, right? It's, you know, you have systems that can get stuck in local optima and it doesn't always work.  There's no general principle saying that they should. And, you know, if you cared about something like proving the Riemann hypothesis or breaking Bitcoin, right, then you would have an enormous dimensional and incredibly rugged and complicated landscape of possible solutions, right, with no reason to think that local optimization will get you anywhere close to the right answer. Okay, and that's what people find when they try it in practice. Now, you know, in some cases, local optimization really does work well. Proteins in particular have been favored by natural selection specifically to fold in an easy and reliable way. Even then, they don't always do it. So prions, which are the agents of mad cow disease, seem to be proteins that just folded into a local optimum that was not the global optimum of the energy. So, yeah, but you know, the shortest answer to all of these claims is like, well, if you were right, why aren't you rich? Right, and you know, one can, especially in the age of cryptocurrencies, one can ask that question. Okay, so all right, so now let's move on to quantum computers. Okay, so, you know, you knew that was coming at some point. So do they change the picture? So, okay, so in order to tell you about this, I first need like a slide to explain what a quantum computer is.  You know, I mean, what I usually say is that, you know, if any of you haven't seen quantum mechanics, my advice would be that it's actually not nearly as hard as you may have been led to believe, especially once you take the physics out of it, okay? So, which is, you know, in quantum information, the way we tend to think about quantum mechanics is as a certain generalization of the rules of probability. And by the way, we typically only care about finite-dimensional Hilbert spaces and not about infinite-dimensional ones. So right away, that like cuts.


What if to build the world of our dreams could be fast, could be free, and could be fun? What if to build the world or the planet of our dreams could be fast, free, and fun?  I read this book, Collapse from Jared Diamond. He explained scientifically how we're going to go. We are running into a collapse that we all already know, but he described how and why. And together with this event, the book was a lot of tsunamis and disasters and flood, so I decided to stop everything in my life and take two years out to travel all around the world to try to understand what could be my best contribution to save the planet. So I decided something like very small, because I'm just one person, so I decided a target to mobilize two billion people in four years. You know, I have to be serious, like seven billion people, we cannot mobilize, but two billion people maybe can go there. So I went there and traveled all over the world to try to understand what's going on, what are the disasters that are going there, and how people are really ready to do some change. You know, how could I reach two billion people? So here in Brazil we have a traditional game that's called Jincana, that mobilized the whole city to play a few days in possible missions. For example, you ask them to find a pink elephant in two hours. So you know there's no pink elephant and there's no elephant in Brazil. But within two hours, guess what? They go find a circus in a neighborhood, they paint a pink elephant and the pink elephant is there. So in this situation, the whole city don't complain about the impossible mission. They have a lack of resource, they have a lack of time, but they have a lot of fun. So I wanted to mobilize two billion people, what if we could create a global Jincana? So what I did was to travel around because I'm a serious person. So is it possible?  So I traveled for 25 countries and playing games with them like impossible missions. To build a playground in Islam, in India, in two days with no money. They have no money, so they have to do it with no money anyway. And they made it. I went to Kenya the same, Cambodia the same, even United States the same. Argentina not yet. I have no time. So what I learned from this trip, playing a game, impossible mission, people can easily create freely, they become friends right away. People have readiness, they are ready to play impossible things because of the game. People find and reach spectacularity, it doesn't exist in English but it's nice, it should be. It's spectacular. Results like a pink elephant. Wait. And during this whole process of hard things, they have a lot of fun.  They make more playgrounds in squares, things that they never did before. So okay, what's the second thing that I learned?   So they are ready to do it as much as it's fun. The other thing, the best leaders ever, the powerful guys that were able to mobilize the whole community were kids. And the third thing, we have abundance of free resource. So any one of us, even in slums in Nigeria, wherever, we have connections. We can reach the best resource, the best methodologies and the best way to mobilize people all over the world. And we are seeing that all over. So we create a game called Play the Call, a global game. And basically it's quite simple, three steps. So you go to internet and you receive an impossible mission, a missing possible, like the movie. So you have to shape it fast, free and fun. So you are able to call all your friends to join you. And the third time you go to make it happen and then you share. You take pictures and you share on internet to inspire more people. Quite simple, you have three weeks to plant 1,000 trees. So go to internet, ask 300 of your friends, please each one of you plant three trees.  You're going to give people a platform, a very easy platform, especially for kids playing. That can articulate all the tools that you have on internet, Facebook, YouTube. All the tools that are free to mobilize people. All the technologies that you can go there. So basically you're going to allow kids to be superheroes in a very simple way. We have a collective platform so you can measure how many people are, when you are getting to the two billion. And you can see who are the people, the point of lights in the global. You can see how many people are playing around the world and impacting the planet. We have technology for that. So basically this whole mass of possibility and information we're going to organize in that simple platform that I showed you. And then people are going to be able to use this platform to change their own neighborhood. Planting trees, vegetable garden, natural food, painting, pimping the carousels around. So they're going to do everything locally, but inspiring people all over the world. Oh my gosh. So is it true or is it possible? So my last 30 seconds, we did it. It's possible because three years ago before I started my trip, we tested. So Santa Catalina 6 cities underwater. The situation was like that. We started to create a game to bring back the joy of life.   We create three different teams. And weekly impossible missions to train people all over the country to be able to join Santa Catalina to help them. The fire team, people that want to go there to help support them. The air team, people that are going to circulate the resource on internet. And the land team that were there. So three months later, 800 people there. 800 people, 12 communities, 10 days. We create grids, we create playgrounds, we create playgrounds and squares, motocross things. And after that, one year after, just by watching what we did there on internet, more than 90 ways all over the world that we didn't need anything, they just saw us. So it's possible. So in a while, after December 21st, someone is going to knock your door. They start to call you to play. So say yes and join us and change the world. Thank you.


What's going on everybody got Kyle and Shawn here with Lane sports cards. He's coming for me This one's gonna be for Jennifer why we're doing a personal to leaf exotic baseball Good luck, Jennifer. Come on Jennifer right here right now always enjoy the leaf stuff right here all right now baseball first Reginald presciato Reggie presci on the tiger strike one of ten Reggie presci, Reginald presciato on the tiger strike Behind it. We got a chase the lotter chase the lotter their twins prospect. What is number three out of seven? Chase the louder and on the back. We got slamming Sammy Sosa nice one there on the auto Sammy Sosa number two out of five Is Sammy Sosa that's playing Sammy on the snake skin machine Sammy. Yeah, definitely have two out of five on the snake skin I don't know box two Very similar. Yeah All right box two Come on Jennifer You can manage though. Yeah, you guys this a baseball. You're saying early. Yeah, it's D. Sure as hell. Did one of those guys One of the feet one of the couple guys There's a Jacob Barry Jacob Barry the Marlins prospect. That's gonna be on the tiger strike One is numbered five out of ten Marlins prospect right behind me we have a see her prospect for the cubbies Chicago there numbered one out of five See her and on the back. We have a J. C. Oh Tigers prospect there on these snakeskin. That's Josh's brother ten out of ten Nice Jase there the Tigers And last one here for Jennifer we got a 23 leaf ultimate hockey Good luck. Good luck. These are always fun First up say ultimate memorabilia that'll be all game used Olaf Kozik Evgeny and Nabokov Heinrich Lundquist Jose Theodore Nikolai Kabooglin, there's a Mika Kibrusoff Thomas Vulcan and Marty Turko on the Game use memorabilia there nine out of 25 Hockey eight all game use men Behind that we have a this one's gonna be willy-o ree willy-o ree on the auto there two out of five Oh, we willy-o ree On the auto there that one is the movie poster stop Really oh ree two out of five Throwback auto and we have signatures eight. All right. We got Gila Fleur Marcell Dion Jean Retel Daryl Sittler Johnny Buck Pete Mahovlik got Yvonne Korner Corner you know you're an lawyer and Dennis Holt We go Jennifer on the hockey eight-way auto three out of three The leaf hockey is really cool to me. It's all like Retire players all game used. It's really cool stuff. Manchester you the Fleur Dion Retel Sittler Buck Mahovlik Kanolier and Holt Really sick one there congrats Jennifer get those right out to you on those couple of leaf boxes appreciate you


Okay. Hello. Good morning. Thanks to Nasser's nice introduction. I don't have to say much. We will now tell you about our simulation study who's trying to find a very good way to compensate for various missing data patterns we find in biosocial research. This is rather becoming important since many of British or European or worldwide studies actually have already included biomarker data or planning to do so in the very near future. So this data, this kind of data from biological samples is becoming more and more accessible in larger numbers which allows for statistical analysis. So for our our studies we're using the English longitudinal study of aging or called ELSA which in its first wave had more than 11,000 men and women over the age of 50. This data comes originally as a sample from the Health Survey of England and additionally to the main questionnaires which happen in every wave. In wave 2, 4 and 6 there have been health examinations which included a nurse visit and blood samples and additionally in wave 6 we have also hair samples. So this day this biomarker data is available every two years for the same individuals. Missing information and Natalie already touched on that can happen at different stages in the data sets. For ELSA it's in three main parts. It can happen in the main interview. People just do not participate, for instance in wave 2 where it's the first time for us we can see or we collect biomarker data set. So that can be already attrition from wave 1 to wave 2. Then there has to be the burden of asking them for consent of a nurse visit to health examination. There might also be not eligible for a nurse visit due to bad health already. And then we have a next step where people can drop out when they're either not consent or not eligible to actually provide a blood or a hair sample. We're dealing mostly with three different kinds of missing data. One is missing completely at random, one is at random and one is not at random. And to just visualize the difference between all of them. If we analyze a relationship between an explanatory variable of X on the outcome variable of Y and we have a missing pattern, but the missing pattern is not related to Y or X. That's completely at random. So the missingness has nothing to do with either component of the relationship we're trying to observe. However, if the missing pattern is actually related to some of the explanatory variables, this is called missing at random. Since it is still independent from our outcome variable and if we take all the the explanatory variable where the missingness is depending on into account, put them into our model, we pretty much adjust for those missingness bias which would be otherwise introduced. Really problematic it becomes when we have an impact from the missingness patterns, which is also related to our outcome variables. So this becomes much more complicated and only including explanatory variables in your model will not compensate for the bias produced by the missingness. So over the years this is not a new problem and it's only a new problem now since we're trying to adjust it to biomarker research. But missing data has always occurred as long as there has been survey data. There's several methods out there. The problem is that there is a bit of concern and confusion about the effectiveness of several of those methods to compensate for missing data. So that unfortunately leads in many cases to either ignoring missing data bias completely or just simply assuming m-car or ma situation where data is random and no further compensation methods are used to. Therefore this study tries first to test and evaluate the effect of several of the analytical approaches to compensate for missing data. This is done by using a simulation study which is based on real data from ELSA Wave 2 where it's the first time we have biomarker data in ELSA. We compare five different analytical methods to compensate for missing data and we test those five methods on the three types of missing patterns missing completely at random, at random and not at random. This is done in six rather simple steps. We are only interested in the effectiveness of the methods on the missing patterns, but in order to run that we have to have a substantive model. So first we choose our substantive model, then we move on to ELSA and choose our variables for the substantive models. We then expand our sample size a bit. So we have a nice big sample size we can run our simulation on. In step three we run our baseline model on all the full data set to get our baseline true values for our true model. And in step four we create, from our full data set, we create data sets which have missing information and missing information in those three categories of again completely at random, at random and not at random. Then we test our five analytical approaches on those data sets with missing data and in the very end we compare the results of that with our true model from step three to see how close can we get to the truth using those different approaches. First step is our substantive model. We have chosen to analyze something to the relationship between socioeconomic status and some level of biomarker for the socioeconomic status. We look at education as occupation as well as wealth quintiles. For our biomarker we chosen CRP, that's C-reactive protein in the blood, which is usually a good indicator for chronic inflammation. And looking through the literature on exactly this kind of substantive models, we have chosen quite a variety of different confounding variables to isolate the effect of socioeconomic level status on the biomarker. You can see here below. Then we go to ELSA and have a look at which data we have. We have 8,780 core members in wave two, which is our base level. The problem is that we have, not the problem, but the situation is that almost only 70% of those have eligible blood samples and over 30% have actually no eligible blood samples in this data set. This can be broken down even further at which stage and why we don't have a blood sample. There could be a refuse to nurse for the refuse to have a blood sample. No blood was in the end taken because people were not eligible or something else happened with the actual blood sample. So we still end up with no information. So one second. So we end up with 5,899 eligible blood sample and pulse, which is then by the methods of bootstrapping boosted to a sample of 10,000 individuals. And this 10,000 is our baseline model. We run the model like a normal logistic regression on the locked CRP values, which gives us our baseline values for the coefficients of SES as having an impact on the CRP value. And this looks pretty much like these. So we have education where the reference group is high education and we can see a gradient, upward gradient of the lower the education, the higher CRP values is. So it is a negative impact on the CRP values for occupation, which is an ordinal classification. We see a bit more variety. And for wealth quantites, we see a very nice, the richer, the higher quintile the people become in their wealth quintile, the lower the CRP value is. And it's actually significance as well to most of the reference category of the lowest quintile. So this is our true model, which we define true model. And all the outcomes in the end when we have run our simulations will be compared to this to see how close can we get using the different approaches to this data. So now we're creating missing data, which was always fun. So completely at random means that we created missingness completely at random has nothing to do with our outcome where we CRP and it had nothing to do with any of the explanatory variables we put in for the mass situation. We created 16 strata, which are based on age, sex, wealth and health, which are both, which all four are used as a dichotomous variable, which creates 16 strata. And those 16 strata have different amount of missingness according to what we observed in real Elsa data. And creating not, not at random missingness. We have used those 16 strata and added another strata for those which are having higher CRP levels, have higher rates of missingness than those which are by a benchmark have lower CRP levels. So there we create the, the connection between missing pattern and the outcome variable of CRP. For each of those data sets, we created a hundred data sets, which, which has missing data. And these are the five approaches we test through. The first one is rather simple, it's complete case. That means we only run the models on all the individuals who have all the information available, which has CRP values, which, as we already see, is about seven, a slightly less than 70% of our model. The second approach is inverse propensity weighting. Before we come to the substantive model, we run a probate model which takes most of the explanatory variable plus some extra variables into account to predict if people have a valid CRP level or not. And this is then transferred to inverse propensity weight, which is then used in the substantial model to adjust for this. Then we have used in the third model a selection model using the mills ratio. And then we have two types of multiple importation. Multiple importation in general takes into account the pattern and distribution of explanatory variables in the data sets of all the data we have to implement data for where we have missing data sets. This is the, usually that is done a couple of times, hence the multiple import, multiple, in multiple importation. And a substantive model is then run on those iterations of incutations. And then our very sophisticated version is we combine a mills ratio and multiple importation. This is done in pretty much three steps. First, we run a multiple importation on all the explanatory variable to fill the gaps for that, but not on CRP. It has all the missing is still in there, but we use the complete set of explanatory variables then to calculate a mills ratio, which is then used in the imputation stage of another multiple importation to generate or to fill all the gaps for the outcome variable of CRP. So while the complicated stage, that was lots of computation. So in the end, again, we have our five model, five approaches from complete case to the combination of mills ratio and multiple importation. And we have our three kinds of missingness. And our expectation looked a bit like that. We thought if there's no bias from the missingness, so we have the M-car situation, probably not adjusting for anything is the best solution because anything else would just introduce bias, introduce patterns of missingness, which is just not there, so hence the green field. And we think the more complicated and the more sophisticated the compensation model becomes in the end, the more bias is actually introduced to a data set. So for M-car, we would probably see very bad results trying the latter approaches. It's exactly opposite for the amount of M-car situation, not compensating, just using the complete case will probably not compensate for any of the bias already existing. But then using some of the compensating methods, so using some of the compensation methods will improve the model in case compared to not using any compensation. So that's what we expected. And we'll come back to that and how good our expectations line up with our results. So as you can see, we have three different variables for socioeconomic outcome status, which is education, occupation, wealth. We have five different models, approaches, and we have three different missingness patterns. If I would show you all the results, it would still be in the afternoon. So I'm sticking to the wealth quintiles because they had such a nice tendency or nice gradient of an effect. We see here the results from three different models for M-car, Ma and Ma, but I put them in the same graph so we can compare them very nicely. You see these are the box plots of the 100 simulations and the 100 coefficients for each of the wealth quintiles. You see now here in its distribution. The dotted line is always the true model coefficients as our reference. So you can see that in most cases, we actually see that the inner 50% of the box plot is nicely lining up with our true model. So the whiskers are not too wide, so the distribution is not too wide, and we actually get rather nice results for that. So the results for M-car are very close to even over 100 simulations. It's very close to our true values. That's what we expected. What we didn't expect is that it actually works rather nicely as well with Ma and even not too bad to Emna as well. You can see with the higher quintiles of the fourth and highest quintile in Emna that it's moving slightly away from our references. So it is fitting worse than for the other missingness pattern, but still it doesn't do too bad. So complete case as an approach is not doing very bad. If you look at inverse propensity rating, we see a very similar picture that the approach is compensating very nicely or it's not introducing much bias for the M-car situation. It's still producing rather close variables to the close coefficients for the Ma situation, and it's not much worse for the Emna either. And we see something similar again for the mills ratio, which is doing rather good. Even it's not producing, you can see that the box plots are moving slightly away for the M-car, but not too much. And Ma and Ma situation doesn't look too, it doesn't look very different from the other approaches. We would have definitely expected to see larger differences. Then we have multiple imputation, and we had very high hopes of multiple imputation getting rid of most of the bias introduced for Ma and Ma. And we can see that it's producing rather nice results, but it's not very different from, again, the other approaches. So there doesn't seem to be much of an advantage of using multiple imputation over the others. And then came our brilliant idea of combining the multiple imputation and the mills ratio. And we can see here that it is producing, it's actually introducing quite a lot of bias, especially seen here now in the third quintile, where the inner 50% are not even close, anywhere close to our reference from the two model. And this in all three missingness patterns, the worst seems to be for M-car, which is expected because there was no bias for to begin with. So from our expectations of this should happen, it does look very different. First of all, our approach of using multiple imputation and the mills ratio together didn't perform well independent of the missingness pattern. It did worse with the M-car, but it's still doing quite badly for the M-car. So this is definitely not the solution compensating for missingness in the M-car situation. We also didn't see, what we didn't see was that the model was getting much worse using no compensation whatsoever in the complete case approach for ma and ma. So this was rather surprising. And in total, we can unfortunately only say at the moment that it's not very clear pattern. So the clear patterns we expected to see just didn't pop up. And the approach of combining multiple imputation and the mills ratio just didn't have the effects we were hoping for. And it's introducing bias for all three of them. So we do not recommend you to use it at all. We also saw that the M-car and the ma situation had rather robust results independent of which of the approaches we used. So distributions of the hundred simulation coefficient were rather close to each other. And the bias which was originally already in the M-na missingness pattern could not be compensated with any of the approaches we tested. So we still have to work on that and we will. So to improve this paper, we have two more ideas. One would be to extend the number of iterations of simulations we're running because for simulation study a hundred simulations is actually not too, it's not a very big number. Usually simulations are run on a much higher number but it takes a lot of computing time for us to do so since it's, in between steps are very complicated. But we're still thinking of maybe running another hundred just to be sure if situation changes. We're also thinking of simplifying our substantial model because we are only interested in the effectiveness of the analytical approaches for compensating onto the missingness patterns. So using a much more simpler model with less confounding covariates or at the moment we also have all three of our SES factors in the same model, we might have to isolate those effects to see clearer patterns. So we're still thinking about changing a couple of these and hope maybe the results look a bit distant after that. After that, we leave this simulation study behind and we want to test of the approaches we have now learned working well and not working very well. How much does the effectiveness have changed if we change the proportion of missingness? At the moment we run the simulation on the missingness scale, very similar to those in the ELSA dataset. But what if we say instead of 30% missingness, we bump it up to 60% missingness? Does the approaches we have seen which produce rather nice results, do they still produce good results? Or does it all depend on how much your dataset actually has missing values? So that's one paper. And the last one is we want to then extend our thesis to a longitudinal approach. That means we include data from the biomarkers as well from way four and six. And we run some growth curve modelling to see what happens over time with missingness. And that's all for me. Thank you for listening. If you have questions, we are very happy to hear about it. Thank you.


Dyrector, I have the honour of requesting that you confer an honorary fellowship upon Professor Farouq Toppan. By virtue of the authority invested in me, I award the honorary fellowship of so as University of London on Professor Farouq Toppan. Professor Farouq Toppan will now address us. Chairman, Lord Michael Hastings, Director, Adam Habib, Professor Adam Habib, distinguished guests, ladies and gentlemen, dear graduands, good morning. I'm deeply grateful to SOAS for the award of honorary fellowship which I accept with utmost humility. It's all the more special as it is from my own alma mater. And on this special occasion may I also acknowledge and convey congratulations to my fellow recipients of honorary awards in these winter graduations, Aqiyabad, Aisabu, Misan, Hariman, Ensanda, Wajiambu, and I thank SOAS for acknowledging the contributions of people in the African diaspora. Thank you. Congratulations to all graduands on your achievement and it goes without saying that your success has involved hard work and very good and perhaps at times difficult management of time and resources during these years both inside and outside of SOAS. I joined SOAS as a student in 1962 just over 60 years ago when I climbed those famous steps and although the internal and even external geography of SOAS has undergone some changes since then, a constant has been its increasing engagement with diversity. That engagement I have seen from the outside, I have seen it from the inside has developed over the years and somehow SOAS inculcates this ethos of diversity in its student body in various ways, sometimes even imperceptibly. If I may give a minor but perhaps a telling example from the Sohelia Broad program which we introduced in 1995 that is over two decades ago. Two students, let's call them Miriam and Kate, were the pioneers of the program. They had gone to the University of Dar es Salaam and as they were the pioneers we did not have any of course any precedent and therefore we were slightly or perhaps even more than slightly anxious on how the year would unfold. For them. But I shouldn't have worried because they took the change of environment and culture in the stride. They knew that Dar es Salaam is Dar es Salaam. It is an African city, a city with its own personality, its own way of life, its own culture, its own language which they were there to learn. They totally understood that they were not there to impose their values on others. That to me was pluralism in action. The acceptance and respect of difference is normal. And when I was thinking about this example what went through my mind was a thought. Some might say it is anachronistic thought but perhaps some might say it is not because human values have always been with us. And it is one of those if onlys of history. If only. If only the navigators, explorers and the discoverers of the 15th century, the ones in 1492 who went west and the ones in 1498 who went east around the Cape of Good Hall. And those who came in their wake. If only they had the attitude and understanding of Miriam and Kate towards the people they had encountered. If only then our world would probably be rather different today. And so finally my fellow alumni because you're on the verge of becoming alumni. This period of one and half years or two years of the pandemic has not been easy for many, many of us. But we have come through and this is now the time to build on what we have achieved, indeed what you have achieved. And not just academically but in many walks of human life. A time to move forward with confidence, self confidence with understanding with self assurance and the feeling that you are not alone. Just look around you, look on the stage, look at us. So good luck and wherever your own will and destiny takes you, we wish you well. We wish you good luck with best wishes. And in Swahili we say quairini. Quairini is goodbye but goodbye with our blessings, with our thoughts, with our prayers. And may I have a little request that wherever you may end up in the world here abroad, anywhere, please take a little piece of sauce and keep it in your hearts to be very comforting. I have done that and it hasn't done me any harm. So good luck and in Swahili we say asante ni sana thank you very much for your presence and for your contributions.


The United States Army Research Lab is extending our geographical presence through our open campus research model. The Texas Region and Army Research Laboratory makes an outstanding opportunity for partnership just based on the amount of population of Army soldiers in the state of Texas. It's all about the future soldier. Technologies created today will go into development phase that create outputs that affect our future. To have ARL South located at the flagship of a system of 14 institutions is incredibly powerful. ARL has been the catalyst for the universities in the state of Texas to start working together addressing common research questions and providing unique solutions that will advance technology for the state but also for the nation and the armed forces. Open campus is really about establishing partnerships and relationships and really matching the brilliant scientists together but what's just as important is being able to reach out to our experts that are here at the universities. There is a lot of expertise within the universities across Texas and the South that probably wasn't tapped into from an Army perspective. By having more people involved from across the country you get the expertise to make a more diverse approach to solve some of the Army problems. The Army Research Lab has the thought leaders and they know the application space and we have the capacity to solve problems in the fundamental space and so together we can identify problems and really make a difference. In Capital Factory we can connect with young entrepreneurs who are working in the technical areas and industry. It's about modernizing our troops. The weapons of the soldier of the future they're computers and information and because of that they need to be connecting into the startup community to find out the most current and best ways that they can use that information to give them the insights they need to be successful. Virtual reality and artificial intelligence are two ways that Army Research Laboratory South and the entire Texas Network are making great advances for soldiers. Innovation and research is very important for the soldier of the future. Some of the leading experts in many of the areas we're interested in from additive manufacturing to autonomous systems they're right here in Texas and they're already working on problems that are related to the problems we have in the Army. One of the main missions for the Army Research Lab is to ensure that the technologies that soldiers use out in the field are secure. We're leveraging the University in ways to develop research that'll help us to accomplish our mission. Our students are quite unique. For them service is the first priority with this partnership with ARL South. These leaders will also have an opportunity to be integrated with our laboratory facilities, work with ARL researchers who will be embedded here and our researchers here at Texas A&M. This collaboration is making us aware of problems that we need to be solving as researchers and this is a really unique opportunity to be able to see problems that are of practical importance. And really for us in the Army Research Lab it's about getting the technology accelerated quickly so that it gets into the hands of our warfighter. I've seen it as a commander in the field, the importance of research to the soldier. If it keeps my soldiers alive and helps me beat the enemy we want to connect it to the soldier as quickly as possible. We all get to work at the highest level in trying to meet our objectives and our missions and for us this is creating new knowledge and seeing that our solutions actually solve problems and save lives. We come with new ideas, new concepts and it's leading to the bottom line with the stage in soldier's lives. That's why it's an outstanding opportunity for the State of Texas Army Research Laboratory to get together openly collaborate and work with Army unique problems.


Hey, what's up, guys? This is Coach Ben from buildmysportsbiz.com. This is where we help tons of coaches all around the world start their private or group training business, no matter the sport. So if you are a coach with brand new here, make sure that you subscribe to our YouTube channel here. All you got to do is click on the red Subscribe button below this video. That's why you stay updated with all the new videos that we're going to be releasing. Now, in today's video, I'm going to be talking about the number one skill that you need in order to become a successful sports business owner. Now, this is not going to have to do with anything, with training. I assume if you're watching this video, you're a good trainer. Let's just face it, you're a good trainer. You know you're a good trainer. You want to build your business. And that's why you're here right now. If you are not a good trainer, you would probably be learning how to be a better coach. That's not really what I teach. I help coaches become better coaches by becoming better business owners. That way they can coach more kids and have a bigger impact at a higher level in whatever community they live in, in whatever sport that is. So the number one thing that you need to develop and get good at is the ability to sell to parents specifically. So no matter what sport you're in, let's just take baseball, for example. If you are a baseball coach and you want to start a private pitching business or a private batting practice or batting, any position doesn't matter. You want to be very good at communication with parents over the phone and in person. And you need to understand what your true value is behind your program. When you do that, it shows confidence when you talk to parents in person or over the phone. If you're ever talking to anybody and they can sense that you are not confident about when you're trying to sell your program and you're trying to get them closer to what you're doing, that's going to turn people away. And I'm telling you, from day one, that was my biggest problem. I was not confident. I would mumble when I would talk. I had zero energy, energy, zero enthusiasm. And that was something that was under my control. And I was just not used to getting outside of my comfort zone and selling anything. And this reality, if I stayed in my little comfort zone back then, it took me about two or three years to get out of that. But if I stayed in that, the 0% chance I would be making this video right now. 0% chance I would have been able to help as many coaches as we have now. But because I stepped outside of my comfort zone, it allowed me to be way more aggressive as far as wanting to help more people. If I want to help more people, I have to be selling all day long, all of the time. Because I know if I have a service that can truly help somebody, it's really my obligation and duty to sell that. If I know I have something that can help somebody, I need to make sure that they understand that I am here to help them. And that's the exact approach I took with my soccer training business here. Once I understood, you know what, Ben? If you want to be successful with this, you have to get better at communication. You have to get better at selling. And once I started to really understand that and start to practice that more, my business went from zero people to we've trained over 5,000 kids now in our program for soccer, and that's through private training, group training, camps, clinics. And really my goal at this point is to help coaches repeat the same success.  There's good coaches everywhere, especially in my industry and soccer, there's great coaches everywhere. There's coaches who are better coaches than me. I'm going to put my ego to the side here. There's coaches who are way better coaches than me. But that has nothing to do with growing a business. I mean, that can in some sense have a lot to do with it. But if you don't have your business set up the correct way, and you are not selling, and you are not actively talking to parents over and over and over all the time, and you're not forming those relationships, you're not going to be in business. So I hope all that makes sense. I just feel very strong about that because I see a lot of people, they come to me, I talk to coaches all the time on Saturdays, who have been watching this YouTube channel. I talk to people all the time, and they're asking me how to get clients. You know, what can I be doing to get clients? Well, how many people are you talking to?  Like what are you actively doing instead of making excuses, right? So if you become a master salesperson, that is going to elevate your business to a level that you might think is just not imaginable, right? So that is why I'm here, guys. I hope this message helped you. If you want my personal one-on-one help and you want to get better at selling, you want to learn how I do that with parents over the phone, learn how I structure my programs where I get parents to commit to six months or one year of my soccer training program, and you want to do that in your sport, then I highly recommend you reach out to me this weekend. All right, all you have to do is very simple. Just go to my website, it's buildmysportsbiz.com. Okay, when you go there, there is a big blue button at the top of the homepage. You just click on that. I have an easy application form. You just go through that. Once you go through that, I will text you. We will get on the phone sometime this weekend, and I would love just to see how I can help you out. That's the whole purpose of the call. I just want to see where you're at, what you need to help with, and I will show you a game plan that we can attack this way. You can build your business to a completely different level, all right? So that's it for the video, guys. Make sure before you go, if you haven't subscribed to this channel yet, hit that red subscribe button here on YouTube. That's going to help our channel grow. That's going to keep you updated as far as what are the new videos we have coming out every single week for the rest of the year. I am 100% sure that we're going to have at least two videos every single week that get published here, and they're really going to help you out. So that's it, guys. I'll see you soon.